xszheng2020/the_stack_dedup_python_hits_6 · Datasets at Hugging Face

edf14d2a03d990185ba5001e02c7bfcb38e6d92d

2,627

py

Python

test/generate/crf_mix.py

feiranwang/dimmwitted

bd1b593bfc0f7f76f13a71423740c1f5e77e3336

[ "Apache-2.0" ]

null

test/generate/crf_mix.py

feiranwang/dimmwitted

bd1b593bfc0f7f76f13a71423740c1f5e77e3336

[ "Apache-2.0" ]

null

test/generate/crf_mix.py

feiranwang/dimmwitted

bd1b593bfc0f7f76f13a71423740c1f5e77e3336

[ "Apache-2.0" ]

null

import struct import factor_graph_pb2 import random NVAR = 1000000 NQVAR = 1000000 fo = open("crf_mix/graph.variables.pb", "wb") for i in range(0,NVAR): v = factor_graph_pb2.Variable() v.id = i v.initialValue = 0 if random.random() < 0.8: v.initialValue = 1 v.dataType = 0 v.isEvidence = True v.cardinality = 1 size = v.ByteSize() fo.write(struct.pack("i", size + 3)) fo.write(v.SerializeToString()) #break for i in range(NVAR, NVAR+NQVAR): v = factor_graph_pb2.Variable() v.id = i v.initialValue = 0 v.dataType = 0 v.isEvidence = False v.cardinality = 1 size = v.ByteSize() fo.write(struct.pack("i", size + 3)) fo.write(v.SerializeToString()) fo.close() fo = open("crf_mix/graph.factors.pb", "wb") for i in range(0,NVAR): f = factor_graph_pb2.Factor() f.id = i f.weightId = 0 f.factorFunction = 0 fo.write(struct.pack("i", f.ByteSize()+3)) fo.write(f.SerializeToString()) for i in range(NVAR, NVAR+NQVAR): f = factor_graph_pb2.Factor() f.id = i f.weightId = 0 f.factorFunction = 0 fo.write(struct.pack("i", f.ByteSize()+3)) fo.write(f.SerializeToString()) for i in range(NVAR+NQVAR,NVAR+NQVAR+NVAR+NQVAR): f = factor_graph_pb2.Factor() f.id = i f.weightId = 1 f.factorFunction = 0 fo.write(struct.pack("i", f.ByteSize()+3)) fo.write(f.SerializeToString()) fo.close() fo = open("crf_mix/graph.edges.pb", "wb") for i in range(0,NVAR): e = factor_graph_pb2.GraphEdge() e.variableId = i e.factorId = i e.position = 0 e.isPositive = True fo.write(struct.pack("i", e.ByteSize()+3)) fo.write(e.SerializeToString()) for i in range(NVAR, NVAR+NQVAR): e = factor_graph_pb2.GraphEdge() e.variableId = i e.factorId = i e.position = 0 e.isPositive = True fo.write(struct.pack("i", e.ByteSize()+3)) fo.write(e.SerializeToString()) for i in range(NVAR+NQVAR,NVAR+NQVAR+NVAR+NQVAR-1): start_id = i-NVAR-NQVAR end_id = start_id+1 e = factor_graph_pb2.GraphEdge() e.variableId = start_id e.factorId = i e.position = 0 e.isPositive = True fo.write(struct.pack("i", e.ByteSize()+3)) fo.write(e.SerializeToString()) e = factor_graph_pb2.GraphEdge() e.variableId = end_id e.factorId = i e.position = 1 e.isPositive = True fo.write(struct.pack("i", e.ByteSize()+3)) fo.write(e.SerializeToString()) fo.close() fo = open("crf_mix/graph.weights.pb", "wb") w = factor_graph_pb2.Weight() w.id = 0 w.initialValue = 0 w.isFixed = False fo.write(struct.pack("i", w.ByteSize()+3)) fo.write(w.SerializeToString()) w = factor_graph_pb2.Weight() w.id = 1 w.initialValue = 0.001 w.isFixed = True fo.write(struct.pack("i", w.ByteSize()+3)) fo.write(w.SerializeToString()) fo.close()

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py

Python

pySDC/tests/test_projects/test_parallelSDC/test_fisher.py

brownbaerchen/pySDC

31293859d731646aa09cef4345669eac65501550

[ "BSD-2-Clause" ]

20

2015-03-21T09:02:55.000Z

2022-02-26T20:22:21.000Z

pySDC/tests/test_projects/test_parallelSDC/test_fisher.py

brownbaerchen/pySDC

31293859d731646aa09cef4345669eac65501550

[ "BSD-2-Clause" ]

61

2015-03-02T09:35:55.000Z

2022-03-17T12:42:48.000Z

pySDC/tests/test_projects/test_parallelSDC/test_fisher.py

brownbaerchen/pySDC

31293859d731646aa09cef4345669eac65501550

[ "BSD-2-Clause" ]

19

2015-02-20T11:52:33.000Z

2022-02-02T10:46:27.000Z

from pySDC.projects.parallelSDC.newton_vs_sdc import main as main_newton_vs_sdc from pySDC.projects.parallelSDC.newton_vs_sdc import plot_graphs as plot_graphs_newton_vs_sdc from pySDC.projects.parallelSDC.nonlinear_playground import main, plot_graphs def test_main(): main() plot_graphs() def test_newton_vs_sdc(): main_newton_vs_sdc() plot_graphs_newton_vs_sdc()

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py

Python

socialnews/news/static.py

agiliq/django-socialnews

aa4a1a4a0e3279e6c7999071648ba37c71df9d15

[ "BSD-3-Clause" ]

30

2015-01-18T16:34:03.000Z

2021-05-23T20:05:54.000Z

socialnews/news/static.py

agiliq/django-socialnews

aa4a1a4a0e3279e6c7999071648ba37c71df9d15

[ "BSD-3-Clause" ]

null

socialnews/news/static.py

agiliq/django-socialnews

aa4a1a4a0e3279e6c7999071648ba37c71df9d15

[ "BSD-3-Clause" ]

11

2015-02-21T10:45:41.000Z

2021-01-24T21:08:20.000Z

from helpers import render def aboutus(request): return render(request, {}, 'news/aboutus.html') def help(request): return render(request, {}, 'news/help.html') def buttons(request): return render(request, {}, 'news/buttons.html')

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py

Python

smaug/__init__.py

xyzsam/smaug

899d84a53561d66f40648a0910da5d3d9af792ab

[ "BSD-3-Clause" ]

null

smaug/__init__.py

xyzsam/smaug

899d84a53561d66f40648a0910da5d3d9af792ab

[ "BSD-3-Clause" ]

null

smaug/__init__.py

xyzsam/smaug

899d84a53561d66f40648a0910da5d3d9af792ab

[ "BSD-3-Clause" ]

null

from smaug.core.types_pb2 import * from smaug.python.ops import math_ops as math from smaug.python.ops import array_ops as tensor from smaug.python.ops import nn from smaug.python.ops.control_flow_ops import merge, switch, cond from smaug.python.ops.data_op import input_data from smaug.python.graph import Graph from smaug.python.tensor import Tensor from smaug.python.node import Node

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fccc34cc439d38eeab72433c518086b7b0c84e47

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py

Python

Chapter 01/Chap01_Example1.140.py

Anancha/Programming-Techniques-using-Python

e80c329d2a27383909d358741a5cab03cb22fd8b

[ "MIT" ]

null

Chapter 01/Chap01_Example1.140.py

Anancha/Programming-Techniques-using-Python

e80c329d2a27383909d358741a5cab03cb22fd8b

[ "MIT" ]

null

Chapter 01/Chap01_Example1.140.py

Anancha/Programming-Techniques-using-Python

e80c329d2a27383909d358741a5cab03cb22fd8b

[ "MIT" ]

null

t1 = (5,6,2,1) t1[0] = 4

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1

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null

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6

1e125c7b1e76d71e286e07cfb46ee380014d59a8

87

py

Python

tests/test_dummy.py

lazyoracle/crude-codecov

5b55a9c8e9c3affa1eafcf74c51774e09d9c6b3a

[ "MIT" ]

null

tests/test_dummy.py

lazyoracle/crude-codecov

5b55a9c8e9c3affa1eafcf74c51774e09d9c6b3a

[ "MIT" ]

4

2021-08-30T22:36:23.000Z

2021-08-31T18:58:02.000Z

tests/test_dummy.py

lazyoracle/crude-codecov

5b55a9c8e9c3affa1eafcf74c51774e09d9c6b3a

[ "MIT" ]

null

import crude_codecov def test_dummy(): assert crude_codecov.__version__ == "0.0.1"

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py

Python

pirl/__init__.py

HumanCompatibleAI/population-irl

c0881829adb750a9e43e90ce632851eed3e3a5e5

[ "MIT" ]

18

2018-07-26T05:36:24.000Z

2022-02-25T11:45:31.000Z

pirl/__init__.py

HumanCompatibleAI/population-irl

c0881829adb750a9e43e90ce632851eed3e3a5e5

[ "MIT" ]

9

2018-04-22T22:05:22.000Z

2022-01-17T02:39:35.000Z

pirl/__init__.py

HumanCompatibleAI/population-irl

c0881829adb750a9e43e90ce632851eed3e3a5e5

[ "MIT" ]

2

2019-04-20T01:09:41.000Z

2020-04-01T09:39:04.000Z

from pirl import agents, envs, experiments

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0.833333

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1ea193886dbde1ae847205688a6660e83c3a296f

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py

Python

exercises/practice/armstrong-numbers/.meta/example.py

tamireinhorn/python

027e94759dd3281b0633c82171e377a28dc5a92e

[ "MIT" ]

1,177

2017-06-21T20:24:06.000Z

2022-03-29T02:30:55.000Z

exercises/practice/armstrong-numbers/.meta/example.py

tamireinhorn/python

027e94759dd3281b0633c82171e377a28dc5a92e

[ "MIT" ]

1,890

2017-06-18T20:06:10.000Z

2022-03-31T18:35:51.000Z

exercises/practice/armstrong-numbers/.meta/example.py

stigjb-forks/exercism-python

cfb620d1603eb9b08511f96f00f872c67cac0d05

[ "MIT" ]

1,095

2017-06-26T23:06:19.000Z

2022-03-29T03:25:38.000Z

def is_armstrong_number(number): return sum(pow(int(digit), len(str(number))) for digit in str(number)) == number

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949b32ab20611380a25c5fe37ef151f3b6be25c0

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py

Python

weather/commands/__init__.py

aziezahmed/openweathermap-cli

510f9a61b67b36b1a659a777165ca0411abc4da0

[ "MIT" ]

7

2017-04-29T09:44:47.000Z

2020-07-16T22:01:03.000Z

weather/commands/__init__.py

aziezahmed/openweathermap-cli

510f9a61b67b36b1a659a777165ca0411abc4da0

[ "MIT" ]

1

2019-10-20T22:16:16.000Z

weather/commands/__init__.py

aziezahmed/openweathermap-cli

510f9a61b67b36b1a659a777165ca0411abc4da0

[ "MIT" ]

null

from .today import * from .week import *

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94e9443efd65946de1392a95a28ea037fcc3a503

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py

Python

examples/good/black/noqa/test_cmd.py

jamescooke/flake8-aaa

9df248e10538946531b67da4564bb229a91baece

[ "MIT" ]

44

2018-04-08T21:25:43.000Z

2022-01-20T14:28:16.000Z

examples/good/black/noqa/test_cmd.py

jamescooke/flake8-aaa

9df248e10538946531b67da4564bb229a91baece

[ "MIT" ]

72

2018-03-30T14:30:48.000Z

2022-03-31T16:18:16.000Z

examples/good/black/noqa/test_cmd.py

jamescooke/flake8-aaa

9df248e10538946531b67da4564bb229a91baece

[ "MIT" ]

1

2018-10-17T18:49:25.000Z

def test(): # noqa assert 1 + 1 == 2 def test_multi_line_args(math_fixture, *args, **kwargs): # noqa assert 1 + 1 == 2

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py

Python

IMLearn/desent_methods/__init__.py

LidarAb/IML.HUJI

798c99f9b1c29a701c1e06e923a429cae639937f

[ "MIT" ]

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2022-03-06T11:29:52.000Z

2022-03-13T13:51:37.000Z

IMLearn/desent_methods/__init__.py

LidarAb/IML.HUJI

798c99f9b1c29a701c1e06e923a429cae639937f

[ "MIT" ]

null

IMLearn/desent_methods/__init__.py

LidarAb/IML.HUJI

798c99f9b1c29a701c1e06e923a429cae639937f

[ "MIT" ]

null

from .gradient_descent import GradientDescent from .learning_rate import FixedLR, ExponentialLR, AdaptiveLR __all__ = ["GradientDescent", "FixedLR", "ExponentialLR", "AdaptiveLR"]

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py

Python

dataset/__init__.py

kekayan/TabFormer

96f9f219d06750e5df4f431b4bc3c19590a9b9c6

[ "Apache-2.0" ]

158

2020-11-04T03:21:37.000Z

2022-03-31T17:43:37.000Z

dataset/__init__.py

kekayan/TabFormer

96f9f219d06750e5df4f431b4bc3c19590a9b9c6

[ "Apache-2.0" ]

27

2020-12-03T16:35:52.000Z

2022-03-01T02:02:15.000Z

dataset/__init__.py

kekayan/TabFormer

96f9f219d06750e5df4f431b4bc3c19590a9b9c6

[ "Apache-2.0" ]

32

2020-12-16T02:12:27.000Z

2022-03-21T18:40:30.000Z

from .datacollator import TransDataCollatorForLanguageModeling

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py

Python

flask_app/redis.py

vmalloc/logpile

466a7404e17878ac688542a4e048eaf1a414c007

[ "BSD-3-Clause" ]

1

2016-12-28T07:41:09.000Z

flask_app/redis.py

vmalloc/logpile

466a7404e17878ac688542a4e048eaf1a414c007

[ "BSD-3-Clause" ]

null

flask_app/redis.py

vmalloc/logpile

466a7404e17878ac688542a4e048eaf1a414c007

[ "BSD-3-Clause" ]

null

from __future__ import absolute_import import redis def get_connection(db=0): return redis.StrictRedis()

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py

Python

custom/icds_reports/data_pull/exceptions.py

scottwedge/commcare-hq

900ccf81c9f23fb3b435962f065648669817f37a

[ "BSD-3-Clause" ]

null

custom/icds_reports/data_pull/exceptions.py

scottwedge/commcare-hq

900ccf81c9f23fb3b435962f065648669817f37a

[ "BSD-3-Clause" ]

null

custom/icds_reports/data_pull/exceptions.py

scottwedge/commcare-hq

900ccf81c9f23fb3b435962f065648669817f37a

[ "BSD-3-Clause" ]

null

class UnboundDataPullException(Exception): pass class DataPullInProgressError(Exception): pass

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py

Python

adminRole/views.py

YashashwiniDixit/EDEZE

79f264fd0def7062e57de8306dee7b16f349e73a

[ "MIT" ]

null

adminRole/views.py

YashashwiniDixit/EDEZE

79f264fd0def7062e57de8306dee7b16f349e73a

[ "MIT" ]

null

adminRole/views.py

YashashwiniDixit/EDEZE

79f264fd0def7062e57de8306dee7b16f349e73a

[ "MIT" ]

null

from django.shortcuts import render from utility_functions.date_functions import * from utility_functions.db_functions import * def adminIndex(request): return render(request,'adminIndex.html') def addTeacher(request): if request.session.has_key('user_id') and not request.session.has_key('teacher_id') and not request.session.has_key('student_id'): if request.method == "POST": try: username = request.POST.get('username') password = request.POST.get('password') email_id = request.POST.get('email_id') first_name = request.POST.get('first_name') last_name = request.POST.get('last_name') qualification = request.POST.get('qualification') research_interests = request.POST.get('research_interests') con = DBConnection.getConnection() cur = con.cursor() query = "INSERT INTO users(username,password,type) VALUES(%s,%s,%s);" cur.execute(query, (username, password, "teacher")) con.commit() user_id = cur.lastrowid query = "INSERT INTO teachers(user_id,first_name,last_name,qualification,research_interests,email_id) VALUES (%s,%s,%s,%s,%s,%s)" cur.execute(query,(user_id,first_name,last_name,qualification,research_interests,email_id)) con.commit() return render(request,'successful.html') except Exception as e: return render(request, 'unsuccessful.html',{'e':e}) else: return render(request, 'addTeacher.html') return render(request, 'aboutus.html') def addAdmin(request): if request.session.has_key('user_id') and not request.session.has_key('teacher_id') and not request.session.has_key('student_id'): if request.method == "POST": try: username = request.POST.get('username') password = request.POST.get('password') con = DBConnection.getConnection() cur = con.cursor() query = "INSERT INTO users(username,password,type) VALUES(%s,%s,%s);" cur.execute(query, (username, password, "admin")) con.commit() return render(request, 'successful.html') except Exception as e: return render(request, 'unsuccessful.html',{'e':e}) else: return render(request, 'addAdmin.html') return render(request, 'aboutus.html') def addStudent(request): if request.session.has_key('user_id') and request.session.has_key('teacher_id'): if request.method == "POST": try: username = request.POST.get('username') password = request.POST.get('password') email_id = request.POST.get('email_id') first_name = request.POST.get('first_name') last_name = request.POST.get('last_name') about_me = request.POST.get('about_me') class_id = request.POST.get('class_id') con = DBConnection.getConnection() cur = con.cursor() query = "INSERT INTO users(username,password,type) VALUES(%s,%s,%s);" cur.execute(query, (username, password, "student")) con.commit() user_id = cur.lastrowid query = "INSERT INTO students(user_id,first_name,last_name,about_me,class_id,email_id) VALUES (%s,%s,%s,%s,%s,%s)" cur.execute(query,(user_id,first_name,last_name,about_me,class_id,email_id)) con.commit() return render(request,'successful.html') except Exception as e: print(e) return render(request, 'unsuccessful.html',{'e':e}) else: con = DBConnection.getConnection() cur = con.cursor() query = "SELECT id,name FROM class WHERE incharge_id = %s;" cur.execute(query, (request.session['teacher_id'])) classes = cur.fetchall() return render(request, 'addStudent.html',{'classes':classes}) return render(request, 'aboutus.html')

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1

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null

0

1

0

6

44db578da92603ec7ba1c45af150b53499ae5509

30

py

Python

firebasescrypt/__init__.py

6degrees/firebase-scrypt-python

6bde9476643abb149a104e921847be99c97728a0

[ "MIT" ]

4

2021-02-04T04:13:39.000Z

2022-03-30T15:22:55.000Z

firebasescrypt/__init__.py

6degrees/firebase-scrypt-python

6bde9476643abb149a104e921847be99c97728a0

[ "MIT" ]

3

2021-02-15T00:56:56.000Z

2022-02-21T13:52:45.000Z

firebasescrypt/__init__.py

6degrees/firebase-scrypt-python

6bde9476643abb149a104e921847be99c97728a0

[ "MIT" ]

2

2022-02-17T16:53:56.000Z

2022-03-30T16:17:46.000Z

from .firebasescrypt import *

15

29

0.8

3

30

8

1

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0.133333

30

1

30

0.923077

0

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0

1

0

6

44ea598d1d89c8d431f77985d18643ca5e7d04e9

198

py

Python

src/python/zensols/deeplearn/__init__.py

plandes/deeplearn

925f02200c62a7dc798e474ed94a86e009fd1ebf

[ "MIT" ]

2

2021-04-30T17:19:14.000Z

2021-05-04T03:48:59.000Z

src/python/zensols/deeplearn/__init__.py

plandes/deeplearn

925f02200c62a7dc798e474ed94a86e009fd1ebf

[ "MIT" ]

null

src/python/zensols/deeplearn/__init__.py

plandes/deeplearn

925f02200c62a7dc798e474ed94a86e009fd1ebf

[ "MIT" ]

null

"""A deep learning framework to make training, validating and testing models with PyTorch easier. """ from .torchtype import * from .torchconfig import * from .domain import * from .plot import *

19.8

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null

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1

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1

0

1

0

6

78384cc5b43052a6842dcecb2c6c7c07487d4e9a

74

py

Python

CNN_Model/__init__.py

AFahri/ANBU

d14fe39d6bd37a01add4a3369b479c6474537305

[ "MIT" ]

1

2019-11-01T14:36:41.000Z

CNN_Model/__init__.py

AFahri/ANBU

d14fe39d6bd37a01add4a3369b479c6474537305

[ "MIT" ]

5

2020-09-26T00:18:38.000Z

2021-08-25T15:49:37.000Z

CNN_Model/__init__.py

AFahri/ANBU

d14fe39d6bd37a01add4a3369b479c6474537305

[ "MIT" ]

null

from . import PREDICT from . import train_classifier from . import UTILS

14.8

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0.783784

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null

0

1

0

1

0

1

0

6

785bdcf84872bf8e8d72655a3e4d406e0e7fd8cf

3,835

py

Python

test/test_rtrie.py

ncloudioj/hustle

890793b996d5ba0660f4f16dd71c88abc86ae5b5

[ "MIT" ]

88

2015-01-07T16:57:29.000Z

2021-05-31T15:11:45.000Z

test/test_rtrie.py

ncloudioj/hustle

890793b996d5ba0660f4f16dd71c88abc86ae5b5

[ "MIT" ]

3

2015-08-17T09:42:20.000Z

2018-01-12T18:31:12.000Z

test/test_rtrie.py

ncloudioj/hustle

890793b996d5ba0660f4f16dd71c88abc86ae5b5

[ "MIT" ]

10

2015-04-05T14:41:32.000Z

2018-12-02T20:46:57.000Z

# -*- coding: utf-8 -*- import unittest import rtrie import mdb from wtrie import Trie class TestRTrie(unittest.TestCase): def test_rtrie_in_memory(self): s = unicode(u'séllsink').encode('utf-8') #print "HELLSINK: %s" % s t = Trie() self.assertEqual(t.add('hello'), 1) self.assertEqual(t.add('hell'), 2) self.assertEqual(t.add('hello'), 1) self.assertEqual(t.add('hellothere'), 3) self.assertEqual(t.add('good'), 4) self.assertEqual(t.add('goodbye'), 5) self.assertEqual(t.add('hello'), 1) self.assertEqual(t.add('hellsink'), 6) self.assertEqual(t.add(s), 7) t.print_it() nodes, kids, _ = t.serialize() nodeaddr, nodelen = nodes.buffer_info() kidaddr, kidlen = kids.buffer_info() print "LENS %s %s" % (nodelen, kidlen) for i in range(8): val = rtrie.value_for_vid(nodeaddr, kidaddr, i) print "Value", i, val self.assertEqual(rtrie.vid_for_value(nodeaddr, kidaddr, 'hello'), 1) self.assertEqual(rtrie.vid_for_value(nodeaddr, kidaddr, 'hell'), 2) self.assertEqual(rtrie.vid_for_value(nodeaddr, kidaddr, 'goodbye'), 5) self.assertEqual(rtrie.vid_for_value(nodeaddr, kidaddr, 'hellsink'), 6) self.assertEqual(rtrie.vid_for_value(nodeaddr, kidaddr, 'hellothere'), 3) self.assertEqual(rtrie.vid_for_value(nodeaddr, kidaddr, 'good'), 4) self.assertEqual(rtrie.vid_for_value(nodeaddr, kidaddr, s), 7) self.assertIsNone(rtrie.vid_for_value(nodeaddr, kidaddr, 'notthere')) self.assertIsNone(rtrie.vid_for_value(nodeaddr, kidaddr, 'h')) self.assertIsNone(rtrie.vid_for_value(nodeaddr, kidaddr, 'he')) self.assertIsNone(rtrie.vid_for_value(nodeaddr, kidaddr, 'hel')) self.assertIsNone(rtrie.vid_for_value(nodeaddr, kidaddr, 'hells')) def test_rtrie_in_mdb(self): t = Trie() self.assertEqual(t.add('hello'), 1) self.assertEqual(t.add('hell'), 2) self.assertEqual(t.add('hello'), 1) self.assertEqual(t.add('hellothere'), 3) self.assertEqual(t.add('good'), 4) self.assertEqual(t.add('goodbye'), 5) self.assertEqual(t.add('hello'), 1) self.assertEqual(t.add('hellsink'), 6) nodes, kids, _ = t.serialize() nodeaddr, nodelen = nodes.buffer_info() kidaddr, kidlen = kids.buffer_info() try: env = mdb.Env('/tmp/test_rtrie', flags=mdb.MDB_WRITEMAP | mdb.MDB_NOSYNC | mdb.MDB_NOSUBDIR) txn = env.begin_txn() db = env.open_db(txn, name='_meta_', flags=mdb.MDB_CREATE) db.put_raw(txn, 'nodes', nodeaddr, nodelen) db.put_raw(txn, 'kids', kidaddr, kidlen) n, ns = db.get_raw(txn, 'nodes') k, ks = db.get_raw(txn, 'kids') txn.commit() env.close() env = mdb.Env('/tmp/test_rtrie', flags=mdb.MDB_NOSYNC | mdb.MDB_NOSUBDIR) txn = env.begin_txn() db = env.open_db(txn, name='_meta_') n, ns = db.get_raw(txn, 'nodes') k, ks = db.get_raw(txn, 'kids') self.assertEqual(rtrie.vid_for_value(n, k, 'hello'), 1) self.assertEqual(rtrie.vid_for_value(n, k, 'hell'), 2) self.assertEqual(rtrie.vid_for_value(n, k, 'goodbye'), 5) self.assertEqual(rtrie.vid_for_value(n, k, 'hellsink'), 6) self.assertEqual(rtrie.vid_for_value(n, k, 'hellothere'), 3) self.assertEqual(rtrie.vid_for_value(n, k, 'good'), 4) self.assertIsNone(rtrie.vid_for_value(n, k, 'notthere')) txn.commit() env.close() finally: import os os.unlink('/tmp/test_rtrie') os.unlink('/tmp/test_rtrie-lock')

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1

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null

0

1

0

6

7866c2304834046c91a1c1aaf6a8606f8efc1840

138

py

Python

tests/test_form_builder_routes.py

aryan040501/wikilabels

ea110da2b969cc978a0f288c4da6250dc9d67e72

[ "MIT" ]

15

2015-07-16T17:56:43.000Z

2018-08-20T14:59:16.000Z

tests/test_form_builder_routes.py

aryan040501/wikilabels

ea110da2b969cc978a0f288c4da6250dc9d67e72

[ "MIT" ]

122

2015-06-10T15:58:11.000Z

2018-08-16T14:56:23.000Z

tests/test_form_builder_routes.py

aryan040501/wikilabels

ea110da2b969cc978a0f288c4da6250dc9d67e72

[ "MIT" ]

27

2015-07-15T22:12:35.000Z

2018-08-06T23:10:28.000Z

from .routes_test_fixture import app # noqa def test_form_builder(client): assert client.get("/form_builder/")._status_code == 200

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1

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1

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1

0

6

7876baf44727b21ba7c4931a06b6f997a31b0a13

63

py

Python

python_packages_static/pyemu/pyemu_warnings.py

usgs/neversink_workflow

acd61435b8553e38d4a903c8cd7a3afc612446f9

[ "CC0-1.0" ]

null

python_packages_static/pyemu/pyemu_warnings.py

usgs/neversink_workflow

acd61435b8553e38d4a903c8cd7a3afc612446f9

[ "CC0-1.0" ]

null

python_packages_static/pyemu/pyemu_warnings.py

usgs/neversink_workflow

acd61435b8553e38d4a903c8cd7a3afc612446f9

[ "CC0-1.0" ]

null

import warnings class PyemuWarning(RuntimeWarning): pass

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true

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null

0

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null

0

1

0

1

0

6

78b06dd8cab70752b356b191a9940d6db31b4227

24

py

Python

src/models/__init__.py

shirley-wu/text_to_table

44cb100b8ff2543b5b4efe1461502c00c34ef846

[ "MIT" ]

3

2022-03-17T05:55:23.000Z

2022-03-30T08:34:14.000Z

src/models/__init__.py

shirley-wu/text_to_table

44cb100b8ff2543b5b4efe1461502c00c34ef846

[ "MIT" ]

1

2022-03-30T09:04:54.000Z

src/models/__init__.py

shirley-wu/text_to_table

44cb100b8ff2543b5b4efe1461502c00c34ef846

[ "MIT" ]

null

from . import bart_ours

12

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0.791667

4

24

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1

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0.166667

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1

24

0.9

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1

0

null

0

1

0

1

0

1

0

6

78bdd21c27bad04113312d94ba53668ff41d19c7

27

py

Python

tenark/models/__init__.py

knowark/tenark

27d51972ff32c47ba8da423752fa7c32bd0ea6df

[ "MIT" ]

1

2019-05-16T04:05:21.000Z

tenark/models/__init__.py

knowark/tenark

27d51972ff32c47ba8da423752fa7c32bd0ea6df

[ "MIT" ]

1

2020-06-13T20:29:14.000Z

tenark/models/__init__.py

knowark/tenark

27d51972ff32c47ba8da423752fa7c32bd0ea6df

[ "MIT" ]

null

from .tenant import Tenant

13.5

26

0.814815

4

27

5.5

0.75

0

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27

1

27

0.956522

0

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true

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0

1

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1

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null

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1

0

null

0

1

0

1

0

1

0

6

151ea8b46aa415d082d28820af146e120e228a8e

1,199

py

Python

src/python/hello_world/messages/animals.py

jaximan/pexample

8820e82b01b4ef84746351ddf2e1c8af1ff6b0a1

[ "Apache-2.0" ]

null

src/python/hello_world/messages/animals.py

jaximan/pexample

8820e82b01b4ef84746351ddf2e1c8af1ff6b0a1

[ "Apache-2.0" ]

null

src/python/hello_world/messages/animals.py

jaximan/pexample

8820e82b01b4ef84746351ddf2e1c8af1ff6b0a1

[ "Apache-2.0" ]

null

from textwrap import dedent def cow(message): return dedent(""" ________________ < {} > ---------------- \\ ^__^ \\ (oo)\\_______ (__)\\ )\\/ ||----w | || || """).format(message) def unicorn(message): return dedent(""" \\ \\ \\\\ \\\\ >\\/7 _.-(6' \\ (=___._/` \\ ) \\ | / / | ________________ / > / < {} > j < _\\ ---------------- _.-' : ``. \\ r=._\\ `. <`\\\\_ \\ .`-. \\ r-7 `-. ._ ' . `\\ \\`, `-.`7 7) ) \\/ \\| \\' / `-._ || .' \\\\ ( >\\ > ,.-' >.' <.'_.'' <' """).format(message)

27.25

52

0.141785

28

1,199

4.107143

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0.226087

0.330435

0

0.012531

0.667223

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43

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false

0

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null

1

0

1

0

1

0

1

0

null

0

6

153a15bee5776e41afcddb375f3002f88c90686d

146

py

Python

app/profile/__init__.py

natacha-beck/conp-portal

28950b4cd957b157260b288cfea2488658ac2d53

[ "MIT" ]

10

2019-02-27T22:55:28.000Z

2021-06-15T12:55:10.000Z

app/profile/__init__.py

natacha-beck/conp-portal

28950b4cd957b157260b288cfea2488658ac2d53

[ "MIT" ]

325

2019-02-27T22:58:32.000Z

2022-03-17T15:48:54.000Z

app/profile/__init__.py

natacha-beck/conp-portal

28950b4cd957b157260b288cfea2488658ac2d53

[ "MIT" ]

31

2019-03-05T16:04:01.000Z

2021-12-22T15:25:15.000Z

# -*- coding: utf-8 -*- from flask import Blueprint profile_bp = Blueprint('profile', __name__) from app.profile import routes # noqa: E402,F401

29.2

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null

0

1

0

1

0

6

155c1a98a441f82486cd2ddeae5ca2947ac9a586

81

py

Python

src/views.py

hey-mako/heroku-botops

d9d8795eddbbb69ef5acddd584fcb108a0c8e794

[ "MIT" ]

null

src/views.py

hey-mako/heroku-botops

d9d8795eddbbb69ef5acddd584fcb108a0c8e794

[ "MIT" ]

null

src/views.py

hey-mako/heroku-botops

d9d8795eddbbb69ef5acddd584fcb108a0c8e794

[ "MIT" ]

null

from . import application @application.route('/') def index(): return '', 200

11.571429

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null

0

1

0

null

0

1

0

1

0

6

156d396d9f1ca32dba075ec687c077308bf4d0f8

4,362

py

Python

unit-test/test_nvme_scan.py

dedwards-micron/nvme-scan

2d41b42a178cd4fcc694c104ec53253affa8809b

[ "BSD-3-Clause" ]

null

unit-test/test_nvme_scan.py

dedwards-micron/nvme-scan

2d41b42a178cd4fcc694c104ec53253affa8809b

[ "BSD-3-Clause" ]

null

unit-test/test_nvme_scan.py

dedwards-micron/nvme-scan

2d41b42a178cd4fcc694c104ec53253affa8809b

[ "BSD-3-Clause" ]

null

import unittest import json from nvme_scan import get_args, NvmeDeviceCollector class NvmeScanTestCase(unittest.TestCase): def test_01_linux_scan_all(self): test_args = [] args = get_args(test_args) self.assertFalse(args.use_spdk) self.assertIsNone(args.spdk_path) self.assertEqual(args.scan_type, 'ALL') self.assertIsNone(args.dev_ref) self.assertFalse(args.diff_scan) self.assertIsNone(args.data_file) def test_02_linux_scan_bdf(self): test_args = [ "-b", "0000:0f:00.0" ] args = get_args(test_args) self.assertFalse(args.use_spdk) self.assertIsNone(args.spdk_path) self.assertEqual(args.scan_type, 'BDF') self.assertEqual(args.dev_ref, test_args[1]) self.assertFalse(args.diff_scan) self.assertIsNone(args.data_file) def test_03_linux_scan_devnode(self): test_args = [ "-n", "/dev/nvme0" ] args = get_args(test_args) self.assertFalse(args.use_spdk) self.assertIsNone(args.spdk_path) self.assertEqual(args.scan_type, 'NODE') self.assertEqual(args.dev_ref, test_args[1]) self.assertFalse(args.diff_scan) self.assertIsNone(args.data_file) def test_04_linux_diff_all(self): test_args = [ "-f", "sample_data_file.json" ] args = get_args(test_args) self.assertFalse(args.use_spdk) self.assertIsNone(args.spdk_path) self.assertEqual(args.scan_type, 'ALL') self.assertIsNone(args.dev_ref) self.assertTrue(args.diff_scan) self.assertEqual(args.data_file, test_args[1]) def test_05_linux_diff_bdf(self): test_args = [ "-b", "0000:0E:00.0", "-f", "sample_data_file.json" ] args = get_args(test_args) self.assertFalse(args.use_spdk) self.assertIsNone(args.spdk_path) self.assertEqual(args.scan_type, 'BDF') self.assertEqual(args.dev_ref, test_args[1]) self.assertTrue(args.diff_scan) self.assertEqual(args.data_file, test_args[3]) def test_06_linux_diff_devnode(self): test_args = [ "-n", "/dev/nvme0", "-f", "sample_data_file.json" ] args = get_args(test_args) self.assertFalse(args.use_spdk) self.assertIsNone(args.spdk_path) self.assertEqual(args.scan_type, 'NODE') self.assertEqual(args.dev_ref, test_args[1]) self.assertTrue(args.diff_scan) self.assertEqual(args.data_file, test_args[3]) def test_07_linux_diff_missing_data(self): test_args = [ "-n", "/dev/nvme0", "-f", "nonexisting_in_file.json" ] args = get_args(test_args) self.assertFalse(args.use_spdk) self.assertIsNone(args.spdk_path) self.assertEqual(args.scan_type, 'NODE') self.assertEqual(args.dev_ref, test_args[1]) self.assertFalse(args.diff_scan) self.assertIsNone(args.data_file) def test_08_linux_diff_with_spdk(self): test_args = [ "-n", "/dev/nvme0", "-f", "sample_data_file.json", "--spdk", "empty_spdk_dir" ] args = get_args(test_args) self.assertTrue(args.use_spdk) self.assertEqual(args.spdk_path, test_args[5]) self.assertEqual(args.scan_type, 'NODE') self.assertEqual(args.dev_ref, test_args[1]) self.assertTrue(args.diff_scan) self.assertEqual(args.data_file, test_args[3]) def test_09_linux_diff_missing_spdk(self): test_args = [ "-n", "/dev/nvme0", "-f", "sample_data_file.json", "--spdk", "nonexisting_spdk" ] args = get_args(test_args) self.assertFalse(args.use_spdk) self.assertIsNone(args.spdk_path) self.assertEqual(args.scan_type, 'NODE') self.assertEqual(args.dev_ref, test_args[1]) self.assertTrue(args.diff_scan) self.assertEqual(args.data_file, test_args[3]) def test_10_linux_collect_full_scan(self): nvme_hlpr = NvmeDeviceCollector() dev_data = nvme_hlpr.new_scan() print("Device Collector Data:\n{}".format(json.dumps(dev_data))) if __name__ == '__main__': unittest.main()

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ec7b4b5eee2e7175f6ffd40bc3aac24579e12026

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py

Python

deepaccess/interpret/__init__.py

jhammelman/DeepAccessTransfer

8ca978873e2fcb1b95d90902e3fb38e710027776

[ "MIT" ]

2

2021-08-16T18:34:59.000Z

2022-02-19T16:05:21.000Z

deepaccess/interpret/__init__.py

jhammelman/DeepAccessTransfer

8ca978873e2fcb1b95d90902e3fb38e710027776

[ "MIT" ]

null

deepaccess/interpret/__init__.py

jhammelman/DeepAccessTransfer

8ca978873e2fcb1b95d90902e3fb38e710027776

[ "MIT" ]

1

2021-05-26T21:54:53.000Z

from deepaccess.ensemble_utils import * from deepaccess.train.DeepAccessModel import * from .ExpectedPatternEffect import * from .interpret import * from .importance_utils import *

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Python

tests/test_nn.py

KaiyuYue/torchshard

89e21def180bf6063ceb2e312a61631173abc7e7

[ "Apache-2.0" ]

265

2021-04-27T12:06:45.000Z

2022-03-17T11:13:17.000Z

tests/test_nn.py

poodarchu/torchshard

667cfce9ed3e2170c7768d910a71aa07897857e7

[ "Apache-2.0" ]

7

2021-05-24T06:54:44.000Z

2022-01-01T18:47:38.000Z

tests/test_nn.py

KaiyuYue/torchshard

89e21def180bf6063ceb2e312a61631173abc7e7

[ "Apache-2.0" ]

