xszheng2020/the_stack_dedup_python_hits_6 · Datasets at Hugging Face

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managedtenants/__init__.py

sugarraysam/managed-tenants-cli

59609583a86d1674f2c7376b601ce9ddfeee48de

[ "Apache-2.0" ]

null

managedtenants/__init__.py

sugarraysam/managed-tenants-cli

59609583a86d1674f2c7376b601ce9ddfeee48de

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managedtenants/__init__.py

sugarraysam/managed-tenants-cli

59609583a86d1674f2c7376b601ce9ddfeee48de

[ "Apache-2.0" ]

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from managedtenants.core.tasks_loader.pre_task import PreTask from managedtenants.core.tasks_loader.task import Task from managedtenants.core.tasks_loader.post_task import PostTask from managedtenants.core.status import Status __all__ = ["PreTask", "Task", "PostTask", "Status"]

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venv/lib/python3.8/site-packages/pip/_vendor/progress/counter.py

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venv/lib/python3.8/site-packages/pip/_vendor/progress/counter.py

DesmoSearch/Desmobot

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[ "MIT" ]

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bookorbooks/quiz/api/serializers/__init__.py

talhakoylu/SummerInternshipBackend

4ecedf5c97f73e3d32d5a534769e86aac3e4b6d3

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2021-08-10T22:24:17.000Z

bookorbooks/quiz/api/serializers/__init__.py

talhakoylu/SummerInternshipBackend

4ecedf5c97f73e3d32d5a534769e86aac3e4b6d3

[ "MIT" ]

null

bookorbooks/quiz/api/serializers/__init__.py

talhakoylu/SummerInternshipBackend

4ecedf5c97f73e3d32d5a534769e86aac3e4b6d3

[ "MIT" ]

null

from .quiz_serializers import QuizStandardSerializer from .question_serializers import QuestionWithQuizSerializer from .taking_quiz_serializers import TakingQuizDetailsForParentSerializer, TakingQuizDetailsForInstructorSerializer, TakingQuizDetailsForSpecificClassSerializer, TakingQuizSerializer, TakingQuizCreateSerializer, TakingQuizAnswerCreateSerializer

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src/yfoption/__init__.py

socbase/yfoption

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[ "MIT" ]

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src/yfoption/__init__.py

socbase/yfoption

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[ "MIT" ]

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src/yfoption/__init__.py

socbase/yfoption

31ca5231b52fcf73c6edb56419e1ccba06523c45

[ "MIT" ]

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from .yfoption import *

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CIM15/IEC61970/Informative/InfOperations/__init__.py

MaximeBaudette/PyCIM

d68ee5ccfc1d32d44c5cd09fb173142fb5ff4f14

[ "MIT" ]

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2015-04-22T10:41:03.000Z

2022-03-29T16:04:34.000Z

CIM15/IEC61970/Informative/InfOperations/__init__.py

MaximeBaudette/PyCIM

d68ee5ccfc1d32d44c5cd09fb173142fb5ff4f14

[ "MIT" ]

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2015-08-26T03:57:23.000Z

2020-12-11T20:14:42.000Z

CIM15/IEC61970/Informative/InfOperations/__init__.py

MaximeBaudette/PyCIM

d68ee5ccfc1d32d44c5cd09fb173142fb5ff4f14

[ "MIT" ]

35

2015-01-10T12:21:03.000Z

2020-09-09T08:18:16.000Z

# Copyright (C) 2010-2011 Richard Lincoln # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to # deal in the Software without restriction, including without limitation the # rights to use, copy, modify, merge, publish, distribute, sublicense, and/or # sell copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING # FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS # IN THE SOFTWARE. """TODO: The following has been copied from a very old version of draft Part 11, so the references are wrong, but we store the knowledge here to reuse later: 'The Documentation package is used for the modeling of business documents. Some of these may be electronic realizations of legacy paper document, and some may be electronic information exchanges or collections. Documents will typically reference or describe one or more CIM objects. The DataSets package is used to describe documents tyically used for exchange of collections of object descriptions (e.g., NetworkDataSet). The operational package is used to define documents related to distribution operations business processes (e.g., OperationalRestriction, SwitchingSchedule). TroubleTickets are used by Customers to report problems related to the elctrical distribution network. TroubleTickets may be grouped and be related to a PlannedOutage, OutageNotification and/or PowerSystemResource. The Outage package defines classes related to outage management (OutageStep, OutageRecord, OutageReport).' """ from CIM15.IEC61970.Informative.InfOperations.OutageRecord import OutageRecord from CIM15.IEC61970.Informative.InfOperations.OutageReport import OutageReport from CIM15.IEC61970.Informative.InfOperations.ChangeItem import ChangeItem from CIM15.IEC61970.Informative.InfOperations.PSREvent import PSREvent from CIM15.IEC61970.Informative.InfOperations.PlannedOutage import PlannedOutage from CIM15.IEC61970.Informative.InfOperations.CircuitSection import CircuitSection from CIM15.IEC61970.Informative.InfOperations.SafetyDocument import SafetyDocument from CIM15.IEC61970.Informative.InfOperations.OperationalRestriction import OperationalRestriction from CIM15.IEC61970.Informative.InfOperations.ChangeSet import ChangeSet from CIM15.IEC61970.Informative.InfOperations.SwitchingSchedule import SwitchingSchedule from CIM15.IEC61970.Informative.InfOperations.Circuit import Circuit from CIM15.IEC61970.Informative.InfOperations.NetworkDataSet import NetworkDataSet from CIM15.IEC61970.Informative.InfOperations.OutageStep import OutageStep from CIM15.IEC61970.Informative.InfOperations.OrgPsrRole import OrgPsrRole from CIM15.IEC61970.Informative.InfOperations.OutageCode import OutageCode from CIM15.IEC61970.Informative.InfOperations.IncidentCode import IncidentCode from CIM15.IEC61970.Informative.InfOperations.LandBase import LandBase from CIM15.IEC61970.Informative.InfOperations.ErpPersonScheduleStepRole import ErpPersonScheduleStepRole from CIM15.IEC61970.Informative.InfOperations.SwitchingStep import SwitchingStep from CIM15.IEC61970.Informative.InfOperations.CallBack import CallBack from CIM15.IEC61970.Informative.InfOperations.TroubleTicket import TroubleTicket from CIM15.IEC61970.Informative.InfOperations.IncidentRecord import IncidentRecord from CIM15.IEC61970.Informative.InfOperations.OutageNotification import OutageNotification from CIM15.IEC61970.Informative.InfOperations.OutageStepPsrRole import OutageStepPsrRole nsURI = "http://iec.ch/TC57/2010/CIM-schema-cim15#InfOperations" nsPrefix = "cimInfOperations" class SwitchingStepStatusKind(str): """Values are: instructed, confirmed, proposed, aborted, skipped """ pass class CircuitConnectionKind(str): """Values are: electricallyConnected, nominallyConnected, asBuilt, other """ pass class PSREventKind(str): """Values are: pendingRemove, pendingReplace, outOfService, pendingAdd, unknown, inService, other """ pass class TroubleReportingKind(str): """Values are: email, call, letter, other """ pass class ChangeItemKind(str): """Values are: add, modify, delete """ pass class OutageKind(str): """Values are: fixed, flexible, forced """ pass

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graspy/simulations/__init__.py

dfrancisco1998/graspy

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graspy/simulations/__init__.py

dfrancisco1998/graspy

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graspy/simulations/__init__.py

dfrancisco1998/graspy

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[ "Apache-2.0" ]

null

from .simulations import * from .simulations_corr import * from .rdpg_corr import *

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Python

model/module/__init__.py

nihalsid/dcm-net

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[ "MIT" ]

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2022-03-24T10:09:22.000Z

model/module/__init__.py

nihalsid/dcm-net

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[ "MIT" ]

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2021-12-02T19:22:09.000Z

model/module/__init__.py

nihalsid/dcm-net

343608e2cdc565137d6d2958be8bbd751ef20f7d

[ "MIT" ]

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2021-12-07T07:10:07.000Z

from .edge_conv_translation_invariance import *

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Python

piston/__init__.py

Roshan-Here/Eval-Code

3def7f40ac84991e347a622ec6063d8827bf379d

[ "Apache-2.0" ]

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piston/__init__.py

Roshan-Here/Eval-Code

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[ "Apache-2.0" ]

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piston/__init__.py

Roshan-Here/Eval-Code

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[ "Apache-2.0" ]

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from .client import Piston

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Python

kidcoords.py

coolreader18/imagemapping

abe418df5524a86e1e39cf98e06d4776ffb79325

[ "MIT" ]

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2019-08-07T04:53:00.000Z

2019-08-07T06:42:17.000Z

kidcoords.py

coolreader18/imagemapping

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kidcoords.py

coolreader18/imagemapping

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dependencies/panda/Panda3D-1.10.0-x64/panda3d/dtoolconfig.py

CrankySupertoon01/Toontown-2

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dependencies/panda/Panda3D-1.10.0-x64/panda3d/dtoolconfig.py

CrankySupertoonArchive/Toontown-2

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2018-07-30T18:28:34.000Z

dependencies/panda/Panda3D-1.10.0-x64/panda3d/dtoolconfig.py

CrankySupertoonArchive/Toontown-2

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[ "MIT" ]

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2019-12-02T01:39:10.000Z

2021-02-13T22:41:00.000Z

# This file is automatically generated by makepanda.py. Do not modify. from __future__ import absolute_import from .interrogatedb import *

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Computer Vision/imports/__init__.py

leommiranda/Practical-Deep-Learning-for-Coders-2.0

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2020-09-01T04:23:40.000Z

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Computer Vision/imports/__init__.py

leommiranda/Practical-Deep-Learning-for-Coders-2.0

b13076975abb9d2d01e48a573b89dce751cef0f0

[ "MIT" ]

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2020-09-04T05:46:38.000Z

2021-03-21T12:00:05.000Z

Computer Vision/imports/__init__.py

leommiranda/Practical-Deep-Learning-for-Coders-2.0

b13076975abb9d2d01e48a573b89dce751cef0f0

[ "MIT" ]

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2020-08-31T13:02:06.000Z

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from .model import * from .utils import * from .metrics import *

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easybot/__init__.py

BlizardWizard/Easybot

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[ "MIT" ]

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easybot/__init__.py

BlizardWizard/Easybot

7ef1f4eb156e1a263f4a12d310f3bb79c06d3d5b

[ "MIT" ]

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2019-02-13T01:58:10.000Z

2019-11-06T08:02:32.000Z

easybot/__init__.py

BlizardWizard/easybot

7ef1f4eb156e1a263f4a12d310f3bb79c06d3d5b

[ "MIT" ]

null

from easybot.Client import Client

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Python

tests/test_protocol_integration.py

samuelcolvin/em2

a587eaa80c09a2b44d9c221d09a563aad5b05d78

[ "MIT" ]

5

2019-03-20T19:07:45.000Z

2020-10-03T01:16:05.000Z

tests/test_protocol_integration.py

samuelcolvin/em2

a587eaa80c09a2b44d9c221d09a563aad5b05d78

[ "MIT" ]

51

2019-03-12T16:19:46.000Z

2021-03-09T00:52:24.000Z

tests/test_protocol_integration.py

samuelcolvin/em2

a587eaa80c09a2b44d9c221d09a563aad5b05d78

[ "MIT" ]

1

2019-05-31T14:41:18.000Z

from arq import Worker from pytest_toolbox.comparison import CloseToNow from em2.core import Action, ActionTypes, construct_conv from .conftest import Factory async def test_publish_em2(factory: Factory, worker: Worker, alt_cli, alt_db_conn, alt_conns): await factory.create_user(email='testing@local.example.com') recipient = 'recipient@alt.example.com' await alt_db_conn.fetchval("insert into auth_users (email, account_status) values ($1, 'active')", recipient) assert await alt_db_conn.fetchval('select count(*) from conversations') == 0 conv = await factory.create_conv(participants=[{'email': recipient}], publish=True) assert await worker.run_check(max_burst_jobs=2) == 2 assert await alt_db_conn.fetchval('select count(*) from conversations') == 1 user_id = await alt_db_conn.fetchval('select id from users where email=$1', recipient) conv = await construct_conv(alt_conns, user_id, conv.key) assert conv == { 'subject': 'Test Subject', 'created': CloseToNow(), 'messages': [ { 'ref': 3, 'author': 'testing@local.example.com', 'body': 'Test Message', 'created': CloseToNow(), 'format': 'markdown', 'active': True, } ], 'participants': {'testing@local.example.com': {'id': 1}, 'recipient@alt.example.com': {'id': 2}}, } async def test_em2_second_message(factory: Factory, worker: Worker, alt_factory: Factory, conns, alt_conns): a = 'testing@local.example.com' await factory.create_user(email=a) recip = 'recipient@alt.example.com' await alt_factory.create_user(email=recip) assert await alt_conns.main.fetchval('select count(*) from users') == 1 conv = await factory.create_conv(participants=[{'email': recip}], publish=True) assert await worker.run_check() == 3 assert await conns.main.fetchval('select count(*) from conversations') == 1 assert await conns.main.fetchval('select count(*) from actions') == 4 assert await alt_conns.main.fetchval('select count(*) from conversations') == 1 assert await alt_conns.main.fetchval('select count(*) from actions') == 4 action = Action(actor_id=factory.user.id, act=ActionTypes.msg_add, body='msg 2') await factory.act(conv.id, action) assert await worker.run_check() == 6 conv_summary = await construct_conv(conns, factory.user.id, conv.key) assert conv_summary == { 'subject': 'Test Subject', 'created': CloseToNow(), 'messages': [ { 'ref': 3, 'author': a, 'body': 'Test Message', 'created': CloseToNow(), 'format': 'markdown', 'active': True, }, {'ref': 5, 'author': a, 'body': 'msg 2', 'created': CloseToNow(), 'format': 'markdown', 'active': True}, ], 'participants': {'testing@local.example.com': {'id': 1}, 'recipient@alt.example.com': {'id': 2}}, } alt_conv_summary = await construct_conv(alt_conns, alt_factory.user.id, conv.key) assert conv_summary == alt_conv_summary async def test_em2_reply(factory: Factory, worker: Worker, alt_factory: Factory, conns, alt_conns, alt_worker: Worker): sender = 'sender@local.example.com' await factory.create_user(email=sender) recip = 'recipient@alt.example.com' await alt_factory.create_user(email=recip) assert await conns.main.fetchval('select count(*) from conversations') == 0 assert await alt_conns.main.fetchval('select count(*) from conversations') == 0 conv = await factory.create_conv(participants=[{'email': recip}], publish=True) assert await conns.main.fetchval('select count(*) from conversations') == 1 assert await conns.main.fetchval('select count(*) from actions') == 4 assert await alt_conns.main.fetchval('select count(*) from conversations') == 0 assert await worker.run_check() == 3 assert await conns.main.fetchval('select count(*) from conversations') == 1 assert await conns.main.fetchval('select count(*) from actions') == 4 assert await alt_conns.main.fetchval('select count(*) from conversations') == 1 assert await alt_conns.main.fetchval('select count(*) from actions') == 4 assert await alt_worker.run_check() == 1 action = Action(actor_id=alt_factory.user.id, act=ActionTypes.msg_add, body='msg 3') alt_conv_id = await alt_conns.main.fetchval('select id from conversations where key=$1', conv.key) await alt_factory.act(alt_conv_id, action) assert await conns.main.fetchval('select count(*) from actions') == 4 assert await alt_conns.main.fetchval('select count(*) from actions') == 4 assert await alt_worker.run_check() == 2 assert await conns.main.fetchval('select count(*) from actions') == 5 assert await alt_conns.main.fetchval('select count(*) from actions') == 4 assert await worker.run_check() == 6 assert await conns.main.fetchval('select count(*) from actions') == 5 assert await alt_conns.main.fetchval('select count(*) from actions') == 5 conv_summary = await construct_conv(conns, factory.user.id, conv.key) assert conv_summary == { 'subject': 'Test Subject', 'created': CloseToNow(), 'messages': [ { 'ref': 3, 'author': 'sender@local.example.com', 'body': 'Test Message', 'created': CloseToNow(), 'format': 'markdown', 'active': True, }, { 'ref': 5, 'author': 'recipient@alt.example.com', 'body': 'msg 3', 'created': CloseToNow(), 'format': 'markdown', 'active': True, }, ], 'participants': {'sender@local.example.com': {'id': 1}, 'recipient@alt.example.com': {'id': 2}}, } alt_conv_summary = await construct_conv(alt_conns, alt_factory.user.id, conv.key) assert conv_summary == alt_conv_summary

41.979452

119

0.630935

744

6,129

5.068548

0.114247

0.084593

0.110846

0.134182

0.838239

0.806417

0.765579

0.741978

0.719438

0.674887

0

0.011019

0.230054

6,129

145

120

42.268966

0.788091

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0.57265

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0.262849

0.060695

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0.290598

1

0

false

0

0.034188

0

0.034188

0

null

0

1

0

null

0

6

85cc79f23a2b33ce9494952d7dd4dc50acc9bd8c

45

py

Python

modules/2.79/bpy/types/ShaderNodeBsdfTransparent.py

cmbasnett/fake-bpy-module

acb8b0f102751a9563e5b5e5c7cd69a4e8aa2a55

[ "MIT" ]

null

modules/2.79/bpy/types/ShaderNodeBsdfTransparent.py

cmbasnett/fake-bpy-module

acb8b0f102751a9563e5b5e5c7cd69a4e8aa2a55

[ "MIT" ]

null

modules/2.79/bpy/types/ShaderNodeBsdfTransparent.py

cmbasnett/fake-bpy-module

acb8b0f102751a9563e5b5e5c7cd69a4e8aa2a55

[ "MIT" ]

null

class ShaderNodeBsdfTransparent: pass

7.5

32

0.755556

3

45

11.333333

1

0

0.222222

45

5

33

9

0.971429

0

1

0

true

0.5

0

0.5

0

1

null

0

1

0

null

0

1

0

6

85d950f9708a9c7836ddbacadf382323526fd692

203

py

Python

roseltorg_parser/get_tenders.py

ServerHack-The-First-Law-Of-Robotics/data_engineering

4651d69615eda1eaac518a86b8c9f94b8912146e

[ "MIT" ]

null

roseltorg_parser/get_tenders.py

ServerHack-The-First-Law-Of-Robotics/data_engineering

4651d69615eda1eaac518a86b8c9f94b8912146e

[ "MIT" ]

null

roseltorg_parser/get_tenders.py

ServerHack-The-First-Law-Of-Robotics/data_engineering

4651d69615eda1eaac518a86b8c9f94b8912146e

[ "MIT" ]

null

from parser import TenderParser from config import mapping TenderParser().go_through_pages(mapping['metal'], 'metal_tenders.txt') TenderParser().go_through_pages(mapping['metal'], 'rubber_tenders.txt')

33.833333

71

0.807882

26

203

6.076923

0.5

0.177215

0.265823

0.329114

0.481013

0

0.064039

203

5

72

40.6

0.831579

0

0.221675

0

1

0

true

0

0.5

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0.5

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

85ed7ab8eec9d97fb72895a8a4f28ebd9a5d50a7

48

py

Python

amazon/kth_smallest_in_sorted_matrix.py

dsrao711/DSA-101-HacktoberFest

0d04e2aecee224080c34146e327ff6de15d9ba16

[ "MIT" ]

16

2021-10-02T20:10:51.000Z

2022-03-06T10:31:11.000Z

amazon/kth_smallest_in_sorted_matrix.py

dsrao711/DSA-101-HacktoberFest

0d04e2aecee224080c34146e327ff6de15d9ba16

[ "MIT" ]

55

2021-10-02T07:31:41.000Z

2021-10-30T06:19:26.000Z

amazon/kth_smallest_in_sorted_matrix.py

dsrao711/DSA-101-HacktoberFest

0d04e2aecee224080c34146e327ff6de15d9ba16

[ "MIT" ]

36

2021-10-02T18:00:08.000Z

2022-01-03T18:50:35.000Z

m = [1,5,9,10,11,13,12,13,15] m.sort() print(m)

12

29

0.5625

14

48

1.928571

0.785714

0

0.348837

0.104167

48

3

30

16

0.27907

0

1

0

false

0

0.333333

1

null

0

1

0

1

0

1

0

null

0

6

c83ae4819a8eac4e0ff39464e6d7f657dc54b3bb

16,696

py

Python

training_pipeline/model.py

Elucidation/ChessboardDetect

a5d2a2c2ab2434e4e041b4f384f3cd7d6884d2c4

[ "MIT" ]

43

2016-10-28T02:13:26.000Z

2022-02-16T14:20:32.000Z

training_pipeline/model.py

AnkaChan/ChessboardDetect

a5d2a2c2ab2434e4e041b4f384f3cd7d6884d2c4

[ "MIT" ]

3

2016-11-15T19:04:46.000Z

2020-08-26T20:41:29.000Z

training_pipeline/model.py

AnkaChan/ChessboardDetect

a5d2a2c2ab2434e4e041b4f384f3cd7d6884d2c4

[ "MIT" ]

12

2018-08-22T22:33:21.000Z

2021-08-20T08:40:42.000Z

# CNN model, based off of the Tensorflow CNN Mnist Classifier tutorial. import tensorflow as tf def cnn_model(features, labels, mode, params): """Model function for CNN.""" # Grayscale winsize=10 (21x21) # Input Layer input_layer = tf.reshape(features["x"], [-1, 21, 21, 1]) input_layer = tf.cast(input_layer, tf.float32) # Convert batch of images from uint8 to float64 normalized. # input_layer = tf.map_fn(lambda img: tf.image.per_image_standardization(img), input_layer) if labels is not None: bool_labels = tf.cast(labels, tf.bool) tf.summary.image('Input_Good', tf.boolean_mask(input_layer, bool_labels), max_outputs=10) tf.summary.image('Input_Bad', tf.boolean_mask(input_layer, tf.logical_not(bool_labels)), max_outputs=10) # Convolutional Layer #1 conv1 = tf.layers.conv2d( inputs=input_layer, filters=params['filter_sizes'][0], kernel_size=[5, 5], padding="same", activation=tf.nn.relu) # Pooling Layer #1 pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2], strides=2) # Convolutional Layer #2 and Pooling Layer #2 conv2 = tf.layers.conv2d( inputs=pool1, filters=params['filter_sizes'][1], kernel_size=[5, 5], padding="same", activation=tf.nn.relu) pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2) # Dense Layer pool2_flat = tf.reshape(pool2, [-1, 5 * 5 * params['filter_sizes'][1]]) dense = tf.layers.dense(inputs=pool2_flat, units=params['filter_sizes'][2], activation=tf.nn.relu) dropout = tf.layers.dropout( inputs=dense, rate=0.4, training=mode == tf.estimator.ModeKeys.TRAIN) # Logits Layer logits = tf.layers.dense(inputs=dropout, units=2) return logits def cnn_model_rgb(features, labels, mode, params): """Model function for CNN.""" # RGB winsize=10 (Nx15x15x3) # Input Layer input_layer = tf.reshape(features["x"], [-1, 15, 15, 3], name='ReshapeinModel1') input_layer = tf.cast(input_layer, tf.float32) # Convert batch of images from uint8 to float64 normalized. # input_layer = tf.map_fn(lambda img: tf.image.per_image_standardization(img), input_layer) tf.summary.image('Input', input_layer, max_outputs=5) # Convolutional Layer #1 conv1 = tf.layers.conv2d( inputs=input_layer, filters=params['filter_sizes'][0], kernel_size=[5, 5], padding="same", activation=tf.nn.relu) # Pooling Layer #1 pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2], strides=2) tf.summary.image('pool1', pool1[:,:,:,:4], max_outputs=5) # Convolutional Layer #2 and Pooling Layer #2 conv2 = tf.layers.conv2d( inputs=pool1, filters=params['filter_sizes'][1], kernel_size=[5, 5], padding="same", activation=tf.nn.relu) pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2) tf.summary.image('pool2', pool2[:,:,:,:4], max_outputs=5) # Dense Layer pool2_flat = tf.reshape(pool2, [-1, 3 * 3 * params['filter_sizes'][1]], name='ReshapePool2_flat') dense = tf.layers.dense(inputs=pool2_flat, units=params['filter_sizes'][2], activation=tf.nn.relu) dropout = tf.layers.dropout( inputs=dense, rate=0.1, training=mode == tf.estimator.ModeKeys.TRAIN) # Logits Layer logits = tf.layers.dense(inputs=dropout, units=2) return logits def cnn_model_rgb_small(features, labels, mode, params): """Model function for CNN.""" # RGB winsize=10 (Nx15x15x3) # Input Layer input_layer = tf.reshape(features["x"], [-1, 15, 15, 3], name='ReshapeinModel1') input_layer = tf.cast(input_layer, tf.float32) # Convert batch of images from uint8 to float64 normalized. # input_layer = tf.map_fn(lambda img: tf.image.per_image_standardization(img), input_layer) tf.summary.image('Input', input_layer, max_outputs=5) # Convolutional Layer #1 conv1 = tf.layers.conv2d( inputs=input_layer, filters=params['filter_sizes'][0], kernel_size=[3, 3], padding="same", activation=tf.nn.relu) # Pooling Layer #1 pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2], strides=2) tf.summary.image('pool1', pool1[:,:,:,:4], max_outputs=5) # Dense Layer pool1_flat = tf.reshape(pool1, [-1, 7 * 7 * params['filter_sizes'][0]], name='ReshapePool1_flat') dense = tf.layers.dense(inputs=pool1_flat, units=params['filter_sizes'][2], activation=tf.nn.relu) dropout = tf.layers.dropout( inputs=dense, rate=0.4, training=mode == tf.estimator.ModeKeys.TRAIN) # Logits Layer logits = tf.layers.dense(inputs=dropout, units=2) return logits def cnn_model_small(features, labels, mode, params): """Model function for CNN.""" # Assumes 21x21 input size # Input Layer input_layer = tf.reshape(features["x"], [-1, 21, 21, 1]) input_layer = tf.cast(input_layer, tf.float32) if labels is not None: bool_labels = tf.cast(labels, tf.bool) tf.summary.image('Input_Good', tf.boolean_mask(input_layer, bool_labels), max_outputs=10) tf.summary.image('Input_Bad', tf.boolean_mask(input_layer, tf.logical_not(bool_labels)), max_outputs=10) # Convolutional Layer #1 conv1 = tf.layers.conv2d( inputs=input_layer, filters=params['filter_sizes'][0], kernel_size=[3, 3], padding="same", activation=tf.nn.relu) # Pooling Layer #1 pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2], strides=2) # Dense Layer pool1_flat = tf.reshape(pool1, [-1, 10 * 10 * params['filter_sizes'][0]]) dense = tf.layers.dense(inputs=pool1_flat, units=params['filter_sizes'][1], activation=tf.nn.relu) dropout = tf.layers.dropout( inputs=dense, rate=0.4, training=mode == tf.estimator.ModeKeys.TRAIN) # Logits Layer logits = tf.layers.dense(inputs=dropout, units=2) return logits def cnn_model_ultrasmall(features, labels, mode, params): """Model function for CNN.""" # Assumes 15x15 input size # Input Layer input_layer = tf.reshape(features["x"], [-1, 15, 15, 1]) input_layer = tf.cast(input_layer, tf.float32) if labels is not None: bool_labels = tf.cast(labels, tf.bool) tf.summary.image('Input_Good', tf.boolean_mask(input_layer, bool_labels), max_outputs=5) tf.summary.image('Input_Bad', tf.boolean_mask(input_layer, tf.logical_not(bool_labels)), max_outputs=5) # Convolutional Layer #1 conv1 = tf.layers.conv2d( inputs=input_layer, filters=params['filter_sizes'][0], kernel_size=[5, 5], padding="same", activation=tf.nn.relu) # Pooling Layer #1 pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2], strides=2) tf.summary.image('pool1', pool1[:,:,:,:3], max_outputs=5) # Convolutional Layer #2 and Pooling Layer #2 conv2 = tf.layers.conv2d( inputs=pool1, filters=params['filter_sizes'][1], kernel_size=[5, 5], padding="same", activation=tf.nn.relu) pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2) tf.summary.image('pool2', pool2[:,:,:,:3], max_outputs=5) # Dense Layer pool2_flat = tf.reshape(pool2, [-1, 3 * 3 * params['filter_sizes'][1]]) dense = tf.layers.dense(inputs=pool2_flat, units=params['filter_sizes'][2], activation=tf.nn.relu) dropout = tf.layers.dropout( inputs=dense, rate=0.4, training=mode == tf.estimator.ModeKeys.TRAIN) # Logits Layer logits = tf.layers.dense(inputs=dropout, units=2) return logits def cnn_model_big(features, labels, mode, params): """Model function for CNN.""" channels = 2 # r g b gx gy # Input Layer input_layer = tf.reshape(features["x"], [-1, 15, 15, channels]) input_layer = tf.cast(input_layer, tf.float32) # Convert batch of images from uint8 to float64 normalized. # Note, potentially expensive # input_layer = tf.map_fn(lambda img: tf.image.per_image_standardization(img), input_layer) if labels is not None: bool_labels = tf.cast(labels, tf.bool) # tf.summary.image('Input_Good', # tf.boolean_mask(input_layer[:,:,:,:3], bool_labels), max_outputs=10) # tf.summary.image('Gx Good', # tf.boolean_mask(tf.expand_dims(input_layer[:,:,:,3], axis=-1), bool_labels), max_outputs=10) # tf.summary.image('Gy Good', # tf.boolean_mask(tf.expand_dims(input_layer[:,:,:,4], axis=-1), bool_labels), max_outputs=10) # tf.summary.image('Input_Bad', # tf.boolean_mask(input_layer[:,:,:,:3], tf.logical_not(bool_labels)), max_outputs=10) tf.summary.image('Gx Good', tf.boolean_mask(tf.expand_dims(input_layer[:,:,:,0], axis=-1), bool_labels), max_outputs=10) tf.summary.image('Gy Good', tf.boolean_mask(tf.expand_dims(input_layer[:,:,:,1], axis=-1), bool_labels), max_outputs=10) # Convolutional Layer #1 conv1 = tf.layers.conv2d( inputs=input_layer, filters=params['filter_sizes'][0], kernel_size=[5, 5], padding="same", activation=tf.nn.relu) # 31x31x3 output # Pooling Layer #1 pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2], strides=2) # 15x15x3 output # Convolutional Layer #2 and Pooling Layer #2 conv2 = tf.layers.conv2d( inputs=pool1, filters=params['filter_sizes'][1], kernel_size=[5, 5], padding="same", activation=tf.nn.relu) # 15x15x3 input, 15x15xparams['filter_sizes'][1] output pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2) # 7x7xparams['filter_sizes'][1] output # Dense Layer pool2_flat = tf.reshape(pool2, [-1, 3 * 3 * params['filter_sizes'][1]]) dense = tf.layers.dense(inputs=pool2_flat, units=params['filter_sizes'][2], activation=tf.nn.relu) dropout = tf.layers.dropout( inputs=dense, rate=0.4, training=mode == tf.estimator.ModeKeys.TRAIN) # Logits Layer logits = tf.layers.dense(inputs=dropout, units=2) return logits def dnn_model_rgb(features, labels, mode, params): """Model function for DNN.""" # RGB winsize=10 (Nx15x15x3) # Input Layer input_layer = tf.cast(features['x'], tf.float32) # Convert batch of images from uint8 to float64 normalized. # input_layer = tf.map_fn(lambda img: tf.image.per_image_standardization(img), input_layer) tf.summary.image('Input', input_layer, max_outputs=4) # Dense Layer #1 dense1 = tf.layers.dense( inputs=input_layer, units=params['filter_sizes'][0], activation=tf.nn.relu) # Dense Layer #2 dense2 = tf.layers.dense( inputs=dense1, units=params['filter_sizes'][1], activation=tf.nn.relu) # Dense Layer #3 dense3 = tf.layers.dense( inputs=dense2, units=params['filter_sizes'][2], activation=tf.nn.relu) dropout = tf.layers.dropout( inputs=dense3, rate=0.1, training=mode == tf.estimator.ModeKeys.TRAIN) # Flatten input Nx15x15xparams['filter_sizes'][2] dropout_flat = tf.reshape(dropout, [-1, 15*15*params['filter_sizes'][2]], name='flat_dropout') # Logits Layer logits = tf.layers.dense(inputs=dropout_flat, units=2) return logits def cnn_model_rgb_v2(features, labels, mode, params): """Model function for CNN.""" # RGB winsize=10 (Nx15x15x3) # Input Layer input_layer = tf.reshape(features["x"], [-1, 15, 15, 3], name='ReshapeinModel1') input_layer = tf.cast(input_layer, tf.float32) # Convert batch of images from uint8 to float64 normalized. # input_layer = tf.map_fn(lambda img: tf.image.per_image_standardization(img), input_layer) tf.summary.image('Input', input_layer, max_outputs=4) # Convolutional Layer #1 conv1 = tf.layers.conv2d( inputs=input_layer, filters=128, kernel_size=[3, 3], activation=tf.nn.relu) tf.summary.image('conv1', conv1[:,:,:,:3], max_outputs=4) # Nx13x13x128 # Convolutional Layer #2 conv2 = tf.layers.conv2d( inputs=conv1, filters=64, kernel_size=[3, 3], activation=tf.nn.relu) tf.summary.image('conv2', conv2[:,:,:,:3], max_outputs=4) # Nx11x11x64 # Pooling Layer #1 pool1 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2) # Nx5x5x64 # Dense Layer 1 pool1_flat = tf.reshape(pool1, [-1, 5 * 5 * 64]) dense1 = tf.layers.dense(inputs=pool1_flat, units=128, activation=tf.nn.relu) # Dense Layer 2 dense2 = tf.layers.dense(inputs=dense1, units=128, activation=tf.nn.relu) dropout1 = tf.layers.dropout( inputs=dense2, rate=0.1, training=mode == tf.estimator.ModeKeys.TRAIN) # Logits Layer logits = tf.layers.dense(inputs=dropout1, units=2) return logits def cnn_model_rgb_v3(features, labels, mode, params): """Model function for CNN.""" # RGB winsize=10 (Nx15x15x3) # Input Layer input_layer = tf.reshape(features["x"], [-1, 15, 15, 3], name='ReshapeinModel1') input_layer = tf.cast(input_layer, tf.float32) # Convert batch of images from uint8 to float64 normalized. # input_layer = tf.map_fn(lambda img: tf.image.per_image_standardization(img), input_layer) tf.summary.image('Input', input_layer, max_outputs=4) # Nx15x15xK # Convolutional Layer #1 conv1 = tf.layers.conv2d( inputs=input_layer, filters=64, kernel_size=[3, 3], padding="same", activation=tf.nn.relu) # tf.summary.image('conv1', conv1[:,:,:,:3], max_outputs=4) # Convolutional Layer #2 conv2 = tf.layers.conv2d( inputs=conv1, filters=128, kernel_size=[3, 3], padding="same", activation=tf.nn.relu) # tf.summary.image('conv2', conv2[:,:,:,:3], max_outputs=4) conv3 = tf.layers.conv2d( inputs=conv2, filters=128, kernel_size=[3, 3], padding="same", activation=tf.nn.relu) # tf.summary.image('conv3', conv3[:,:,:,:3], max_outputs=4) # Convolutional Layer #2 conv4 = tf.layers.conv2d( inputs=conv3, filters=128, kernel_size=[3, 3], padding="same", activation=tf.nn.relu) # tf.summary.image('conv4', conv4[:,:,:,:3], max_outputs=4) # Pooling Layer #1 pool1 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[3, 3], strides=3) # Nx5x5xK # Convolutional Layer #1 conv5 = tf.layers.conv2d( inputs=pool1, filters=128, kernel_size=[3, 3], padding="same", activation=tf.nn.relu) # Convolutional Layer #2 conv6 = tf.layers.conv2d( inputs=conv5, filters=128, kernel_size=[3, 3], padding="same", activation=tf.nn.relu) conv7 = tf.layers.conv2d( inputs=conv6, filters=128, kernel_size=[3, 3], padding="same", activation=tf.nn.relu) # Convolutional Layer #2 conv8 = tf.layers.conv2d( inputs=conv7, filters=128, kernel_size=[3, 3], padding="same", activation=tf.nn.relu) # tf.summary.image('conv4', conv4[:,:,:,:3], max_outputs=4) # Dense Layer 1 conv8_flat = tf.reshape(conv8, [-1, 5 * 5 * 128]) dense1 = tf.layers.dense(inputs=conv8_flat, units=512, activation=tf.nn.relu) dropout1 = tf.layers.dropout(inputs=dense1, rate=0.4, training=mode == tf.estimator.ModeKeys.TRAIN) # Logits Layer logits = tf.layers.dense(inputs=dropout1, units=2) return logits def cnn_model_fn(features, labels, mode, params): # logits = cnn_model(features, labels, mode, params) logits = cnn_model_rgb_v3(features, labels, mode, params) # logits = cnn_model_rgb_small(features, labels, mode, params) predictions = { # Generate predictions (for PREDICT and EVAL mode) "classes": tf.argmax(input=logits, axis=1), # Add `softmax_tensor` to the graph. It is used for PREDICT and by the # `logging_hook`. "probabilities": tf.nn.softmax(logits, name="softmax_tensor") } export_output = {'predict': tf.estimator.export.PredictOutput(predictions)} if mode == tf.estimator.ModeKeys.PREDICT: return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions, export_outputs=export_output) # If not predict, then labels is not none labels = tf.cast(labels, tf.int32) # Calculate Loss (for both TRAIN and EVAL modes) loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits) # Configure the Training Op (for TRAIN mode) if mode == tf.estimator.ModeKeys.TRAIN: # optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.001) optimizer = tf.train.AdamOptimizer() train_op = optimizer.minimize( loss=loss, global_step=tf.train.get_global_step()) return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=train_op) # Add evaluation metrics (for EVAL mode) eval_metric_ops = { "accuracy": tf.metrics.accuracy( labels=labels, predictions=predictions["classes"])} return tf.estimator.EstimatorSpec( mode=mode, loss=loss, eval_metric_ops=eval_metric_ops)

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c0870c14227b354df85e47c7ff718cbf3263d0e0

43

py

Python

scvelo/pp.py

WeilerP/scvelo

1805ab4a72d3f34496f0ef246500a159f619d3a2

[ "BSD-3-Clause" ]

272

2018-08-21T08:59:11.000Z

2022-03-30T11:24:19.000Z

scvelo/pp.py

theislab/scvelo

1805ab4a72d3f34496f0ef246500a159f619d3a2

[ "BSD-3-Clause" ]

570

2018-08-21T14:04:03.000Z

2022-03-30T08:48:04.000Z

scvelo/pp.py

WeilerP/scvelo

1805ab4a72d3f34496f0ef246500a159f619d3a2

[ "BSD-3-Clause" ]

105

2018-09-04T14:08:58.000Z

2022-03-17T16:20:14.000Z

from scvelo.preprocessing import * # noqa

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74

py

Python

model/__init__.py

phykn/xai_tree

66f5cb4ea77686364478b1f16f937678b2e544a8

[ "Apache-2.0" ]