11

2021-04-28T04:15:44.000Z

2022-01-26T04:29:30.000Z

import unittest import copy from typing import Optional, List, Callable, Tuple import torch import torch.nn as nn import torch.distributed as dist import torch.multiprocessing as mp import torch.nn.parallel as parallel from torch import Tensor import torchshard as ts from testing import dist_worker, assertEqual, set_seed from testing import LinearModel, LinearStackModel, ConvLinearModel from testing import loss_reduction_type, threshold # global test configurations batch_size = 3 feats_size = 8 seed = 12357 class TestParallelCrossEntropy(unittest.TestCase): @staticmethod def run_test_parallel_cross_entropy(local_rank: int) -> None: set_seed(seed + local_rank) parallel_dim = -1 x = torch.randn(batch_size, feats_size).cuda(local_rank) y = torch.randint(10, (batch_size,)).cuda(local_rank) dist.broadcast(x, 0) dist.broadcast(y, 0) model = LinearModel(feats_size, feats_size*2, bias=True, dim=parallel_dim).cuda(local_rank) raw_model = model.module if hasattr(model, "module") else model # align weight ts.nn.init.shard_init_helper_( torch.nn.init.kaiming_normal_, raw_model.layer2.weight, a=0, mode='fan_in', nonlinearity='relu' ) master_weight = ts.distributed.gather(raw_model.layer2.weight.data, dim=0) raw_model.layer1.weight.data.copy_(master_weight) # align bias ts.nn.init.shard_init_helper_( torch.nn.init.constant_, raw_model.layer2.bias, val=0.5 ) master_bias = ts.distributed.gather(raw_model.layer2.bias.data, dim=0) raw_model.layer1.bias.data.copy_(master_bias) model.train() criterion1 = torch.nn.CrossEntropyLoss(reduction=loss_reduction_type).cuda(local_rank) criterion2 = ts.nn.ParallelCrossEntropyLoss(reduction=loss_reduction_type).cuda(local_rank) y1, y2 = model(x) # 1st assert: forward outputs gathered_y2 = ts.distributed.gather(y2) assertEqual(y1, gathered_y2, threshold=threshold) loss1 = criterion1(y1, y) loss2 = criterion2(y2, y) if loss_reduction_type == 'none': loss1 = loss1.sum() loss2 = loss2.sum() # 2nd assert: forward losses assertEqual(loss1, loss2, threshold=threshold) # 3rd assert: backward gradients loss1.backward() loss2.backward() assertEqual( raw_model.layer1.weight.grad, ts.distributed.gather(raw_model.layer2.weight.grad, dim=0), threshold=threshold ) assertEqual( raw_model.layer1.bias.grad, ts.distributed.gather(raw_model.layer2.bias.grad, dim=0), threshold=threshold ) @staticmethod def run_test_parallel_cross_entropy_within_ddp_mode(local_rank: int) -> None: set_seed(seed + local_rank) parallel_dim = None bias = True x = torch.randn(batch_size, 8, 1, 1).cuda(local_rank) y = torch.randint(10, (batch_size,)).cuda(local_rank) raw_model = ConvLinearModel(feats_size, feats_size*2, bias=bias, dim=parallel_dim).cuda(local_rank) # convert nn.Linear -> nn.ParallelLinear ts.nn.ParallelLinear.convert_parallel_linear(raw_model, dim=parallel_dim) raw_model = parallel.DistributedDataParallel(raw_model, device_ids=[local_rank]) ddp_model = parallel.DistributedDataParallel( ConvLinearModel(feats_size, feats_size*2, bias=bias, dim=parallel_dim).cuda(local_rank), device_ids=[local_rank] ) raw_criterion = ts.nn.ParallelCrossEntropyLoss(reduction=loss_reduction_type).cuda(local_rank) ddp_criterion = torch.nn.CrossEntropyLoss(reduction=loss_reduction_type).cuda(local_rank) # align weight & bias raw_model.module.conv.weight.data.copy_(ddp_model.module.conv.weight.data) raw_model.module.conv.bias.data.copy_(ddp_model.module.conv.bias.data) raw_model.module.fc.weight.data.copy_(ddp_model.module.fc.weight.data) raw_model.module.fc.bias.data.copy_(ddp_model.module.fc.bias.data) # assert weight assertEqual(raw_model.module.conv.weight.data, ddp_model.module.conv.weight.data, threshold=threshold) assertEqual(raw_model.module.conv.bias.data, ddp_model.module.conv.bias.data, threshold=threshold) assertEqual(raw_model.module.fc.weight.data, ddp_model.module.fc.weight.data, threshold=threshold) assertEqual(raw_model.module.fc.bias.data, ddp_model.module.fc.bias.data, threshold=threshold) # switch mode raw_model.train() ddp_model.train() # 1st assert: forward outputs y1 = raw_model(x) y2 = ddp_model(x) assertEqual(y1, y2, threshold=threshold) # 2nd assert: forward losses raw_loss = raw_criterion(y1, y) ddp_loss = ddp_criterion(y2, y) assertEqual(raw_loss, ddp_loss, threshold=threshold) if loss_reduction_type == 'none': raw_loss = raw_loss.sum() ddp_loss = ddp_loss.sum() # 3rd assert: backward gradients raw_loss.backward() ddp_loss.backward() assertEqual(raw_model.module.fc.weight.grad, ddp_model.module.fc.weight.grad, threshold=threshold) assertEqual(raw_model.module.fc.bias.grad, ddp_model.module.fc.bias.grad, threshold=threshold) assertEqual(raw_model.module.conv.weight.grad, ddp_model.module.conv.weight.grad, threshold=threshold) assertEqual(raw_model.module.conv.bias.grad, ddp_model.module.conv.bias.grad, threshold=threshold) @staticmethod def run_test_parallel_cross_entropy_within_ddp_mode_and_row_parallel(local_rank: int) -> None: set_seed(seed + local_rank) parallel_dim = 0 bias = True x = torch.randn(batch_size, 8, 1, 1).cuda(local_rank) y = torch.randint(10, (batch_size,)).cuda(local_rank) raw_model = ConvLinearModel(feats_size, feats_size*2, bias=bias, dim=parallel_dim).cuda(local_rank) # convert nn.Linear -> nn.ParallelLinear ts.nn.ParallelLinear.convert_parallel_linear(raw_model, dim=parallel_dim) raw_model = parallel.DistributedDataParallel(raw_model, device_ids=[local_rank]) ddp_model = parallel.DistributedDataParallel( ConvLinearModel(feats_size, feats_size*2, bias=bias, dim=parallel_dim).cuda(local_rank), device_ids=[local_rank] ) raw_criterion = ts.nn.ParallelCrossEntropyLoss(reduction=loss_reduction_type).cuda(local_rank) ddp_criterion = torch.nn.CrossEntropyLoss(reduction=loss_reduction_type).cuda(local_rank) # align weight & bias raw_model.module.conv.weight.data.copy_(ddp_model.module.conv.weight.data) raw_model.module.conv.bias.data.copy_(ddp_model.module.conv.bias.data) _weight = ts.distributed.scatter(ddp_model.module.fc.weight.data, dim=1) raw_model.module.fc.weight.data.copy_(_weight) raw_model.module.fc.bias.data.copy_(ddp_model.module.fc.bias.data) # assert weight assertEqual(raw_model.module.conv.weight.data, ddp_model.module.conv.weight.data, threshold=threshold) assertEqual(raw_model.module.conv.bias.data, ddp_model.module.conv.bias.data, threshold=threshold) assertEqual( ts.distributed.gather(raw_model.module.fc.weight.data, dim=1), ddp_model.module.fc.weight.data, threshold=threshold ) assertEqual(raw_model.module.fc.bias.data, ddp_model.module.fc.bias.data, threshold=threshold) # switch mode raw_model.train() ddp_model.train() x = ts.distributed.gather(x, dim=0) y = ts.distributed.gather(y, dim=0) y1 = raw_model(x) y2 = ddp_model(x) # 1st assert: forward outputs assertEqual(y1, y2, threshold=threshold) raw_loss = raw_criterion(y1, y) ddp_loss = ddp_criterion(y2, y) if loss_reduction_type == 'none': raw_loss = raw_loss.sum() ddp_loss = ddp_loss.sum() # 2nd assert: forward losses assertEqual(raw_loss, ddp_loss, threshold=threshold) # 3rd assert: backward gradients raw_loss.backward() ddp_loss.backward() assertEqual(ts.distributed.gather(raw_model.module.fc.weight.grad, dim=-1), ddp_model.module.fc.weight.grad, threshold=threshold) assertEqual(raw_model.module.fc.bias.grad, ddp_model.module.fc.bias.grad, threshold=threshold) assertEqual(raw_model.module.conv.weight.grad, ddp_model.module.conv.weight.grad, threshold=threshold) assertEqual(raw_model.module.conv.bias.grad, ddp_model.module.conv.bias.grad, threshold=threshold) @staticmethod def run_test_parallel_cross_entropy_within_ddp_mode_and_col_parallel(local_rank: int) -> None: set_seed(seed + local_rank) parallel_dim = -1 bias = True x = torch.randn(batch_size, 8, 1, 1).cuda(local_rank) y = torch.randint(10, (batch_size,)).cuda(local_rank) raw_model = ConvLinearModel(feats_size, feats_size*2, bias=bias, dim=parallel_dim).cuda(local_rank) # convert nn.Linear -> nn.ParallelLinear ts.nn.ParallelLinear.convert_parallel_linear(raw_model, dim=parallel_dim) raw_model = parallel.DistributedDataParallel(raw_model, device_ids=[local_rank]) ddp_model = parallel.DistributedDataParallel( ConvLinearModel(feats_size, feats_size*2, bias=bias, dim=parallel_dim).cuda(local_rank), device_ids=[local_rank] ) raw_criterion = ts.nn.ParallelCrossEntropyLoss(reduction=loss_reduction_type).cuda(local_rank) ddp_criterion = torch.nn.CrossEntropyLoss(reduction=loss_reduction_type).cuda(local_rank) # align weight & bias raw_model.module.conv.weight.data.copy_(ddp_model.module.conv.weight.data) _weight = ts.distributed.scatter(ddp_model.module.fc.weight.data, dim=0) raw_model.module.fc.weight.data.copy_(_weight) raw_model.module.conv.bias.data.copy_(ddp_model.module.conv.bias.data) _bias = ts.distributed.scatter(ddp_model.module.fc.bias.data, dim=0) raw_model.module.fc.bias.data.copy_(_bias) # assert weight assertEqual(raw_model.module.conv.weight.data, ddp_model.module.conv.weight.data, threshold=threshold) assertEqual(raw_model.module.conv.bias.data, ddp_model.module.conv.bias.data, threshold=threshold) assertEqual( ts.distributed.gather(raw_model.module.fc.weight.data, dim=0), ddp_model.module.fc.weight.data, threshold=threshold ) assertEqual( ts.distributed.gather(raw_model.module.fc.bias.data, dim=0), ddp_model.module.fc.bias.data, threshold=threshold ) # switch mode raw_model.train() ddp_model.train() y1 = raw_model(x) y2 = ddp_model(x) # 1st assert: forward outputs gathered_y1 = ts.distributed.gather(y1, dim=1) gathered_y2 = ts.distributed.gather(y2, dim=0) assertEqual(gathered_y1, gathered_y2, threshold=threshold) # 2nd assert: forward losses gathered_y = ts.distributed.gather(y) raw_loss = raw_criterion(y1, gathered_y) ddp_loss = ddp_criterion(y2, y) if loss_reduction_type == 'none': raw_loss = raw_loss.sum() ddp_loss = ddp_loss.sum() # 3rd assert: backward gradients raw_loss.backward() ddp_loss.backward() if loss_reduction_type == 'mean': linear_w_grad = ddp_model.module.fc.weight.grad linear_b_grad = ddp_model.module.fc.bias.grad else: linear_w_grad = ts.distributed.reduce(ddp_model.module.fc.weight.grad) linear_b_grad = ts.distributed.reduce(ddp_model.module.fc.bias.grad) assertEqual( raw_model.module.fc.weight.grad, ts.distributed.scatter(linear_w_grad, dim=0), threshold=threshold ) assertEqual( raw_model.module.fc.bias.grad, ts.distributed.scatter(linear_b_grad, dim=0), threshold=threshold ) if loss_reduction_type == 'mean': conv_w_grad = ts.distributed.reduce(raw_model.module.conv.weight.grad) conv_b_grad = ts.distributed.reduce(raw_model.module.conv.bias.grad) else: conv_w_grad = raw_model.module.conv.weight.grad conv_b_grad = raw_model.module.conv.bias.grad assertEqual(conv_w_grad, ddp_model.module.conv.weight.grad, threshold=threshold) assertEqual(conv_b_grad, ddp_model.module.conv.bias.grad, threshold=threshold) @unittest.skipIf(not torch.cuda.is_available(), 'CUDA is not available') def test_parallel_cross_entropy(self): ngpus = torch.cuda.device_count() mp.spawn( dist_worker, args=(self.run_test_parallel_cross_entropy, ngpus), nprocs=ngpus ) ts.distributed.destroy_process_group() @unittest.skipIf(not torch.cuda.is_available(), 'CUDA is not available') def test_parallel_cross_entropy_within_ddp_mode(self): ngpus = torch.cuda.device_count() mp.spawn( dist_worker, args=(self.run_test_parallel_cross_entropy_within_ddp_mode, ngpus), nprocs=ngpus ) ts.distributed.destroy_process_group() @unittest.skipIf(not torch.cuda.is_available(), 'CUDA is not available') def test_parallel_cross_entropy_within_ddp_mode_and_row_parallel(self): ngpus = torch.cuda.device_count() mp.spawn( dist_worker, args=(self.run_test_parallel_cross_entropy_within_ddp_mode_and_row_parallel, ngpus), nprocs=ngpus ) ts.distributed.destroy_process_group() @unittest.skipIf(not torch.cuda.is_available(), 'CUDA is not available') def test_parallel_cross_entropy_within_ddp_mode_and_col_parallel(self): ngpus = torch.cuda.device_count() mp.spawn( dist_worker, args=(self.run_test_parallel_cross_entropy_within_ddp_mode_and_col_parallel, ngpus), nprocs=ngpus ) class TestParallelLinearStack(unittest.TestCase): @staticmethod def run_test_parallel_linear_stack(local_rank: int) -> None: set_seed(seed + local_rank) x = torch.randn(batch_size, feats_size).cuda(local_rank) dist.broadcast(x, 0) model = LinearStackModel(feats_size, feats_size*2, bias=True).cuda(local_rank) raw_model = model.module if hasattr(model, "module") else model # align weight for idx, (m1, m2) in enumerate(zip(raw_model.module1.modules(), raw_model.module2.modules())): if idx == 0: continue # align weight and bias ts.nn.init.shard_init_helper_( torch.nn.init.xavier_normal_, m2.weight ) ts.nn.init.shard_init_helper_( torch.nn.init.constant_, m2.bias, val=0.133 ) parallel_dim = getattr(m2.weight, ts._PARALLEL_DIM) if parallel_dim == None: master_weight = m2.weight.data master_bias = m2.bias.data elif parallel_dim == 0: master_weight = ts.distributed.gather(m2.weight.data, dim=1) master_bias = m2.bias.data elif parallel_dim == 1 or parallel_dim == -1: master_weight = ts.distributed.gather(m2.weight.data, dim=0) master_bias = ts.distributed.gather(m2.bias.data, dim=0) else: raise m1.weight.data.copy_(master_weight) m1.bias.data.copy_(master_bias) # forward model.train() y1, y2 = model(x) assertEqual(y1, y2, threshold=threshold) @unittest.skipIf(not torch.cuda.is_available(), 'CUDA is not available') def test_parallel_linear_stack(self): ngpus = torch.cuda.device_count() mp.spawn( dist_worker, args=(self.run_test_parallel_linear_stack, ngpus), nprocs=ngpus ) class TestParallelLinear(unittest.TestCase): @staticmethod def run_test_raw_parallel_linear(local_rank): set_seed(seed + local_rank) parallel_dim = None x = torch.randn(batch_size, feats_size).cuda(local_rank) dist.broadcast(x, 0) model = LinearModel(feats_size, feats_size*2, bias=True, dim=parallel_dim).cuda(local_rank) model = model.module if hasattr(model, "module") else model # align weight ts.nn.init.shard_init_helper_( torch.nn.init.kaiming_normal_, model.layer2.weight, ) model.layer1.weight.data.copy_(model.layer2.weight.data) # align bias ts.nn.init.shard_init_helper_( torch.nn.init.constant_, model.layer2.bias, val=0.1 ) model.layer1.bias.data.copy_(model.layer2.bias.data) # forward model.train() y1, y2 = model(x) assertEqual(y1, y2, threshold=threshold) @staticmethod def run_test_row_parallel_linear(local_rank): set_seed(seed + local_rank) parallel_dim = 0 x = torch.randn(batch_size, feats_size).cuda(local_rank) dist.broadcast(x, 0) model = LinearModel(feats_size, feats_size*2, bias=True, dim=parallel_dim).cuda(local_rank) # align weight ts.nn.init.shard_init_helper_( torch.nn.init.kaiming_normal_, model.layer2.weight, a=0, mode='fan_in', nonlinearity='leaky_relu' ) master_weight = ts.distributed.gather(model.layer2.weight.data, dim=-1) model.layer1.weight.data.copy_(master_weight) # align bias ts.nn.init.shard_init_helper_( torch.nn.init.constant_, model.layer2.bias, val=0.333 ) model.layer1.bias.data.copy_(model.layer2.bias.data) # forward model.train() y1, y2 = model(x) assertEqual(y1, y2, threshold=threshold) @staticmethod def run_test_col_parallel_linear(local_rank): set_seed(seed + local_rank) parallel_dim = -1 x = torch.randn(batch_size, feats_size).cuda(local_rank) dist.broadcast(x, 0) model = LinearModel(feats_size, feats_size*2, bias=True, dim=parallel_dim).cuda(local_rank) model = model.module if hasattr(model, "module") else model # align weight ts.nn.init.shard_init_helper_( torch.nn.init.kaiming_normal_, model.layer2.weight, a=0, mode='fan_in', nonlinearity='leaky_relu' ) master_weight = ts.distributed.gather(model.layer2.weight.data, dim=0) model.layer1.weight.data.copy_(master_weight) # align bias ts.nn.init.shard_init_helper_( torch.nn.init.constant_, model.layer2.bias, val=0.5 ) master_bias = ts.distributed.gather(model.layer2.bias.data, dim=0) model.layer1.bias.data.copy_(master_bias) # forward model.train() y1, y2 = model(x) y2 = ts.distributed.gather(y2) assertEqual(y1, y2, threshold=threshold) @unittest.skipIf(not torch.cuda.is_available(), 'CUDA is not available') def test_col_parallel_linear(self): ngpus = torch.cuda.device_count() mp.spawn( dist_worker, args=(self.run_test_col_parallel_linear, ngpus), nprocs=ngpus ) @unittest.skipIf(not torch.cuda.is_available(), 'CUDA is not available') def test_raw_parallel_linear(self): ngpus = torch.cuda.device_count() mp.spawn( dist_worker, args=(self.run_test_raw_parallel_linear, ngpus), nprocs=ngpus ) @unittest.skipIf(not torch.cuda.is_available(), 'CUDA is not available') def test_row_parallel_linear(self): ngpus = torch.cuda.device_count() mp.spawn( dist_worker, args=(self.run_test_row_parallel_linear, ngpus), nprocs=ngpus ) if __name__ == '__main__': torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False unittest.main()

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null

0

1

0

null

0

6

eca2972394b0f57d3cd9a6ab8060fbb3a83bdf0f

5,830

py

Python

maps.py

IlmastroStefanuzzo/py_cli_snake

c23ffc604f6d9dfbafd598ce6f12fba1d79fef21

[ "MIT" ]

1

2021-07-08T21:59:37.000Z

maps.py

IlmastroStefanuzzo/py_cli_snake

c23ffc604f6d9dfbafd598ce6f12fba1d79fef21

[ "MIT" ]

null

maps.py

IlmastroStefanuzzo/py_cli_snake

c23ffc604f6d9dfbafd598ce6f12fba1d79fef21

[ "MIT" ]

null

# README! # # You can add your own maps, just follow the format: # mapX = ("Name shown to the user", PUT THE THREE QUOTATION MARKS (""") HERE # MAP GOES HERE, you can use any characters, but you can only define the map's borders # PUT THREE QUOTATION MARKS (""") HERE # #################################### # # You MUST put the maps in this order: map1 map2 map3 map4 map5 map6 map7 etc. # Quotation marks like this # V map1 = ("Tall", """ ############################################################ # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # ############################################################ """) # <-- use the quotation marks like this map2 = ("Tall/thin", """ ################################### # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # ################################### """) # <-- use the quotation marks like this map3 = ("T h i c c square", """ @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ """) map4 = ("32x32 (because of characters' nature, which are taller than how large they are, this map will look rectangular", """ QQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQ Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q QQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQ """)

40.769231

125

0.100515

175

5,830

3.348571

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0.215017

0.317406

0.416382

0.204778

0.109215

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5,830

142

126

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1

0

1

0

1

0

1

0

null

0

6

eca36f427e7efeaee8af237fe78f2460dfaad9e1

175

py

Python

openslides/motions/exceptions.py

rolandgeider/OpenSlides

331141c17cb23da26e377d4285efdb4a50753a59

[ "MIT" ]

null

openslides/motions/exceptions.py

rolandgeider/OpenSlides

331141c17cb23da26e377d4285efdb4a50753a59

[ "MIT" ]

null

openslides/motions/exceptions.py

rolandgeider/OpenSlides

331141c17cb23da26e377d4285efdb4a50753a59

[ "MIT" ]

null

from openslides.utils.exceptions import OpenSlidesError class WorkflowError(OpenSlidesError): """Exception raised when errors in a workflow or state accure.""" pass

25

69

0.777143

20

175

6.8

0.95

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0.154286

175

6

70

29.166667

0.918919

0.337143

0

1

0

true

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

6

ecab6477f5c4879a394ee88389e6f54eb5099f04

422

py

Python

September 2020/07-Modules/04/mathematical_module/math_module.py

eclipse-ib/Software-University-Professional-Advanced-Module

636385f9e5521840f680644824d725d074b93c9a

[ "MIT" ]

null

September 2020/07-Modules/04/mathematical_module/math_module.py

eclipse-ib/Software-University-Professional-Advanced-Module

636385f9e5521840f680644824d725d074b93c9a

[ "MIT" ]

null

September 2020/07-Modules/04/mathematical_module/math_module.py

eclipse-ib/Software-University-Professional-Advanced-Module

636385f9e5521840f680644824d725d074b93c9a

[ "MIT" ]

null

import parser def exec(op, n1, n2): return op(n1, n2) # # Moe: # def mathematical_op(n1, sign, n2): # result = 0 # if sign == "/": # result = n1 / n2 # # elif sign == "*": # result = n1 * n2 # # elif sign == "-": # result = n1 - n2 # # elif sign == "+": # result = n1 + n2 # # elif sign == "^": # result = n1 ** n2 # # return f"{result:.2f}"

13.612903

36

0.417062

49

422

3.571429

0.346939

0.16

0.342857

0.4

0.491429

0

0.069767

0.388626

422

30

37

14.066667

0.608527

0.746446

0

1

0.333333

false

0

0.333333

1

0

null

0

1

0

null

0

1

0

1

0

6

ecb168a45086bd341b7fd10b8637a2c14462cea3

129

py

Python

nf_common_source/code/services/dataframe_service/dataframe_helpers/dataframe_split_constants.py

boro-alpha/nf_common

66d6844d9ae9a86a3e5b461f92e1ba0ec15e85ef

[ "MIT" ]

null

nf_common_source/code/services/dataframe_service/dataframe_helpers/dataframe_split_constants.py

boro-alpha/nf_common

66d6844d9ae9a86a3e5b461f92e1ba0ec15e85ef

[ "MIT" ]

null

nf_common_source/code/services/dataframe_service/dataframe_helpers/dataframe_split_constants.py

boro-alpha/nf_common

66d6844d9ae9a86a3e5b461f92e1ba0ec15e85ef

[ "MIT" ]

null

EQUAL_ROWS_DATAFRAME_NAME = \ 'dataframe_of_equal_rows' NON_EQUAL_ROWS_DATAFRAME_NAME = \ 'dataframe_of_non_equal_rows'

21.5

33

0.806202

18

129

5

0.333333

0.4

0.488889

0.733333

0

0.131783

129

5

34

25.8

0.803571

0

0.387597

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0

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0

null

1

0

1

0

1

0

null

0

6

ecbfdebf55eb3fa6ec715c1e978208dad94d160c

7,912

py

Python

cnn/dna_nn/dataset.py

solislemuslab/dna-nn-theory

9e996d5f453e1d620dadca0c276cb4a68e2b68e5

[ "MIT" ]

1

2021-06-02T22:27:46.000Z

cnn/dna_nn/dataset.py

solislemuslab/dna-nn-theory

9e996d5f453e1d620dadca0c276cb4a68e2b68e5

[ "MIT" ]

null

cnn/dna_nn/dataset.py

solislemuslab/dna-nn-theory

9e996d5f453e1d620dadca0c276cb4a68e2b68e5

[ "MIT" ]

1

2020-07-08T19:53:30.000Z

from itertools import product import numpy as np import pandas as pd from sklearn.model_selection import train_test_split import tensorflow as tf from tensorflow import keras from tensorflow.data import Dataset from dna_nn.load import encode, encoded_shape, gen_from_arrays, gen_from_fasta, read_fasta vocab = [' '.join(p) for p in product('ACGT', repeat=1)] vectorize_layer = keras.layers.experimental.preprocessing.TextVectorization( standardize=lambda x: tf.strings.regex_replace(x, '(.)', '\\1 '), ngrams=1 ) vectorize_layer.adapt(vocab) def vectorize_text(x, y): x_index = vectorize_layer(x) return x_index, y def splice(file, word_size=3, region_size=0, expand=True): d = {'EI': 0, 'IE': 1, 'N': 2} data = pd.read_csv(file, header=None, sep=',\\W*', engine='python', usecols=[0, 2]) data.columns = ['class', 'sequence'] for old, new in zip('NDSR', 'ATCG'): data['sequence'] = data['sequence'].str.replace(old, new) data['class'] = data['class'].map(lambda y: d[y]) encode_func = encode(word_size, region_size, expand=expand) x_shape = encoded_shape(data['sequence'][0], word_size, region_size, expand=expand) x, y = data['sequence'].to_numpy(), data['class'].to_numpy() x = np.array([encode_func(_) for _ in x]) x_train, x_test, y_train, y_test = train_test_split(x, y, stratify=y) return x_shape, x_train, x_test, y_train, y_test def h3(file, word_size=3, region_size=0, expand=True): sequences, labels = read_fasta(file) test_size = 0.15 val_size = 0.15 split_options = dict(test_size=test_size, stratify=labels, random_state=3264) x_train_val, x_test, y_train_val, y_test = train_test_split(sequences, labels, **split_options) # normalize val_size and update options split_options.update(dict(test_size=val_size/(1-test_size), stratify=y_train_val)) x_train, x_val, y_train, y_val = train_test_split(x_train_val, y_train_val, **split_options) del x_train_val, y_train_val encode_func = encode(word_size, region_size, expand=expand) x_shape = encoded_shape(sequences[0], word_size, region_size, expand=expand) train_gen = gen_from_arrays(x_train, y_train, encode_func) val_gen = gen_from_arrays(x_val, y_val, encode_func) test_gen = gen_from_arrays(x_test, y_test, encode_func) # datasets batch_size = 32 prefetch = tf.data.experimental.AUTOTUNE output_shapes = (x_shape, ()) output_types = (tf.float32, tf.float32) train_ds = Dataset.from_generator(train_gen, output_types, output_shapes) train_ds = train_ds.shuffle(500).batch(batch_size).prefetch(prefetch) test_ds = Dataset.from_generator(test_gen, output_types, output_shapes) test_ds = test_ds.batch(batch_size).prefetch(prefetch) x_val_encode, y_val_encode = [], [] for x, y in val_gen(): x_val_encode.append(x) y_val_encode.append(y) x_val_encode = np.array(x_val_encode) y_val_encode = np.array(y_val_encode) validation_data = (x_val_encode, y_val_encode) return x_shape, train_ds, validation_data, test_ds def motif_discovery(train_file, test_file, word_size=3, region_size=2, expand=True): subset_size = 690 * 190 x_shape = encoded_shape(range(101), word_size, region_size, expand=expand) encode_func = encode(word_size, region_size, expand=expand) train_gen = gen_from_fasta(train_file, encode_func) test_gen = gen_from_fasta(test_file, encode_func) # datasets bacth_size = 512 prefetch = tf.data.experimental.AUTOTUNE output_shapes = (x_shape, ()) output_types = (tf.float32, tf.float32) train_ds = Dataset.from_generator(train_gen, output_types, output_shapes) # takes about 30 seconds to skip the training data val_ds = train_ds.skip(subset_size).take(690 * 10) train_ds = train_ds.take(subset_size).shuffle(500).batch(bacth_size).prefetch(prefetch) test_ds = Dataset.from_generator(test_gen, output_types, output_shapes) test_ds = test_ds.take(subset_size).batch(bacth_size).prefetch(prefetch) x_val, y_val = [], [] for d in val_ds: x_val.append(d[0]) y_val.append(d[1]) x_val = tf.convert_to_tensor(x_val) y_val = tf.convert_to_tensor(y_val) validation_data = (x_val, y_val) return x_shape, train_ds, validation_data, test_ds def splice_raw(file): d = {'EI': 0, 'IE': 1, 'N': 2} data = pd.read_csv(file, header=None, sep=',\\W*', engine='python', usecols=[0, 2]) data.columns = ['class', 'sequence'] for old, new in zip('NDSR', 'ATCG'): data['sequence'] = data['sequence'].str.replace(old, new) data['class'] = data['class'].map(lambda y: d[y]) x_shape = len(data['sequence'][0]) x, y = data['sequence'].to_numpy(), data['class'].to_numpy() x, y = vectorize_text(x, y) x = x.numpy() x_train, x_test, y_train, y_test = train_test_split(x, y, stratify=y) return x_shape, x_train, x_test, y_train, y_test def h3_raw(file): sequences, labels = read_fasta(file) test_size = 0.15 val_size = 0.15 split_options = dict(test_size=test_size, stratify=labels, random_state=3264) x_train_val, x_test, y_train_val, y_test = train_test_split(sequences, labels, **split_options) # normalize val_size and update options split_options.update(dict(test_size=val_size/(1-test_size), stratify=y_train_val)) x_train, x_val, y_train, y_val = train_test_split(x_train_val, y_train_val, **split_options) del x_train_val, y_train_val x_shape = len(sequences[0]) train_gen = gen_from_arrays(x_train, y_train, None) val_gen = gen_from_arrays(x_val, y_val, None) test_gen = gen_from_arrays(x_test, y_test, None) # datasets batch_size = 32 prefetch = tf.data.experimental.AUTOTUNE output_shapes = ((), ()) output_types = (tf.string, tf.float32) train_ds = Dataset.from_generator(train_gen, output_types, output_shapes) train_ds = train_ds.shuffle(500).batch(batch_size).map(vectorize_text).prefetch(prefetch) val_ds = Dataset.from_generator(val_gen, output_types, output_shapes) val_ds = val_ds.map(vectorize_text).prefetch(prefetch) test_ds = Dataset.from_generator(test_gen, output_types, output_shapes) test_ds = test_ds.batch(batch_size).map(vectorize_text).prefetch(prefetch) x_val_encode, y_val_encode = [], [] for x, y in val_ds: x_val_encode.append(x) y_val_encode.append(y) x_val_encode = np.array(x_val_encode) y_val_encode = np.array(y_val_encode) validation_data = (x_val_encode, y_val_encode) return x_shape, train_ds, validation_data, test_ds def motif_discovery_raw(train_file, test_file): subset_size = 690 * 190 x_shape = len(range(101)) train_gen = gen_from_fasta(train_file, None) test_gen = gen_from_fasta(test_file, None) # datasets bacth_size = 512 prefetch = tf.data.experimental.AUTOTUNE output_shapes = ((), ()) output_types = (tf.string, tf.float32) train_ds = Dataset.from_generator(train_gen, output_types, output_shapes) # takes about 30 seconds to skip the training data val_ds = train_ds.skip(subset_size).take(690 * 10).map(vectorize_text) train_ds = train_ds.take(subset_size).shuffle(500).batch(bacth_size).map(vectorize_text).prefetch(prefetch) test_ds = Dataset.from_generator(test_gen, output_types, output_shapes) test_ds = test_ds.take(subset_size).batch(bacth_size).map(vectorize_text).prefetch(prefetch) x_val, y_val = [], [] for d in val_ds: x_val.append(d[0]) y_val.append(d[1]) x_val = tf.convert_to_tensor(x_val) y_val = tf.convert_to_tensor(y_val) validation_data = (x_val, y_val) return x_shape, train_ds, validation_data, test_ds

38.784314

111

0.697801

1,233

7,912

4.145174

0.117599

0.018783

0.021522

0.03874

0.852671

0.839366

0.816474

0.791235

0.778713

0.729994

0

0.018117

0.183771

7,912

204

112

38.784314

0.773304

0.026416

0

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0

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0

1

0.046667

false

0

0.053333

0

0.146667

0

null

0

1

0

null

0

6

ecd9b4c406020b1ced95ab768eb059219714b2cd

40

py

Python

tellurium/optimization/__init__.py

stanleygu/tellurium

bfa6898eb4b632b31c4d12c0b0c78ce704a1d898

[ "Apache-2.0" ]

null

tellurium/optimization/__init__.py

stanleygu/tellurium

bfa6898eb4b632b31c4d12c0b0c78ce704a1d898

[ "Apache-2.0" ]

null

tellurium/optimization/__init__.py

stanleygu/tellurium

bfa6898eb4b632b31c4d12c0b0c78ce704a1d898

[ "Apache-2.0" ]

null

from DiffEvolution import DiffEvolution

20

39

0.9

4

40

9

0.75

0

0.1

40

1

40

1

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

ece8e6053b1add7a33ef7953723ea3434ef154ae

3,602

py

Python

test_balanced_parentheses.py

rikhallar/AlgorithmsStudy

703ab2e4e120c9a56c26970807d61ce7976c6886

[ "MIT" ]

null

test_balanced_parentheses.py

rikhallar/AlgorithmsStudy

703ab2e4e120c9a56c26970807d61ce7976c6886

[ "MIT" ]

null

test_balanced_parentheses.py

rikhallar/AlgorithmsStudy

703ab2e4e120c9a56c26970807d61ce7976c6886

[ "MIT" ]

null

import balanced_parentheses import unittest class TestBalancedParentheses(unittest.TestCase): def test_returns_false_when_unbalanced_mixed_group_three_types(self): line = "[{{)(}}]" algorithm = balanced_parentheses.BalancedParentheses() result = algorithm.answer(line) self.assertEqual(False, result) def test_returns_false_when_unbalanced_mixed_group_two_types(self): line = "({)}" algorithm = balanced_parentheses.BalancedParentheses() result = algorithm.answer(line) self.assertEqual(False, result) def test_returns_true_when_balanced_mixed_group(self): line = "{()}[[{}]]" algorithm = balanced_parentheses.BalancedParentheses() result = algorithm.answer(line) self.assertEqual(True, result) def test_returns_true_on_balance_of_separate_pairs_of_braces(self): line = "{}{}" algorithm = balanced_parentheses.BalancedParentheses() result = algorithm.answer(line) self.assertEqual(True, result) def test_returns_true_on_balance_of_inclusive_braces(self): line = "{{}}" algorithm = balanced_parentheses.BalancedParentheses() result = algorithm.answer(line) self.assertEqual(True, result) def test_returns_true_on_balance_of_two_braces(self): line = "{}" algorithm = balanced_parentheses.BalancedParentheses() result = algorithm.answer(line) self.assertEqual(True, result) def test_returns_true_on_balance_of_separate_pairs_of_brackets(self): line = "[][]" algorithm = balanced_parentheses.BalancedParentheses() result = algorithm.answer(line) self.assertEqual(True, result) def test_returns_true_on_balance_of_inclusive_brackets(self): line = "[[]]" algorithm = balanced_parentheses.BalancedParentheses() result = algorithm.answer(line) self.assertEqual(True, result) def test_returns_true_on_balance_of_two_brackets(self): line = "[]" algorithm = balanced_parentheses.BalancedParentheses() result = algorithm.answer(line) self.assertEqual(True, result) def test_returns_true_on_balance_of_separate_pairs_of_parentheses(self): line = "()()" algorithm = balanced_parentheses.BalancedParentheses() result = algorithm.answer(line) self.assertEqual(True, result) def test_returns_true_on_balance_of_inclusive_parentheses(self): line = "(())" algorithm = balanced_parentheses.BalancedParentheses() result = algorithm.answer(line) self.assertEqual(True, result) def test_returns_false_when_parentheses_not_opened(self): line = ")()" algorithm = balanced_parentheses.BalancedParentheses() result = algorithm.answer(line) self.assertEqual(False, result) def test_returns_false_when_parentheses_not_closed(self): line = "(()" algorithm = balanced_parentheses.BalancedParentheses() result = algorithm.answer(line) self.assertEqual(False, result) def test_returns_true_on_balance_of_two_parentheses(self): line = "()" algorithm = balanced_parentheses.BalancedParentheses() result = algorithm.answer(line) self.assertEqual(True, result) def test_returns_true_on_void_string(self): line = "" algorithm = balanced_parentheses.BalancedParentheses() result = algorithm.answer(line) self.assertEqual(True, result) if __name__ == '__main__': unittest.main()

36.02

76

0.692115

362

3,602

6.522099

0.116022

0.128759

0.088945

0.158831

0.933503

0.891148

0.885218

0

0.213493

3,602

99

77

36.383838

0.833392

0

0.5625

0

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0

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1

0.1875

false

0

0.025

0

0.225

0

null

0

1

0

null

0

6

01f28c6fc266be29c3850ae3c0a6a3a13a019088

8,627

py

Python

tspec_cmd_impl/lmt_xml_config.py

jeremyko/let-me-test

c227d8522c25108eb0b21f8aa36798ac0611eaaf

[ "MIT" ]

null

tspec_cmd_impl/lmt_xml_config.py

jeremyko/let-me-test

c227d8522c25108eb0b21f8aa36798ac0611eaaf

[ "MIT" ]

null

tspec_cmd_impl/lmt_xml_config.py

jeremyko/let-me-test

c227d8522c25108eb0b21f8aa36798ac0611eaaf

[ "MIT" ]

null

#-*- coding: utf-8 -*- """ xml config handling """ #202007 kojh create try: import xml.etree.cElementTree as ET except ImportError: print ("ImportError") import xml.etree.ElementTree as ET import os from pexpect import pxssh from module_core import lmt_exception from module_core import lmt_util from tspec_cmd_impl import lmt_remote #/////////////////////////////////////////////////////////////////////////////// # auto backup --> when one tspec begins. # auto rollback --> when one tspec ends.. #/////////////////////////////////////////////////////////////////////////////// def set_xml_cfg_ems(runner_ctx, xpath, val): local_xml_path = "{}/{}".format(runner_ctx.temp_internal_use_only_dir_remote, os.path.basename(runner_ctx.ems_xml_cfg_path)) if(runner_ctx.ems_is_xml_config_changed == False): # set_xml_cfg 이 여러번 호출되는 경우 고려. # -> 최초로 set_xml_cfg 호출됬을때만 한번 backup 수행 runner_ctx.logger.debug("{}BACKUP ems xml cfg".format(runner_ctx.cur_indent)) #runner_ctx.backup_config() lmt_remote.backup_remote_file(runner_ctx, runner_ctx.ems_ip,runner_ctx.ems_id, runner_ctx.ems_passwd, runner_ctx.ems_xml_cfg_path) runner_ctx.ems_is_xml_config_changed = True runner_ctx.logger.info("{}ems config path = {}".format(runner_ctx.cur_indent, runner_ctx.ems_xml_cfg_path)) lmt_remote.get_remote_file(runner_ctx,runner_ctx.ems_ip,runner_ctx.ems_id,runner_ctx.ems_passwd, runner_ctx.ems_xml_cfg_path) set_xml_cfg_this_path(runner_ctx, local_xml_path, xpath, val) remote_path = os.path.dirname(runner_ctx.ems_xml_cfg_path) lmt_remote.put_remote_file(runner_ctx,runner_ctx.ems_ip,runner_ctx.ems_id,runner_ctx.ems_passwd, remote_path, runner_ctx.temp_internal_use_only_dir_remote, os.path.basename(local_xml_path), os.path.basename(local_xml_path)) #/////////////////////////////////////////////////////////////////////////////// def set_xml_cfg(runner_ctx, xpath, val): if(runner_ctx.is_xml_config_changed == False): # set_xml_cfg 이 여러번 호출되는 경우 고려. # -> 최초로 set_xml_cfg 호출됬을때만 한번 backup 수행 runner_ctx.logger.debug("{}BACKUP xml cfg".format(runner_ctx.cur_indent)) runner_ctx.backup_config() runner_ctx.is_xml_config_changed = True set_xml_cfg_this_path(runner_ctx, runner_ctx.xml_cfg_path, xpath, val) #/////////////////////////////////////////////////////////////////////////////// def set_xml_cfg_this_path(runner_ctx, file_path, xpath, val): xpath = lmt_util.replace_all_symbols(runner_ctx,xpath) runner_ctx.logger.debug("{}xml_cfg_path = {}".format(runner_ctx.cur_indent,file_path)) runner_ctx.logger.debug("{}xpath = {}".format(runner_ctx.cur_indent,xpath)) try: #------------------------------ doc = ET.parse(file_path) #------------------------------ #doc = ET.parse("error.xml") except Exception as e: err_msg = 'xml parse failed {} :{}'.format(e.__doc__, e.message) runner_ctx.logger.error("{}{}".format(runner_ctx.cur_indent,err_msg)) raise lmt_exception.LmtException(err_msg) if(doc is None): runner_ctx.logger.error("{}parse failed ".format(runner_ctx.cur_indent)) return False try: xml_root = doc.getroot() if(xml_root is None): err_msg = "xml getroot failed" runner_ctx.logger.error(err_msg) raise lmt_exception.LmtException(err_msg) except Exception as e: err_msg = 'xml getroot failed : {} :{}'.format(e.__doc__, e.message) runner_ctx.logger.error("{}{}".format(runner_ctx.cur_indent,err_msg)) raise lmt_exception.LmtException(err_msg) tmp_xpath = './' + xpath # root + xpath #tmp_xpath = './/' + 'DB_CONNECT_INFO/USER_ID' # OK #tmp_xpath = './' + 'COMMON/DB_CONNECT_INFO/USER_ID' # OK try: xml_nodes = xml_root.findall(tmp_xpath) if xml_nodes is None: err_msg = "findall failed = {}".format(tmp_xpath) runner_ctx.logger.error("{}{}".format(runner_ctx.cur_indent,tmp_xpath)) raise lmt_exception.LmtException(err_msg) if len(xml_nodes) == 0: err_msg = "invalid xpath = {}".format(tmp_xpath) runner_ctx.logger.error("{}{}".format(runner_ctx.cur_indent,err_msg)) raise lmt_exception.LmtException(err_msg) #print xml_nodes config_val = xml_nodes[0].text runner_ctx.logger.debug("{}old value = {}".format(runner_ctx.cur_indent,config_val)) runner_ctx.logger.debug("{}new value = {}".format(runner_ctx.cur_indent,val)) #------------------------ # XXX change value XXX xml_nodes[0].text = val #------------------------ #last_updated = ET.SubElement(xml_nodes[0], "test_new") #last_updated.text = 'TEST' #write file #doc.write(runner_ctx.xml_cfg_path, encoding="utf-8", xml_declaration=True) doc.write(file_path) except Exception as e: err_msg = 'error : {} :{}'.format(e.__doc__, e.message) runner_ctx.logger.error("{}{}".format(runner_ctx.cur_indent,err_msg)) raise except (SyntaxError, AttributeError): err_msg = 'Syntax or Attribute error ' runner_ctx.logger.error("{}{}".format(runner_ctx.cur_indent,err_msg)) raise return True #/////////////////////////////////////////////////////////////////////////////// def get_xml_cfg_ems(runner_ctx, xpath): xml_path = "{}/{}".format(runner_ctx.temp_internal_use_only_dir_remote, os.path.basename(runner_ctx.xml_cfg_path)) lmt_remote.get_remote_file(runner_ctx,runner_ctx.ems_ip,runner_ctx.ems_id,runner_ctx.ems_passwd, runner_ctx.ems_xml_cfg_path) out = get_xml_cfg_this_path(runner_ctx, xml_path, xpath) return out #/////////////////////////////////////////////////////////////////////////////// def get_xml_cfg(runner_ctx, xpath): out = get_xml_cfg_this_path(runner_ctx, runner_ctx.xml_cfg_path, xpath) return out #/////////////////////////////////////////////////////////////////////////////// def get_xml_cfg_this_path(runner_ctx, file_path, xpath): xpath = lmt_util.replace_all_symbols(runner_ctx,xpath) runner_ctx.logger.debug("{}xml_cfg_path = {}".format(runner_ctx.cur_indent,file_path)) runner_ctx.logger.debug("{}xpath = {}".format(runner_ctx.cur_indent,xpath)) try: #------------------------------ doc = ET.parse(file_path) #------------------------------ except Exception as e: err_msg = 'xml parse failed :{} -> {} :{}'.format(file_path, e.__doc__, e.message) runner_ctx.logger.error("{}{}".format(runner_ctx.cur_indent,err_msg)) raise lmt_exception.LmtException(err_msg) if(doc is None): runner_ctx.logger.error("parse failed ") return None try: xml_root = doc.getroot() if(xml_root is None): err_msg = "xml getroot failed" runner_ctx.logger.error(err_msg) raise lmt_exception.LmtException(err_msg) except Exception as e: err_msg = 'xml getroot failed : {} :{}'.format(e.__doc__, e.message) runner_ctx.logger.error("{}{}".format(runner_ctx.cur_indent,err_msg)) raise lmt_exception.LmtException(err_msg) tmp_xpath = './' + xpath # root + xpath #tmp_xpath = './/' + 'DB_CONNECT_INFO/USER_ID' # OK #tmp_xpath = './' + 'COMMON/DB_CONNECT_INFO/USER_ID' # OK try: xml_nodes = xml_root.findall(tmp_xpath) if xml_nodes is None: err_msg = "findall failed = {}".format(tmp_xpath) runner_ctx.logger.error("{}{}".format(runner_ctx.cur_indent,tmp_xpath)) return None # no error ! this is just get function #raise lmt_exception.LmtException(err_msg) if len(xml_nodes) == 0: err_msg = "invalid xpath = [{}] get just failed.".format(tmp_xpath) runner_ctx.logger.warning("{}{}".format(runner_ctx.cur_indent,err_msg)) return None # no error ! this is just get function #raise lmt_exception.LmtException(err_msg) config_val = xml_nodes[0].text runner_ctx.logger.info("{}{}={}".format(runner_ctx.cur_indent,xpath, config_val)) return config_val except Exception as e: err_msg = 'error : {} :{}'.format(e.__doc__, e.message) runner_ctx.logger.error("{}".format(err_msg)) raise except (SyntaxError, AttributeError): err_msg = 'Syntax or Attribute error ' runner_ctx.logger.error("{}".format(err_msg)) raise return None

42.497537

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1,128

8,627

4.351064

0.118794

0.172372

0.079462

0.077017

0.852078

0.825387

0.779747

0.746536

0.717604

0.687857

0

0.002004

0.190101

8,627

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42.707921

0.700444

0.191608

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0

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false

0.030769

0.069231

0

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0.007692

0

null

0

1

0

null

0

6

171a74a0ae2ed1b2c6a7c96ffb7835f7de0c7190

110

py

Python

gluoncvth/models/__init__.py

cclauss/gluoncv-torch

937b40d8ea297f52f4b65e0ac3a6922768d788a9

[ "MIT" ]

495

2018-10-12T23:23:28.000Z

2020-05-06T05:48:57.000Z

gluoncvth/models/__init__.py

cclauss/gluoncv-torch

937b40d8ea297f52f4b65e0ac3a6922768d788a9

[ "MIT" ]

19

2018-10-15T17:37:51.000Z

2019-10-14T10:57:31.000Z

gluoncvth/models/__init__.py

cclauss/gluoncv-torch

937b40d8ea297f52f4b65e0ac3a6922768d788a9

[ "MIT" ]

47

2018-10-14T13:01:56.000Z

2020-05-03T15:22:15.000Z

from . import model_zoo from .resnet import * from .fcn import * from .pspnet import * from .deeplab import *