1

2022-02-06T17:49:26.000Z

model/__init__.py

phykn/xai_tree

66f5cb4ea77686364478b1f16f937678b2e544a8

[ "Apache-2.0" ]

null

model/__init__.py

phykn/xai_tree

66f5cb4ea77686364478b1f16f937678b2e544a8

[ "Apache-2.0" ]

null

from .split_data import split_data from .best_model import get_best_model

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6

23ef81393604236ea3044f4542a267131cb4a5c6

102

py

Python

pygecko/lib/cg_api_simple/__init__.py

SrJMaia/coin-gecko-api

2f011f23b104b0ee3a0561e68c6ec974536a59ec

[ "MIT" ]

null

pygecko/lib/cg_api_simple/__init__.py

SrJMaia/coin-gecko-api

2f011f23b104b0ee3a0561e68c6ec974536a59ec

[ "MIT" ]

null

pygecko/lib/cg_api_simple/__init__.py

SrJMaia/coin-gecko-api

2f011f23b104b0ee3a0561e68c6ec974536a59ec

[ "MIT" ]

null

from .price import get_simple_price_from_api from .vs_currency import get_supported_vs_currencies_api

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6

f1b2f079b4b85a4cd5be372290326e4f4a707f94

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py

Python

rxsci_river/evaluate/__init__.py

maki-nage/rxsci-river

19fc66e67aff8dfa7efbf4107c228de15fd75d3a

[ "MIT" ]

2

2021-11-26T20:59:38.000Z

2022-03-14T10:10:00.000Z

rxsci_river/evaluate/__init__.py

maki-nage/rxsci-river

19fc66e67aff8dfa7efbf4107c228de15fd75d3a

[ "MIT" ]

null

rxsci_river/evaluate/__init__.py

maki-nage/rxsci-river

19fc66e67aff8dfa7efbf4107c228de15fd75d3a

[ "MIT" ]

null

from .prequential import prequential

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f1c9e6f419532ebc43d314bbb70920fedab8b857

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py

Python

tools/Vitis-AI-Quantizer/vai_q_pytorch/pytorch_binding/pytorch_nndct/utils/__init__.py

bluetiger9/Vitis-AI

a7728733bbcfc292ff3afa46b9c8b03e94b740b3

[ "Apache-2.0" ]

848

2019-12-03T00:16:17.000Z

2022-03-31T22:53:17.000Z

tools/Vitis-AI-Quantizer/vai_q_pytorch/pytorch_binding/pytorch_nndct/utils/__init__.py

wangyifan778/Vitis-AI

f61061eef7550d98bf02a171604c9a9f283a7c47

[ "Apache-2.0" ]

656

2019-12-03T00:48:46.000Z

2022-03-31T18:41:54.000Z

tools/Vitis-AI-Quantizer/vai_q_pytorch/pytorch_binding/pytorch_nndct/utils/__init__.py

wangyifan778/Vitis-AI

f61061eef7550d98bf02a171604c9a9f283a7c47

[ "Apache-2.0" ]

506

2019-12-03T00:46:26.000Z

2022-03-30T10:34:56.000Z

from .torch_op_attr import * from .nndct2torch_op_map import * from .op_register import * from .torch_const import * from .tensor_utils import * from .schema import *

21

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6

f1e6e51701ba7bf48115ecd46633dd44d4e3ca54

86,590

py

Python

sharpy/structure/utils/lagrangeconstraints.py

AntonioWR/sharpy

c922be8d5a1831c4624b22f39264e2f417a03deb

[ "BSD-3-Clause" ]

null

sharpy/structure/utils/lagrangeconstraints.py

AntonioWR/sharpy

c922be8d5a1831c4624b22f39264e2f417a03deb

[ "BSD-3-Clause" ]

null

sharpy/structure/utils/lagrangeconstraints.py

AntonioWR/sharpy

c922be8d5a1831c4624b22f39264e2f417a03deb

[ "BSD-3-Clause" ]