18.333333

23

0.736364

16

110

5

0.5

0.375

0

0.181818

110

5

24

22

0.888889

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

17340576521957378be05ae8f6f2195901a5b466

11,761

py

Python

UnityEngine/Canvas/__init__.py

Grim-es/udon-pie-auto-completion

c2cd86554ed615cdbbb01e19fa40665eafdfaedc

[ "MIT" ]

null

UnityEngine/Canvas/__init__.py

Grim-es/udon-pie-auto-completion

c2cd86554ed615cdbbb01e19fa40665eafdfaedc

[ "MIT" ]

null

UnityEngine/Canvas/__init__.py

Grim-es/udon-pie-auto-completion

c2cd86554ed615cdbbb01e19fa40665eafdfaedc

[ "MIT" ]

null

from typing import overload from UdonPie import System from UdonPie import UnityEngine from UdonPie.Undefined import * class Canvas: def __new__(cls, arg1=None): ''' :returns: Canvas :rtype: UnityEngine.Canvas ''' pass @staticmethod def op_Implicit(arg1): ''' :param arg1: Object :type arg1: UnityEngine.Object :returns: Boolean :rtype: System.Boolean ''' pass @staticmethod def op_Equality(arg1, arg2): ''' :param arg1: Object :type arg1: UnityEngine.Object :param arg2: Object :type arg2: UnityEngine.Object :returns: Boolean :rtype: System.Boolean ''' pass @staticmethod def op_Inequality(arg1, arg2): ''' :param arg1: Object :type arg1: UnityEngine.Object :param arg2: Object :type arg2: UnityEngine.Object :returns: Boolean :rtype: System.Boolean ''' pass @staticmethod def add_willRenderCanvases(arg1): ''' :param arg1: WillRenderCanvases :type arg1: UnityEngine.WillRenderCanvases ''' pass @staticmethod def remove_willRenderCanvases(arg1): ''' :param arg1: WillRenderCanvases :type arg1: UnityEngine.WillRenderCanvases ''' pass @staticmethod def get_isRootCanvas(): ''' :returns: Boolean :rtype: System.Boolean ''' pass @staticmethod def get_pixelRect(): ''' :returns: Rect :rtype: UnityEngine.Rect ''' pass @staticmethod def get_scaleFactor(): ''' :returns: Single :rtype: System.Single ''' pass @staticmethod def set_scaleFactor(arg1): ''' :param arg1: Single :type arg1: System.Single or float ''' pass @staticmethod def get_referencePixelsPerUnit(): ''' :returns: Single :rtype: System.Single ''' pass @staticmethod def set_referencePixelsPerUnit(arg1): ''' :param arg1: Single :type arg1: System.Single or float ''' pass @staticmethod def get_overridePixelPerfect(): ''' :returns: Boolean :rtype: System.Boolean ''' pass @staticmethod def set_overridePixelPerfect(arg1): ''' :param arg1: Boolean :type arg1: System.Boolean or bool ''' pass @staticmethod def get_pixelPerfect(): ''' :returns: Boolean :rtype: System.Boolean ''' pass @staticmethod def set_pixelPerfect(arg1): ''' :param arg1: Boolean :type arg1: System.Boolean or bool ''' pass @staticmethod def get_planeDistance(): ''' :returns: Single :rtype: System.Single ''' pass @staticmethod def set_planeDistance(arg1): ''' :param arg1: Single :type arg1: System.Single or float ''' pass @staticmethod def get_renderOrder(): ''' :returns: Int32 :rtype: System.Int32 ''' pass @staticmethod def get_overrideSorting(): ''' :returns: Boolean :rtype: System.Boolean ''' pass @staticmethod def set_overrideSorting(arg1): ''' :param arg1: Boolean :type arg1: System.Boolean or bool ''' pass @staticmethod def get_sortingOrder(): ''' :returns: Int32 :rtype: System.Int32 ''' pass @staticmethod def set_sortingOrder(arg1): ''' :param arg1: Int32 :type arg1: System.Int32 or int ''' pass @staticmethod def get_sortingLayerID(): ''' :returns: Int32 :rtype: System.Int32 ''' pass @staticmethod def set_sortingLayerID(arg1): ''' :param arg1: Int32 :type arg1: System.Int32 or int ''' pass @staticmethod def get_cachedSortingLayerValue(): ''' :returns: Int32 :rtype: System.Int32 ''' pass @staticmethod def get_additionalShaderChannels(): ''' :returns: AdditionalCanvasShaderChannels :rtype: UnityEngine.AdditionalCanvasShaderChannels ''' pass @staticmethod def set_additionalShaderChannels(arg1): ''' :param arg1: AdditionalCanvasShaderChannels :type arg1: UnityEngine.AdditionalCanvasShaderChannels ''' pass @staticmethod def get_sortingLayerName(): ''' :returns: String :rtype: System.String ''' pass @staticmethod def set_sortingLayerName(arg1): ''' :param arg1: String :type arg1: System.String or str ''' pass @staticmethod def get_rootCanvas(): ''' :returns: Canvas :rtype: UnityEngine.Canvas ''' pass @staticmethod def get_normalizedSortingGridSize(): ''' :returns: Single :rtype: System.Single ''' pass @staticmethod def set_normalizedSortingGridSize(arg1): ''' :param arg1: Single :type arg1: System.Single or float ''' pass @staticmethod def GetDefaultCanvasMaterial(): ''' :returns: Material :rtype: UnityEngine.Material ''' pass @staticmethod def GetETC1SupportedCanvasMaterial(): ''' :returns: Material :rtype: UnityEngine.Material ''' pass @staticmethod def ForceUpdateCanvases(): pass @staticmethod def get_enabled(): ''' :returns: Boolean :rtype: System.Boolean ''' pass @staticmethod def set_enabled(arg1): ''' :param arg1: Boolean :type arg1: System.Boolean or bool ''' pass @staticmethod def get_transform(): ''' :returns: Transform :rtype: UnityEngine.Transform ''' pass @staticmethod def get_gameObject(): ''' :returns: GameObject :rtype: UnityEngine.GameObject ''' pass @staticmethod @overload def GetComponent(arg1): ''' :param arg1: Type :type arg1: System.Type :returns: Component :rtype: UnityEngine.Component ''' pass @staticmethod @overload def GetComponent(arg1): ''' :param arg1: String :type arg1: System.String or str :returns: Component :rtype: UnityEngine.Component ''' pass @staticmethod def GetComponent(arg1=None): pass @staticmethod @overload def GetComponentInChildren(arg1, arg2): ''' :param arg1: Type :type arg1: System.Type :param arg2: Boolean :type arg2: System.Boolean or bool :returns: Component :rtype: UnityEngine.Component ''' pass @staticmethod @overload def GetComponentInChildren(arg1): ''' :param arg1: Type :type arg1: System.Type :returns: Component :rtype: UnityEngine.Component ''' pass @staticmethod def GetComponentInChildren(arg1=None, arg2=None): pass @staticmethod @overload def GetComponentsInChildren(arg1, arg2): ''' :param arg1: Type :type arg1: System.Type :param arg2: Boolean :type arg2: System.Boolean or bool :returns: ComponentArray :rtype: UnityEngine.ComponentArray ''' pass @staticmethod @overload def GetComponentsInChildren(arg1): ''' :param arg1: Type :type arg1: System.Type :returns: ComponentArray :rtype: UnityEngine.ComponentArray ''' pass @staticmethod @overload def GetComponentsInChildren(arg1, arg2): ''' :param arg1: Boolean :type arg1: System.Boolean or bool :param arg2: Undefined variable :type arg2: ListT.ListT ''' pass @staticmethod @overload def GetComponentsInChildren(arg1): ''' :param arg1: Undefined variable :type arg1: ListT.ListT ''' pass @staticmethod def GetComponentsInChildren(arg1=None, arg2=None): pass @staticmethod @overload def GetComponentInParent(arg1): ''' :param arg1: Type :type arg1: System.Type :returns: Component :rtype: UnityEngine.Component ''' pass @staticmethod def GetComponentInParent(arg1=None): pass @staticmethod @overload def GetComponentsInParent(arg1, arg2): ''' :param arg1: Type :type arg1: System.Type :param arg2: Boolean :type arg2: System.Boolean or bool :returns: ComponentArray :rtype: UnityEngine.ComponentArray ''' pass @staticmethod @overload def GetComponentsInParent(arg1): ''' :param arg1: Type :type arg1: System.Type :returns: ComponentArray :rtype: UnityEngine.ComponentArray ''' pass @staticmethod @overload def GetComponentsInParent(arg1, arg2): ''' :param arg1: Boolean :type arg1: System.Boolean or bool :param arg2: Undefined variable :type arg2: ListT.ListT ''' pass @staticmethod def GetComponentsInParent(arg1=None, arg2=None): pass @staticmethod @overload def GetComponents(arg1): ''' :param arg1: Type :type arg1: System.Type :returns: ComponentArray :rtype: UnityEngine.ComponentArray ''' pass @staticmethod @overload def GetComponents(arg1, arg2): ''' :param arg1: Type :type arg1: System.Type :param arg2: Undefined variable :type arg2: SystemCollectionsGenericList.SystemCollectionsGenericList ''' pass @staticmethod @overload def GetComponents(arg1): ''' :param arg1: Undefined variable :type arg1: ListT.ListT ''' pass @staticmethod def GetComponents(arg1=None, arg2=None): pass @staticmethod def GetInstanceID(): ''' :returns: Int32 :rtype: System.Int32 ''' pass @staticmethod def GetHashCode(): ''' :returns: Int32 :rtype: System.Int32 ''' pass @staticmethod def Equals(arg1): ''' :param arg1: Object :type arg1: System.Object :returns: Boolean :rtype: System.Boolean ''' pass @staticmethod def get_name(): ''' :returns: String :rtype: System.String ''' pass @staticmethod def set_name(arg1): ''' :param arg1: String :type arg1: System.String or str ''' pass @staticmethod def ToString(): ''' :returns: String :rtype: System.String ''' pass @staticmethod def GetType(): ''' :returns: Type :rtype: System.Type ''' pass

20.207904

77

0.531672

947

11,761

6.558606

0.087645

0.172597

0.159073

0.070842

0.795041

0.775237

0.744969

0.741266

0.633875

0.470939

0

0.023139

0.375308

11,761

581

78

20.242685

0.82224

0.352606

0

0.717489

0

1

0.304933

false

0.304933

0.017937

0

0.327354

0

null

0

1

0

1

0

null

0

1

0

1

0

6

17466a1af3ff665782a6663acc291ee96d5bc0ec

243

py

Python

src/control/__init__.py

alfredo-milani/ParseScript

58847537b53bfb7b88710761963dc94b06041195

[ "MIT" ]

null

src/control/__init__.py

alfredo-milani/ParseScript

58847537b53bfb7b88710761963dc94b06041195

[ "MIT" ]

null

src/control/__init__.py

alfredo-milani/ParseScript

58847537b53bfb7b88710761963dc94b06041195

[ "MIT" ]

null

# DataRetrievalController must be the first import from DataRetrievalController import DataRetrievalController from DataRetrievalCLIController import DataRetrievalCLIController from DataRetrievalGUIController import DataRetrievalGUIController

48.6

65

0.917695

18

243

12.388889

0.5

0

0.078189

243

4

66

60.75

0.995536

0.197531

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

1748ef075259057561d15b693fbe73121deb991d

3,768

py

Python

dizoo/minigrid/envs/test_minigrid_env.py

LuciusMos/DI-engine

b040b1c36afce038effec9eb483f625131573824

[ "Apache-2.0" ]

464

2021-07-08T07:26:33.000Z

2022-03-31T12:35:16.000Z

dizoo/minigrid/envs/test_minigrid_env.py

LuciusMos/DI-engine

b040b1c36afce038effec9eb483f625131573824

[ "Apache-2.0" ]

177

2021-07-09T08:22:55.000Z

2022-03-31T07:35:22.000Z

dizoo/minigrid/envs/test_minigrid_env.py

LuciusMos/DI-engine

b040b1c36afce038effec9eb483f625131573824

[ "Apache-2.0" ]

92

2021-07-08T12:16:37.000Z

2022-03-31T09:24:41.000Z

import pytest import os import numpy as np from dizoo.minigrid.envs import MiniGridEnv from easydict import EasyDict import copy # The following two cfg can be tested through TestMiniGridAKTDTnv config = dict( env_id='MiniGrid-AKTDT-13x13-v0', flat_obs=True, ) cfg = EasyDict(copy.deepcopy(config)) cfg.cfg_type = 'MiniGridEnvDict' config2 = dict( env_id='MiniGrid-AKTDT-7x7-1-v0', flat_obs=True, ) cfg2 = EasyDict(copy.deepcopy(config2)) cfg2.cfg_type = 'MiniGridEnvDict' @pytest.mark.envtest class TestMiniGridEnv: def test_naive(self): env = MiniGridEnv(MiniGridEnv.default_config()) env.seed(314) path = './video' if not os.path.exists(path): os.mkdir(path) env.enable_save_replay(path) assert env._seed == 314 obs = env.reset() act_val = env.info().act_space.value min_val, max_val = act_val['min'], act_val['max'] for i in range(env._max_step): random_action = np.random.randint(min_val, max_val, size=(1, )) timestep = env.step(random_action) print(timestep) print(timestep.obs.max()) assert isinstance(timestep.obs, np.ndarray) assert isinstance(timestep.done, bool) assert timestep.obs.shape == (2739, ) assert timestep.reward.shape == (1, ) assert timestep.reward >= env.info().rew_space.value['min'] assert timestep.reward <= env.info().rew_space.value['max'] if timestep.done: env.reset() print(env.info()) env.close() @pytest.mark.envtest class TestMiniGridAKTDTnv: def test_adtkt_13(self): env = MiniGridEnv(cfg2) env.seed(314) path = './video' if not os.path.exists(path): os.mkdir(path) env.enable_save_replay(path) assert env._seed == 314 obs = env.reset() act_val = env.info().act_space.value min_val, max_val = act_val['min'], act_val['max'] for i in range(env._max_step): random_action = np.random.randint(min_val, max_val, size=(1, )) timestep = env.step(random_action) print(timestep) print(timestep.obs.max()) assert isinstance(timestep.obs, np.ndarray) assert isinstance(timestep.done, bool) assert timestep.obs.shape == (2667, ) assert timestep.reward.shape == (1, ) assert timestep.reward >= env.info().rew_space.value['min'] assert timestep.reward <= env.info().rew_space.value['max'] if timestep.done: env.reset() print(env.info()) env.close() def test_adtkt_7(self): env = MiniGridEnv(cfg2) env.seed(314) path = './video' if not os.path.exists(path): os.mkdir(path) env.enable_save_replay(path) assert env._seed == 314 obs = env.reset() act_val = env.info().act_space.value min_val, max_val = act_val['min'], act_val['max'] for i in range(env._max_step): random_action = np.random.randint(min_val, max_val, size=(1, )) timestep = env.step(random_action) print(timestep) print(timestep.obs.max()) assert isinstance(timestep.obs, np.ndarray) assert isinstance(timestep.done, bool) assert timestep.obs.shape == (2619, ) assert timestep.reward.shape == (1, ) assert timestep.reward >= env.info().rew_space.value['min'] assert timestep.reward <= env.info().rew_space.value['max'] if timestep.done: env.reset() print(env.info()) env.close()

34.254545

75

0.589172

468

3,768

4.608974

0.198718

0.038943

0.083449

0.03338

0.770978

0.75058

0

0.020097

0.28689

3,768

109

76

34.568807

0.782657

0.01672

0

0.75

0

0.035917

0.012422

0

0.21

1

0.03

false

0

0.06

0

0.11

0.09

0

null

0

1

0

1

0

null

0

6

1761238101f1087ea5f33452535edd0dcf2f12c9

52

py

Python

app/app_cli.py

Cyber-Mint/py_app_package

a582de288479559780ef82f8f2c25d73272e8d5e

[ "MIT" ]

null

app/app_cli.py

Cyber-Mint/py_app_package

a582de288479559780ef82f8f2c25d73272e8d5e

[ "MIT" ]

null

app/app_cli.py

Cyber-Mint/py_app_package

a582de288479559780ef82f8f2c25d73272e8d5e

[ "MIT" ]

null

import app def main(): print(app.say_hello())

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178a627285a70dde0cb98a7771b27a35cc8995b9

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py

Python

minitf/vjps/__init__.py

guocuimi/minitf

f272a6b1546b82aaec41ec7d2c2d34fa40a40385

[ "MIT" ]

7

2020-02-10T08:16:30.000Z

2021-01-31T14:08:02.000Z

minitf/vjps/__init__.py

guocuimi/minitf

f272a6b1546b82aaec41ec7d2c2d34fa40a40385

[ "MIT" ]

1

2020-02-29T01:57:54.000Z

minitf/vjps/__init__.py

guocuimi/minitf

f272a6b1546b82aaec41ec7d2c2d34fa40a40385

[ "MIT" ]

null

import minitf.vjps.primitive_vjps

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179c6369fdcb2244ac7a1bb6a34b94ab2b0bdc42

199

py

Python

aser/server/cli.py

HKUST-KnowComp/CSKB-Population

7b1b2d25fbd0095b0cf009b933cfd5a62feadd58

[ "MIT" ]

13

2021-09-10T03:41:02.000Z

2022-03-30T09:53:12.000Z

aser/server/cli.py

HKUST-KnowComp/CSKB-Population

7b1b2d25fbd0095b0cf009b933cfd5a62feadd58

[ "MIT" ]

1

2022-02-09T23:08:33.000Z

2022-03-22T22:28:37.000Z

aser/server/cli.py

HKUST-KnowComp/CSKB-Population

7b1b2d25fbd0095b0cf009b933cfd5a62feadd58

[ "MIT" ]

2

2021-10-12T13:15:35.000Z

2021-11-17T08:46:46.000Z

def main(): from aser.server import ASERServer from aser.utils.config import get_server_args_parser parser = get_server_args_parser() args = parser.parse_args() ASERServer(args)

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bd6d661b3d1ff2b3691f9758688d58eb076d1aae

143

py

Python

day11/test_lib.py

heijp06/AoC-2016

684e483e2dfddd4de592f13d1e843d031060ef26

[ "MIT" ]

null

day11/test_lib.py

heijp06/AoC-2016

684e483e2dfddd4de592f13d1e843d031060ef26

[ "MIT" ]

null

day11/test_lib.py

heijp06/AoC-2016

684e483e2dfddd4de592f13d1e843d031060ef26

[ "MIT" ]

null

from data_for_testing import data from lib import part1, part2 def test_part1(): assert part1(data) == 11 def test_part2(): pass

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6

bd889fed7cd54768154a3afda21818eeb1287dcb

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py

Python

tests/test_dataflow/test_dataset/test_pattern.py

alexandreMayerowitz/playground-plums

a6be79e4c30c7abcbade5581f052a4e8035a2057

[ "MIT" ]

null

tests/test_dataflow/test_dataset/test_pattern.py

alexandreMayerowitz/playground-plums

a6be79e4c30c7abcbade5581f052a4e8035a2057

[ "MIT" ]

null

tests/test_dataflow/test_dataset/test_pattern.py

alexandreMayerowitz/playground-plums

a6be79e4c30c7abcbade5581f052a4e8035a2057

[ "MIT" ]

2

2021-02-03T12:37:53.000Z

2022-03-09T03:48:12.000Z

import pathlib import pytest import numpy as np from plums.commons.data import TileWrapper, Record, RecordCollection, DataPoint from plums.dataflow.dataset import PatternDataset def _dummy_tile_driver(paths, **matches): paths = sorted(paths, key=str, reverse=True) print(paths) print(matches) return TileWrapper(np.zeros((12, 12, 3)), filename=paths[0], **matches) def _invalid_return_tile_driver(paths, **matches): print(paths) print(matches) return np.zeros((12, 12, 3)) def _invalid_paths_signature_tile_driver(*paths, **matches): print(paths) print(matches) return TileWrapper(np.zeros((12, 12, 3)), filename=paths[0], **matches) def _invalid_matches_signature_tile_driver(*paths, matches=None): print(paths) print(matches) return TileWrapper(np.zeros((12, 12, 3)), filename=paths[0], **matches) def _invalid_extra_signature_tile_driver(*paths, degenerate=False, **matches): print(paths) print(degenerate) print(matches) return TileWrapper(np.zeros((12, 12, 3)), filename=paths[0], **matches) def _dummy_annotation_driver(paths, **matches): print(paths) print(matches) record = Record([[[0, 0], [0, 1], [1, 1], [1, 0], [0, 0]]], ('label', ), paths=paths, **matches) return RecordCollection(record) def _invalid_return_annotation_driver(paths, **matches): print(paths) print(matches) return matches def _invalid_paths_signature_annotation_driver(*paths, **matches): print(paths) print(matches) record = Record([[[0, 0], [0, 1], [1, 1], [1, 0], [0, 0]]], ('label', ), paths=paths, **matches) return RecordCollection(record) def _invalid_matches_signature_annotation_driver(*paths, matches=None): print(paths) print(matches) record = Record([[[0, 0], [0, 1], [1, 1], [1, 0], [0, 0]]], ('label', ), paths=paths, **matches) return RecordCollection(record) def _invalid_extra_signature_annotation_driver(*paths, degenerate=False, **matches): print(paths) print(degenerate) print(matches) record = Record([[[0, 0], [0, 1], [1, 1], [1, 0], [0, 0]]], ('label', ), paths=paths, **matches) return RecordCollection(record) class TestSignature: def test_type_tile_signature(self): with pytest.raises(TypeError, match='Invalid Tile driver: Expected a callable'): _ = PatternDataset('data/images/{dataset}/{aoi}/{type}/{tile}.jpg', 'data/labels/{dataset}/{aoi}/{type}/{tile}.json', None, _dummy_annotation_driver) def test_invalid_paths_tile_signature(self): with pytest.raises(TypeError, match='Invalid Tile driver: Expected function'): _ = PatternDataset('data/images/{dataset}/{aoi}/{type}/{tile}.jpg', 'data/labels/{dataset}/{aoi}/{type}/{tile}.json', _invalid_paths_signature_tile_driver, _dummy_annotation_driver) def test_invalid_matches_tile_signature(self): with pytest.raises(TypeError, match='Invalid Tile driver: Expected function'): _ = PatternDataset('data/images/{dataset}/{aoi}/{type}/{tile}.jpg', 'data/labels/{dataset}/{aoi}/{type}/{tile}.json', _invalid_matches_signature_tile_driver, _dummy_annotation_driver) def test_invalid_extra_tile_signature(self): with pytest.raises(TypeError, match='Invalid Tile driver: Expected function'): _ = PatternDataset('data/images/{dataset}/{aoi}/{type}/{tile}.jpg', 'data/labels/{dataset}/{aoi}/{type}/{tile}.json', _invalid_extra_signature_tile_driver, _dummy_annotation_driver) def test_type_annotation_signature(self): with pytest.raises(TypeError, match='Invalid Annotation driver: Expected a callable'): _ = PatternDataset('data/images/{dataset}/{aoi}/{type}/{tile}.jpg', 'data/labels/{dataset}/{aoi}/{type}/{tile}.json', _dummy_tile_driver, None) def test_invalid_paths_annotation_signature(self): with pytest.raises(TypeError, match='Invalid Annotation driver: Expected function'): _ = PatternDataset('data/images/{dataset}/{aoi}/{type}/{tile}.jpg', 'data/labels/{dataset}/{aoi}/{type}/{tile}.json', _dummy_tile_driver, _invalid_paths_signature_annotation_driver) def test_invalid_matches_annotation_signature(self): with pytest.raises(TypeError, match='Invalid Annotation driver: Expected function'): _ = PatternDataset('data/images/{dataset}/{aoi}/{type}/{tile}.jpg', 'data/labels/{dataset}/{aoi}/{type}/{tile}.json', _dummy_tile_driver, _invalid_matches_signature_annotation_driver) def test_invalid_extra_annotation_signature(self): with pytest.raises(TypeError, match='Invalid Annotation driver: Expected function'): _ = PatternDataset('data/images/{dataset}/{aoi}/{type}/{tile}.jpg', 'data/labels/{dataset}/{aoi}/{type}/{tile}.json', _dummy_tile_driver, _invalid_extra_signature_annotation_driver) class TestPairMatch: def test_strict(self, strict_pattern_tree): root, path_list = strict_pattern_tree dataset = PatternDataset('data/images/{dataset}/{aoi}/{type}/{tile}.jpg', 'data/labels/{dataset}/{aoi}/{type}/{tile}.json', _dummy_tile_driver, _dummy_annotation_driver, path=str(root)) assert len(dataset) == 8 assert dataset._matching_groups == ('dataset', 'aoi', 'type', 'tile') assert set(dataset._group_index) == {('dataset_1', 'aoi_0', 'simulated', 'tile_00'), ('dataset_1', 'aoi_0', 'simulated', 'tile_01'), ('dataset_1', 'aoi_0', 'labeled', 'tile_00'), ('dataset_1', 'aoi_0', 'labeled', 'tile_01'), ('dataset_1', 'aoi_3', 'simulated', 'tile_00'), ('dataset_1', 'aoi_3', 'simulated', 'tile_01'), ('dataset_1', 'aoi_3', 'labeled', 'tile_00'), ('dataset_1', 'aoi_3', 'labeled', 'tile_01')} def test_sort(self, strict_pattern_tree): root, path_list = strict_pattern_tree dataset = PatternDataset('data/images/{dataset}/{aoi}/{type}/{tile}.jpg', 'data/labels/{dataset}/{aoi}/{type}/{tile}.json', _dummy_tile_driver, _dummy_annotation_driver, path=root, sort_key=lambda x: tuple(reversed(x))) assert len(dataset) == 8 assert dataset._matching_groups == ('dataset', 'aoi', 'type', 'tile') assert dataset._group_index == [ ('dataset_1', 'aoi_0', 'labeled', 'tile_00'), ('dataset_1', 'aoi_3', 'labeled', 'tile_00'), ('dataset_1', 'aoi_0', 'simulated', 'tile_00'), ('dataset_1', 'aoi_3', 'simulated', 'tile_00'), ('dataset_1', 'aoi_0', 'labeled', 'tile_01'), ('dataset_1', 'aoi_3', 'labeled', 'tile_01'), ('dataset_1', 'aoi_0', 'simulated', 'tile_01'), ('dataset_1', 'aoi_3', 'simulated', 'tile_01'), ] def test_cache(self, strict_pattern_tree): root, path_list = strict_pattern_tree dataset = PatternDataset('data/images/{dataset}/{aoi}/{type}/{tile}.jpg', 'data/labels/{dataset}/{aoi}/{type}/{tile}.json', _dummy_tile_driver, _dummy_annotation_driver, path=root) cached = PatternDataset('data/images/{dataset}/{aoi}/{type}/{tile}.jpg', 'data/labels/{dataset}/{aoi}/{type}/{tile}.json', _dummy_tile_driver, _dummy_annotation_driver, path=root, cache=True) assert dataset._tiles_database == cached._tiles_database assert dataset._tiles_index == cached._tiles_index assert dataset._annotations_database == cached._annotations_database assert dataset._annotations_index == cached._annotations_index assert dataset._matching_groups == cached._matching_groups assert dataset._group_index == cached._group_index def test_cache_miss(self, strict_pattern_tree): root, path_list = strict_pattern_tree dataset = PatternDataset('data/images/{dataset}/{type}/{tile}.jpg', 'data/labels/{dataset}/{type}/{tile}.json', _dummy_tile_driver, _dummy_annotation_driver, path=pathlib.Path(str(root)), cache=True) assert len(dataset) == 2 assert dataset._matching_groups == ('dataset', 'type', 'tile') assert set(dataset._group_index) == {('dataset_0', 'labeled', 'tile_00'), ('dataset_0', 'labeled', 'tile_01')} def test_strict_recursive(self, strict_pattern_tree): root, path_list = strict_pattern_tree dataset = PatternDataset('data/images/{dataset}/{aoi/}/{tile}.jpg', 'data/labels/{dataset}/{aoi/}/{tile}.json', _dummy_tile_driver, _dummy_annotation_driver, path=root) assert len(dataset) == 10 assert dataset._matching_groups == ('dataset', 'aoi', 'tile') assert set(dataset._group_index) == {('dataset_0', 'labeled', 'tile_00'), ('dataset_0', 'labeled', 'tile_01'), ('dataset_1', 'aoi_0/simulated', 'tile_00'), ('dataset_1', 'aoi_0/simulated', 'tile_01'), ('dataset_1', 'aoi_0/labeled', 'tile_00'), ('dataset_1', 'aoi_0/labeled', 'tile_01'), ('dataset_1', 'aoi_3/simulated', 'tile_00'), ('dataset_1', 'aoi_3/simulated', 'tile_01'), ('dataset_1', 'aoi_3/labeled', 'tile_00'), ('dataset_1', 'aoi_3/labeled', 'tile_01')} def test_tile_degeneracy_fail(self, loose_pattern_tree): root, path_list = loose_pattern_tree with pytest.raises(ValueError, match='Tile pattern degeneracy is not supported'): _ = PatternDataset('data/images/tile.jpg', 'data/labels/{dataset_id}/{aoi_id}/{type_id}/{tile_id}.json', _dummy_tile_driver, _dummy_annotation_driver, path=root) def test_no_common_group_fail(self, loose_pattern_tree): root, path_list = loose_pattern_tree with pytest.raises(ValueError, match='No common group could be found in between patterns'): _ = PatternDataset('data/images/{dataset}/{aoi}/{type}/{tile}.jpg', 'data/labels/{dataset_id}/{aoi_id}/{type_id}/{tile_id}.json', _dummy_tile_driver, _dummy_annotation_driver, path=root) def test_no_match_fail(self, loose_pattern_tree): root, path_list = loose_pattern_tree with pytest.raises(ValueError, match='No matches where found between tiles and annotation'): _ = PatternDataset('data/images/{dataset}/{aoi}/{type}/{tile}.jpg', 'data/labels/{dataset}/{aoi}/{type}/{tile}.JSON', _dummy_tile_driver, _dummy_annotation_driver, path=root, strict=False) def test_loose_fail(self, loose_pattern_tree): root, path_list = loose_pattern_tree with pytest.raises(ValueError, match='does not have a matching annotation'): _ = PatternDataset('data/images/{dataset}/{aoi}/{type}/{tile}.jpg', 'data/labels/{dataset}/{aoi}/{type}/{tile}.json', _dummy_tile_driver, _dummy_annotation_driver, path=root) def test_loose(self, loose_pattern_tree): root, path_list = loose_pattern_tree dataset = PatternDataset('data/images/{dataset}/{aoi}/{type}/{tile}.jpg', 'data/labels/{dataset}/{aoi}/{type}/{tile}.json', _dummy_tile_driver, _dummy_annotation_driver, path=root, strict=False) assert len(dataset) == 6 assert dataset._matching_groups == ('dataset', 'aoi', 'type', 'tile') assert set(dataset._group_index) == {('dataset_1', 'aoi_0', 'simulated', 'tile_00'), ('dataset_1', 'aoi_0', 'simulated', 'tile_01'), ('dataset_1', 'aoi_3', 'simulated', 'tile_00'), ('dataset_1', 'aoi_3', 'simulated', 'tile_01'), ('dataset_1', 'aoi_3', 'labeled', 'tile_00'), ('dataset_1', 'aoi_3', 'labeled', 'tile_01')} def test_loose_alternative(self, loose_pattern_tree): root, path_list = loose_pattern_tree dataset = PatternDataset('data/images/{dataset}/{aoi}/{type}/{tile}.jpg', 'data/images/{dataset}/{aoi}/{type}/{tile}.json', _dummy_tile_driver, _dummy_annotation_driver, path=root, strict=False) assert len(dataset) == 1 assert dataset._matching_groups == ('dataset', 'aoi', 'type', 'tile') assert set(dataset._group_index) == {('dataset_1', 'aoi_0', 'labeled', 'tile_00')} dataset = PatternDataset('data/images/{dataset}/{aoi}/{type}/{tile}.jpg', 'data/images/{dataset}/{aoi}/{type}/{tile}.[json|geojson]', _dummy_tile_driver, _dummy_annotation_driver, path=root, strict=False) assert len(dataset) == 2 assert set(dataset._group_index) == {('dataset_1', 'aoi_0', 'labeled', 'tile_00'), ('dataset_1', 'aoi_0', 'labeled', 'tile_01')} def test_loose_duplicate(self, loose_pattern_tree): root, path_list = loose_pattern_tree with pytest.raises(ValueError, match='does not have a matching annotation'): _ = PatternDataset('data/images/{dataset}/{type}/{prior}/{tile}.jpg', 'data/labels/{dataset}/{type}/{tile}.json', _dummy_tile_driver, _dummy_annotation_driver, path=root) dataset = PatternDataset('data/images/{dataset}/{type}/{prior}/{tile}.jpg', 'data/labels/{dataset}/{type}/{tile}.json', _dummy_tile_driver, _dummy_annotation_driver, path=root, strict=False) assert len(dataset) == 2 assert dataset._matching_groups == ('dataset', 'type', 'tile') assert set(dataset._group_index) == {('dataset_0', 'labeled', 'tile_00'), ('dataset_0', 'labeled', 'tile_01')} assert len(dataset._tiles_database[('dataset_0', 'labeled', 'tile_00')]) == 2 assert len(dataset._tiles_database[('dataset_0', 'labeled', 'tile_01')]) == 2 assert len(dataset._annotations_database[('dataset_0', 'labeled', 'tile_00')]) == 1 assert len(dataset._annotations_database[('dataset_0', 'labeled', 'tile_01')]) == 1 def test_degenerate(self, loose_pattern_tree): root, path_list = loose_pattern_tree dataset = PatternDataset('data/images/{dataset}/{type}/{prior}/{tile}.jpg', 'data/images.json', _dummy_tile_driver, _dummy_annotation_driver, path=root) assert len(dataset) == 12 assert dataset._matching_groups == ('dataset', 'type', 'prior', 'tile') assert set(dataset._group_index) == {('dataset_0', 'labeled', 'prior', 'tile_00'), ('dataset_0', 'labeled', 'prior', 'tile_01'), ('dataset_0', 'labeled', 'posterior', 'tile_00'), ('dataset_0', 'labeled', 'posterior', 'tile_01'), ('dataset_1', 'aoi_0', 'simulated', 'tile_00'), ('dataset_1', 'aoi_0', 'simulated', 'tile_01'), ('dataset_1', 'aoi_0', 'labeled', 'tile_00'), ('dataset_1', 'aoi_0', 'labeled', 'tile_01'), ('dataset_1', 'aoi_3', 'simulated', 'tile_00'), ('dataset_1', 'aoi_3', 'simulated', 'tile_01'), ('dataset_1', 'aoi_3', 'labeled', 'tile_00'), ('dataset_1', 'aoi_3', 'labeled', 'tile_01')} class TestDriver: def test_call_argument(self, loose_pattern_tree): root, path_list = loose_pattern_tree dataset = PatternDataset('data/images/{dataset}/{nature}/{prior}/{tile}.jpg', 'data/labels/{dataset}/{nature}/{tile}.json', _dummy_tile_driver, _dummy_annotation_driver, path=root, strict=False, sort_key=lambda x: x) assert isinstance(dataset[0], DataPoint) assert dataset[0].tiles.iloc[0].filename == root / 'data/images/dataset_0/labeled/prior/tile_00.jpg' assert dataset[0].tiles.iloc[0].dataset == 'dataset_0' assert dataset[0].tiles.iloc[0].nature == 'labeled' assert dataset[0].tiles.iloc[0].tile == 'tile_00' assert not hasattr(dataset[0].tiles.iloc[0], 'prior') assert dataset[0].annotation[0].paths == (root / 'data/labels/dataset_0/labeled/tile_00.json', ) assert dataset[0].annotation[0].dataset == 'dataset_0' assert dataset[0].annotation[0].nature == 'labeled' assert dataset[0].annotation[0].tile == 'tile_00' assert not hasattr(dataset[0].annotation[0], 'prior') def test_degenerate_call_argument(self, loose_pattern_tree): root, path_list = loose_pattern_tree dataset = PatternDataset('data/images/{dataset}/{nature}/{prior}/{tile}.jpg', 'data/images.json', _dummy_tile_driver, _dummy_annotation_driver, path=root, sort_key=lambda x: x) assert isinstance(dataset[0], DataPoint) assert dataset[0].tiles.iloc[0].filename == root / 'data/images/dataset_0/labeled/posterior/tile_00.jpg' assert dataset[0].tiles.iloc[0].dataset == 'dataset_0' assert dataset[0].tiles.iloc[0].nature == 'labeled' assert dataset[0].tiles.iloc[0].prior == 'posterior' assert dataset[0].tiles.iloc[0].tile == 'tile_00' assert dataset[0].annotation[0].paths == (root / 'data/images.json', ) assert dataset[0].annotation[0].dataset == 'dataset_0' assert dataset[0].annotation[0].nature == 'labeled' assert dataset[0].annotation[0].prior == 'posterior' assert dataset[0].annotation[0].tile == 'tile_00' def test_call_invalid_tile_type(self, loose_pattern_tree): root, path_list = loose_pattern_tree dataset = PatternDataset('data/images/{dataset}/{nature}/{prior}/{tile}.jpg', 'data/labels/{dataset}/{nature}/{tile}.json', _invalid_return_tile_driver, _dummy_annotation_driver, path=root, strict=False) with pytest.raises(TypeError): _ = dataset[0] def test_call_invalid_annotation_type(self, loose_pattern_tree): root, path_list = loose_pattern_tree dataset = PatternDataset('data/images/{dataset}/{nature}/{prior}/{tile}.jpg', 'data/labels/{dataset}/{nature}/{tile}.json', _dummy_tile_driver, _invalid_return_annotation_driver, path=root, strict=False) with pytest.raises(TypeError): _ = dataset[0]

54.572559

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0.173203

0.071895

0

null

0

1

0

null

0

6

bdb1072f6721289fe90380ba674416faf59de888

152

py

Python

crankycoin/test/test_transaction.py

Dysproh/martian-sporks

c8580817759303fe4560a4fa4664222193bbc298

[ "MIT" ]

null

crankycoin/test/test_transaction.py

Dysproh/martian-sporks

c8580817759303fe4560a4fa4664222193bbc298

[ "MIT" ]

null

crankycoin/test/test_transaction.py

Dysproh/martian-sporks

c8580817759303fe4560a4fa4664222193bbc298

[ "MIT" ]

null

import unittest from mock import patch, Mock, MagicMock, call from crankycoin.transaction import * class TestTransaction(unittest.TestCase): pass

19

45

0.796053

18

152

6.722222

0.722222

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0.144737

152

7

46

21.714286

0.930769

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true

0.2

0.6

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1

0

null

0

1

0

1

0

6

da3835a334a56b499f1b5514a6f98c4bb1c425e4

13,173

py

Python

sdk/python/pulumi_gitlab/group_label.py

pulumi/pulumi-gitlab

5627240bf718fc765d3a2068acd20621383514c8

[ "ECL-2.0", "Apache-2.0" ]

11

2019-09-17T20:41:23.000Z

2021-12-02T20:39:23.000Z

sdk/python/pulumi_gitlab/group_label.py

pulumi/pulumi-gitlab

5627240bf718fc765d3a2068acd20621383514c8

[ "ECL-2.0", "Apache-2.0" ]

67

2019-06-21T18:30:30.000Z

2022-03-31T21:27:20.000Z

sdk/python/pulumi_gitlab/group_label.py

pulumi/pulumi-gitlab

5627240bf718fc765d3a2068acd20621383514c8

[ "ECL-2.0", "Apache-2.0" ]

2

2019-10-05T10:36:36.000Z

2021-05-13T18:14:59.000Z

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union, overload from . import _utilities __all__ = ['GroupLabelArgs', 'GroupLabel'] @pulumi.input_type class GroupLabelArgs: def __init__(__self__, *, color: pulumi.Input[str], group: pulumi.Input[str], description: Optional[pulumi.Input[str]] = None, name: Optional[pulumi.Input[str]] = None): """ The set of arguments for constructing a GroupLabel resource. :param pulumi.Input[str] color: The color of the label given in 6-digit hex notation with leading '#' sign (e.g. #FFAABB) or one of the [CSS color names](https://developer.mozilla.org/en-US/docs/Web/CSS/color_value#Color_keywords). :param pulumi.Input[str] group: The name or id of the group to add the label to. :param pulumi.Input[str] description: The description of the label. :param pulumi.Input[str] name: The name of the label. """ pulumi.set(__self__, "color", color) pulumi.set(__self__, "group", group) if description is not None: pulumi.set(__self__, "description", description) if name is not None: pulumi.set(__self__, "name", name) @property @pulumi.getter def color(self) -> pulumi.Input[str]: """ The color of the label given in 6-digit hex notation with leading '#' sign (e.g. #FFAABB) or one of the [CSS color names](https://developer.mozilla.org/en-US/docs/Web/CSS/color_value#Color_keywords). """ return pulumi.get(self, "color") @color.setter def color(self, value: pulumi.Input[str]): pulumi.set(self, "color", value) @property @pulumi.getter def group(self) -> pulumi.Input[str]: """ The name or id of the group to add the label to. """ return pulumi.get(self, "group") @group.setter def group(self, value: pulumi.Input[str]): pulumi.set(self, "group", value) @property @pulumi.getter def description(self) -> Optional[pulumi.Input[str]]: """ The description of the label. """ return pulumi.get(self, "description") @description.setter def description(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "description", value) @property @pulumi.getter def name(self) -> Optional[pulumi.Input[str]]: """ The name of the label. """ return pulumi.get(self, "name") @name.setter def name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "name", value) @pulumi.input_type class _GroupLabelState: def __init__(__self__, *, color: Optional[pulumi.Input[str]] = None, description: Optional[pulumi.Input[str]] = None, group: Optional[pulumi.Input[str]] = None, name: Optional[pulumi.Input[str]] = None): """ Input properties used for looking up and filtering GroupLabel resources. :param pulumi.Input[str] color: The color of the label given in 6-digit hex notation with leading '#' sign (e.g. #FFAABB) or one of the [CSS color names](https://developer.mozilla.org/en-US/docs/Web/CSS/color_value#Color_keywords). :param pulumi.Input[str] description: The description of the label. :param pulumi.Input[str] group: The name or id of the group to add the label to. :param pulumi.Input[str] name: The name of the label. """ if color is not None: pulumi.set(__self__, "color", color) if description is not None: pulumi.set(__self__, "description", description) if group is not None: pulumi.set(__self__, "group", group) if name is not None: pulumi.set(__self__, "name", name) @property @pulumi.getter def color(self) -> Optional[pulumi.Input[str]]: """ The color of the label given in 6-digit hex notation with leading '#' sign (e.g. #FFAABB) or one of the [CSS color names](https://developer.mozilla.org/en-US/docs/Web/CSS/color_value#Color_keywords). """ return pulumi.get(self, "color") @color.setter def color(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "color", value) @property @pulumi.getter def description(self) -> Optional[pulumi.Input[str]]: """ The description of the label. """ return pulumi.get(self, "description") @description.setter def description(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "description", value) @property @pulumi.getter def group(self) -> Optional[pulumi.Input[str]]: """ The name or id of the group to add the label to. """ return pulumi.get(self, "group") @group.setter def group(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "group", value) @property @pulumi.getter def name(self) -> Optional[pulumi.Input[str]]: """ The name of the label. """ return pulumi.get(self, "name") @name.setter def name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "name", value) class GroupLabel(pulumi.CustomResource): @overload def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, color: Optional[pulumi.Input[str]] = None, description: Optional[pulumi.Input[str]] = None, group: Optional[pulumi.Input[str]] = None, name: Optional[pulumi.Input[str]] = None, __props__=None): """ ## # gitlab\_group\_label This resource allows you to create and manage labels for your GitLab groups. For further information on labels, consult the [gitlab documentation](https://docs.gitlab.com/ee/user/project/labels.html#group-labels). ## Example Usage ```python import pulumi import pulumi_gitlab as gitlab fixme = gitlab.GroupLabel("fixme", color="#ffcc00", description="issue with failing tests", group="example") ``` ## Import Gitlab group labels can be imported using an id made up of `{group_id}:{group_label_id}`, e.g. ```sh $ pulumi import gitlab:index/groupLabel:GroupLabel example 12345:fixme ``` :param str resource_name: The name of the resource. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[str] color: The color of the label given in 6-digit hex notation with leading '#' sign (e.g. #FFAABB) or one of the [CSS color names](https://developer.mozilla.org/en-US/docs/Web/CSS/color_value#Color_keywords). :param pulumi.Input[str] description: The description of the label. :param pulumi.Input[str] group: The name or id of the group to add the label to. :param pulumi.Input[str] name: The name of the label. """ ... @overload def __init__(__self__, resource_name: str, args: GroupLabelArgs, opts: Optional[pulumi.ResourceOptions] = None): """ ## # gitlab\_group\_label This resource allows you to create and manage labels for your GitLab groups. For further information on labels, consult the [gitlab documentation](https://docs.gitlab.com/ee/user/project/labels.html#group-labels). ## Example Usage ```python import pulumi import pulumi_gitlab as gitlab fixme = gitlab.GroupLabel("fixme", color="#ffcc00", description="issue with failing tests", group="example") ``` ## Import Gitlab group labels can be imported using an id made up of `{group_id}:{group_label_id}`, e.g. ```sh $ pulumi import gitlab:index/groupLabel:GroupLabel example 12345:fixme ``` :param str resource_name: The name of the resource. :param GroupLabelArgs args: The arguments to use to populate this resource's properties. :param pulumi.ResourceOptions opts: Options for the resource. """ ... def __init__(__self__, resource_name: str, *args, **kwargs): resource_args, opts = _utilities.get_resource_args_opts(GroupLabelArgs, pulumi.ResourceOptions, *args, **kwargs) if resource_args is not None: __self__._internal_init(resource_name, opts, **resource_args.__dict__) else: __self__._internal_init(resource_name, *args, **kwargs) def _internal_init(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, color: Optional[pulumi.Input[str]] = None, description: Optional[pulumi.Input[str]] = None, group: Optional[pulumi.Input[str]] = None, name: Optional[pulumi.Input[str]] = None, __props__=None): if opts is None: opts = pulumi.ResourceOptions() if not isinstance(opts, pulumi.ResourceOptions): raise TypeError('Expected resource options to be a ResourceOptions instance') if opts.version is None: opts.version = _utilities.get_version() if opts.id is None: if __props__ is not None: raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource') __props__ = GroupLabelArgs.__new__(GroupLabelArgs) if color is None and not opts.urn: raise TypeError("Missing required property 'color'") __props__.__dict__["color"] = color __props__.__dict__["description"] = description if group is None and not opts.urn: raise TypeError("Missing required property 'group'") __props__.__dict__["group"] = group __props__.__dict__["name"] = name super(GroupLabel, __self__).__init__( 'gitlab:index/groupLabel:GroupLabel', resource_name, __props__, opts) @staticmethod def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions] = None, color: Optional[pulumi.Input[str]] = None, description: Optional[pulumi.Input[str]] = None, group: Optional[pulumi.Input[str]] = None, name: Optional[pulumi.Input[str]] = None) -> 'GroupLabel': """ Get an existing GroupLabel resource's state with the given name, id, and optional extra properties used to qualify the lookup. :param str resource_name: The unique name of the resulting resource. :param pulumi.Input[str] id: The unique provider ID of the resource to lookup. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[str] color: The color of the label given in 6-digit hex notation with leading '#' sign (e.g. #FFAABB) or one of the [CSS color names](https://developer.mozilla.org/en-US/docs/Web/CSS/color_value#Color_keywords). :param pulumi.Input[str] description: The description of the label. :param pulumi.Input[str] group: The name or id of the group to add the label to. :param pulumi.Input[str] name: The name of the label. """ opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id)) __props__ = _GroupLabelState.__new__(_GroupLabelState) __props__.__dict__["color"] = color __props__.__dict__["description"] = description __props__.__dict__["group"] = group __props__.__dict__["name"] = name return GroupLabel(resource_name, opts=opts, __props__=__props__) @property @pulumi.getter def color(self) -> pulumi.Output[str]: """ The color of the label given in 6-digit hex notation with leading '#' sign (e.g. #FFAABB) or one of the [CSS color names](https://developer.mozilla.org/en-US/docs/Web/CSS/color_value#Color_keywords). """ return pulumi.get(self, "color") @property @pulumi.getter def description(self) -> pulumi.Output[Optional[str]]: """ The description of the label. """ return pulumi.get(self, "description") @property @pulumi.getter def group(self) -> pulumi.Output[str]: """ The name or id of the group to add the label to. """ return pulumi.get(self, "group") @property @pulumi.getter def name(self) -> pulumi.Output[str]: """ The name of the label. """ return pulumi.get(self, "name")

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da7578156cf111ab80ce38dad6b975ba02cfa587

85

py

Python

epippy/geographics/__init__.py

montefesp/EPIPPy

7de873cf70d06986e83a434b6ab4b8997694a269

[ "MIT" ]

null

epippy/geographics/__init__.py

montefesp/EPIPPy

7de873cf70d06986e83a434b6ab4b8997694a269

[ "MIT" ]

null

epippy/geographics/__init__.py

montefesp/EPIPPy

7de873cf70d06986e83a434b6ab4b8997694a269

[ "MIT" ]

null

from .shapes import * from .points import * from .codes import * from .areas import *

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py

Python

tests/test_example/test_hello.py

jb-delafosse/dbt-subdocs

a7b2b09bc3131015b2540cb2f3dcb4cd99dbb12e

[ "MIT" ]

12

2022-01-19T14:15:44.000Z

2022-02-24T14:53:50.000Z

tests/test_example/test_hello.py

jb-delafosse/dbt-subdocs

a7b2b09bc3131015b2540cb2f3dcb4cd99dbb12e

[ "MIT" ]

51

2022-01-19T12:16:07.000Z

2022-03-31T14:31:24.000Z

tests/test_example/test_hello.py

jb-delafosse/dbt-subdocs

a7b2b09bc3131015b2540cb2f3dcb4cd99dbb12e

[ "MIT" ]

null

"""Tests example.""" import pytest def test_dummy(): assert True

10.142857

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6

da8b99d2e7602017fe382598beb9de7cffc38c57

153

py

Python

bno055_usb_stick_py/__init__.py

selyunin/bno055_usb_stick_linux_driver

ed698e4917f4c52c34d0127b55e4a618627aa8b3

[ "MIT" ]

10

2018-12-29T18:39:25.000Z

2021-12-16T07:41:57.000Z

bno055_usb_stick_py/__init__.py

selyunin/bno055_usb_stick_linux_driver

ed698e4917f4c52c34d0127b55e4a618627aa8b3

[ "MIT" ]

5

2018-12-29T00:46:05.000Z

2020-12-19T21:03:59.000Z

bno055_usb_stick_py/__init__.py

selyunin/bno055_usb_stick_linux_driver

ed698e4917f4c52c34d0127b55e4a618627aa8b3

[ "MIT" ]

1

2020-02-17T06:53:11.000Z

name = "bno055_usb_stick_py" version = "0.9.5" from bno055_usb_stick_py.bno055_usb_stick import BnoUsbStick from bno055_usb_stick_py.bno055 import BNO055

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py

Python

zetta/db/influx/__init__.py

irfan-nst/zetta

13ca51604bdf418b31f20db6aaf95c428fc306d1

[ "MIT" ]

null

zetta/db/influx/__init__.py

irfan-nst/zetta

13ca51604bdf418b31f20db6aaf95c428fc306d1

[ "MIT" ]

null

zetta/db/influx/__init__.py

irfan-nst/zetta

13ca51604bdf418b31f20db6aaf95c428fc306d1

[ "MIT" ]

null

from .main import InfluxDB

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py

Python

sdk/python/pulumi_google_native/pubsub/v1beta1a/subscription.py

AaronFriel/pulumi-google-native

75d1cda425e33d4610348972cd70bddf35f1770d

[ "Apache-2.0" ]

44

2021-04-18T23:00:48.000Z

2022-02-14T17:43:15.000Z

sdk/python/pulumi_google_native/pubsub/v1beta1a/subscription.py

AaronFriel/pulumi-google-native

75d1cda425e33d4610348972cd70bddf35f1770d

[ "Apache-2.0" ]

354

2021-04-16T16:48:39.000Z

2022-03-31T17:16:39.000Z

sdk/python/pulumi_google_native/pubsub/v1beta1a/subscription.py

AaronFriel/pulumi-google-native

75d1cda425e33d4610348972cd70bddf35f1770d

[ "Apache-2.0" ]

8

2021-04-24T17:46:51.000Z

2022-01-05T10:40:21.000Z

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi SDK Generator. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union, overload from ... import _utilities from . import outputs from ._inputs import * __all__ = ['SubscriptionArgs', 'Subscription'] @pulumi.input_type class SubscriptionArgs: def __init__(__self__, *, ack_deadline_seconds: Optional[pulumi.Input[int]] = None, name: Optional[pulumi.Input[str]] = None, push_config: Optional[pulumi.Input['PushConfigArgs']] = None, topic: Optional[pulumi.Input[str]] = None): """ The set of arguments for constructing a Subscription resource. :param pulumi.Input[int] ack_deadline_seconds: For either push or pull delivery, the value is the maximum time after a subscriber receives a message before the subscriber should acknowledge or Nack the message. If the Ack deadline for a message passes without an Ack or a Nack, the Pub/Sub system will eventually redeliver the message. If a subscriber acknowledges after the deadline, the Pub/Sub system may accept the Ack, but it is possible that the message has been already delivered again. Multiple Acks to the message are allowed and will succeed. For push delivery, this value is used to set the request timeout for the call to the push endpoint. For pull delivery, this value is used as the initial value for the Ack deadline. It may be overridden for each message using its corresponding ack_id with ModifyAckDeadline. While a message is outstanding (i.e. it has been delivered to a pull subscriber and the subscriber has not yet Acked or Nacked), the Pub/Sub system will not deliver that message to another pull subscriber (on a best-effort basis). :param pulumi.Input[str] name: Name of the subscription. :param pulumi.Input['PushConfigArgs'] push_config: If push delivery is used with this subscription, this field is used to configure it. :param pulumi.Input[str] topic: The name of the topic from which this subscription is receiving messages. """ if ack_deadline_seconds is not None: pulumi.set(__self__, "ack_deadline_seconds", ack_deadline_seconds) if name is not None: pulumi.set(__self__, "name", name) if push_config is not None: pulumi.set(__self__, "push_config", push_config) if topic is not None: pulumi.set(__self__, "topic", topic) @property @pulumi.getter(name="ackDeadlineSeconds") def ack_deadline_seconds(self) -> Optional[pulumi.Input[int]]: """ For either push or pull delivery, the value is the maximum time after a subscriber receives a message before the subscriber should acknowledge or Nack the message. If the Ack deadline for a message passes without an Ack or a Nack, the Pub/Sub system will eventually redeliver the message. If a subscriber acknowledges after the deadline, the Pub/Sub system may accept the Ack, but it is possible that the message has been already delivered again. Multiple Acks to the message are allowed and will succeed. For push delivery, this value is used to set the request timeout for the call to the push endpoint. For pull delivery, this value is used as the initial value for the Ack deadline. It may be overridden for each message using its corresponding ack_id with ModifyAckDeadline. While a message is outstanding (i.e. it has been delivered to a pull subscriber and the subscriber has not yet Acked or Nacked), the Pub/Sub system will not deliver that message to another pull subscriber (on a best-effort basis). """ return pulumi.get(self, "ack_deadline_seconds") @ack_deadline_seconds.setter def ack_deadline_seconds(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "ack_deadline_seconds", value) @property @pulumi.getter def name(self) -> Optional[pulumi.Input[str]]: """ Name of the subscription. """ return pulumi.get(self, "name") @name.setter def name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "name", value) @property @pulumi.getter(name="pushConfig") def push_config(self) -> Optional[pulumi.Input['PushConfigArgs']]: """ If push delivery is used with this subscription, this field is used to configure it. """ return pulumi.get(self, "push_config") @push_config.setter def push_config(self, value: Optional[pulumi.Input['PushConfigArgs']]): pulumi.set(self, "push_config", value) @property @pulumi.getter def topic(self) -> Optional[pulumi.Input[str]]: """ The name of the topic from which this subscription is receiving messages. """ return pulumi.get(self, "topic") @topic.setter def topic(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "topic", value) class Subscription(pulumi.CustomResource): @overload def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, ack_deadline_seconds: Optional[pulumi.Input[int]] = None, name: Optional[pulumi.Input[str]] = None, push_config: Optional[pulumi.Input[pulumi.InputType['PushConfigArgs']]] = None, topic: Optional[pulumi.Input[str]] = None, __props__=None): """ Creates a subscription on a given topic for a given subscriber. If the subscription already exists, returns ALREADY_EXISTS. If the corresponding topic doesn't exist, returns NOT_FOUND. If the name is not provided in the request, the server will assign a random name for this subscription on the same project as the topic. :param str resource_name: The name of the resource. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[int] ack_deadline_seconds: For either push or pull delivery, the value is the maximum time after a subscriber receives a message before the subscriber should acknowledge or Nack the message. If the Ack deadline for a message passes without an Ack or a Nack, the Pub/Sub system will eventually redeliver the message. If a subscriber acknowledges after the deadline, the Pub/Sub system may accept the Ack, but it is possible that the message has been already delivered again. Multiple Acks to the message are allowed and will succeed. For push delivery, this value is used to set the request timeout for the call to the push endpoint. For pull delivery, this value is used as the initial value for the Ack deadline. It may be overridden for each message using its corresponding ack_id with ModifyAckDeadline. While a message is outstanding (i.e. it has been delivered to a pull subscriber and the subscriber has not yet Acked or Nacked), the Pub/Sub system will not deliver that message to another pull subscriber (on a best-effort basis). :param pulumi.Input[str] name: Name of the subscription. :param pulumi.Input[pulumi.InputType['PushConfigArgs']] push_config: If push delivery is used with this subscription, this field is used to configure it. :param pulumi.Input[str] topic: The name of the topic from which this subscription is receiving messages. """ ... @overload def __init__(__self__, resource_name: str, args: Optional[SubscriptionArgs] = None, opts: Optional[pulumi.ResourceOptions] = None): """ Creates a subscription on a given topic for a given subscriber. If the subscription already exists, returns ALREADY_EXISTS. If the corresponding topic doesn't exist, returns NOT_FOUND. If the name is not provided in the request, the server will assign a random name for this subscription on the same project as the topic. :param str resource_name: The name of the resource. :param SubscriptionArgs args: The arguments to use to populate this resource's properties. :param pulumi.ResourceOptions opts: Options for the resource. """ ... def __init__(__self__, resource_name: str, *args, **kwargs): resource_args, opts = _utilities.get_resource_args_opts(SubscriptionArgs, pulumi.ResourceOptions, *args, **kwargs) if resource_args is not None: __self__._internal_init(resource_name, opts, **resource_args.__dict__) else: __self__._internal_init(resource_name, *args, **kwargs) def _internal_init(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, ack_deadline_seconds: Optional[pulumi.Input[int]] = None, name: Optional[pulumi.Input[str]] = None, push_config: Optional[pulumi.Input[pulumi.InputType['PushConfigArgs']]] = None, topic: Optional[pulumi.Input[str]] = None, __props__=None): if opts is None: opts = pulumi.ResourceOptions() if not isinstance(opts, pulumi.ResourceOptions): raise TypeError('Expected resource options to be a ResourceOptions instance') if opts.version is None: opts.version = _utilities.get_version() if opts.id is None: if __props__ is not None: raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource') __props__ = SubscriptionArgs.__new__(SubscriptionArgs) __props__.__dict__["ack_deadline_seconds"] = ack_deadline_seconds __props__.__dict__["name"] = name __props__.__dict__["push_config"] = push_config __props__.__dict__["topic"] = topic super(Subscription, __self__).__init__( 'google-native:pubsub/v1beta1a:Subscription', resource_name, __props__, opts) @staticmethod def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions] = None) -> 'Subscription': """ Get an existing Subscription resource's state with the given name, id, and optional extra properties used to qualify the lookup. :param str resource_name: The unique name of the resulting resource. :param pulumi.Input[str] id: The unique provider ID of the resource to lookup. :param pulumi.ResourceOptions opts: Options for the resource. """ opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id)) __props__ = SubscriptionArgs.__new__(SubscriptionArgs) __props__.__dict__["ack_deadline_seconds"] = None __props__.__dict__["name"] = None __props__.__dict__["push_config"] = None __props__.__dict__["topic"] = None return Subscription(resource_name, opts=opts, __props__=__props__) @property @pulumi.getter(name="ackDeadlineSeconds") def ack_deadline_seconds(self) -> pulumi.Output[int]: """ For either push or pull delivery, the value is the maximum time after a subscriber receives a message before the subscriber should acknowledge or Nack the message. If the Ack deadline for a message passes without an Ack or a Nack, the Pub/Sub system will eventually redeliver the message. If a subscriber acknowledges after the deadline, the Pub/Sub system may accept the Ack, but it is possible that the message has been already delivered again. Multiple Acks to the message are allowed and will succeed. For push delivery, this value is used to set the request timeout for the call to the push endpoint. For pull delivery, this value is used as the initial value for the Ack deadline. It may be overridden for each message using its corresponding ack_id with ModifyAckDeadline. While a message is outstanding (i.e. it has been delivered to a pull subscriber and the subscriber has not yet Acked or Nacked), the Pub/Sub system will not deliver that message to another pull subscriber (on a best-effort basis). """ return pulumi.get(self, "ack_deadline_seconds") @property @pulumi.getter def name(self) -> pulumi.Output[str]: """ Name of the subscription. """ return pulumi.get(self, "name") @property @pulumi.getter(name="pushConfig") def push_config(self) -> pulumi.Output['outputs.PushConfigResponse']: """ If push delivery is used with this subscription, this field is used to configure it. """ return pulumi.get(self, "push_config") @property @pulumi.getter def topic(self) -> pulumi.Output[str]: """ The name of the topic from which this subscription is receiving messages. """ return pulumi.get(self, "topic")

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61.995238

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0

1

0

null

0

6

16ff532cbef4b2524787321b2546b76716178dd1

39

py

Python

tests/test_0000_test_suite.py

korantu/lona

5039fa59f37cc32b9c789753af2ed8a8670ab611

[ "MIT" ]

230

2021-08-15T20:46:24.000Z

2022-03-30T10:17:43.000Z

tests/test_0000_test_suite.py

korantu/lona

5039fa59f37cc32b9c789753af2ed8a8670ab611

[ "MIT" ]

176

2021-08-18T08:19:37.000Z

2022-03-29T16:45:06.000Z

tests/test_0000_test_suite.py

korantu/lona

5039fa59f37cc32b9c789753af2ed8a8670ab611

[ "MIT" ]

13

2021-08-20T10:35:04.000Z

2022-01-17T15:49:40.000Z

def test_test_suite(): assert True

13

22

0.717949

6

39

4.333333

0.833333

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0.205128

39

2

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0.83871

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6

e50960698d51602fd4c68a2c3eb9703556125a00

34

py

Python

python-checker/Examples/FULL/cob_command_gui/src/command_gui_buttons/__init__.py

andersfischernielsen/ROS-dependency-checker

50ed13b23fe47a5e124875d4dd99482bef033c1b

[ "MIT" ]

null

python-checker/Examples/FULL/cob_command_gui/src/command_gui_buttons/__init__.py

andersfischernielsen/ROS-dependency-checker

50ed13b23fe47a5e124875d4dd99482bef033c1b

[ "MIT" ]

1

2020-03-05T12:39:21.000Z

2020-03-09T12:01:27.000Z

python-checker/Examples/FULL/cob_command_gui/src/command_gui_buttons/__init__.py

andersfischernielsen/ROS-dependency-checker

50ed13b23fe47a5e124875d4dd99482bef033c1b

[ "MIT" ]

2

2019-10-04T12:46:09.000Z

2020-01-27T15:25:09.000Z

from command_gui_buttons import *

17

33

0.852941

5

34

5.4

1

0

0.117647

34

1

34

0.9

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1

0

6

e540a8fbeca0ec33a2dcd501943b0809910daa85

336

py

Python

pyzmq/run_test.py

nikicc/anaconda-recipes

9c611a5854bf41bbc5e7ed9853dc71c0851a62ef

[ "BSD-3-Clause" ]

130

2015-07-28T03:41:21.000Z

2022-03-16T03:07:41.000Z

pyzmq/run_test.py

nikicc/anaconda-recipes

9c611a5854bf41bbc5e7ed9853dc71c0851a62ef

[ "BSD-3-Clause" ]

119

2015-08-01T00:54:06.000Z

2021-01-05T13:00:46.000Z

pyzmq/run_test.py

nikicc/anaconda-recipes

9c611a5854bf41bbc5e7ed9853dc71c0851a62ef

[ "BSD-3-Clause" ]

72

2015-07-29T02:35:56.000Z

2022-02-26T14:31:15.000Z

import zmq.backend.cython._device import zmq.backend.cython._poll import zmq.backend.cython._version import zmq.backend.cython.constants import zmq.backend.cython.context import zmq.backend.cython.error import zmq.backend.cython.message import zmq.backend.cython.socket import zmq.backend.cython.utils import zmq.devices.monitoredqueue

30.545455

35

0.854167

49

336

5.795918

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0.316901

0.507042

0.697183

0

0.059524

336

10

36

33.6

0.898734

0

1

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true

0

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0

null

1

0

null

0

1

0

1

0

1

0

6

e552e7153359c62ed08145355367e17c59582140

359

py

Python

apps/utils/errors.py

osw4l/villas-de-san-pablo

89f00dfbbfbfee5111bd9852ddfbdb8727d10ed2

[ "MIT" ]

null

apps/utils/errors.py

osw4l/villas-de-san-pablo

89f00dfbbfbfee5111bd9852ddfbdb8727d10ed2

[ "MIT" ]

null

apps/utils/errors.py

osw4l/villas-de-san-pablo

89f00dfbbfbfee5111bd9852ddfbdb8727d10ed2

[ "MIT" ]

null

__author__ = 'osw4l' from django.shortcuts import render def error400(request): return render(request, '400.html', status=400) def error403(request): return render(request, '403.html', status=403) def error404(request): return render(request, '404.html', status=404) def error500(request): return render(request, '500.html', status=500)

21.117647

50

0.718663

47

359

5.404255

0.425532

0.204724

0.299213

0.409449

0

0.120915

0.147632

359

17

51

21.117647

0.70915

0

0.102778

0

1

0.4

false

0

0.1

0.4

0.9

0

null

1

0

null

0

1

0

1

0

6

e56fd9b29de971668a90c6df02dad73905330fe9

641

py

Python

sdk/python/pulumi_oci/email/__init__.py

EladGabay/pulumi-oci

6841e27d4a1a7e15c672306b769912efbfd3ba99

[ "ECL-2.0", "Apache-2.0" ]

5

2021-08-17T11:14:46.000Z

2021-12-31T02:07:03.000Z

sdk/python/pulumi_oci/email/__init__.py

pulumi-oci/pulumi-oci

6841e27d4a1a7e15c672306b769912efbfd3ba99

[ "ECL-2.0", "Apache-2.0" ]

1

2021-09-06T11:21:29.000Z

sdk/python/pulumi_oci/email/__init__.py

pulumi-oci/pulumi-oci

6841e27d4a1a7e15c672306b769912efbfd3ba99

[ "ECL-2.0", "Apache-2.0" ]

2

2021-08-24T23:31:30.000Z

2022-01-02T19:26:54.000Z

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** from .. import _utilities import typing # Export this package's modules as members: from .dkim import * from .email_domain import * from .get_dkim import * from .get_dkims import * from .get_email_domain import * from .get_email_domains import * from .get_sender import * from .get_senders import * from .get_suppression import * from .get_suppressions import * from .sender import * from .suppression import * from ._inputs import * from . import outputs

29.136364

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641

21

88

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null

0

1

0

1

0

1

0

6

e57b7d1c0f719230b127692ca3f4b8905350f853

230

py

Python

claripy/frontends/__init__.py

embg/claripy

1a5e0ca61d3f480e541226f103900e983f025e4a

[ "BSD-2-Clause" ]

211

2015-08-06T23:25:01.000Z

2022-03-26T19:34:49.000Z

claripy/frontends/__init__.py

embg/claripy

1a5e0ca61d3f480e541226f103900e983f025e4a

[ "BSD-2-Clause" ]

175

2015-09-03T11:09:18.000Z

2022-03-09T20:24:33.000Z

claripy/frontends/__init__.py

embg/claripy

1a5e0ca61d3f480e541226f103900e983f025e4a

[ "BSD-2-Clause" ]

99

2015-08-07T10:30:08.000Z

2022-03-26T10:32:09.000Z

from .light_frontend import LightFrontend from .full_frontend import FullFrontend from .hybrid_frontend import HybridFrontend from .composite_frontend import CompositeFrontend from .replacement_frontend import ReplacementFrontend

38.333333

53

0.891304

25

230

8

0.52

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0

0.086957

230

5

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46

0.952381

0

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true

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1

0

1

0

6

e5939eb0603b78e2ad8f082adab31466f9d066c8

1,128

py

Python

bank_bot/banking_system/exceptions.py

Tengro/larp_bankbot

22d5ea49d5f507da74fb3b1f106c24ad52cb9e68

[ "MIT" ]

3

2019-07-27T15:20:49.000Z

2019-10-14T13:10:55.000Z

bank_bot/banking_system/exceptions.py

Tengro/larp_bankbot

22d5ea49d5f507da74fb3b1f106c24ad52cb9e68

[ "MIT" ]

1

2021-06-01T23:55:12.000Z

bank_bot/banking_system/exceptions.py

Tengro/larp_bankbot

22d5ea49d5f507da74fb3b1f106c24ad52cb9e68

[ "MIT" ]

null

class TransactionError(Exception): def __init__(self, message): self.message = message # Call the base class constructor with the parameters it needs super().__init__(message) class UserError(Exception): def __init__(self, message): self.message = message # Call the base class constructor with the parameters it needs super().__init__(message) class HackerError(Exception): def __init__(self, message, low_level=False, victim_chat_id=None): self.message = message self.low_level = low_level self.victim_chat_id = victim_chat_id # Call the base class constructor with the parameters it needs super().__init__(message) class AddressRecordError(Exception): def __init__(self, message): self.message = message # Call the base class constructor with the parameters it needs super().__init__(message) class MessageError(Exception): def __init__(self, message): self.message = message # Call the base class constructor with the parameters it needs super().__init__(message)

35.25

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0.68883

134

1,128

5.432836

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0.151099

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0.137363

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0.73489

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1,128

31

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null

0

1

0

null

0

1

0

6

e59b28398c507c22236bce92cccdca7d89f8094e

46

py

Python

test.py

nate-hunter/xml-csv-api

68d2ad840ea5a4955bcfd6d67d48fe9a750474cd

[ "MIT" ]

null

test.py

nate-hunter/xml-csv-api

68d2ad840ea5a4955bcfd6d67d48fe9a750474cd

[ "MIT" ]

6

2021-03-30T13:54:54.000Z

2021-09-22T19:23:45.000Z

test.py

nate-hunter/xml-csv-api

68d2ad840ea5a4955bcfd6d67d48fe9a750474cd

[ "MIT" ]

null

print('...Test Python file in Vagrant Box...')

46

0.673913

7

46

4.428571

1

0

0.108696

46

1

46

0.756098

0

0.787234

0

1

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true

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

e5c108a2f45f841e8343197173d7732a4abe2f0a

16,136

py

Python

geofence_monitor_test.py

x2y/monitors

103bda6ffff7b9d22931b0fdb26a997332a913ec

[ "MIT" ]

null

geofence_monitor_test.py

x2y/monitors

103bda6ffff7b9d22931b0fdb26a997332a913ec

[ "MIT" ]

null

geofence_monitor_test.py

x2y/monitors

103bda6ffff7b9d22931b0fdb26a997332a913ec

[ "MIT" ]

null

import geofence_monitor import io import mock import mocks import monitor import re import requests import time import unittest CAR_NEGATIVE_1_404_RESPONSE = requests.Response() CAR_NEGATIVE_1_404_RESPONSE.status_code = 404 CAR_0_NO_COORDINATES_RESPONSE = requests.Response() CAR_0_NO_COORDINATES_RESPONSE.status_code = 200 CAR_0_NO_COORDINATES_RESPONSE.raw = io.BytesIO(b''' { "type": "FeatureCollection", "features": [{ "type": "Feature", "geometry": { "type": "Polygon", "coordinates": [[ [-118.5, 34.0], [-118.5, 34.1], [-118.3, 34.1], [-118.3, 34.0], [-118.5, 34.0] ]] }, "properties": {"name": "Los Angeles"} }] }''') CAR_1_INSIDE_GEOFENCE_RESPONSE = requests.Response() CAR_1_INSIDE_GEOFENCE_RESPONSE.status_code = 200 CAR_1_INSIDE_GEOFENCE_RESPONSE.raw = io.BytesIO(b''' { "type": "FeatureCollection", "features": [{ "type": "Feature", "geometry": {"type": "Point", "coordinates": [-118.4, 34.05]}, "properties": {"id": 1, "description": "In Los Angeles geofence"} }, { "type": "Feature", "geometry": { "type": "Polygon", "coordinates": [[ [-118.5, 34.0], [-118.5, 34.1], [-118.3, 34.1], [-118.3, 34.0], [-118.5, 34.0] ]] }, "properties": {"name": "Los Angeles"} }] }''') CAR_2_INSIDE_SECOND_GEOFENCE_RESPONSE = requests.Response() CAR_2_INSIDE_SECOND_GEOFENCE_RESPONSE.status_code = 200 CAR_2_INSIDE_SECOND_GEOFENCE_RESPONSE.raw = io.BytesIO(b''' { "type": "FeatureCollection", "features": [{ "type": "Feature", "geometry": {"type": "Point", "coordinates": [-118.45, 34.075]}, "properties": {"id": 2, "description": "In Los Angeles geofence"} }, { "type": "Feature", "geometry": { "type": "Polygon", "coordinates": [[ [-122.5, 37.7], [-122.5, 37.8], [-122.4, 37.8], [-122.4, 37.7], [-122.5, 37.7] ]] }, "properties": {"name": "San Francisco"} }, { "type": "Feature", "geometry": { "type": "Polygon", "coordinates": [[ [-118.5, 34.0], [-118.5, 34.1], [-118.3, 34.1], [-118.3, 34.0], [-118.5, 34.0] ]] }, "properties": {"name": "Los Angeles"} }] }''') CAR_3_OUTSIDE_ITS_GEOFENCES_RESPONSE = requests.Response() CAR_3_OUTSIDE_ITS_GEOFENCES_RESPONSE.status_code = 200 CAR_3_OUTSIDE_ITS_GEOFENCES_RESPONSE.raw = io.BytesIO(b''' { "type": "FeatureCollection", "features": [{ "type": "Feature", "geometry": {"type": "Point", "coordinates": [-73.98, 40.76]}, "properties": {"id": 2, "description": "In New York City, outside geofences"} }, { "type": "Feature", "geometry": { "type": "Polygon", "coordinates": [[ [-122.5, 37.7], [-122.5, 37.8], [-122.4, 37.8], [-122.4, 37.7], [-122.5, 37.7] ]] }, "properties": {"name": "San Francisco"} }, { "type": "Feature", "geometry": { "type": "Polygon", "coordinates": [[ [-118.5, 34.0], [-118.5, 34.1], [-118.3, 34.1], [-118.3, 34.0], [-118.5, 34.0] ]] }, "properties": {"name": "Los Angeles"} }] }''') class GeofenceMonitorTest(unittest.TestCase): def setUp(self): geofence_monitor.server.config['TESTING'] = True self.server = geofence_monitor.server.test_client() mock.patch('threading.Timer', mocks.MockTimer).start() def tearDown(self): self.server = None mock.patch.stopall() monitor.reset() def test_parse_args_without_car_ids(self): with self.assertRaises(SystemExit) as e: geofence_monitor.start([]) self.assertEqual(e.exception.code, 2) def test_parse_args_defaults(self): geofence_monitor.start(['1', 'http://test.com']) self.assertEqual(monitor.args.car_ids, [1]) self.assertEqual(monitor.args.car_status_url, 'http://skurt-interview-api.herokuapp.com/carStatus/%s') self.assertEqual(monitor.args.query_delay_s, 1.0) def test_parse_args_with_complex_args(self): geofence_monitor.start([ '1-11', '13', '15-16', 'http://test.com', '--car_status_url=http://test.com/carStatus/%s', '--max_query_qps=2.0', ]) self.assertEqual(monitor.args.car_ids, [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 15, 16]) self.assertEqual(monitor.args.car_status_url, 'http://test.com/carStatus/%s') self.assertEqual(monitor.args.query_delay_s, 0.5) def test_parse_args_with_overlapping_car_id_ranges(self): geofence_monitor.start([ '5-10', '1-7', '3', 'http://test.com', '--car_status_url=http://test.com/carStatus/%s', '--max_query_qps=2.0', ]) self.assertEqual(monitor.args.car_ids, [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]) self.assertEqual(monitor.args.car_status_url, 'http://test.com/carStatus/%s') self.assertEqual(monitor.args.query_delay_s, 0.5) def test_polling_one_car_that_times_out(self): def time_out(url, timeout=999): raise requests.exceptions.Timeout('Request timed out') with mock.patch('monitor.alert') as mock_alert: with mock.patch('requests.get', side_effect=time_out) as mock_get: geofence_monitor.start([ '-2', 'http://test.com', '--car_status_url=http://test.com/carStatus/%s', '--max_query_qps=2.0', '--poll_period_s=10', '--min_poll_padding_period_s=0', ]) monitor.poll_timer.mock_tick(1.0) mock_get.assert_called_once_with('http://test.com/carStatus/-2', timeout=10) mock_alert.assert_called_once_with('Geofence monitor errors', 'geofence_monitor_errors', {'car_errors': [(-2, 'FETCH_TIMED_OUT')]}) def test_polling_one_car_with_404_response(self): with mock.patch('monitor.alert') as mock_alert: with mock.patch('requests.get', return_value=CAR_NEGATIVE_1_404_RESPONSE) as mock_get: geofence_monitor.start([ '-1', 'http://test.com', '--car_status_url=http://test.com/carStatus/%s', '--max_query_qps=2.0', '--poll_period_s=10', '--min_poll_padding_period_s=0', ]) monitor.poll_timer.mock_tick(1.0) mock_get.assert_called_once_with('http://test.com/carStatus/-1', timeout=10) mock_alert.assert_called_once_with('Geofence monitor errors', 'geofence_monitor_errors', {'car_errors': [(-1, 'INVALID_FETCH_RESPONSE')]}) def test_polling_one_car_with_no_coordinates(self): with mock.patch('monitor.alert') as mock_alert: with mock.patch('requests.get', return_value=CAR_0_NO_COORDINATES_RESPONSE) as mock_get: geofence_monitor.start([ '0', 'http://test.com', '--car_status_url=http://test.com/carStatus/%s', '--max_query_qps=2.0', '--poll_period_s=10', '--min_poll_padding_period_s=0', ]) monitor.poll_timer.mock_tick(1.0) mock_get.assert_called_once_with('http://test.com/carStatus/0', timeout=10) mock_alert.assert_called_once_with('Geofence monitor errors', 'geofence_monitor_errors', {'car_errors': [(0, 'NO_CAR_COORDS')]}) def test_polling_one_inside_geofence(self): with mock.patch('monitor.alert') as mock_alert: with mock.patch('requests.get', return_value=CAR_1_INSIDE_GEOFENCE_RESPONSE) as mock_get: geofence_monitor.start([ '1', 'http://test.com', '--car_status_url=http://test.com/carStatus/%s', '--max_query_qps=2.0', '--poll_period_s=10', '--min_poll_padding_period_s=0', ]) monitor.poll_timer.mock_tick(1.0) mock_get.assert_called_once_with('http://test.com/carStatus/1', timeout=10) mock_alert.assert_not_called() def test_polling_one_inside_its_second_geofence(self): with mock.patch('monitor.alert') as mock_alert: with mock.patch('requests.get', return_value=CAR_2_INSIDE_SECOND_GEOFENCE_RESPONSE) as mock_get: geofence_monitor.start([ '2', 'http://test.com', '--car_status_url=http://test.com/carStatus/%s', '--max_query_qps=2.0', '--poll_period_s=10', '--min_poll_padding_period_s=0', ]) monitor.poll_timer.mock_tick(1.0) mock_get.assert_called_once_with('http://test.com/carStatus/2', timeout=10) mock_alert.assert_not_called() def test_polling_one_outside_its_geofences(self): with mock.patch('monitor.alert') as mock_alert: with mock.patch('requests.get', return_value=CAR_3_OUTSIDE_ITS_GEOFENCES_RESPONSE) as mock_get: geofence_monitor.start([ '3', 'http://test.com', '--car_status_url=http://test.com/carStatus/%s', '--google_maps_api_key=1234567890', '--max_query_qps=2.0', '--poll_period_s=10', '--min_poll_padding_period_s=0', ]) monitor.poll_timer.mock_tick(1.0) mock_get.assert_called_once_with('http://test.com/carStatus/3', timeout=10) mock_alert.assert_called_once_with( 'Cars outside of geofences', 'geofence_monitor_geofence', {'car_coords': [(3, [-73.98, 40.76])], 'google_maps_api_key': '1234567890'}) def test_polling_all_inside_geofences(self): def mock_get_response(url, timeout=999): return { '1': CAR_1_INSIDE_GEOFENCE_RESPONSE, '2': CAR_2_INSIDE_SECOND_GEOFENCE_RESPONSE, }[re.search(r'-?\d+$', url).group()] with mock.patch('monitor.alert') as mock_alert: with mock.patch('requests.get', side_effect=mock_get_response) as mock_get: geofence_monitor.start([ '1-2', 'http://test.com', '--car_status_url=http://test.com/carStatus/%s', '--max_query_qps=2.0', '--poll_period_s=10', '--min_poll_padding_period_s=0', ]) monitor.poll_timer.mock_tick(1.0) mock_get.assert_has_calls([ mock.call('http://test.com/carStatus/1', timeout=10), mock.call('http://test.com/carStatus/2', timeout=10), ]) self.assertEqual(mock_get.call_count, 2) mock_alert.assert_not_called() def test_polling_some_inside_some_outside_their_geofences(self): def mock_get_response(url, timeout=999): return { '1': CAR_1_INSIDE_GEOFENCE_RESPONSE, '2': CAR_2_INSIDE_SECOND_GEOFENCE_RESPONSE, '3': CAR_3_OUTSIDE_ITS_GEOFENCES_RESPONSE, }[re.search(r'-?\d+$', url).group()] with mock.patch('monitor.alert') as mock_alert: with mock.patch('requests.get', side_effect=mock_get_response) as mock_get: geofence_monitor.start([ '1-3', 'http://test.com', '--car_status_url=http://test.com/carStatus/%s', '--google_maps_api_key=1234567890', '--max_query_qps=2.0', '--poll_period_s=10', '--min_poll_padding_period_s=0', ]) monitor.poll_timer.mock_tick(1.0) mock_get.assert_has_calls([ mock.call('http://test.com/carStatus/1', timeout=10), mock.call('http://test.com/carStatus/2', timeout=10), mock.call('http://test.com/carStatus/3', timeout=10), ]) self.assertEqual(mock_get.call_count, 3) mock_alert.assert_called_once_with( 'Cars outside of geofences', 'geofence_monitor_geofence', {'car_coords': [(3, [-73.98, 40.76])], 'google_maps_api_key': '1234567890'}) def test_polling_triggering_both_alerts(self): def mock_get_response(url, timeout=999): if url[-2:] == '-2': raise requests.exceptions.Timeout('Request timed out') return { '-1': CAR_NEGATIVE_1_404_RESPONSE, '0': CAR_0_NO_COORDINATES_RESPONSE, '1': CAR_1_INSIDE_GEOFENCE_RESPONSE, '2': CAR_2_INSIDE_SECOND_GEOFENCE_RESPONSE, '3': CAR_3_OUTSIDE_ITS_GEOFENCES_RESPONSE, }[re.search(r'-?\d+$', url).group()] with mock.patch('monitor.alert') as mock_alert: with mock.patch('requests.get', side_effect=mock_get_response) as mock_get: geofence_monitor.start([ '-2', '-1', '0-3', 'http://test.com', '--car_status_url=http://test.com/carStatus/%s', '--google_maps_api_key=1234567890', '--max_query_qps=2.0', '--poll_period_s=10', '--min_poll_padding_period_s=0', ]) monitor.poll_timer.mock_tick(1.0) mock_get.assert_has_calls([ mock.call('http://test.com/carStatus/-2', timeout=10), mock.call('http://test.com/carStatus/-1', timeout=10), mock.call('http://test.com/carStatus/0', timeout=10), mock.call('http://test.com/carStatus/1', timeout=10), mock.call('http://test.com/carStatus/2', timeout=10), mock.call('http://test.com/carStatus/3', timeout=10), ]) self.assertEqual(mock_get.call_count, 6) mock_alert.assert_has_calls([ mock.call('Cars outside of geofences', 'geofence_monitor_geofence', {'car_coords': [(3, [-73.98, 40.76])], 'google_maps_api_key': '1234567890'}), mock.call('Geofence monitor errors', 'geofence_monitor_errors', {'car_errors': [ (-2, 'FETCH_TIMED_OUT'), (-1, 'INVALID_FETCH_RESPONSE'), (0, 'NO_CAR_COORDS') ]}), ], any_order=True) def test_polling_with_duplicate_car_ids(self): def mock_get_response(url, timeout=999): return { '1': CAR_1_INSIDE_GEOFENCE_RESPONSE, '2': CAR_2_INSIDE_SECOND_GEOFENCE_RESPONSE, }[re.search(r'-?\d+$', url).group()] with mock.patch('monitor.alert') as mock_alert: with mock.patch('requests.get', side_effect=mock_get_response) as mock_get: geofence_monitor.start([ '1-2', '1', '2', 'http://test.com', '--car_status_url=http://test.com/carStatus/%s', '--max_query_qps=2.0', '--poll_period_s=10', '--min_poll_padding_period_s=0', ]) monitor.poll_timer.mock_tick(1.0) mock_get.assert_has_calls([ mock.call('http://test.com/carStatus/1', timeout=10), mock.call('http://test.com/carStatus/2', timeout=10), ]) # Assert that it's only making two requests. self.assertEqual(mock_get.call_count, 2) mock_alert.assert_not_called() def test_polling_request_throttling(self): request_times = [] def mock_get_response(url, timeout=999): request_times.append(time.time()) time.sleep(0.1) return { '1': CAR_1_INSIDE_GEOFENCE_RESPONSE, '2': CAR_2_INSIDE_SECOND_GEOFENCE_RESPONSE, }[re.search(r'-?\d+$', url).group()] with mock.patch('requests.get', side_effect=mock_get_response) as mock_get: geofence_monitor.start([ '1-2', 'http://test.com', '--car_status_url=http://test.com/carStatus/%s', '--max_query_qps=2.0', '--poll_period_s=10', '--min_poll_padding_period_s=0', ]) monitor.poll_timer.mock_tick(1.0) mock_get.assert_has_calls([ mock.call('http://test.com/carStatus/1', timeout=10), mock.call('http://test.com/carStatus/2', timeout=10), ]) self.assertEqual(mock_get.call_count, 2) self.assertTrue(request_times[1] - request_times[0] >= 0.5) if __name__ == '__main__': unittest.main()

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e5fae66cec05256fc40aa3b06fae7ab4da2d8c3a

26

py

Python

pyvision/gans/wasserstein_gan/__init__.py

indiradutta/PyVision

cf74da32a3469ddcce9917ac1f2fcaaeefdeacdf

[ "BSD-3-Clause" ]

31

2020-05-03T07:03:01.000Z

2022-01-29T15:29:22.000Z

pyvision/gans/wasserstein_gan/__init__.py

indiradutta/PyVision

cf74da32a3469ddcce9917ac1f2fcaaeefdeacdf

[ "BSD-3-Clause" ]

13

2020-05-25T14:23:46.000Z

2021-08-04T10:38:02.000Z

pyvision/gans/wasserstein_gan/__init__.py

indiradutta/PyVision

cf74da32a3469ddcce9917ac1f2fcaaeefdeacdf

[ "BSD-3-Clause" ]

12

2020-05-24T22:26:59.000Z

2021-08-03T18:30:51.000Z

from .model import WassGAN

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0093878d04965d4a664d586ef86a54d374b8e646

431

py

Python

PYTHON/Operators/assignments.py

YakazaSTG/Python-Basics

35cbc9a7b30dd985922bb288d67a1cf10d4da40c

[ "MIT" ]

null

PYTHON/Operators/assignments.py

YakazaSTG/Python-Basics

35cbc9a7b30dd985922bb288d67a1cf10d4da40c

[ "MIT" ]

null

PYTHON/Operators/assignments.py

YakazaSTG/Python-Basics

35cbc9a7b30dd985922bb288d67a1cf10d4da40c

[ "MIT" ]

null

# x = 5 # y = 10 # z = 20 # x, y, z = 5, 16, 20 # x, y = y, x # x += 5 #x = x + 5 # x -= 5 #x = x - 5 # x *= 5 #x = x * 5 # x /= 5 #x = x / 5 # x %= 5 #x = x % 5 # y //= 5 #y = y // 5 # y **= z #y = y ** z values = 1, 2, 3, 4, 5 print(values) print(type(values)) x, y, *z = values print(x, y, z) print(x, y, z[1])

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00e3eb0d436953a3bfa91a7e2a369c88e208e027

5,822

py

Python

src/attention.py

RobinQrtz/egglayingwoolmilksow

fea7d6a58f9387c4139c4cc2c96b353c9dcd0fca

[ "MIT" ]

null

src/attention.py

RobinQrtz/egglayingwoolmilksow

fea7d6a58f9387c4139c4cc2c96b353c9dcd0fca

[ "MIT" ]

null

src/attention.py

RobinQrtz/egglayingwoolmilksow

fea7d6a58f9387c4139c4cc2c96b353c9dcd0fca

[ "MIT" ]

null

import torch import torch.nn.functional as F from utils import batched_concat_per_row, create_parameter class Attention: @staticmethod def edge_factory(dim, attention_type): if attention_type == "bilinear": return BilinearEdgeAttention(dim) elif attention_type == "biaffine": return BiaffineEdgeAttention(dim) elif attention_type == "affine": return AffineEdgeAttention(dim) else: raise Exception("{attention_type} is not a valid attention type".format(attention_type)) @staticmethod def label_factory(dim, n_labels, attention_type): if attention_type == "bilinear": return BilinearLabelAttention(dim, n_labels) elif attention_type == "biaffine": return BiaffineLabelAttention(dim, n_labels) elif attention_type == "affine": return AffineLabelAttention(dim, n_labels) else: raise Exception("{attention_type} is not a valid attention type".format(attention_type)) def get_label_scores(self, head, dep): # head, dep: [sequence x batch x mlp] raise NotImplementedError() def get_edge_scores(self, head, dep): # head, dep: [sequence x batch x mlp] raise NotImplementedError() class BilinearEdgeAttention(torch.nn.Module): def __init__(self, dim): super().__init__() self.edge_U = create_parameter(dim, dim) def forward(self, head, dep): # head, dep: [batch x sequence x mlp] # (batch x seq x seq) return torch.einsum("bij,jk,bok->bio", (head, self.edge_U, dep)) class BilinearLabelAttention(torch.nn.Module): def __init__(self, dim, n_labels): super().__init__() self.label_U_diag = create_parameter(n_labels, dim) def forward(self, head, dep): # head, dep: [batch x sequence x mlp] # (batch x label x seq x seq) return torch.einsum("bij,lj,boj->blio", (head, self.label_U_diag, dep)) class BiaffineEdgeAttention(torch.nn.Module): def __init__(self, dim): super().__init__() self.edge_U = create_parameter(dim, dim) self.edge_W = create_parameter(1, 2 * dim) self.edge_b = create_parameter(1) def forward(self, head, dep): # head, dep: [batch x sequence x mlp] batch_size = head.size(0) sequence_size = head.size(1) # (batch x seq x seq) t1 = torch.einsum("bij,jk,bok->bio", (head, self.edge_U, dep)) # (batch x seq*seq x 2mlp) concated = batched_concat_per_row(head, dep) # (1 x 2mlp) @ (batch x 2mlp x seq*seq) => (batch x 1 x seq*seq) t2 = self.edge_W @ concated.transpose(1, 2) # (batch x 1 x seq*seq) => (batch x seq x seq) t2 = t2.view(batch_size, sequence_size, sequence_size) return t1 + t2 + self.edge_b class BiaffineLabelAttention(torch.nn.Module): def __init__(self, dim, n_labels): super().__init__() self.label_U_diag = create_parameter(n_labels, dim) self.label_W = create_parameter(n_labels, 2 * dim) self.label_b = create_parameter(n_labels) self.n_labels = n_labels def forward(self, head, dep): # head, dep: [batch x sequence x mlp] batch_size = head.size(0) sequence_size = head.size(1) # (batch x label x seq x seq) t1 = torch.einsum("bij,lj,boj->blio", (head, self.label_U_diag, dep)) # (batch x seq*seq x 2mlp) concated = batched_concat_per_row(head, dep) # (labels x 2mlp) @ (batch x 2mlp x seq*seq) => (batch x labels x seq*seq) t2 = self.label_W @ concated.transpose(1, 2) # (batch x labels x seq*seq) => (batch x labels x seq x seq) t2 = t2.view(batch_size, self.n_labels, sequence_size, sequence_size) return t1 + t2 + self.label_b[None, :, None, None] class AffineLabelAttention(torch.nn.Module): def __init__(self, dim, n_labels): super().__init__() self.label_W = create_parameter(n_labels, 2 * dim) self.label_b = create_parameter(n_labels) self.n_labels = n_labels def forward(self, head, dep): # head, dep: [batch x sequence x mlp] batch_size = head.size(0) sequence_size = head.size(1) # (batch x seq*seq x 2mlp) concated = batched_concat_per_row(head, dep) # (labels x 2mlp) @ (batch x 2mlp x seq*seq) => (batch x labels x seq*seq) t2 = self.label_W @ concated.transpose(1, 2) # (batch x labels x seq*seq) => (batch x labels x seq x seq) t2 = t2.view(batch_size, self.n_labels, sequence_size, sequence_size) return t2 + self.label_b[None, :, None, None] class AffineEdgeAttention(torch.nn.Module): def __init__(self, dim): super().__init__() self.edge_W = create_parameter(1, 2 * dim) self.edge_b = create_parameter(1) def forward(self, head, dep): # head, dep: [batch x sequence x mlp] batch_size = head.size(0) sequence_size = head.size(1) # (batch x seq*seq x 2mlp) concated = batched_concat_per_row(head, dep) # (1 x 2mlp) @ (batch x 2mlp x seq*seq) => (batch x 1 x seq*seq) t2 = self.edge_W @ concated.transpose(1, 2) # (batch x 1 x seq*seq) => (batch x seq x seq) t2 = t2.view(batch_size, sequence_size, sequence_size) return t2 + self.edge_b class DotProductAttention(torch.nn.Module): def __init__(self, dim): super().__init__() self.dk = dim ** 0.5#torch.sqrt(dk) def forward(self, attention_matrix, output): # TODO really dim=1? attention_matrix = attention_matrix am = F.softmax(attention_matrix.transpose(-2,-1) * self.dk, dim=1) @ output return am