null

""" LagrangeConstraints library Library used to create the matrices associated to boundary conditions through the method of Lagrange Multipliers. The source code includes four different sections. * Basic structures: basic functions and variables needed to organise the library with different Lagrange Constraints to enhance the interaction with this library. * Auxiliar functions: basic queries that are performed repeatedly. * Equations: functions that generate the equations associated to the constraint of basic degrees of freedom. * Lagrange Constraints: different available Lagrange Constraints. They tipically use the basic functions in "Equations" to assembly the required set of equations. Attributes: dict_of_lc (dict): Dictionary including the available Lagrange Contraint identifier (``_lc_id``) and the associated ``BaseLagrangeConstraint`` class Notes: To use this library: import sharpy.structure.utils.lagrangeconstraints as lagrangeconstraints Args: lc_list (list): list of all the defined contraints MBdict (dict): dictionary with the MultiBody and LagrangeMultipliers information MB_beam (list): list of :class:`~sharpy.structure.models.beam.Beam` of each of the bodies that form the system MB_tstep (list): list of :class:`~sharpy.utils.datastructures.StructTimeStepInfo` of each of the bodies that form the system num_LM_eq (int): number of new equations needed to define the boundary boundary conditions sys_size (int): total number of degrees of freedom of the multibody system dt (float): time step Lambda (np.ndarray): list of Lagrange multipliers values Lambda_dot (np.ndarray): list of the first derivative of the Lagrange multipliers values dynamic_or_static (str): string defining if the computation is dynamic or static LM_C (np.ndarray): Damping matrix associated to the Lagrange Multipliers equations LM_K (np.ndarray): Stiffness matrix associated to the Lagrange Multipliers equations LM_Q (np.ndarray): Vector of independent terms associated to the Lagrange Multipliers equations """ from abc import ABCMeta, abstractmethod import sharpy.utils.cout_utils as cout import os import ctypes as ct import numpy as np import sharpy.utils.algebra as ag ############################################################################### # Basic structures ############################################################################### dict_of_lc = {} lc = {} # for internal working # decorator def lagrangeconstraint(arg): """ Decorator used to create the dictionary (``dict_of_lc``) that links constraints id (``_lc_id``) to the associated ``BaseLagrangeConstraint`` class """ global dict_of_lc try: arg._lc_id except AttributeError: raise AttributeError('Class defined as lagrange constraint has no _lc_id attribute') dict_of_lc[arg._lc_id] = arg return arg def print_available_lc(): """ Prints the available Lagrange Constraints """ cout.cout_wrap('The available lagrange constraints on this session are:', 2) for name, i_lc in dict_of_lc.items(): cout.cout_wrap('%s ' % i_lc._lc_id, 2) def lc_from_string(string): """ Returns the ``BaseLagrangeConstraint`` class associated to a constraint id (``_lc_id``) """ return dict_of_lc[string] def lc_list_from_path(cwd): onlyfiles = [f for f in os.listdir(cwd) if os.path.isfile(os.path.join(cwd, f))] for i_file in range(len(onlyfiles)): if ".py" in onlyfiles[i_file]: if onlyfiles[i_file] == "__init__.py": onlyfiles[i_file] = "" continue onlyfiles[i_file] = onlyfiles[i_file].replace('.py', '') else: onlyfiles[i_file] = "" files = [file for file in onlyfiles if not file == ""] return files def initialise_lc(lc_name, print_info=True): """ Initialises the Lagrange Constraints """ if print_info: cout.cout_wrap('Generating an instance of %s' % lc_name, 2) cls_type = lc_from_string(lc_name) lc = cls_type() return lc class BaseLagrangeConstraint(metaclass=ABCMeta): __doc__ = """ BaseLagrangeConstraint Base class for LagrangeConstraints showing the methods required. They will be inherited by all the Lagrange Constraints Attributes: _n_eq (int): Number of equations required by a LagrangeConstraint _ieq (int): Number of the first equation associated to the Lagrange Constraint in the whole set of Lagrange equations """ _lc_id = 'BaseLagrangeConstraint' def __init__(self): """ Initialisation """ self._n_eq = None self._ieq = None @abstractmethod def get_n_eq(self): """ Returns the number of equations required by the Lagrange Constraint """ return self._n_eq @abstractmethod # def initialise(self, **kwargs): def initialise(self, MBdict_entry, ieq): """ Initialisation """ self._ieq = ieq return self._ieq + self._n_eq @abstractmethod # def staticmat(self, **kwargs): def staticmat(self, LM_C, LM_K, LM_Q, MB_beam, MB_tstep, ts, num_LM_eq, sys_size, dt, Lambda, Lambda_dot): """ Generates the structural matrices (damping, stiffness) and the independent vector associated to the LagrangeConstraint in a static simulation """ return np.zeros((6, 6)) @abstractmethod # def dynamicmat(self, **kwargs): def dynamicmat(self, LM_C, LM_K, LM_Q, MB_beam, MB_tstep, ts, num_LM_eq, sys_size, dt, Lambda, Lambda_dot): """ Generates the structural matrices (damping, stiffness) and the independent vector associated to the LagrangeConstraint in a dynamic simulation """ return np.zeros((10, 10)) @abstractmethod # def staticpost(self, **kwargs): def staticpost(self, lc_list, MB_beam, MB_tstep): """ Postprocess operations needed by the LagrangeConstraint in a static simulation """ return @abstractmethod # def dynamicpost(self, **kwargs): def dynamicpost(self, lc_list, MB_beam, MB_tstep): """ Postprocess operations needed by the LagrangeConstraint in a dynamic simulation """ return ################################################################################ # Auxiliar functions ################################################################################ def define_node_dof(MB_beam, node_body, num_node): """ define_node_dof Define the position of the first degree of freedom associated to a certain node Args: MB_beam(list): list of :class:`~sharpy.structure.models.beam.Beam` node_body(int): body to which the node belongs num_node(int): number os the node within the body Returns: node_dof(int): first degree of freedom associated to the node """ node_dof = 0 for ibody in range(node_body): node_dof += MB_beam[ibody].num_dof.value if MB_beam[ibody].FoR_movement == 'free': node_dof += 10 node_dof += 6*MB_beam[node_body].vdof[num_node] return node_dof def define_FoR_dof(MB_beam, FoR_body): """ define_FoR_dof Define the position of the first degree of freedom associated to a certain frame of reference Args: MB_beam(list): list of :class:`~sharpy.structure.models.beam.Beam` node_body(int): body to which the node belongs num_node(int): number os the node within the body Returns: node_dof(int): first degree of freedom associated to the node """ FoR_dof = 0 for ibody in range(FoR_body): FoR_dof += MB_beam[ibody].num_dof.value if MB_beam[ibody].FoR_movement == 'free': FoR_dof += 10 FoR_dof += MB_beam[FoR_body].num_dof.value return FoR_dof def set_value_or_default(dictionary, key, default_val): try: value = dictionary[key] except KeyError: value = default_val return value ################################################################################ # Equations ################################################################################ def equal_pos_node_FoR(MB_tstep, MB_beam, FoR_body, node_body, inode_in_body, node_FoR_dof, node_dof, FoR_dof, sys_size, Lambda, scalingFactor, penaltyFactor, ieq, LM_K, LM_C, LM_Q): """ This function generates the stiffness and damping matrices and the independent vector associated to a constraint that imposes equal positions between a node and a frame of reference See ``LagrangeConstraints`` for the description of variables Args: node_FoR_dof (int): position of the first degree of freedom of the FoR to which the "node" belongs node_dof (int): position of the first degree of freedom associated to the "node" FoR_body (int): body number of the "FoR" FoR_dof (int): position of the first degree of freedom associated to the "FoR" """ num_LM_eq_specific = 3 Bnh = np.zeros((num_LM_eq_specific, sys_size), dtype=ct.c_double, order = 'F') B = np.zeros((num_LM_eq_specific, sys_size), dtype=ct.c_double, order = 'F') # Simplify notation node_cga = MB_tstep[node_body].cga() node_pos = MB_tstep[node_body].pos[inode_in_body, :] node_FoR_pos = MB_tstep[node_body].for_pos[0:3] FoR_pos = MB_tstep[FoR_body].for_pos[0:3] # if MB_beam[node_body].FoR_movement == 'free': B[:, node_FoR_dof:node_FoR_dof+3] = np.eye(3) B[:, node_dof:node_dof+3] = node_cga B[:, FoR_dof:FoR_dof+3] = -np.eye(3) LM_K[sys_size + ieq : sys_size + ieq + num_LM_eq_specific, :sys_size] += scalingFactor*B LM_K[:sys_size, sys_size + ieq : sys_size + ieq + num_LM_eq_specific] += scalingFactor*np.transpose(B) LM_Q[:sys_size] += scalingFactor*np.dot(np.transpose(B), Lambda[ieq:ieq+num_LM_eq_specific]) LM_Q[sys_size+ieq:sys_size+ieq+num_LM_eq_specific] += scalingFactor*(node_FoR_pos + np.dot(node_cga, node_pos) - FoR_pos) LM_C[node_dof:node_dof+3, node_FoR_dof+6:node_FoR_dof+10] += scalingFactor*ag.der_CquatT_by_v(MB_tstep[node_body].quat, Lambda[ieq : ieq + num_LM_eq_specific]) if penaltyFactor: q = np.zeros((sys_size, )) q[node_FoR_dof:node_FoR_dof+3] = node_FoR_pos q[node_dof:node_dof+3] = node_pos q[FoR_dof:FoR_dof+3] = FoR_pos LM_Q[:sys_size] += penaltyFactor*np.dot(B.T, np.dot(B, q)) LM_K[node_FoR_dof:node_FoR_dof+3, node_FoR_dof:node_FoR_dof+3] += penaltyFactor*np.eye(3) LM_K[node_FoR_dof:node_FoR_dof+3, node_dof:node_dof+3] += penaltyFactor*node_cga LM_K[node_FoR_dof:node_FoR_dof+3, FoR_dof:FoR_dof+3] += -penaltyFactor*np.eye(3) LM_C[node_FoR_dof:node_FoR_dof+3, node_FoR_dof+6:node_FoR_dof+10] += penaltyFactor*ag.der_Cquat_by_v(MB_tstep[node_body].quat, node_pos) LM_K[node_dof:node_dof+3, node_FoR_dof:node_FoR_dof+3] += penaltyFactor*node_cga.T LM_K[node_dof:node_dof+3, node_dof:node_dof+3] += penaltyFactor*np.eye(3) LM_K[node_dof:node_dof+3, FoR_dof:FoR_dof+3] += -penaltyFactor*node_cga.T LM_C[node_dof:node_dof+3, node_FoR_dof+6:node_FoR_dof+10] += penaltyFactor*(ag.der_CquatT_by_v(MB_tstep[node_body].quat, node_FoR_pos - FoR_pos)) LM_K[FoR_dof:FoR_dof+3, node_FoR_dof:node_FoR_dof+3] += -penaltyFactor*np.eye(3) LM_K[FoR_dof:FoR_dof+3, node_dof:node_dof+3] += -penaltyFactor*node_cga.T LM_K[FoR_dof:FoR_dof+3, FoR_dof:FoR_dof+3] += penaltyFactor*np.eye(3) LM_C[FoR_dof:FoR_dof+3, node_FoR_dof+6:node_FoR_dof+10] += -penaltyFactor*ag.der_Cquat_by_v(MB_tstep[node_body].quat, node_pos) ieq += 3 return ieq def equal_lin_vel_node_FoR(MB_tstep, MB_beam, FoR_body, node_body, node_number, node_FoR_dof, node_dof, FoR_dof, sys_size, Lambda_dot, scalingFactor, penaltyFactor, ieq, LM_K, LM_C, LM_Q): """ This function generates the stiffness and damping matrices and the independent vector associated to a constraint that imposes equal linear velocities between a node and a frame of reference See ``LagrangeConstraints`` for the description of variables Args: node_number (int): number of the "node" within its own body node_body (int): body number of the "node" node_FoR_dof (int): position of the first degree of freedom of the FoR to which the "node" belongs node_dof (int): position of the first degree of freedom associated to the "node" FoR_body (int): body number of the "FoR" FoR_dof (int): position of the first degree of freedom associated to the "FoR" """ num_LM_eq_specific = 3 Bnh = np.zeros((num_LM_eq_specific, sys_size), dtype=ct.c_double, order = 'F') B = np.zeros((num_LM_eq_specific, sys_size), dtype=ct.c_double, order = 'F') # Simplify notation node_cga = MB_tstep[node_body].cga() node_FoR_va = MB_tstep[node_body].for_vel[0:3] node_FoR_wa = MB_tstep[node_body].for_vel[3:6] node_Ra = MB_tstep[node_body].pos[node_number,:] node_dot_Ra = MB_tstep[node_body].pos_dot[node_number,:] FoR_cga = MB_tstep[FoR_body].cga() FoR_va = MB_tstep[FoR_body].for_vel[0:3] Bnh[:, FoR_dof:FoR_dof+3] = FoR_cga Bnh[:, node_dof:node_dof+3] = -1.0*node_cga if MB_beam[node_body].FoR_movement == 'free': Bnh[:, node_FoR_dof:node_FoR_dof+3] = -1.0*node_cga Bnh[:, node_FoR_dof+3:node_FoR_dof+6] = np.dot(node_cga,ag.skew(node_Ra)) LM_C[sys_size+ieq:sys_size+ieq+num_LM_eq_specific,:sys_size] += scalingFactor*Bnh LM_C[:sys_size,sys_size+ieq:sys_size+ieq+num_LM_eq_specific] += scalingFactor*np.transpose(Bnh) LM_Q[:sys_size] += scalingFactor*np.dot(np.transpose(Bnh), Lambda_dot[ieq:ieq+num_LM_eq_specific]) LM_Q[sys_size+ieq:sys_size+ieq+num_LM_eq_specific] += scalingFactor*(np.dot(FoR_cga, FoR_va) + -1.0*np.dot(node_cga, node_dot_Ra + node_FoR_va + -1.0*np.dot(ag.skew(node_Ra), node_FoR_wa))) LM_C[FoR_dof:FoR_dof+3, FoR_dof+6:FoR_dof+10] += scalingFactor*ag.der_CquatT_by_v(MB_tstep[FoR_body].quat, Lambda_dot[ieq:ieq+num_LM_eq_specific]) if MB_beam[node_body].FoR_movement == 'free': LM_C[node_dof:node_dof+3,node_FoR_dof+6:node_FoR_dof+10] -= scalingFactor*ag.der_CquatT_by_v(MB_tstep[node_body].quat, Lambda_dot[ieq:ieq+num_LM_eq_specific]) LM_C[node_FoR_dof:node_FoR_dof+3,node_FoR_dof+6:node_FoR_dof+10] -= scalingFactor*ag.der_CquatT_by_v(MB_tstep[node_body].quat,Lambda_dot[ieq:ieq+num_LM_eq_specific]) LM_C[node_FoR_dof+3:node_FoR_dof+6,node_FoR_dof+6:node_FoR_dof+10] += scalingFactor*np.dot(ag.skew(node_Ra).T, ag.der_CquatT_by_v(MB_tstep[node_body].quat, Lambda_dot[ieq:ieq+num_LM_eq_specific])) LM_K[node_FoR_dof+3:node_FoR_dof+6,node_dof:node_dof+3] += scalingFactor*ag.skew(np.dot(node_cga.T,Lambda_dot[ieq:ieq+num_LM_eq_specific])) if penaltyFactor: q = np.zeros((sys_size)) q[FoR_dof:FoR_dof+3] = FoR_va q[node_dof:node_dof+3] = node_dot_Ra if MB_beam[node_body].FoR_movement == 'free': q[node_FoR_dof:node_FoR_dof+3] = node_FoR_va q[node_FoR_dof+3:node_FoR_dof+6] = node_FoR_wa LM_Q[:sys_size] += penaltyFactor*np.dot(np.dot(Bnh.T, Bnh), q) LM_C[:sys_size, :sys_size] += penaltyFactor*np.dot(Bnh.T, Bnh) # Derivatives wrt the FoR quaterion LM_C[FoR_dof:FoR_dof+3, FoR_dof+6:FoR_dof+10] -= penaltyFactor*ag.der_CquatT_by_v(MB_tstep[FoR_body].quat, np.dot(node_cga, node_dot_Ra + node_FoR_va + np.dot(ag.skew(node_Ra), node_FoR_wa))) LM_C[node_dof:node_dof+3, FoR_dof+6:FoR_dof+10] -= penaltyFactor*np.dot(node_cga.T, ag.der_CquatT_by_v(MB_tstep[FoR_body].quat, FoR_va)) if MB_beam[node_body].FoR_movement == 'free': LM_C[node_FoR_dof:node_FoR_dof+3, FoR_dof+6:FoR_dof+10] -= penaltyFactor*np.dot(node_cga.T, ag.der_CquatT_by_v(MB_tstep[FoR_body].quat, FoR_va)) mat = ag.multiply_matrices(ag.skew(node_Ra).T, node_cga.T) LM_C[node_FoR_dof+3:node_FoR_dof+6, FoR_dof+6:FoR_dof+10] += penaltyFactor*np.dot(mat, ag.der_CquatT_by_v(MB_tstep[FoR_body].quat, FoR_va)) # Derivatives wrt the node quaternion if MB_beam[node_body].FoR_movement == 'free': vec = -node_dot_Ra - node_FoR_va + np.dot(ag.skew(node_Ra), node_FoR_wa) LM_C[FoR_dof:FoR_dof+3, node_FoR_dof+6:node_FoR_dof+10] += penaltyFactor*np.dot(FoR_cga.T, ag.der_Cquat_by_v(MB_tstep[node_body].quat, vec)) derivative = -ag.der_CquatT_by_v(MB_tstep[node_body].quat, np.dot(FoR_cga, FoR_va)) LM_C[node_dof:node_dof+3, node_FoR_dof+6:node_FoR_dof+10] += penaltyFactor*derivative LM_C[node_FoR_dof:node_FoR_dof+3, node_FoR_dof+6:node_FoR_dof+10] += penaltyFactor*derivative LM_C[node_FoR_dof+3:node_FoR_dof+6, node_FoR_dof+6:node_FoR_dof+10] -= penaltyFactor*np.dot(ag.skew(node_Ra), derivative) # Derivatives wrt the node Ra LM_K[FoR_dof:FoR_dof+3, node_dof:node_dof+3] -= penaltyFactor*ag.multiply_matrices(FoR_cga.T, node_cga, ag.skew(node_FoR_wa)) LM_K[node_dof:node_dof+3, node_dof:node_dof+3] += penaltyFactor*ag.skew(node_FoR_wa) if MB_beam[node_body].FoR_movement == 'free': LM_K[node_FoR_dof:node_FoR_dof+3, node_dof:node_dof+3] += penaltyFactor*ag.skew(node_FoR_wa) vec = ag.multiply_matrices(node_cga.T, FoR_cga, FoR_va) - node_dot_Ra - node_FoR_va LM_K[node_FoR_dof+3:node_FoR_dof+6, node_dof:node_dof+3] += penaltyFactor*ag.skew(vec) LM_K[node_FoR_dof+3:node_FoR_dof+6, node_dof:node_dof+3] -= penaltyFactor*ag.der_skewp_skewp_v(node_Ra, node_FoR_wa) ieq += 3 return ieq def def_rot_axis_FoR_wrt_node_general(MB_tstep, MB_beam, FoR_body, node_body, node_number, node_FoR_dof, node_dof, FoR_dof, sys_size, Lambda_dot, rot_axisB, scalingFactor, penaltyFactor, ieq, LM_K, LM_C, LM_Q, indep): """ This function generates the stiffness and damping matrices and the independent vector associated to a joint that forces the rotation axis of a FoR to be parallel to a certain direction. This direction is defined in the B FoR of a node and, thus, might change along the simulation. See ``LagrangeConstraints`` for the description of variables Args: rot_axisB (np.ndarray): Rotation axis with respect to the node B FoR indep (np.ndarray): Number of the equations that are used as independent node_number (int): number of the "node" within its own body node_body (int): body number of the "node" node_FoR_dof (int): position of the first degree of freedom of the FoR to which the "node" belongs node_dof (int): position of the first degree of freedom associated to the "node" FoR_body (int): body number of the "FoR" FoR_dof (int): position of the first degree of freedom associated to the "FoR" """ ielem, inode_in_elem = MB_beam[node_body].node_master_elem[node_number] # Simplify notation cab = ag.crv2rotation(MB_tstep[node_body].psi[ielem,inode_in_elem,:]) node_cga = MB_tstep[node_body].cga() FoR_cga = MB_tstep[FoR_body].cga() FoR_wa = MB_tstep[FoR_body].for_vel[3:6] if not indep: aux_Bnh = ag.multiply_matrices(ag.skew(rot_axisB), cab.T, node_cga.T, FoR_cga) # indep = None n0 = np.linalg.norm(aux_Bnh[0,:]) n1 = np.linalg.norm(aux_Bnh[1,:]) n2 = np.linalg.norm(aux_Bnh[2,:]) if ((n0 < n1) and (n0 < n2)): # indep = np.array([1,2], dtype = int) indep[:] = [1, 2] # new_Lambda_dot = np.array([0., Lambda_dot[ieq], Lambda_dot[ieq+1]]) elif ((n1 < n0) and (n1 < n2)): # indep = np.array([0,2], dtype = int) indep[:] = [0, 2] # new_Lambda_dot = np.array([Lambda_dot[ieq], 0.0, Lambda_dot[ieq+1]]) elif ((n2 < n0) and (n2 < n1)): # indep = np.array([0,1], dtype = int) indep[:] = [0, 1] # new_Lambda_dot = np.array([Lambda_dot[ieq], Lambda_dot[ieq+1], 0.0]) new_Lambda_dot = np.zeros(3) new_Lambda_dot[indep[0]] = Lambda_dot[ieq] new_Lambda_dot[indep[1]] = Lambda_dot[ieq+1] num_LM_eq_specific = 2 Bnh = np.zeros((num_LM_eq_specific, sys_size), dtype=ct.c_double, order = 'F') B = np.zeros((num_LM_eq_specific, sys_size), dtype=ct.c_double, order = 'F') # Lambda_dot[ieq:ieq+num_LM_eq_specific] # np.concatenate((Lambda_dot[ieq:ieq+num_LM_eq_specific], np.array([0.]))) Bnh[:, FoR_dof+3:FoR_dof+6] = ag.multiply_matrices(ag.skew(rot_axisB), cab.T, node_cga.T, FoR_cga)[indep,:] # Constrain angular velocities LM_Q[:sys_size] += scalingFactor*np.dot(np.transpose(Bnh), Lambda_dot[ieq:ieq+num_LM_eq_specific]) LM_Q[sys_size+ieq:sys_size+ieq+num_LM_eq_specific] += scalingFactor*ag.multiply_matrices(ag.skew(rot_axisB), cab.T, node_cga.T, FoR_cga, FoR_wa)[indep] LM_C[sys_size+ieq:sys_size+ieq+num_LM_eq_specific,:sys_size] += scalingFactor*Bnh LM_C[:sys_size,sys_size+ieq:sys_size+ieq+num_LM_eq_specific] += scalingFactor*np.transpose(Bnh) if MB_beam[node_body].FoR_movement == 'free': LM_C[FoR_dof+3:FoR_dof+6,node_FoR_dof+6:node_FoR_dof+10] += scalingFactor*np.dot(FoR_cga.T, ag.der_Cquat_by_v(MB_tstep[node_body].quat, ag.multiply_matrices(cab, ag.skew(rot_axisB).T, new_Lambda_dot))) LM_C[FoR_dof+3:FoR_dof+6,FoR_dof+6:FoR_dof+10] += scalingFactor*ag.der_CquatT_by_v(MB_tstep[FoR_body].quat, ag.multiply_matrices(node_cga, cab, ag.skew(rot_axisB).T, new_Lambda_dot)) LM_K[FoR_dof+3:FoR_dof+6,node_dof+3:node_dof+6] += scalingFactor*ag.multiply_matrices(FoR_cga.T, node_cga, cab, ag.skew(rot_axisB).T, new_Lambda_dot) if penaltyFactor: q = np.zeros((sys_size,)) q[FoR_dof+3:FoR_dof+6] = MB_tstep[FoR_body].for_vel[3:6] LM_Q[:sys_size] += penaltyFactor*np.dot(Bnh.T, np.dot(Bnh, q)) LM_C[:sys_size, :sys_size] += penaltyFactor*np.dot(Bnh.T, Bnh) sq_rot_axisB = np.dot(ag.skew(rot_axisB).T, ag.skew(rot_axisB)) # Derivatives with the quaternion of the FoR vec = ag.multiply_matrices(node_cga, cab, sq_rot_axisB, cab.T, node_cga.T, FoR_cga, FoR_wa) LM_C[FoR_dof+3:FoR_dof+6, FoR_dof+6:FoR_dof+10] += penaltyFactor*ag.der_CquatT_by_v(MB_tstep[FoR_body].quat, vec) mat = ag.multiply_matrices(FoR_cga.T, node_cga, cab, sq_rot_axisB, cab.T, node_cga.T) LM_C[FoR_dof+3:FoR_dof+6, FoR_dof+6:FoR_dof+10] += penaltyFactor*np.dot(mat, ag.der_Cquat_by_v(MB_tstep[FoR_body].quat, FoR_wa)) if MB_beam[node_body].FoR_movement == 'free': # Derivatives with the quaternion of the FoR of the node vec = ag.multiply_matrices(cab, sq_rot_axisB, cab.T, node_cga.T, FoR_cga, FoR_wa) LM_C[FoR_dof+3:FoR_dof+6, node_FoR_dof+6:node_FoR_dof+10] += penaltyFactor*np.dot(FoR_cga.T, ag.der_Cquat_by_v(MB_tstep[node_body].quat, vec)) mat = ag.multiply_matrices(FoR_cga.T, node_cga, cab, sq_rot_axisB, cab.T) vec = np.dot(FoR_cga, FoR_wa) LM_C[FoR_dof+3:FoR_dof+6, node_FoR_dof+6:node_FoR_dof+10] += penaltyFactor*np.dot(mat, ag.der_CquatT_by_v(MB_tstep[node_body].quat, vec)) # Derivatives with the CRV mat = np.dot(FoR_cga.T, node_cga) vec = ag.multiply_matrices(sq_rot_axisB, cab.T, node_cga.T, FoR_cga, FoR_wa) LM_K[FoR_dof+3:FoR_dof+6, node_dof+3:node_dof+6] += penaltyFactor*np.dot(mat, ag.der_Ccrv_by_v(MB_tstep[node_body].psi[ielem,inode_in_elem,:], vec)) mat = ag.multiply_matrices(FoR_cga.T, node_cga, cab, sq_rot_axisB) vec = ag.multiply_matrices(node_cga.T, FoR_cga, FoR_wa) LM_K[FoR_dof+3:FoR_dof+6, node_dof+3:node_dof+6] += penaltyFactor*np.dot(mat, ag.der_CcrvT_by_v(MB_tstep[node_body].psi[ielem,inode_in_elem,:], vec)) ieq += 2 return ieq def def_rot_axis_FoR_wrt_node_xyz(MB_tstep, MB_beam, FoR_body, node_body, node_number, node_FoR_dof, node_dof, FoR_dof, sys_size, Lambda_dot, rot_axisB, scalingFactor, penaltyFactor, ieq, LM_K, LM_C, LM_Q, zero_comp): """ This function generates the stiffness and damping matrices and the independent vector associated to a joint that forces the rotation axis of a FoR to be parallel to a certain direction. This direction is defined in the B FoR of a node and parallel to x, y or z See ``LagrangeConstraints`` for the description of variables Args: rot_axisB (np.ndarray): Rotation axis with respect to the node B FoR indep (np.ndarray): Number of the equations that are used as independent node_number (int): number of the "node" within its own body node_body (int): body number of the "node" node_FoR_dof (int): position of the first degree of freedom of the FoR to which the "node" belongs node_dof (int): position of the first degree of freedom associated to the "node" FoR_body (int): body number of the "FoR" FoR_dof (int): position of the first degree of freedom associated to the "FoR" """ ielem, inode_in_elem = MB_beam[node_body].node_master_elem[node_number] num_LM_eq_specific = 2 Bnh = np.zeros((num_LM_eq_specific, sys_size), dtype=ct.c_double, order = 'F') B = np.zeros((num_LM_eq_specific, sys_size), dtype=ct.c_double, order = 'F') # Simplify notation cab = ag.crv2rotation(MB_tstep[node_body].psi[ielem,inode_in_elem,:]) node_cga = MB_tstep[node_body].cga() FoR_cga = MB_tstep[FoR_body].cga() FoR_wa = MB_tstep[FoR_body].for_vel[3:6] psi = MB_tstep[node_body].psi[ielem,inode_in_elem,:] psi_dot = MB_tstep[node_body].psi_dot[ielem,inode_in_elem,:] # Components to be zero Z = np.zeros((2,3)) Z[:, zero_comp] = np.eye(2) Bnh[:, FoR_dof+3:FoR_dof+6] += ag.multiply_matrices(Z, cab.T, node_cga.T, FoR_cga) Bnh[:, node_dof+3:node_dof+6] -= ag.multiply_matrices(Z, ag.crv2tan(psi)) Bnh[:, node_FoR_dof+3:node_FoR_dof+6] -= ag.multiply_matrices(Z, cab.T) # Constrain angular velocities LM_Q[:sys_size] += scalingFactor*np.dot(np.transpose(Bnh), Lambda_dot[ieq:ieq+num_LM_eq_specific]) LM_Q[sys_size+ieq:sys_size+ieq+num_LM_eq_specific] += scalingFactor*ag.multiply_matrices(Z, cab.T, node_cga.T, FoR_cga, FoR_wa) LM_Q[sys_size+ieq:sys_size+ieq+num_LM_eq_specific] -= scalingFactor*ag.multiply_matrices(Z, ag.crv2tan(psi), psi_dot) LM_Q[sys_size+ieq:sys_size+ieq+num_LM_eq_specific] -= scalingFactor*ag.multiply_matrices(Z, cab.T, MB_tstep[node_body].for_vel[3:6]) LM_C[sys_size+ieq:sys_size+ieq+num_LM_eq_specific,:sys_size] += scalingFactor*Bnh LM_C[:sys_size,sys_size+ieq:sys_size+ieq+num_LM_eq_specific] += scalingFactor*np.transpose(Bnh) vec = ag.multiply_matrices(node_cga, cab, Z.T, Lambda_dot[ieq:ieq+num_LM_eq_specific]) LM_C[FoR_dof+3:FoR_dof+6, FoR_dof+6:FoR_dof+10] += scalingFactor*ag.der_CquatT_by_v(MB_tstep[FoR_body].quat, vec) if MB_beam[node_body].FoR_movement == 'free': vec = ag.multiply_matrices(cab, Z.T, Lambda_dot[ieq:ieq+num_LM_eq_specific]) LM_C[FoR_dof+3:FoR_dof+6, node_FoR_dof+6:node_FoR_dof+10] += scalingFactor*ag.multiply_matrices(FoR_cga.T, ag.der_Cquat_by_v(MB_tstep[node_body].quat, vec)) LM_K[FoR_dof+3:FoR_dof+6, node_dof+3:node_dof+6] += scalingFactor*ag.multiply_matrices(FoR_cga.T, node_cga, ag.der_Ccrv_by_v(MB_tstep[node_body].psi[ielem,inode_in_elem,:], np.dot(Z.T, Lambda_dot[ieq:ieq+num_LM_eq_specific]))) LM_K[node_dof+3:node_dof+6, node_dof+3:node_dof+6] -= scalingFactor*ag.der_TanT_by_xv(psi, ag.multiply_matrices(Z.T, Lambda_dot[ieq:ieq+num_LM_eq_specific])) LM_K[node_FoR_dof+3:node_FoR_dof+6, node_dof+3:node_dof+6] -= scalingFactor*ag.der_Ccrv_by_v(psi, ag.multiply_matrices(Z.T, Lambda_dot[ieq:ieq+num_LM_eq_specific])) if penaltyFactor: q = np.zeros((sys_size,)) q[FoR_dof+3:FoR_dof+6] = FoR_wa LM_Q[:sys_size] += penaltyFactor*np.dot(Bnh.T, np.dot(Bnh, q)) LM_C[:sys_size, :sys_size] += penaltyFactor*np.dot(Bnh.T, Bnh) ZTZ = np.dot(Z.T, Z) # Derivatives with the quaternion of the FoR vec = ag.multiply_matrices(node_cga, cab, ZTZ, cab.T, node_cga.T, FoR_cga, FoR_wa) LM_C[FoR_dof+3:FoR_dof+6, FoR_dof+6:FoR_dof+10] += penaltyFactor*ag.der_CquatT_by_v(MB_tstep[FoR_body].quat, vec) mat = ag.multiply_matrices(FoR_cga.T, node_cga, cab, ZTZ, cab.T, node_cga.T) LM_C[FoR_dof+3:FoR_dof+6, FoR_dof+6:FoR_dof+10] += penaltyFactor*np.dot(mat, ag.der_Cquat_by_v(MB_tstep[FoR_body].quat, FoR_wa)) if MB_beam[node_body].FoR_movement == 'free': # Derivatives with the quaternion of the FoR of the node vec = ag.multiply_matrices(cab, ZTZ, cab.T, node_cga.T, FoR_cga, FoR_wa) LM_C[FoR_dof+3:FoR_dof+6, node_FoR_dof+6:node_FoR_dof+10] += penaltyFactor*np.dot(FoR_cga.T, ag.der_Cquat_by_v(MB_tstep[node_body].quat, vec)) mat = ag.multiply_matrices(FoR_cga.T, node_cga, cab, ZTZ, cab.T) vec = np.dot(FoR_cga, FoR_wa) LM_C[FoR_dof+3:FoR_dof+6, node_FoR_dof+6:node_FoR_dof+10] += penaltyFactor*np.dot(mat, ag.der_CquatT_by_v(MB_tstep[node_body].quat, vec)) # Derivatives with the CRV mat = np.dot(FoR_cga.T, node_cga) vec = ag.multiply_matrices(ZTZ, cab.T, node_cga.T, FoR_cga, FoR_wa) LM_K[FoR_dof+3:FoR_dof+6, node_dof+3:node_dof+6] += penaltyFactor*np.dot(mat, ag.der_Ccrv_by_v(MB_tstep[node_body].psi[ielem,inode_in_elem,:], vec)) mat = ag.multiply_matrices(FoR_cga.T, node_cga, cab, ZTZ) vec = ag.multiply_matrices(node_cga.T, FoR_cga, FoR_wa) LM_K[FoR_dof+3:FoR_dof+6, node_dof+3:node_dof+6] += penaltyFactor*np.dot(mat, ag.der_CcrvT_by_v(MB_tstep[node_body].psi[ielem,inode_in_elem,:], vec)) ieq += 2 return ieq def def_rot_vel_FoR_wrt_node(MB_tstep, MB_beam, FoR_body, node_body, node_number, node_FoR_dof, node_dof, FoR_dof, sys_size, Lambda_dot, rot_axisB, rot_vel, scalingFactor, penaltyFactor, ieq, LM_K, LM_C, LM_Q): """ This function generates the stiffness and damping matrices and the independent vector associated to a joint that forces the rotation velocity of a FoR with respect to a node See ``LagrangeConstraints`` for the description of variables Args: rot_axisB (np.ndarray): Rotation axis with respect to the node B FoR rot_vel (float): Rotation velocity node_number (int): number of the "node" within its own body node_body (int): body number of the "node" node_FoR_dof (int): position of the first degree of freedom of the FoR to which the "node" belongs node_dof (int): position of the first degree of freedom associated to the "node" FoR_body (int): body number of the "FoR" FoR_dof (int): position of the first degree of freedom associated to the "FoR" """ num_LM_eq_specific = 1 Bnh = np.zeros((num_LM_eq_specific, sys_size), dtype=ct.c_double, order = 'F') B = np.zeros((num_LM_eq_specific, sys_size), dtype=ct.c_double, order = 'F') ielem, inode_in_elem = MB_beam[node_body].node_master_elem[node_number] Bnh[:, FoR_dof+3:FoR_dof+6] = ag.multiply_matrices(rot_axisB, ag.crv2rotation(MB_tstep[node_body].psi[ielem,inode_in_elem,:]).T, MB_tstep[node_body].cga().T, MB_tstep[FoR_body].cga()) # Constrain angular velocities LM_Q[:sys_size] += scalingFactor*np.dot(np.transpose(Bnh),Lambda_dot[ieq:ieq+num_LM_eq_specific]) LM_Q[sys_size+ieq:sys_size+ieq+num_LM_eq_specific] += scalingFactor*ag.multiply_matrices(rot_axisB, ag.crv2rotation(MB_tstep[node_body].psi[ielem,inode_in_elem,:]).T, MB_tstep[node_body].cga().T, MB_tstep[FoR_body].cga(), MB_tstep[FoR_body].for_vel[3:6]) - scalingFactor*rot_vel LM_C[sys_size+ieq:sys_size+ieq+num_LM_eq_specific,:sys_size] += scalingFactor*Bnh LM_C[:sys_size,sys_size+ieq:sys_size+ieq+num_LM_eq_specific] += scalingFactor*np.transpose(Bnh) if MB_beam[node_body].FoR_movement == 'free': LM_C[FoR_dof+3:FoR_dof+6,node_FoR_dof+6:node_FoR_dof+10] += scalingFactor*np.dot(MB_tstep[FoR_body].cga().T, ag.der_Cquat_by_v(MB_tstep[node_body].quat, ag.multiply_matrices(ag.crv2rotation(MB_tstep[node_body].psi[ielem,inode_in_elem,:]), # rot_axisB.T, rot_axisB.T*Lambda_dot[ieq:ieq+num_LM_eq_specific]))) LM_C[FoR_dof+3:FoR_dof+6,FoR_dof+6:FoR_dof+10] += scalingFactor*ag.der_CquatT_by_v(MB_tstep[FoR_body].quat, ag.multiply_matrices(MB_tstep[node_body].cga(), ag.crv2rotation(MB_tstep[node_body].psi[ielem,inode_in_elem,:]).T, rot_axisB.T*Lambda_dot[ieq:ieq+num_LM_eq_specific])) LM_K[FoR_dof+3:FoR_dof+6,node_dof+3:node_dof+6] += scalingFactor*ag.multiply_matrices(MB_tstep[FoR_body].cga().T, MB_tstep[node_body].cga(), ag.der_Ccrv_by_v(MB_tstep[node_body].psi[ielem,inode_in_elem,:], rot_axisB.T*Lambda_dot[ieq:ieq+num_LM_eq_specific])) ieq += 1 return ieq def def_rot_vect_FoR_wrt_node(MB_tstep, MB_beam, FoR_body, node_body, node_number, node_FoR_dof, node_dof, FoR_dof, sys_size, Lambda_dot, rot_vect, scalingFactor, penaltyFactor, ieq, LM_K, LM_C, LM_Q): """ This function fixes the rotation velocity VECTOR of a FOR equal to a velocity vector defined in the B FoR of a node This function is a new implementation that combines and simplifies the use of 'def_rot_vel_FoR_wrt_node' and 'def_rot_axis_FoR_wrt_node' together """ num_LM_eq_specific = 3 Bnh = np.zeros((num_LM_eq_specific, sys_size), dtype=ct.c_double, order = 'F') # Simplify notation ielem, inode_in_elem = MB_beam[node_body].node_master_elem[node_number] node_cga = MB_tstep[node_body].cga() cab = ag.crv2rotation(MB_tstep[node_body].psi[ielem,inode_in_elem,:]) FoR_cga = MB_tstep[FoR_body].cga() FoR_wa = MB_tstep[FoR_body].for_vel[3:6] Bnh[:, FoR_dof+3:FoR_dof+6] = ag.multiply_matrices(cab.T, node_cga.T, FoR_cga) # Constrain angular velocities LM_Q[:sys_size] += scalingFactor*np.dot(np.transpose(Bnh), Lambda_dot[ieq:ieq+num_LM_eq_specific]) LM_Q[sys_size+ieq:sys_size+ieq+num_LM_eq_specific] += scalingFactor*(np.dot(Bnh[:, FoR_dof+3:FoR_dof+6], FoR_wa) - rot_vect) LM_C[sys_size+ieq:sys_size+ieq+num_LM_eq_specific,:sys_size] += scalingFactor*Bnh LM_C[:sys_size,sys_size+ieq:sys_size+ieq+num_LM_eq_specific] += scalingFactor*np.transpose(Bnh) if MB_beam[node_body].FoR_movement == 'free': LM_C[FoR_dof+3:FoR_dof+6,node_FoR_dof+6:node_FoR_dof+10] += scalingFactor*np.dot(FoR_cga.T, ag.der_Cquat_by_v(MB_tstep[node_body].quat, np.dot(cab, Lambda_dot[ieq:ieq+num_LM_eq_specific]))) LM_C[FoR_dof+3:FoR_dof+6,FoR_dof+6:FoR_dof+10] += scalingFactor*ag.der_CquatT_by_v(MB_tstep[FoR_body].quat, ag.multiply_matrices(node_cga, cab, Lambda_dot[ieq:ieq+num_LM_eq_specific])) LM_K[FoR_dof+3:FoR_dof+6,node_dof+3:node_dof+6] += scalingFactor*ag.multiply_matrices(FoR_cga.T, node_cga, ag.der_Ccrv_by_v(MB_tstep[node_body].psi[ielem,inode_in_elem,:], Lambda_dot[ieq:ieq+num_LM_eq_specific])) if penaltyFactor: LM_C[FoR_dof+3:FoR_dof+6, FoR_dof+3:FoR_dof+6] += penaltyFactor*np.eye(3) q = np.zeros((sys_size)) q[FoR_dof+3:FoR_dof+6] = FoR_wa LM_Q[:sys_size] += penaltyFactor*np.dot(np.dot(Bnh.T, Bnh), q) ieq += 3 return ieq ################################################################################ # Lagrange constraints ################################################################################ @lagrangeconstraint class hinge_node_FoR(BaseLagrangeConstraint): __doc__ = """ hinge_node_FoR This constraint forces a hinge behaviour between a node and a FoR See ``LagrangeConstraints`` for the description of variables Attributes: node_number (int): number of the "node" within its own body node_body (int): body number of the "node" FoR_body (int): body number of the "FoR" rot_axisB (np.ndarray): Rotation axis with respect to the node B FoR """ _lc_id = 'hinge_node_FoR' def __init__(self): self.required_parameters = ['node_in_body', 'body', 'body_FoR', 'rot_axisB'] self._n_eq = 5 def get_n_eq(self): return self._n_eq def initialise(self, MBdict_entry, ieq, print_info=True): self.node_number = MBdict_entry['node_in_body'] self.node_body = MBdict_entry['body'] self.FoR_body = MBdict_entry['body_FoR'] self.rot_axisB = MBdict_entry['rot_axisB'] self._ieq = ieq self.scalingFactor = set_value_or_default(MBdict_entry, "scalingFactor", 1.) self.penaltyFactor = set_value_or_default(MBdict_entry, "penaltyFactor", 0.) if (self.rot_axisB[[1, 2]] == 0).all(): self.rot_dir = 'x' self.zero_comp = np.array([1, 2], dtype=int) elif (self.rot_axisB[[0, 2]] == 0).all(): self.rot_dir = 'y' self.zero_comp = np.array([0, 2], dtype=int) elif (self.rot_axisB[[0, 1]] == 0).all(): self.rot_dir = 'z' self.zero_comp = np.array([0, 1], dtype=int) else: self.rot_dir = 'general' self.indep = [] return self._ieq + self._n_eq def staticmat(self, LM_C, LM_K, LM_Q, MB_beam, MB_tstep, ts, num_LM_eq, sys_size, dt, Lambda, Lambda_dot): return def dynamicmat(self, LM_C, LM_K, LM_Q, MB_beam, MB_tstep, ts, num_LM_eq, sys_size, dt, Lambda, Lambda_dot): # Define the position of the first degree of freedom associated to the node node_dof = define_node_dof(MB_beam, self.node_body, self.node_number) node_FoR_dof = define_FoR_dof(MB_beam, self.node_body) FoR_dof = define_FoR_dof(MB_beam, self.FoR_body) ieq = self._ieq # Define the equations # ieq = equal_pos_node_FoR(MB_tstep, MB_beam, self.FoR_body, self.node_body, self.node_number, node_FoR_dof, node_dof, FoR_dof, sys_size, Lambda, self.scalingFactor, self.penaltyFactor, ieq, LM_K, LM_C, LM_Q) ieq = equal_lin_vel_node_FoR(MB_tstep, MB_beam, self.FoR_body, self.node_body, self.node_number, node_FoR_dof, node_dof, FoR_dof, sys_size, Lambda_dot, self.scalingFactor, self.penaltyFactor, ieq, LM_K, LM_C, LM_Q) if self.rot_dir == 'general': ieq = def_rot_axis_FoR_wrt_node_general(MB_tstep, MB_beam, self.FoR_body, self.node_body, self.node_number, node_FoR_dof, node_dof, FoR_dof, sys_size, Lambda_dot, self.rot_axisB, self.scalingFactor, self.penaltyFactor, ieq, LM_K, LM_C, LM_Q, self.indep) else: ieq = def_rot_axis_FoR_wrt_node_xyz(MB_tstep, MB_beam, self.FoR_body, self.node_body, self.node_number, node_FoR_dof, node_dof, FoR_dof, sys_size, Lambda_dot, self.rot_axisB, self.scalingFactor, self.penaltyFactor, ieq, LM_K, LM_C, LM_Q, self.zero_comp) return def staticpost(self, lc_list, MB_beam, MB_tstep): return def dynamicpost(self, lc_list, MB_beam, MB_tstep): MB_tstep[self.FoR_body].for_pos[0:3] = np.dot(MB_tstep[self.node_body].cga(), MB_tstep[self.node_body].pos[self.node_number,:]) + MB_tstep[self.node_body].for_pos[0:3] return @lagrangeconstraint class hinge_node_FoR_constant_vel(BaseLagrangeConstraint): __doc__ = """ hinge_node_FoR_constant_vel This constraint forces a hinge behaviour between a node and a FoR and a constant rotation velocity at the join See ``LagrangeConstraints`` for the description of variables Attributes: node_number (int): number of the "node" within its own body node_body (int): body number of the "node" FoR_body (int): body number of the "FoR" rot_vect (np.ndarray): Rotation velocity vector in the node B FoR """ _lc_id = 'hinge_node_FoR_constant_vel' def __init__(self): self.required_parameters = ['node_in_body', 'body', 'body_FoR', 'rot_vect'] self._n_eq = 6 def get_n_eq(self): return self._n_eq def initialise(self, MBdict_entry, ieq, print_info=True): self.node_number = MBdict_entry['node_in_body'] self.node_body = MBdict_entry['body'] self.FoR_body = MBdict_entry['body_FoR'] self.rot_vect = MBdict_entry['rot_vect'] self._ieq = ieq self.indep = [] self.scalingFactor = set_value_or_default(MBdict_entry, "scalingFactor", 1.) self.penaltyFactor = set_value_or_default(MBdict_entry, "penaltyFactor", 0.) # self.static_constraint = fully_constrained_node_FoR() # self.static_constraint.initialise(MBdict_entry, ieq) return self._ieq + self._n_eq def staticmat(self, LM_C, LM_K, LM_Q, MB_beam, MB_tstep, ts, num_LM_eq, sys_size, dt, Lambda, Lambda_dot): return def dynamicmat(self, LM_C, LM_K, LM_Q, MB_beam, MB_tstep, ts, num_LM_eq, sys_size, dt, Lambda, Lambda_dot): # Define the position of the first degree of freedom associated to the node node_dof = define_node_dof(MB_beam, self.node_body, self.node_number) node_FoR_dof = define_FoR_dof(MB_beam, self.node_body) FoR_dof = define_FoR_dof(MB_beam, self.FoR_body) ieq = self._ieq # Define the equations # ieq = equal_pos_node_FoR(MB_tstep, MB_beam, self.FoR_body, self.node_body, self.node_number, node_FoR_dof, node_dof, FoR_dof, sys_size, Lambda, self.scalingFactor, self.penaltyFactor, ieq, LM_K, LM_C, LM_Q) ieq = equal_lin_vel_node_FoR(MB_tstep, MB_beam, self.FoR_body, self.node_body, self.node_number, node_FoR_dof, node_dof, FoR_dof, sys_size, Lambda_dot, self.scalingFactor, self.penaltyFactor, ieq, LM_K, LM_C, LM_Q) ieq = def_rot_vect_FoR_wrt_node(MB_tstep, MB_beam, self.FoR_body, self.node_body, self.node_number, node_FoR_dof, node_dof, FoR_dof, sys_size, Lambda_dot, self.rot_vect, self.scalingFactor, self.penaltyFactor, ieq, LM_K, LM_C, LM_Q) # ieq = def_rot_axis_FoR_wrt_node(MB_tstep, MB_beam, self.FoR_body, self.node_body, self.node_number, node_FoR_dof, node_dof, FoR_dof, sys_size, Lambda_dot, self.rot_axisB, self.scalingFactor, self.penaltyFactor, ieq, LM_K, LM_C, LM_Q, self.indep) # ieq = def_rot_vel_FoR_wrt_node(MB_tstep, MB_beam, self.FoR_body, self.node_body, self.node_number, node_FoR_dof, node_dof, FoR_dof, sys_size, Lambda_dot, self.rot_axisB, self.rot_vel, self.scalingFactor, self.penaltyFactor, ieq, LM_K, LM_C, LM_Q) return def staticpost(self, lc_list, MB_beam, MB_tstep): return def dynamicpost(self, lc_list, MB_beam, MB_tstep): MB_tstep[self.FoR_body].for_pos[0:3] = np.dot(MB_tstep[self.node_body].cga(), MB_tstep[self.node_body].pos[self.node_number,:]) + MB_tstep[self.node_body].for_pos[0:3] ielem, inode_in_elem = MB_beam[self.node_body].node_master_elem[self.node_number] node_cga = MB_tstep[self.node_body].cga() cab = ag.crv2rotation(MB_tstep[self.node_body].psi[ielem,inode_in_elem,:]) FoR_cga = MB_tstep[self.FoR_body].cga() rot_vect_A = ag.multiply_matrices(FoR_cga.T, node_cga, cab, self.rot_vect) MB_tstep[self.FoR_body].for_vel[3:6] = rot_vect_A.copy() return @lagrangeconstraint class spherical_node_FoR(BaseLagrangeConstraint): __doc__ = """ spherical_node_FoR This constraint forces a spherical join between a node and a FoR See ``LagrangeConstraints`` for the description of variables Attributes: node_number (int): number of the "node" within its own body node_body (int): body number of the "node" FoR_body (int): body number of the "FoR" """ _lc_id = 'spherical_node_FoR' def __init__(self): self.required_parameters = ['node_in_body', 'body', 'body_FoR'] self._n_eq = 3 def get_n_eq(self): return self._n_eq def initialise(self, MBdict_entry, ieq, print_info=True): self.node_number = MBdict_entry['node_in_body'] self.node_body = MBdict_entry['body'] self.FoR_body = MBdict_entry['body_FoR'] self._ieq = ieq self.scalingFactor = set_value_or_default(MBdict_entry, "scalingFactor", 1.) self.penaltyFactor = set_value_or_default(MBdict_entry, "penaltyFactor", 0.) return self._ieq + self._n_eq def staticmat(self, LM_C, LM_K, LM_Q, MB_beam, MB_tstep, ts, num_LM_eq, sys_size, dt, Lambda, Lambda_dot): return def dynamicmat(self, LM_C, LM_K, LM_Q, MB_beam, MB_tstep, ts, num_LM_eq, sys_size, dt, Lambda, Lambda_dot): # Define the position of the first degree of freedom associated to the node node_dof = define_node_dof(MB_beam, self.node_body, self.node_number) node_FoR_dof = define_FoR_dof(MB_beam, self.node_body) FoR_dof = define_FoR_dof(MB_beam, self.FoR_body) ieq = self._ieq # Define the equations ieq = equal_lin_vel_node_FoR(MB_tstep, MB_beam, self.FoR_body, self.node_body, self.node_number, node_FoR_dof, node_dof, FoR_dof, sys_size, Lambda_dot, self.scalingFactor, self.penaltyFactor, ieq, LM_K, LM_C, LM_Q) return def staticpost(self, lc_list, MB_beam, MB_tstep): return def dynamicpost(self, lc_list, MB_beam, MB_tstep): MB_tstep[self.FoR_body].for_pos[0:3] = np.dot(MB_tstep[self.node_body].cga(), MB_tstep[self.node_body].pos[self.node_number,:]) + MB_tstep[self.node_body].for_pos[0:3] return @lagrangeconstraint class free(BaseLagrangeConstraint): _lc_id = 'free' __doc__ = _lc_id def __init__(self): self.required_parameters = [] self._n_eq = 0 def get_n_eq(self): return self._n_eq def initialise(self, MBdict_entry, ieq, print_info=True): self._ieq = ieq return self._ieq + self._n_eq def staticmat(self, LM_C, LM_K, LM_Q, MB_beam, MB_tstep, ts, num_LM_eq, sys_size, dt, Lambda, Lambda_dot): return def dynamicmat(self, LM_C, LM_K, LM_Q, MB_beam, MB_tstep, ts, num_LM_eq, sys_size, dt, Lambda, Lambda_dot): return def staticpost(self, lc_list, MB_beam, MB_tstep): return def dynamicpost(self, lc_list, MB_beam, MB_tstep): return @lagrangeconstraint class spherical_FoR(BaseLagrangeConstraint): __doc__ = """ spherical_FoR This constraint forces a spherical join at a FoR See ``LagrangeConstraints`` for the description of variables Attributes: body_FoR (int): body number of the "FoR" """ _lc_id = 'spherical_FoR' def __init__(self): self.required_parameters = ['body_FoR'] self._n_eq = 3 def get_n_eq(self): return self._n_eq def initialise(self, MBdict_entry, ieq, print_info=True): self.body_FoR = MBdict_entry['body_FoR'] self._ieq = ieq self.scalingFactor = set_value_or_default(MBdict_entry, "scalingFactor", 1.) self.penaltyFactor = set_value_or_default(MBdict_entry, "penaltyFactor", 0.) return self._ieq + self._n_eq def staticmat(self, LM_C, LM_K, LM_Q, MB_beam, MB_tstep, ts, num_LM_eq, sys_size, dt, Lambda, Lambda_dot): return def dynamicmat(self, LM_C, LM_K, LM_Q, MB_beam, MB_tstep, ts, num_LM_eq, sys_size, dt, Lambda, Lambda_dot): num_LM_eq_specific = self._n_eq Bnh = np.zeros((num_LM_eq_specific, sys_size), dtype=ct.c_double, order = 'F') B = np.zeros((num_LM_eq_specific, sys_size), dtype=ct.c_double, order = 'F') # Define the position of the first degree of freedom associated to the FoR FoR_dof = define_FoR_dof(MB_beam, self.body_FoR) ieq = self._ieq Bnh[:3, FoR_dof:FoR_dof+3] = 1.0*np.eye(3) LM_C[sys_size+ieq:sys_size+ieq+num_LM_eq_specific,:sys_size] += self.scalingFactor*Bnh LM_C[:sys_size,sys_size+ieq:sys_size+ieq+num_LM_eq_specific] += self.scalingFactor*np.transpose(Bnh) LM_Q[:sys_size] += self.scalingFactor*np.dot(np.transpose(Bnh),Lambda_dot[ieq:ieq+num_LM_eq_specific]) LM_Q[sys_size+ieq:sys_size+ieq+3] += self.scalingFactor*MB_tstep[self.body_FoR].for_vel[0:3].astype(dtype=ct.c_double, copy=True, order='F') ieq += 3 return def staticpost(self, lc_list, MB_beam, MB_tstep): return def dynamicpost(self, lc_list, MB_beam, MB_tstep): return @lagrangeconstraint class hinge_FoR(BaseLagrangeConstraint): __doc__ = """ hinge_FoR This constraint forces a hinge at a FoR See ``LagrangeConstraints`` for the description of variables Attributes: body_FoR (int): body number of the "FoR" rot_axis_AFoR (np.ndarray): Rotation axis with respect to the node A FoR """ _lc_id = 'hinge_FoR' def __init__(self): self.required_parameters = ['body_FoR', 'rot_axis_AFoR'] self._n_eq = 5 def get_n_eq(self): return self._n_eq def initialise(self, MBdict_entry, ieq, print_info=True): self.body_FoR = MBdict_entry['body_FoR'] self.rot_axis = MBdict_entry['rot_axis_AFoR'] self._ieq = ieq self.scalingFactor = set_value_or_default(MBdict_entry, "scalingFactor", 1.) self.penaltyFactor = set_value_or_default(MBdict_entry, "penaltyFactor", 0.) if (self.rot_axis[[1, 2]] == 0).all(): self.rot_dir = 'x' self.zero_comp = np.array([1, 2], dtype=int) elif (self.rot_axis[[0, 2]] == 0).all(): self.rot_dir = 'y' self.zero_comp = np.array([0, 2], dtype=int) elif (self.rot_axis[[0, 1]] == 0).all(): self.rot_dir = 'z' self.zero_comp = np.array([0, 1], dtype=int) else: self.rot_dir = 'general' return self._ieq + self._n_eq def staticmat(self, LM_C, LM_K, LM_Q, MB_beam, MB_tstep, ts, num_LM_eq, sys_size, dt, Lambda, Lambda_dot): return def dynamicmat(self, LM_C, LM_K, LM_Q, MB_beam, MB_tstep, ts, num_LM_eq, sys_size, dt, Lambda, Lambda_dot): num_LM_eq_specific = self._n_eq Bnh = np.zeros((num_LM_eq_specific, sys_size), dtype=ct.c_double, order = 'F') B = np.zeros((num_LM_eq_specific, sys_size), dtype=ct.c_double, order = 'F') # Define the position of the first degree of freedom associated to the FoR FoR_dof = define_FoR_dof(MB_beam, self.body_FoR) ieq = self._ieq Bnh[:3, FoR_dof:FoR_dof+3] = 1.0*np.eye(3) if self.rot_dir == 'general': # Only two of these equations are linearly independent skew_rot_axis = ag.skew(self.rot_axis) n0 = np.linalg.norm(skew_rot_axis[0,:]) n1 = np.linalg.norm(skew_rot_axis[1,:]) n2 = np.linalg.norm(skew_rot_axis[2,:]) if ((n0 < n1) and (n0 < n2)): row0 = 1 row1 = 2 elif ((n1 < n0) and (n1 < n2)): row0 = 0 row1 = 2 elif ((n2 < n0) and (n2 < n1)): row0 = 0 row1 = 1 Bnh[3:5, FoR_dof+3:FoR_dof+6] = skew_rot_axis[[row0,row1],:] else: Bnh[3:5, FoR_dof+3+self.zero_comp] = np.eye(2) LM_C[sys_size+ieq:sys_size+ieq+num_LM_eq_specific,:sys_size] += self.scalingFactor*Bnh LM_C[:sys_size,sys_size+ieq:sys_size+ieq+num_LM_eq_specific] += self.scalingFactor*np.transpose(Bnh) LM_Q[:sys_size] += self.scalingFactor*np.dot(np.transpose(Bnh),Lambda_dot[ieq:ieq+num_LM_eq_specific]) LM_Q[sys_size+ieq:sys_size+ieq+3] += self.scalingFactor*MB_tstep[self.body_FoR].for_vel[0:3].astype(dtype=ct.c_double, copy=True, order='F') if self.rot_dir == 'general': LM_Q[sys_size+ieq+3:sys_size+ieq+5] += self.scalingFactor*np.dot(skew_rot_axis[[row0,row1],:], MB_tstep[self.body_FoR].for_vel[3:6]) else: LM_Q[sys_size+ieq+3:sys_size+ieq+5] += self.scalingFactor*MB_tstep[self.body_FoR].for_vel[3 + self.zero_comp] if self.penaltyFactor: LM_Q[FoR_dof:FoR_dof+3] += self.penaltyFactor*MB_tstep[self.body_FoR].for_vel[0:3] LM_C[FoR_dof:FoR_dof+3, FoR_dof:FoR_dof+3] += self.penaltyFactor*np.eye(3) if self.rot_dir == 'general': sq_rot_axis = np.dot(ag.skew(self.rot_axis).T, ag.skew(self.rot_axis)) LM_Q[FoR_dof+3:FoR_dof+6] += self.penaltyFactor*np.dot(sq_rot_axis, MB_tstep[self.body_FoR].for_vel[3:6]) LM_C[FoR_dof+3:FoR_dof+6, FoR_dof+3:FoR_dof+6] += self.penaltyFactor*sq_rot_axis else: LM_Q[FoR_dof+3:FoR_dof+6] += self.penaltyFactor*MB_tstep[self.body_FoR].for_vel[3:6] LM_C[FoR_dof+3:FoR_dof+6, FoR_dof+3:FoR_dof+6] += self.penaltyFactor*np.eye(3) ieq += 5 return def staticpost(self, lc_list, MB_beam, MB_tstep): return def dynamicpost(self, lc_list, MB_beam, MB_tstep): return @lagrangeconstraint class hinge_FoR_wrtG(BaseLagrangeConstraint): __doc__ = """ hinge_FoR_wrtG This constraint forces a hinge at a FoR See ``LagrangeConstraints`` for the description of variables Attributes: body_FoR (int): body number of the "FoR" rot_axis_AFoR (np.ndarray): Rotation axis with respect to the node G FoR """ _lc_id = 'hinge_FoR_wrtG' def __init__(self): self.required_parameters = ['body_FoR', 'rot_axis_AFoR'] self._n_eq = 5 def get_n_eq(self): return self._n_eq def initialise(self, MBdict_entry, ieq, print_info=True): self.body_FoR = MBdict_entry['body_FoR'] self.rot_axis = MBdict_entry['rot_axis_AFoR'] self._ieq = ieq self.scalingFactor = set_value_or_default(MBdict_entry, "scalingFactor", 1.) self.penaltyFactor = set_value_or_default(MBdict_entry, "penaltyFactor", 0.) return self._ieq + self._n_eq def staticmat(self, LM_C, LM_K, LM_Q, MB_beam, MB_tstep, ts, num_LM_eq, sys_size, dt, Lambda, Lambda_dot): return def dynamicmat(self, LM_C, LM_K, LM_Q, MB_beam, MB_tstep, ts, num_LM_eq, sys_size, dt, Lambda, Lambda_dot): num_LM_eq_specific = self._n_eq Bnh = np.zeros((num_LM_eq_specific, sys_size), dtype=ct.c_double, order = 'F') B = np.zeros((num_LM_eq_specific, sys_size), dtype=ct.c_double, order = 'F') # Define the position of the first degree of freedom associated to the FoR FoR_dof = define_FoR_dof(MB_beam, self.body_FoR) ieq = self._ieq Bnh[:3, FoR_dof:FoR_dof+3] = MB_tstep[self.body_FoR].cga() # Only two of these equations are linearly independent skew_rot_axis = ag.skew(self.rot_axis) n0 = np.linalg.norm(skew_rot_axis[0,:]) n1 = np.linalg.norm(skew_rot_axis[1,:]) n2 = np.linalg.norm(skew_rot_axis[2,:]) if ((n0 < n1) and (n0 < n2)): row0 = 1 row1 = 2 elif ((n1 < n0) and (n1 < n2)): row0 = 0 row1 = 2 elif ((n2 < n0) and (n2 < n1)): row0 = 0 row1 = 1 Bnh[3:5, FoR_dof+3:FoR_dof+6] = skew_rot_axis[[row0,row1],:] LM_C[sys_size+ieq:sys_size+ieq+num_LM_eq_specific,:sys_size] += self.scalingFactor*Bnh LM_C[:sys_size,sys_size+ieq:sys_size+ieq+num_LM_eq_specific] += self.scalingFactor*np.transpose(Bnh) LM_C[FoR_dof:FoR_dof+3,FoR_dof+6:FoR_dof+10] += self.scalingFactor*ag.der_CquatT_by_v(MB_tstep[self.body_FoR].quat,Lambda_dot[ieq:ieq+3]) LM_Q[:sys_size] += self.scalingFactor*np.dot(np.transpose(Bnh),Lambda_dot[ieq:ieq+num_LM_eq_specific]) LM_Q[sys_size+ieq:sys_size+ieq+3] += self.scalingFactor*np.dot(MB_tstep[self.body_FoR].cga(),MB_tstep[self.body_FoR].for_vel[0:3]) LM_Q[sys_size+ieq+3:sys_size+ieq+5] += self.scalingFactor*np.dot(skew_rot_axis[[row0,row1],:], MB_tstep[self.body_FoR].for_vel[3:6]) ieq += 5 return def staticpost(self, lc_list, MB_beam, MB_tstep): return def dynamicpost(self, lc_list, MB_beam, MB_tstep): return @lagrangeconstraint class fully_constrained_node_FoR(BaseLagrangeConstraint): __doc__ = """ fully_constrained_node_FoR This constraint forces linear and angular displacements between a node and a FoR to be the same See ``LagrangeConstraints`` for the description of variables Attributes: node_number (int): number of the "node" within its own body node_body (int): body number of the "node" FoR_body (int): body number of the "FoR" """ _lc_id = 'fully_constrained_node_FoR' def __init__(self): self.required_parameters = ['node_in_body', 'body', 'body_FoR'] self._n_eq = 6 def get_n_eq(self): return self._n_eq def initialise(self, MBdict_entry, ieq, print_info=True): cout.cout_wrap("WARNING: do not use fully_constrained_node_FoR. It is outdated. Definetly not working if 'body' has velocity", 3) self.node_number = MBdict_entry['node_in_body'] self.node_body = MBdict_entry['body'] self.FoR_body = MBdict_entry['body_FoR'] self._ieq = ieq self.scalingFactor = set_value_or_default(MBdict_entry, "scalingFactor", 1.) self.penaltyFactor = set_value_or_default(MBdict_entry, "penaltyFactor", 0.) return self._ieq + self._n_eq def staticmat(self, LM_C, LM_K, LM_Q, MB_beam, MB_tstep, ts, num_LM_eq, sys_size, dt, Lambda, Lambda_dot): return def dynamicmat(self, LM_C, LM_K, LM_Q, MB_beam, MB_tstep, ts, num_LM_eq, sys_size, dt, Lambda, Lambda_dot): num_LM_eq_specific = self._n_eq Bnh = np.zeros((num_LM_eq_specific, sys_size), dtype=ct.c_double, order = 'F') B = np.zeros((num_LM_eq_specific, sys_size), dtype=ct.c_double, order = 'F') node_dof = define_node_dof(MB_beam, self.node_body, self.node_number) FoR_dof = define_FoR_dof(MB_beam, self.FoR_body) ieq = self._ieq # Option with non holonomic constraints # BC for linear velocities Bnh[:3, node_dof:node_dof+3] = -1.0*np.eye(3) quat = ag.quat_bound(MB_tstep[self.FoR_body].quat) Bnh[:3, FoR_dof:FoR_dof+3] = ag.quat2rotation(quat) # BC for angular velocities Bnh[3:6,FoR_dof+3:FoR_dof+6] = -1.0*ag.quat2rotation(quat) ielem, inode_in_elem = MB_beam[0].node_master_elem[self.node_number] Bnh[3:6,node_dof+3:node_dof+6] = ag.crv2tan(MB_tstep[0].psi[ielem, inode_in_elem, :]) LM_C[sys_size+ieq:sys_size+ieq+num_LM_eq_specific,:sys_size] += self.scalingFactor*Bnh LM_C[:sys_size,sys_size+ieq:sys_size+ieq+num_LM_eq_specific] += self.scalingFactor*np.transpose(Bnh) LM_Q[:sys_size] += self.scalingFactor*np.dot(np.transpose(Bnh),Lambda_dot[ieq:ieq+num_LM_eq_specific]) LM_Q[sys_size+ieq:sys_size+ieq+3] += -self.scalingFactor*MB_tstep[0].pos_dot[-1,:] + np.dot(ag.quat2rotation(quat),MB_tstep[1].for_vel[0:3]) LM_Q[sys_size+ieq+3:sys_size+ieq+6] += self.scalingFactor*(np.dot(ag.crv2tan(MB_tstep[0].psi[ielem, inode_in_elem, :]),MB_tstep[0].psi_dot[ielem, inode_in_elem, :]) - np.dot(ag.quat2rotation(quat), MB_tstep[self.FoR_body].for_vel[3:6])) #LM_K[FoR_dof:FoR_dof+3,FoR_dof+6:FoR_dof+10] = ag.der_CquatT_by_v(MB_tstep[body_FoR].quat,Lambda_dot) LM_C[FoR_dof:FoR_dof+3,FoR_dof+6:FoR_dof+10] += self.scalingFactor*ag.der_CquatT_by_v(quat,Lambda_dot[ieq:ieq+3]) LM_C[FoR_dof+3:FoR_dof+6,FoR_dof+6:FoR_dof+10] -= self.scalingFactor*ag.der_CquatT_by_v(quat,Lambda_dot[ieq+3:ieq+6]) LM_K[node_dof+3:node_dof+6,node_dof+3:node_dof+6] += self.scalingFactor*ag.der_TanT_by_xv(MB_tstep[0].psi[ielem, inode_in_elem, :],Lambda_dot[ieq+3:ieq+6]) ieq += 6 return def staticpost(self, lc_list, MB_beam, MB_tstep): return def dynamicpost(self, lc_list, MB_beam, MB_tstep): # MB_tstep[self.FoR_body].for_pos[0:3] = np.dot(ag.quat2rotation(MB_tstep[self.node_body].quat), MB_tstep[self.node_body].pos[self.node_number,:]) + MB_tstep[self.node_body].for_pos[0:3] return @lagrangeconstraint class constant_rot_vel_FoR(BaseLagrangeConstraint): __doc__ = """ constant_rot_vel_FoR This constraint forces a constant rotation velocity of a FoR See ``LagrangeConstraints`` for the description of variables Attributes: FoR_body (int): body number of the "FoR" """ _lc_id = 'constant_rot_vel_FoR' def __init__(self): self.required_parameters = ['FoR_body', 'rot_vel'] self._n_eq = 3 def get_n_eq(self): return self._n_eq def initialise(self, MBdict_entry, ieq, print_info=True): self.rot_vel = MBdict_entry['rot_vel'] self.FoR_body = MBdict_entry['FoR_body'] self._ieq = ieq self.scalingFactor = set_value_or_default(MBdict_entry, "scalingFactor", 1.) self.penaltyFactor = set_value_or_default(MBdict_entry, "penaltyFactor", 0.) return self._ieq + self._n_eq def staticmat(self, LM_C, LM_K, LM_Q, MB_beam, MB_tstep, ts, num_LM_eq, sys_size, dt, Lambda, Lambda_dot): return def dynamicmat(self, LM_C, LM_K, LM_Q, MB_beam, MB_tstep, ts, num_LM_eq, sys_size, dt, Lambda, Lambda_dot): num_LM_eq_specific = self._n_eq Bnh = np.zeros((num_LM_eq_specific, sys_size), dtype=ct.c_double, order = 'F') B = np.zeros((num_LM_eq_specific, sys_size), dtype=ct.c_double, order = 'F') # Define the position of the first degree of freedom associated to the FoR FoR_dof = define_FoR_dof(MB_beam, self.FoR_body) ieq = self._ieq Bnh[:3,FoR_dof+3:FoR_dof+6] = np.eye(3) LM_C[sys_size+ieq:sys_size+ieq+num_LM_eq_specific,:sys_size] += self.scalingFactor*Bnh LM_C[:sys_size,sys_size+ieq:sys_size+ieq+num_LM_eq_specific] += self.scalingFactor*np.transpose(Bnh) LM_Q[:sys_size] += self.scalingFactor*np.dot(np.transpose(Bnh),Lambda_dot[ieq:ieq+num_LM_eq_specific]) LM_Q[sys_size+ieq:sys_size+ieq+num_LM_eq_specific] += self.scalingFactor*(MB_tstep[self.FoR_body].for_vel[3:6] - self.rot_vel) ieq += 3 return def staticpost(self, lc_list, MB_beam, MB_tstep): return def dynamicpost(self, lc_list, MB_beam, MB_tstep): return @lagrangeconstraint class constant_vel_FoR(BaseLagrangeConstraint): __doc__ = """ constant_vel_FoR This constraint forces a constant velocity of a FoR See ``LagrangeConstraints`` for the description of variables Attributes: FoR_body (int): body number of the "FoR" vel (np.ndarray): 6 components of the desired velocity """ _lc_id = 'constant_vel_FoR' def __init__(self): self.required_parameters = ['FoR_body', 'vel'] self._n_eq = 6 def get_n_eq(self): return self._n_eq def initialise(self, MBdict_entry, ieq, print_info=True): self.vel = MBdict_entry['vel'] self.FoR_body = MBdict_entry['FoR_body'] self._ieq = ieq self.scalingFactor = set_value_or_default(MBdict_entry, "scalingFactor", 1.) self.penaltyFactor = set_value_or_default(MBdict_entry, "penaltyFactor", 0.) return self._ieq + self._n_eq def staticmat(self, LM_C, LM_K, LM_Q, MB_beam, MB_tstep, ts, num_LM_eq, sys_size, dt, Lambda, Lambda_dot): return def dynamicmat(self, LM_C, LM_K, LM_Q, MB_beam, MB_tstep, ts, num_LM_eq, sys_size, dt, Lambda, Lambda_dot): num_LM_eq_specific = self._n_eq Bnh = np.zeros((num_LM_eq_specific, sys_size), dtype=ct.c_double, order='F') B = np.zeros((num_LM_eq_specific, sys_size), dtype=ct.c_double, order='F') # Define the position of the first degree of freedom associated to the FoR FoR_dof = define_FoR_dof(MB_beam, self.FoR_body) ieq = self._ieq Bnh[:num_LM_eq_specific, FoR_dof:FoR_dof+6] = np.eye(6) LM_C[sys_size + ieq:sys_size + ieq + num_LM_eq_specific, :sys_size] += self.scalingFactor * Bnh LM_C[:sys_size, sys_size + ieq:sys_size + ieq + num_LM_eq_specific] += self.scalingFactor * np.transpose(Bnh) LM_Q[:sys_size] += self.scalingFactor * np.dot(np.transpose(Bnh), Lambda_dot[ieq:ieq + num_LM_eq_specific]) LM_Q[sys_size + ieq:sys_size + ieq + num_LM_eq_specific] += self.scalingFactor*(MB_tstep[self.FoR_body].for_vel - self.vel) ieq += 6 return def staticpost(self, lc_list, MB_beam, MB_tstep): return def dynamicpost(self, lc_list, MB_beam, MB_tstep): return @lagrangeconstraint class lin_vel_node_wrtA(BaseLagrangeConstraint): __doc__ = """ lin_vel_node_wrtA This constraint forces the linear velocity of a node to have a certain value with respect to the A FoR See ``LagrangeConstraints`` for the description of variables Attributes: node_number (int): number of the "node" within its own body body_number (int): body number of the "node" vel (np.ndarray): 6 components of the desired velocity with respect to the A FoR """ _lc_id = 'lin_vel_node_wrtA' def __init__(self): self.required_parameters = ['velocity', 'body_number', 'node_number'] self._n_eq = 3 def get_n_eq(self): return self._n_eq def initialise(self, MBdict_entry, ieq, print_info=True): self.vel = MBdict_entry['velocity'] self.body_number = MBdict_entry['body_number'] self.node_number = MBdict_entry['node_number'] self._ieq = ieq self.scalingFactor = set_value_or_default(MBdict_entry, "scalingFactor", 1.) self.penaltyFactor = set_value_or_default(MBdict_entry, "penaltyFactor", 0.) return self._ieq + self._n_eq def staticmat(self, LM_C, LM_K, LM_Q, MB_beam, MB_tstep, ts, num_LM_eq, sys_size, dt, Lambda, Lambda_dot): num_LM_eq_specific = self._n_eq B = np.zeros((num_LM_eq_specific, sys_size), dtype=ct.c_double, order='F') # Define the position of the first degree of freedom associated to the FoR node_dof = define_node_dof(MB_beam, self.body_number, self.node_number) ieq = self._ieq B[:num_LM_eq_specific, node_dof:node_dof+3] = np.eye(3) LM_K[sys_size + ieq:sys_size + ieq + num_LM_eq_specific, :sys_size] += self.scalingFactor * B LM_K[:sys_size, sys_size + ieq:sys_size + ieq + num_LM_eq_specific] += self.scalingFactor * np.transpose(B) LM_Q[:sys_size] += self.scalingFactor * np.dot(np.transpose(B), Lambda[ieq:ieq + num_LM_eq_specific]) LM_Q[sys_size + ieq:sys_size + ieq + num_LM_eq_specific] += self.scalingFactor*(MB_tstep[self.body_number].pos[self.node_number,:] - MB_beam[self.body_number].ini_info.pos[self.node_number,:]) ieq += 3 return def dynamicmat(self, LM_C, LM_K, LM_Q, MB_beam, MB_tstep, ts, num_LM_eq, sys_size, dt, Lambda, Lambda_dot): if len(self.vel.shape) > 1: current_vel = self.vel[ts-1, :] else: current_vel = self.vel num_LM_eq_specific = self._n_eq Bnh = np.zeros((num_LM_eq_specific, sys_size), dtype=ct.c_double, order='F') B = np.zeros((num_LM_eq_specific, sys_size), dtype=ct.c_double, order='F') # Define the position of the first degree of freedom associated to the FoR node_dof = define_node_dof(MB_beam, self.body_number, self.node_number) ieq = self._ieq Bnh[:num_LM_eq_specific, node_dof:node_dof+3] = np.eye(3) LM_C[sys_size + ieq:sys_size + ieq + num_LM_eq_specific, :sys_size] += self.scalingFactor * Bnh LM_C[:sys_size, sys_size + ieq:sys_size + ieq + num_LM_eq_specific] += self.scalingFactor * np.transpose(Bnh) LM_Q[:sys_size] += self.scalingFactor * np.dot(np.transpose(Bnh), Lambda_dot[ieq:ieq + num_LM_eq_specific]) LM_Q[sys_size + ieq:sys_size + ieq + num_LM_eq_specific] += self.scalingFactor*(MB_tstep[self.body_number].pos_dot[self.node_number,:] - current_vel) ieq += 3 return def staticpost(self, lc_list, MB_beam, MB_tstep): return def dynamicpost(self, lc_list, MB_beam, MB_tstep): return @lagrangeconstraint class lin_vel_node_wrtG(BaseLagrangeConstraint): __doc__ = """ lin_vel_node_wrtG This constraint forces the linear velocity of a node to have a certain value with respect to the G FoR See ``LagrangeConstraints`` for the description of variables Attributes: node_number (int): number of the "node" within its own body body_number (int): body number of the "node" vel (np.ndarray): 6 components of the desired velocity with respect to the G FoR """ _lc_id = 'lin_vel_node_wrtG' def __init__(self): self.required_parameters = ['velocity', 'body_number', 'node_number'] self._n_eq = 3 def get_n_eq(self): return self._n_eq def initialise(self, MBdict_entry, ieq, print_info=True): self.vel = MBdict_entry['velocity'] self.body_number = MBdict_entry['body_number'] self.node_number = MBdict_entry['node_number'] self._ieq = ieq self.scalingFactor = set_value_or_default(MBdict_entry, "scalingFactor", 1.) self.penaltyFactor = set_value_or_default(MBdict_entry, "penaltyFactor", 0.) return self._ieq + self._n_eq def staticmat(self, LM_C, LM_K, LM_Q, MB_beam, MB_tstep, ts, num_LM_eq, sys_size, dt, Lambda, Lambda_dot): num_LM_eq_specific = self._n_eq B = np.zeros((num_LM_eq_specific, sys_size), dtype=ct.c_double, order='F') # Define the position of the first degree of freedom associated to the FoR node_dof = define_node_dof(MB_beam, self.body_number, self.node_number) ieq = self._ieq B[:num_LM_eq_specific, node_dof:node_dof+3] = MB_tstep[self.body_number].cga() LM_K[sys_size + ieq:sys_size + ieq + num_LM_eq_specific, :sys_size] += self.scalingFactor * B LM_K[:sys_size, sys_size + ieq:sys_size + ieq + num_LM_eq_specific] += self.scalingFactor * np.transpose(B) LM_Q[:sys_size] += self.scalingFactor * np.dot(np.transpose(B), Lambda[ieq:ieq + num_LM_eq_specific]) LM_Q[sys_size + ieq:sys_size + ieq + num_LM_eq_specific] += self.scalingFactor*(np.dot(MB_tstep[self.body_number].cga(), MB_tstep[self.body_number].pos[self.node_number,:]) + MB_tstep[self.body_number].for_pos) LM_Q[sys_size + ieq:sys_size + ieq + num_LM_eq_specific] -= self.scalingFactor*(np.dot(MB_beam[self.body_number].ini_info.cga(), MB_beam[self.body_number].ini_info.pos[self.node_number,:]) + MB_beam[self.body_number].ini_info.for_pos) ieq += 3 return def dynamicmat(self, LM_C, LM_K, LM_Q, MB_beam, MB_tstep, ts, num_LM_eq, sys_size, dt, Lambda, Lambda_dot): if len(self.vel.shape) > 1: current_vel = self.vel[ts-1, :] else: current_vel = self.vel num_LM_eq_specific = self._n_eq Bnh = np.zeros((num_LM_eq_specific, sys_size), dtype=ct.c_double, order='F') B = np.zeros((num_LM_eq_specific, sys_size), dtype=ct.c_double, order='F') # Define the position of the first degree of freedom associated to the FoR FoR_dof = define_FoR_dof(MB_beam, self.body_number) node_dof = define_node_dof(MB_beam, self.body_number, self.node_number) ieq = self._ieq if MB_beam[self.body_number].FoR_movement == 'free': Bnh[:num_LM_eq_specific, FoR_dof:FoR_dof+3] = MB_tstep[self.body_number].cga() Bnh[:num_LM_eq_specific, FoR_dof+3:FoR_dof+6] = -np.dot(MB_tstep[self.body_number].cga(), ag.skew(MB_tstep[self.body_number].pos[self.node_number,:])) Bnh[:num_LM_eq_specific, node_dof:node_dof+3] = MB_tstep[self.body_number].cga() LM_C[sys_size + ieq:sys_size + ieq + num_LM_eq_specific, :sys_size] += self.scalingFactor * Bnh LM_C[:sys_size, sys_size + ieq:sys_size + ieq + num_LM_eq_specific] += self.scalingFactor * np.transpose(Bnh) if MB_beam[self.body_number].FoR_movement == 'free': LM_C[FoR_dof:FoR_dof+3, FoR_dof+6:FoR_dof+10] += self.scalingFactor*ag.der_CquatT_by_v(MB_tstep[self.body_number].quat,Lambda_dot[ieq:ieq + num_LM_eq_specific]) LM_C[node_dof:node_dof+3, FoR_dof+6:FoR_dof+10] += self.scalingFactor*ag.der_CquatT_by_v(MB_tstep[self.body_number].quat,Lambda_dot[ieq:ieq + num_LM_eq_specific]) LM_C[FoR_dof+3:FoR_dof+6, FoR_dof+6:FoR_dof+10] += self.scalingFactor*np.dot(ag.skew(MB_tstep[self.body_number].pos[self.node_number,:]), ag.der_CquatT_by_v(MB_tstep[self.body_number].quat,Lambda_dot[ieq:ieq + num_LM_eq_specific])) LM_K[FoR_dof+3:FoR_dof+6, node_dof:node_dof+3] -= self.scalingFactor*ag.skew(np.dot(MB_tstep[self.body_number].cga().T, Lambda_dot[ieq:ieq + num_LM_eq_specific])) LM_Q[:sys_size] += self.scalingFactor * np.dot(np.transpose(Bnh), Lambda_dot[ieq:ieq + num_LM_eq_specific]) LM_Q[sys_size + ieq:sys_size + ieq + num_LM_eq_specific] += self.scalingFactor*(np.dot( MB_tstep[self.body_number].cga(), ( MB_tstep[self.body_number].for_vel[0:3] + np.dot(ag.skew(MB_tstep[self.body_number].for_vel[3:6]), MB_tstep[self.body_number].pos[self.node_number,:]) + MB_tstep[self.body_number].pos_dot[self.node_number,:])) - current_vel) ieq += 3 return def staticpost(self, lc_list, MB_beam, MB_tstep): return def dynamicpost(self, lc_list, MB_beam, MB_tstep): return ################################################################################ # Funtions to interact with this Library ################################################################################ def initialize_constraints(MBdict): index_eq = 0 num_constraints = MBdict['num_constraints'] lc_list = list() # Read the dictionary and create the constraints for iconstraint in range(num_constraints): lc_list.append(lc_from_string(MBdict["constraint_%02d" % iconstraint]['behaviour'])()) index_eq = lc_list[-1].initialise(MBdict["constraint_%02d" % iconstraint], index_eq) return lc_list def define_num_LM_eq(lc_list): """ define_num_LM_eq Define the number of equations needed to define the boundary boundary conditions Args: lc_list(): list of all the defined contraints Returns: num_LM_eq(int): number of new equations needed to define the boundary boundary conditions Examples: num_LM_eq = lagrangeconstraints.define_num_LM_eq(lc_list) Notes: """ num_LM_eq = 0 # Compute the number of equations for lc in lc_list: num_LM_eq += lc.get_n_eq() return num_LM_eq def generate_lagrange_matrix(lc_list, MB_beam, MB_tstep, ts, num_LM_eq, sys_size, dt, Lambda, Lambda_dot, dynamic_or_static): """ generate_lagrange_matrix Generates the matrices associated to the Lagrange multipliers boundary conditions Args: lc_list(): list of all the defined contraints MBdict(dict): dictionary with the MultiBody and LagrangeMultipliers information MB_beam(list): list of 'beams' of each of the bodies that form the system MB_tstep(list): list of 'StructTimeStepInfo' of each of the bodies that form the system num_LM_eq(int): number of new equations needed to define the boundary boundary conditions sys_size(int): total number of degrees of freedom of the multibody system dt(float): time step Lambda(np.ndarray): list of Lagrange multipliers values Lambda_dot(np.ndarray): list of the first derivative of the Lagrange multipliers values dynamic_or_static (str): string defining if the computation is dynamic or static Returns: LM_C (np.ndarray): Damping matrix associated to the Lagrange Multipliers equations LM_K (np.ndarray): Stiffness matrix associated to the Lagrange Multipliers equations LM_Q (np.ndarray): Vector of independent terms associated to the Lagrange Multipliers equations """ # Initialize matrices LM_C = np.zeros((sys_size + num_LM_eq,sys_size + num_LM_eq), dtype=ct.c_double, order = 'F') LM_K = np.zeros((sys_size + num_LM_eq,sys_size + num_LM_eq), dtype=ct.c_double, order = 'F') LM_Q = np.zeros((sys_size + num_LM_eq,),dtype=ct.c_double, order = 'F') # Define the matrices associated to the constratints # TODO: Is there a better way to deal with ieq? # ieq = 0 for lc in lc_list: if dynamic_or_static.lower() == "static": lc.staticmat(LM_C=LM_C, LM_K=LM_K, LM_Q=LM_Q, MB_beam=MB_beam, MB_tstep=MB_tstep, ts=ts, num_LM_eq=num_LM_eq, sys_size=sys_size, dt=dt, Lambda=Lambda, Lambda_dot=Lambda_dot) elif dynamic_or_static.lower() == "dynamic": lc.dynamicmat(LM_C=LM_C, LM_K=LM_K, LM_Q=LM_Q, MB_beam=MB_beam, MB_tstep=MB_tstep, ts=ts, num_LM_eq=num_LM_eq, sys_size=sys_size, dt=dt, Lambda=Lambda, Lambda_dot=Lambda_dot) return LM_C, LM_K, LM_Q def postprocess(lc_list, MB_beam, MB_tstep, dynamic_or_static): """ Run the postprocess of all the Lagrange Constraints in the system """ for lc in lc_list: if dynamic_or_static.lower() == "static": lc.staticpost(lc_list = lc_list, MB_beam = MB_beam, MB_tstep = MB_tstep) elif dynamic_or_static.lower() == "dynamic": lc.dynamicpost(lc_list = lc_list, MB_beam = MB_beam, MB_tstep = MB_tstep) return def remove_constraint(MBdict, constraint): """ Removes a constraint from the list. This function is thought to release constraints at some point during a dynamic simulation """ try: del(MBdict[constraint]) MBdict['num_constraints'] -= 1 except KeyError: # The entry did not exist in the dict, pass without substracting 1 to # num_constraints pass ################################################################################ ################################################################################ ################################################################################ print_available_lc()