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null

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da9def58cdcc6a2b60f4591db5bc4085cea327c1

107

py

Python

pybns/__init__.py

datadesk/py-bns

0754bc3e839b1e8b2a3dea74e151e0b7e146ffbd

[ "MIT" ]

3

2018-10-29T10:09:56.000Z

2021-03-07T19:21:02.000Z

pybns/__init__.py

datadesk/pyBNS

0754bc3e839b1e8b2a3dea74e151e0b7e146ffbd

[ "MIT" ]

4

2018-04-30T19:29:57.000Z

2018-04-30T19:31:34.000Z

pybns/__init__.py

datadesk/py-bns

0754bc3e839b1e8b2a3dea74e151e0b7e146ffbd

[ "MIT" ]

null

from __future__ import (absolute_import, division, print_function, unicode_literals) from builtins import *

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97b06a5a304ed3edceb84deed17fa403883798ac

20

py

Python

kubam/app/aci/__init__.py

cmconner156/KUBaM

d4cd132374c69b91dd7df0e099c9ec3b44a0b3ec

[ "Apache-2.0" ]

14

2017-07-21T18:10:18.000Z

2021-11-10T21:12:01.000Z

kubam/app/aci/__init__.py

cmconner156/KUBaM

d4cd132374c69b91dd7df0e099c9ec3b44a0b3ec

[ "Apache-2.0" ]

23

2017-08-28T19:43:19.000Z

2022-03-15T00:49:16.000Z

kubam/app/aci/__init__.py

CiscoUcs/KUBaM

0718a8245d56be060838e41f44765c746fbcdc4c

[ "Apache-2.0" ]

19

2017-09-19T19:18:56.000Z

2021-09-13T01:21:26.000Z

from aci import aci

10

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1

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6

97c801478e1c01fbc15205221d8ed0b87e2b5bb1

8,246

py

Python

jpake/parameters.py

bwhmather/python-jpake

7c9f3ebf2e5458f5721984b7295c59dc8390d4be

[ "BSD-3-Clause" ]

1

2016-10-10T21:36:22.000Z

jpake/parameters.py

bwhmather/python-jpake

7c9f3ebf2e5458f5721984b7295c59dc8390d4be

[ "BSD-3-Clause" ]

3

2017-09-07T19:24:17.000Z

2017-09-07T19:42:26.000Z

jpake/parameters.py

bwhmather/python-jpake

7c9f3ebf2e5458f5721984b7295c59dc8390d4be

[ "BSD-3-Clause" ]

2

2020-05-13T02:06:38.000Z

2020-05-13T20:05:22.000Z

class Parameters(object): def __init__(self, *, p, q, g): if isinstance(p, bytes): p = int.from_bytes(p, 'big') self.p = p if isinstance(q, bytes): q = int.from_bytes(q, 'big') self.q = q if isinstance(g, bytes): g = int.from_bytes(g, 'big') self.g = g NIST_80 = Parameters( p=( b'\xfd\x7f\x53\x81\x1d\x75\x12\x29\x52\xdf\x4a\x9c\x2e\xec\xe4\xe7' b'\xf6\x11\xb7\x52\x3c\xef\x44\x00\xc3\x1e\x3f\x80\xb6\x51\x26\x69' b'\x45\x5d\x40\x22\x51\xfb\x59\x3d\x8d\x58\xfa\xbf\xc5\xf5\xba\x30' b'\xf6\xcb\x9b\x55\x6c\xd7\x81\x3b\x80\x1d\x34\x6f\xf2\x66\x60\xb7' b'\x6b\x99\x50\xa5\xa4\x9f\x9f\xe8\x04\x7b\x10\x22\xc2\x4f\xbb\xa9' b'\xd7\xfe\xb7\xc6\x1b\xf8\x3b\x57\xe7\xc6\xa8\xa6\x15\x0f\x04\xfb' b'\x83\xf6\xd3\xc5\x1e\xc3\x02\x35\x54\x13\x5a\x16\x91\x32\xf6\x75' b'\xf3\xae\x2b\x61\xd7\x2a\xef\xf2\x22\x03\x19\x9d\xd1\x48\x01\xc7' ), q=( b'\x97\x60\x50\x8f\x15\x23\x0b\xcc\xb2\x92\xb9\x82\xa2\xeb\x84\x0b' b'\xf0\x58\x1c\xf5' ), g=( b'\xf7\xe1\xa0\x85\xd6\x9b\x3d\xde\xcb\xbc\xab\x5c\x36\xb8\x57\xb9' b'\x79\x94\xaf\xbb\xfa\x3a\xea\x82\xf9\x57\x4c\x0b\x3d\x07\x82\x67' b'\x51\x59\x57\x8e\xba\xd4\x59\x4f\xe6\x71\x07\x10\x81\x80\xb4\x49' b'\x16\x71\x23\xe8\x4c\x28\x16\x13\xb7\xcf\x09\x32\x8c\xc8\xa6\xe1' b'\x3c\x16\x7a\x8b\x54\x7c\x8d\x28\xe0\xa3\xae\x1e\x2b\xb3\xa6\x75' b'\x91\x6e\xa3\x7f\x0b\xfa\x21\x35\x62\xf1\xfb\x62\x7a\x01\x24\x3b' b'\xcc\xa4\xf1\xbe\xa8\x51\x90\x89\xa8\x83\xdf\xe1\x5a\xe5\x9f\x06' b'\x92\x8b\x66\x5e\x80\x7b\x55\x25\x64\x01\x4c\x3b\xfe\xcf\x49\x2a' ), ) NIST_112 = Parameters( p=( b'\xC1\x96\xBA\x05\xAC\x29\xE1\xF9\xC3\xC7\x2D\x56\xDF\xFC\x61\x54' b'\xA0\x33\xF1\x47\x7A\xC8\x8E\xC3\x7F\x09\xBE\x6C\x5B\xB9\x5F\x51' b'\xC2\x96\xDD\x20\xD1\xA2\x8A\x06\x7C\xCC\x4D\x43\x16\xA4\xBD\x1D' b'\xCA\x55\xED\x10\x66\xD4\x38\xC3\x5A\xEB\xAA\xBF\x57\xE7\xDA\xE4' b'\x28\x78\x2A\x95\xEC\xA1\xC1\x43\xDB\x70\x1F\xD4\x85\x33\xA3\xC1' b'\x8F\x0F\xE2\x35\x57\xEA\x7A\xE6\x19\xEC\xAC\xC7\xE0\xB5\x16\x52' b'\xA8\x77\x6D\x02\xA4\x25\x56\x7D\xED\x36\xEA\xBD\x90\xCA\x33\xA1' b'\xE8\xD9\x88\xF0\xBB\xB9\x2D\x02\xD1\xD2\x02\x90\x11\x3B\xB5\x62' b'\xCE\x1F\xC8\x56\xEE\xB7\xCD\xD9\x2D\x33\xEE\xA6\xF4\x10\x85\x9B' b'\x17\x9E\x7E\x78\x9A\x8F\x75\xF6\x45\xFA\xE2\xE1\x36\xD2\x52\xBF' b'\xFA\xFF\x89\x52\x89\x45\xC1\xAB\xE7\x05\xA3\x8D\xBC\x2D\x36\x4A' b'\xAD\xE9\x9B\xE0\xD0\xAA\xD8\x2E\x53\x20\x12\x14\x96\xDC\x65\xB3' b'\x93\x0E\x38\x04\x72\x94\xFF\x87\x78\x31\xA1\x6D\x52\x28\x41\x8D' b'\xE8\xAB\x27\x5D\x7D\x75\x65\x1C\xEF\xED\x65\xF7\x8A\xFC\x3E\xA7' b'\xFE\x4D\x79\xB3\x5F\x62\xA0\x40\x2A\x11\x17\x59\x9A\xDA\xC7\xB2' b'\x69\xA5\x9F\x35\x3C\xF4\x50\xE6\x98\x2D\x3B\x17\x02\xD9\xCA\x83' ), q=( b'\x90\xEA\xF4\xD1\xAF\x07\x08\xB1\xB6\x12\xFF\x35\xE0\xA2\x99\x7E' b'\xB9\xE9\xD2\x63\xC9\xCE\x65\x95\x28\x94\x5C\x0D' ), g=( b'\xA5\x9A\x74\x9A\x11\x24\x2C\x58\xC8\x94\xE9\xE5\xA9\x18\x04\xE8' b'\xFA\x0A\xC6\x4B\x56\x28\x8F\x8D\x47\xD5\x1B\x1E\xDC\x4D\x65\x44' b'\x4F\xEC\xA0\x11\x1D\x78\xF3\x5F\xC9\xFD\xD4\xCB\x1F\x1B\x79\xA3' b'\xBA\x9C\xBE\xE8\x3A\x3F\x81\x10\x12\x50\x3C\x81\x17\xF9\x8E\x50' b'\x48\xB0\x89\xE3\x87\xAF\x69\x49\xBF\x87\x84\xEB\xD9\xEF\x45\x87' b'\x6F\x2E\x6A\x5A\x49\x5B\xE6\x4B\x6E\x77\x04\x09\x49\x4B\x7F\xEE' b'\x1D\xBB\x1E\x4B\x2B\xC2\xA5\x3D\x4F\x89\x3D\x41\x8B\x71\x59\x59' b'\x2E\x4F\xFF\xDF\x69\x69\xE9\x1D\x77\x0D\xAE\xBD\x0B\x5C\xB1\x4C' b'\x00\xAD\x68\xEC\x7D\xC1\xE5\x74\x5E\xA5\x5C\x70\x6C\x4A\x1C\x5C' b'\x88\x96\x4E\x34\xD0\x9D\xEB\x75\x3A\xD4\x18\xC1\xAD\x0F\x4F\xDF' b'\xD0\x49\xA9\x55\xE5\xD7\x84\x91\xC0\xB7\xA2\xF1\x57\x5A\x00\x8C' b'\xCD\x72\x7A\xB3\x76\xDB\x6E\x69\x55\x15\xB0\x5B\xD4\x12\xF5\xB8' b'\xC2\xF4\xC7\x7E\xE1\x0D\xA4\x8A\xBD\x53\xF5\xDD\x49\x89\x27\xEE' b'\x7B\x69\x2B\xBB\xCD\xA2\xFB\x23\xA5\x16\xC5\xB4\x53\x3D\x73\x98' b'\x0B\x2A\x3B\x60\xE3\x84\xED\x20\x0A\xE2\x1B\x40\xD2\x73\x65\x1A' b'\xD6\x06\x0C\x13\xD9\x7F\xD6\x9A\xA1\x3C\x56\x11\xA5\x1B\x90\x85' ), ) NIST_128 = Parameters( p=( b'\x90\x06\x64\x55\xB5\xCF\xC3\x8F\x9C\xAA\x4A\x48\xB4\x28\x1F\x29' b'\x2C\x26\x0F\xEE\xF0\x1F\xD6\x10\x37\xE5\x62\x58\xA7\x79\x5A\x1C' b'\x7A\xD4\x60\x76\x98\x2C\xE6\xBB\x95\x69\x36\xC6\xAB\x4D\xCF\xE0' b'\x5E\x67\x84\x58\x69\x40\xCA\x54\x4B\x9B\x21\x40\xE1\xEB\x52\x3F' b'\x00\x9D\x20\xA7\xE7\x88\x0E\x4E\x5B\xFA\x69\x0F\x1B\x90\x04\xA2' b'\x78\x11\xCD\x99\x04\xAF\x70\x42\x0E\xEF\xD6\xEA\x11\xEF\x7D\xA1' b'\x29\xF5\x88\x35\xFF\x56\xB8\x9F\xAA\x63\x7B\xC9\xAC\x2E\xFA\xAB' b'\x90\x34\x02\x22\x9F\x49\x1D\x8D\x34\x85\x26\x1C\xD0\x68\x69\x9B' b'\x6B\xA5\x8A\x1D\xDB\xBE\xF6\xDB\x51\xE8\xFE\x34\xE8\xA7\x8E\x54' b'\x2D\x7B\xA3\x51\xC2\x1E\xA8\xD8\xF1\xD2\x9F\x5D\x5D\x15\x93\x94' b'\x87\xE2\x7F\x44\x16\xB0\xCA\x63\x2C\x59\xEF\xD1\xB1\xEB\x66\x51' b'\x1A\x5A\x0F\xBF\x61\x5B\x76\x6C\x58\x62\xD0\xBD\x8A\x3F\xE7\xA0' b'\xE0\xDA\x0F\xB2\xFE\x1F\xCB\x19\xE8\xF9\x99\x6A\x8E\xA0\xFC\xCD' b'\xE5\x38\x17\x52\x38\xFC\x8B\x0E\xE6\xF2\x9A\xF7\xF6\x42\x77\x3E' b'\xBE\x8C\xD5\x40\x24\x15\xA0\x14\x51\xA8\x40\x47\x6B\x2F\xCE\xB0' b'\xE3\x88\xD3\x0D\x4B\x37\x6C\x37\xFE\x40\x1C\x2A\x2C\x2F\x94\x1D' b'\xAD\x17\x9C\x54\x0C\x1C\x8C\xE0\x30\xD4\x60\xC4\xD9\x83\xBE\x9A' b'\xB0\xB2\x0F\x69\x14\x4C\x1A\xE1\x3F\x93\x83\xEA\x1C\x08\x50\x4F' b'\xB0\xBF\x32\x15\x03\xEF\xE4\x34\x88\x31\x0D\xD8\xDC\x77\xEC\x5B' b'\x83\x49\xB8\xBF\xE9\x7C\x2C\x56\x0E\xA8\x78\xDE\x87\xC1\x1E\x3D' b'\x59\x7F\x1F\xEA\x74\x2D\x73\xEE\xC7\xF3\x7B\xE4\x39\x49\xEF\x1A' b'\x0D\x15\xC3\xF3\xE3\xFC\x0A\x83\x35\x61\x70\x55\xAC\x91\x32\x8E' b'\xC2\x2B\x50\xFC\x15\xB9\x41\xD3\xD1\x62\x4C\xD8\x8B\xC2\x5F\x3E' b'\x94\x1F\xDD\xC6\x20\x06\x89\x58\x1B\xFE\xC4\x16\xB4\xB2\xCB\x73' ), q=( b'\xCF\xA0\x47\x8A\x54\x71\x7B\x08\xCE\x64\x80\x5B\x76\xE5\xB1\x42' b'\x49\xA7\x7A\x48\x38\x46\x9D\xF7\xF7\xDC\x98\x7E\xFC\xCF\xB1\x1D' ), g=( b'\x5E\x5C\xBA\x99\x2E\x0A\x68\x0D\x88\x5E\xB9\x03\xAE\xA7\x8E\x4A' b'\x45\xA4\x69\x10\x3D\x44\x8E\xDE\x3B\x7A\xCC\xC5\x4D\x52\x1E\x37' b'\xF8\x4A\x4B\xDD\x5B\x06\xB0\x97\x0C\xC2\xD2\xBB\xB7\x15\xF7\xB8' b'\x28\x46\xF9\xA0\xC3\x93\x91\x4C\x79\x2E\x6A\x92\x3E\x21\x17\xAB' b'\x80\x52\x76\xA9\x75\xAA\xDB\x52\x61\xD9\x16\x73\xEA\x9A\xAF\xFE' b'\xEC\xBF\xA6\x18\x3D\xFC\xB5\xD3\xB7\x33\x2A\xA1\x92\x75\xAF\xA1' b'\xF8\xEC\x0B\x60\xFB\x6F\x66\xCC\x23\xAE\x48\x70\x79\x1D\x59\x82' b'\xAA\xD1\xAA\x94\x85\xFD\x8F\x4A\x60\x12\x6F\xEB\x2C\xF0\x5D\xB8' b'\xA7\xF0\xF0\x9B\x33\x97\xF3\x93\x7F\x2E\x90\xB9\xE5\xB9\xC9\xB6' b'\xEF\xEF\x64\x2B\xC4\x83\x51\xC4\x6F\xB1\x71\xB9\xBF\xA9\xEF\x17' b'\xA9\x61\xCE\x96\xC7\xE7\xA7\xCC\x3D\x3D\x03\xDF\xAD\x10\x78\xBA' b'\x21\xDA\x42\x51\x98\xF0\x7D\x24\x81\x62\x2B\xCE\x45\x96\x9D\x9C' b'\x4D\x60\x63\xD7\x2A\xB7\xA0\xF0\x8B\x2F\x49\xA7\xCC\x6A\xF3\x35' b'\xE0\x8C\x47\x20\xE3\x14\x76\xB6\x72\x99\xE2\x31\xF8\xBD\x90\xB3' b'\x9A\xC3\xAE\x3B\xE0\xC6\xB6\xCA\xCE\xF8\x28\x9A\x2E\x28\x73\xD5' b'\x8E\x51\xE0\x29\xCA\xFB\xD5\x5E\x68\x41\x48\x9A\xB6\x6B\x5B\x4B' b'\x9B\xA6\xE2\xF7\x84\x66\x08\x96\xAF\xF3\x87\xD9\x28\x44\xCC\xB8' b'\xB6\x94\x75\x49\x6D\xE1\x9D\xA2\xE5\x82\x59\xB0\x90\x48\x9A\xC8' b'\xE6\x23\x63\xCD\xF8\x2C\xFD\x8E\xF2\xA4\x27\xAB\xCD\x65\x75\x0B' b'\x50\x6F\x56\xDD\xE3\xB9\x88\x56\x7A\x88\x12\x6B\x91\x4D\x78\x28' b'\xE2\xB6\x3A\x6D\x7E\xD0\x74\x7E\xC5\x9E\x0E\x0A\x23\xCE\x7D\x8A' b'\x74\xC1\xD2\xC2\xA7\xAF\xB6\xA2\x97\x99\x62\x0F\x00\xE1\x1C\x33' b'\x78\x7F\x7D\xED\x3B\x30\xE1\xA2\x2D\x09\xF1\xFB\xDA\x1A\xBB\xBF' b'\xBF\x25\xCA\xE0\x5A\x13\xF8\x12\xE3\x45\x63\xF9\x94\x10\xE7\x3B' ), )

56.479452

75

0.62782

1,784

8,246

2.8963

0.154148

0.003097

0.006967

0

0.279752

0.139947

8,246

145

76

56.868966

0.448816

0

0.131387

0

0.737226

0.784987

0.781955

0

1

0

1

0.007299

false

0

0.014599

0

null

0

1

0

1

0

1

null

1

0

6

c104adfbd5de2599d82f6d9ab507bf60b410239f

26

py

Python

modules/tests/asset/__init__.py

andygimma/eden

716d5e11ec0030493b582fa67d6f1c35de0af50d

[ "MIT" ]

1

2019-08-20T16:32:33.000Z

modules/tests/asset/__init__.py

andygimma/eden

716d5e11ec0030493b582fa67d6f1c35de0af50d

[ "MIT" ]

null

modules/tests/asset/__init__.py

andygimma/eden

716d5e11ec0030493b582fa67d6f1c35de0af50d

[ "MIT" ]

null

from create_asset import *

26

0.846154

4

26

5.25

1

0

0.115385

26

1

26

0.913043

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

c119faacd6c334952a693360789b0f7ab17fc630

52

py

Python

catwatch/blueprints/stream/__init__.py

Pythonian/catwatch

25730faa9d8ec6564b075de78bbbf4ff125ada97

[ "MIT" ]

null

catwatch/blueprints/stream/__init__.py

Pythonian/catwatch

25730faa9d8ec6564b075de78bbbf4ff125ada97

[ "MIT" ]

null

catwatch/blueprints/stream/__init__.py

Pythonian/catwatch

25730faa9d8ec6564b075de78bbbf4ff125ada97

[ "MIT" ]

2

2018-08-04T16:46:55.000Z

2019-07-02T19:30:24.000Z

from catwatch.blueprints.stream.views import stream

26

51

0.865385

7

52

6.428571

0.857143

0

0.076923

52

1

52

0.9375

0

1

0

true

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

c146074d79cc5fe42bb7a094b7e410c627d8cd36

30,685

py

Python

python/manipulator_x_demo/manipulatorx_6dof.py

ROBOTIS-Leon/ManipulatorXdemo

c491a15211bdbe64cd89149b6e3a96f2744b0b93

[ "BSD-3-Clause" ]

null

python/manipulator_x_demo/manipulatorx_6dof.py

ROBOTIS-Leon/ManipulatorXdemo

c491a15211bdbe64cd89149b6e3a96f2744b0b93

[ "BSD-3-Clause" ]

1

2016-07-11T08:51:30.000Z

python/manipulator_x_demo/manipulatorx_6dof.py

ROBOTIS-Leon/ManipulatorXdemo

c491a15211bdbe64cd89149b6e3a96f2744b0b93

[ "BSD-3-Clause" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- # # sync_read_write.py # # Created on: 2016. 6. 16. # Author: Ryu Woon Jung (Leon) # # # ********* Sync Read and Sync Write Example ********* # # # Available Dynamixel model on this example : All models using Protocol 2.0 # This example is designed for using two Dynamixel PRO 54-200, and an USB2DYNAMIXEL. # To use another Dynamixel model, such as X series, see their details in E-Manual(support.robotis.com) and edit below variables yourself. # Be sure that Dynamixel PRO properties are already set as %% ID : 1 / Baudnum : 3 (Baudrate : 1000000 [1M]) # import os, ctypes, time if os.name == 'nt': import msvcrt def getch(): return msvcrt.getch().decode() else: import sys, tty, termios fd = sys.stdin.fileno() old_settings = termios.tcgetattr(fd) def getch(): try: tty.setraw(sys.stdin.fileno()) ch = sys.stdin.read(1) finally: termios.tcsetattr(fd, termios.TCSADRAIN, old_settings) return ch os.sys.path.append('../dynamixel_functions_py') # Path setting import dynamixel_functions as dynamixel # Uses Dynamixel SDK library # Control table address ADDR_XM430_ACCELERATION_LIMIT = 40 # Control table address is different in Dynamixel model ADDR_XM430_VELOCITY_LIMIT = 44 ADDR_XM430_TORQUE_ENABLE = 64 ADDR_XM430_POSITION_P_GAIN = 84 ADDR_XM430_PROF_ACCELERATION = 108 ADDR_XM430_PROF_VELOCITY = 112 ADDR_XM430_GOAL_POSITION = 116 ADDR_XM430_PRESENT_POSITION = 132 # Data Byte Length LEN_XM430_GOAL_POSITION = 4 LEN_XM430_PRESENT_POSITION = 4 LEN_XM430_GOAL_VELOCITY = 2 # Protocol version PROTOCOL_VERSION = 2 # See which protocol version is used in the Dynamixel # Default setting DXL1_ID = 1 # Dynamixel ID: 1 DXL2_ID = 2 # Dynamixel ID: 2 DXL3_ID = 3 # Dynamixel ID: 3 DXL4_ID = 4 # Dynamixel ID: 4 DXL5_ID = 5 # Dynamixel ID: 5 DXL6_ID = 6 # Dynamixel ID: 6 DXL7_ID = 7 # Dynamixel ID: 7 BAUDRATE = 1000000 DEVICENAME = "/dev/ttyUSB0".encode('utf-8') # Check which port is being used on your controller # ex) Windows: "COM1" Linux: "/dev/ttyUSB0" TORQUE_ENABLE = 1 # Value for enabling the torque TORQUE_DISABLE = 0 # Value for disabling the torque DXL_MOVING_STATUS_THRESHOLD = 30 # Dynamixel moving status threshold ESC_ASCII_VALUE = 0x1b COMM_SUCCESS = 0 # Communication Success result value COMM_TX_FAIL = -1001 # Communication Tx Failed # Initialize PortHandler Structs # Set the port path # Get methods and members of PortHandlerLinux or PortHandlerWindows port_num = dynamixel.portHandler(DEVICENAME) # Initialize PacketHandler Structs dynamixel.packetHandler() # Initialize Groupsyncwrite instance groupwrite_num = dynamixel.groupSyncWrite(port_num, PROTOCOL_VERSION, ADDR_XM430_GOAL_POSITION, LEN_XM430_GOAL_POSITION) # Initialize Groupsyncread Structs for Present Position groupread_num = dynamixel.groupSyncRead(port_num, PROTOCOL_VERSION, ADDR_XM430_PRESENT_POSITION, LEN_XM430_PRESENT_POSITION) index = 0 dxl_comm_result = COMM_TX_FAIL # Communication result dxl_addparam_result = 0 # AddParam result dxl_getdata_result = 0 # GetParam result dxl_goal_position = [ [2048, 2048, 3072, 2048, 2048, 2048, 2700], # Init v [1649, 2028, 1626, 1527, 2683, 2426, 2000], # Bow v [1853, 2677, 2373, 1731, 2446, 2340, 2000], # Move v [1853, 2677, 2373, 1731, 2446, 2340, 2560], # Grab [2048, 2048, 3072, 2048, 2048, 2048, 2560], # Center [ 1, 2048, 3072, 2048, 2048, 2048, 2560], # Center 2 [ 229, 1122, 3280, 2595, 1793, 1517, 2560], # Move [ 229, 1122, 3280, 2595, 1793, 1517, 2000], # Loose [ 1, 2048, 3072, 2048, 2048, 2048, 2700], # Center [2048, 2048, 3072, 2048, 2048, 2048, 2700]] # Center2 # [2176, 2048, 750, 2400, 2400], # Bow # [2048, 2048, 2048, 2048, 2700]] # Center dxl_position_p_gain = 700 dxl_acc_limit = 4 dxl_vel_limit = 20 dxl_prof_acc = 4 dxl_prof_vel = 20 dxl_error = 0 # Dynamixel error # Open port if dynamixel.openPort(port_num): print("Succeeded to open the port!") else: print("Failed to open the port!") print("Press any key to terminate...") getch() quit() # Set port baudrate if dynamixel.setBaudRate(port_num, BAUDRATE): print("Succeeded to change the baudrate!") else: print("Failed to change the baudrate!") print("Press any key to terminate...") getch() quit() # Acceleration Limit #1 dynamixel.write4ByteTxRx(port_num, PROTOCOL_VERSION, DXL1_ID, ADDR_XM430_ACCELERATION_LIMIT, dxl_acc_limit) # Acceleration Limit #2 dynamixel.write4ByteTxRx(port_num, PROTOCOL_VERSION, DXL2_ID, ADDR_XM430_ACCELERATION_LIMIT, dxl_acc_limit) # Acceleration Limit #3 dynamixel.write4ByteTxRx(port_num, PROTOCOL_VERSION, DXL3_ID, ADDR_XM430_ACCELERATION_LIMIT, dxl_acc_limit) # Acceleration Limit #4 dynamixel.write4ByteTxRx(port_num, PROTOCOL_VERSION, DXL4_ID, ADDR_XM430_ACCELERATION_LIMIT, dxl_acc_limit) # Acceleration Limit #5 dynamixel.write4ByteTxRx(port_num, PROTOCOL_VERSION, DXL5_ID, ADDR_XM430_ACCELERATION_LIMIT, dxl_acc_limit) # Acceleration Limit #6 dynamixel.write4ByteTxRx(port_num, PROTOCOL_VERSION, DXL6_ID, ADDR_XM430_ACCELERATION_LIMIT, dxl_acc_limit) # Acceleration Limit #7 dynamixel.write4ByteTxRx(port_num, PROTOCOL_VERSION, DXL7_ID, ADDR_XM430_ACCELERATION_LIMIT, dxl_acc_limit) # Velocity Limit #1 dynamixel.write4ByteTxRx(port_num, PROTOCOL_VERSION, DXL1_ID, ADDR_XM430_VELOCITY_LIMIT, dxl_vel_limit) # Velocity Limit #2 dynamixel.write4ByteTxRx(port_num, PROTOCOL_VERSION, DXL2_ID, ADDR_XM430_VELOCITY_LIMIT, dxl_vel_limit) # Velocity Limit #3 dynamixel.write4ByteTxRx(port_num, PROTOCOL_VERSION, DXL3_ID, ADDR_XM430_VELOCITY_LIMIT, dxl_vel_limit) # Velocity Limit #4 dynamixel.write4ByteTxRx(port_num, PROTOCOL_VERSION, DXL4_ID, ADDR_XM430_VELOCITY_LIMIT, dxl_vel_limit) # Velocity Limit #5 dynamixel.write4ByteTxRx(port_num, PROTOCOL_VERSION, DXL5_ID, ADDR_XM430_VELOCITY_LIMIT, dxl_vel_limit) # Velocity Limit #6 dynamixel.write4ByteTxRx(port_num, PROTOCOL_VERSION, DXL6_ID, ADDR_XM430_VELOCITY_LIMIT, dxl_vel_limit) # Velocity Limit #7 dynamixel.write4ByteTxRx(port_num, PROTOCOL_VERSION, DXL7_ID, ADDR_XM430_VELOCITY_LIMIT, dxl_vel_limit) # Enable Dynamixel#1 Torque dynamixel.write1ByteTxRx(port_num, PROTOCOL_VERSION, DXL1_ID, ADDR_XM430_TORQUE_ENABLE, TORQUE_ENABLE) if dynamixel.getLastTxRxResult(port_num, PROTOCOL_VERSION) != COMM_SUCCESS: dynamixel.printTxRxResult(PROTOCOL_VERSION, dynamixel.getLastTxRxResult(port_num, PROTOCOL_VERSION)) elif dynamixel.getLastRxPacketError(port_num, PROTOCOL_VERSION) != 0: dynamixel.printRxPacketError(PROTOCOL_VERSION, dynamixel.getLastRxPacketError(port_num, PROTOCOL_VERSION)) else: print("Dynamixel#1 has been successfully connected") # Enable Dynamixel#2 Torque dynamixel.write1ByteTxRx(port_num, PROTOCOL_VERSION, DXL2_ID, ADDR_XM430_TORQUE_ENABLE, TORQUE_ENABLE) if dynamixel.getLastTxRxResult(port_num, PROTOCOL_VERSION) != COMM_SUCCESS: dynamixel.printTxRxResult(PROTOCOL_VERSION, dynamixel.getLastTxRxResult(port_num, PROTOCOL_VERSION)) elif dynamixel.getLastRxPacketError(port_num, PROTOCOL_VERSION) != 0: dynamixel.printRxPacketError(PROTOCOL_VERSION, dynamixel.getLastRxPacketError(port_num, PROTOCOL_VERSION)) else: print("Dynamixel#2 has been successfully connected") # Enable Dynamixel#3 Torque dynamixel.write1ByteTxRx(port_num, PROTOCOL_VERSION, DXL3_ID, ADDR_XM430_TORQUE_ENABLE, TORQUE_ENABLE) if dynamixel.getLastTxRxResult(port_num, PROTOCOL_VERSION) != COMM_SUCCESS: dynamixel.printTxRxResult(PROTOCOL_VERSION, dynamixel.getLastTxRxResult(port_num, PROTOCOL_VERSION)) elif dynamixel.getLastRxPacketError(port_num, PROTOCOL_VERSION) != 0: dynamixel.printRxPacketError(PROTOCOL_VERSION, dynamixel.getLastRxPacketError(port_num, PROTOCOL_VERSION)) else: print("Dynamixel#3 has been successfully connected") # Enable Dynamixel#4 Torque dynamixel.write1ByteTxRx(port_num, PROTOCOL_VERSION, DXL4_ID, ADDR_XM430_TORQUE_ENABLE, TORQUE_ENABLE) if dynamixel.getLastTxRxResult(port_num, PROTOCOL_VERSION) != COMM_SUCCESS: dynamixel.printTxRxResult(PROTOCOL_VERSION, dynamixel.getLastTxRxResult(port_num, PROTOCOL_VERSION)) elif dynamixel.getLastRxPacketError(port_num, PROTOCOL_VERSION) != 0: dynamixel.printRxPacketError(PROTOCOL_VERSION, dynamixel.getLastRxPacketError(port_num, PROTOCOL_VERSION)) else: print("Dynamixel#4 has been successfully connected") # Enable Dynamixel#5 Torque dynamixel.write1ByteTxRx(port_num, PROTOCOL_VERSION, DXL5_ID, ADDR_XM430_TORQUE_ENABLE, TORQUE_ENABLE) if dynamixel.getLastTxRxResult(port_num, PROTOCOL_VERSION) != COMM_SUCCESS: dynamixel.printTxRxResult(PROTOCOL_VERSION, dynamixel.getLastTxRxResult(port_num, PROTOCOL_VERSION)) elif dynamixel.getLastRxPacketError(port_num, PROTOCOL_VERSION) != 0: dynamixel.printRxPacketError(PROTOCOL_VERSION, dynamixel.getLastRxPacketError(port_num, PROTOCOL_VERSION)) else: print("Dynamixel#5 has been successfully connected") # Enable Dynamixel#6 Torque dynamixel.write1ByteTxRx(port_num, PROTOCOL_VERSION, DXL6_ID, ADDR_XM430_TORQUE_ENABLE, TORQUE_ENABLE) if dynamixel.getLastTxRxResult(port_num, PROTOCOL_VERSION) != COMM_SUCCESS: dynamixel.printTxRxResult(PROTOCOL_VERSION, dynamixel.getLastTxRxResult(port_num, PROTOCOL_VERSION)) elif dynamixel.getLastRxPacketError(port_num, PROTOCOL_VERSION) != 0: dynamixel.printRxPacketError(PROTOCOL_VERSION, dynamixel.getLastRxPacketError(port_num, PROTOCOL_VERSION)) else: print("Dynamixel#6 has been successfully connected") # Enable Dynamixel#7 Torque dynamixel.write1ByteTxRx(port_num, PROTOCOL_VERSION, DXL7_ID, ADDR_XM430_TORQUE_ENABLE, TORQUE_ENABLE) if dynamixel.getLastTxRxResult(port_num, PROTOCOL_VERSION) != COMM_SUCCESS: dynamixel.printTxRxResult(PROTOCOL_VERSION, dynamixel.getLastTxRxResult(port_num, PROTOCOL_VERSION)) elif dynamixel.getLastRxPacketError(port_num, PROTOCOL_VERSION) != 0: dynamixel.printRxPacketError(PROTOCOL_VERSION, dynamixel.getLastRxPacketError(port_num, PROTOCOL_VERSION)) else: print("Dynamixel#7 has been successfully connected") # Profile Acceleration Limit #1 dynamixel.write4ByteTxRx(port_num, PROTOCOL_VERSION, DXL1_ID, ADDR_XM430_PROF_ACCELERATION, dxl_prof_acc) # Profile Acceleration Limit #2 dynamixel.write4ByteTxRx(port_num, PROTOCOL_VERSION, DXL2_ID, ADDR_XM430_PROF_ACCELERATION, dxl_prof_acc) # Profile Acceleration Limit #3 dynamixel.write4ByteTxRx(port_num, PROTOCOL_VERSION, DXL3_ID, ADDR_XM430_PROF_ACCELERATION, dxl_prof_acc) # Profile Acceleration Limit #4 dynamixel.write4ByteTxRx(port_num, PROTOCOL_VERSION, DXL4_ID, ADDR_XM430_PROF_ACCELERATION, dxl_prof_acc) # Profile Acceleration Limit #5 dynamixel.write4ByteTxRx(port_num, PROTOCOL_VERSION, DXL5_ID, ADDR_XM430_PROF_ACCELERATION, dxl_prof_acc) # Profile Acceleration Limit #6 dynamixel.write4ByteTxRx(port_num, PROTOCOL_VERSION, DXL6_ID, ADDR_XM430_PROF_ACCELERATION, dxl_prof_acc) # Profile Acceleration Limit #7 dynamixel.write4ByteTxRx(port_num, PROTOCOL_VERSION, DXL7_ID, ADDR_XM430_PROF_ACCELERATION, dxl_prof_acc) # Profile Velocity Limit #1 dynamixel.write4ByteTxRx(port_num, PROTOCOL_VERSION, DXL1_ID, ADDR_XM430_PROF_VELOCITY, dxl_prof_vel) # Profile Velocity Limit #2 dynamixel.write4ByteTxRx(port_num, PROTOCOL_VERSION, DXL2_ID, ADDR_XM430_PROF_VELOCITY, dxl_prof_vel) # Profile Velocity Limit #3 dynamixel.write4ByteTxRx(port_num, PROTOCOL_VERSION, DXL3_ID, ADDR_XM430_PROF_VELOCITY, dxl_prof_vel) # Profile Velocity Limit #4 dynamixel.write4ByteTxRx(port_num, PROTOCOL_VERSION, DXL4_ID, ADDR_XM430_PROF_VELOCITY, dxl_prof_vel) # Profile Velocity Limit #5 dynamixel.write4ByteTxRx(port_num, PROTOCOL_VERSION, DXL5_ID, ADDR_XM430_PROF_VELOCITY, dxl_prof_vel) # Profile Velocity Limit #6 dynamixel.write4ByteTxRx(port_num, PROTOCOL_VERSION, DXL6_ID, ADDR_XM430_PROF_VELOCITY, dxl_prof_vel) # Profile Velocity Limit #7 dynamixel.write4ByteTxRx(port_num, PROTOCOL_VERSION, DXL7_ID, ADDR_XM430_PROF_VELOCITY, dxl_prof_vel) # Enable position p gain #1 dynamixel.write2ByteTxRx(port_num, PROTOCOL_VERSION, DXL1_ID, ADDR_XM430_POSITION_P_GAIN, dxl_position_p_gain) if dynamixel.getLastTxRxResult(port_num, PROTOCOL_VERSION) != COMM_SUCCESS: dynamixel.printTxRxResult(PROTOCOL_VERSION, dynamixel.getLastTxRxResult(port_num, PROTOCOL_VERSION)) elif dynamixel.getLastRxPacketError(port_num, PROTOCOL_VERSION) != 0: dynamixel.printRxPacketError(PROTOCOL_VERSION, dynamixel.getLastRxPacketError(port_num, PROTOCOL_VERSION)) # Enable position p gain #2 dynamixel.write2ByteTxRx(port_num, PROTOCOL_VERSION, DXL2_ID, ADDR_XM430_POSITION_P_GAIN, dxl_position_p_gain) if dynamixel.getLastTxRxResult(port_num, PROTOCOL_VERSION) != COMM_SUCCESS: dynamixel.printTxRxResult(PROTOCOL_VERSION, dynamixel.getLastTxRxResult(port_num, PROTOCOL_VERSION)) elif dynamixel.getLastRxPacketError(port_num, PROTOCOL_VERSION) != 0: dynamixel.printRxPacketError(PROTOCOL_VERSION, dynamixel.getLastRxPacketError(port_num, PROTOCOL_VERSION)) # Enable position p gain #3 dynamixel.write2ByteTxRx(port_num, PROTOCOL_VERSION, DXL3_ID, ADDR_XM430_POSITION_P_GAIN, dxl_position_p_gain) if dynamixel.getLastTxRxResult(port_num, PROTOCOL_VERSION) != COMM_SUCCESS: dynamixel.printTxRxResult(PROTOCOL_VERSION, dynamixel.getLastTxRxResult(port_num, PROTOCOL_VERSION)) elif dynamixel.getLastRxPacketError(port_num, PROTOCOL_VERSION) != 0: dynamixel.printRxPacketError(PROTOCOL_VERSION, dynamixel.getLastRxPacketError(port_num, PROTOCOL_VERSION)) # Enable position p gain #4 dynamixel.write2ByteTxRx(port_num, PROTOCOL_VERSION, DXL4_ID, ADDR_XM430_POSITION_P_GAIN, dxl_position_p_gain) if dynamixel.getLastTxRxResult(port_num, PROTOCOL_VERSION) != COMM_SUCCESS: dynamixel.printTxRxResult(PROTOCOL_VERSION, dynamixel.getLastTxRxResult(port_num, PROTOCOL_VERSION)) elif dynamixel.getLastRxPacketError(port_num, PROTOCOL_VERSION) != 0: dynamixel.printRxPacketError(PROTOCOL_VERSION, dynamixel.getLastRxPacketError(port_num, PROTOCOL_VERSION)) # Enable position p gain #5 dynamixel.write2ByteTxRx(port_num, PROTOCOL_VERSION, DXL5_ID, ADDR_XM430_POSITION_P_GAIN, dxl_position_p_gain) if dynamixel.getLastTxRxResult(port_num, PROTOCOL_VERSION) != COMM_SUCCESS: dynamixel.printTxRxResult(PROTOCOL_VERSION, dynamixel.getLastTxRxResult(port_num, PROTOCOL_VERSION)) elif dynamixel.getLastRxPacketError(port_num, PROTOCOL_VERSION) != 0: dynamixel.printRxPacketError(PROTOCOL_VERSION, dynamixel.getLastRxPacketError(port_num, PROTOCOL_VERSION)) # Enable position p gain #6 dynamixel.write2ByteTxRx(port_num, PROTOCOL_VERSION, DXL6_ID, ADDR_XM430_POSITION_P_GAIN, dxl_position_p_gain) if dynamixel.getLastTxRxResult(port_num, PROTOCOL_VERSION) != COMM_SUCCESS: dynamixel.printTxRxResult(PROTOCOL_VERSION, dynamixel.getLastTxRxResult(port_num, PROTOCOL_VERSION)) elif dynamixel.getLastRxPacketError(port_num, PROTOCOL_VERSION) != 0: dynamixel.printRxPacketError(PROTOCOL_VERSION, dynamixel.getLastRxPacketError(port_num, PROTOCOL_VERSION)) # Enable position p gain #7 dynamixel.write2ByteTxRx(port_num, PROTOCOL_VERSION, DXL7_ID, ADDR_XM430_POSITION_P_GAIN, dxl_position_p_gain) if dynamixel.getLastTxRxResult(port_num, PROTOCOL_VERSION) != COMM_SUCCESS: dynamixel.printTxRxResult(PROTOCOL_VERSION, dynamixel.getLastTxRxResult(port_num, PROTOCOL_VERSION)) elif dynamixel.getLastRxPacketError(port_num, PROTOCOL_VERSION) != 0: dynamixel.printRxPacketError(PROTOCOL_VERSION, dynamixel.getLastRxPacketError(port_num, PROTOCOL_VERSION)) # Add parameter storage for Dynamixel#1 present position value dxl_addparam_result = ctypes.c_ubyte(dynamixel.groupSyncReadAddParam(groupread_num, DXL1_ID)).value if dxl_addparam_result != 1: print("[ID:%03d] groupSyncRead addparam failed" % (DXL1_ID)) quit() # Add parameter storage for Dynamixel#2 present position value dxl_addparam_result = ctypes.c_ubyte(dynamixel.groupSyncReadAddParam(groupread_num, DXL2_ID)).value if dxl_addparam_result != 1: print("[ID:%03d] groupSyncRead addparam failed" % (DXL2_ID)) quit() # Add parameter storage for Dynamixel#3 present position value dxl_addparam_result = ctypes.c_ubyte(dynamixel.groupSyncReadAddParam(groupread_num, DXL3_ID)).value if dxl_addparam_result != 1: print("[ID:%03d] groupSyncRead addparam failed" % (DXL3_ID)) quit() # Add parameter storage for Dynamixel#4 present position value dxl_addparam_result = ctypes.c_ubyte(dynamixel.groupSyncReadAddParam(groupread_num, DXL4_ID)).value if dxl_addparam_result != 1: print("[ID:%03d] groupSyncRead addparam failed" % (DXL4_ID)) quit() # Add parameter storage for Dynamixel#5 present position value dxl_addparam_result = ctypes.c_ubyte(dynamixel.groupSyncReadAddParam(groupread_num, DXL5_ID)).value if dxl_addparam_result != 1: print("[ID:%03d] groupSyncRead addparam failed" % (DXL5_ID)) quit() # Add parameter storage for Dynamixel#6 present position value dxl_addparam_result = ctypes.c_ubyte(dynamixel.groupSyncReadAddParam(groupread_num, DXL6_ID)).value if dxl_addparam_result != 1: print("[ID:%03d] groupSyncRead addparam failed" % (DXL6_ID)) quit() # Add parameter storage for Dynamixel#7 present position value dxl_addparam_result = ctypes.c_ubyte(dynamixel.groupSyncReadAddParam(groupread_num, DXL7_ID)).value if dxl_addparam_result != 1: print("[ID:%03d] groupSyncRead addparam failed" % (DXL7_ID)) quit() while 1: print("Press any key to continue! (or press ESC to quit!)") if getch() == chr(ESC_ASCII_VALUE): break for pose in range(0, 10): # Add Dynamixel#1 goal position value to the Syncwrite storage dxl_addparam_result = ctypes.c_ubyte(dynamixel.groupSyncWriteAddParam(groupwrite_num, DXL1_ID, dxl_goal_position[pose][DXL1_ID - 1], LEN_XM430_GOAL_POSITION)).value if dxl_addparam_result != 1: print("[ID:%03d] groupSyncWrite addparam failed" % (DXL1_ID)) quit() # Add Dynamixel#2 goal position value to the Syncwrite parameter storage dxl_addparam_result = ctypes.c_ubyte(dynamixel.groupSyncWriteAddParam(groupwrite_num, DXL2_ID, dxl_goal_position[pose][DXL2_ID - 1], LEN_XM430_GOAL_POSITION)).value if dxl_addparam_result != 1: print("[ID:%03d] groupSyncWrite addparam failed" % (DXL2_ID)) quit() # Add Dynamixel#3 goal position value to the Syncwrite parameter storage dxl_addparam_result = ctypes.c_ubyte(dynamixel.groupSyncWriteAddParam(groupwrite_num, DXL3_ID, dxl_goal_position[pose][DXL3_ID - 1], LEN_XM430_GOAL_POSITION)).value if dxl_addparam_result != 1: print("[ID:%03d] groupSyncWrite addparam failed" % (DXL3_ID)) quit() # Add Dynamixel#4 goal position value to the Syncwrite parameter storage dxl_addparam_result = ctypes.c_ubyte(dynamixel.groupSyncWriteAddParam(groupwrite_num, DXL4_ID, dxl_goal_position[pose][DXL4_ID - 1], LEN_XM430_GOAL_POSITION)).value if dxl_addparam_result != 1: print("[ID:%03d] groupSyncWrite addparam failed" % (DXL4_ID)) quit() # Add Dynamixel#5 goal position value to the Syncwrite parameter storage dxl_addparam_result = ctypes.c_ubyte(dynamixel.groupSyncWriteAddParam(groupwrite_num, DXL5_ID, dxl_goal_position[pose][DXL5_ID - 1], LEN_XM430_GOAL_POSITION)).value if dxl_addparam_result != 1: print("[ID:%03d] groupSyncWrite addparam failed" % (DXL5_ID)) quit() # Add Dynamixel#6 goal position value to the Syncwrite parameter storage dxl_addparam_result = ctypes.c_ubyte(dynamixel.groupSyncWriteAddParam(groupwrite_num, DXL6_ID, dxl_goal_position[pose][DXL6_ID - 1], LEN_XM430_GOAL_POSITION)).value if dxl_addparam_result != 1: print("[ID:%03d] groupSyncWrite addparam failed" % (DXL6_ID)) quit() # Add Dynamixel#7 goal position value to the Syncwrite parameter storage dxl_addparam_result = ctypes.c_ubyte(dynamixel.groupSyncWriteAddParam(groupwrite_num, DXL7_ID, dxl_goal_position[pose][DXL7_ID - 1], LEN_XM430_GOAL_POSITION)).value if dxl_addparam_result != 1: print("[ID:%03d] groupSyncWrite addparam failed" % (DXL7_ID)) quit() # Syncwrite goal position dynamixel.groupSyncWriteTxPacket(groupwrite_num) if dynamixel.getLastTxRxResult(port_num, PROTOCOL_VERSION) != COMM_SUCCESS: dynamixel.printTxRxResult(PROTOCOL_VERSION, dynamixel.getLastTxRxResult(port_num, PROTOCOL_VERSION)) # Clear syncwrite parameter storage dynamixel.groupSyncWriteClearParam(groupwrite_num) while 1: # Syncread present position dynamixel.groupSyncReadTxRxPacket(groupread_num) if dynamixel.getLastTxRxResult(port_num, PROTOCOL_VERSION) != COMM_SUCCESS: dynamixel.printTxRxResult(PROTOCOL_VERSION, dynamixel.getLastTxRxResult(port_num, PROTOCOL_VERSION)) # Check if groupsyncread data of Dynamixel#1 is available dxl_getdata_result = ctypes.c_ubyte(dynamixel.groupSyncReadIsAvailable(groupread_num, DXL1_ID, ADDR_XM430_PRESENT_POSITION, LEN_XM430_PRESENT_POSITION)).value if dxl_getdata_result != 1: print("[ID:%03d] groupSyncRead getdata failed" % (DXL1_ID)) quit() # Check if groupsyncread data of Dynamixel#2 is available dxl_getdata_result = ctypes.c_ubyte(dynamixel.groupSyncReadIsAvailable(groupread_num, DXL2_ID, ADDR_XM430_PRESENT_POSITION, LEN_XM430_PRESENT_POSITION)).value if dxl_getdata_result != 1: print("[ID:%03d] groupSyncRead getdata failed" % (DXL2_ID)) quit() # Check if groupsyncread data of Dynamixel#3 is available dxl_getdata_result = ctypes.c_ubyte(dynamixel.groupSyncReadIsAvailable(groupread_num, DXL3_ID, ADDR_XM430_PRESENT_POSITION, LEN_XM430_PRESENT_POSITION)).value if dxl_getdata_result != 1: print("[ID:%03d] groupSyncRead getdata failed" % (DXL3_ID)) quit() # Check if groupsyncread data of Dynamixel#4 is available dxl_getdata_result = ctypes.c_ubyte(dynamixel.groupSyncReadIsAvailable(groupread_num, DXL4_ID, ADDR_XM430_PRESENT_POSITION, LEN_XM430_PRESENT_POSITION)).value if dxl_getdata_result != 1: print("[ID:%03d] groupSyncRead getdata failed" % (DXL4_ID)) quit() # Check if groupsyncread data of Dynamixel#5 is available dxl_getdata_result = ctypes.c_ubyte(dynamixel.groupSyncReadIsAvailable(groupread_num, DXL5_ID, ADDR_XM430_PRESENT_POSITION, LEN_XM430_PRESENT_POSITION)).value if dxl_getdata_result != 1: print("[ID:%03d] groupSyncRead getdata failed" % (DXL5_ID)) quit() # Check if groupsyncread data of Dynamixel#6 is available dxl_getdata_result = ctypes.c_ubyte(dynamixel.groupSyncReadIsAvailable(groupread_num, DXL6_ID, ADDR_XM430_PRESENT_POSITION, LEN_XM430_PRESENT_POSITION)).value if dxl_getdata_result != 1: print("[ID:%03d] groupSyncRead getdata failed" % (DXL6_ID)) quit() # Check if groupsyncread data of Dynamixel#7 is available dxl_getdata_result = ctypes.c_ubyte(dynamixel.groupSyncReadIsAvailable(groupread_num, DXL7_ID, ADDR_XM430_PRESENT_POSITION, LEN_XM430_PRESENT_POSITION)).value if dxl_getdata_result != 1: print("[ID:%03d] groupSyncRead getdata failed" % (DXL7_ID)) quit() # Get Dynamixel#1 present position value dxl1_present_position = dynamixel.groupSyncReadGetData(groupread_num, DXL1_ID, ADDR_XM430_PRESENT_POSITION, LEN_XM430_PRESENT_POSITION) # Get Dynamixel#2 present position value dxl2_present_position = dynamixel.groupSyncReadGetData(groupread_num, DXL2_ID, ADDR_XM430_PRESENT_POSITION, LEN_XM430_PRESENT_POSITION) # Get Dynamixel#3 present position value dxl3_present_position = dynamixel.groupSyncReadGetData(groupread_num, DXL3_ID, ADDR_XM430_PRESENT_POSITION, LEN_XM430_PRESENT_POSITION) # Get Dynamixel#4 present position value dxl4_present_position = dynamixel.groupSyncReadGetData(groupread_num, DXL4_ID, ADDR_XM430_PRESENT_POSITION, LEN_XM430_PRESENT_POSITION) # Get Dynamixel#5 present position value dxl5_present_position = dynamixel.groupSyncReadGetData(groupread_num, DXL5_ID, ADDR_XM430_PRESENT_POSITION, LEN_XM430_PRESENT_POSITION) # Get Dynamixel#6 present position value dxl6_present_position = dynamixel.groupSyncReadGetData(groupread_num, DXL6_ID, ADDR_XM430_PRESENT_POSITION, LEN_XM430_PRESENT_POSITION) # Get Dynamixel#7 present position value dxl7_present_position = dynamixel.groupSyncReadGetData(groupread_num, DXL7_ID, ADDR_XM430_PRESENT_POSITION, LEN_XM430_PRESENT_POSITION) print("[ID:%03d] GoalPos:%03d PresPos:%03d\t[ID:%03d] GoalPos:%03d PresPos:%03d\t[ID:%03d] GoalPos:%03d PresPos:%03d\t[ID:%03d] GoalPos:%03d PresPos:%03d\t[ID:%03d] GoalPos:%03d PresPos:%03d\t[ID:%03d] GoalPos:%03d PresPos:%03d\t[ID:%03d] GoalPos:%03d PresPos:%03d" % (DXL1_ID, dxl_goal_position[pose][DXL1_ID - 1], dxl1_present_position, DXL2_ID, dxl_goal_position[pose][DXL2_ID - 1], dxl2_present_position, DXL3_ID, dxl_goal_position[pose][DXL3_ID - 1], dxl3_present_position, DXL4_ID, dxl_goal_position[pose][DXL4_ID - 1], dxl4_present_position, DXL5_ID, dxl_goal_position[pose][DXL5_ID - 1], dxl5_present_position, DXL6_ID, dxl_goal_position[pose][DXL6_ID - 1], dxl6_present_position, DXL7_ID, dxl_goal_position[pose][DXL7_ID - 1], dxl7_present_position)) if not ((abs(dxl_goal_position[pose][DXL1_ID - 1] - dxl1_present_position) > DXL_MOVING_STATUS_THRESHOLD) or (abs(dxl_goal_position[pose][DXL2_ID - 1] - dxl2_present_position) > DXL_MOVING_STATUS_THRESHOLD)): break time.sleep(2.5) # Disable Dynamixel#1 Torque dynamixel.write1ByteTxRx(port_num, PROTOCOL_VERSION, DXL1_ID, ADDR_XM430_TORQUE_ENABLE, TORQUE_DISABLE) if dynamixel.getLastTxRxResult(port_num, PROTOCOL_VERSION) != COMM_SUCCESS: dynamixel.printTxRxResult(PROTOCOL_VERSION, dynamixel.getLastTxRxResult(port_num, PROTOCOL_VERSION)) elif dynamixel.getLastRxPacketError(port_num, PROTOCOL_VERSION) != 0: dynamixel.printRxPacketError(PROTOCOL_VERSION, dynamixel.getLastRxPacketError(port_num, PROTOCOL_VERSION)) # Disable Dynamixel#2 Torque dynamixel.write1ByteTxRx(port_num, PROTOCOL_VERSION, DXL2_ID, ADDR_XM430_TORQUE_ENABLE, TORQUE_DISABLE) if dynamixel.getLastTxRxResult(port_num, PROTOCOL_VERSION) != COMM_SUCCESS: dynamixel.printTxRxResult(PROTOCOL_VERSION, dynamixel.getLastTxRxResult(port_num, PROTOCOL_VERSION)) elif dynamixel.getLastRxPacketError(port_num, PROTOCOL_VERSION) != 0: dynamixel.printRxPacketError(PROTOCOL_VERSION, dynamixel.getLastRxPacketError(port_num, PROTOCOL_VERSION)) # Disable Dynamixel#3 Torque dynamixel.write1ByteTxRx(port_num, PROTOCOL_VERSION, DXL3_ID, ADDR_XM430_TORQUE_ENABLE, TORQUE_DISABLE) if dynamixel.getLastTxRxResult(port_num, PROTOCOL_VERSION) != COMM_SUCCESS: dynamixel.printTxRxResult(PROTOCOL_VERSION, dynamixel.getLastTxRxResult(port_num, PROTOCOL_VERSION)) elif dynamixel.getLastRxPacketError(port_num, PROTOCOL_VERSION) != 0: dynamixel.printRxPacketError(PROTOCOL_VERSION, dynamixel.getLastRxPacketError(port_num, PROTOCOL_VERSION)) # Disable Dynamixel#4 Torque dynamixel.write1ByteTxRx(port_num, PROTOCOL_VERSION, DXL4_ID, ADDR_XM430_TORQUE_ENABLE, TORQUE_DISABLE) if dynamixel.getLastTxRxResult(port_num, PROTOCOL_VERSION) != COMM_SUCCESS: dynamixel.printTxRxResult(PROTOCOL_VERSION, dynamixel.getLastTxRxResult(port_num, PROTOCOL_VERSION)) elif dynamixel.getLastRxPacketError(port_num, PROTOCOL_VERSION) != 0: dynamixel.printRxPacketError(PROTOCOL_VERSION, dynamixel.getLastRxPacketError(port_num, PROTOCOL_VERSION)) # Disable Dynamixel#5 Torque dynamixel.write1ByteTxRx(port_num, PROTOCOL_VERSION, DXL5_ID, ADDR_XM430_TORQUE_ENABLE, TORQUE_DISABLE) if dynamixel.getLastTxRxResult(port_num, PROTOCOL_VERSION) != COMM_SUCCESS: dynamixel.printTxRxResult(PROTOCOL_VERSION, dynamixel.getLastTxRxResult(port_num, PROTOCOL_VERSION)) elif dynamixel.getLastRxPacketError(port_num, PROTOCOL_VERSION) != 0: dynamixel.printRxPacketError(PROTOCOL_VERSION, dynamixel.getLastRxPacketError(port_num, PROTOCOL_VERSION)) # Disable Dynamixel#6 Torque dynamixel.write1ByteTxRx(port_num, PROTOCOL_VERSION, DXL6_ID, ADDR_XM430_TORQUE_ENABLE, TORQUE_DISABLE) if dynamixel.getLastTxRxResult(port_num, PROTOCOL_VERSION) != COMM_SUCCESS: dynamixel.printTxRxResult(PROTOCOL_VERSION, dynamixel.getLastTxRxResult(port_num, PROTOCOL_VERSION)) elif dynamixel.getLastRxPacketError(port_num, PROTOCOL_VERSION) != 0: dynamixel.printRxPacketError(PROTOCOL_VERSION, dynamixel.getLastRxPacketError(port_num, PROTOCOL_VERSION)) # Disable Dynamixel#7 Torque dynamixel.write1ByteTxRx(port_num, PROTOCOL_VERSION, DXL7_ID, ADDR_XM430_TORQUE_ENABLE, TORQUE_DISABLE) if dynamixel.getLastTxRxResult(port_num, PROTOCOL_VERSION) != COMM_SUCCESS: dynamixel.printTxRxResult(PROTOCOL_VERSION, dynamixel.getLastTxRxResult(port_num, PROTOCOL_VERSION)) elif dynamixel.getLastRxPacketError(port_num, PROTOCOL_VERSION) != 0: dynamixel.printRxPacketError(PROTOCOL_VERSION, dynamixel.getLastRxPacketError(port_num, PROTOCOL_VERSION)) # Close port dynamixel.closePort(port_num)

55.089767

512

0.755027

3,714

30,685

5.935649

0.081583

0.126559

0.094579

0.138716

0.852665

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0.793423

0.775822

0.747834

0.729508

0

0.04433

0.164869

30,685

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513

55.188849

0.815929

0.147075

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0.445748

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0.002933

0.063586

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0

1

0.005865

false

0

0.01173

0.002933

0.02346

0.234604

0

null

0

1

0

null

0

6

c1ab18bf2beb0d647cb319a0d633196bb02ce0d4

44

py

Python

hello-world.py

MellowDesert/astr-119

f90a7008046ab1bf78a5bdebf451ce468cdd3c42

[ "MIT" ]

null

hello-world.py

MellowDesert/astr-119

f90a7008046ab1bf78a5bdebf451ce468cdd3c42

[ "MIT" ]

9

2021-09-23T22:41:24.000Z

2021-11-17T18:29:15.000Z

hello-world.py

MellowDesert/astr-119

f90a7008046ab1bf78a5bdebf451ce468cdd3c42

[ "MIT" ]

null

print("Hello World") #printing Hello World

22

43

0.75

6

44

5.5

0.666667

0.606061

0

0.136364

44

1

44

0.868421

0.454545

0

0.5

0

1

0

true

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

de152b1876677377626258f749524ce63a69cd57

16,817

py

Python

venv/lib/python3.6/site-packages/ansible_collections/dellemc/openmanage/tests/unit/plugins/modules/test_dellemc_configure_idrac_eventing.py

usegalaxy-no/usegalaxy

75dad095769fe918eb39677f2c887e681a747f3a

[ "MIT" ]

1

2020-01-22T13:11:23.000Z

venv/lib/python3.6/site-packages/ansible_collections/dellemc/openmanage/tests/unit/plugins/modules/test_dellemc_configure_idrac_eventing.py

usegalaxy-no/usegalaxy

75dad095769fe918eb39677f2c887e681a747f3a

[ "MIT" ]

12

2020-02-21T07:24:52.000Z

2020-04-14T09:54:32.000Z

venv/lib/python3.6/site-packages/ansible_collections/dellemc/openmanage/tests/unit/plugins/modules/test_dellemc_configure_idrac_eventing.py

usegalaxy-no/usegalaxy

75dad095769fe918eb39677f2c887e681a747f3a

[ "MIT" ]

null

# -*- coding: utf-8 -*- # # Dell EMC OpenManage Ansible Modules # Version 3.0.0 # Copyright (C) 2020-2021 Dell Inc. or its subsidiaries. All Rights Reserved. # GNU General Public License v3.0+ (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt) # from __future__ import (absolute_import, division, print_function) __metaclass__ = type import pytest from ansible_collections.dellemc.openmanage.plugins.modules import dellemc_configure_idrac_eventing from ansible_collections.dellemc.openmanage.tests.unit.plugins.modules.common import FakeAnsibleModule, Constants from ansible_collections.dellemc.openmanage.tests.unit.compat.mock import MagicMock, patch, Mock, PropertyMock from pytest import importorskip importorskip("omsdk.sdkfile") importorskip("omsdk.sdkcreds") class TestConfigureEventing(FakeAnsibleModule): module = dellemc_configure_idrac_eventing @pytest.fixture def idrac_configure_eventing_mock(self, mocker): omsdk_mock = MagicMock() idrac_obj = MagicMock() omsdk_mock.file_share_manager = idrac_obj omsdk_mock.config_mgr = idrac_obj type(idrac_obj).create_share_obj = Mock(return_value="Status") type(idrac_obj).set_liason_share = Mock(return_value="Status") return idrac_obj @pytest.fixture def idrac_file_manager_config_eventing_mock(self, mocker): try: file_manager_obj = mocker.patch( 'ansible_collections.dellemc.openmanage.plugins.modules.dellemc_configure_idrac_eventing.file_share_manager') except AttributeError: file_manager_obj = MagicMock() obj = MagicMock() file_manager_obj.create_share_obj.return_value = obj return file_manager_obj @pytest.fixture def is_changes_applicable_eventing_mock(self, mocker): try: changes_applicable_obj = mocker.patch( 'ansible_collections.dellemc.openmanage.plugins.modules.dellemc_configure_idrac_eventing.config_mgr') except AttributeError: changes_applicable_obj = MagicMock() obj = MagicMock() changes_applicable_obj.is_change_applicable.return_value = obj return changes_applicable_obj @pytest.fixture def idrac_connection_configure_eventing_mock(self, mocker, idrac_configure_eventing_mock): idrac_conn_class_mock = mocker.patch('ansible_collections.dellemc.openmanage.plugins.modules.' 'dellemc_configure_idrac_eventing.iDRACConnection', return_value=idrac_configure_eventing_mock) idrac_conn_class_mock.return_value.__enter__.return_value = idrac_configure_eventing_mock return idrac_configure_eventing_mock def test_main_configure_eventing_success_case01(self, idrac_connection_configure_eventing_mock, idrac_default_args, mocker, idrac_file_manager_config_eventing_mock): idrac_default_args.update({"share_name": "sharename", 'share_password': None, "destination_number": 1, "destination": "1.1.1.1", 'share_mnt': None, 'share_user': None}) message = {'msg': 'Successfully configured the idrac eventing settings.', 'eventing_status': {"Id": "JID_12345123456", "JobState": "Completed"}, 'changed': True} mocker.patch('ansible_collections.dellemc.openmanage.plugins.modules.' 'dellemc_configure_idrac_eventing.run_idrac_eventing_config', return_value=message) result = self._run_module(idrac_default_args) assert result["msg"] == "Successfully configured the iDRAC eventing settings." def test_run_idrac_eventing_config_success_case01(self, idrac_connection_configure_eventing_mock, idrac_file_manager_config_eventing_mock, idrac_default_args, is_changes_applicable_eventing_mock): idrac_default_args.update({"share_name": "sharename", "share_mnt": "mountname", "share_user": "shareuser", "share_password": "sharepassword", "destination_number": 1, "destination": "1.1.1.1", "snmp_v3_username": "snmpuser", "snmp_trap_state": "Enabled", "alert_number": 4, "email_alert_state": "Enabled", "address": "abc@xyz", "custom_message": "test", "enable_alerts": "Enabled", "authentication": "Enabled", "smtp_ip_address": "192.168.0.1", "smtp_port": 443, "username": "uname", "password": "pwd"}) message = {"changes_applicable": True, "message": "Changes found to commit!"} idrac_connection_configure_eventing_mock.config_mgr.is_change_applicable.return_value = message f_module = self.get_module_mock(params=idrac_default_args, check_mode=True) with pytest.raises(Exception) as ex: self.module.run_idrac_eventing_config(idrac_connection_configure_eventing_mock, f_module) assert "Changes found to commit!" == ex.value.args[0] def test_run_idrac_eventing_config_success_case02(self, idrac_connection_configure_eventing_mock, idrac_file_manager_config_eventing_mock, idrac_default_args): idrac_default_args.update({"share_name": "sharename", "share_mnt": "mountname", "share_user": "shareuser", "share_password": "sharepassword", "destination_number": 1, "destination": "1.1.1.1", "snmp_v3_username": "snmpuser", "snmp_trap_state": "Enabled", "alert_number": 4, "email_alert_state": "Enabled", "address": "abc@xyz", "custom_message": "test", "enable_alerts": "Enabled", "authentication": "Enabled", "smtp_ip_address": "192.168.0.1", "smtp_port": 443, "username": "uname", "password": "pwd"}) message = {"changes_applicable": True, "message": "changes found to commit!", "changed": True, "Status": "Success"} idrac_connection_configure_eventing_mock.config_mgr.apply_changes.return_value = message f_module = self.get_module_mock(params=idrac_default_args) f_module.check_mode = False result = self.module.run_idrac_eventing_config(idrac_connection_configure_eventing_mock, f_module) assert result['message'] == 'changes found to commit!' def test_run_idrac_eventing_config_success_case03(self, idrac_connection_configure_eventing_mock, idrac_file_manager_config_eventing_mock, idrac_default_args): idrac_default_args.update({"share_name": "sharename", "share_mnt": "mountname", "share_user": "shareuser", "share_password": "sharepassword", "destination_number": 1, "destination": "1.1.1.1", "snmp_v3_username": "snmpuser", "snmp_trap_state": "Enabled", "alert_number": 4, "email_alert_state": "Enabled", "address": "abc@xyz", "custom_message": "test", "enable_alerts": "Enabled", "authentication": "Enabled", "smtp_ip_address": "192.168.0.1", "smtp_port": 443, "username": "uname", "password": "pwd"}) message = {"changes_applicable": False, "Message": "No changes found to commit!", "changed": False, "Status": "Success"} idrac_connection_configure_eventing_mock.config_mgr.apply_changes.return_value = message f_module = self.get_module_mock(params=idrac_default_args) f_module.check_mode = False result = self.module.run_idrac_eventing_config(idrac_connection_configure_eventing_mock, f_module) assert result["Message"] == 'No changes found to commit!' def test_run_idrac_eventing_config_success_case04(self, idrac_connection_configure_eventing_mock, idrac_default_args, idrac_file_manager_config_eventing_mock): idrac_default_args.update({"share_name": "sharename", "share_mnt": "mountname", "share_user": "shareuser", "share_password": "sharepassword", "destination_number": 1, "destination": "1.1.1.1", "snmp_v3_username": "snmpuser", "snmp_trap_state": "Enabled", "alert_number": 4, "email_alert_state": "Enabled", "address": "abc@xyz", "custom_message": "test", "enable_alerts": "Enabled", "authentication": "Enabled", "smtp_ip_address": "192.168.0.1", "smtp_port": 443, "username": "uname", "password": "pwd"}) message = {"changes_applicable": False, "Message": "No changes were applied", "changed": False, "Status": "Success"} idrac_connection_configure_eventing_mock.config_mgr.apply_changes.return_value = message f_module = self.get_module_mock(params=idrac_default_args) f_module.check_mode = False result = self.module.run_idrac_eventing_config(idrac_connection_configure_eventing_mock, f_module) assert result['Message'] == 'No changes were applied' def test_run_idrac_eventing_config_success_case05(self, idrac_connection_configure_eventing_mock, idrac_file_manager_config_eventing_mock, idrac_default_args): idrac_default_args.update({"share_name": "sharename", "share_mnt": "mountname", "share_user": "shareuser", "share_password": "sharepassword", "destination_number": None, "destination": None, "snmp_v3_username": None, "snmp_trap_state": None, "alert_number": None, "email_alert_state": None, "address": None, "custom_message": None, "enable_alerts": None, "authentication": None, "smtp_ip_address": None, "smtp_port": None, "username": None, "password": None}) message = {"changes_applicable": False, "Message": "No changes were applied", "changed": False, "Status": "Success"} obj = MagicMock() idrac_connection_configure_eventing_mock.config_mgr = obj type(obj).configure_snmp_trap_destination = PropertyMock(return_value=message) type(obj).configure_email_alerts = PropertyMock(return_value=message) type(obj).configure_idrac_alerts = PropertyMock(return_value=message) type(obj).configure_smtp_server_settings = PropertyMock(return_value=message) idrac_connection_configure_eventing_mock.config_mgr.apply_changes.return_value = message f_module = self.get_module_mock(params=idrac_default_args) f_module.check_mode = False result = self.module.run_idrac_eventing_config(idrac_connection_configure_eventing_mock, f_module) assert result['Message'] == 'No changes were applied' def test_run_idrac_eventing_config_failed_case01(self, idrac_connection_configure_eventing_mock, idrac_file_manager_config_eventing_mock, idrac_default_args): idrac_default_args.update({"share_name": "sharename", "share_mnt": "mountname", "share_user": "shareuser", "share_password": "sharepassword", "destination_number": 1, "destination": "1.1.1.1", "snmp_v3_username": "snmpuser", "snmp_trap_state": "Enabled", "alert_number": 4, "email_alert_state": "Enabled", "address": "abc@xyz", "custom_message": "test", "enable_alerts": "Enabled", "authentication": "Enabled", "smtp_ip_address": "192.168.0.1", "smtp_port": 443, "username": "uname", "password": "pwd"}) message = {'Status': 'Failed', "Data": {'Message': 'status failed in checking Data'}} idrac_connection_configure_eventing_mock.file_share_manager.create_share_obj.return_value = "mnt/iso" idrac_connection_configure_eventing_mock.config_mgr.set_liason_share.return_value = message f_module = self.get_module_mock(params=idrac_default_args) with pytest.raises(Exception) as ex: self.module.run_idrac_eventing_config(idrac_connection_configure_eventing_mock, f_module) assert ex.value.args[0] == 'status failed in checking Data' def test_run_idrac_eventing_config_failed_case02(self, idrac_connection_configure_eventing_mock, idrac_default_args, idrac_file_manager_config_eventing_mock): idrac_default_args.update({"share_name": "sharename", "share_mnt": "mountname", "share_user": "shareuser", "share_password": "sharepassword", "destination_number": 1, "destination": "1.1.1.1", "snmp_v3_username": "snmpuser", "snmp_trap_state": "Enabled", "alert_number": 4, "email_alert_state": "Enabled", "address": "abc@xyz", "custom_message": "test", "enable_alerts": "Enabled", "authentication": "Enabled", "smtp_ip_address": "192.168.0.1", "smtp_port": 443, "username": "uname", "password": "pwd"}) message = {"changes_applicable": False, "Message": "No changes were applied", "changed": False, "Status": "failed"} idrac_connection_configure_eventing_mock.config_mgr.apply_changes.return_value = message f_module = self.get_module_mock(params=idrac_default_args) f_module.check_mode = False result = self.module.run_idrac_eventing_config(idrac_connection_configure_eventing_mock, f_module) assert result['Message'] == 'No changes were applied' def test_run_idrac_eventing_config_failed_case03(self, idrac_connection_configure_eventing_mock, idrac_default_args, idrac_file_manager_config_eventing_mock): idrac_default_args.update({"share_name": "sharename", "share_mnt": "mountname", "share_user": "shareuser", "share_password": "sharepassword", "destination_number": 1, "destination": "1.1.1.1", "snmp_v3_username": "snmpuser", "snmp_trap_state": "Enabled", "alert_number": 4, "email_alert_state": "Enabled", "address": "abc@xyz", "custom_message": "test", "enable_alerts": "Enabled", "authentication": "Enabled", "smtp_ip_address": "192.168.0.1", "smtp_port": 443, "username": "uname", "password": "pwd"}) message = {'Status': 'Failed', "Data": {'Message': "Failed to found changes"}} idrac_connection_configure_eventing_mock.file_share_manager.create_share_obj.return_value = "mnt/iso" idrac_connection_configure_eventing_mock.config_mgr.set_liason_share.return_value = message f_module = self.get_module_mock(params=idrac_default_args) with pytest.raises(Exception) as ex: self.module.run_idrac_eventing_config(idrac_connection_configure_eventing_mock, f_module) assert ex.value.args[0] == 'Failed to found changes' @pytest.mark.parametrize("exc_type", [ImportError, ValueError, RuntimeError]) def test_main_configure_eventing_exception_handling_case(self, exc_type, mocker, idrac_default_args, idrac_connection_configure_eventing_mock, idrac_file_manager_config_eventing_mock): idrac_default_args.update({"share_name": "sharename", 'share_password': None, 'share_mnt': None, 'share_user': None}) mocker.patch('ansible_collections.dellemc.openmanage.plugins.modules.' 'dellemc_configure_idrac_eventing.run_idrac_eventing_config', side_effect=exc_type('test')) result = self._run_module_with_fail_json(idrac_default_args) assert 'msg' in result assert result['failed'] is True

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1

0

null

0

1

0

6

a9a9239f86846bcd866237495963b98c25fcbf9f

25,431

py

Python

spark_fhir_schemas/stu3/complex_types/guidanceresponse.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/guidanceresponse.py

icanbwell/SparkFhirSchemas

8c828313c39850b65f8676e67f526ee92b7d624e

[ "Apache-2.0" ]

null

spark_fhir_schemas/stu3/complex_types/guidanceresponse.py

icanbwell/SparkFhirSchemas

8c828313c39850b65f8676e67f526ee92b7d624e

[ "Apache-2.0" ]

null

from typing import Union, List, Optional from pyspark.sql.types import ( StructType, StructField, StringType, ArrayType, DataType, TimestampType, ) # This file is auto-generated by generate_schema so do not edit manually # noinspection PyPep8Naming class GuidanceResponseSchema: """ A guidance response is the formal response to a guidance request, including any output parameters returned by the evaluation, as well as the description of any proposed actions to be taken. """ # 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]: """ A guidance response is the formal response to a guidance request, including any output parameters returned by the evaluation, as well as the description of any proposed actions to be taken. id: The logical id of the resource, as used in the URL for the resource. Once assigned, this value never changes. extension: May be used to represent additional information that is not part of the basic definition of the resource. 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. meta: The metadata about the resource. This is content that is maintained by the infrastructure. Changes to the content may not always be associated with version changes to the resource. implicitRules: A reference to a set of rules that were followed when the resource was constructed, and which must be understood when processing the content. language: The base language in which the resource is written. text: A human-readable narrative that contains a summary of the resource, and may be used to represent the content of the resource to a human. The narrative need not encode all the structured data, but is required to contain sufficient detail to make it "clinically safe" for a human to just read the narrative. Resource definitions may define what content should be represented in the narrative to ensure clinical safety. contained: These resources do not have an independent existence apart from the resource that contains them - they cannot be identified independently, and nor can they have their own independent transaction scope. resourceType: This is a GuidanceResponse resource requestId: The id of the request associated with this response. If an id was given as part of the request, it will be reproduced here to enable the requester to more easily identify the response in a multi-request scenario. identifier: Allows a service to provide a unique, business identifier for the response. module: A reference to the knowledge module that was invoked. status: The status of the response. If the evaluation is completed successfully, the status will indicate success. However, in order to complete the evaluation, the engine may require more information. In this case, the status will be data-required, and the response will contain a description of the additional required information. If the evaluation completed successfully, but the engine determines that a potentially more accurate response could be provided if more data was available, the status will be data-requested, and the response will contain a description of the additional requested information. subject: The patient for which the request was processed. context: Allows the context of the guidance response to be provided if available. In a service context, this would likely be unavailable. occurrenceDateTime: Indicates when the guidance response was processed. performer: Provides a reference to the device that performed the guidance. reasonCodeableConcept: Indicates the reason the request was initiated. This is typically provided as a parameter to the evaluation and echoed by the service, although for some use cases, such as subscription- or event-based scenarios, it may provide an indication of the cause for the response. reasonReference: Indicates the reason the request was initiated. This is typically provided as a parameter to the evaluation and echoed by the service, although for some use cases, such as subscription- or event-based scenarios, it may provide an indication of the cause for the response. note: Provides a mechanism to communicate additional information about the response. evaluationMessage: Messages resulting from the evaluation of the artifact or artifacts. As part of evaluating the request, the engine may produce informational or warning messages. These messages will be provided by this element. outputParameters: The output parameters of the evaluation, if any. Many modules will result in the return of specific resources such as procedure or communication requests that are returned as part of the operation result. However, modules may define specific outputs that would be returned as the result of the evaluation, and these would be returned in this element. result: The actions, if any, produced by the evaluation of the artifact. dataRequirement: If the evaluation could not be completed due to lack of information, or additional information would potentially result in a more accurate response, this element will a description of the data required in order to proceed with the evaluation. A subsequent request to the service should include this data. """ from spark_fhir_schemas.stu3.complex_types.extension import ExtensionSchema from spark_fhir_schemas.stu3.complex_types.meta import MetaSchema from spark_fhir_schemas.stu3.complex_types.narrative import NarrativeSchema from spark_fhir_schemas.stu3.simple_types.resourcelist import ResourceListSchema from spark_fhir_schemas.stu3.complex_types.identifier import IdentifierSchema from spark_fhir_schemas.stu3.complex_types.reference import ReferenceSchema from spark_fhir_schemas.stu3.complex_types.codeableconcept import ( CodeableConceptSchema, ) from spark_fhir_schemas.stu3.complex_types.annotation import AnnotationSchema from spark_fhir_schemas.stu3.complex_types.datarequirement import ( DataRequirementSchema, ) if ( max_recursion_limit and nesting_list.count("GuidanceResponse") >= 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 + ["GuidanceResponse"] schema = StructType( [ # The logical id of the resource, as used in the URL for the resource. Once # assigned, this value never changes. StructField("id", StringType(), True), # May be used to represent additional information that is not part of the basic # definition of the resource. 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 metadata about the resource. This is content that is maintained by the # infrastructure. Changes to the content may not always be associated with # version changes to the resource. StructField( "meta", MetaSchema.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, ), # A reference to a set of rules that were followed when the resource was # constructed, and which must be understood when processing the content. StructField("implicitRules", StringType(), True), # The base language in which the resource is written. StructField("language", StringType(), True), # A human-readable narrative that contains a summary of the resource, and may be # used to represent the content of the resource to a human. The narrative need # not encode all the structured data, but is required to contain sufficient # detail to make it "clinically safe" for a human to just read the narrative. # Resource definitions may define what content should be represented in the # narrative to ensure clinical safety. StructField( "text", NarrativeSchema.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, ), # These resources do not have an independent existence apart from the resource # that contains them - they cannot be identified independently, and nor can they # have their own independent transaction scope. StructField( "contained", ArrayType( ResourceListSchema.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, ), # This is a GuidanceResponse resource StructField("resourceType", StringType(), True), # The id of the request associated with this response. If an id was given as # part of the request, it will be reproduced here to enable the requester to # more easily identify the response in a multi-request scenario. StructField("requestId", StringType(), True), # Allows a service to provide a unique, business identifier for the response. StructField( "identifier", IdentifierSchema.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, ), # A reference to the knowledge module that was invoked. StructField( "module", ReferenceSchema.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 status of the response. If the evaluation is completed successfully, the # status will indicate success. However, in order to complete the evaluation, # the engine may require more information. In this case, the status will be # data-required, and the response will contain a description of the additional # required information. If the evaluation completed successfully, but the engine # determines that a potentially more accurate response could be provided if more # data was available, the status will be data-requested, and the response will # contain a description of the additional requested information. StructField("status", StringType(), True), # The patient for which the request was processed. StructField( "subject", ReferenceSchema.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, ), # Allows the context of the guidance response to be provided if available. In a # service context, this would likely be unavailable. StructField( "context", ReferenceSchema.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 when the guidance response was processed. StructField("occurrenceDateTime", TimestampType(), True), # Provides a reference to the device that performed the guidance. StructField( "performer", ReferenceSchema.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 reason the request was initiated. This is typically provided as # a parameter to the evaluation and echoed by the service, although for some use # cases, such as subscription- or event-based scenarios, it may provide an # indication of the cause for the response. StructField( "reasonCodeableConcept", 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 reason the request was initiated. This is typically provided as # a parameter to the evaluation and echoed by the service, although for some use # cases, such as subscription- or event-based scenarios, it may provide an # indication of the cause for the response. StructField( "reasonReference", ReferenceSchema.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, ), # Provides a mechanism to communicate additional information about the response. StructField( "note", ArrayType( AnnotationSchema.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, ), # Messages resulting from the evaluation of the artifact or artifacts. As part # of evaluating the request, the engine may produce informational or warning # messages. These messages will be provided by this element. StructField( "evaluationMessage", ArrayType( ReferenceSchema.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 output parameters of the evaluation, if any. Many modules will result in # the return of specific resources such as procedure or communication requests # that are returned as part of the operation result. However, modules may define # specific outputs that would be returned as the result of the evaluation, and # these would be returned in this element. StructField( "outputParameters", ReferenceSchema.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 actions, if any, produced by the evaluation of the artifact. StructField( "result", ReferenceSchema.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 the evaluation could not be completed due to lack of information, or # additional information would potentially result in a more accurate response, # this element will a description of the data required in order to proceed with # the evaluation. A subsequent request to the service should include this data. StructField( "dataRequirement", ArrayType( DataRequirementSchema.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, ), ] ) if not include_extension: schema.fields = [ c if c.name != "extension" else StructField("extension", StringType(), True) for c in schema.fields ] return schema

52.871102

108

0.574496

2,528

25,431

5.606013

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0.037045

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0.385828

25,431

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109

52.98125

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false

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0.04886

0

null

0

1

0

null

0

6

a9ee9589873f013b2bd559695924c9a0bde5ba87

1,851

py

Python

tests/test_polygon.py

timskovjacobsen/computational-geometry

ffce747dc2112426bdc6f4e76c6164e4d812fa93

[ "MIT" ]

1

2021-08-30T22:05:20.000Z

tests/test_polygon.py

timskovjacobsen/computational-geometry

ffce747dc2112426bdc6f4e76c6164e4d812fa93

[ "MIT" ]

null

tests/test_polygon.py

timskovjacobsen/computational-geometry

ffce747dc2112426bdc6f4e76c6164e4d812fa93

[ "MIT" ]

null

from numpy.testing import assert_almost_equal from computational_geometry.polygon import polygon_area def test_polygon_with_counterclockwise_rectangle(): # ----- Setup ----- # Define the vertices of a rectangle (B x H = 10 x 10) x = [0, 10, 10, 0] y = [0, 0, 10, 10] # ----- Exercise ----- # Compute the area of the hexagon actual = polygon_area(x, y) actual_signed = polygon_area(x, y, signed=True) # The CORRECT result for the area is 100.00 and 100.00 expected = 100.00 expected_signed = 100.00 # ----- Verify ----- assert_almost_equal(actual, expected, decimal=2) assert_almost_equal(actual_signed, expected_signed, decimal=2) def test_polygon_with_clockwise_rectangle(): # ----- Setup ----- # Define the vertices of a rectangle (B x H = 10 x 10) x = [0, 0, 10, 10] y = [0, 10, 10, 0] # ----- Exercise ----- # Compute the area of the hexagon actual = polygon_area(x, y) actual_signed = polygon_area(x, y, signed=True) # The CORRECT result for the area is 100.00 and -100.00 expected = 100.00 expected_signed = -100.00 # ----- Verify ----- assert_almost_equal(actual, expected, decimal=2) assert_almost_equal(actual_signed, expected_signed, decimal=2) def test_polygon_with_counterclockwise_hexagon(): # ----- Setup ----- # Define the vertices of a hexagon x = [3, 4, 7, 8, 8.5, 3] y = [5, 3, 0, 1, 3, 5] # ----- Exercise ----- # Compute the area of the hexagon actual = polygon_area(x, y) actual_signed = polygon_area(x, y, signed=True) # The CORRECT result for the area is 12.0 and 12.0 expected = 12.0 expected_signed = 12.0 # ----- Verify ----- assert_almost_equal(actual, expected, decimal=2) assert_almost_equal(actual_signed, expected_signed, decimal=2)

27.626866

66

0.635332

268

1,851

4.216418

0.197761

0.035398

0.10531

0.069027

0.834513

0.790265

0.768142

0

0.072433

0.231767

1,851

66

67

28.045455

0.722222

0.3047

0

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0

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1

0.103448

false

0

0.068966

0

0.172414

0

null

0

1

0

null

0

6

e75367f70688e3f747086ccaf048e9e5fa422cde

63

py

Python

cuesdk/__init__.py

thops/cue-sdk-python

ee14846958163b1c18268e44d0bf0a852514e564

[ "MIT" ]

34

2020-03-25T08:57:23.000Z

2022-03-26T16:30:06.000Z

cuesdk/__init__.py

thops/cue-sdk-python

ee14846958163b1c18268e44d0bf0a852514e564

[ "MIT" ]

12

2020-03-25T08:56:28.000Z

2022-02-18T15:20:51.000Z

cuesdk/__init__.py

thops/cue-sdk-python

ee14846958163b1c18268e44d0bf0a852514e564

[ "MIT" ]

17

2020-07-24T13:29:06.000Z

2022-02-02T22:13:43.000Z

from .enums import * from .structs import * from .api import *

15.75

22

0.714286

9

63

5

0.555556

0.444444

0

0.190476

63

3

23

21

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true

0

1

0

1

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1

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null

1

0

1

0

null

0

1

0

1

0

1

0

6

e7acc997f285ea7a39e003af771e930a1f11775e

30

py

Python

swami-control/usr/lib/python2.7/dist-packages/swami_wallpaper/__init__.py

Feneric/bodhi3packages

f325307ffa53a91c060c20e1da1793b26122ca1b

[ "BSD-3-Clause" ]

2

2016-04-10T14:38:52.000Z

2018-08-31T21:41:37.000Z

swami-control/usr/lib/python2.7/dist-packages/swami_wallpaper/__init__.py

Feneric/bodhi3packages

f325307ffa53a91c060c20e1da1793b26122ca1b

[ "BSD-3-Clause" ]