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Python

zeva12can/__init__.py

sectioncritical/zeva12can

7dbb426a18b8ded8d6c118df998c1cad2d2fdd67

[ "MIT" ]

null

zeva12can/__init__.py

sectioncritical/zeva12can

7dbb426a18b8ded8d6c118df998c1cad2d2fdd67

[ "MIT" ]

null

zeva12can/__init__.py

sectioncritical/zeva12can

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[ "MIT" ]

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Python

app/src/main/python/film.py

108360224/watch_video

bfbcd0fbe617eceb974d8c1e9c976f47ad7b0814

[ "MIT" ]

null

app/src/main/python/film.py

108360224/watch_video

bfbcd0fbe617eceb974d8c1e9c976f47ad7b0814

[ "MIT" ]

null

app/src/main/python/film.py

108360224/watch_video

bfbcd0fbe617eceb974d8c1e9c976f47ad7b0814

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Created on Sat May 2 10:45:29 2020 @author: max """ from bs4 import BeautifulSoup import requests import re from make_first_page import make_first_page import numpy as np import cv2 class Film(): def __init__(self,URL): html = requests.get('http://www.99kubo.tv'+URL).text soup = BeautifulSoup(html, 'lxml') a=soup.select_one('body > div.main > div.list > div.listlf > dl > span > a:nth-child(3)') self.URL='http://www.99kubo.tv/'+a['href'] html = requests.get(self.URL).text self.soup = BeautifulSoup(html, 'lxml') url_list=() img_list=() title_list=() ul=self.soup.select_one('body > div.main > div.list > div.listlf > ul') for li in ul.select('li'): a=li.select('a')[0] url_list+=(a['href'],) img=a.find_all('img')[0] im=img['data-original'] img_list+=(im,) title_list+=(img['alt'],) self.film_list=(url_list,title_list,img_list) def get_film_list(self): return self.film_list def sort_by(self,sort): url_list=() img_list=() title_list=() tag=self.soup.select_one('body > div.main > div.list > div.listlf > div') a=tag.find_all('a')[-1] self.URL=re.sub(r'order.+%20desc','order-'+sort+'%20desc',self.URL) html = requests.get(self.URL).text self.soup = BeautifulSoup(html, 'lxml') ul=self.soup.select_one('body > div.main > div.list > div.listlf > ul') for li in ul.select('li'): a=li.select('a')[0] url_list+=(a['href'],) img=a.find_all('img')[0] im=img['data-original'] img_list+=(im,) title_list+=(img['alt'],) self.film_list=(url_list,title_list,img_list) def goto_area(self,area): url_list=() img_list=() title_list=() tag=self.soup.select_one('body > div.main > div.list > div.listlf > div') a=tag.find_all('a')[-1] self.URL=re.sub(r'area.+tag','area-'+area+'-tag',self.URL) html = requests.get(self.URL).text self.soup = BeautifulSoup(html, 'lxml') ul=self.soup.select_one('body > div.main > div.list > div.listlf > ul') for li in ul.select('li'): a=li.select('a')[0] url_list+=(a['href'],) img=a.find_all('img')[0] im=img['data-original'] img_list+=(im,) title_list+=(img['alt'],) self.film_list=(url_list,title_list,img_list) def load_new_film(self): url_list=() img_list=() title_list=() tag=self.soup.select_one('body > div.main > div.list > div.listlf > div') a=tag.find_all('a')[-1] html = requests.get('http://www.99kubo.tv/'+a['href']).text self.soup = BeautifulSoup(html, 'lxml') ul=self.soup.select_one('body > div.main > div.list > div.listlf > ul') for li in ul.select('li'): a=li.select('a')[0] url_list+=(a['href'],) img=a.find_all('img')[0] im=img['data-original'] img_list+=(im,) title_list+=(img['alt'],) self.film_list=(url_list,title_list,img_list)

32.152381

97

0.531991

473

3,376

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0.048667

0.060255

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0.293839

3,376

104

98

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0.708893

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0

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0

null

0

1

0

1

0

null

0

6

9e4006cadb828a36d14809ba6e15176ea16ed232

8,391

py

Python

tests/test_meshes/test_mesh_topology.py

pysofe/pysofe

088d4061fcf194a85ff3332e7bdd3bde095e4f69

[ "BSD-3-Clause" ]

null

tests/test_meshes/test_mesh_topology.py

pysofe/pysofe

088d4061fcf194a85ff3332e7bdd3bde095e4f69

[ "BSD-3-Clause" ]

null

tests/test_meshes/test_mesh_topology.py

pysofe/pysofe

088d4061fcf194a85ff3332e7bdd3bde095e4f69

[ "BSD-3-Clause" ]

null

""" Tests for mesh topologies. """ import numpy as np import pytest from pysofe import meshes # the 1D test mesh # # 1---(1)---2---(2)---3---(3)---4 class TestMeshTopology1D(object): # the 1D test mesh connectivity array cells1D = np.array([[1,2], [2,3], [3,4]]) topo = meshes.topology.MeshTopology(cells=cells1D, dimension=1) def test_attributes(self): assert self.topo._dimension == 1 assert np.all(self.topo._n_vertices == [1, 2]) def test_incidence_1_0_and_0_1(self): assert np.all(self.topo.get_connectivity(1,0).toarray() == np.array([[1,1,0,0], [0,1,1,0], [0,0,1,1]])) assert np.all(self.topo.get_connectivity(0,1).toarray() == np.array([[1,0,0], [1,1,0], [0,1,1], [0,0,1]])) def test_incidence_1_1(self): assert np.all(self.topo.get_connectivity(1,1).toarray() == np.array([[0,1,0], [1,0,1], [0,1,0]])) def test_incidence_0_0(self): assert np.all(self.topo.get_connectivity(0,0).toarray() == np.array([[1,0,0,0], [0,1,0,0], [0,0,1,0], [0,0,0,1]])) def test_boundary(self): assert np.all(self.topo.get_boundary(0) == np.array([1,0,0,1])) assert np.all(self.topo.get_boundary(1) == np.array([1,0,1])) # the 2D test mesh # # 4---------3 # |\ /| # | \ (3) / | # | \ / | # | \ / | # |(4) 5 (2)| # | / \ | # | / \ | # | / (1) \ | # |/ \| # 1---------2 class TestMeshTopology2D(object): # the 2D test mesh connectivity array cells2D = np.array([[1,2,5], [2,3,5], [3,4,5], [4,1,5]]) topo = meshes.topology.MeshTopology(cells=cells2D, dimension=2) def test_attributes(self): assert self.topo._dimension == 2 assert np.all(self.topo._n_vertices == [1, 2, 3]) def test_incidence_2_0_and_0_2(self): assert np.all(self.topo.get_connectivity(2,0).toarray() == np.array([[1,1,0,0,1], [0,1,1,0,1], [0,0,1,1,1], [1,0,0,1,1]])) assert np.all(self.topo.get_connectivity(0,2).toarray() == np.array([[1,0,0,1], [1,1,0,0], [0,1,1,0], [0,0,1,1], [1,1,1,1]])) def test_incidence_1_0_and_0_1(self): assert np.all(self.topo.get_connectivity(1,0).toarray() == np.array([[1,1,0,0,0], [1,0,0,1,0], [1,0,0,0,1], [0,1,1,0,0], [0,1,0,0,1], [0,0,1,1,0], [0,0,1,0,1], [0,0,0,1,1]])) assert np.all(self.topo.get_connectivity(0,1).toarray() == np.array([[1,1,1,0,0,0,0,0], [1,0,0,1,1,0,0,0], [0,0,0,1,0,1,1,0], [0,1,0,0,0,1,0,1], [0,0,1,0,1,0,1,1]])) def test_incidence_2_1_and_1_2(self): assert np.all(self.topo.get_connectivity(2,1).toarray() == np.array([[1,0,1,0,1,0,0,0], [0,0,0,1,1,0,1,0], [0,0,0,0,0,1,1,1], [0,1,1,0,0,0,0,1]])) assert np.all(self.topo.get_connectivity(1,2).toarray() == np.array([[1,0,0,0], [0,0,0,1], [1,0,0,1], [0,1,0,0], [1,1,0,0], [0,0,1,0], [0,1,1,0], [0,0,1,1]])) def test_incidence_2_2(self): assert np.all(self.topo.get_connectivity(2,2).toarray() == np.array([[0,1,0,1], [1,0,1,0], [0,1,0,1], [1,0,1,0]])) def test_incidence_1_1(self): assert np.all(self.topo.get_connectivity(1,1).toarray() == np.array([[0,1,1,1,1,0,0,0], [1,0,1,0,0,1,0,1], [1,1,0,0,1,0,1,1], [1,0,0,0,1,1,1,0], [1,0,1,1,0,0,1,1], [0,1,0,1,0,0,1,1], [0,0,1,1,1,1,0,1], [0,1,1,0,1,1,1,0]])) def test_incidence_0_0(self): assert np.all(self.topo.get_connectivity(0,0).toarray() == np.eye(5)) def test_boundary(self): assert np.all(self.topo.get_boundary(0) == np.array([1,1,1,1,0])) assert np.all(self.topo.get_boundary(1) == np.array([1,1,0,1,0,1,0,0])) assert np.all(self.topo.get_boundary(2) == np.array([1,1,1,1])) class TestMeshTopology3D(object): # the 3D test mesh connectivity array cells3D = np.array([[1,2,3,5], [3,4,1,5]]) topo = meshes.topology.MeshTopology(cells=cells3D, dimension=3) def test_attributes(self): assert self.topo._dimension == 3 assert np.all(self.topo._n_vertices == [1, 2, 3, 4]) def test_incidence_3_0_and_0_3(self): assert np.all(self.topo.get_connectivity(3,0).toarray() == np.array([[1,1,1,0,1], [1,0,1,1,1]])) assert np.all(self.topo.get_connectivity(0,3).toarray() == np.array([[1,1], [1,0], [1,1], [0,1], [1,1]])) def test_incidence_2_0_and_0_2(self): assert np.all(self.topo.get_connectivity(2,0).toarray() == np.array([[1,1,1,0,0], [1,1,0,0,1], [1,0,1,1,0], [1,0,1,0,1], [1,0,0,1,1], [0,1,1,0,1], [0,0,1,1,1]])) assert np.all(self.topo.get_connectivity(0,2).toarray() == np.array([[1,1,1,1,1,0,0], [1,1,0,0,0,1,0], [1,0,1,1,0,1,1], [0,0,1,0,1,0,1], [0,1,0,1,1,1,1]])) def test_incidence_1_0_and_0_1(self): assert np.all(self.topo.get_connectivity(1,0).toarray() == np.array([[1,1,0,0,0], [1,0,1,0,0], [1,0,0,1,0], [1,0,0,0,1], [0,1,1,0,0], [0,1,0,0,1], [0,0,1,1,0], [0,0,1,0,1], [0,0,0,1,1]])) assert np.all(self.topo.get_connectivity(0,1).toarray() == np.array([[1,1,1,1,0,0,0,0,0], [1,0,0,0,1,1,0,0,0], [0,1,0,0,1,0,1,1,0], [0,0,1,0,0,0,1,0,1], [0,0,0,1,0,1,0,1,1]])) if __name__ == '__main__': from IPython import embed as IPS IPS()

36.802632

67

0.342987

1,088

8,391

2.550551

0.05239

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0.075676

0.054775

0.833874

0.817297

0.803243

0.783423

0.664865

0.627387

0

0.156799

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8,391

227

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36.964758

0.492628

0.039685

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0.101796

false

0

0.023952

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0.179641

0

null

0

1

0

1

0

1

0

null

0

6

9e9e26044eba959869c3dad33521581589e496a1

114

py

Python

vhoster/cli/__init__.py

GerardBalaoro/VHoster

991a10f30308103d7b187c8d5dba636f0e14b669

[ "MIT" ]

2

2020-10-30T12:02:21.000Z

2020-12-11T23:42:12.000Z

vhoster/cli/__init__.py

GerardBalaoro/VHoster

991a10f30308103d7b187c8d5dba636f0e14b669

[ "MIT" ]

null

vhoster/cli/__init__.py

GerardBalaoro/VHoster

991a10f30308103d7b187c8d5dba636f0e14b669

[ "MIT" ]

null

"""Command Line Interface""" from .core import * from .site import * from .config import * from .services import *

22.8

28

0.719298

15

114

5.466667

0.6

0.365854

0

0.157895

114

5

29

22.8

0.854167

0.192982

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

7b90a1ab3033703f4758ac0994cb6b6d6c162b4b

155

py

Python

solutions/python3/293.py

sm2774us/amazon_interview_prep_2021

f580080e4a6b712b0b295bb429bf676eb15668de

[ "MIT" ]

42

2020-08-02T07:03:49.000Z

2022-03-26T07:50:15.000Z

solutions/python3/293.py

ajayv13/leetcode

de02576a9503be6054816b7444ccadcc0c31c59d

[ "MIT" ]

null

solutions/python3/293.py

ajayv13/leetcode

de02576a9503be6054816b7444ccadcc0c31c59d

[ "MIT" ]

40

2020-02-08T02:50:24.000Z

2022-03-26T15:38:10.000Z

class Solution: def generatePossibleNextMoves(self, s): return [s[:i] + "--" + s[i + 2:] for i in range(len(s) - 1) if s[i] == s[i + 1] == "+"]

51.666667

95

0.516129

25

155

3.2

0.6

0.1

0.075

0.1

0

0.025641

0.245161

155

3

95

51.666667

0.65812

0

1

0

0.019231

0

1

0.333333

false

0

0.333333

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

7ba574977a2fe82549fd52a48bfa1b4330c8c575

32

py

Python

test/output/011.py

EliRibble/pyfmt

e84a5531a7c06703eddd9dbc2072b0c8deae8c57

[ "MIT" ]

null

test/output/011.py

EliRibble/pyfmt

e84a5531a7c06703eddd9dbc2072b0c8deae8c57

[ "MIT" ]

null

test/output/011.py

EliRibble/pyfmt

e84a5531a7c06703eddd9dbc2072b0c8deae8c57

[ "MIT" ]

null

from functools import lru_cache

16

31

0.875

5

32

5.4

1

0

0.125

32

1

32

0.964286

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

7baa30029056c36bdf044b8018674d3d2750c3f6

118

py

Python

utils/tensor_utils.py

Stelath/geoguessr-ai

08f5ae7ca8d1e50d586ee66222814589f4095a6d

[ "MIT" ]

null

utils/tensor_utils.py

Stelath/geoguessr-ai

08f5ae7ca8d1e50d586ee66222814589f4095a6d

[ "MIT" ]

null

utils/tensor_utils.py

Stelath/geoguessr-ai

08f5ae7ca8d1e50d586ee66222814589f4095a6d

[ "MIT" ]

null

import torch def round_tensor(tensor, decimals=4): return torch.round(tensor * 10 ** decimals) / (10 ** decimals)

29.5

66

0.70339

16

118

5.125

0.5625

0.268293

0

0.050505

0.161017

118

4

66

29.5

0.777778

0

1

0.333333

false

0

0.333333

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

7bcd28f5615c722fe263714b607adff2637af99d

234

py

Python

orator/schema/grammars/__init__.py

HeathLee/sorator

271668865bf0d039908643e3df9b98c966b9d956

[ "MIT" ]

null

orator/schema/grammars/__init__.py

HeathLee/sorator

271668865bf0d039908643e3df9b98c966b9d956

[ "MIT" ]

null

orator/schema/grammars/__init__.py

HeathLee/sorator

271668865bf0d039908643e3df9b98c966b9d956

[ "MIT" ]

null

# -*- coding: utf-8 -*- from .grammar import SchemaGrammar # noqa from .sqlite_grammar import SQLiteSchemaGrammar # noqa from .postgres_grammar import PostgresSchemaGrammar # noqa from .mysql_grammar import MySQLSchemaGrammar # noqa

33.428571

58

0.799145

26

234

7.076923

0.538462

0.282609

0

0.004902

0.128205

234

6

59

39

0.897059

0.175214

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

c8a9ac03016a0b7c7bce32e3d85f02927fffc51c

83

py

Python

lib/python2.7/site-packages/weblib/system.py

Jatin-Nagpal/FlaskApp

e94184059810e22c82db812c658407a4fcbba4b5

[ "MIT" ]

22

2015-04-18T19:07:17.000Z

2021-02-19T07:30:09.000Z

lib/python2.7/site-packages/weblib/system.py

Jatin-Nagpal/FlaskApp

e94184059810e22c82db812c658407a4fcbba4b5

[ "MIT" ]

7

2015-05-18T06:39:39.000Z

2022-03-01T15:06:29.000Z

lib/python2.7/site-packages/weblib/system.py

Jatin-Nagpal/FlaskApp

e94184059810e22c82db812c658407a4fcbba4b5

[ "MIT" ]

10

2015-04-27T11:23:59.000Z

2021-02-19T07:30:12.000Z

def check_ares_support(): import pycurl return 'c-ares' in pycurl.version

16.6

37

0.710843

12

83

4.75

0.833333

0

0.204819

83

4

38

20.75

0.863636

0

0.072289

0

1

0.333333

true

0

0.333333

0

1

0

1

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null

0

1

0

null

0

1

0

1

0

1

0

6

c8e99fe28862e8a2bbc3a820038cd2d47ced7c31

11,889

py

Python

spark_fhir_schemas/stu3/complex_types/plandefinition_target.py

icanbwell/SparkFhirSchemas

8c828313c39850b65f8676e67f526ee92b7d624e

[ "Apache-2.0" ]

2

2020-10-31T23:25:01.000Z

2021-06-09T14:12:42.000Z

spark_fhir_schemas/stu3/complex_types/plandefinition_target.py

icanbwell/SparkFhirSchemas

8c828313c39850b65f8676e67f526ee92b7d624e

[ "Apache-2.0" ]

null

spark_fhir_schemas/stu3/complex_types/plandefinition_target.py

icanbwell/SparkFhirSchemas

8c828313c39850b65f8676e67f526ee92b7d624e

[ "Apache-2.0" ]

null

from typing import Union, List, Optional from pyspark.sql.types import StructType, StructField, StringType, ArrayType, DataType # This file is auto-generated by generate_schema so do not edit manually # noinspection PyPep8Naming class PlanDefinition_TargetSchema: """ This resource allows for the definition of various types of plans as a sharable, consumable, and executable artifact. The resource is general enough to support the description of a broad range of clinical artifacts such as clinical decision support rules, order sets and protocols. """ # noinspection PyDefaultArgument @staticmethod def get_schema( max_nesting_depth: Optional[int] = 6, nesting_depth: int = 0, nesting_list: List[str] = [], max_recursion_limit: Optional[int] = 2, include_extension: Optional[bool] = False, extension_fields: Optional[List[str]] = [ "valueBoolean", "valueCode", "valueDate", "valueDateTime", "valueDecimal", "valueId", "valueInteger", "valuePositiveInt", "valueString", "valueTime", "valueUnsignedInt", "valueUri", "valueQuantity", ], extension_depth: int = 0, max_extension_depth: Optional[int] = 2, ) -> Union[StructType, DataType]: """ This resource allows for the definition of various types of plans as a sharable, consumable, and executable artifact. The resource is general enough to support the description of a broad range of clinical artifacts such as clinical decision support rules, order sets and protocols. id: unique id for the element within a resource (for internal references). This may be any string value that does not contain spaces. extension: May be used to represent additional information that is not part of the basic definition of the element. In order to make the use of extensions safe and manageable, there is a strict set of governance applied to the definition and use of extensions. Though any implementer is allowed to define an extension, there is a set of requirements that SHALL be met as part of the definition of the extension. measure: The parameter whose value is to be tracked, e.g. body weigth, blood pressure, or hemoglobin A1c level. detailQuantity: The target value of the measure to be achieved to signify fulfillment of the goal, e.g. 150 pounds or 7.0%. Either the high or low or both values of the range can be specified. Whan a low value is missing, it indicates that the goal is achieved at any value at or below the high value. Similarly, if the high value is missing, it indicates that the goal is achieved at any value at or above the low value. detailRange: The target value of the measure to be achieved to signify fulfillment of the goal, e.g. 150 pounds or 7.0%. Either the high or low or both values of the range can be specified. Whan a low value is missing, it indicates that the goal is achieved at any value at or below the high value. Similarly, if the high value is missing, it indicates that the goal is achieved at any value at or above the low value. detailCodeableConcept: The target value of the measure to be achieved to signify fulfillment of the goal, e.g. 150 pounds or 7.0%. Either the high or low or both values of the range can be specified. Whan a low value is missing, it indicates that the goal is achieved at any value at or below the high value. Similarly, if the high value is missing, it indicates that the goal is achieved at any value at or above the low value. due: Indicates the timeframe after the start of the goal in which the goal should be met. """ from spark_fhir_schemas.stu3.complex_types.extension import ExtensionSchema from spark_fhir_schemas.stu3.complex_types.codeableconcept import ( CodeableConceptSchema, ) from spark_fhir_schemas.stu3.complex_types.quantity import QuantitySchema from spark_fhir_schemas.stu3.complex_types.range import RangeSchema from spark_fhir_schemas.stu3.complex_types.duration import DurationSchema if ( max_recursion_limit and nesting_list.count("PlanDefinition_Target") >= max_recursion_limit ) or (max_nesting_depth and nesting_depth >= max_nesting_depth): return StructType([StructField("id", StringType(), True)]) # add my name to recursion list for later my_nesting_list: List[str] = nesting_list + ["PlanDefinition_Target"] schema = StructType( [ # unique id for the element within a resource (for internal references). This # may be any string value that does not contain spaces. StructField("id", StringType(), True), # May be used to represent additional information that is not part of the basic # definition of the element. In order to make the use of extensions safe and # manageable, there is a strict set of governance applied to the definition and # use of extensions. Though any implementer is allowed to define an extension, # there is a set of requirements that SHALL be met as part of the definition of # the extension. StructField( "extension", ArrayType( ExtensionSchema.get_schema( max_nesting_depth=max_nesting_depth, nesting_depth=nesting_depth + 1, nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth, max_extension_depth=max_extension_depth, ) ), True, ), # The parameter whose value is to be tracked, e.g. body weigth, blood pressure, # or hemoglobin A1c level. StructField( "measure", CodeableConceptSchema.get_schema( max_nesting_depth=max_nesting_depth, nesting_depth=nesting_depth + 1, nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth + 1, max_extension_depth=max_extension_depth, ), True, ), # The target value of the measure to be achieved to signify fulfillment of the # goal, e.g. 150 pounds or 7.0%. Either the high or low or both values of the # range can be specified. Whan a low value is missing, it indicates that the # goal is achieved at any value at or below the high value. Similarly, if the # high value is missing, it indicates that the goal is achieved at any value at # or above the low value. StructField( "detailQuantity", QuantitySchema.get_schema( max_nesting_depth=max_nesting_depth, nesting_depth=nesting_depth + 1, nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth + 1, max_extension_depth=max_extension_depth, ), True, ), # The target value of the measure to be achieved to signify fulfillment of the # goal, e.g. 150 pounds or 7.0%. Either the high or low or both values of the # range can be specified. Whan a low value is missing, it indicates that the # goal is achieved at any value at or below the high value. Similarly, if the # high value is missing, it indicates that the goal is achieved at any value at # or above the low value. StructField( "detailRange", RangeSchema.get_schema( max_nesting_depth=max_nesting_depth, nesting_depth=nesting_depth + 1, nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth + 1, max_extension_depth=max_extension_depth, ), True, ), # The target value of the measure to be achieved to signify fulfillment of the # goal, e.g. 150 pounds or 7.0%. Either the high or low or both values of the # range can be specified. Whan a low value is missing, it indicates that the # goal is achieved at any value at or below the high value. Similarly, if the # high value is missing, it indicates that the goal is achieved at any value at # or above the low value. StructField( "detailCodeableConcept", CodeableConceptSchema.get_schema( max_nesting_depth=max_nesting_depth, nesting_depth=nesting_depth + 1, nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth + 1, max_extension_depth=max_extension_depth, ), True, ), # Indicates the timeframe after the start of the goal in which the goal should # be met. StructField( "due", DurationSchema.get_schema( max_nesting_depth=max_nesting_depth, nesting_depth=nesting_depth + 1, nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth + 1, max_extension_depth=max_extension_depth, ), True, ), ] ) if not include_extension: schema.fields = [ c if c.name != "extension" else StructField("extension", StringType(), True) for c in schema.fields ] return schema

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py

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clynmut/__init__.py

jeffhsu3/ClynMut

c215ea2f8263016a249d6556c762410b39165546

[ "MIT" ]

18

2021-03-12T20:04:57.000Z

2022-01-11T03:16:31.000Z

clynmut/__init__.py

jeffhsu3/ClynMut

c215ea2f8263016a249d6556c762410b39165546

[ "MIT" ]

null

clynmut/__init__.py

jeffhsu3/ClynMut

c215ea2f8263016a249d6556c762410b39165546

[ "MIT" ]

2

2021-03-16T18:41:12.000Z

2021-06-04T02:03:01.000Z

from clynmut.clynmut import *

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py

Python

code_summary/onmt/bin/__init__.py

Nrgeup/review_assistant

bf03d62773501b84069afcc8b3da66d6d7829218

[ "Apache-2.0" ]