5

2015-10-23T06:49:33.000Z

2018-10-20T00:46:58.000Z

swami-control/usr/lib/python2.7/dist-packages/swami_wallpaper/__init__.py

Feneric/bodhi3packages

f325307ffa53a91c060c20e1da1793b26122ca1b

[ "BSD-3-Clause" ]

5

2017-05-20T14:44:54.000Z

2019-10-05T15:59:33.000Z

from swami_wallpaper import *

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99c2e2576fd5b890c04f444b61a62ed629e2fdcd

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py

Python

template/__init__.py

khanhcsc/tts-bot

d29d6297eec4cec338f1944384d4ef08bb11c48a

[ "MIT" ]

null

template/__init__.py

khanhcsc/tts-bot

d29d6297eec4cec338f1944384d4ef08bb11c48a

[ "MIT" ]

null

template/__init__.py

khanhcsc/tts-bot

d29d6297eec4cec338f1944384d4ef08bb11c48a

[ "MIT" ]

1

2021-06-14T11:43:00.000Z

from template.bot import Bot from template.context import Context from template.handle import handle

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6

99f0e4956fba01448ac2830edda3c70ef8ca8219

18

py

Python

scanner/__init__.py

xavierskip/LANinfo

53c14731a665436ea5fd956b2353672b1a15f92c

[ "MIT" ]

null

scanner/__init__.py

xavierskip/LANinfo

53c14731a665436ea5fd956b2353672b1a15f92c

[ "MIT" ]

null

scanner/__init__.py

xavierskip/LANinfo

53c14731a665436ea5fd956b2353672b1a15f92c

[ "MIT" ]

null

from scan import *

18

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6

8216621cf819730d305afdda72e9c16adee6758f

177

py

Python

Source/Services/RPSLS.PythonPlayer.Api/app/pick/rpsls_dto.py

geverghe/RockPaperScissorsLizardSpock

1b13088032e413bcd1e79e32274b54396f89c79f

[ "MIT" ]

572

2019-11-05T15:26:43.000Z

2022-03-21T19:01:58.000Z

Source/Services/RPSLS.PythonPlayer.Api/app/pick/rpsls_dto.py

geverghe/RockPaperScissorsLizardSpock

1b13088032e413bcd1e79e32274b54396f89c79f

[ "MIT" ]

21

2019-11-07T15:47:10.000Z

2022-02-13T00:03:22.000Z

Source/Services/RPSLS.PythonPlayer.Api/app/pick/rpsls_dto.py

geverghe/RockPaperScissorsLizardSpock

1b13088032e413bcd1e79e32274b54396f89c79f

[ "MIT" ]

258

2019-11-05T16:10:44.000Z

2022-03-24T23:43:52.000Z

import socket from flask import jsonify def get_rpsls_dto_json(pick): return jsonify(text = pick.name, value = pick.value, player=socket.gethostname(), playerType="python")

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8233865fec5325b1f427958bb50e12b73b46ce49

10,638

py

Python

sdk/python/pulumi_aws/outputs.py

rapzo/pulumi-aws

390a098221315d98a54ba97d1559e750dc3053b7

[ "ECL-2.0", "Apache-2.0" ]

260

2018-06-18T14:57:00.000Z

2022-03-29T11:41:03.000Z

sdk/python/pulumi_aws/outputs.py

rapzo/pulumi-aws

390a098221315d98a54ba97d1559e750dc3053b7

[ "ECL-2.0", "Apache-2.0" ]

1,154

2018-06-19T20:38:20.000Z

2022-03-31T19:48:16.000Z

sdk/python/pulumi_aws/outputs.py

rapzo/pulumi-aws

390a098221315d98a54ba97d1559e750dc3053b7

[ "ECL-2.0", "Apache-2.0" ]

115

2018-06-28T03:20:27.000Z

2022-03-29T11:41:06.000Z

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union, overload from . import _utilities from ._enums import * __all__ = [ 'GetAmiBlockDeviceMappingResult', 'GetAmiFilterResult', 'GetAmiIdsFilterResult', 'GetAmiProductCodeResult', 'GetAutoscalingGroupsFilterResult', 'GetAvailabilityZoneFilterResult', 'GetAvailabilityZonesFilterResult', 'GetElasticIpFilterResult', 'GetPrefixListFilterResult', 'GetRegionsFilterResult', ] @pulumi.output_type class GetAmiBlockDeviceMappingResult(dict): def __init__(__self__, *, device_name: str, ebs: Mapping[str, str], no_device: str, virtual_name: str): """ :param str device_name: The physical name of the device. :param Mapping[str, str] ebs: Map containing EBS information, if the device is EBS based. Unlike most object attributes, these are accessed directly (e.g. `ebs.volume_size` or `ebs["volume_size"]`) rather than accessed through the first element of a list (e.g. `ebs[0].volume_size`). :param str no_device: Suppresses the specified device included in the block device mapping of the AMI. :param str virtual_name: The virtual device name (for instance stores). """ pulumi.set(__self__, "device_name", device_name) pulumi.set(__self__, "ebs", ebs) pulumi.set(__self__, "no_device", no_device) pulumi.set(__self__, "virtual_name", virtual_name) @property @pulumi.getter(name="deviceName") def device_name(self) -> str: """ The physical name of the device. """ return pulumi.get(self, "device_name") @property @pulumi.getter def ebs(self) -> Mapping[str, str]: """ Map containing EBS information, if the device is EBS based. Unlike most object attributes, these are accessed directly (e.g. `ebs.volume_size` or `ebs["volume_size"]`) rather than accessed through the first element of a list (e.g. `ebs[0].volume_size`). """ return pulumi.get(self, "ebs") @property @pulumi.getter(name="noDevice") def no_device(self) -> str: """ Suppresses the specified device included in the block device mapping of the AMI. """ return pulumi.get(self, "no_device") @property @pulumi.getter(name="virtualName") def virtual_name(self) -> str: """ The virtual device name (for instance stores). """ return pulumi.get(self, "virtual_name") @pulumi.output_type class GetAmiFilterResult(dict): def __init__(__self__, *, name: str, values: Sequence[str]): """ :param str name: The name of the AMI that was provided during image creation. """ pulumi.set(__self__, "name", name) pulumi.set(__self__, "values", values) @property @pulumi.getter def name(self) -> str: """ The name of the AMI that was provided during image creation. """ return pulumi.get(self, "name") @property @pulumi.getter def values(self) -> Sequence[str]: return pulumi.get(self, "values") @pulumi.output_type class GetAmiIdsFilterResult(dict): def __init__(__self__, *, name: str, values: Sequence[str]): pulumi.set(__self__, "name", name) pulumi.set(__self__, "values", values) @property @pulumi.getter def name(self) -> str: return pulumi.get(self, "name") @property @pulumi.getter def values(self) -> Sequence[str]: return pulumi.get(self, "values") @pulumi.output_type class GetAmiProductCodeResult(dict): def __init__(__self__, *, product_code_id: str, product_code_type: str): pulumi.set(__self__, "product_code_id", product_code_id) pulumi.set(__self__, "product_code_type", product_code_type) @property @pulumi.getter(name="productCodeId") def product_code_id(self) -> str: return pulumi.get(self, "product_code_id") @property @pulumi.getter(name="productCodeType") def product_code_type(self) -> str: return pulumi.get(self, "product_code_type") @pulumi.output_type class GetAutoscalingGroupsFilterResult(dict): def __init__(__self__, *, name: str, values: Sequence[str]): """ :param str name: The name of the filter. The valid values are: `auto-scaling-group`, `key`, `value`, and `propagate-at-launch`. :param Sequence[str] values: The value of the filter. """ pulumi.set(__self__, "name", name) pulumi.set(__self__, "values", values) @property @pulumi.getter def name(self) -> str: """ The name of the filter. The valid values are: `auto-scaling-group`, `key`, `value`, and `propagate-at-launch`. """ return pulumi.get(self, "name") @property @pulumi.getter def values(self) -> Sequence[str]: """ The value of the filter. """ return pulumi.get(self, "values") @pulumi.output_type class GetAvailabilityZoneFilterResult(dict): def __init__(__self__, *, name: str, values: Sequence[str]): """ :param str name: The name of the filter field. Valid values can be found in the [EC2 DescribeAvailabilityZones API Reference](https://docs.aws.amazon.com/AWSEC2/latest/APIReference/API_DescribeAvailabilityZones.html). :param Sequence[str] values: Set of values that are accepted for the given filter field. Results will be selected if any given value matches. """ pulumi.set(__self__, "name", name) pulumi.set(__self__, "values", values) @property @pulumi.getter def name(self) -> str: """ The name of the filter field. Valid values can be found in the [EC2 DescribeAvailabilityZones API Reference](https://docs.aws.amazon.com/AWSEC2/latest/APIReference/API_DescribeAvailabilityZones.html). """ return pulumi.get(self, "name") @property @pulumi.getter def values(self) -> Sequence[str]: """ Set of values that are accepted for the given filter field. Results will be selected if any given value matches. """ return pulumi.get(self, "values") @pulumi.output_type class GetAvailabilityZonesFilterResult(dict): def __init__(__self__, *, name: str, values: Sequence[str]): """ :param str name: The name of the filter field. Valid values can be found in the [EC2 DescribeAvailabilityZones API Reference](https://docs.aws.amazon.com/AWSEC2/latest/APIReference/API_DescribeAvailabilityZones.html). :param Sequence[str] values: Set of values that are accepted for the given filter field. Results will be selected if any given value matches. """ pulumi.set(__self__, "name", name) pulumi.set(__self__, "values", values) @property @pulumi.getter def name(self) -> str: """ The name of the filter field. Valid values can be found in the [EC2 DescribeAvailabilityZones API Reference](https://docs.aws.amazon.com/AWSEC2/latest/APIReference/API_DescribeAvailabilityZones.html). """ return pulumi.get(self, "name") @property @pulumi.getter def values(self) -> Sequence[str]: """ Set of values that are accepted for the given filter field. Results will be selected if any given value matches. """ return pulumi.get(self, "values") @pulumi.output_type class GetElasticIpFilterResult(dict): def __init__(__self__, *, name: str, values: Sequence[str]): pulumi.set(__self__, "name", name) pulumi.set(__self__, "values", values) @property @pulumi.getter def name(self) -> str: return pulumi.get(self, "name") @property @pulumi.getter def values(self) -> Sequence[str]: return pulumi.get(self, "values") @pulumi.output_type class GetPrefixListFilterResult(dict): def __init__(__self__, *, name: str, values: Sequence[str]): """ :param str name: The name of the filter field. Valid values can be found in the [EC2 DescribePrefixLists API Reference](https://docs.aws.amazon.com/AWSEC2/latest/APIReference/API_DescribePrefixLists.html). :param Sequence[str] values: Set of values that are accepted for the given filter field. Results will be selected if any given value matches. """ pulumi.set(__self__, "name", name) pulumi.set(__self__, "values", values) @property @pulumi.getter def name(self) -> str: """ The name of the filter field. Valid values can be found in the [EC2 DescribePrefixLists API Reference](https://docs.aws.amazon.com/AWSEC2/latest/APIReference/API_DescribePrefixLists.html). """ return pulumi.get(self, "name") @property @pulumi.getter def values(self) -> Sequence[str]: """ Set of values that are accepted for the given filter field. Results will be selected if any given value matches. """ return pulumi.get(self, "values") @pulumi.output_type class GetRegionsFilterResult(dict): def __init__(__self__, *, name: str, values: Sequence[str]): """ :param str name: The name of the filter field. Valid values can be found in the [describe-regions AWS CLI Reference][1]. :param Sequence[str] values: Set of values that are accepted for the given filter field. Results will be selected if any given value matches. """ pulumi.set(__self__, "name", name) pulumi.set(__self__, "values", values) @property @pulumi.getter def name(self) -> str: """ The name of the filter field. Valid values can be found in the [describe-regions AWS CLI Reference][1]. """ return pulumi.get(self, "name") @property @pulumi.getter def values(self) -> Sequence[str]: """ Set of values that are accepted for the given filter field. Results will be selected if any given value matches. """ return pulumi.get(self, "values")

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1

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1

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null

0

6

413c645c0502c638a805eb95f1600299b5cd8316

46,901

py

Python

unittests/test_models.py

YangyangFu/MPCPy

c9980cbfe7b5ea21b003c2c0bab800099dccf3f1

[ "BSD-3-Clause-LBNL" ]

null

unittests/test_models.py

YangyangFu/MPCPy

c9980cbfe7b5ea21b003c2c0bab800099dccf3f1

[ "BSD-3-Clause-LBNL" ]

null

unittests/test_models.py

YangyangFu/MPCPy

c9980cbfe7b5ea21b003c2c0bab800099dccf3f1

[ "BSD-3-Clause-LBNL" ]

1

2021-11-20T03:23:13.000Z

# -*- coding: utf-8 -*- """ This module contains the classes for testing the model module of mpcpy. """ import unittest from mpcpy import models from mpcpy import exodata from mpcpy import utility from mpcpy import systems from mpcpy import units from mpcpy import variables from testing import TestCaseMPCPy import pandas as pd import numpy as np from matplotlib import pyplot as plt import pickle import os #%% class SimpleRC(TestCaseMPCPy): '''Test simple model simulate and estimate. ''' def setUp(self): self.start_time = '1/1/2017'; self.final_time = '1/2/2017'; # Set measurements self.measurements = {}; self.measurements['T_db'] = {'Sample' : variables.Static('T_db_sample', 1800, units.s)}; def tearDown(self): del self.start_time del self.final_time del self.measurements def test_simulate(self): '''Test simulation of a model.''' # Set model paths mopath = os.path.join(self.get_unittest_path(), 'resources', 'model', 'Simple.mo'); modelpath = 'Simple.RC_nostart'; # Gather control inputs control_csv_filepath = os.path.join(self.get_unittest_path(), 'resources', 'model', 'SimpleRC_Input.csv'); variable_map = {'q_flow_csv' : ('q_flow', units.W)}; controls = exodata.ControlFromCSV(control_csv_filepath, variable_map); controls.collect_data(self.start_time, self.final_time); # Instantiate model model = models.Modelica(models.JModelica, \ models.RMSE, \ self.measurements, \ moinfo = (mopath, modelpath, {}), \ control_data = controls.data); # Simulate model model.simulate(self.start_time, self.final_time); # Check references df_test = model.display_measurements('Simulated'); self.check_df(df_test, 'simulate_display.csv'); df_test = model.get_base_measurements('Simulated'); self.check_df(df_test, 'simulate_base.csv'); def test_simulate_with_save_parameter_input_data(self): '''Test simulation of a model.''' # Set model paths mopath = os.path.join(self.get_unittest_path(), 'resources', 'model', 'Simple.mo'); modelpath = 'Simple.RC_nostart'; # Gather control inputs control_csv_filepath = os.path.join(self.get_unittest_path(), 'resources', 'model', 'SimpleRC_Input.csv'); variable_map = {'q_flow_csv' : ('q_flow', units.W)}; controls = exodata.ControlFromCSV(control_csv_filepath, variable_map); controls.collect_data(self.start_time, self.final_time); # Instantiate model model = models.Modelica(models.JModelica, \ models.RMSE, \ self.measurements, \ moinfo = (mopath, modelpath, {}), \ control_data = controls.data, save_parameter_input_data=True); # Simulate model model.simulate(self.start_time, self.final_time); # Check references df_test = model.display_measurements('Simulated'); self.check_df(df_test, 'simulate_display.csv'); df_test = model.get_base_measurements('Simulated'); self.check_df(df_test, 'simulate_base.csv'); def test_estimate_one_par(self): '''Test the estimation of one parameter of a model.''' # Set model paths mopath = os.path.join(self.get_unittest_path(), 'resources', 'model', 'Simple.mo'); modelpath = 'Simple.RC_noinputs'; # Instantiate system system = systems.EmulationFromFMU(self.measurements, \ moinfo = (mopath, modelpath, {})); system.collect_measurements(self.start_time, self.final_time); # Define parameters parameter_data = {}; parameter_data['heatCapacitor.C'] = {}; parameter_data['heatCapacitor.C']['Value'] = variables.Static('C_Value', 55000, units.J_K); parameter_data['heatCapacitor.C']['Minimum'] = variables.Static('C_Min', 10000, units.J_K); parameter_data['heatCapacitor.C']['Maximum'] = variables.Static('C_Max', 1000000, units.J_K); parameter_data['heatCapacitor.C']['Free'] = variables.Static('C_Free', True, units.boolean); # Instantiate model model = models.Modelica(models.JModelica, \ models.RMSE, \ self.measurements, \ moinfo = (mopath, modelpath, {}), \ parameter_data = parameter_data); # Estimate models model.estimate(self.start_time, self.final_time, ['T_db']) # Check references data = [model.parameter_data['heatCapacitor.C']['Value'].display_data()] index = ['heatCapacitor.C'] df_test = pd.DataFrame(data=data, index=index, columns=['Value']) self.check_df(df_test, 'estimate_one_par.csv', timeseries=False) def test_estimate_two_par(self): '''Test the estimation of two parameters of a model.''' # Set model paths mopath = os.path.join(self.get_unittest_path(), 'resources', 'model', 'Simple.mo'); modelpath = 'Simple.RC_noinputs'; # Instantiate system system = systems.EmulationFromFMU(self.measurements, \ moinfo = (mopath, modelpath, {})); system.collect_measurements(self.start_time, self.final_time); # Define parameters parameter_data = {}; parameter_data['heatCapacitor.C'] = {}; parameter_data['heatCapacitor.C']['Value'] = variables.Static('C_Value', 55000, units.J_K); parameter_data['heatCapacitor.C']['Minimum'] = variables.Static('C_Min', 10000, units.J_K); parameter_data['heatCapacitor.C']['Maximum'] = variables.Static('C_Max', 1000000, units.J_K); parameter_data['heatCapacitor.C']['Free'] = variables.Static('C_Free', True, units.boolean); parameter_data['thermalResistor.R'] = {}; parameter_data['thermalResistor.R']['Value'] = variables.Static('R_Value', 0.02, units.K_W); parameter_data['thermalResistor.R']['Minimum'] = variables.Static('R_Min', 0.001, units.K_W); parameter_data['thermalResistor.R']['Maximum'] = variables.Static('R_Max', 0.1, units.K_W); parameter_data['thermalResistor.R']['Free'] = variables.Static('R_Free', True, units.boolean); # Instantiate model model = models.Modelica(models.JModelica, \ models.RMSE, \ self.measurements, \ moinfo = (mopath, modelpath, {}), \ parameter_data = parameter_data); # Estimate models model.estimate(self.start_time, self.final_time, ['T_db']) # Check references data = [model.parameter_data['heatCapacitor.C']['Value'].display_data(), model.parameter_data['thermalResistor.R']['Value'].display_data(),] index = ['heatCapacitor.C', 'thermalResistor.R'] df_test = pd.DataFrame(data=data, index=index, columns=['Value']) self.check_df(df_test, 'estimate_two_par.csv', timeseries=False) def test_simulate_continue(self): '''Test simulation of a model in steps.''' # Set model paths mopath = os.path.join(self.get_unittest_path(), 'resources', 'model', 'Simple.mo'); modelpath = 'Simple.RC_nostart'; # Gather control inputs control_csv_filepath = os.path.join(self.get_unittest_path(), 'resources', 'model', 'SimpleRC_Input.csv'); variable_map = {'q_flow_csv' : ('q_flow', units.W)}; controls = exodata.ControlFromCSV(control_csv_filepath, variable_map); controls.collect_data(self.start_time, self.final_time); # Instantiate model model = models.Modelica(models.JModelica, \ models.RMSE, \ self.measurements, \ moinfo = (mopath, modelpath, {}), \ control_data = controls.data); # Simulate model model.simulate(self.start_time, self.final_time); # Check references df_test = model.display_measurements('Simulated'); self.check_df(df_test, 'simulate_display.csv'); # Simulate model in 4-hour chunks sim_steps = pd.date_range(self.start_time, self.final_time, freq=str('8H')) for i in range(len(sim_steps)-1): if i == 0: model.simulate(sim_steps[i], sim_steps[i+1]); else: model.simulate('continue', sim_steps[i+1]); # Check references df_test = model.display_measurements('Simulated'); self.check_df(df_test, 'simulate_step{0}.csv'.format(i)); def test_simulate_noinputs(self): '''Test simulation of a model with no external inputs.''' # Set model paths mopath = os.path.join(self.get_unittest_path(), 'resources', 'model', 'Simple.mo'); modelpath = 'Simple.RC_noinputs'; # Instantiate model model = models.Modelica(models.JModelica, \ models.RMSE, \ self.measurements, \ moinfo = (mopath, modelpath, {})); # Simulate model model.simulate(self.start_time, self.final_time); # Check references df_test = model.display_measurements('Simulated'); self.check_df(df_test, 'simulate_noinputs.csv'); def test_estimate_error_nofreeparameters(self): '''Test error raised if no free parameters passed.''' # Set model paths mopath = os.path.join(self.get_unittest_path(), 'resources', 'model', 'Simple.mo'); modelpath = 'Simple.RC_noinputs'; # Instantiate model model_no_params = models.Modelica(models.JModelica, \ models.RMSE, \ self.measurements, \ moinfo = (mopath, modelpath, {})); # Check error raised with no parameters with self.assertRaises(ValueError): model_no_params.estimate(self.start_time, self.final_time, []); # Set parameters parameter_data = {}; parameter_data['heatCapacitor.C'] = {}; parameter_data['heatCapacitor.C']['Value'] = variables.Static('C_Value', 55000, units.J_K); parameter_data['heatCapacitor.C']['Minimum'] = variables.Static('C_Min', 10000, units.J_K); parameter_data['heatCapacitor.C']['Maximum'] = variables.Static('C_Max', 100000, units.J_K); parameter_data['heatCapacitor.C']['Free'] = variables.Static('C_Free', False, units.boolean); # Instantiate model model_no_free = models.Modelica(models.JModelica, \ models.RMSE, \ self.measurements, \ moinfo = (mopath, modelpath, {}), \ parameter_data = parameter_data); # Check error raised with no free parameters with self.assertRaises(ValueError): model_no_params.estimate(self.start_time, self.final_time, []); def test_estimate_error_nomeasurements(self): '''Test error raised if measurement_variable_list not in measurements dictionary.''' # Set model paths mopath = os.path.join(self.get_unittest_path(), 'resources', 'model', 'Simple.mo'); modelpath = 'Simple.RC_noinputs'; # Set parameters parameter_data = {}; parameter_data['heatCapacitor.C'] = {}; parameter_data['heatCapacitor.C']['Value'] = variables.Static('C_Value', 55000, units.J_K); parameter_data['heatCapacitor.C']['Minimum'] = variables.Static('C_Min', 10000, units.J_K); parameter_data['heatCapacitor.C']['Maximum'] = variables.Static('C_Max', 100000, units.J_K); parameter_data['heatCapacitor.C']['Free'] = variables.Static('C_Free', True, units.boolean); # Instantiate model model_no_meas = models.Modelica(models.JModelica, \ models.RMSE, \ self.measurements, \ moinfo = (mopath, modelpath, {}), \ parameter_data = parameter_data); # Check error raised with no free parameters with self.assertRaises(ValueError): model_no_meas.estimate(self.start_time, self.final_time, ['wrong_meas']); def test_instantiate_error_incompatible_estimation(self): '''Test error raised if estimation method is incompatible with model.''' # Set model path fmupath = os.path.join(self.get_unittest_path(), 'resources', 'building', 'LBNL71T_Emulation_JModelica_v1.fmu'); with self.assertRaises(ValueError): model = models.Modelica(models.JModelica, models.RMSE, {}, fmupath=fmupath); #%% class EstimateFromJModelicaRealCSV(TestCaseMPCPy): '''Test parameter estimation of a model using JModelica from real csv data. ''' def setUp(self): ## Setup building fmu emulation self.building_source_file_path_est = os.path.join(self.get_unittest_path(), 'resources', 'building', 'RealMeasurements_est.csv'); self.building_source_file_path_val = os.path.join(self.get_unittest_path(), 'resources', 'building', 'RealMeasurements_val.csv'); self.building_source_file_path_val_missing = os.path.join(self.get_unittest_path(), 'resources', 'building', 'RealMeasurements_val_missing.csv'); self.zone_names = ['wes', 'hal', 'eas']; self.weather_path = os.path.join(self.get_unittest_path(), 'resources', 'weather', 'USA_IL_Chicago-OHare.Intl.AP.725300_TMY3.epw'); self.internal_path = os.path.join(self.get_unittest_path(), 'resources', 'internal', 'sampleCSV.csv'); self.internal_variable_map = {'intRad_wes' : ('wes', 'intRad', units.W_m2), \ 'intCon_wes' : ('wes', 'intCon', units.W_m2), \ 'intLat_wes' : ('wes', 'intLat', units.W_m2), \ 'intRad_hal' : ('hal', 'intRad', units.W_m2), \ 'intCon_hal' : ('hal', 'intCon', units.W_m2), \ 'intLat_hal' : ('hal', 'intLat', units.W_m2), \ 'intRad_eas' : ('eas', 'intRad', units.W_m2), \ 'intCon_eas' : ('eas', 'intCon', units.W_m2), \ 'intLat_eas' : ('eas', 'intLat', units.W_m2)}; self.control_path = os.path.join(self.get_unittest_path(), 'resources', 'building', 'ControlCSV_0.csv'); self.control_variable_map = {'conHeat_wes' : ('conHeat_wes', units.unit1), \ 'conHeat_hal' : ('conHeat_hal', units.unit1), \ 'conHeat_eas' : ('conHeat_eas', units.unit1)}; # Measurements self.measurements = {}; self.measurements['wesTdb'] = {'Sample' : variables.Static('wesTdb_sample', 1800, units.s)}; self.measurements['halTdb'] = {'Sample' : variables.Static('halTdb_sample', 1800, units.s)}; self.measurements['easTdb'] = {'Sample' : variables.Static('easTdb_sample', 1800, units.s)}; self.measurements['wesPhvac'] = {'Sample' : variables.Static('easTdb_sample', 1800, units.s)}; self.measurements['halPhvac'] = {'Sample' : variables.Static('easTdb_sample', 1800, units.s)}; self.measurements['easPhvac'] = {'Sample' : variables.Static('easTdb_sample', 1800, units.s)}; self.measurements['Ptot'] = {'Sample' : variables.Static('easTdb_sample', 1800, units.s)}; self.measurement_variable_map = {'wesTdb_mea' : ('wesTdb', units.K), 'halTdb_mea' : ('halTdb', units.K), 'easTdb_mea' : ('easTdb', units.K), 'wesPhvac_mea' : ('wesPhvac', units.W), 'halPhvac_mea' : ('halPhvac', units.W), 'easPhvac_mea' : ('easPhvac', units.W), 'Ptot_mea' : ('Ptot', units.W)} ## Setup model self.mopath = os.path.join(self.get_unittest_path(), 'resources', 'model', 'LBNL71T_MPC.mo'); self.modelpath = 'LBNL71T_MPC.MPC'; self.libraries = os.environ.get('MODELICAPATH'); self.estimate_method = models.JModelica; self.validation_method = models.RMSE; # Instantiate exo data sources self.weather = exodata.WeatherFromEPW(self.weather_path); self.internal = exodata.InternalFromCSV(self.internal_path, self.internal_variable_map, tz_name = self.weather.tz_name); self.control = exodata.ControlFromCSV(self.control_path, self.control_variable_map, tz_name = self.weather.tz_name); # Parameters self.parameters = exodata.ParameterFromCSV(os.path.join(self.get_unittest_path(), 'resources', 'model', 'LBNL71T_Parameters.csv')); self.parameters.collect_data(); self.parameters.data['lat'] = {}; self.parameters.data['lat']['Value'] = self.weather.lat; # Instantiate test building self.building_est = systems.RealFromCSV(self.building_source_file_path_est, self.measurements, self.measurement_variable_map, tz_name = self.weather.tz_name); # Exogenous collection time self.start_time_exodata = '1/1/2015'; self.final_time_exodata = '1/30/2015'; # Estimation time self.start_time_estimation = '1/1/2015'; self.final_time_estimation = '1/4/2015'; # Validation time self.start_time_validation = '1/4/2015'; self.final_time_validation = '1/5/2015'; # Measurement variables for estimate self.measurement_variable_list = ['wesTdb', 'easTdb', 'halTdb']; # Exodata self.weather.collect_data(self.start_time_exodata, self.final_time_exodata); self.internal.collect_data(self.start_time_exodata, self.final_time_exodata); self.control.collect_data(self.start_time_exodata, self.final_time_exodata); # Collect measurement data self.building_est.collect_measurements(self.start_time_estimation, self.final_time_estimation); # Instantiate model self.model = models.Modelica(self.estimate_method, \ self.validation_method, \ self.building_est.measurements, \ moinfo = (self.mopath, self.modelpath, self.libraries), \ zone_names = self.zone_names, \ weather_data = self.weather.data, \ internal_data = self.internal.data, \ control_data = self.control.data, \ parameter_data = self.parameters.data, \ tz_name = self.weather.tz_name); # Simulate model with initial guess self.model.simulate(self.start_time_estimation, self.final_time_estimation) def tearDown(self): del self.model del self.building_est del self.weather del self.internal del self.control del self.parameters del self.measurements def test_estimate_and_validate(self): '''Test the estimation of a model's coefficients based on measured data.''' plt.close('all'); # Check references df_test = self.model.display_measurements('Simulated'); self.check_df(df_test, 'simulate_initial_parameters.csv'); # Estimate model based on emulated data self.model.estimate(self.start_time_estimation, self.final_time_estimation, self.measurement_variable_list); # Finish test self._finish_estimate_validate('') def test_estimate_and_validate_missing_measurements(self): '''Test the estimation of a model's coefficients based on measured data. Some of the validation measurement data is missing. ''' plt.close('all'); # Check references df_test = self.model.display_measurements('Simulated'); self.check_df(df_test, 'simulate_initial_parameters.csv'); # Estimate model based on emulated data self.model.estimate(self.start_time_estimation, self.final_time_estimation, self.measurement_variable_list); # Validate model based on estimation data self.model.validate(self.start_time_estimation, self.final_time_estimation, \ os.path.join(self.get_unittest_path(), 'outputs', 'model_estimation_csv'), plot=0) # Check references RMSE = {}; for key in self.model.RMSE.keys(): RMSE[key] = {}; RMSE[key]['Value'] = self.model.RMSE[key].display_data(); df_test = pd.DataFrame(data = RMSE); self.check_df(df_test, 'estimate_RMSE.csv', timeseries=False); # Instantiate validate building building_val = systems.RealFromCSV(self.building_source_file_path_val_missing, self.measurements, self.measurement_variable_map, tz_name = self.weather.tz_name); # Validate on validation data building_val.collect_measurements(self.start_time_validation, self.final_time_validation); self.model.measurements = building_val.measurements; self.model.validate(self.start_time_validation, self.final_time_validation, \ os.path.join(self.get_unittest_path(), 'outputs', 'model_validation_csv'), plot=0); # Check references RMSE = {}; for key in self.model.RMSE.keys(): RMSE[key] = {}; RMSE[key]['Value'] = self.model.RMSE[key].display_data(); df_test = pd.DataFrame(data = RMSE); self.check_df(df_test, 'validate_RMSE_missing.csv', timeseries=False); def test_estimate_and_validate_global_start_init(self): '''Test the estimation of a model's coefficients based on measured data using global start and user-defined initial value.''' plt.close('all'); # Estimate model based on emulated data self.model.estimate(self.start_time_estimation, self.final_time_estimation, self.measurement_variable_list, global_start=7, seed=0, use_initial_values=True); # Finish test self._finish_estimate_validate('_global_start_winit') def test_estimate_and_validate_global_start_woinit(self): '''Test the estimation of a model's coefficients based on measured data using global start and no user-defined initial value.''' plt.close('all'); # Estimate model based on emulated data self.model.estimate(self.start_time_estimation, self.final_time_estimation, self.measurement_variable_list, global_start=7, seed=0, use_initial_values=False); # Finish test self._finish_estimate_validate('_global_start_woinit') def test_estimate_and_validate_global_start_maxexceeded(self): '''Test the estimation of a model's coefficients based on measured data using global start and maximum cpu time and iterations.''' plt.close('all'); # Set maximum cpu time for JModelica opt_options = self.model._estimate_method.opt_problem.get_optimization_options(); opt_options['IPOPT_options']['max_cpu_time'] = 60; opt_options['IPOPT_options']['max_iter'] = 100; self.model._estimate_method.opt_problem.set_optimization_options(opt_options); # Estimate model based on emulated data self.model.estimate(self.start_time_estimation, self.final_time_estimation, self.measurement_variable_list, global_start=7, seed=0, use_initial_values=True); # Finish test self._finish_estimate_validate('_global_start_maxexceeded') def _finish_estimate_validate(self,tag): '''Internal method for finishing the estimate and valudate tests.''' # Validate model based on estimation data self.model.validate(self.start_time_estimation, self.final_time_estimation, \ os.path.join(self.get_unittest_path(), 'outputs', 'model_estimation_csv'), plot=0) # Check references RMSE = {}; for key in self.model.RMSE.keys(): RMSE[key] = {}; RMSE[key]['Value'] = self.model.RMSE[key].display_data(); df_test = pd.DataFrame(data = RMSE); self.check_df(df_test, 'estimate_RMSE{0}.csv'.format(tag), timeseries=False); # All estimates if global estimate try: glo_est_data_test = self.model.get_global_estimate_data() self.check_json(glo_est_data_test, 'estimate_gloest{0}.txt'.format(tag)); except: pass # Instantiate validate building self.building_val = systems.RealFromCSV(self.building_source_file_path_val, self.measurements, self.measurement_variable_map, tz_name = self.weather.tz_name); # Validate on validation data self.building_val.collect_measurements(self.start_time_validation, self.final_time_validation); self.model.measurements = self.building_val.measurements; self.model.validate(self.start_time_validation, self.final_time_validation, \ os.path.join(self.get_unittest_path(), 'outputs', 'model_validation_csv'), plot=0); # Check references RMSE = {}; for key in self.model.RMSE.keys(): RMSE[key] = {}; RMSE[key]['Value'] = self.model.RMSE[key].display_data(); df_test = pd.DataFrame(data = RMSE); self.check_df(df_test, 'validate_RMSE{0}.csv'.format(tag), timeseries=False); class EstimateFromJModelicaEmulationFMU(TestCaseMPCPy): '''Test emulation-based parameter estimation of a model using JModelica. ''' def setUp(self): ## Setup building fmu emulation self.building_source_file_path = os.path.join(self.get_unittest_path(), 'resources', 'building', 'LBNL71T_Emulation_JModelica_v2.fmu'); self.zone_names = ['wes', 'hal', 'eas']; self.weather_path = os.path.join(self.get_unittest_path(), 'resources', 'weather', 'USA_IL_Chicago-OHare.Intl.AP.725300_TMY3.epw'); self.internal_path = os.path.join(self.get_unittest_path(), 'resources', 'internal', 'sampleCSV.csv'); self.internal_variable_map = {'intRad_wes' : ('wes', 'intRad', units.W_m2), \ 'intCon_wes' : ('wes', 'intCon', units.W_m2), \ 'intLat_wes' : ('wes', 'intLat', units.W_m2), \ 'intRad_hal' : ('hal', 'intRad', units.W_m2), \ 'intCon_hal' : ('hal', 'intCon', units.W_m2), \ 'intLat_hal' : ('hal', 'intLat', units.W_m2), \ 'intRad_eas' : ('eas', 'intRad', units.W_m2), \ 'intCon_eas' : ('eas', 'intCon', units.W_m2), \ 'intLat_eas' : ('eas', 'intLat', units.W_m2)}; self.control_path = os.path.join(self.get_unittest_path(), 'resources', 'building', 'ControlCSV_0.csv'); self.control_variable_map = {'conHeat_wes' : ('conHeat_wes', units.unit1), \ 'conHeat_hal' : ('conHeat_hal', units.unit1), \ 'conHeat_eas' : ('conHeat_eas', units.unit1)}; # Measurements self.measurements = {}; self.measurements['wesTdb'] = {'Sample' : variables.Static('wesTdb_sample', 1800, units.s)}; self.measurements['halTdb'] = {'Sample' : variables.Static('halTdb_sample', 1800, units.s)}; self.measurements['easTdb'] = {'Sample' : variables.Static('easTdb_sample', 1800, units.s)}; self.measurements['wesPhvac'] = {'Sample' : variables.Static('easTdb_sample', 1800, units.s)}; self.measurements['halPhvac'] = {'Sample' : variables.Static('easTdb_sample', 1800, units.s)}; self.measurements['easPhvac'] = {'Sample' : variables.Static('easTdb_sample', 1800, units.s)}; self.measurements['Ptot'] = {'Sample' : variables.Static('easTdb_sample', 1800, units.s)}; ## Setup model self.mopath = os.path.join(self.get_unittest_path(), 'resources', 'model', 'LBNL71T_MPC.mo'); self.modelpath = 'LBNL71T_MPC.MPC'; self.libraries = os.environ.get('MODELICAPATH'); self.estimate_method = models.JModelica; self.validation_method = models.RMSE; # Instantiate exo data sources self.weather = exodata.WeatherFromEPW(self.weather_path); self.internal = exodata.InternalFromCSV(self.internal_path, self.internal_variable_map, tz_name = self.weather.tz_name); self.control = exodata.ControlFromCSV(self.control_path, self.control_variable_map, tz_name = self.weather.tz_name); # Parameters self.parameters = exodata.ParameterFromCSV(os.path.join(self.get_unittest_path(), 'resources', 'model', 'LBNL71T_Parameters.csv')); self.parameters.collect_data(); self.parameters.data['lat'] = {}; self.parameters.data['lat']['Value'] = self.weather.lat; # Instantiate building building_parameters_data = {}; building_parameters_data['lat'] = {}; building_parameters_data['lat']['Value'] = self.weather.lat; self.building = systems.EmulationFromFMU(self.measurements, \ fmupath = self.building_source_file_path, \ zone_names = self.zone_names, \ parameter_data = building_parameters_data); def tearDown(self): del self.building del self.weather del self.internal del self.control del self.parameters del self.measurements def test_estimate_and_validate(self): '''Test the estimation of a model's coefficients based on measured data.''' plt.close('all'); # Exogenous collection time self.start_time_exodata = '1/1/2015'; self.final_time_exodata = '1/30/2015'; # Estimation time self.start_time_estimation = '1/1/2015'; self.final_time_estimation = '1/4/2015'; # Validation time self.start_time_validation = '1/4/2015'; self.final_time_validation = '1/5/2015'; # Measurement variables for estimate self.measurement_variable_list = ['wesTdb', 'easTdb', 'halTdb']; # Exodata self.weather.collect_data(self.start_time_exodata, self.final_time_exodata); self.internal.collect_data(self.start_time_exodata, self.final_time_exodata); self.control.collect_data(self.start_time_exodata, self.final_time_exodata); # Set exodata to building emulation self.building.weather_data = self.weather.data; self.building.internal_data = self.internal.data; self.building.control_data = self.control.data; self.building.tz_name = self.weather.tz_name; # Collect measurement data self.building.collect_measurements(self.start_time_estimation, self.final_time_estimation); # Instantiate model self.model = models.Modelica(self.estimate_method, \ self.validation_method, \ self.building.measurements, \ moinfo = (self.mopath, self.modelpath, self.libraries), \ zone_names = self.zone_names, \ weather_data = self.weather.data, \ internal_data = self.internal.data, \ control_data = self.control.data, \ parameter_data = self.parameters.data, \ tz_name = self.weather.tz_name, save_parameter_input_data=True); # Simulate model with initial guess self.model.simulate(self.start_time_estimation, self.final_time_estimation) # Check references df_test = self.model.display_measurements('Simulated'); self.check_df(df_test, 'simulate_initial_parameters.csv'); # Check parameter and input data were saved df_test = pd.read_csv('mpcpy_simulation_inputs_model.csv', index_col='Time'); df_test.index = pd.to_datetime(df_test.index).tz_localize('UTC') self.check_df(df_test, 'mpcpy_simulation_inputs_model.csv'); df_test = pd.read_csv('mpcpy_simulation_parameters_model.csv', index_col='parameter'); self.check_df(df_test, 'mpcpy_simulation_parameters_model.csv', timeseries=False); # Estimate model based on emulated data self.model.estimate(self.start_time_estimation, self.final_time_estimation, self.measurement_variable_list); # Validate model based on estimation data self.model.validate(self.start_time_estimation, self.final_time_estimation, \ os.path.join(self.get_unittest_path(), 'outputs', 'model_estimation'), plot=0) # Check references RMSE = {}; for key in self.model.RMSE.keys(): RMSE[key] = {}; RMSE[key]['Value'] = self.model.RMSE[key].display_data(); df_test = pd.DataFrame(data = RMSE); self.check_df(df_test, 'estimate_RMSE.csv', timeseries=False); # Validate on validation data self.building.collect_measurements(self.start_time_validation, self.final_time_validation); self.model.measurements = self.building.measurements; self.model.validate(self.start_time_validation, self.final_time_validation, \ os.path.join(self.get_unittest_path(), 'outputs', 'model_validation'), plot=0); # Check references RMSE = {}; for key in self.model.RMSE.keys(): RMSE[key] = {}; RMSE[key]['Value'] = self.model.RMSE[key].display_data(); df_test = pd.DataFrame(data = RMSE); self.check_df(df_test, 'validate_RMSE.csv', timeseries=False); def test_estimate_error_continue(self): '''Test that an error is thrown for estimation start_time of continue. ''' plt.close('all'); # Exogenous collection time start_time_exodata = '1/1/2015'; final_time_exodata = '1/30/2015'; # Estimation time start_time_estimation = 'continue'; final_time_estimation = '1/4/2015'; # Measurement variables for estimate self.measurement_variable_list = ['wesTdb', 'easTdb', 'halTdb']; # Exodata self.weather.collect_data(start_time_exodata, final_time_exodata); self.internal.collect_data(start_time_exodata, final_time_exodata); self.control.collect_data(start_time_exodata, final_time_exodata); # Instantiate model self.model = models.Modelica(self.estimate_method, \ self.validation_method, \ self.building.measurements, \ moinfo = (self.mopath, self.modelpath, self.libraries), \ zone_names = self.zone_names, \ weather_data = self.weather.data, \ internal_data = self.internal.data, \ control_data = self.control.data, \ parameter_data = self.parameters.data, \ tz_name = self.weather.tz_name); # Error when estimate model with self.assertRaises(ValueError): self.model.estimate(start_time_estimation, final_time_estimation, self.measurement_variable_list); #%% class EstimateFromUKF(TestCaseMPCPy): '''Test the parameter estimation of a model using UKF. ''' def setUp(self): self.start_time = '1/1/2017'; self.final_time = '1/10/2017'; # Set measurements self.measurements = {}; self.measurements['T_db'] = {'Sample' : variables.Static('T_db_sample', 1800, units.s)}; # Set model paths mopath = os.path.join(self.get_unittest_path(), 'resources', 'model', 'Simple.mo'); modelpath = 'Simple.RC_nostart'; self.moinfo = (mopath, modelpath, {}) # Gather parameters parameter_csv_filepath = os.path.join(self.get_unittest_path(), 'resources', 'model', 'SimpleRC_Parameters.csv'); self.parameters = exodata.ParameterFromCSV(parameter_csv_filepath); self.parameters.collect_data(); # Gather control inputs control_csv_filepath = os.path.join(self.get_unittest_path(), 'resources', 'model', 'SimpleRC_Input.csv'); variable_map = {'q_flow_csv' : ('q_flow', units.W)}; self.controls = exodata.ControlFromCSV(control_csv_filepath, variable_map); self.controls.collect_data(self.start_time, self.final_time); # Instantiate system self.system = systems.EmulationFromFMU(self.measurements, \ moinfo = self.moinfo, \ control_data = self.controls.data); # Get measurements self.system.collect_measurements(self.start_time, self.final_time); def tearDown(self): del self.system del self.controls del self.parameters del self.measurements def test_estimate_and_validate(self): '''Test the estimation of a model's coefficients based on measured data.''' # Instantiate model model = models.Modelica(models.UKF, \ models.RMSE, \ self.system.measurements, \ moinfo = self.moinfo, \ parameter_data = self.parameters.data, \ control_data = self.controls.data, \ version = '1.0'); # Estimate model.estimate(self.start_time, self.final_time, ['T_db']); # Validate model.validate(self.start_time, self.final_time, 'validate', plot = 0); # Check references RMSE = {}; for key in model.RMSE.keys(): RMSE[key] = {}; RMSE[key]['Value'] = model.RMSE[key].display_data(); df_test = pd.DataFrame(data = RMSE); self.check_df(df_test, 'validate_RMSE.csv', timeseries=False); def test_error_fmu_version(self): '''Test error raised if wrong fmu version.''' # Check error raised with wrong fmu version (2.0 instead of 1.0) with self.assertRaises(ValueError): # Instantiate model model = models.Modelica(models.UKF, \ models.RMSE, \ self.system.measurements, \ moinfo = self.moinfo, \ parameter_data = self.parameters.data, \ control_data = self.controls.data, \ version = '2.0'); #%% Occupancy tests class OccupancyFromQueueing(TestCaseMPCPy): '''Test the occupancy model using a queueing approach. ''' def setUp(self): # Testing time self.start_time = '3/8/2013'; self.final_time = '3/15/2013 23:59'; # Setup building measurement collection from csv self.csv_filepath = os.path.join(self.get_unittest_path(), 'resources', 'building', 'OccData.csv'); # Measurements self.measurements = {}; self.measurements['occupancy'] = {'Sample' : variables.Static('occupancy_sample', 300, units.s)}; self.measurement_variable_map = {'Total People Count for the whole building (+)' : ('occupancy', units.unit1)}; # Instantiate building measurement source self.building = systems.RealFromCSV(self.csv_filepath, \ self.measurements, self.measurement_variable_map, time_header = 'Date'); # Where to save ref occupancy model self.occupancy_model_file = self.get_ref_path() + os.sep +'occupancy_model_estimated.txt'; def tearDown(self): del self.building del self.measurements def test_estimate(self): '''Test the estimation method.''' plt.close('all'); # Training Time start_time = '2/1/2013'; final_time = '7/24/2013 23:59'; # Collect measurements self.building.collect_measurements(start_time, final_time); # Instantiate occupancy model occupancy = models.Occupancy(models.QueueModel, self.building.measurements); # Estimate occupancy model parameters np.random.seed(1); occupancy.estimate(start_time, final_time); try: with open(self.occupancy_model_file, 'r') as f: occupancy = pickle.load(f); except IOError: try: os.makedirs(self.get_ref_path()); except OSError: pass; with open(self.occupancy_model_file, 'w') as f: pickle.dump(occupancy, f); def test_simulate(self): '''Test occupancy prediction.''' plt.close('all'); # Load occupancy model with open(self.occupancy_model_file, 'r') as f: occupancy = pickle.load(f); # Simulate occupancy model np.random.seed(1); occupancy.simulate(self.start_time, self.final_time); # Check references df_test = occupancy.display_measurements('Simulated'); self.check_df(df_test, 'simulate_display.csv'); df_test = occupancy.get_base_measurements('Simulated'); self.check_df(df_test, 'simulate_base.csv'); def test_validate(self): '''Test occupancy prediction comparison with measured data.''' plt.close('all'); # Load occupancy model with open(self.occupancy_model_file, 'r') as f: occupancy = pickle.load(f); # Collect validation measurements self.building.collect_measurements(self.start_time, self.final_time); # Set valiation measurements in occupancy model occupancy.measurements = self.building.measurements; # Validate occupancy model with simulation options simulate_options = occupancy.get_simulate_options(); simulate_options['iter_num'] = 5; occupancy.set_simulate_options(simulate_options); np.random.seed(1); occupancy.validate(self.start_time, self.final_time, \ os.path.join(self.get_unittest_path(), 'outputs', \ 'occupancy_model_validate')); # Check references RMSE = {}; for key in occupancy.RMSE.keys(): RMSE[key] = {}; RMSE[key]['Value'] = occupancy.RMSE[key].display_data(); df_test = pd.DataFrame(data = RMSE); self.check_df(df_test, 'validate_RMSE.csv', timeseries=False); def test_get_load(self): '''Test generation of occupancy load data using occupancy prediction.''' plt.close('all'); # Load occupancy model with open(self.occupancy_model_file, 'r') as f: occupancy = pickle.load(f); # Simulate occupancy model simulate_options = occupancy.get_simulate_options(); simulate_options['iter_num'] = 5; np.random.seed(1); occupancy.simulate(self.start_time, self.final_time); load = occupancy.get_load(100); # Check references df_test = load.to_frame(name='load'); df_test.index.name = 'Time'; self.check_df(df_test, 'get_load.csv'); def test_get_constraint(self): '''Test generation of occupancy constraint data using occupancy prediction.''' plt.close('all'); # Load occupancy model with open(self.occupancy_model_file, 'r') as f: occupancy = pickle.load(f); # Simulate occupancy model simulate_options = occupancy.get_simulate_options(); simulate_options['iter_num'] = 5; np.random.seed(1); occupancy.simulate(self.start_time, self.final_time); constraint = occupancy.get_constraint(20, 25); # Check references df_test = constraint.to_frame(name='constraint'); df_test.index.name = 'Time'; self.check_df(df_test, 'get_constraint.csv'); def test_error_points_per_day(self): '''Test occupancy prediction.''' plt.close('all'); # Time self.start_time = '3/1/2013'; self.final_time = '3/7/2013 23:59'; # Load occupancy model with open(self.occupancy_model_file, 'r') as f: occupancy = pickle.load(f); # Change occupant measurements to not be whole number in points per day occupancy.measurements['occupancy']['Sample'] = variables.Static('occupancy_sample', 299, units.s); # Estimate occupancy model parameters and expect error with self.assertRaises(ValueError): np.random.seed(1); occupancy.estimate(self.start_time, self.final_time); if __name__ == '__main__': unittest.main()