1

2020-01-17T00:41:51.000Z

code_summary/onmt/bin/__init__.py

Nrgeup/review_assistant

bf03d62773501b84069afcc8b3da66d6d7829218

[ "Apache-2.0" ]

null

code_summary/onmt/bin/__init__.py

Nrgeup/review_assistant

bf03d62773501b84069afcc8b3da66d6d7829218

[ "Apache-2.0" ]

null

from code_summary import onmt

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6

cdc978204c202ab5d5fdd20bd95c516cb51e35c3

25

py

Python

gypse/__init__.py

aeroxis/gypsy

bfcdb64e9ca61fac6a2b41780b11e87c7df759b2

[ "MIT" ]

3

2019-04-10T22:02:36.000Z

2020-12-13T21:29:28.000Z

gypse/__init__.py

aeroxis/gypsy

bfcdb64e9ca61fac6a2b41780b11e87c7df759b2

[ "MIT" ]

null

gypse/__init__.py

aeroxis/gypsy

bfcdb64e9ca61fac6a2b41780b11e87c7df759b2

[ "MIT" ]

null

__version__ = "00.0.0.0"

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null

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6

a810b613e673d036490a1cd9fdcdb5a455cafdaf

103

py

Python

app/models/k8s_resource/io/k8s/api/apps/__init__.py

zephyrxvxx7/KubeZephyr-Backend

242410bc236e1f7204c24d635eb3346b0c256dc8

[ "MIT" ]

2

2021-04-25T01:49:45.000Z

2021-11-25T09:10:40.000Z

app/models/k8s_resource/io/k8s/apimachinery/pkg/apis/meta/__init__.py

zephyrxvxx7/KubeZephyr-Backend

242410bc236e1f7204c24d635eb3346b0c256dc8

[ "MIT" ]

null

app/models/k8s_resource/io/k8s/apimachinery/pkg/apis/meta/__init__.py

zephyrxvxx7/KubeZephyr-Backend

242410bc236e1f7204c24d635eb3346b0c256dc8

[ "MIT" ]

null

# generated by datamodel-codegen: # filename: swagger.json # timestamp: 2021-03-29T08:55:23+00:00

25.75

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103

4.933333

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null

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6

b552f9b13868fe566e3371852979afc588234dce

64

py

Python

multilingual_t5/baseline_ta/__init__.py

sumanthd17/mt5

c99b4e3ad1c69908c852c730a1323ccb52d48f58

[ "Apache-2.0" ]

null

multilingual_t5/baseline_ta/__init__.py

sumanthd17/mt5

c99b4e3ad1c69908c852c730a1323ccb52d48f58

[ "Apache-2.0" ]

null

multilingual_t5/baseline_ta/__init__.py

sumanthd17/mt5

c99b4e3ad1c69908c852c730a1323ccb52d48f58

[ "Apache-2.0" ]

null

"""baseline_ta dataset.""" from .baseline_ta import BaselineTa

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21.333333

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6

b58315fe1a2b68d34f59c37c70e8fbbacda9271c

47

py

Python

scripts/portal/in_cygnusGarden.py

G00dBye/YYMS

1de816fc842b6598d5b4b7896b6ab0ee8f7cdcfb

[ "MIT" ]

54

2019-04-16T23:24:48.000Z

2021-12-18T11:41:50.000Z

scripts/portal/in_cygnusGarden.py

G00dBye/YYMS

1de816fc842b6598d5b4b7896b6ab0ee8f7cdcfb

[ "MIT" ]

3

2019-05-19T15:19:41.000Z

2020-04-27T16:29:16.000Z

scripts/portal/in_cygnusGarden.py

G00dBye/YYMS

1de816fc842b6598d5b4b7896b6ab0ee8f7cdcfb

[ "MIT" ]

49

2020-11-25T23:29:16.000Z

2022-03-26T16:20:24.000Z

# 271030600 sm.warp(271040000, 5) sm.dispose()

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a98b737a1373777fbb01ab298b247bc51454fb60

96

py

Python

venv/lib/python3.8/site-packages/poetry/core/masonry/__init__.py

GiulianaPola/select_repeats

17a0d053d4f874e42cf654dd142168c2ec8fbd11

[ "MIT" ]

2

2022-03-13T01:58:52.000Z

2022-03-31T06:07:54.000Z

venv/lib/python3.8/site-packages/poetry/core/masonry/__init__.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

19

2021-11-20T04:09:18.000Z

2022-03-23T15:05:55.000Z

venv/lib/python3.8/site-packages/poetry/core/masonry/__init__.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

null

/home/runner/.cache/pip/pool/2c/01/5e/0dc808506426b1f51c286ea153e1fd17e20ffa8fbc785c857b3ee15787

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0

null

1

0

1

0

6

8d620bec9d775d32398986febe26c308f4849774

177

py

Python

MySchool/Dashboard/views.py

Bhavesh052/MySchool

7902ffd23b7ea82b3eb79943fe6fe80e02ec2580

[ "MIT" ]

null

MySchool/Dashboard/views.py

Bhavesh052/MySchool

7902ffd23b7ea82b3eb79943fe6fe80e02ec2580

[ "MIT" ]

null

MySchool/Dashboard/views.py

Bhavesh052/MySchool

7902ffd23b7ea82b3eb79943fe6fe80e02ec2580

[ "MIT" ]

null

from django.shortcuts import render from django.http import HttpResponse # Create your views here. def Courses(request): return HttpResponse('<h1>Ths is my Home Page</h1>')

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8d714e25c9a6296ebbf7e275a3de6d321bfb8fb4

216

py

Python

pyclue/tf1/models/engine/__init__.py

CLUEbenchmark/PyCLUE

c16af32dd7dc195e77f352b6b3d2d5b963e193ba

[ "MIT" ]

122

2019-12-04T14:42:34.000Z

2022-03-01T08:12:30.000Z

pyclue/tf1/tasks/classification/multi_label/__init__.py

CLUEbenchmark/PyCLUE

c16af32dd7dc195e77f352b6b3d2d5b963e193ba

[ "MIT" ]

9

2020-06-05T00:42:13.000Z

2022-02-09T23:39:31.000Z

pyclue/tf1/tasks/sentence_pair/siamese/__init__.py

CLUEbenchmark/PyCLUE

c16af32dd7dc195e77f352b6b3d2d5b963e193ba

[ "MIT" ]

12

2019-12-06T01:58:31.000Z

2021-12-22T09:51:13.000Z

#!/usr/bin/python3 """ @Author: Liu Shaoweihua @Site: https://github.com/liushaoweihua """ from __future__ import absolute_import from __future__ import division from __future__ import print_function

16.615385

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8d9d97b1564f2c5e10dd73e6bf6ae9eb2d4399da

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py

Python

orangecontrib/recommendation/evaluation/__init__.py

robertcv/orange3-recommendation

db421c32f85f123b1f3058865438df1b996772cd

[ "BSD-2-Clause" ]

22

2016-09-11T11:40:17.000Z

2019-07-27T21:45:21.000Z

orangecontrib/recommendation/evaluation/__init__.py

robertcv/orange3-recommendation

db421c32f85f123b1f3058865438df1b996772cd

[ "BSD-2-Clause" ]

14

2016-08-16T22:19:31.000Z

2020-12-17T00:03:34.000Z

orangecontrib/recommendation/evaluation/__init__.py

robertcv/orange3-recommendation

db421c32f85f123b1f3058865438df1b996772cd

[ "BSD-2-Clause" ]

19

2016-08-16T20:06:57.000Z

2021-09-16T11:42:11.000Z

from .ranking import *

22

0.772727

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a5c3edf7e2af07b8820adc1cf58a393f9d57d03f

39

py

Python

app/models/__init__.py

archit47/Cricket-Run-Chase-Simulator

9ccdc6ec459587ed6a8b4d742f15f4d6f72fefb9

[ "MIT" ]

null

app/models/__init__.py

archit47/Cricket-Run-Chase-Simulator

9ccdc6ec459587ed6a8b4d742f15f4d6f72fefb9

[ "MIT" ]

null

app/models/__init__.py

archit47/Cricket-Run-Chase-Simulator

9ccdc6ec459587ed6a8b4d742f15f4d6f72fefb9

[ "MIT" ]

null

from app.models.players import Player

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a5cf7fc19dc004e446539b34cce6621cb081e391

226

py

Python

examples/property_prediction/MTL/model/__init__.py

siboehm/dgl-lifesci

f8a176414b21b72c5ca1f8c7eb8d64702432ae24

[ "Apache-2.0" ]

390

2020-06-05T13:16:18.000Z

2022-03-31T07:36:34.000Z

examples/property_prediction/MTL/model/__init__.py

siboehm/dgl-lifesci

f8a176414b21b72c5ca1f8c7eb8d64702432ae24

[ "Apache-2.0" ]

71

2020-06-12T05:26:56.000Z

2022-03-29T06:26:39.000Z

examples/property_prediction/MTL/model/__init__.py

siboehm/dgl-lifesci

f8a176414b21b72c5ca1f8c7eb8d64702432ae24

[ "Apache-2.0" ]

113

2020-06-08T18:48:18.000Z

2022-03-22T01:16:26.000Z

from .gcn import GCNRegressor, GCNRegressorBypass from .gat import GATRegressor, GATRegressorBypass from .mpnn import MPNNRegressor, MPNNRegressorBypass from .attentivefp import AttentiveFPRegressor, AttentiveFPRegressorBypass

56.5

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0.880531

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true

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1

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6

a5e4d2c172658de89500c497025ae8a81d6ceab5

90

py

Python

pollbot/display/__init__.py

tigerdar004/RweddingPoll

8617c63798dbebe6aee3ea7bd61d995a588fc048

[ "MIT" ]

112

2019-06-11T17:52:57.000Z

2022-03-18T00:05:21.000Z

pollbot/display/__init__.py

tigerdar004/RweddingPoll

8617c63798dbebe6aee3ea7bd61d995a588fc048

[ "MIT" ]

91

2019-05-28T11:33:40.000Z

2022-02-27T12:12:07.000Z

pollbot/display/__init__.py

tigerdar004/RweddingPoll

8617c63798dbebe6aee3ea7bd61d995a588fc048

[ "MIT" ]

69

2019-07-10T16:58:06.000Z

2022-03-30T22:09:44.000Z

# Import for easier re-export from .poll import * # noqa from .settings import * # noqa

22.5

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1

0

1

0

6

938c1a29e3112a81c9ce52c6005bb5a287a585b3

4,210

py

Python

analysis/eval_phys_data.py

BoyuanChen/neural-state-variables

10483d93ac8c006f3786c434fb57d70d9ab465ec

[ "MIT" ]

17

2021-12-29T16:48:46.000Z

2022-03-25T01:57:13.000Z

analysis/eval_phys_data.py

BoyuanChen/neural-state-variables

10483d93ac8c006f3786c434fb57d70d9ab465ec

[ "MIT" ]

null

analysis/eval_phys_data.py

BoyuanChen/neural-state-variables

10483d93ac8c006f3786c434fb57d70d9ab465ec

[ "MIT" ]

1

2022-01-22T11:26:09.000Z

import os import sys import cv2 import numpy as np from tqdm import tqdm def eval_phys_data_single_pendulum(data_filepath, num_vids, num_frms, save_path): from eval_phys_single_pendulum import eval_physics, phys_vars_list phys = {p_var:[] for p_var in phys_vars_list} for n in tqdm(range(num_vids)): seq_filepath = os.path.join(data_filepath, str(n)) frames = [] for p in range(num_frms): frame_p = cv2.imread(os.path.join(seq_filepath, str(p)+'.png')) frames.append(frame_p) phys_tmp = eval_physics(frames) for p_var in phys_vars_list: phys[p_var].append(phys_tmp[p_var]) for p_var in phys_vars_list: phys[p_var] = np.array(phys[p_var]) np.save(save_path, phys) def eval_phys_data_double_pendulum(data_filepath, num_vids, num_frms, save_path): from eval_phys_double_pendulum import eval_physics, phys_vars_list phys = {p_var:[] for p_var in phys_vars_list} for n in tqdm(range(num_vids)): seq_filepath = os.path.join(data_filepath, str(n)) frames = [] for p in range(num_frms): frame_p = cv2.imread(os.path.join(seq_filepath, str(p)+'.png')) frames.append(frame_p) phys_tmp = eval_physics(frames) for p_var in phys_vars_list: phys[p_var].append(phys_tmp[p_var]) for p_var in phys_vars_list: phys[p_var] = np.array(phys[p_var]) # remove outliers thresh_1 = np.nanpercentile(np.abs(phys['vel_theta_1']), 98) thresh_2 = np.nanpercentile(np.abs(phys['vel_theta_2']), 98) for n in range(num_vids): for p in range(num_frms): if (not np.isnan(phys['vel_theta_1'][n, p]) and np.abs(phys['vel_theta_1'][n, p]) >= thresh_1) \ or (not np.isnan(phys['vel_theta_2'][n, p]) and np.abs(phys['vel_theta_2'][n, p]) >= thresh_2): phys['vel_theta_1'][n, p] = np.nan phys['vel_theta_2'][n, p] = np.nan phys['kinetic energy'][n, p] = np.nan phys['total energy'][n, p] = np.nan np.save(save_path, phys) def eval_phys_data_elastic_pendulum(data_filepath, num_vids, num_frms, save_path): from eval_phys_elastic_pendulum import eval_physics, phys_vars_list phys = {p_var:[] for p_var in phys_vars_list} for n in tqdm(range(num_vids)): seq_filepath = os.path.join(data_filepath, str(n)) frames = [] for p in range(num_frms): frame_p = cv2.imread(os.path.join(seq_filepath, str(p)+'.png')) frames.append(frame_p) phys_tmp = eval_physics(frames) for p_var in phys_vars_list: phys[p_var].append(phys_tmp[p_var]) for p_var in phys_vars_list: phys[p_var] = np.array(phys[p_var]) # remove outliers thresh_1 = np.nanpercentile(np.abs(phys['vel_theta_1']), 98) thresh_2 = np.nanpercentile(np.abs(phys['vel_theta_2']), 98) thresh_z = np.nanpercentile(np.abs(phys['vel_z']), 98) for n in range(num_vids): for p in range(num_frms): if (not np.isnan(phys['vel_theta_1'][n, p]) and np.abs(phys['vel_theta_1'][n, p]) >= thresh_1) \ or (not np.isnan(phys['vel_theta_2'][n, p]) and np.abs(phys['vel_theta_2'][n, p]) >= thresh_2) \ or (not np.isnan(phys['vel_z'][n, p]) and np.abs(phys['vel_z'][n, p]) >= thresh_z): phys['vel_theta_1'][n, p] = np.nan phys['vel_theta_2'][n, p] = np.nan phys['vel_z'][n, p] = np.nan phys['kinetic energy'][n, p] = np.nan phys['total energy'][n, p] = np.nan np.save(save_path, phys) if __name__ == '__main__': dataset = str(sys.argv[1]) data_filepath = str(sys.argv[2]) save_path = os.path.join(data_filepath, 'phys_vars.npy') if dataset == 'single_pendulum': eval_phys_data_single_pendulum(data_filepath, 1200, 60, save_path) elif dataset == 'double_pendulum': eval_phys_data_double_pendulum(data_filepath, 1100, 60, save_path) elif dataset == 'elastic_pendulum': eval_phys_data_elastic_pendulum(data_filepath, 1200, 60, save_path) else: assert False, 'Unknown system...'

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py

Python

blue_st_sdk/features/audio/adpcm/__init__.py

cchangeur/BlueSTSDK_Python

e5c6e4bc5a58680bad0d867633dd9d92012b9baf

[ "BSD-3-Clause" ]

43

2019-03-08T08:03:19.000Z

2022-01-20T11:51:11.000Z

blue_st_sdk/features/audio/adpcm/__init__.py

cchangeur/BlueSTSDK_Python

e5c6e4bc5a58680bad0d867633dd9d92012b9baf

[ "BSD-3-Clause" ]

24

2019-04-01T20:50:40.000Z

2022-03-16T17:00:54.000Z

blue_st_sdk/features/audio/adpcm/__init__.py

cchangeur/BlueSTSDK_Python

e5c6e4bc5a58680bad0d867633dd9d92012b9baf

[ "BSD-3-Clause" ]

19

2019-02-20T08:41:20.000Z

2021-11-21T11:39:50.000Z

__all__ = [ 'feature_audio_adpcm', \ 'feature_audio_adpcm_sync', \ 'bv_audio_sync_manager' ]

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py

Python

experiments/training/__init__.py

selflein/manifold-flow

2cc91c7acf61c8b4df07a940f0311ee93c39f0c7

[ "MIT" ]

199

2020-03-31T22:45:31.000Z

2022-03-18T14:57:23.000Z

experiments/training/__init__.py

selflein/manifold-flow

2cc91c7acf61c8b4df07a940f0311ee93c39f0c7

[ "MIT" ]

4

2020-04-04T18:45:33.000Z

2022-01-05T03:16:07.000Z

experiments/training/__init__.py

selflein/manifold-flow

2cc91c7acf61c8b4df07a940f0311ee93c39f0c7

[ "MIT" ]

25

2020-04-01T11:04:11.000Z

2022-03-30T17:21:44.000Z

from . import losses from .trainer import ForwardTrainer, ConditionalForwardTrainer, AdversarialTrainer, ConditionalAdversarialTrainer, SCANDALForwardTrainer from .alternate import AlternatingTrainer

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py

Python

study.py

GongBenWuZang123456/go_study

048b1d4124d3b9784668d1911650be29f3930cbd

[ "MIT" ]

null

study.py

GongBenWuZang123456/go_study

048b1d4124d3b9784668d1911650be29f3930cbd

[ "MIT" ]

null

study.py

GongBenWuZang123456/go_study

048b1d4124d3b9784668d1911650be29f3930cbd

[ "MIT" ]

null

sum = 1 nnn = 2 mmm = 666666666

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py

Python

Packs/SOCRadar/Integrations/SOCRadarThreatFusion/SOCRadarThreatFusion_test.py

diCagri/content

c532c50b213e6dddb8ae6a378d6d09198e08fc9f

[ "MIT" ]

799

2016-08-02T06:43:14.000Z

2022-03-31T11:10:11.000Z

Packs/SOCRadar/Integrations/SOCRadarThreatFusion/SOCRadarThreatFusion_test.py

diCagri/content

c532c50b213e6dddb8ae6a378d6d09198e08fc9f

[ "MIT" ]

9,317

2016-08-07T19:00:51.000Z

2022-03-31T21:56:04.000Z

Packs/SOCRadar/Integrations/SOCRadarThreatFusion/SOCRadarThreatFusion_test.py

diCagri/content

c532c50b213e6dddb8ae6a378d6d09198e08fc9f

[ "MIT" ]

1,297

2016-08-04T13:59:00.000Z

2022-03-31T23:43:06.000Z

import json import io import pytest from CommonServerPython import DemistoException, FeedIndicatorType, CommandResults def util_load_json(path): with io.open(path, mode='r', encoding='utf-8') as f: return json.loads(f.read()) SOCRADAR_API_ENDPOINT = 'https://platform.socradar.com/api' CALCULATE_DBOT_SCORE_INPUTS = [ (900, 3), (800, 2), (450, 2), (300, 1), (100, 1), (0, 0), ] def test_test_module(requests_mock): """Tests the test_module validation command. """ from SOCRadarThreatFusion import Client, test_module mock_socradar_api_key = "APIKey" suffix = f'threat/analysis/check/auth?key={mock_socradar_api_key}' mock_response = util_load_json('test_data/check_auth_response.json') requests_mock.get(f'{SOCRADAR_API_ENDPOINT}/{suffix}', json=mock_response) client = Client( base_url=SOCRADAR_API_ENDPOINT, api_key=mock_socradar_api_key, verify=False, proxy=False ) response = test_module(client) assert response == 'ok' def test_test_module_handles_authorization_error(requests_mock): """Tests the test_module validation command authorization error. """ from SOCRadarThreatFusion import Client, test_module, MESSAGES mock_socradar_api_key = "WrongAPIKey" suffix = f'threat/analysis/check/auth?key={mock_socradar_api_key}' mock_response = util_load_json('test_data/check_auth_response_auth_error.json') requests_mock.get(f'{SOCRADAR_API_ENDPOINT}/{suffix}', json=mock_response, status_code=401) client = Client( base_url=SOCRADAR_API_ENDPOINT, api_key=mock_socradar_api_key, verify=False, proxy=False ) with pytest.raises(DemistoException, match=MESSAGES['AUTHORIZATION_ERROR']): test_module(client) def test_ip_command(requests_mock): """Tests the ip_command function. Configures requests_mock instance to generate the appropriate SOCRadar ThreatFusion API response, loaded from a local JSON file. Checks the output of the command function with the expected output. """ from SOCRadarThreatFusion import Client, ip_command mock_socradar_api_key = "APIKey" mock_response = util_load_json('test_data/score_ip_response.json') suffix = 'threat/analysis' requests_mock.get(f'{SOCRADAR_API_ENDPOINT}/{suffix}', json=mock_response) mock_args = {'ip': '1.1.1.1'} client = Client( base_url=SOCRADAR_API_ENDPOINT, api_key=mock_socradar_api_key, verify=False, proxy=False ) result = ip_command( client=client, args=mock_args, ) expected_output = util_load_json('test_data/score_ip_expected_output.json') expected_context = util_load_json('test_data/score_ip_expected_context_generic_command.json') assert isinstance(result, list) assert result != [] assert '### SOCRadar - Analysis results for IP: 1.1.1.1' in result[0].readable_output assert result[0].outputs == expected_context assert result[0].raw_response == expected_output def test_ip_command_handles_incorrect_entity_type(): """Tests the ip_command function incorrect entity type error. """ from SOCRadarThreatFusion import Client, ip_command mock_socradar_api_key = "APIKey" mock_args = {'ip': 'INCORRECT IP ADDRESS'} client = Client( base_url=SOCRADAR_API_ENDPOINT, api_key=mock_socradar_api_key, verify=False, proxy=False ) with pytest.raises(ValueError): ip_command( client=client, args=mock_args, ) def test_domain_command(requests_mock): """Tests the domain_command function. Configures requests_mock instance to generate the appropriate SOCRadar ThreatFusion API response, loaded from a local JSON file. Checks the output of the command function with the expected output. """ from SOCRadarThreatFusion import Client, domain_command mock_socradar_api_key = "APIKey" mock_response = util_load_json('test_data/score_domain_response.json') suffix = 'threat/analysis' requests_mock.get(f'{SOCRADAR_API_ENDPOINT}/{suffix}', json=mock_response) mock_args = {'domain': 'paloaltonetworks.com'} client = Client( base_url=SOCRADAR_API_ENDPOINT, api_key=mock_socradar_api_key, verify=False, proxy=False ) result = domain_command( client=client, args=mock_args, ) expected_output = util_load_json('test_data/score_domain_expected_output.json') expected_context = util_load_json('test_data/score_domain_expected_context_generic_command.json') assert isinstance(result, list) assert result != [] assert '### SOCRadar - Analysis results for domain: paloaltonetworks.com' in result[0].readable_output assert result[0].outputs == expected_context assert result[0].raw_response == expected_output def test_domain_command_handles_incorrect_entity_type(): """Tests the domain_command function incorrect entity type error. """ from SOCRadarThreatFusion import Client, domain_command mock_socradar_api_key = "APIKey" mock_args = {'domain': 'INCORRECT DOMAIN'} client = Client( base_url=SOCRADAR_API_ENDPOINT, api_key=mock_socradar_api_key, verify=False, proxy=False ) with pytest.raises(ValueError): domain_command( client=client, args=mock_args, ) def test_file_command(requests_mock): """Tests the file_command function. Configures requests_mock instance to generate the appropriate SOCRadar ThreatFusion API response, loaded from a local JSON file. Checks the output of the command function with the expected output. """ from SOCRadarThreatFusion import Client, file_command mock_socradar_api_key = "APIKey" mock_response = util_load_json('test_data/score_hash_response.json') suffix = 'threat/analysis' requests_mock.get(f'{SOCRADAR_API_ENDPOINT}/{suffix}', json=mock_response) mock_args = {'file': '3b7b359ea17ac76341957573e332a2d6bcac363401ac71c8df94dac93df6d792'} client = Client( base_url=SOCRADAR_API_ENDPOINT, api_key=mock_socradar_api_key, verify=False, proxy=False ) result = file_command( client=client, args=mock_args, ) expected_output = util_load_json('test_data/score_hash_expected_output.json') expected_context = util_load_json('test_data/score_hash_expected_context_generic_command.json') assert isinstance(result, list) assert result != [] assert '### SOCRadar - Analysis results for hash: 3b7b359ea17ac76341957573e332a2d6bcac363401ac71c8df94dac93df6d792' \ in result[0].readable_output assert result[0].outputs == expected_context assert result[0].raw_response == expected_output def test_file_command_handles_incorrect_entity_type(): """Tests the file_command function incorrect entity type error. """ from SOCRadarThreatFusion import Client, file_command mock_socradar_api_key = "APIKey" mock_args = {'file': 'INCORRECT HASH'} client = Client( base_url=SOCRADAR_API_ENDPOINT, api_key=mock_socradar_api_key, verify=False, proxy=False ) with pytest.raises(ValueError): file_command( client=client, args=mock_args, ) def test_score_ip(requests_mock): """Tests the score_ip_command function. Configures requests_mock instance to generate the appropriate SOCRadar ThreatFusion API response, loaded from a local JSON file. Checks the output of the command function with the expected output. """ from SOCRadarThreatFusion import Client, score_ip_command mock_socradar_api_key = "APIKey" mock_response = util_load_json('test_data/score_ip_response.json') suffix = 'threat/analysis' requests_mock.get(f'{SOCRADAR_API_ENDPOINT}/{suffix}', json=mock_response) mock_args = {'ip': '1.1.1.1'} client = Client( base_url=SOCRADAR_API_ENDPOINT, api_key=mock_socradar_api_key, verify=False, proxy=False ) result = score_ip_command( client=client, args=mock_args, ) expected_output = util_load_json('test_data/score_ip_expected_output.json') expected_context = util_load_json('test_data/score_ip_expected_context.json') assert isinstance(result, CommandResults) assert '### SOCRadar - Analysis results for IP: 1.1.1.1' in result.readable_output assert result.outputs == expected_context assert result.raw_response == expected_output def test_score_ip_handles_incorrect_entity_type(): """Tests the score_ip_command function incorrect entity type error. """ from SOCRadarThreatFusion import Client, score_ip_command mock_socradar_api_key = "APIKey" mock_args = {'ip': 'INCORRECT IP ADDRESS'} client = Client( base_url=SOCRADAR_API_ENDPOINT, api_key=mock_socradar_api_key, verify=False, proxy=False ) with pytest.raises(ValueError): score_ip_command( client=client, args=mock_args, ) def test_score_domain(requests_mock): """Tests the score_domain_command function. Configures requests_mock instance to generate the appropriate SOCRadar ThreatFusion API response, loaded from a local JSON file. Checks the output of the command function with the expected output. """ from SOCRadarThreatFusion import Client, score_domain_command mock_socradar_api_key = "APIKey" mock_response = util_load_json('test_data/score_domain_response.json') suffix = 'threat/analysis' requests_mock.get(f'{SOCRADAR_API_ENDPOINT}/{suffix}', json=mock_response) mock_args = {'domain': 'paloaltonetworks.com'} client = Client( base_url=SOCRADAR_API_ENDPOINT, api_key=mock_socradar_api_key, verify=False, proxy=False ) result = score_domain_command( client=client, args=mock_args, ) expected_output = util_load_json('test_data/score_domain_expected_output.json') expected_context = util_load_json('test_data/score_domain_expected_context.json') assert isinstance(result, CommandResults) assert '### SOCRadar - Analysis results for domain: paloaltonetworks.com' in result.readable_output assert result.outputs == expected_context assert result.raw_response == expected_output def test_score_domain_handles_incorrect_entity_type(): """Tests the score_domain_command function incorrect entity type error. """ from SOCRadarThreatFusion import Client, score_domain_command mock_socradar_api_key = "APIKey" mock_args = {'domain': 'INCORRECT DOMAIN'} client = Client( base_url=SOCRADAR_API_ENDPOINT, api_key=mock_socradar_api_key, verify=False, proxy=False ) with pytest.raises(ValueError): score_domain_command( client=client, args=mock_args, ) def test_score_hash(requests_mock): """Tests the score_hash_command function. Configures requests_mock instance to generate the appropriate SOCRadar ThreatFusion API response, loaded from a local JSON file. Checks the output of the command function with the expected output. """ from SOCRadarThreatFusion import Client, score_hash_command mock_socradar_api_key = "APIKey" mock_response = util_load_json('test_data/score_hash_response.json') suffix = 'threat/analysis' requests_mock.get(f'{SOCRADAR_API_ENDPOINT}/{suffix}', json=mock_response) mock_args = {'hash': '3b7b359ea17ac76341957573e332a2d6bcac363401ac71c8df94dac93df6d792'} client = Client( base_url=SOCRADAR_API_ENDPOINT, api_key=mock_socradar_api_key, verify=False, proxy=False ) result = score_hash_command( client=client, args=mock_args ) expected_output = util_load_json('test_data/score_hash_expected_output.json') expected_context = util_load_json('test_data/score_hash_expected_context.json') assert isinstance(result, CommandResults) assert '### SOCRadar - Analysis results for hash: 3b7b359ea17ac76341957573e332a2d6bcac363401ac71c8df94dac93df6d792' \ in result.readable_output assert result.outputs == expected_context assert result.raw_response == expected_output def test_score_hash_handles_incorrect_entity_type(): """Tests the score_hash_command function incorrect entity type error. """ from SOCRadarThreatFusion import Client, score_hash_command mock_socradar_api_key = "APIKey" mock_args = {'hash': 'INCORRECT HASH'} client = Client( base_url=SOCRADAR_API_ENDPOINT, api_key=mock_socradar_api_key, verify=False, proxy=False ) with pytest.raises(ValueError): score_hash_command( client=client, args=mock_args, ) @pytest.mark.parametrize('socradar_score, dbot_score', CALCULATE_DBOT_SCORE_INPUTS) def test_calculate_dbot_score(socradar_score, dbot_score): from SOCRadarThreatFusion import calculate_dbot_score assert calculate_dbot_score(socradar_score) == dbot_score def test_map_indicator_type(): from SOCRadarThreatFusion import map_indicator_type assert FeedIndicatorType.IP == map_indicator_type('ipv4') assert FeedIndicatorType.IPv6 == map_indicator_type('ipv6') assert FeedIndicatorType.Domain == map_indicator_type('hostname') assert FeedIndicatorType.File == map_indicator_type('hash') assert None is map_indicator_type('IP') assert None is map_indicator_type('invalid')

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1926dfeb97d8f0f20d83a85e8229ffec681ec175

112

py

Python

pytextgears/__init__.py

khmurakami/pytextgears

a45722382d5cec1586e5aaeab354ecea9e506f91

[ "MIT" ]

1

2021-05-19T04:45:06.000Z

pytextgears/__init__.py

khmurakami/pytextgears

a45722382d5cec1586e5aaeab354ecea9e506f91

[ "MIT" ]

null

pytextgears/__init__.py

khmurakami/pytextgears

a45722382d5cec1586e5aaeab354ecea9e506f91

[ "MIT" ]

null

from .pytextgears import TextGear from .utils import * from .json_parser import * from .error_handling import *

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py

Python

tests/test_spc.py

andrewheusser/quail

fce1152a3f7dc983f4a3143698fdc3e27f61d1d2

[ "MIT" ]

17

2017-04-12T15:45:37.000Z

2021-07-12T21:25:50.000Z

tests/test_spc.py

vishalbelsare/quail

6c847a49f31d953f3264294439576a23588b84d8

[ "MIT" ]

80

2017-04-12T18:54:10.000Z

2021-06-05T17:28:33.000Z

tests/test_spc.py

vishalbelsare/quail

6c847a49f31d953f3264294439576a23588b84d8

[ "MIT" ]

8

2018-02-01T18:53:46.000Z

2020-01-12T17:36:33.000Z

from quail.egg import Egg import numpy as np import pytest def test_spc(): presented=[[['cat', 'bat', 'hat', 'goat'],['zoo', 'animal', 'zebra', 'horse']]] recalled=[[['bat', 'cat', 'goat', 'hat'],['animal', 'horse', 'zoo']]] egg = Egg(pres=presented,rec=recalled) assert np.array_equal(egg.analyze('spc').data.values,[np.array([ 1., 1., 1., 1.]),np.array([ 1., 1., 0., 1.])]) def test_analysis_spc_multisubj(): presented=[[['cat', 'bat', 'hat', 'goat'],['zoo', 'animal', 'zebra', 'horse']],[['cat', 'bat', 'hat', 'goat'],['zoo', 'animal', 'zebra', 'horse']]] recalled=[[['bat', 'cat', 'goat', 'hat'],['animal', 'horse', 'zoo']],[['bat', 'cat', 'goat', 'hat'],['animal', 'horse', 'zoo']]] multisubj_egg = Egg(pres=presented,rec=recalled) assert np.allclose(multisubj_egg.analyze('spc').data.values,np.array([[ 1., 1., 1., 1.],[ 1., 1., 0., 1.],[ 1., 1., 1., 1.],[ 1., 1., 0., 1.]])) def test_spc_best_euclidean(): presented=[[[10, 20, 30, 40],[10, 20, 30, 40]]] recalled=[[[20, 10, 40, 30],[20, 40, 10]]] egg = Egg(pres=presented,rec=recalled) assert np.allclose(egg.analyze('spc', match='best', distance='euclidean', features='item').data.values,[np.array([1., 1., 1., 1.]),np.array([1., 1., 0., 1.])]) def test_spc_best_euclidean(): presented = [[[{'item' : i, 'feature1' : i*10} for i in range(1, 5)] for i in range(2)]] recalled=[[[{'item' : i, 'feature1' : i*10} for i in [2, 1, 4, 3]],[{'item' : i, 'feature1' : i*10} for i in [2, 4, 1]]]] egg = Egg(pres=presented,rec=recalled) assert np.allclose(egg.analyze('spc', match='best', distance='euclidean', features='feature1').data.values,[np.array([1., 1., 1., 1.]),np.array([1., 1., 0., 1.])]) def test_spc_best_euclidean_3d(): presented = [[[{'item' : i, 'feature1' : [i*10, 0, 0]} for i in range(1, 5)] for i in range(2)]] recalled=[[[{'item' : i, 'feature1' : [i*10, 0, 0]} for i in [2, 1, 4, 3]],[{'item' : i, 'feature1' : [i*10, 0, 0]} for i in [2, 4, 1]]]] egg = Egg(pres=presented,rec=recalled) assert np.array_equal(egg.analyze('spc', match='best', distance='euclidean', features='feature1').data.values,[np.array([1., 1., 1., 1.]),np.array([1., 1., 0., 1.])]) def test_spc_best_euclidean_3d_2features(): presented = [[[{'item' : i, 'feature1' : [i*10, 0, 0], 'feature2' : [i*10, 0, 0]} for i in range(1, 5)] for i in range(2)]] recalled=[[[{'item' : i, 'feature1' : [i*10, 0, 0], 'feature2': [i*10, 0, 0]} for i in [2, 1, 4, 3]],[{'item' : i, 'feature1' : [i*10, 0, 0], 'feature2': [i*10, 0, 0]} for i in [2, 4, 1]]]] egg = Egg(pres=presented,rec=recalled) assert np.array_equal(egg.analyze('spc', match='best', distance='euclidean', features=['feature1', 'feature2']).data.values,[np.array([1., 1., 1., 1.]),np.array([1., 1., 0., 1.])]) def test_spc_best_euclidean_3d_features_not_set(): presented = [[[{'item' : i, 'feature1' : [i*10, 0, 0]} for i in range(1, 5)] for i in range(2)]] recalled=[[[{'item' : i, 'feature1' : [i*10, 0, 0]} for i in [2, 1, 4, 3]],[{'item' : i, 'feature1' : [i*10, 0, 0]} for i in [2, 4, 1]]]] egg = Egg(pres=presented,rec=recalled) assert np.array_equal(egg.analyze('spc', match='best', distance='euclidean', features='feature1').data.values,[np.array([1., 1., 1., 1.]),np.array([1., 1., 0., 1.])]) def test_spc_best_euclidean_3d_exception_no_features(): presented=[[[[10, 0, 0], [20, 0, 0], [30, 0, 0], [40, 0, 0]], [[10, 0, 0], [20, 0, 0], [30, 0, 0], [40, 0, 0]]]] recalled=[[[[20, 0, 0], [10, 0, 0], [40, 0, 0], [30, 0, 0]], [[20, 0, 0], [40, 0, 0], [10, 0, 0]]]] egg = Egg(pres=presented,rec=recalled) with pytest.raises(Exception): assert np.array_equal(egg.analyze('spc', match='best', distance='euclidean').data.values,[np.array([1., 1., 1., 1.]),np.array([1., 1., 0., 1.])]) def test_spc_best_euclidean_3d_exception_item_specified(): presented=[[[[10, 0, 0], [20, 0, 0], [30, 0, 0], [40, 0, 0]], [[10, 0, 0], [20, 0, 0], [30, 0, 0], [40, 0, 0]]]] recalled=[[[[20, 0, 0], [10, 0, 0], [40, 0, 0], [30, 0, 0]], [[20, 0, 0], [40, 0, 0], [10, 0, 0]]]] egg = Egg(pres=presented,rec=recalled) assert np.array_equal(egg.analyze('spc', match='best', distance='euclidean', features='item').data.values,[np.array([1., 1., 1., 1.]),np.array([1., 1., 0., 1.])]) def test_spc_best_correlation_3d(): presented=[[[[10, 0, 10], [20, 0, 0], [30, 0, -10], [40, 0, -20]], [[10, 0, 10], [20, 0, 0], [30, 0, -10], [40, 0, -20]]]] recalled=[[[[20, 0, 0], [10, 0, 10], [40, 0, -20], [30, 0, -10]], [[20, 0, 0], [40, 0, -20], [10, 0, 10]]]] egg = Egg(pres=presented,rec=recalled) assert np.array_equal(egg.analyze('spc', match='best', distance='correlation', features='item').data.values,[np.array([1., 1., 1., 1.]),np.array([1., 1., 0., 1.])]) def test_spc_smooth_correlation_3d(): presented=[[[[10, 0, 10], [20, 0, 0], [30, 0, -10], [40, 0, -20]], [[10, 0, 10], [20, 0, 0], [30, 0, -10], [40, 0, -20]]]] recalled=[[[[20, 0, 0], [10, 0, 10], [40, 0, -20], [30, 0, -10]], [[20, 0, 0], [40, 0, -20], [10, 0, 10]]]] egg = Egg(pres=presented,rec=recalled) egg.analyze('spc', match='smooth', distance='euclidean', features='item').data.values

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1931e0564e7a7ea346e585ae507436d9fe70eebb

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py

Python

plugins/plugin_huffman/__init__.py

G-AshwinKumar/experiment-notebook

aae1c5fb9ef8f84dce5d75989ed8975797282f37

[ "MIT" ]

null

plugins/plugin_huffman/__init__.py

G-AshwinKumar/experiment-notebook

aae1c5fb9ef8f84dce5d75989ed8975797282f37

[ "MIT" ]

null

plugins/plugin_huffman/__init__.py

G-AshwinKumar/experiment-notebook

aae1c5fb9ef8f84dce5d75989ed8975797282f37

[ "MIT" ]

null

from . import huffman_codec

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1996e266c1b531fd6cc4ed2ce80391b2c5054e51

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py

Python

answers.py

DenisLozhnikov/iamhome

07d8b32c556ded345a915a7e61620b3c951d670d

[ "MIT" ]

1

2021-11-14T16:50:18.000Z

answers.py

DenisLozhnikov/iamhome

07d8b32c556ded345a915a7e61620b3c951d670d

[ "MIT" ]

26

2021-09-22T14:18:23.000Z

2021-10-07T05:57:26.000Z

answers.py

DenisLozhnikov/iamhome

07d8b32c556ded345a915a7e61620b3c951d670d

[ "MIT" ]

1

2021-09-24T11:37:39.000Z

import random POSITIVE_ANSWERS = ['Хорошо', 'Поняла', 'Отлично', 'Поняла Вас', 'Ясно', 'Понятно', 'Записала'] def add_positive_answer(text): return random.choice(POSITIVE_ANSWERS) + '. ' + text

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199f640cef151891e8177a4f7886d0fe16df1c6b

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py

Python

output/models/nist_data/list_pkg/nmtokens/schema_instance/nistschema_sv_iv_list_nmtokens_enumeration_1_xsd/nistschema_sv_iv_list_nmtokens_enumeration_1.py

tefra/xsdata-w3c-tests

b6b6a4ac4e0ab610e4b50d868510a8b7105b1a5f

[ "MIT" ]

1

2021-08-14T17:59:21.000Z

output/models/nist_data/list_pkg/nmtokens/schema_instance/nistschema_sv_iv_list_nmtokens_enumeration_1_xsd/nistschema_sv_iv_list_nmtokens_enumeration_1.py

tefra/xsdata-w3c-tests

b6b6a4ac4e0ab610e4b50d868510a8b7105b1a5f

[ "MIT" ]

4

2020-02-12T21:30:44.000Z

2020-04-15T20:06:46.000Z

output/models/nist_data/list_pkg/nmtokens/schema_instance/nistschema_sv_iv_list_nmtokens_enumeration_1_xsd/nistschema_sv_iv_list_nmtokens_enumeration_1.py

tefra/xsdata-w3c-tests

b6b6a4ac4e0ab610e4b50d868510a8b7105b1a5f

[ "MIT" ]

null

from dataclasses import dataclass, field from enum import Enum from typing import Optional __NAMESPACE__ = "NISTSchema-SV-IV-list-NMTOKENS-enumeration-1-NS" class NistschemaSvIvListNmtokensEnumeration1Type(Enum): IMPROVED_IS_STANDARDS_ADVENT_XML_THE_RETRIEVE_WITH_C_INVESTIGATION_SPECIFICATIONS_IS_ONLY_INFO_OF_ADDITIONALLY_TO_BASED_THE_EB_XML_PARTNERSHIPS_OASI_DEVELOPERS_ENGINEERIN_THE_DISCUSSIONS_EFFORTS_PA = ( "improved-is-standards.advent-XML_the_retrieve.with:C", "investigation.specifications.is-Only.info", "of.Additionally_to_based:The.ebXML_partnerships_OASI", "Developers.engineerin", "the_discussions:efforts_pa", ) PROBLEMS_OF_DOM_FOR_DEFINE_INDUSTRY_HAS_I_TECHNOLOGIES_CHAIN_F_DISTRIBUTED_ENSURE_THE_ADDRESSING_A_INCLUDING_IN_THE_PROVIDE_DIAGNOS_FROM_TO_VARIETY_UN_THE_TECHNICAL_SCHEMAS_FOR_USE_PROJECTOR_AND_DISPLAYING_REVOL_FILE_COLLABORATING_DESCRIPTION_U_PARTNERSHIPS_THIS_TO_OASIS_PERVASIVE_THE_XML_THE_INTERNATIONAL_AND_KNOWN_AN_XML_TO_PAGES_DISCOVER_FED_DISSEMINATE_FOR_CAN_TO_UTILITIES_CHAINS_INTEROPERABILITY = ( "problems_of_DOM_for.define-industry:has_i", "technologies.chain:f", "distributed:ensure", "the-addressing.a_including:in.the-provide-diagnos", "from_to:variety.un", "the_Technical_Schemas_for:use.projector_and.displaying.revol", "file-collaborating_Description.u", "partnerships_this:to-OASIS_Pervasive.the:", "XML.the.international:and.known_an.XML-to.pages_discover:fed", "disseminate-for:can:to_utilities.chains-interoperability:", ) DEPENDABILITY_ENFORCEMENT_BETWEEN_PARTICIPATE_NIST_OF_THE_OF_WITH_GROUPS_COLLABORATE_DISCOVERY_THEIR_TO_GRAPHIC_REVIEWED_AND_LANGU_PROMISES_DOCUMENTS_SOLUTIONS_IN_THE_IN_SOFT = ( "dependability-enforcement.between:participate_NIST-of", "the-of.with_Groups.collaborate.discovery:", "their-to_graphic", "reviewed.and.Langu", "promises-documents-solutions.in-the_in:soft", ) MANIPULATE_PERV_OFFER_TO_AND_E_WITH_REFE_TIME_THE_ANY_AMBIGUITIES_TO_ORGANIZATION_THE_LOCALIZED_JI_OF_PROVIDES_A_INTEROPERABILITY_BUSINESS_NIST_PERVASIVE_A_FIL_AND_IS_THE_BACK_XML_TEST_DOCUMENTS_AND_A_REFERENCE_IS_THE_P_AND_OUR_THE_ARE_AND_R = ( "manipulate:perv", "offer:to.and:e-with.refe", "time_the.any-ambiguities.to_organization-the-", "localized_Ji", "of.provides-a:interoperability-business-NIST-pervasive_a:fil", "and-is.the_back-XML.test-documents_and.a-reference_is.the_p", "and-our:the-are.and.r", ) DEVELOPERS_S_A_BUILD_IS_ENABLING_THAT_T_ISSUES_SUCH_THE_THE_ASKED_RELATED_TO_BE_HAMPERED_O_AND_INFO_MUST_TO_WITH_THE_THE_B_VOCA_AROU_USING_VERTICAL_THIS_WIDELY_WITHI = ( "developers.s_a_build:is-enabling.that-t", "issues_such:The_The.asked:related-to.be-hampered.o", "and_info", "must_to:with-the.the-b", "voca", "arou", "using:vertical:This.widely:withi", ) CAN_WITH_PROTOTYPE_TECHNOLOGIES_A_HELPING_ENFORCEMENT_CO_MANY_AND_EMBEDDED_SEMANTICS_PERVASIVE_UNAMBIGUO_LAW_THE_TODAY_SIGNATURES_DESKTOP_NEW_XML_A_OBJECTIVE_IT_THE_AND_FOR_MADE_KEY_ORGANIZATIONS_QUALITY_AB_MARKUP_ARE_ABOUT_OASI_THE_A_IS_ABILITY_TO_IS_FOR_SUCCESS_OF_INDUSTRY_DOM_PC_WHICH_THE_LED_AUTOMATIC = ( "can.with:prototype:technologies.a:helping.enforcement:_co", "many.and:embedded-semantics.Pervasive.unambiguo", "law.The_today_signatures:desktop:new.XML:A", "objective.it", "The:and-for-made-key:organizations.quality:ab", "Markup-are-about:OASI", "the.A_is.ability-to.is:for-success.of.industry-DOM.PC_which", "The:led.automatic-", ) @dataclass class NistschemaSvIvListNmtokensEnumeration1: class Meta: name = "NISTSchema-SV-IV-list-NMTOKENS-enumeration-1" namespace = "NISTSchema-SV-IV-list-NMTOKENS-enumeration-1-NS" value: Optional[NistschemaSvIvListNmtokensEnumeration1Type] = field( default=None, metadata={ "required": True, } )

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19a199ba3c2923def0bd8f6bad9da5d847c4cf4f

180

py

Python

autoarray/inversion/regularization/__init__.py

caoxiaoyue/PyAutoArray

e10d3d6a5b8dd031f2ad277486bd539bd5858b2a

[ "MIT" ]

null

autoarray/inversion/regularization/__init__.py

caoxiaoyue/PyAutoArray

e10d3d6a5b8dd031f2ad277486bd539bd5858b2a

[ "MIT" ]

null

autoarray/inversion/regularization/__init__.py

caoxiaoyue/PyAutoArray

e10d3d6a5b8dd031f2ad277486bd539bd5858b2a

[ "MIT" ]

null

from .constant import Constant from .constant import ConstantSplit from .adaptive_brightness import AdaptiveBrightness from .adaptive_brightness import AdaptiveBrightnessSplit

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270d7b756a53b40a23192377c06dd1774d77b516

113

py

Python

utils/base.py

saisyam/scrapers

1e34c2e3d9b052f0516c72210f0bcbdb8f631d89

[ "Apache-2.0" ]

null

utils/base.py

saisyam/scrapers

1e34c2e3d9b052f0516c72210f0bcbdb8f631d89

[ "Apache-2.0" ]

5

2021-03-13T07:07:41.000Z

2021-03-23T11:28:21.000Z

utils/base.py

saisyam/scrapers

1e34c2e3d9b052f0516c72210f0bcbdb8f631d89

[ "Apache-2.0" ]

null

class BaseScraper: def __init__(self, url): self.url = url def scrape(self): return {}

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27213f0b38b74db7851c15191be45d4d09a8c6c5

284

py

Python

via_cms/model/internal/__init__.py

jeanjacquesp/via-cms

12b212f8005e3d667c23ffc4da831e4d3e653999

[ "MIT" ]

null

via_cms/model/internal/__init__.py

jeanjacquesp/via-cms

12b212f8005e3d667c23ffc4da831e4d3e653999

[ "MIT" ]

null

via_cms/model/internal/__init__.py

jeanjacquesp/via-cms

12b212f8005e3d667c23ffc4da831e4d3e653999

[ "MIT" ]

null

# Copyright 2020 Pax Syriana Foundation. Licensed under the Apache License, Version 2.0 # from via_cms.model.internal._id_manager import * from via_cms.model.internal.role_dao import * from via_cms.model.internal.user_dao import * from via_cms.model.internal.workflow_dao import *

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2734575d3d6f659e2dac1b63b4c20102436119ef

137

py

Python

scripts/reactor/autogen_ludiquest2.py

doriyan13/doristory

438caf3b123922da3f5f3b16fcc98a26a8ab85ce

[ "MIT" ]

null

scripts/reactor/autogen_ludiquest2.py

doriyan13/doristory

438caf3b123922da3f5f3b16fcc98a26a8ab85ce

[ "MIT" ]

null

scripts/reactor/autogen_ludiquest2.py

doriyan13/doristory

438caf3b123922da3f5f3b16fcc98a26a8ab85ce

[ "MIT" ]

null

# ParentID: 2202002 # Character field ID when accessed: 220020400 # ObjectID: 1000011 # Object Position X: -650 # Object Position Y: 162

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7e0e3c048f76ae11f44a3a1cc7ad7ccd70653470

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py

Python

quspin/basis/_reshape_subsys.py

anton-buyskikh/QuSpin

4e46b495e399414d9361d659e186492a1ac5b511

[ "BSD-3-Clause" ]

195

2016-10-24T18:05:31.000Z

2022-03-29T10:11:56.000Z

quspin/basis/_reshape_subsys.py

cileeky/QuSpin

769d3817870f6ff55c4283af46f94e11c36f4121

[ "BSD-3-Clause" ]

303

2016-10-25T20:08:11.000Z

2022-03-31T16:52:09.000Z

quspin/basis/_reshape_subsys.py

cileeky/QuSpin

769d3817870f6ff55c4283af46f94e11c36f4121

[ "BSD-3-Clause" ]

54

2017-01-03T18:47:52.000Z

2022-03-16T06:54:33.000Z

import numpy as _np import scipy.sparse as _sp from ._basis_utils import _shuffle_sites #################################################### # set of helper functions to implement the partial # # trace of lattice density matrices. They do not # # have any checks and states are assumed to be # # in the non-symmetry reduced basis. # #################################################### def _lattice_partial_trace_pure(psi,sub_sys_A,L,sps,return_rdm="A"): """ This function computes the partial trace of a dense pure state psi over set of sites sub_sys_A and returns reduced DM. Vectorisation available. """ psi_v=_lattice_reshape_pure(psi,sub_sys_A,L,sps) if return_rdm == "A": return _np.squeeze(_np.einsum("...ij,...kj->...ik",psi_v,psi_v.conj())),None elif return_rdm == "B": return None,_np.squeeze(_np.einsum("...ji,...jk->...ik",psi_v.conj(),psi_v)) elif return_rdm == "both": return _np.squeeze(_np.einsum("...ij,...kj->...ik",psi_v,psi_v.conj())),_np.squeeze(_np.einsum("...ji,...jk->...ik",psi_v.conj(),psi_v)) def _lattice_partial_trace_mixed(rho,sub_sys_A,L,sps,return_rdm="A"): """ This function computes the partial trace of a set of dense mixed states rho over set of sites sub_sys_A and returns reduced DM. Vectorisation available. """ rho_v=_lattice_reshape_mixed(rho,sub_sys_A,L,sps) if return_rdm == "A": return _np.einsum("...jlkl->...jk",rho_v),None elif return_rdm == "B": return None,_np.einsum("...ljlk->...jk",rho_v.conj()) elif return_rdm == "both": return _np.einsum("...jlkl->...jk",rho_v),_np.einsum("...ljlk->...jk",rho_v.conj()) def _lattice_partial_trace_sparse_pure(psi,sub_sys_A,L,sps,return_rdm="A"): """ This function computes the partial trace of a sparse pure state psi over set of sites sub_sys_A and returns reduced DM. """ psi=_lattice_reshape_sparse_pure(psi,sub_sys_A,L,sps) if return_rdm == "A": return psi.dot(psi.H),None elif return_rdm == "B": return None,psi.H.dot(psi) elif return_rdm == "both": return psi.dot(psi.H),psi.H.dot(psi) def _lattice_reshape_pure(psi,sub_sys_A,L,sps): """ This function reshapes the dense pure state psi over the Hilbert space defined by sub_sys_A and its complement. Vectorisation available. """ extra_dims = psi.shape[:-1] n_dims = len(extra_dims) sub_sys_B = set(range(L))-set(sub_sys_A) sub_sys_A = tuple(sub_sys_A) sub_sys_B = tuple(sub_sys_B) L_A = len(sub_sys_A) L_B = len(sub_sys_B) Ns_A = (sps**L_A) Ns_B = (sps**L_B) T_tup = sub_sys_A+sub_sys_B psi_v = _shuffle_sites(sps,T_tup,psi) psi_v = psi_v.reshape(extra_dims+(Ns_A,Ns_B)) return psi_v ''' def _lattice_reshape_pure(psi,sub_sys_A,L,sps): """ This function reshapes the dense pure state psi over the Hilbert space defined by sub_sys_A and its complement. Vectorisation available. """ extra_dims = psi.shape[:-1] n_dims = len(extra_dims) sub_sys_B = set(range(L))-set(sub_sys_A) sub_sys_A = tuple(sub_sys_A) sub_sys_B = tuple(sub_sys_B) L_A = len(sub_sys_A) L_B = len(sub_sys_B) Ns_A = (sps**L_A) Ns_B = (sps**L_B) T_tup = sub_sys_A+sub_sys_B T_tup = tuple(range(n_dims)) + tuple(n_dims + s for s in T_tup) R_tup = extra_dims + tuple(sps for i in range(L)) psi_v = psi.reshape(R_tup) # DM where index is given per site as rho_v[i_1,...,i_L,j_1,...j_L] psi_v = psi_v.transpose(T_tup) # take transpose to reshuffle indices psi_v = psi_v.reshape(extra_dims+(Ns_A,Ns_B)) return psi_v ''' def _lattice_reshape_mixed(rho,sub_sys_A,L,sps): """ This function reshapes the dense mixed state psi over the Hilbert space defined by sub_sys_A and its complement. Vectorisation available. """ extra_dims = rho.shape[:-2] n_dims = len(extra_dims) sub_sys_B = set(range(L))-set(sub_sys_A) sub_sys_A = tuple(sub_sys_A) sub_sys_B = tuple(sub_sys_B) L_A = len(sub_sys_A) L_B = len(sub_sys_B) Ns_A = (sps**L_A) Ns_B = (sps**L_B) # T_tup tells numpy how to reshuffle the indices such that when I reshape the array to the # 4-_tensor rho_{ik,jl} i,j are for sub_sys_A and k,l are for sub_sys_B # which means I need (sub_sys_A,sub_sys_B,sub_sys_A+L,sub_sys_B+L) T_tup = sub_sys_A+sub_sys_B T_tup = tuple(T_tup) + tuple(L+s for s in T_tup) rho = rho.reshape(extra_dims+(-1,)) rho_v = _shuffle_sites(sps,T_tup,rho) return rho_v.reshape(extra_dims+(Ns_A,Ns_B,Ns_A,Ns_B)) ''' def _lattice_reshape_mixed(rho,sub_sys_A,L,sps): """ This function reshapes the dense mixed state psi over the Hilbert space defined by sub_sys_A and its complement. Vectorisation available. """ extra_dims = rho.shape[:-2] n_dims = len(extra_dims) sub_sys_B = set(range(L))-set(sub_sys_A) sub_sys_A = tuple(sub_sys_A) sub_sys_B = tuple(sub_sys_B) L_A = len(sub_sys_A) L_B = len(sub_sys_B) Ns_A = (sps**L_A) Ns_B = (sps**L_B) # T_tup tells numpy how to reshuffle the indices such that when I reshape the array to the # 4-_tensor rho_{ik,jl} i,j are for sub_sys_A and k,l are for sub_sys_B # which means I need (sub_sys_A,sub_sys_B,sub_sys_A+L,sub_sys_B+L) T_tup = sub_sys_A+sub_sys_B T_tup = tuple(range(n_dims)) + tuple(s+n_dims for s in T_tup) + tuple(L+n_dims+s for s in T_tup) R_tup = extra_dims + tuple(sps for i in range(2*L)) rho_v = rho.reshape(R_tup) # DM where index is given per site as rho_v[i_1,...,i_L,j_1,...j_L] rho_v = rho_v.transpose(T_tup) # take transpose to reshuffle indices return rho_v.reshape(extra_dims+(Ns_A,Ns_B,Ns_A,Ns_B)) ''' def _lattice_reshape_sparse_pure(psi,sub_sys_A,L,sps): """ This function reshapes the sparse pure state psi over the Hilbert space defined by sub_sys_A and its complement. """ sub_sys_B = set(range(L))-set(sub_sys_A) sub_sys_A = tuple(sub_sys_A) sub_sys_B = tuple(sub_sys_B) L_A = len(sub_sys_A) L_B = len(sub_sys_B) Ns_A = (sps**L_A) Ns_B = (sps**L_B) psi = psi.tocoo() T_tup = sub_sys_A+sub_sys_B # reshuffle indices for the sub-systems. # j = sum( j[i]*(sps**i) for i in range(L)) # this reshuffles the j[i] similar to the transpose operation # on the dense arrays psi_v.transpose(T_tup) if T_tup != tuple(range(L)): indx = _np.zeros(psi.col.shape,dtype=psi.col.dtype) for i_old,i_new in enumerate(T_tup): indx += ((psi.col//(sps**(L-i_new-1))) % sps)*(sps**(L-i_old-1)) else: indx = psi.col # A = _np.array([0,1,2,3,4,5,6,7,8,9,10,11]) # print("make shift way of reshaping array") # print("A = {}".format(A)) # print("A.reshape((3,4)): \n {}".format(A.reshape((3,4)))) # print("rows: A.reshape((3,4))/4: \n {}".format(A.reshape((3,4))/4)) # print("cols: A.reshape((3,4))%4: \n {}".format(A.reshape((3,4))%4)) psi._shape = (Ns_A,Ns_B) psi.row[:] = indx / Ns_B psi.col[:] = indx % Ns_B return psi.tocsr() def _tensor_reshape_pure(psi,sub_sys_A,Ns_l,Ns_r): extra_dims = psi.shape[:-1] if sub_sys_A == "left": return psi.reshape(extra_dims+(Ns_l,Ns_r)) else: n_dims = len(extra_dims) T_tup = tuple(range(n_dims))+(n_dims+1,n_dims) psi_v = psi.reshape(extra_dims+(Ns_l,Ns_r)) return psi_v.transpose(T_tup) def _tensor_reshape_sparse_pure(psi,sub_sys_A,Ns_l,Ns_r): psi = psi.tocoo() # make shift way of reshaping array # j = j_l + Ns_r * j_l # j_l = j / Ns_r # j_r = j % Ns_r if sub_sys_A == "left": psi._shape = (Ns_l,Ns_r) psi.row[:] = psi.col / Ns_r psi.col[:] = psi.col % Ns_r return psi.tocsr() else: psi._shape = (Ns_l,Ns_r) psi.row[:] = psi.col / Ns_r psi.col[:] = psi.col % Ns_r return psi.T.tocsr() def _tensor_reshape_mixed(rho,sub_sys_A,Ns_l,Ns_r): extra_dims = rho.shape[:-2] if sub_sys_A == "left": return rho.reshape(extra_dims+(Ns_l,Ns_r,Ns_l,Ns_r)) else: n_dims = len(extra_dims) T_tup = tuple(range(n_dims))+(n_dims+1,n_dims)+(n_dims+3,n_dims+2) rho_v = rho.reshape(extra_dims+(Ns_l,Ns_r,Ns_l,Ns_r)) return rho_v.transpose(T_tup) def _tensor_partial_trace_pure(psi,sub_sys_A,Ns_l,Ns_r,return_rdm="A"): psi_v = _tensor_reshape_pure(psi,sub_sys_A,Ns_l,Ns_r) if return_rdm == "A": return _np.squeeze(_np.einsum("...ij,...kj->...ik",psi_v,psi_v.conj())),None elif return_rdm == "B": return None,_np.squeeze(_np.einsum("...ji,...jk->...ik",psi_v.conj(),psi_v)) elif return_rdm == "both": return _np.squeeze(_np.einsum("...ij,...kj->...ik",psi_v,psi_v.conj())),_np.squeeze(_np.einsum("...ji,...jk->...ik",psi_v.conj(),psi_v)) def _tensor_partial_trace_sparse_pure(psi,sub_sys_A,Ns_l,Ns_r,return_rdm="A"): psi = _tensor_reshape_sparse_pure(psi,sub_sys_A,Ns_l,Ns_r) if return_rdm == "A": return psi.dot(psi.H),None elif return_rdm == "B": return None,psi.H.dot(psi) elif return_rdm == "both": return psi.dot(psi.H),psi.H.dot(psi) def _tensor_partial_trace_mixed(rho,sub_sys_A,Ns_l,Ns_r,return_rdm="A"): rho_v = _tensor_reshape_mixed(rho,sub_sys_A,Ns_l,Ns_r) if return_rdm == "A": return _np.squeeze(_np.einsum("...ijkj->...ik",rho_v)),None elif return_rdm == "B": return None,_np.squeeze(_np.einsum("...jijk->...ik",rho_v.conj())) elif return_rdm == "both": return _np.squeeze(_np.einsum("...ijkj->...ik",rho_v)),_np.squeeze(_np.einsum("...jijk->...ik",rho_v.conj()))

31.578397

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1,778

9,063

3.197975

0.092801

0.098136

0.076328

0.025325

0.857545

0.83732

0.804784

0.780865

0.764685

0.714386

0

0.006417

0.140241

9,063

286

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31.688811

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0.323077

0

null

0

1

0

null

0

6

fdfd689db547e2e4c0b8c5c6cfc4b95d54f0ad6a

1,053

py

Python

2019/16/part1.py

mihaip/adventofcode

3725668595bfcf619fe6c97d12e2f14b42e3f0cb

[ "Apache-2.0" ]

null

2019/16/part1.py

mihaip/adventofcode

3725668595bfcf619fe6c97d12e2f14b42e3f0cb

[ "Apache-2.0" ]

null

2019/16/part1.py

mihaip/adventofcode

3725668595bfcf619fe6c97d12e2f14b42e3f0cb

[ "Apache-2.0" ]

null

#!/usr/local/bin/python3 INPUT = "59766299734185935790261115703620877190381824215209853207763194576128635631359682876612079355215350473577604721555728904226669021629637829323357312523389374096761677612847270499668370808171197765497511969240451494864028712045794776711862275853405465401181390418728996646794501739600928008413106803610665694684578514524327181348469613507611935604098625200707607292339397162640547668982092343405011530889030486280541249694798815457170337648425355693137656149891119757374882957464941514691345812606515925579852852837849497598111512841599959586200247265784368476772959711497363250758706490540128635133116613480058848821257395084976935351858829607105310340" L = len(INPUT) BASE_PATTERN = [0, 1, 0, -1] def phase(input): result = [] for i in range(L): r = 0 for pi in range(L): p = BASE_PATTERN[(pi + 1) // (i + 1) % 4] r += input[pi] * p result.append(abs(r) % 10) return result input = list(map(int, INPUT)) for i in range(100): input = phase(input) print("answer: %s" % "".join(map(str, input[:8])))

45.782609

661

0.834758

71

1,053

12.352113

0.521127

0.023945

0.013683

0.025086

0

0.689834

0.08452

1,053

22

662

47.863636

0.219917

0.021842

0

0.641399

0.631681

0

1

0

1

0.0625

false

0

0.125

0.0625

0

1

null

0

1

0

1

0

1

null

1

0

6

e3016272421ec74b2f4005da3d3ead38f7f113ab

131

py

Python

screens/__init__.py

suchyDev/Kivy-Dynamic-Screens-Template

7fbab74c5693430ea486ac359fa7a032596c232b

[ "MIT" ]

13

2016-11-13T17:56:31.000Z

2022-03-03T21:17:00.000Z

screens/__init__.py

suchyDev/Kivy-Dynamic-Screens-Template

7fbab74c5693430ea486ac359fa7a032596c232b

[ "MIT" ]

4

2018-03-16T00:40:34.000Z

2020-10-26T19:51:02.000Z

screens/__init__.py

suchyDev/Kivy-Dynamic-Screens-Template

7fbab74c5693430ea486ac359fa7a032596c232b

[ "MIT" ]

9

2019-03-26T19:19:05.000Z

2021-08-06T17:06:23.000Z

'''Screens package containing all the app screens.''' from resource_registers import register_kv_and_data register_kv_and_data()

21.833333

53

0.824427

19

131

5.315789

0.736842

0.19802

0.257426

0.336634

0

0.10687

131

5

54

26.2

0.863248

0.358779

0

1

0

true

0

0.5

0

0.5

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

e337de7410b876ca26bbd0376992a347aa78b144

36

py

Python

jsonPackage/__init__.py

Hammer2900/json_help_object

1154f87a53973d239c3a37d01b52410c2abc9101

[ "MIT" ]

null

jsonPackage/__init__.py

Hammer2900/json_help_object

1154f87a53973d239c3a37d01b52410c2abc9101

[ "MIT" ]

1

2021-03-25T21:48:46.000Z

jsonPackage/__init__.py

Hammer2900/json_help_object

1154f87a53973d239c3a37d01b52410c2abc9101

[ "MIT" ]

null

from . import main_core, fast_utils

18

35

0.805556

6

36

4.5

1

0

0.138889

36

1

36

0.870968

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

8b5ef5fd1128e81f186da18e36e1301932b9ed4c

31

py

Python

test.py

GCreaner/Week1

6709aafa8ccc962026319f7a9435f1e98d4548b5

[ "Apache-2.0" ]

null

test.py

GCreaner/Week1

6709aafa8ccc962026319f7a9435f1e98d4548b5

[ "Apache-2.0" ]

null

test.py

GCreaner/Week1

6709aafa8ccc962026319f7a9435f1e98d4548b5

[ "Apache-2.0" ]

null

print "This is a test message"

15.5

30

0.741935

6

31

3.833333

1

0

0.193548

31

1

31

0.92

0

0.709677

0

null

0

null

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

8b9fbd4952fb354426b9eaf821c069013e84a0ae

35

py

Python

bbauto/api/router.py

BB-Auto-Detailing/backend

b60a5107ef0bb6716429ccedcacd8aa987acc866

[ "BSD-2-Clause" ]

4

2018-07-20T15:37:01.000Z

2019-02-20T23:39:09.000Z

freenit/project/project/api/router.py

mekanix/flask-bootstrap-sql-rest

13b4e4dc093e268e40ec56caa25b03157634a087

[ "BSD-2-Clause" ]

4

2020-01-31T12:12:56.000Z

2021-01-13T12:37:23.000Z

freenit/project/project/api/router.py

mekanix/flask-bootstrap-sql-rest

13b4e4dc093e268e40ec56caa25b03157634a087

[ "BSD-2-Clause" ]

5

2018-06-19T19:32:27.000Z

2019-10-02T20:11:30.000Z

from freenit.api.router import api

17.5

34

0.828571

6

35

4.833333

0.833333

0

0.114286

35

1

35

0.935484

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

8ba24f2ead601e33028c7ee972070160c1370c9e

110

py

Python

fathom/residual/__init__.py

Aetf/fathom

1f0dafa9fe3b7988708522d93ecda7f282cb2090

[ "Apache-2.0" ]

1

2021-06-30T04:59:22.000Z

fathom/residual/__init__.py

Aetf/fathom

1f0dafa9fe3b7988708522d93ecda7f282cb2090

[ "Apache-2.0" ]

null

fathom/residual/__init__.py

Aetf/fathom

1f0dafa9fe3b7988708522d93ecda7f282cb2090

[ "Apache-2.0" ]

null

from __future__ import absolute_import, print_function, division from .residual import Residual, ResidualFwd

27.5

64

0.854545

13

110

6.769231

0.692308

0

0.109091

110

3

65

36.666667

0.897959

0

1

0

true

0

1

0

1

0.5

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

8bcb060f9c9e9b1a05e7378d1176a09bd72f8629

12,884

py

Python

menpo/math/test/decomposition_base_test.py

jacksoncsy/menpo

3cac491fe30454935ed12fcaa89f453c5f6ec878

[ "BSD-3-Clause" ]

null

menpo/math/test/decomposition_base_test.py

jacksoncsy/menpo

3cac491fe30454935ed12fcaa89f453c5f6ec878

[ "BSD-3-Clause" ]

null

menpo/math/test/decomposition_base_test.py

jacksoncsy/menpo

3cac491fe30454935ed12fcaa89f453c5f6ec878

[ "BSD-3-Clause" ]

null

import numpy as np from numpy.testing import assert_almost_equal from menpo.math import eigenvalue_decomposition, \ principal_component_decomposition # Positive semi-definite matrix cov_matrix = np.array([[3, 1], [1, 3]]) # Data values taken from: # http://www.cs.otago.ac.nz/cosc453/student_tutorials/principal_components.pdf # Tested values are equal large_samples_data_matrix = np.array([[2.5, 2.4], [0.5, 0.7], [2.2, 2.9], [1.9, 2.2], [3.1, 3.0], [2.3, 2.7], [2.0, 1.6], [1.0, 1.1], [1.5, 1.6], [1.1, 0.9]]) centered_eigenvectors_s = np.array([[0.6778734, 0.73517866], [-0.73517866, 0.6778734]]) non_centered_eigenvectors_s = np.array([[0.68647784, 0.72715072], [-0.72715072, 0.68647784]]) mean_vector_s = np.array([1.81, 1.91]) eigenvalues_no_centre_no_bias_s = np.array([8.97738481, 0.04928186]) eigenvalues_centered_biased_s = np.array([1.15562494, 0.04417506]) eigenvalues_no_centre_biased_s = np.array([8.07964633, 0.04435367]) eigenvalues_centered_no_bias_s = np.array([1.28402771, 0.0490834]) centered_eigenvectors_f = np.array([[-0.09901475, 0.19802951, 0.69310328, 0.29704426, -0.09901475, 0.39605902, -0.39605902, 0.09901475, 0.09901475, -0.19802951]]) centered_eigenvectors_biased_f = np.array([[-0.13864839, 0.27729678, 0.97053872, 0.41594517, -0.13864839, 0.55459355, -0.55459355, 0.13864839, 0.13864839, -0.27729678]]) non_centered_eigenvectors_biased_f = np.array( [[0.04284044, 0.01054804, 0.04479142, 0.03594266, 0.05333815, 0.0438411, 0.03139242, 0.01839615, 0.02714423, 0.01744583], [-0.3840268, 0.26369659, 0.88167249, 0.29008842, -0.43904756, 0.40818153, -0.802497, 0.06307234, 0.0172217, -0.41041863]]) non_centered_eigenvectors_f = np.array( [[0.38507927, 0.09481302, 0.40261598, 0.32307722, 0.4794398, 0.39407387, 0.28217662, 0.16535718, 0.24399096, 0.15681507], [-0.25575629, 0.17561812, 0.58718113, 0.19319469, -0.29239933, 0.27184299, -0.5344514, 0.04200527, 0.01146941, -0.27333287]]) mean_vector_f = np.array([2.45, 0.6, 2.55, 2.05, 3.05, 2.5, 1.8, 1.05, 1.55, 1.]) eigenvalues_no_centre_no_bias_f = np.array([80.79646326, 0.44353674]) eigenvalues_centered_biased_f = np.array([0.255]) eigenvalues_no_centre_biased_f = np.array([40.39823163, 0.22176837]) eigenvalues_centered_no_bias_f = np.array([0.51]) # whiten,centre,bias (samples) # 000 def pcd_samples_nowhiten_nocentre_nobias_test(): output = principal_component_decomposition(large_samples_data_matrix, centre=False, whiten=False, bias=False) eigenvectors, eigenvalues, mean_vector = output assert_almost_equal(eigenvalues, eigenvalues_no_centre_no_bias_s) assert_almost_equal(eigenvectors, non_centered_eigenvectors_s) assert_almost_equal(mean_vector, [0.0, 0.0]) # 001 def pcd_samples_nowhiten_nocentre_yesbias_test(): output = principal_component_decomposition(large_samples_data_matrix, centre=False, bias=True, whiten=False) eigenvectors, eigenvalues, mean_vector = output assert_almost_equal(eigenvalues, eigenvalues_no_centre_biased_s) assert_almost_equal(eigenvectors, non_centered_eigenvectors_s) assert_almost_equal(mean_vector, [0.0, 0.0]) # 010 def pcd_samples_nowhiten_yescentre_nobias_test(): output = principal_component_decomposition(large_samples_data_matrix, whiten=False, centre=True, bias=False) eigenvectors, eigenvalues, mean_vector = output assert_almost_equal(eigenvalues, eigenvalues_centered_no_bias_s) assert_almost_equal(eigenvectors, centered_eigenvectors_s) assert_almost_equal(mean_vector, mean_vector_s) # 011 def pcd_samples_nowhiten_yescentre_yesbias_test(): output = principal_component_decomposition(large_samples_data_matrix, bias=True, centre=True, whiten=False) eigenvectors, eigenvalues, mean_vector = output assert_almost_equal(eigenvalues, eigenvalues_centered_biased_s) assert_almost_equal(eigenvectors, centered_eigenvectors_s) assert_almost_equal(mean_vector, mean_vector_s) # 100 def pcd_samples_yeswhiten_nocentre_nobias_test(): output = principal_component_decomposition(large_samples_data_matrix, centre=False, whiten=True, bias=False) eigenvectors, eigenvalues, mean_vector = output assert_almost_equal(eigenvalues, eigenvalues_no_centre_no_bias_s) assert_almost_equal(eigenvectors.T / np.sqrt(1.0 / eigenvalues), non_centered_eigenvectors_s.T) assert_almost_equal(mean_vector, [0.0, 0.0]) # 101 def pcd_samples_yeswhiten_nocentre_yesbias_test(): output = principal_component_decomposition(large_samples_data_matrix, bias=True, centre=False, whiten=True) eigenvectors, eigenvalues, mean_vector = output assert_almost_equal(eigenvalues, eigenvalues_no_centre_biased_s) assert_almost_equal(eigenvectors.T / np.sqrt(1.0 / eigenvalues), non_centered_eigenvectors_s.T) assert_almost_equal(mean_vector, [0.0, 0.0]) # 110 def pcd_samples_yeswhiten_yescentre_nobias_test(): output = principal_component_decomposition(large_samples_data_matrix, whiten=True, centre=True, bias=False) eigenvectors, eigenvalues, mean_vector = output assert_almost_equal(eigenvalues, eigenvalues_centered_no_bias_s) assert_almost_equal(eigenvectors.T / np.sqrt(1.0 / eigenvalues), centered_eigenvectors_s.T) assert_almost_equal(mean_vector, mean_vector_s) # 111 def pcd_samples_yeswhiten_yescentre_yesbias_test(): output = principal_component_decomposition(large_samples_data_matrix, whiten=True, centre=True, bias=True) eigenvectors, eigenvalues, mean_vector = output assert_almost_equal(eigenvalues, eigenvalues_centered_biased_s) assert_almost_equal(eigenvectors.T / np.sqrt(1.0 / eigenvalues), centered_eigenvectors_s.T) assert_almost_equal(mean_vector, mean_vector_s) # whiten,centre,bias (features) # 000 def pcd_features_nowhiten_nocentre_nobias_test(): output = principal_component_decomposition(large_samples_data_matrix.T, centre=False, whiten=False, bias=False) eigenvectors, eigenvalues, mean_vector = output assert_almost_equal(eigenvalues, eigenvalues_no_centre_no_bias_f) assert_almost_equal(eigenvectors, non_centered_eigenvectors_f) assert_almost_equal(mean_vector, np.zeros(10)) def pcd_features_nowhiten_nocentre_nobias_inplace_test(): # important to copy as this will now destructively effect the input data # matrix (due to inplace) output = principal_component_decomposition(large_samples_data_matrix.T.copy(), centre=False, whiten=False, bias=False, inplace=True) eigenvectors, eigenvalues, mean_vector = output assert_almost_equal(eigenvalues, eigenvalues_no_centre_no_bias_f) assert_almost_equal(eigenvectors, non_centered_eigenvectors_f) assert_almost_equal(mean_vector, np.zeros(10)) # 001 def pcd_features_nowhiten_nocentre_yesbias_test(): output = principal_component_decomposition(large_samples_data_matrix.T, centre=False, bias=True, whiten=False) eigenvectors, eigenvalues, mean_vector = output assert_almost_equal(eigenvalues, eigenvalues_no_centre_biased_f) assert_almost_equal(eigenvectors, non_centered_eigenvectors_f) assert_almost_equal(mean_vector, np.zeros(10)) # 010 def pcd_features_nowhiten_yescentre_nobias_test(): output = principal_component_decomposition(large_samples_data_matrix.T, whiten=False, centre=True, bias=False) eigenvectors, eigenvalues, mean_vector = output assert_almost_equal(eigenvalues, eigenvalues_centered_no_bias_f) assert_almost_equal(eigenvectors, centered_eigenvectors_f) assert_almost_equal(mean_vector, mean_vector_f) # 011 def pcd_features_nowhiten_yescentre_yesbias_test(): output = principal_component_decomposition(large_samples_data_matrix.T, bias=True, centre=True, whiten=False) eigenvectors, eigenvalues, mean_vector = output assert_almost_equal(eigenvalues, eigenvalues_centered_biased_f) assert_almost_equal(eigenvectors, centered_eigenvectors_f) assert_almost_equal(mean_vector, mean_vector_f) # 100 def pcd_features_yeswhiten_nocentre_nobias_test(): output = principal_component_decomposition(large_samples_data_matrix.T, centre=False, whiten=True, bias=False) eigenvectors, eigenvalues, mean_vector = output assert_almost_equal(eigenvalues, eigenvalues_no_centre_no_bias_f) assert_almost_equal(eigenvectors.T / np.sqrt(1.0 / eigenvalues), non_centered_eigenvectors_f.T) assert_almost_equal(mean_vector, np.zeros(10)) # 101 def pcd_features_yeswhiten_nocentre_yesbias_test(): output = principal_component_decomposition(large_samples_data_matrix.T, bias=True, centre=False, whiten=True) eigenvectors, eigenvalues, mean_vector = output assert_almost_equal(eigenvalues, eigenvalues_no_centre_biased_f) assert_almost_equal(eigenvectors, non_centered_eigenvectors_biased_f) assert_almost_equal(mean_vector, np.zeros(10)) # 110 def pcd_features_yeswhiten_yescentre_nobias_test(): output = principal_component_decomposition(large_samples_data_matrix.T, whiten=True, centre=True, bias=False) eigenvectors, eigenvalues, mean_vector = output assert_almost_equal(eigenvalues, eigenvalues_centered_no_bias_f) assert_almost_equal(eigenvectors, centered_eigenvectors_biased_f) assert_almost_equal(mean_vector, mean_vector_f) # 111 def pcd_features_yeswhiten_yescentre_yesbias_test(): output = principal_component_decomposition(large_samples_data_matrix.T, whiten=True, centre=True, bias=True) eigenvectors, eigenvalues, mean_vector = output assert_almost_equal(eigenvalues, eigenvalues_centered_biased_f) assert_almost_equal(eigenvectors, centered_eigenvectors_biased_f) assert_almost_equal(mean_vector, mean_vector_f) def eigenvalue_decomposition_default_epsilon_test(): pos_eigenvectors, pos_eigenvalues = eigenvalue_decomposition(cov_matrix) assert_almost_equal(pos_eigenvalues, [4.0, 2.0]) sqrt_one_over_2 = np.sqrt(2.0) / 2.0 assert_almost_equal(pos_eigenvectors, [[sqrt_one_over_2, -sqrt_one_over_2], [sqrt_one_over_2, sqrt_one_over_2]]) def eigenvalue_decomposition_large_epsilon_test(): pos_eigenvectors, pos_eigenvalues = eigenvalue_decomposition(cov_matrix, eps=0.5) assert_almost_equal(pos_eigenvalues, [4.0]) sqrt_one_over_2 = np.sqrt(2.0) / 2.0 assert_almost_equal(pos_eigenvectors, [[sqrt_one_over_2], [sqrt_one_over_2]])

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null

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null

0

6

8bd1792860d88ade8bd38146c2ce1074d33e66b0

1,322

py

Python

Sprint 2/backend/src/controllers/envio_email.py

IsraelAugusto0110/API_5-SEM

c0d872453c1a0b6e34764810176525456dc6d559

[ "MIT" ]

null

Sprint 2/backend/src/controllers/envio_email.py

IsraelAugusto0110/API_5-SEM

c0d872453c1a0b6e34764810176525456dc6d559

[ "MIT" ]

null

Sprint 2/backend/src/controllers/envio_email.py

IsraelAugusto0110/API_5-SEM

c0d872453c1a0b6e34764810176525456dc6d559

[ "MIT" ]

null

import smtplib class Email: def email_cadastro(a, c): SUBJECT = "Cadastro realizado com sucesso!!!" TO = a FROM = "contato.bycar@gmail.com" PASSWORD = "@bycarApp2021" text = f"SENHA TEMPORÁRIA: {c}" BODY = "\r\n".join(( f"From: {FROM}", f"To: {TO}", f"Subject: {SUBJECT}", "", text)) server = smtplib.SMTP('smtp.gmail.com', 587) server.starttls() server.login(FROM, PASSWORD) print("Login funfou") server.sendmail(FROM, TO, BODY) print("Email enviado para", TO) server.quit() return "200" def email_redefinicao(a): SUBJECT = "Solicitação de redefinição de senha aceita" TO = a print(a) FROM = "contato.bycar@gmail.com" PASSWORD = "@bycarApp2021" text = "use esse código para redefinir sua senha" BODY = "\r\n".join(( f"From: {FROM}", f"To: {TO}", f"Subject: {SUBJECT}", "", text)) server = smtplib.SMTP('smtp.gmail.com', 587) server.starttls() server.login(FROM, PASSWORD) print("Login funfou") server.sendmail(FROM, TO, BODY) print("Email enviado para", TO) server.quit() return "200"

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py

Python

pyastbuilder/tests/sparqlparser/func_unittest.py

TNO/pyAST

f202b1cd2bcc7cf5c5ad9ef5402d5ebea490a496

[ "MIT" ]

4

2017-12-05T12:43:46.000Z

2021-08-12T11:37:45.000Z

pyastbuilder/tests/sparqlparser/func_unittest.py

TNO/pyAST

f202b1cd2bcc7cf5c5ad9ef5402d5ebea490a496

[ "MIT" ]

9

2017-08-24T08:55:17.000Z

2017-10-18T11:57:14.000Z

pyastbuilder/tests/sparqlparser/func_unittest.py

TNO/pyAST

f202b1cd2bcc7cf5c5ad9ef5402d5ebea490a496

[ "MIT" ]

null

''' Created on 20 apr. 2016 @author: jeroenbruijning ''' import unittest from parsertools.parsers.sparqlparser import SPARQLParser, SPARQLParseException from parsertools.parsers.sparqlparser import stripComments, parseQuery, unescapeUcode class Test(unittest.TestCase): def setUp(self): pass def tearDown(self): pass # ParseStruct tests def testParse(self): s = "'work' ^^<work:>" r = SPARQLParser.RDFLiteral(s) assert r.check() def testCopy(self): s = "'work' ^^<work:>" r = SPARQLParser.RDFLiteral(s) r_copy = r.copy() assert r_copy == r assert not r_copy is r def testStr(self): s = "'work' ^^<work:>" r = SPARQLParser.RDFLiteral(s) assert r.__str__() == "'work' ^^ <work:>" def testLabelDotAccess(self): s = "'work' ^^<work:>" r = SPARQLParser.RDFLiteral(s) assert str(r.lexical_form) == "'work'", r.lexical_form r_copy = r.copy() try: r_copy.lexical_form = SPARQLParser.String("'work2'") except AttributeError as e: assert str(e) == 'Direct setting of attributes not allowed. To change an element e, try e.updateWith() instead.' s = '<c:check#22?> ( $var, ?var )' r = SPARQLParser.PrimaryExpression(s, postParseCheck=False) assert r.iriOrFunction.iri == SPARQLParser.iri('<c:check#22?>', postParseCheck=False) def testUpdateWith(self): s = "'work' ^^<work:>" r = SPARQLParser.RDFLiteral(s) r_copy = r.copy() r_copy.lexical_form.updateWith("'work2'") assert r_copy != r r_copy.lexical_form.updateWith("'work'") assert r_copy == r q = ''' PREFIX foaf: <http://xmlns.com/foaf/0.1/> SELECT ?p WHERE { ?p a foaf:Person } ''' r = parseQuery(q) r.expandIris() subjpath = r.searchElements(element_type=SPARQLParser.IRIREF, value=None)[1] assert str(subjpath.getParent()) == '<http://xmlns.com/foaf/0.1/Person>' assert str(subjpath.getAncestors()) == '[iri("<http://xmlns.com/foaf/0.1/Person>"), GraphTerm("<http://xmlns.com/foaf/0.1/Person>"), VarOrTerm("<http://xmlns.com/foaf/0.1/Person>"), GraphNodePath("<http://xmlns.com/foaf/0.1/Person>"), ObjectPath("<http://xmlns.com/foaf/0.1/Person>"), ObjectListPath("<http://xmlns.com/foaf/0.1/Person>"), PropertyListPathNotEmpty("a <http://xmlns.com/foaf/0.1/Person>"), TriplesSameSubjectPath("?p a <http://xmlns.com/foaf/0.1/Person>"), TriplesBlock("?p a <http://xmlns.com/foaf/0.1/Person>"), GroupGraphPatternSub("?p a <http://xmlns.com/foaf/0.1/Person>"), GroupGraphPattern("{ ?p a <http://xmlns.com/foaf/0.1/Person> }"), WhereClause("WHERE { ?p a <http://xmlns.com/foaf/0.1/Person> }"), SelectQuery("SELECT ?p WHERE { ?p a <http://xmlns.com/foaf/0.1/Person> }"), Query("PREFIX foaf: <http://xmlns.com/foaf/0.1/> SELECT ?p WHERE { ?p a <http://xmlns.com/foaf/0.1/Person> }"), QueryUnit("PREFIX foaf: <http://xmlns.com/foaf/0.1/> SELECT ?p WHERE { ?p a <http://xmlns.com/foaf/0.1/Person> }")]' assert r.hasParentPointers() def testBranchAndAtom(self): s = "'work' ^^<work:>" r = SPARQLParser.RDFLiteral(s) assert r.isBranch() assert not r.isAtom() assert (SPARQLParser.IRIREF('<ftp://test>')).isAtom() def testDescend(self): s = '(DISTINCT "*Expression*", "*Expression*", "*Expression*" )' r = SPARQLParser.ArgList(s) assert r.descend() == r v = r.searchElements(element_type=SPARQLParser.STRING_LITERAL2) assert v[0].isAtom() assert not v[0].isBranch() e = r.searchElements(element_type=SPARQLParser.Expression)[0] d = e.descend() assert d.isAtom() def testStripComments(self): s1 = """ <c:check#22?> ( $var, ?var ) # bla 'sdfasf# sdfsfd' # comment """[1:-1].split('\n') s2 = """ <c:check#22?> ( $var, ?var ) 'sdfasf# sdfsfd' """[1:-1] assert stripComments(s1) == s2 def testSearchElements(self): s = '<c:check#22?> ( $var, ?var )' r = SPARQLParser.PrimaryExpression(s, postParseCheck=False) found = r.searchElements() assert len(found) == 32, len(found) found = r.searchElements(labeledOnly=False) assert len(found) == 32, len(found) found = r.searchElements(labeledOnly=True) assert len(found) == 4, len(found) found = r.searchElements(value='<c:check#22?>') assert len(found) == 2, len(found) assert type(found[0]) == SPARQLParser.iri assert found[0].getLabel() == 'iri' assert found[0].__str__() == '<c:check#22?>' def testGetChildOrAncestors(self): s = '<c:check#22?> ( $var, ?var )' r = SPARQLParser.PrimaryExpression(s, postParseCheck=False) found = r.searchElements(element_type=SPARQLParser.ArgList) arglist = found[0] assert(len(arglist.getChildren())) == 4, len(arglist.getChildren()) ancestors = arglist.getAncestors() assert str(ancestors) == '[iriOrFunction("<c:check#22?> ( $var , ?var )"), PrimaryExpression("<c:check#22?> ( $var , ?var )")]', str(ancestors) def testParseQuery(self): s = 'BASE <work:22?> SELECT REDUCED $var1 ?var2 (("*Expression*") AS $var3) { SELECT * {} } GROUP BY ROUND ( "*Expression*") VALUES $S { <t:testIri> <t:testIri> }' parseQuery(s) s = 'BASE <prologue:22> PREFIX prologue: <prologue:33> LOAD <t:testIri> ; BASE <prologue2:42> PREFIX prologue2: <prologue3:33>' parseQuery(s) def testDump(self): s = '(DISTINCT "*Expression*", "*Expression*", "*Expression*" )' s_dump = ''' [ArgList] /( DISTINCT "*Expression*" , "*Expression*" , "*Expression*" )/ | [LPAR] /(/ | | ( | > distinct: | [DISTINCT] /DISTINCT/ | | DISTINCT | > argument: | [Expression] /"*Expression*"/ | | [ConditionalOrExpression] /"*Expression*"/ | | | [ConditionalAndExpression] /"*Expression*"/ | | | | [ValueLogical] /"*Expression*"/ | | | | | [RelationalExpression] /"*Expression*"/ | | | | | | [NumericExpression] /"*Expression*"/ | | | | | | | [AdditiveExpression] /"*Expression*"/ | | | | | | | | [MultiplicativeExpression] /"*Expression*"/ | | | | | | | | | [UnaryExpression] /"*Expression*"/ | | | | | | | | | | [PrimaryExpression] /"*Expression*"/ | | | | | | | | | | | [RDFLiteral] /"*Expression*"/ | | | | | | | | | | | | > lexical_form: | | | | | | | | | | | | [String] /"*Expression*"/ | | | | | | | | | | | | | [STRING_LITERAL2] /"*Expression*"/ | | | | | | | | | | | | | | "*Expression*" | , | > argument: | [Expression] /"*Expression*"/ | | [ConditionalOrExpression] /"*Expression*"/ | | | [ConditionalAndExpression] /"*Expression*"/ | | | | [ValueLogical] /"*Expression*"/ | | | | | [RelationalExpression] /"*Expression*"/ | | | | | | [NumericExpression] /"*Expression*"/ | | | | | | | [AdditiveExpression] /"*Expression*"/ | | | | | | | | [MultiplicativeExpression] /"*Expression*"/ | | | | | | | | | [UnaryExpression] /"*Expression*"/ | | | | | | | | | | [PrimaryExpression] /"*Expression*"/ | | | | | | | | | | | [RDFLiteral] /"*Expression*"/ | | | | | | | | | | | | > lexical_form: | | | | | | | | | | | | [String] /"*Expression*"/ | | | | | | | | | | | | | [STRING_LITERAL2] /"*Expression*"/ | | | | | | | | | | | | | | "*Expression*" | , | > argument: | [Expression] /"*Expression*"/ | | [ConditionalOrExpression] /"*Expression*"/ | | | [ConditionalAndExpression] /"*Expression*"/ | | | | [ValueLogical] /"*Expression*"/ | | | | | [RelationalExpression] /"*Expression*"/ | | | | | | [NumericExpression] /"*Expression*"/ | | | | | | | [AdditiveExpression] /"*Expression*"/ | | | | | | | | [MultiplicativeExpression] /"*Expression*"/ | | | | | | | | | [UnaryExpression] /"*Expression*"/ | | | | | | | | | | [PrimaryExpression] /"*Expression*"/ | | | | | | | | | | | [RDFLiteral] /"*Expression*"/ | | | | | | | | | | | | > lexical_form: | | | | | | | | | | | | [String] /"*Expression*"/ | | | | | | | | | | | | | [STRING_LITERAL2] /"*Expression*"/ | | | | | | | | | | | | | | "*Expression*" | [RPAR] /)/ | | ) '''[1:] r = SPARQLParser.ArgList(s) assert r.dump() == s_dump def testPrefixesAndBase(self): s = 'BASE <prologue:22/> PREFIX prologue1: <prologue:33> LOAD <t:testIri> ; BASE <prologue:44> BASE </exttra> PREFIX prologue2: <prologue:55>' r = parseQuery(s) answer1 = ''' UpdateUnit BASE <prologue:22/> PREFIX prologue1: <prologue:33> LOAD <t:testIri> ; BASE <prologue:44> BASE </exttra> PREFIX prologue2: <prologue:55> [] None UpdateUnit BASE <prologue:22/> PREFIX prologue1: <prologue:33> LOAD <t:testIri> ; BASE <prologue:44> BASE </exttra> PREFIX prologue2: <prologue:55> [] None Update BASE <prologue:22/> PREFIX prologue1: <prologue:33> LOAD <t:testIri> ; BASE <prologue:44> BASE </exttra> PREFIX prologue2: <prologue:55> [] None Prologue BASE <prologue:22/> PREFIX prologue1: <prologue:33> [('prologue1:', 'prologue:33')] prologue:22/ BaseDecl BASE <prologue:22/> [('prologue1:', 'prologue:33')] prologue:22/ BASE BASE [('prologue1:', 'prologue:33')] prologue:22/ IRIREF <prologue:22/> [('prologue1:', 'prologue:33')] prologue:22/ PrefixDecl PREFIX prologue1: <prologue:33> [('prologue1:', 'prologue:33')] prologue:22/ PREFIX PREFIX [('prologue1:', 'prologue:33')] prologue:22/ PNAME_NS prologue1: [('prologue1:', 'prologue:33')] prologue:22/ IRIREF <prologue:33> [('prologue1:', 'prologue:33')] prologue:22/ Update1 LOAD <t:testIri> [('prologue1:', 'prologue:33')] prologue:22/ Load LOAD <t:testIri> [('prologue1:', 'prologue:33')] prologue:22/ LOAD LOAD [('prologue1:', 'prologue:33')] prologue:22/ iri <t:testIri> [('prologue1:', 'prologue:33')] prologue:22/ IRIREF <t:testIri> [('prologue1:', 'prologue:33')] prologue:22/ SEMICOL ; [('prologue1:', 'prologue:33')] prologue:22/ Update BASE <prologue:44> BASE </exttra> PREFIX prologue2: <prologue:55> [('prologue1:', 'prologue:33')] prologue:22/ Prologue BASE <prologue:44> BASE </exttra> PREFIX prologue2: <prologue:55> [('prologue1:', 'prologue:33'), ('prologue2:', 'prologue:55')] prologue:/exttra BaseDecl BASE <prologue:44> [('prologue1:', 'prologue:33'), ('prologue2:', 'prologue:55')] prologue:/exttra BASE BASE [('prologue1:', 'prologue:33'), ('prologue2:', 'prologue:55')] prologue:/exttra IRIREF <prologue:44> [('prologue1:', 'prologue:33'), ('prologue2:', 'prologue:55')] prologue:/exttra BaseDecl BASE </exttra> [('prologue1:', 'prologue:33'), ('prologue2:', 'prologue:55')] prologue:/exttra BASE BASE [('prologue1:', 'prologue:33'), ('prologue2:', 'prologue:55')] prologue:/exttra IRIREF </exttra> [('prologue1:', 'prologue:33'), ('prologue2:', 'prologue:55')] prologue:/exttra PrefixDecl PREFIX prologue2: <prologue:55> [('prologue1:', 'prologue:33'), ('prologue2:', 'prologue:55')] prologue:/exttra PREFIX PREFIX [('prologue1:', 'prologue:33'), ('prologue2:', 'prologue:55')] prologue:/exttra PNAME_NS prologue2: [('prologue1:', 'prologue:33'), ('prologue2:', 'prologue:55')] prologue:/exttra IRIREF <prologue:55> [('prologue1:', 'prologue:33'), ('prologue2:', 'prologue:55')] prologue:/exttra ''' r_answer1 = '' for elt in r.searchElements(): for e in [elt.__class__.__name__, elt, sorted(elt.getPrefixes().items()), elt.getBaseiri()]: r_answer1 += str(e) + '\n' r_answer1 += '\n' assert answer1.strip() == r_answer1.strip() r = parseQuery(s, base='ftp://nothing/') answer2 = ''' UpdateUnit BASE <prologue:22/> PREFIX prologue1: <prologue:33> LOAD <t:testIri> ; BASE <prologue:44> BASE </exttra> PREFIX prologue2: <prologue:55> [] ftp://nothing/ UpdateUnit BASE <prologue:22/> PREFIX prologue1: <prologue:33> LOAD <t:testIri> ; BASE <prologue:44> BASE </exttra> PREFIX prologue2: <prologue:55> [] ftp://nothing/ Update BASE <prologue:22/> PREFIX prologue1: <prologue:33> LOAD <t:testIri> ; BASE <prologue:44> BASE </exttra> PREFIX prologue2: <prologue:55> [] ftp://nothing/ Prologue BASE <prologue:22/> PREFIX prologue1: <prologue:33> [('prologue1:', 'prologue:33')] prologue:22/ BaseDecl BASE <prologue:22/> [('prologue1:', 'prologue:33')] prologue:22/ BASE BASE [('prologue1:', 'prologue:33')] prologue:22/ IRIREF <prologue:22/> [('prologue1:', 'prologue:33')] prologue:22/ PrefixDecl PREFIX prologue1: <prologue:33> [('prologue1:', 'prologue:33')] prologue:22/ PREFIX PREFIX [('prologue1:', 'prologue:33')] prologue:22/ PNAME_NS prologue1: [('prologue1:', 'prologue:33')] prologue:22/ IRIREF <prologue:33> [('prologue1:', 'prologue:33')] prologue:22/ Update1 LOAD <t:testIri> [('prologue1:', 'prologue:33')] prologue:22/ Load LOAD <t:testIri> [('prologue1:', 'prologue:33')] prologue:22/ LOAD LOAD [('prologue1:', 'prologue:33')] prologue:22/ iri <t:testIri> [('prologue1:', 'prologue:33')] prologue:22/ IRIREF <t:testIri> [('prologue1:', 'prologue:33')] prologue:22/ SEMICOL ; [('prologue1:', 'prologue:33')] prologue:22/ Update BASE <prologue:44> BASE </exttra> PREFIX prologue2: <prologue:55> [('prologue1:', 'prologue:33')] prologue:22/ Prologue BASE <prologue:44> BASE </exttra> PREFIX prologue2: <prologue:55> [('prologue1:', 'prologue:33'), ('prologue2:', 'prologue:55')] prologue:/exttra BaseDecl BASE <prologue:44> [('prologue1:', 'prologue:33'), ('prologue2:', 'prologue:55')] prologue:/exttra BASE BASE [('prologue1:', 'prologue:33'), ('prologue2:', 'prologue:55')] prologue:/exttra IRIREF <prologue:44> [('prologue1:', 'prologue:33'), ('prologue2:', 'prologue:55')] prologue:/exttra BaseDecl BASE </exttra> [('prologue1:', 'prologue:33'), ('prologue2:', 'prologue:55')] prologue:/exttra BASE BASE [('prologue1:', 'prologue:33'), ('prologue2:', 'prologue:55')] prologue:/exttra IRIREF </exttra> [('prologue1:', 'prologue:33'), ('prologue2:', 'prologue:55')] prologue:/exttra PrefixDecl PREFIX prologue2: <prologue:55> [('prologue1:', 'prologue:33'), ('prologue2:', 'prologue:55')] prologue:/exttra PREFIX PREFIX [('prologue1:', 'prologue:33'), ('prologue2:', 'prologue:55')] prologue:/exttra PNAME_NS prologue2: [('prologue1:', 'prologue:33'), ('prologue2:', 'prologue:55')] prologue:/exttra IRIREF <prologue:55> [('prologue1:', 'prologue:33'), ('prologue2:', 'prologue:55')] prologue:/exttra ''' r_answer2 = '' for elt in r.searchElements(): for e in [elt.__class__.__name__, elt, sorted(elt.getPrefixes().items()), elt.getBaseiri()]: r_answer2 += str(e) + '\n' r_answer2 += '\n' assert answer2.strip() == r_answer2.strip() def testExpandIris(self): s1 = ''' PREFIX dc: <http://purl.org/dc/elements/1.1/> SELECT ?title WHERE { <http://example.org/book/book1> dc:title ?title } '''[1:-1] s2 = ''' PREFIX dc: <http://purl.org/dc/elements/1.1/> PREFIX : <http://example.org/book/> SELECT $title WHERE { :book1 dc:title $title } '''[1:-1] s3 = ''' BASE <http://example.org/book/> PREFIX dc: <http://purl.org/dc/elements/1.1/> SELECT $title WHERE { <book1> dc:title ?title } '''[1:-1] r1 = parseQuery(s1) r2 = parseQuery(s2) r3 = parseQuery(s3) r1.expandIris() r2.expandIris() r3.expandIris() assert str(r1) == 'PREFIX dc: <http://purl.org/dc/elements/1.1/> SELECT ?title WHERE { <http://example.org/book/book1> <http://purl.org/dc/elements/1.1/title> ?title }' assert str(r2) == 'PREFIX dc: <http://purl.org/dc/elements/1.1/> PREFIX : <http://example.org/book/> SELECT $title WHERE { <http://example.org/book/book1> <http://purl.org/dc/elements/1.1/title> $title }' assert str(r3) == 'BASE <http://example.org/book/> PREFIX dc: <http://purl.org/dc/elements/1.1/> SELECT $title WHERE { <http://example.org/book/book1> <http://purl.org/dc/elements/1.1/title> ?title }' def testUnescapeUcode(self): s = 'abra\\U000C00AAcada\\u00AAbr\u99DDa' assert unescapeUcode(s) == 'abra󀂪cadaªbr駝a' if __name__ == "__main__": #import sys;sys.argv = ['', 'Test.testName'] unittest.main()

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1,038

0.58814

1,774

16,729

5.506201

0.124577

0.067568

0.122543

0.082924

0.765663

0.727273

0.70864

0.693898

0.687654

0.668714

0

0.045164

0.20587

16,729

569

1,039

29.400703

0.690026

0.006635

0

0.722101

0

0.030635

0.692595

0.028296

0

0.078775

1

0.037199

false

0.004376

0.006565

0

0.045952

0

null

0

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0

1

0

1

0

1

0

null

0

6

4744e9d3ab3b10fbf604ae8ab2a9fc793da2f674

2,547

py

Python

openprescribing/frontend/tests/test_managers.py

rebkwok/openprescribing

28c7500a7e4cb725fc6cda0f8c58b07ac7e916a4

[ "MIT" ]

null

openprescribing/frontend/tests/test_managers.py

rebkwok/openprescribing

28c7500a7e4cb725fc6cda0f8c58b07ac7e916a4

[ "MIT" ]

null

openprescribing/frontend/tests/test_managers.py

rebkwok/openprescribing

28c7500a7e4cb725fc6cda0f8c58b07ac7e916a4

[ "MIT" ]

null

from django.test import TestCase from frontend.models import MeasureValue class MeasureValueManagerTests(TestCase): fixtures = ['one_month_of_measures'] def test_by_ccg_with_no_org(self): mvs = MeasureValue.objects.by_ccg([]) self.assertEqual(len(mvs), 2) def test_by_ccg_with_org(self): mvs = MeasureValue.objects.by_ccg(['04D']) self.assertEqual(len(mvs), 1) def test_by_ccg_with_orgs(self): mvs = MeasureValue.objects.by_ccg(['04D', '02Q']) self.assertEqual(len(mvs), 2) def test_by_ccg_with_measure(self): mvs = MeasureValue.objects.by_ccg([], measure_id='cerazette') self.assertEqual(len(mvs), 2) mvs = MeasureValue.objects.by_ccg([], measure_id='bananas') self.assertEqual(len(mvs), 0) def test_by_ccg_with_tag(self): mvs = MeasureValue.objects.by_ccg([], tags=['core']) self.assertEqual(len(mvs), 2) mvs = MeasureValue.objects.by_ccg([], tags=['lowpriority']) self.assertEqual(len(mvs), 0) def test_by_ccg_with_tags(self): mvs = MeasureValue.objects.by_ccg([], tags=['core', 'lowpriority']) self.assertEqual(len(mvs), 0) def test_by_practice_with_no_org(self): mvs = MeasureValue.objects.by_practice([]) self.assertEqual(len(mvs), 9) def test_by_practice_with_pct_org(self): mvs = MeasureValue.objects.by_practice(['04D']) self.assertEqual(len(mvs), 1) def test_by_practice_with_practice_org(self): mvs = MeasureValue.objects.by_practice(['C83051']) self.assertEqual(len(mvs), 1) def test_by_practice_with_orgs(self): mvs = MeasureValue.objects.by_practice(['C83051', '02Q']) self.assertEqual(len(mvs), 8) def test_by_practice_with_measure(self): mvs = MeasureValue.objects.by_practice( ['C83051'], measure_id='cerazette') self.assertEqual(len(mvs), 1) mvs = MeasureValue.objects.by_practice( ['C83051'], measure_id='bananas') self.assertEqual(len(mvs), 0) def test_by_practice_with_tag(self): mvs = MeasureValue.objects.by_practice(['C83051'], tags=['core']) self.assertEqual(len(mvs), 1) mvs = MeasureValue.objects.by_practice( ['C83051'], tags=['lowpriority']) self.assertEqual(len(mvs), 0) def test_by_practice_with_tags(self): mvs = MeasureValue.objects.by_practice( ['C83051'], tags=['core', 'lowpriority']) self.assertEqual(len(mvs), 0)

33.513158

75

0.649784

321

2,547

4.906542

0.140187

0.161905

0.23746

0.259048

0.897778

0.846984

0.649524

0.504762

0.417778

0

0.030861

0.211229

2,547

75

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null

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1

0

null

0

1

0

6

4776d8a97cc5e406953b981c6556a6d522c11b46

214

py

Python

api/words_vector/models.py

leandrocamposcardoso/VetorDePalavras

76d442d0343e85a0edc55ca91b76480c30b3127a

[ "MIT" ]

null

api/words_vector/models.py

leandrocamposcardoso/VetorDePalavras

76d442d0343e85a0edc55ca91b76480c30b3127a

[ "MIT" ]

null

api/words_vector/models.py

leandrocamposcardoso/VetorDePalavras

76d442d0343e85a0edc55ca91b76480c30b3127a

[ "MIT" ]

null

from django.db import models from custom_fields import ListField class Logs(models.Model): files = ListField() vocabulary = ListField(null=True, blank=True) vectors = ListField(null=True, blank=True)

23.777778

49

0.742991

28

214

5.642857

0.607143

0.164557

0.21519

0.278481

0.329114

0

0.163551

214

8

50

26.75

0.882682

0

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0

1

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null

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1

0

1

0

null

0

1

0

1

0

6

47bb152600fc41de2ec8a1427ba054b5e6f16288

170

py

Python

University Admission Procedure/task/university_stage1.py

andreimaftei28/projects-on-JetBrainAcademy

8c2b8ab7bab5757db94e9f0b6d55c33852f64ee1

[ "MIT" ]

null

University Admission Procedure/task/university_stage1.py

andreimaftei28/projects-on-JetBrainAcademy

8c2b8ab7bab5757db94e9f0b6d55c33852f64ee1

[ "MIT" ]

null

University Admission Procedure/task/university_stage1.py

andreimaftei28/projects-on-JetBrainAcademy

8c2b8ab7bab5757db94e9f0b6d55c33852f64ee1

[ "MIT" ]

3

2020-12-19T13:48:06.000Z

2021-08-12T18:36:33.000Z

def get_mean(a, b, c): return sum((a, b, c)) / 3 mean = get_mean(int(input()), int(input()), int(input())) print(mean, "Congratulations, you are accepted!", sep="\n")

42.5

59

0.617647

29

170

3.551724

0.62069

0.23301

0.058252

0.31068

0

0.006849

0.141176

170

4

59

42.5

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0.210526

0

1

0.25

false

0

0.25

0.5

0.25

1

0

null

1

0

1

0

1

0

null

0

1

0

1

0

6

47dd782d49714e6fbc9cb1c79e2c4462f5851fb8

770

py

Python

contrib/python/Jinja2/jinja2/_identifier.py

timgates42/catboost

2fa492f5e32ba14c890dc4b3313cfe1024ca4839

[ "Apache-2.0" ]

2

2021-01-29T04:27:28.000Z

2021-01-29T04:28:27.000Z

contrib/python/Jinja2/jinja2/_identifier.py

birichie/catboost

de75c6af12cf490700e76c22072fbdc15b35d679

[ "Apache-2.0" ]

1

2021-12-09T23:08:25.000Z

contrib/python/Jinja2/jinja2/_identifier.py

birichie/catboost

de75c6af12cf490700e76c22072fbdc15b35d679

[ "Apache-2.0" ]

1

2021-04-27T23:40:09.000Z

# -*- coding: utf-8 -*- import re # generated by scripts/generate_identifier_pattern.py pattern = re.compile( r"[\w·̀-ͯ·҃-֑҇-ׇֽֿׁׂׅׄؐ-ًؚ-ٰٟۖ-ۜ۟-۪ۤۧۨ-ܑۭܰ-݊ަ-ް߫-߳ࠖ-࠙ࠛ-ࠣࠥ-ࠧࠩ-࡙࠭-࡛ࣔ-ࣣ࣡-ःऺ-़ा-ॏ॑-ॗॢॣঁ-ঃ়া-ৄেৈো-্ৗৢৣਁ-ਃ਼ਾ-ੂੇੈੋ-੍ੑੰੱੵઁ-ઃ઼ા-ૅે-ૉો-્ૢૣଁ-ଃ଼ା-ୄେୈୋ-୍ୖୗୢୣஂா-ூெ-ைொ-்ௗఀ-ఃా-ౄె-ైొ-్ౕౖౢౣಁ-ಃ಼ಾ-ೄೆ-ೈೊ-್ೕೖೢೣഁ-ഃാ-ൄെ-ൈൊ-്ൗൢൣංඃ්ා-ුූෘ-ෟෲෳัิ-ฺ็-๎ັິ-ູົຼ່-ໍ༹༘༙༵༷༾༿ཱ-྄྆྇ྍ-ྗྙ-ྼ࿆ါ-ှၖ-ၙၞ-ၠၢ-ၤၧ-ၭၱ-ၴႂ-ႍႏႚ-ႝ፝-፟ᜒ-᜔ᜲ-᜴ᝒᝓᝲᝳ឴-៓៝᠋-᠍ᢅᢆᢩᤠ-ᤫᤰ-᤻ᨗ-ᨛᩕ-ᩞ᩠-᩿᩼᪰-᪽ᬀ-ᬄ᬴-᭄᭫-᭳ᮀ-ᮂᮡ-ᮭ᯦-᯳ᰤ-᰷᳐-᳔᳒-᳨᳭ᳲ-᳴᳸᳹᷀-᷵᷻-᷿‿⁀⁔⃐-⃥⃜⃡-⃰℘℮⳯-⵿⳱ⷠ-〪ⷿ-゙゚〯꙯ꙴ-꙽ꚞꚟ꛰꛱ꠂ꠆ꠋꠣ-ꠧꢀꢁꢴ-ꣅ꣠-꣱ꤦ-꤭ꥇ-꥓ꦀ-ꦃ꦳-꧀ꧥꨩ-ꨶꩃꩌꩍꩻ-ꩽꪰꪲ-ꪴꪷꪸꪾ꪿꫁ꫫ-ꫯꫵ꫶ꯣ-ꯪ꯬꯭ﬞ︀-️︠-︯︳︴﹍-﹏＿𐇽𐋠𐍶-𐍺𐨁-𐨃𐨅𐨆𐨌-𐨏𐨸-𐨿𐨺𐫦𐫥𑀀-𑀂𑀸-𑁆𑁿-𑂂𑂰-𑂺𑄀-𑄂𑄧-𑅳𑄴𑆀-𑆂𑆳-𑇊𑇀-𑇌𑈬-𑈷𑈾𑋟-𑋪𑌀-𑌃𑌼𑌾-𑍄𑍇𑍈𑍋-𑍍𑍗𑍢𑍣𑍦-𑍬𑍰-𑍴𑐵-𑑆𑒰-𑓃𑖯-𑖵𑖸-𑗀𑗜𑗝𑘰-𑙀𑚫-𑚷𑜝-𑜫𑰯-𑰶𑰸-𑰿𑲒-𑲧𑲩-𑲶𖫰-𖫴𖬰-𖬶𖽑-𖽾𖾏-𖾒𛲝𛲞𝅥-𝅩𝅭-𝅲𝅻-𝆂𝆅-𝆋𝆪-𝆭𝉂-𝉄𝨀-𝨶𝨻-𝩬𝩵𝪄𝪛-𝪟𝪡-𝪯𞀀-𞀆𞀈-𞀘𞀛-𞀡𞀣𞀤𞀦-𞣐𞀪-𞣖𞥄-𞥊󠄀-󠇯]+" # noqa: B950 )

96.25

656

0.119481

185

770

3.005405

0.989189

0

0.005369

0.032468

770

7

657

110

0.115436

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0

1

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0.931085

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1

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false

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null

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1

0

1

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1

null

0

6

9a22867cccb5bc200a032cfb57d090ec6851db04

95

py

Python

Models/__init__.py

yasesf93/CrossGradientAggregation

276940b893fcc5f740dcb273efe7f06990c985b9

[ "BSD-3-Clause" ]

4

2021-06-24T17:25:41.000Z

2021-11-16T07:08:30.000Z

Models/__init__.py

yasesf93/CrossGradientAggregation

276940b893fcc5f740dcb273efe7f06990c985b9

[ "BSD-3-Clause" ]

2

2021-07-14T11:13:57.000Z

2021-07-16T21:50:31.000Z

Models/__init__.py

yasesf93/CrossGradientAggregation

276940b893fcc5f740dcb273efe7f06990c985b9

[ "BSD-3-Clause" ]

1

2021-06-24T17:25:43.000Z

from .preresnet import * from .vgg import * from .wide_resnet import * from .resnet import *

23.75

27

0.726316

13

95

5.230769

0.461538

0.441176

0

0.189474

95

4

28

23.75

0.883117

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1

0

true

0

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1

0

1

0

null

0

1

0

1

0

1

0

6

7be3e1c3d4f1eed485f71b969c725501ed0213a3

50

py

Python

molsysmt/tools/networkx_Graph/__init__.py

dprada/molsysmt

83f150bfe3cfa7603566a0ed4aed79d9b0c97f5d

[ "MIT" ]

null

molsysmt/tools/networkx_Graph/__init__.py

dprada/molsysmt

83f150bfe3cfa7603566a0ed4aed79d9b0c97f5d

[ "MIT" ]

null

molsysmt/tools/networkx_Graph/__init__.py

dprada/molsysmt

83f150bfe3cfa7603566a0ed4aed79d9b0c97f5d

[ "MIT" ]

null

from .is_networkx_Graph import is_networkx_Graph

16.666667

48

0.88

8

50

5

0.625

0.5

0.75

0

0.1

50

2

49

25

0.888889

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

d06f56cfab0a3093ce641eec454894d1a17df5af

41

py

Python

genomics_demo/__init__.py

nickdelgrosso/genomics_workshop_demo

9890017a4348d9a97eda8f5977a8a02ed24610c3

[ "MIT" ]

1

2019-04-12T02:40:54.000Z

genomics_demo/__init__.py

nickdelgrosso/genomics_workshop_demo

9890017a4348d9a97eda8f5977a8a02ed24610c3

[ "MIT" ]

1

2018-10-01T13:11:51.000Z

2018-10-01T13:14:17.000Z

genomics_demo/__init__.py

nickdelgrosso/genomics_workshop_demo

9890017a4348d9a97eda8f5977a8a02ed24610c3

[ "MIT" ]

12

2018-10-01T09:35:35.000Z

2018-10-01T09:49:27.000Z

from .dna import DNA from .rna import RNA

20.5

20

0.780488

8

41

4

0.5

0

0.170732

41

2

21

20.5

0.941176

0

1

0

true

0

1

0

1

0

1

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null

0

1

0

null

0

1

0

1

0

1

0

6

d097793f041e36848acc849e03b9787f4833511e

136

py

Python

cw_wrapper/scope/__init__.py

BeneciaLee/cw_wrapper

a1562aa04e11acf9c1646777e2edc52981df9d2e

[ "MIT" ]

3

2021-06-30T05:36:48.000Z

2021-07-01T10:24:59.000Z

cw_wrapper/scope/__init__.py

BeneciaLee/cw_wrapper

a1562aa04e11acf9c1646777e2edc52981df9d2e

[ "MIT" ]

1

2021-07-12T12:11:35.000Z

cw_wrapper/scope/__init__.py

BeneciaLee/cw_wrapper

a1562aa04e11acf9c1646777e2edc52981df9d2e

[ "MIT" ]

2

2021-06-30T08:13:41.000Z

2021-07-01T09:18:04.000Z

__all__ = ['CWScope', 'cw_firmware_auto_update'] from .cw_scope import CWScope from .cw_firmware_update import cw_firmware_auto_update

27.2

55

0.830882

20

136

5

0.45

0.3

0.28

0.4

0

0.095588

136

4

56

34

0.813008

0

0.220588

0.169118

0

1

0

false

0

0.666667

0

0.666667

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1

0

null

1

0

1

0

null

0

1

0

6

d0a698bc9e8dbf90047ce807983a957fa1a05a85

94

py

Python

rawsec_cli/__init__.py

noraj/rawsec_cli

e671ad0d86d8627a0eff1ff13abf1227b6d26114

[ "MIT" ]

null

rawsec_cli/__init__.py

noraj/rawsec_cli

e671ad0d86d8627a0eff1ff13abf1227b6d26114

[ "MIT" ]

null

rawsec_cli/__init__.py

noraj/rawsec_cli

e671ad0d86d8627a0eff1ff13abf1227b6d26114

[ "MIT" ]

null

# autogenerated __version__ = "1.1.1" __commit__ = "0403f8d920e7940a633d25f2b7a8a3e9a31af7f0"

23.5

55

0.819149

7

94

9.857143

0.714286

0.057971

0

0.337209

0.085106

94

3

56

31.333333

0.465116

0.138298

0

1

0

0.56962

0.506329

0

1

0

false

0

1

0

null

0

1

0

1

0

1

0

1

0

1

null

0

6

d0bce9ec07ed3815d6afb9d9ba75f8ec8084f54c

31

py

Python

pil_resize_aspect_ratio/enums/__init__.py

kkristof200/py_resize_image

33824c691481b2166ade18e7fa6b5583ceeaa4f6

[ "MIT" ]

null

pil_resize_aspect_ratio/enums/__init__.py

kkristof200/py_resize_image

33824c691481b2166ade18e7fa6b5583ceeaa4f6

[ "MIT" ]

null

pil_resize_aspect_ratio/enums/__init__.py

kkristof200/py_resize_image

33824c691481b2166ade18e7fa6b5583ceeaa4f6

[ "MIT" ]

null

from .fill_type import FillType

31

0.870968

5

31

5.2

1

0

0.096774

31

1

31

0.928571

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

d0cc4bfdff292b18362e240a3786014dd325e463

195

py

Python

tests/conftest.py

mmEissen/deptree

bc0fac36418ccfa805fc9f405442409cf1a28ade

[ "MIT" ]

1

2018-06-13T16:54:38.000Z

tests/conftest.py

mmEissen/deptree

bc0fac36418ccfa805fc9f405442409cf1a28ade

[ "MIT" ]

6

2018-03-26T09:20:46.000Z

2018-04-23T09:50:05.000Z

tests/conftest.py

mmEissen/importgraph

bc0fac36418ccfa805fc9f405442409cf1a28ade

[ "MIT" ]

null

import pytest from unittest.mock import MagicMock from importgraph import ImportAction @pytest.fixture def import_action(): return ImportAction('some.module.name', {}, [], 0, MagicMock())

19.5

67

0.753846

23

195

6.347826

0.695652

0

0.005917

0.133333

195

9

68

21.666667

0.857988

0

0.082051

0

1

0.166667

true

0

0.833333

0.166667

1.166667

0

1

0

null

0

1

0

null

0

1

0

1

0

6

d0db185a759b784bebfcd2771e58b1735fb88677

58

py

Python

parting/__init__.py

danfairs/django-parting

80f24781396ab8778960ed4b2a86e96dd9f18bb0

[ "BSD-3-Clause" ]

8

2015-04-06T19:59:01.000Z

2019-01-22T16:39:26.000Z

parting/__init__.py

danfairs/django-parting

80f24781396ab8778960ed4b2a86e96dd9f18bb0

[ "BSD-3-Clause" ]

1

2016-04-14T08:49:31.000Z

parting/__init__.py

danfairs/django-parting

80f24781396ab8778960ed4b2a86e96dd9f18bb0

[ "BSD-3-Clause" ]

1

2018-12-20T13:43:39.000Z

from .models import PartitionForeignKey, PartitionManager

29

57

0.87931

5

58

10.2

1

0

0.086207

58

1

58

0.962264

0

1

0

true

0

1

0

1

0

1

0

1

null

0

1

0

null

0

1

0

1

0

1

0

6

d0e95b95265adc4750b71c90b865388e389c5b3e

3,364

py

Python

src/store/baseviews.py

wqian94/retrodb

e0a5655a5986f46872230d0ace8a4a28dab71f28

[ "MIT" ]

null

src/store/baseviews.py

wqian94/retrodb

e0a5655a5986f46872230d0ace8a4a28dab71f28

[ "MIT" ]

null

src/store/baseviews.py

wqian94/retrodb

e0a5655a5986f46872230d0ace8a4a28dab71f28

[ "MIT" ]

null

""" Library for base classes of retroactively-updatable views. """ import numbers class NumericView(numbers.Integral): """ Wrapper for numeric functions so that views can behave like numbers. """ # General magic functions def __repr__(self): return self._value.__repr__() # Number-related functions def __abs__(self, *args, **kwargs): return self._value.__abs__(*args, **kwargs) def __add__(self, *args, **kwargs): return self._value.__add__(*args, **kwargs) def __and__(self, *args, **kwargs): return self._value.__and__(*args, **kwargs) def __ceil__(self, *args, **kwargs): return self._value.__ceil__(*args, **kwargs) def __eq__(self, *args, **kwargs): return self._value.__eq__(*args, **kwargs) def __floor__(self, *args, **kwargs): return self._value.__floor__(*args, **kwargs) def __floordiv__(self, *args, **kwargs): return self._value.__floordiv__(*args, **kwargs) def __int__(self, *args, **kwargs): return self._value.__int__(*args, **kwargs) def __invert__(self, *args, **kwargs): return self._value.__invert__(*args, **kwargs) def __le__(self, *args, **kwargs): return self._value.__le__(*args, **kwargs) def __lshift__(self, *args, **kwargs): return self._value.__lshift__(*args, **kwargs) def __lt__(self, *args, **kwargs): return self._value.__lt__(*args, **kwargs) def __mod__(self, *args, **kwargs): return self._value.__mod__(*args, **kwargs) def __mul__(self, *args, **kwargs): return self._value.__mul__(*args, **kwargs) def __neg__(self, *args, **kwargs): return self._value.__neg__(*args, **kwargs) def __or__(self, *args, **kwargs): return self._value.__or__(*args, **kwargs) def __pos__(self, *args, **kwargs): return self._value.__pos__(*args, **kwargs) def __pow__(self, *args, **kwargs): return self._value.__pow__(*args, **kwargs) def __radd__(self, *args, **kwargs): return self._value.__radd__(*args, **kwargs) def __rand__(self, *args, **kwargs): return self._value.__rand__(*args, **kwargs) def __rfloordiv__(self, *args, **kwargs): return self._value.__rfloordiv__(*args, **kwargs) def __rlshift__(self, *args, **kwargs): return self._value.__rlshift__(*args, **kwargs) def __rmod__(self, *args, **kwargs): return self._value.__rmod__(*args, **kwargs) def __rmul__(self, *args, **kwargs): return self._value.__rmul__(*args, **kwargs) def __ror__(self, *args, **kwargs): return self._value.__ror__(*args, **kwargs) def __round__(self, *args, **kwargs): return self._value.__round__(*args, **kwargs) def __rpow__(self, *args, **kwargs): return self._value.__rpow__(*args, **kwargs) def __rrshift__(self, *args, **kwargs): return self._value.__rrshift__(*args, **kwargs) def __rshift__(self, *args, **kwargs): return self._value.__rshift__(*args, **kwargs) def __rtruediv__(self, *args, **kwargs): return self._value.__rtruediv__(*args, **kwargs) def __rxor__(self, *args, **kwargs): return self._value.__rxor__(*args, **kwargs) def __truediv__(self, *args, **kwargs): return self._value.__truediv__(*args, **kwargs) def __trunc__(self, *args, **kwargs): return self._value.__trunc__(*args, **kwargs) def __xor__(self, *args, **kwargs): return self._value.__xor__(*args, **kwargs)

29.508772

78

0.672711

412

3,364

4.728155

0.160194

0.349076

0.269507

0.349076

0.50616

0

0.156956

3,364

113

79

29.769912

0.686883

0.05321

0

1

0.486111

false

0

0.013889

0.486111

1

0

null

1

0

null

0

1

0

1

0

6

ef7ae166dd3c5655215dfc997707002c4e44707d

2,853

py

Python

tests/test_datasets.py

ricosjp/siml

8fc07d798cdedd77622c16221ee44a575d36bad0

[ "Apache-2.0" ]

11

2020-12-28T16:22:33.000Z

2021-11-14T17:09:27.000Z

tests/test_datasets.py

ricosjp/siml

8fc07d798cdedd77622c16221ee44a575d36bad0

[ "Apache-2.0" ]

null

tests/test_datasets.py

ricosjp/siml

8fc07d798cdedd77622c16221ee44a575d36bad0

[ "Apache-2.0" ]

2

2021-04-28T09:41:47.000Z

2021-07-01T21:18:51.000Z

import unittest import numpy as np import torch import siml.datasets as datasets class TestDatasets(unittest.TestCase): def test_merge_sparse_tensors_square(self): stripped_sparse_info = [ { 'size': [2, 2], 'row': torch.Tensor([0, 1, 1]), 'col': torch.Tensor([0, 0, 1]), 'values': torch.Tensor([1., 2., 3.]), }, { 'size': [2, 2], 'row': torch.Tensor([0, 1, 1]), 'col': torch.Tensor([0, 0, 1]), 'values': torch.Tensor([10., 20., 30.]), }, { 'size': [2, 2], 'row': torch.Tensor([0, 1, 1]), 'col': torch.Tensor([0, 0, 1]), 'values': torch.Tensor([100., 200., 300.]), }, ] expected_sparse = np.array([ [1., 0., 0., 0., 0., 0.], [2., 3., 0., 0., 0., 0.], [0., 0., 10., 0., 0., 0.], [0., 0., 20., 30., 0., 0.], [0., 0., 0., 0., 100., 0.], [0., 0., 0., 0., 200., 300.], ]) merged_sparse = datasets.merge_sparse_tensors(stripped_sparse_info) np.testing.assert_almost_equal( merged_sparse.to_dense().numpy(), expected_sparse) def test_merge_sparse_tensors_rectangle(self): stripped_sparse_info = [ { 'size': [2, 5], 'row': torch.Tensor([0, 1, 1, 1]), 'col': torch.Tensor([0, 0, 1, 4]), 'values': torch.Tensor([1., 2., 3., 4.]), }, { 'size': [3, 4], 'row': torch.Tensor([0, 1, 1, 2]), 'col': torch.Tensor([0, 0, 1, 3]), 'values': torch.Tensor([10., 20., 30., 40.]), }, { 'size': [4, 2], 'row': torch.Tensor([0, 1, 1, 3]), 'col': torch.Tensor([0, 0, 1, 1]), 'values': torch.Tensor([100., 200., 300., 400.]), }, ] expected_sparse = np.array([ [1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], [2., 3., 0., 0., 4., 0., 0., 0., 0., 0., 0.], [0., 0., 0., 0., 0., 10., 0., 0., 0., 0., 0.], [0., 0., 0., 0., 0., 20., 30., 0., 0., 0., 0.], [0., 0., 0., 0., 0., 0., 0., 0., 40., 0., 0.], [0., 0., 0., 0., 0., 0., 0., 0., 0., 100., 0.], [0., 0., 0., 0., 0., 0., 0., 0., 0., 200., 300.], [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 400.], ]) merged_sparse = datasets.merge_sparse_tensors(stripped_sparse_info) np.testing.assert_almost_equal( merged_sparse.to_dense().numpy(), expected_sparse)

36.113924

75

0.375745

357

2,853

2.907563

0.137255

0.206166

0.254335

0.292871

0.861272

0.813102

0.651252

0.587669

0.569364

0.563584

0

0.146597

0.397476

2,853

78

76

36.576923

0.457243

0

0.291667

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0.033649

0

0.027778

1

0.027778

false

0

0.055556

0

0.097222

0

null

1

0

1

0

null

0

6

4bdd85033c224d066f356287d15f6c1d5c50ca18

119

py

Python

blues/datasets/__init__.py

Kageshimasu/blues

a808fb8da86224f2e597916b04bdbd29376af6bb

[ "MIT" ]

null

blues/datasets/__init__.py

Kageshimasu/blues

a808fb8da86224f2e597916b04bdbd29376af6bb

[ "MIT" ]

null

blues/datasets/__init__.py

Kageshimasu/blues

a808fb8da86224f2e597916b04bdbd29376af6bb

[ "MIT" ]

1

2021-02-15T07:54:17.000Z

from .classification_dataset import ClassificationDataset from .object_detection_dataset import ObjectDetectionDataset

39.666667

60

0.915966

11

119

9.636364

0.727273

0.245283

0

0.067227

119

2

61

59.5

0.954955

0

1

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4bf04507b422bc4361f0e650c85b766ee0d864c7

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py

Python

hdcs_manager/source/hsm/hsm/db/__init__.py

isabella232/HDCS

1a5ae046e3ff947cc7a42219b9b1959766687612

[ "Apache-2.0" ]

null

hdcs_manager/source/hsm/hsm/db/__init__.py

isabella232/HDCS

1a5ae046e3ff947cc7a42219b9b1959766687612

[ "Apache-2.0" ]

1

2021-02-23T19:10:26.000Z

hdcs_manager/source/hsm/hsm/db/__init__.py

isabella232/HDCS

1a5ae046e3ff947cc7a42219b9b1959766687612

[ "Apache-2.0" ]

null

from hsm.db.api import *

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py

Python

corpus/mnm_early.py

mnm-rnd/elsa-voice-asr

5c350b6610753629e7f5580468f54ec796f04e16

[ "MIT" ]

1

2021-10-01T00:03:00.000Z

corpus/mnm_early.py

mnm-rnd/elsa-voice-asr

5c350b6610753629e7f5580468f54ec796f04e16

[ "MIT" ]

null

corpus/mnm_early.py

mnm-rnd/elsa-voice-asr

5c350b6610753629e7f5580468f54ec796f04e16

[ "MIT" ]

1

2021-10-01T00:00:08.000Z

from torch.utils.data import Dataset from tqdm import tqdm from pathlib import Path def read_text(text_file): with open(text_file, 'r', encoding='utf-8') as out: return out.readlines()[0].strip() class MnMAudioDataset(Dataset): def __init__(self, path, manifest_csv_file, tokenizer, data_transformer, bucket_size, path_from_home=True): if path_from_home: main_path = Path.home() else: main_path = Path(".") corpus_path = main_path.joinpath(path) manifest_csv_path = corpus_path.joinpath(manifest_csv_file) self.file_text_pair = [] self.data_transformer = data_transformer self.tokenizer = tokenizer self.bucket_size = bucket_size with open(manifest_csv_path, 'r', encoding='utf-8') as mp: for x in tqdm(mp): str_vals = x.strip().split(",") # Preprocess the text text = read_text(str_vals[-1]) text = self.data_transformer(text) text = self.tokenizer.encode(text) self.file_text_pair.append((str_vals[0], text)) def __len__(self): return len(self.file_text_pair) def __getitem__(self, index): if self.bucket_size > 1: # Return a bucket index = min(len(self) - self.bucket_size, index) return self.file_text_pair[index:index+self.bucket_size] # Return a single sample return self.file_text_pair[index] class MnMAudioTextDataset(Dataset): def __init__(self, path, manifest_csv_file, tokenizer, data_transformer, bucket_size, path_from_home=True): if path_from_home: main_path = Path.home() else: main_path = Path(".") corpus_path = main_path.joinpath(path) manifest_csv_path = corpus_path.joinpath(manifest_csv_file) self.texts = [] self.data_transformer = data_transformer self.tokenizer = tokenizer self.bucket_size = bucket_size with open(manifest_csv_path, 'r', encoding='utf-8') as mp: for x in tqdm(mp): str_vals = x.strip().split(",") # Preprocess the text text = read_text(str_vals[-1]) text = self.data_transformer(text) text = self.tokenizer.encode(text) self.texts.append(text) def __len__(self): return len(self.texts) def __getitem__(self, index): if self.bucket_size > 1: # Return a bucket index = min(len(self) - self.bucket_size, index) return self.texts[index:index+self.bucket_size] # Return a single sample return self.texts[index]

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326d44ee5dc00c3eaaea9db2bb520b0e1108e7ef

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py

Python

django_profile_middleware/__init__.py

Pear0/django-profile-middleware

0d9d44cfac4351a4dea874165c95e792fbef16ae

[ "MIT" ]

33

2017-06-29T15:04:20.000Z

2020-06-01T08:12:19.000Z

django_profile_middleware/__init__.py

inovizz/django-profile-middleware

0d9d44cfac4351a4dea874165c95e792fbef16ae

[ "MIT" ]

null

django_profile_middleware/__init__.py

inovizz/django-profile-middleware

0d9d44cfac4351a4dea874165c95e792fbef16ae

[ "MIT" ]

8

2017-08-23T21:50:35.000Z

2019-11-11T02:31:31.000Z

from middleware import * from decorator import *

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1

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6

327ca0932ce7b15e1a9b71e8093d4bcbafc1236f

66

py

Python

gobigger/agents/__init__.py

luanshaotong/GoBigger

00c347a89a660134677d633f39c39123c5ab3deb

[ "Apache-2.0" ]

189

2021-10-08T07:55:10.000Z

2022-03-31T23:49:43.000Z

gobigger/agents/__init__.py

luanshaotong/GoBigger

00c347a89a660134677d633f39c39123c5ab3deb

[ "Apache-2.0" ]

25

2021-11-01T06:59:30.000Z

2022-03-22T11:22:27.000Z

gobigger/agents/__init__.py

luanshaotong/GoBigger

00c347a89a660134677d633f39c39123c5ab3deb

[ "Apache-2.0" ]

28

2021-10-14T12:23:14.000Z

2022-03-31T23:49:45.000Z

from .base_agent import BaseAgent from .bot_agent import BotAgent

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328c30ddef776f4ee405a847595c2a217f3ab3a0

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py

Python

tests/test_queries.py

openclimatefix/perceiver-pytorch

62c314b302aec95571796684732b2bcd0a81cc75

[ "MIT" ]

7

2021-07-30T22:06:26.000Z

2022-02-24T09:39:02.000Z

tests/test_queries.py

openclimatefix/perceiver-pytorch

62c314b302aec95571796684732b2bcd0a81cc75

[ "MIT" ]

16

2021-07-27T09:58:03.000Z

2021-12-16T12:26:53.000Z

tests/test_queries.py

openclimatefix/perceiver-pytorch

62c314b302aec95571796684732b2bcd0a81cc75

[ "MIT" ]

null

import pytest import torch from perceiver_pytorch.queries import LearnableQuery from perceiver_pytorch.perceiver_io import PerceiverIO from perceiver_pytorch.utils import encode_position import einops @pytest.mark.parametrize("layer_shape", ["2d", "3d"]) def test_learnable_query(layer_shape): query_creator = LearnableQuery( channel_dim=32, query_shape=(6, 16, 16), conv_layer=layer_shape, max_frequency=64.0, num_frequency_bands=128, sine_only=False, generate_fourier_features=True, ) x = torch.randn((4, 6, 12, 16, 16)) out = query_creator(x) # Output is flattened, so should be [B, T*H*W, C] # Channels is from channel_dim + 3*(num_frequency_bands * 2 + 1) # 32 + 3*(257) = 771 + 32 = 803 assert out.shape == (4, 16 * 16 * 6, 803) @pytest.mark.parametrize("layer_shape", ["2d", "3d"]) def test_learnable_query_no_fourier(layer_shape): query_creator = LearnableQuery( channel_dim=32, query_shape=(6, 16, 16), conv_layer=layer_shape, max_frequency=64.0, num_frequency_bands=128, sine_only=False, generate_fourier_features=False, ) x = torch.randn((4, 6, 12, 16, 16)) out = query_creator(x) assert out.shape == (4, 16 * 16 * 6, 32) @pytest.mark.parametrize("layer_shape", ["2d", "3d"]) def test_learnable_query_qpplication(layer_shape): output_shape = (6, 16, 16) query_creator = LearnableQuery( channel_dim=32, query_shape=output_shape, conv_layer=layer_shape, max_frequency=64.0, num_frequency_bands=32, sine_only=False, generate_fourier_features=True, ) with torch.no_grad(): query_creator.eval() x = torch.randn((2, 6, 12, 16, 16)) out = query_creator(x) model = PerceiverIO(depth=2, dim=100, queries_dim=query_creator.output_shape()[-1]) model.eval() model_input = torch.randn((2, 256, 100)) model_out = model(model_input, queries=out) # Reshape back to correct shape model_out = einops.rearrange( model_out, "b (t h w) c -> b t c h w", t=output_shape[0], h=output_shape[1], w=output_shape[2], ) assert model_out.shape == (2, 6, 227, 16, 16) @pytest.mark.parametrize("layer_shape", ["2d", "3d"]) def test_learnable_query_precomputed_fourier_only(layer_shape): precomputed_features = encode_position( 1, # Batch size, 1 for this as it will be adapted in forward axis=(10, 16, 16), # 4 history + 6 future steps max_frequency=16.0, num_frequency_bands=128, sine_only=False, ) # Only take future ones precomputed_features = precomputed_features[:, 4:] query_creator = LearnableQuery( channel_dim=32, query_shape=(6, 16, 16), conv_layer=layer_shape, max_frequency=64.0, num_frequency_bands=16, sine_only=False, precomputed_fourier=precomputed_features, generate_fourier_features=False, ) x = torch.randn((4, 6, 12, 16, 16)) out = query_creator(x) # Output is flattened, so should be [B, T*H*W, C] # Channels is from channel_dim + 3*(num_frequency_bands * 2 + 1) # 32 + 3*(257) = 771 + 32 = 803 assert out.shape == (4, 16 * 16 * 6, 803) @pytest.mark.parametrize("layer_shape", ["2d", "3d"]) def test_learnable_query_precomputed_and_generated_fourer(layer_shape): precomputed_features = encode_position( 1, # Batch size, 1 for this as it will be adapted in forward axis=(10, 16, 16), # 4 history + 6 future steps max_frequency=16.0, num_frequency_bands=128, sine_only=False, ) # Only take future ones precomputed_features = precomputed_features[:, 4:] query_creator = LearnableQuery( channel_dim=32, query_shape=(6, 16, 16), conv_layer=layer_shape, max_frequency=64.0, num_frequency_bands=128, sine_only=False, precomputed_fourier=precomputed_features, generate_fourier_features=True, ) x = torch.randn((4, 6, 12, 16, 16)) out = query_creator(x) # Output is flattened, so should be [B, T*H*W, C] # Channels is from channel_dim + 3*(num_frequency_bands * 2 + 1) # 32 + 3*(257) = 771 + 32 = 803 # Then add 771 from the precomputed features, to get 803 + 771 assert out.shape == (4, 16 * 16 * 6, 803 + 771) @pytest.mark.parametrize("layer_shape", ["2d", "3d"]) def test_learnable_query_pass_in_fourier(layer_shape): precomputed_features = encode_position( 4, axis=(10, 16, 16), # 4 history + 6 future steps max_frequency=16.0, num_frequency_bands=64, sine_only=False, ) # Only take future ones precomputed_features = precomputed_features[:, 4:] query_creator = LearnableQuery( channel_dim=32, query_shape=(6, 16, 16), conv_layer=layer_shape, max_frequency=64.0, num_frequency_bands=128, sine_only=False, generate_fourier_features=False, ) x = torch.randn((4, 6, 12, 16, 16)) out = query_creator(x, precomputed_features) # Output is flattened, so should be [B, T*H*W, C] # Channels is from channel_dim + 3*(num_frequency_bands * 2 + 1) # 3*(129) = 389 + 32 = 419 # Since this is less than what is passed to LearnableQuery, we know its using the passed in features assert out.shape == (4, 16 * 16 * 6, 419) @pytest.mark.parametrize("layer_shape", ["2d", "3d"]) def test_learnable_query_all_fouriers(layer_shape): batch_ff = encode_position( 4, axis=(10, 16, 16), # 4 history + 6 future steps max_frequency=16.0, num_frequency_bands=32, sine_only=False, ) # Only take future ones batch_ff = batch_ff[:, 4:] precomputed_features = encode_position( 1, axis=(10, 16, 16), # 4 history + 6 future steps max_frequency=16.0, num_frequency_bands=64, sine_only=False, ) # Only take future ones precomputed_features = precomputed_features[:, 4:] query_creator = LearnableQuery( channel_dim=32, query_shape=(6, 16, 16), conv_layer=layer_shape, max_frequency=64.0, num_frequency_bands=128, sine_only=False, precomputed_fourier=precomputed_features, generate_fourier_features=True, ) x = torch.randn((4, 6, 12, 16, 16)) out = query_creator(x, batch_ff) # Output is flattened, so should be [B, T*H*W, C] # Channels is from channel_dim + 3*(num_frequency_bands * 2 + 1) # 3*(129) = 389 + 32 = 419 + 771 from the generated ones + 195 from the batch features # Since this is less than what is passed to LearnableQuery, we know its using the passed in features assert out.shape == (4, 16 * 16 * 6, 1385)

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328fda97178b8faeaff2544f59328b4292d8af0e

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py

Python

uncertaintorch/__init__.py

jcreinhold/uncertaintorch

0cdc9f25fefad938c9f0bd3a6b40dfaa362dfca5

[ "Apache-2.0" ]

1

2021-03-21T23:13:45.000Z

uncertaintorch/__init__.py

jcreinhold/uncertaintorch

0cdc9f25fefad938c9f0bd3a6b40dfaa362dfca5

[ "Apache-2.0" ]

null

uncertaintorch/__init__.py

jcreinhold/uncertaintorch

0cdc9f25fefad938c9f0bd3a6b40dfaa362dfca5

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- from .learn import * from .models import * from .plot import * from .util import *

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py

Python

catalog.py

TerenceFox/itemcatalog

88621ea3e0383d9b43fe9341fa355b3f4928ead4

[ "MIT" ]

null

catalog.py

TerenceFox/itemcatalog

88621ea3e0383d9b43fe9341fa355b3f4928ead4

[ "MIT" ]

null

catalog.py

TerenceFox/itemcatalog

88621ea3e0383d9b43fe9341fa355b3f4928ead4

[ "MIT" ]

null

from app import app, db from app.models.category import Category from app.models.item import Item from app.models.user import User @app.shell_context_processor def make_shell_context(): return {'db': db, 'User': User, 'Category': Category, 'Item': Item}

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py

Python

estimators/gym-network_classification_env/gym_network_classification_env/envs/__init__.py

boyuruan/Anomaly-ReactionRL

a82da87e2da28ad333a7e19af5a0608390c3312c

[ "MIT" ]

75

2018-06-12T10:51:50.000Z

2022-03-24T14:16:40.000Z

estimators/gym-network_classification_env/gym_network_classification_env/envs/__init__.py

draryan/Anomaly-ReactionRL

590fbc89dfa761be324c35e0dcf5d08f6086df77

[ "MIT" ]

11

2018-07-21T17:56:29.000Z

2021-10-24T00:48:21.000Z

estimators/gym-network_classification_env/gym_network_classification_env/envs/__init__.py

draryan/Anomaly-ReactionRL

590fbc89dfa761be324c35e0dcf5d08f6086df77

[ "MIT" ]

35

2018-09-27T06:03:14.000Z

2022-03-28T13:54:37.000Z

from gym_network_classification_env.envs.network_classification import NetworkClassificationEnv from gym_network_classification_env.envs.helpers_data_preprocessing import data_cls

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6

08ce7a9a118e4e8d9608c2ee615a3276f371115d

34

py

Python

src/sage/repl/all.py

bopopescu/classic_diff_geom

2b1d88becbc8cb30962e0995cc78e429e0f5589f

[ "BSL-1.0" ]

2

2015-08-11T05:05:47.000Z

2019-05-15T17:27:25.000Z

src/sage/repl/all.py

bopopescu/classic_diff_geom

2b1d88becbc8cb30962e0995cc78e429e0f5589f

[ "BSL-1.0" ]

null

src/sage/repl/all.py

bopopescu/classic_diff_geom

2b1d88becbc8cb30962e0995cc78e429e0f5589f

[ "BSL-1.0" ]

1

2020-07-24T11:56:55.000Z

from interpreter import preparser

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6

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py

Python

bumpversion/__main__.py

jaap3/bump2version

22294dd1a1692d6aea4b826b590643c62b5b1eb9

[ "MIT" ]

1,289

2015-01-03T02:42:58.000Z

2022-03-31T11:32:59.000Z

bumpversion/__main__.py

jaap3/bump2version

22294dd1a1692d6aea4b826b590643c62b5b1eb9

[ "MIT" ]

151

2015-01-02T15:02:40.000Z

2022-03-15T19:57:12.000Z

bumpversion/__main__.py

jaap3/bump2version

22294dd1a1692d6aea4b826b590643c62b5b1eb9

[ "MIT" ]

173

2015-01-09T21:43:44.000Z

2022-03-02T22:39:23.000Z

__import__('bumpversion').main()

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py

Python

app/utils/__init__.py

hashtagSELFIE/memegen

3c50b21b557cb7f52b7a46ea33a6b16e3f10011b

[ "MIT" ]

null

app/utils/__init__.py

hashtagSELFIE/memegen

3c50b21b557cb7f52b7a46ea33a6b16e3f10011b

[ "MIT" ]

null

app/utils/__init__.py

hashtagSELFIE/memegen

3c50b21b557cb7f52b7a46ea33a6b16e3f10011b

[ "MIT" ]

null

from . import html, images, text

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3eb083bc4d39832694e8d5194fdca91c29d2e518

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py

Python

dataent/patches/v11_0/rename_workflow_action_to_workflow_action_master.py

dataent/dataent

c41bd5942ffe5513f4d921c4c0595c84bbc422b4

[ "MIT" ]

null

dataent/patches/v11_0/rename_workflow_action_to_workflow_action_master.py

dataent/dataent

c41bd5942ffe5513f4d921c4c0595c84bbc422b4

[ "MIT" ]

6

2020-03-24T17:15:56.000Z

2022-02-10T18:41:31.000Z

dataent/patches/v11_0/rename_workflow_action_to_workflow_action_master.py

dataent/dataent

c41bd5942ffe5513f4d921c4c0595c84bbc422b4

[ "MIT" ]

null

from __future__ import unicode_literals import dataent from dataent.model.rename_doc import rename_doc def execute(): if dataent.db.table_exists("Workflow Action") and not dataent.db.table_exists("Workflow Action Master"): rename_doc('DocType', 'Workflow Action', 'Workflow Action Master') dataent.reload_doc('workflow', 'doctype', 'workflow_action_master')

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3ed98593aeb222b0e161b15194d2d05f4a2cde78

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py

Python

cookiecutter_mbam/scan/__init__.py

tiburona/cookiecutter_mbam

13788774a4c1426c133b3f689f98d8f0c54de9c6

[ "BSD-3-Clause" ]

null

cookiecutter_mbam/scan/__init__.py

tiburona/cookiecutter_mbam

13788774a4c1426c133b3f689f98d8f0c54de9c6

[ "BSD-3-Clause" ]

null

cookiecutter_mbam/scan/__init__.py

tiburona/cookiecutter_mbam

13788774a4c1426c133b3f689f98d8f0c54de9c6

[ "BSD-3-Clause" ]

null

from . import views from .models import Scan from . import service

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py

Python

tests/system/get_cars_positive_test.py

ikostan/REST_API_AUTOMATION

cdb4d30fbc7457b2a403b4dad6fe1efa2e754681

[ "Unlicense" ]

8

2020-03-17T09:15:28.000Z

2022-01-29T19:50:45.000Z

tests/system/get_cars_positive_test.py

ikostan/REST_API_AUTOMATION

cdb4d30fbc7457b2a403b4dad6fe1efa2e754681

[ "Unlicense" ]

1

2021-06-02T00:26:58.000Z

tests/system/get_cars_positive_test.py

ikostan/REST_API_AUTOMATION

cdb4d30fbc7457b2a403b4dad6fe1efa2e754681

[ "Unlicense" ]

1

2021-11-22T16:10:27.000Z

#!/path/to/interpreter """ Flask App REST API testing: GET """ # Created by Egor Kostan. # GitHub: https://github.com/ikostan # LinkedIn: https://www.linkedin.com/in/egor-kostan/ import allure import requests from tests.system.base_test import BaseTestCase from api.cars_app import CARS_LIST, USER_LIST @allure.epic('Simple Flask App') @allure.parent_suite('REST API') @allure.suite("System Tests") @allure.sub_suite("Positive Tests") @allure.feature("GET") @allure.story('Cars') class GetCarsPositiveTestCase(BaseTestCase): """ Simple Flask App Positive Test: GET call > cars """ def setUp(self) -> None: """ Test data preparation :return: """ with allure.step("Arrange expected results (cars list)"): self.cars_url = '/cars' self.CARS_HATCHBACK = [car for car in CARS_LIST if car["car_type"] == 'hatchback'] self.CARS_SEDAN = [car for car in CARS_LIST if car["car_type"] == 'sedan'] self.CARS_LIST = CARS_LIST def test_get_list_of_cars_admin(self): """ Get full list of cars using admin user credentials. :return: """ allure.dynamic.title("Get list of cars " "using admin user credentials") allure.dynamic.severity(allure.severity_level.BLOCKER) with allure.step("Verify user permissions"): username = USER_LIST[0]['name'] password = USER_LIST[0]['password'] self.assertEqual("admin", USER_LIST[0]['perm']) with allure.step("Send GET request"): response = requests.get(self.URL + self.cars_url, auth=(username, password)) with allure.step("Verify status code"): self.assertEqual(200, response.status_code) with allure.step("Verify 'successful' flag"): self.assertTrue(response.json()['successful']) with allure.step("Verify retrieved cars list"): self.assertTrue(all(True for car in self.CARS_LIST if car in response.json()['cars_list'])) def test_get_list_of_cars_non_admin(self): """ Get full list of cars using non admin user credentials. :return: """ allure.dynamic.title("Get list of cars " "using non admin user credentials") allure.dynamic.severity(allure.severity_level.BLOCKER) with allure.step("Verify user permissions"): username = USER_LIST[1]['name'] password = USER_LIST[1]['password'] self.assertEqual("non_admin", USER_LIST[1]['perm']) with allure.step("Send GET request"): response = requests.get(self.URL + self.cars_url, auth=(username, password)) with allure.step("Verify status code"): self.assertEqual(200, response.status_code) with allure.step("Verify 'successful' flag"): self.assertTrue(response.json()['successful']) with allure.step("Verify retrieved cars list"): self.assertTrue(all(True for car in self.CARS_LIST if car in response.json()['cars_list'])) def test_get_list_of_cars_non_admin_sedan(self): """ Get full list of cars of type = 'sedan' using non admin user credentials. :return: """ allure.dynamic.title("Get list of cars of type = 'sedan' " "using non admin user credentials") allure.dynamic.severity(allure.severity_level.BLOCKER) with allure.step("Verify user permissions"): username = USER_LIST[1]['name'] password = USER_LIST[1]['password'] self.assertEqual("non_admin", USER_LIST[1]['perm']) with allure.step("Send GET request"): response = requests.get(self.URL + self.cars_url + '/filter/sedan', auth=(username, password)) with allure.step("Verify status code"): self.assertEqual(200, response.status_code) with allure.step("Verify retrieved cars list of type sedan"): self.assertTrue(all(True for car in self.CARS_SEDAN if car in response.json()['cars'])) def test_get_list_of_cars_admin_hatchback(self): """ Get full list of cars from type = 'hatchback' using admin user credentials. :return: """ allure.dynamic.title("Get list of cars of type = 'hatchback' " "using admin user credentials") allure.dynamic.severity(allure.severity_level.BLOCKER) with allure.step("Verify user permissions"): username = USER_LIST[0]['name'] password = USER_LIST[0]['password'] self.assertEqual("admin", USER_LIST[0]['perm']) with allure.step("Send GET request"): response = requests.get(self.URL + self.cars_url + '/filter/hatchback', auth=(username, password)) with allure.step("Verify status code"): self.assertEqual(200, response.status_code) with allure.step("Verify retrieved cars list of type hatchback"): self.assertTrue(all(True for car in self.CARS_HATCHBACK if car in response.json()['cars'])) def test_get_car_by_name_non_admin_swift(self): """ Get car data by name = 'swift' using non admin user credentials. :return: """ allure.dynamic.title("Get car data by name using " "non admin user credentials") allure.dynamic.severity(allure.severity_level.BLOCKER) with allure.step("Verify user permissions"): username = USER_LIST[1]['name'] password = USER_LIST[1]['password'] self.assertEqual("non_admin", USER_LIST[1]['perm']) with allure.step("Prepare expected results"): car = {"brand": "Maruti", "car_type": "hatchback", "name": "Swift", "price_range": "3-5 lacs"} with allure.step("Send GET request"): response = requests.get(self.URL + self.cars_url + '/Swift', auth=(username, password)) with allure.step("Verify status code"): self.assertEqual(200, response.status_code) with allure.step("Verify 'successful' flag"): self.assertTrue(response.json()['successful']) with allure.step("Verify retrieved car"): self.assertTrue(car == response.json()['car'])

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6

f5f5c6edab85cfd630b4745ea1e44b090793640b

49

py

Python

examples/i18nurls/__init__.py

adityavs/werkzeug

03bf010f239255049b62f41e37e2e53006ad2398

[ "BSD-3-Clause" ]

4,200

2016-03-29T16:32:32.000Z

2022-03-30T15:37:03.000Z

examples/i18nurls/__init__.py

northernSage/werkzeug

048cdfd9b969c0c3a133d7ff43b8ad1ad6a673ec

[ "BSD-3-Clause" ]

1,203

2016-03-29T15:46:57.000Z

2022-03-31T21:15:00.000Z

examples/i18nurls/__init__.py

northernSage/werkzeug

048cdfd9b969c0c3a133d7ff43b8ad1ad6a673ec

[ "BSD-3-Clause" ]

1,403

2016-03-29T16:50:37.000Z

2022-03-29T09:18:38.000Z

from .application import Application as make_app

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