53.847302

166

0.596107

5,065

46,901

5.300099

0.070286

0.023803

0.02954

0.019296

0.832334

0.798286

0.766325

0.723859

0.707879

0.70244

0

0.012749

0.289205

46,901

870

167

53.909195

0.792513

0.116053

0

0.687601

0

0.11548

0.016584

0

0.011272

1

0.056361

false

0.003221

0.020934

0

0.085346

0

null

0

1

0

null

0

6

414686fe483b31fbdec4638fec6bb79af5c8e9c6

2,050

py

Python

epytope/Data/pssms/smmpmbec/mat/B_45_01_8.py

christopher-mohr/epytope

8ac9fe52c0b263bdb03235a5a6dffcb72012a4fd

[ "BSD-3-Clause" ]

7

2021-02-01T18:11:28.000Z

2022-01-31T19:14:07.000Z

epytope/Data/pssms/smmpmbec/mat/B_45_01_8.py

christopher-mohr/epytope

8ac9fe52c0b263bdb03235a5a6dffcb72012a4fd

[ "BSD-3-Clause" ]

22

2021-01-02T15:25:23.000Z

2022-03-14T11:32:53.000Z

epytope/Data/pssms/smmpmbec/mat/B_45_01_8.py

christopher-mohr/epytope

8ac9fe52c0b263bdb03235a5a6dffcb72012a4fd

[ "BSD-3-Clause" ]

4

2021-05-28T08:50:38.000Z

2022-03-14T11:45:32.000Z

B_45_01_8 = {0: {'A': -0.439, 'C': -0.065, 'E': -0.014, 'D': 0.021, 'G': -0.043, 'F': -0.078, 'I': -0.046, 'H': 0.125, 'K': 0.102, 'M': 0.047, 'L': 0.036, 'N': 0.101, 'Q': 0.113, 'P': -0.068, 'S': 0.031, 'R': 0.132, 'T': 0.046, 'W': 0.029, 'V': -0.05, 'Y': 0.019}, 1: {'A': 0.201, 'C': -0.049, 'E': -0.422, 'D': -0.182, 'G': -0.018, 'F': 0.074, 'I': 0.067, 'H': 0.073, 'K': 0.142, 'M': -0.0, 'L': 0.064, 'N': -0.107, 'Q': -0.132, 'P': 0.144, 'S': -0.035, 'R': 0.107, 'T': 0.002, 'W': -0.118, 'V': 0.132, 'Y': 0.057}, 2: {'A': -0.067, 'C': 0.025, 'E': -0.032, 'D': 0.053, 'G': 0.024, 'F': 0.107, 'I': 0.133, 'H': -0.023, 'K': 0.005, 'M': -0.344, 'L': -0.242, 'N': -0.067, 'Q': -0.174, 'P': 0.319, 'S': -0.01, 'R': -0.015, 'T': 0.054, 'W': 0.064, 'V': 0.028, 'Y': 0.161}, 3: {'A': -0.039, 'C': 0.033, 'E': 0.065, 'D': 0.1, 'G': 0.17, 'F': -0.05, 'I': -0.24, 'H': 0.221, 'K': 0.186, 'M': -0.091, 'L': -0.248, 'N': 0.104, 'Q': -0.031, 'P': -0.011, 'S': 0.106, 'R': 0.242, 'T': -0.035, 'W': -0.119, 'V': -0.368, 'Y': 0.003}, 4: {'A': -0.287, 'C': 0.052, 'E': 0.076, 'D': -0.032, 'G': -0.058, 'F': 0.2, 'I': -0.003, 'H': 0.047, 'K': 0.011, 'M': 0.033, 'L': 0.118, 'N': 0.072, 'Q': -0.041, 'P': 0.011, 'S': -0.098, 'R': 0.002, 'T': -0.166, 'W': 0.062, 'V': -0.063, 'Y': 0.065}, 5: {'A': -0.133, 'C': 0.005, 'E': -0.007, 'D': -0.086, 'G': 0.063, 'F': -0.027, 'I': 0.208, 'H': -0.101, 'K': -0.014, 'M': 0.066, 'L': 0.037, 'N': 0.018, 'Q': 0.096, 'P': 0.104, 'S': 0.066, 'R': -0.119, 'T': 0.014, 'W': -0.131, 'V': 0.108, 'Y': -0.166}, 6: {'A': -0.531, 'C': -0.151, 'E': -0.118, 'D': -0.032, 'G': -0.044, 'F': -0.014, 'I': -0.127, 'H': 0.212, 'K': 0.156, 'M': 0.016, 'L': -0.112, 'N': 0.162, 'Q': 0.062, 'P': 0.081, 'S': -0.096, 'R': 0.199, 'T': -0.055, 'W': 0.194, 'V': -0.062, 'Y': 0.26}, 7: {'A': -0.767, 'C': -0.05, 'E': -0.01, 'D': -0.01, 'G': -0.176, 'F': 0.016, 'I': -0.026, 'H': 0.196, 'K': 0.057, 'M': 0.206, 'L': 0.236, 'N': 0.181, 'Q': 0.161, 'P': -0.226, 'S': -0.1, 'R': 0.082, 'T': -0.083, 'W': 0.29, 'V': -0.158, 'Y': 0.181}, -1: {'con': 4.58524}}

2,050

0.394634

496

2,050

1.625

0.288306

0.019851

0.012407

0.014888

0.034739

0

0.37369

0.161951

2,050

1

2,050

0.09546

0

0.079473

0

1

0

false

0

1

null

0

1

0

1

0

null

0

6

4173b4f344ed9e126bdab074dbf27d9591abe674

254

py

Python

tools/get_resolution.py

2flps/python-autodrawer

6ee06e8fbb78a4f9f7946e95a40b1c563a6f18c0

[ "MIT" ]

1

2021-12-23T02:39:53.000Z

tools/get_resolution.py

FelipeFlohr/python-autodrawer

6ee06e8fbb78a4f9f7946e95a40b1c563a6f18c0

[ "MIT" ]

null

tools/get_resolution.py

FelipeFlohr/python-autodrawer

6ee06e8fbb78a4f9f7946e95a40b1c563a6f18c0

[ "MIT" ]

null

import pyautogui print("EN: Monitor's X size | PT-BR: Tamanho X do monitor: {}\nEN: Monitor's Y size | PT-BR: Tamanho Y do monitor: {}\n".format(pyautogui.size()[0], pyautogui.size()[1])) input("EN: Press enter to exit | PT-BR: Aperte enter para sair ")

63.5

170

0.685039

45

254

3.866667

0.577778

0.068966

0.091954

0.172414

0

0.009174

0.141732

254

4

171

63.5

0.788991

0

0.333333

0.658824

0

1

0

true

0

0.333333

0

0.333333

0

null

0

1

0

1

0

1

0

null

0

1

0

1

0

6

418e2ac8656a23792d5262c1faf2985c89870af5

27,635

py

Python

commands/nellie.py

MuffinAmor/nellie

eace65ac7d7d1730c131345e6e5e5b7d39b078ef

[ "MIT" ]

1

2022-03-12T17:34:05.000Z

commands/nellie.py

MuffinAmor/nellie

eace65ac7d7d1730c131345e6e5e5b7d39b078ef

[ "MIT" ]

null

commands/nellie.py

MuffinAmor/nellie

eace65ac7d7d1730c131345e6e5e5b7d39b078ef

[ "MIT" ]

null

from datetime import datetime import discord from discord.ext import commands from lib.general import prefix bot = commands.Bot(command_prefix='nl!') botcolor = 0x00ff06 bot.remove_command('help') Nellie = "[Nellie](https://discordapp.com/oauth2/authorize?" \ "client_id=631149405965385759&permissions=388305&redirect_uri=https%3A%2F%2Fdiscord.gg&scope=bot)" url = 'https://cdn.discordapp.com/attachments/522437022095245313/546359964101509151/Neko_Logo.png' def current(bot, message): current = prefix(str(message.guild.id)) return current support = "Do you need help? {}support".format(current) class nellie(commands.Cog): def __init__(self, bot): self.bot = bot self.Nellie = "https://discordapp.com/oauth2/authorize?" \ "client_id=631149405965385759&permissions=388305&redirect_uri=https%3A%2F%2Fdiscord.gg&scope=bot" ######################################################################################################################## @commands.command() async def invite(self, ctx): if ctx.author.bot is False: embed = discord.Embed(color=ctx.author.color) embed.add_field( name=":tools: Nellie Invite Link :tools:", value="[Do like invite me? Click me!]({})".format(self.Nellie), inline=False) embed.set_footer(text='Message was requested by {}'.format(ctx.author), icon_url=ctx.author.avatar_url) embed.timestamp = datetime.utcnow() await ctx.send(embed=embed) @commands.Cog.listener() async def on_message(self, message): if message.author.bot is False: current = prefix(str(message.guild.id)) support = "Do you need help? {}support".format(current) if message.content.startswith("n!cmdhelp"): if "createroom" in message.content: embed = discord.Embed(title="Command Help: createroom", description="Command Number 201", color=message.author.color) embed.add_field(name='Bot:', value=Nellie, inline=True) embed.add_field(name='Command', value='createroom *id* *name*', inline=True) embed.add_field(name='Example:', value='{}createroom 636702313758851102² #Neko Dev. Army³\n' '*² the ID of a Channel from the other Server, *³Your choosen name.'.format( current), inline=False) embed.add_field(name='Description', value='The Command **createroom** make possible to create your personal Globalchatroom ' 'beetween two different Servers.\n' 'It create a connection beetween the Command and the ID-Channel.', inline=False) embed.add_field(name='Argument fields:', value='__**id**__:\n' 'In this field you put in the Channel-id of the Channel which you like create ' 'the Chatroom in the other Server.\n' '\n' '__**name**__:\n' 'In the name field you put in your own choosen name, ' 'how you like name your personal Chatgroup. ' 'This name can not be changed after the Command excecute.', inline=False) embed.add_field(name='Required Permissions:', value='__**Bot**__:\n' '-Manage Channels\n' '-Embed Links\n' '-Message send\n' '-Manage Messages\n' '-Message read\n' '\n' '__**Command Excecuter**__:\n' 'Administrator in both Server', inline=False) embed.set_thumbnail(url=url) embed.set_footer(text=support) embed.timestamp = datetime.utcnow() await message.channel.send(embed=embed) elif "unlink" in message.content: embed = discord.Embed(title="Command Help: unlink", description="Command Number 202", color=message.author.color) embed.add_field(name='Bot:', value=Nellie, inline=True) embed.add_field(name='Command', value='unlink *id* *name*', inline=True) embed.add_field(name='Example:', value='{}unlink 636702313758851102² #Neko Dev. Army³\n' '*² the ID of the Channel you like unlink, *³The Chatroom name.'.format( current), inline=False) embed.add_field(name='Description', value='The Command **unlink** cut the connection beetween the ID-Channel ' 'and the Chatroom.', inline=False) embed.add_field(name='Argument fields:', value='__**id**__:\nIn this field you put in the Channel-id of the Channel which you ' 'like disconnect from the Chatroom.\n' '\n' '__**name**__:\n' 'In the **name** field you put in, from which Chatroom do you like disconnect ' 'the ID-Channel.', inline=False) embed.add_field(name='Required Permissions:', value='__**Bot**__:\n' '-Manage Channels\n' '-Embed Links\n' '-Message send\n' '-Manage Messages\n' '-Message read\n' '\n' '__**Command Excecuter**__:\n' 'Administrator in the Server of the ID-Channel', inline=False) embed.set_thumbnail(url=url) embed.set_footer(text=support) embed.timestamp = datetime.utcnow() msg = await message.channel.send(embed=embed) ################################################################# elif "roominfo" in message.content: embed = discord.Embed(title="Command Help: roominfo", description="Command Number 203", color=message.author.color) embed.add_field(name='Bot:', value=Nellie, inline=True) embed.add_field(name='Command', value='roominfo *name*', inline=True) embed.add_field(name='Example:', value='{}roominfo #Neko Dev. Army³\n*³The Chatroom name.'.format(current), inline=False) embed.add_field(name='Description', value='The Command **roominfo** gives you infos about the named room.', inline=False) embed.add_field(name='Argument fields:', value='__**name**__:\nIn the **name** field you put in, from which Chatroom do you like have Infos.', inline=False) embed.add_field(name='Required Permissions:', value='__**Bot**__:\n-Embed Links\n-Send Messages\n-Read Messages\n\n__**Command Excecuter**__:\n-Send Messages\n-Read Messages\n', inline=False) embed.set_thumbnail(url=url) embed.set_footer(text=support) embed.timestamp = datetime.utcnow() msg = await message.channel.send(embed=embed) ################################################################# elif "namecheck" in message.content: embed = discord.Embed(title="Command Help: namecheck", description="Command Number 204", color=message.author.color) embed.add_field(name='Bot:', value=Nellie, inline=True) embed.add_field(name='Command', value='namecheck *name*', inline=True) embed.add_field(name='Example:', value='{}namecheck #Neko Dev. Army³\n*³The checked name.'.format(current), inline=False) embed.add_field(name='Description', value='The Command **namecheck** tells you, if this Chatroomname is allready given or avaible.', inline=False) embed.add_field(name='Argument fields:', value='__**name**__:\nIn the **name** field you put in, which name do you like check.', inline=False) embed.add_field(name='Required Permissions:', value='__**Bot**__:\n-Embed Links\n-Send Messages\n-Read Messages\n\n__**Command Excecuter**__:\n-Send Messages\n-Read Messages\n', inline=False) embed.set_thumbnail(url=url) embed.set_footer(text=support) embed.timestamp = datetime.utcnow() msg = await message.channel.send(embed=embed) ################################################################# if "delroom" in message.content: embed = discord.Embed(title="Command Help: delroom", description="Command Number 205", color=message.author.color) embed.add_field(name='Bot:', value=Nellie, inline=True) embed.add_field(name='Command', value='delroom *name*', inline=True) embed.add_field(name='Example:', value='{}delroom #Neko Dev. Army³\n*³The Name of the Chatroom that you like delete.'.format( current), inline=False) embed.add_field(name='Description', value='The Command **delroom** delete the named Chatroom, if you owns them.', inline=False) embed.add_field(name='Argument fields:', value='__**name**__:\nIn the **name** field you put in, which Chatroom do you like delete.', inline=False) embed.add_field(name='Required Permissions:', value='__**Bot**__:\n-Embed Links\n-Send Messages\n-Read Messages\n\n__**Command Excecuter**__:\nNeed to be the Owner of the named Chatroom.', inline=False) embed.set_thumbnail(url=url) embed.set_footer(text=support) embed.timestamp = datetime.utcnow() msg = await message.channel.send(embed=embed) ################################################################# elif "addmod" in message.content: embed = discord.Embed(title="Command Help: addmod", description="Command Number 206", color=message.author.color) embed.add_field(name='Bot:', value=Nellie, inline=True) embed.add_field(name='Command', value='addmod *member* *name*', inline=True) embed.add_field(name='Example:', value='{}addmod <@474947907913515019>² #Neko Dev. Army³\n*² the User that you would like add, *³The Name of the Chatroom in which you like add the Mod.'.format( current), inline=False) embed.add_field(name='Description', value='The Command **addmod** add the mentioned Member as Mod to your Chatroom.', inline=False) embed.add_field(name='Argument fields:', value='__**member**__:\nThis field you mention the member that you like add as Mod to your Chatroom.\n\n__**name**__:\nIn the **name** field you put in, in which Chatroom do you like add the Mod.', inline=False) embed.add_field(name='Required Permissions:', value='__**Bot**__:\n-Manage Channels\n-Embed Links\n-Message send\n-Manage Messages\n-Message read\n\n__**Command Excecuter**__:\n-Need to be the Owner of the named Chatroom.', inline=False) embed.set_thumbnail(url=url) embed.set_footer(text=support) embed.timestamp = datetime.utcnow() msg = await message.channel.send(embed=embed) ################################################################# elif "add" in message.content: embed = discord.Embed(title="Command Help: add", description="Command Number 207", color=message.author.color) embed.add_field(name='Bot:', value=Nellie, inline=True) embed.add_field(name='Command', value='add *id* *name*', inline=True) embed.add_field(name='Example:', value='{}add 636702313758851102² #Neko Dev. Army³\n*² the ID of the Channel which you would like add, *³The Name of the Chatroom that you like add the Channel.'.format( current), inline=False) embed.add_field(name='Description', value='The Command **add** connect the ID-Channel with your Chatroom.', inline=False) embed.add_field(name='Argument fields:', value='__**id**__:\nIn this field you put in the Channel-id of the Channel which you like connect with the named Chatroom.\n\n__**name**__:\nIn the **name** field you put in, in which Chatroom do you like add the ID-Channel.', inline=False) embed.add_field(name='Required Permissions:', value='__**Bot**__:\n-Manage Channels\n-Embed Links\n-Message send\n-Manage Messages\n-Message read\n\n__**Command Excecuter**__:\n-Need to be the Owner of the named Chatroom or the Chatroom need to set as Public.\n-You need to have administrator permissions of the ID-Channel Server', inline=False) embed.set_thumbnail(url=url) embed.set_footer(text=support) embed.timestamp = datetime.utcnow() msg = await message.channel.send(embed=embed) ################################################################# elif "removemod" in message.content: embed = discord.Embed(title="Command Help: removemod", description="Command Number 208", color=message.author.color) embed.add_field(name='Bot:', value=Nellie, inline=True) embed.add_field(name='Command', value='removemod *member* *name*', inline=True) embed.add_field(name='Example:', value='{}removemod <@474947907913515019>² #Neko Dev. Army³\n*² the Member which you like remove as Mod, *³The Name of the Chatroom in which you like remove the Mod.'.format( current), inline=False) embed.add_field(name='Description', value='The Command **removemod** remove the mentioned Member as Mod from your Chatroom.', inline=False) embed.add_field(name='Argument fields:', value='__**member**__:\nThis field you mention the member that you like remove the Mod from your Chatroom.\n\n__**name**__:\nIn the **name** field you put in, from which Chatroom do you like remove the Mod.', inline=False) embed.add_field(name='Required Permissions:', value='__**Bot**__:\n-Embed Links\n-Message send\n-Message read\n\n__**Command Excecuter**__:\n-Need to be the Owner of the named Chatroom.', inline=False) embed.set_thumbnail(url=url) embed.set_footer(text=support) embed.timestamp = datetime.utcnow() msg = await message.channel.send(embed=embed) ################################################################# elif "showmod" in message.content: embed = discord.Embed(title="Command Help: showmod", description="Command Number 209", color=message.author.color) embed.add_field(name='Bot:', value=Nellie, inline=True) embed.add_field(name='Command', value='showmod *name*', inline=True) embed.add_field(name='Example:', value='{}showmod #Neko Dev. Army³\n*³The Name of the Chatroom from which you like see the Mods.'.format( current), inline=False) embed.add_field(name='Description', value='The Command **showmod** shows you the current Mods of the named Chatgroup.', inline=False) embed.add_field(name='Argument fields:', value='__**name**__:\nIn the **name** field you put in, from which Chatroom do you like see the Mods.', inline=False) embed.add_field(name='Required Permissions:', value='__**Bot**__:\n-Embed Links\n-Message send\n-Message read\n\n__**Command Excecuter**__:\n-Need to be the Owner of the named Chatroom.', inline=False) embed.set_thumbnail(url=url) embed.set_footer(text=support) embed.timestamp = datetime.utcnow() msg = await message.channel.send(embed=embed) ################################################################# elif "unban" in message.content: embed = discord.Embed(title="Command Help: unban", description="Command Number 210", color=message.author.color) embed.add_field(name='Bot:', value=Nellie, inline=True) embed.add_field(name='Command', value='unban *id* *name*', inline=True) embed.add_field(name='Example:', value='{}unban 474947907913515019² #Neko Dev. Army³\n*² the ID of the User that you would unban *³The Name of the Chatroom from which you like unban the User.'.format( current), inline=False) embed.add_field(name='Description', value='The Command **unban** allows you to unban a user from the named Chatgroup.', inline=False) embed.add_field(name='Argument fields:', value='__**id**__:\nIn the field **id** you put in the ID of the User who you like unban the the named Chatroom.\n\n__**name**__:\nIn the **name** field you put in, from which Chatroom you like unban the User.', inline=False) embed.add_field(name='Required Permissions:', value='__**Bot**__:\n-Manage Channels\n-Embed Links\n-Message send\n-Manage Messages\n-Message read\n\n__**Command Excecuter**__:\n-Need to be a Moderator of the named Chatroom.', inline=False) embed.set_thumbnail(url=url) embed.set_footer(text=support) embed.timestamp = datetime.utcnow() msg = await message.channel.send(embed=embed) ################################################################# elif "ban" in message.content: embed = discord.Embed(title="Command Help: ban", description="Command Number 211", color=message.author.color) embed.add_field(name='Bot:', value=Nellie, inline=True) embed.add_field(name='Command', value='ban *id* *name*', inline=True) embed.add_field(name='Example:', value='{}ban 474947907913515019 #Neko Dev. Army³\n*² the ID of the User that you would ban, *³The Name of the Chatroom from which you like ban the User.'.format( current), inline=False) embed.add_field(name='Description', value='The Command **ban** allows you to ban a user out of the named Chatgroup.', inline=False) embed.add_field(name='Argument fields:', value='__**id**__:\nIn the field **id** you put in the ID of the User who you like ban out of the named Chatroom.\n\n__**name**__:\nIn the **name** field you put in, from which Chatroom you like ban the User.', inline=False) embed.add_field(name='Required Permissions:', value='__**Bot**__:\n-Manage Channels\n-Embed Links\n-Message send\n-Manage Messages\n-Message read\n\n__**Command Excecuter**__:\n-Need to be a Moderator of the named Chatroom.', inline=False) embed.set_thumbnail(url=url) embed.set_footer(text=support) embed.timestamp = datetime.utcnow() msg = await message.channel.send(embed=embed) ################################################################# elif "slowmode" in message.content: embed = discord.Embed(title="Command Help: slowmode", description="Command Number 212", color=message.author.color) embed.add_field(name='Bot:', value=Nellie, inline=True) embed.add_field(name='Command', value='slowmode *sec* *name*', inline=True) embed.add_field(name='Example:', value='{}slowmode 3² #Neko Dev. Army³\n*² the seconds beetween the messages, *³The Name of the Chatroom in which you like set the slowmode.'.format( current), inline=False) embed.add_field(name='Description', value='The Command **slowmode** allows you to set the slowmode for your Chatroom.', inline=False) embed.add_field(name='Argument fields:', value='__**sec**__:\nIn the field **sec** you put in, the difference time beetween two messages from the same user.\n\n__**name**__:\nIn the **name** field you put in, from which Chatroom you like set the slowmode.', inline=False) embed.add_field(name='Required Permissions:', value='__**Bot**__:\n-Manage Channels\n-Embed Links\n-Message send\n-Manage Messages\n-Message read\n\n__**Command Excecuter**__:\n-Need to be the Owner of the named Chatroom.', inline=False) embed.set_thumbnail(url=url) embed.set_footer(text=support) embed.timestamp = datetime.utcnow() msg = await message.channel.send(embed=embed) ################################################################# elif "openrooms" in message.content: embed = discord.Embed(title="Command Help: openrooms", description="Command Number 213", color=message.author.color) embed.add_field(name='Bot:', value=Nellie, inline=True) embed.add_field(name='Command', value='openrooms', inline=True) embed.add_field(name='Example:', value='{}openroom'.format(current), inline=False) embed.add_field(name='Description', value='The Command **openroom** shows you all Public Chatrooms.', inline=False) embed.add_field(name='Argument fields:', value='No argument fields.', inline=False) embed.add_field(name='Required Permissions:', value='__**Bot**__:\n-Embed Links\n-Message send\n-Message read\n\n__**Command Excecuter**__:\n-Send Messages\n-Read Messages', inline=False) embed.set_thumbnail(url=url) embed.set_footer(text=support) embed.timestamp = datetime.utcnow() msg = await message.channel.send(embed=embed) ################################################################# elif "roomsetup" in message.content: embed = discord.Embed(title="Command Help: roomsetup", description="Command Number 215", color=message.author.color) embed.add_field(name='Bot:', value=Nellie, inline=True) embed.add_field(name='Command', value='roomsetup *name*', inline=True) embed.add_field(name='Example:', value='{}roomsetup #Neko Dev. Army³\n*³The name of the room which you like setup.'.format( current), inline=False) embed.add_field(name='Description', value='The Command **roomsetup** starts a setup with your Chatroom.', inline=False) embed.add_field(name='Argument fields:', value='__**name**__:\nIn the **name** field you put in, which Chatroom do you like setup.', inline=False) embed.add_field(name='Required Permissions:', value='__**Bot**__:\n-Embed Links\n-Message send\n-Message read\n\n__**Command Excecuter**__:\n-Need to be the Owner of the named Chatroom.', inline=False) embed.set_thumbnail(url=url) embed.set_footer(text=support) embed.timestamp = datetime.utcnow() msg = await message.channel.send(embed=embed) ################################################################# def setup(bot): bot.add_cog(nellie(bot))

71.041131

318

0.498788

2,791

27,635

4.822286

0.084916

0.050524

0.082101

0.107363

0.801397

0.785274

0.776209

0.77346

0.755703

0.654878

0

0.017575

0.367903

27,635

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319

71.224227

0.75292

0

0.618644

0

0.09322

0.352066

0.032646

0

0.000304

0

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0.008475

false

0

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0

0.025424

0

null

0

1

0

null

0

6

418f71a356a465739726e562a662134f75238225

136

py

Python

ubicacion/views/__init__.py

jlopez0591/SIGIA

e857e2273daa43ab64fa78df254275af2dbcc2a5

[ "MIT" ]

null

ubicacion/views/__init__.py

jlopez0591/SIGIA

e857e2273daa43ab64fa78df254275af2dbcc2a5

[ "MIT" ]

7

2020-02-12T00:42:15.000Z

2022-03-11T23:23:48.000Z

ubicacion/views/__init__.py

jlopez0591/SIGIA

e857e2273daa43ab64fa78df254275af2dbcc2a5

[ "MIT" ]

null

from .api import * # from .main import * from .main_v2 import * from .graph import * from .autocomplete import * from .reportes import *

22.666667

27

0.727941

19

136

5.157895

0.421053

0.510204

0.285714

0

0.008929

0.176471

136

6

28

22.666667

0.866071

0.139706

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

41a57d0de2f917b4e84a7842bc42a34cf914970c

63

py

Python

nicpolpy/__init__.py

ysBach/NICpolpy

62def479b954a782ee50997a1437da30e0e9dae1

[ "MIT" ]

null

nicpolpy/__init__.py

ysBach/NICpolpy

62def479b954a782ee50997a1437da30e0e9dae1

[ "MIT" ]

null

nicpolpy/__init__.py

ysBach/NICpolpy

62def479b954a782ee50997a1437da30e0e9dae1

[ "MIT" ]

null

from .util import * from .preproc import * from .phot import *

15.75

22

0.714286

9

63

5

0.555556

0.444444

0

0.190476

63

3

23

21

0.882353

0

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true

0

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null

0

1

0

1

0

1

0

6

41c13f72eb5b6a61879d3be0b4b992003af13536

3,777

py

Python

nebula/tests/test_event_default.py

threathunterX/nebula_web

2e32e6e7b225e0bd87ee8c847c22862f12c51bb1

[ "Apache-2.0" ]

2

2019-05-01T09:42:32.000Z

2019-05-31T01:08:37.000Z

nebula/tests/test_event_default.py

threathunterX/nebula_web

2e32e6e7b225e0bd87ee8c847c22862f12c51bb1

[ "Apache-2.0" ]

1

2021-06-01T23:30:04.000Z

nebula/tests/test_event_default.py

threathunterX/nebula_web

2e32e6e7b225e0bd87ee8c847c22862f12c51bb1

[ "Apache-2.0" ]

5

2019-05-14T09:30:12.000Z

2020-09-29T04:57:26.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- import json from sqlalchemy.orm import sessionmaker from nebula.views import event_default from nebula.dao.eventmeta_dao import EventMetaDefaultDao from nebula.models import EventMeta from base import WebTestCase, wsgi_safe, Auth_Code with open('nebula/tests/data/event.json') as json_file: events = json.load(json_file) # global application scope. create Session class, engine Session = sessionmaker() @wsgi_safe class TestDefaultEventListHandler(WebTestCase): def get_handlers(self): return [(r"/default/events", event_default.EventListHandler)] @classmethod def setUpClass(cls): super(TestDefaultEventListHandler, cls).setUpClass() cls.event_dao = EventMetaDefaultDao() cls.event_dao.clear() def tearDown(self): self.event_dao.clear() def test_add_events(self): url = "/default/events?auth={}".format(Auth_Code) post_args = json.dumps(events) response = self.fetch(url, method='POST', body=post_args) res = json.loads(response.body) self.assertEqual(res['status'], 0) self.assertEqual(res['msg'], 'ok') self.assertEqual(self.event_dao.count(), 1) def test_get_events(self): for e in events: self.event_dao.add_meta(EventMeta.from_dict(e)) url = "/default/events?auth={}".format(Auth_Code) response = self.fetch(url) res = json.loads(response.body) self.assertEqual(res['status'], 0) self.assertEqual(res['msg'], 'ok') self.assertEqual(len(res['values']), 1) def test_delete_events(self): for e in events: self.event_dao.add_meta(EventMeta.from_dict(e)) url = "/default/events?auth={}".format(Auth_Code) response = self.fetch(url, method='DELETE') res = json.loads(response.body) self.assertEqual(res['status'], 0) self.assertEqual(res['msg'], 'ok') self.assertEqual(self.event_dao.count(), 0) class TestDefaultEventQueryHandler(WebTestCase): def get_handlers(self): return [(r"/default/events/event/(.*)/(.*)", event_default.EventQueryHandler)] @classmethod def setUpClass(cls): super(TestDefaultEventQueryHandler, cls).setUpClass() cls.event_dao = EventMetaDefaultDao() cls.event_dao.clear() def tearDown(self): self.event_dao.clear() def test_add_event(self): url = "/default/events/event/{}/{}?auth={}".format( events[0]['app'], events[0]['name'], Auth_Code) post_args = json.dumps(events[0]) response = self.fetch(url, method='POST', body=post_args) res = json.loads(response.body) self.assertEqual(res['status'], 0) self.assertEqual(res['msg'], 'ok') self.assertEqual(self.event_dao.count(), 1) def test_get_event(self): for e in events: self.event_dao.add_meta(EventMeta.from_dict(e)) url = "/default/events/event/{}/{}?auth={}".format( events[0]['app'], events[0]['name'], Auth_Code) response = self.fetch(url) res = json.loads(response.body) self.assertEqual(res['status'], 0) self.assertEqual(res['msg'], 'ok') self.assertEqual(len(res['values']), 1) def test_delete_event(self): for e in events: self.event_dao.add_meta(EventMeta.from_dict(e)) url = "/default/events/event/{}/{}?auth={}".format( events[0]['app'], events[0]['name'], Auth_Code) response = self.fetch(url, method='DELETE') res = json.loads(response.body) self.assertEqual(res['status'], 0) self.assertEqual(res['msg'], 'ok') self.assertEqual(self.event_dao.count(), 0)

33.723214

86

0.63516

465

3,777

5.04086

0.189247

0.115188

0.09215

0.051195

0.763652

0.736348

0.702218

0

0.006752

0.21578

3,777

111

87

34.027027

0.784605

0.025947

0

0.717647

0

0.099837

0.063384

0

0.211765

1

0.141176

false

0

0.070588

0.023529

0.258824

0

null

0

1

0

1

0

null

0

6

ec1702fdea2ca90bd3c369766f374a4b12f2bbdc

2,140

py

Python

dispatch/tests/test_state.py

pyronicide/dispatch

d10fa3e7bf5a711415c3fb9dafea331ac5273bf5

[ "Apache-2.0" ]

null

dispatch/tests/test_state.py

pyronicide/dispatch

d10fa3e7bf5a711415c3fb9dafea331ac5273bf5

[ "Apache-2.0" ]

null

dispatch/tests/test_state.py

pyronicide/dispatch

d10fa3e7bf5a711415c3fb9dafea331ac5273bf5

[ "Apache-2.0" ]

null

from mock import patch, mock_open import dispatch.state as state class TestState(object): @patch('dispatch.state.open', create=True) @patch('os.path.exists', new=lambda x: True) @patch('dispatch.state.ARGS', create=True) def test_persisted_queue_two(self, mock_args, mock_open_queue): file_data = '[{"id":"one", "location": "bar", "port": 1234, '\ '"resource": "baz", "running": true, ' \ '"data": "somescript", "uris": ["http://foo/"]}, ' \ '{"id":"two", "location": "bar", "port": 456, '\ '"resource": "baz", "running": true, ' \ '"data": "somescript", "uris": ["http://bar/"]}]' mock_open(mock=mock_open_queue, read_data=file_data) mock_args.queue_dir = 'foo' state.CURRENT = state.State() assert len(state.CURRENT.queue.queue) == 2 @patch('dispatch.state.open', create=True) @patch('os.path.exists', new=lambda x: True) @patch('dispatch.state.ARGS', create=True) def test_persisted_queue_one(self, mock_args, mock_open_queue): file_data = '[{"id":"foo", "location": "bar", "port": 1234, '\ '"resource": "baz", "running": true, ' \ '"data": "somescript", "uris": ["http://foo/"]}]' mock_open(mock=mock_open_queue, read_data=file_data) mock_args.queue_dir = 'foo' state.CURRENT = state.State() assert len(state.CURRENT.queue.queue) == 1 @patch('dispatch.state.open', create=True) @patch('os.path.exists', new=lambda x: True) @patch('dispatch.state.ARGS', create=True) def test_persisted_queue_zero(self, mock_args, mock_open_queue): file_data = '[]' mock_open(mock=mock_open_queue, read_data=file_data) mock_args.queue_dir = 'foo' state.CURRENT = state.State() assert len(state.CURRENT.queue.queue) == 0 @patch('os.path.exists', new=lambda x: False) @patch('dispatch.state.ARGS', create=True) def test_persisted_queue_no_file(self, mock_args): mock_args.queue_dir = 'foo' state.CURRENT = state.State() assert len(state.CURRENT.queue.queue) == 0

43.673469

70

0.61215

275

2,140

4.581818

0.192727

0.063492

0.1

0.053968

0.888889

0.86746

0.806349

0.750794

0

0.008886

0.211215

2,140

48

71

44.583333

0.737559

0

0.604651

0

0.276636

0

0.093023

1

0.093023

false

0

0.046512

0

0.162791

0

null

0

1

0

null

0

6

ec3907343bb7146a933d648ace011599185b6269

22

py

Python

inspectio/__init__.py

nicholasmalaya/paleologos

11959056caa80d3c910759b714a0f8e42f986f0f

[ "MIT" ]

1

2021-11-04T17:49:42.000Z

inspectio/__init__.py

nicholasmalaya/paleologos

11959056caa80d3c910759b714a0f8e42f986f0f

[ "MIT" ]

null

inspectio/__init__.py

nicholasmalaya/paleologos

11959056caa80d3c910759b714a0f8e42f986f0f

[ "MIT" ]

2

2019-01-04T16:08:18.000Z

2019-12-16T19:34:24.000Z

# # nick # 4/20/14 #

4.4

9

0.409091

4

22

2.25

1

0

0.333333

0.318182

22

4

10

5.5

0.266667

0.590909

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

1

0

1

0

null

0

1

0

6

ec5861b58ea09313d6128451d9519b45fb543959

6,956

py

Python

tests/test_16_cc_oauth2_service.py

AntonLazovsky/JWTConnect-Python-OidcRP

8a0447287b428d40ad8189baf117951a4901e9c0

[ "Apache-2.0" ]

49

2020-01-31T01:05:09.000Z

2022-02-14T11:56:33.000Z

tests/test_16_cc_oauth2_service.py

AntonLazovsky/JWTConnect-Python-OidcRP

8a0447287b428d40ad8189baf117951a4901e9c0

[ "Apache-2.0" ]

25

2020-02-11T09:53:49.000Z

2022-03-05T14:35:25.000Z

tests/test_16_cc_oauth2_service.py

IdentityPython/oidcrp

cef27f13eeebcedf67651632615c8055d038cd7d

[ "Apache-2.0" ]

16

2018-06-22T07:07:27.000Z

2019-11-09T01:42:59.000Z

from oidcmsg.oauth2 import AccessTokenResponse import pytest from oidcrp.entity import Entity from oidcrp.util import rndstr KEYDEF = [{"type": "EC", "crv": "P-256", "use": ["sig"]}] class TestRP(): @pytest.fixture(autouse=True) def create_service(self): client_config = { 'client_id': 'client_id', 'client_secret': 'another password' } services = { 'token': { 'class': 'oidcrp.oauth2.client_credentials.cc_access_token.CCAccessToken' }, 'refresh_token': { 'class': 'oidcrp.oauth2.client_credentials.cc_refresh_access_token' '.CCRefreshAccessToken' } } self.entity = Entity(config=client_config, services=services) self.entity.client_get("service",'accesstoken').endpoint = 'https://example.com/token' self.entity.client_get("service",'refresh_token').endpoint = 'https://example.com/token' def test_token_get_request(self): request_args = {'grant_type': 'client_credentials'} _srv = self.entity.client_get("service",'accesstoken') _info = _srv.get_request_parameters(request_args=request_args) assert _info['method'] == 'POST' assert _info['url'] == 'https://example.com/token' assert _info['body'] == 'grant_type=client_credentials' assert _info['headers'] == { 'Authorization': 'Basic Y2xpZW50X2lkOmFub3RoZXIrcGFzc3dvcmQ=', 'Content-Type': 'application/x-www-form-urlencoded' } def test_token_parse_response(self): request_args = {'grant_type': 'client_credentials'} _srv = self.entity.client_get("service",'accesstoken') _request_info = _srv.get_request_parameters(request_args=request_args) response = AccessTokenResponse(**{ "access_token": "2YotnFZFEjr1zCsicMWpAA", "token_type": "example", "expires_in": 3600, "refresh_token": "tGzv3JOkF0XG5Qx2TlKWIA", "example_parameter": "example_value" }) _response = _srv.parse_response(response.to_json(), sformat="json") # since no state attribute is involved, a key is minted _key = rndstr(16) _srv.update_service_context(_response, key=_key) info = _srv.client_get("service_context").state.get_item(AccessTokenResponse, 'token_response', _key) assert '__expires_at' in info def test_refresh_token_get_request(self): _srv = self.entity.client_get("service",'accesstoken') _srv.update_service_context({ "access_token": "2YotnFZFEjr1zCsicMWpAA", "token_type": "example", "expires_in": 3600, "refresh_token": "tGzv3JOkF0XG5Qx2TlKWIA", "example_parameter": "example_value" }) _srv = self.entity.client_get("service",'refresh_token') _id = rndstr(16) _info = _srv.get_request_parameters(state_id=_id) assert _info['method'] == 'POST' assert _info['url'] == 'https://example.com/token' assert _info[ 'body'] == 'grant_type=refresh_token' assert _info['headers'] == { 'Authorization': 'Bearer tGzv3JOkF0XG5Qx2TlKWIA', 'Content-Type': 'application/x-www-form-urlencoded' } def test_refresh_token_parse_response(self): request_args = {'grant_type': 'client_credentials'} _srv = self.entity.client_get("service",'accesstoken') _request_info = _srv.get_request_parameters(request_args=request_args) response = AccessTokenResponse(**{ "access_token": "2YotnFZFEjr1zCsicMWpAA", "token_type": "example", "expires_in": 3600, "refresh_token": "tGzv3JOkF0XG5Qx2TlKWIA", "example_parameter": "example_value" }) _response = _srv.parse_response(response.to_json(), sformat="json") # since no state attribute is involved, a key is minted _key = rndstr(16) _srv.update_service_context(_response, key=_key) info = _srv.client_get("service_context").state.get_item(AccessTokenResponse, 'token_response', _key) assert '__expires_at' in info # Move from token to refresh token service _srv = self.entity.client_get("service",'refresh_token') _request_info = _srv.get_request_parameters(request_args=request_args, state=_key) refresh_response = AccessTokenResponse(**{ "access_token": 'wy4R01DmMoB5xkI65nNkVv1l', "token_type": "example", "expires_in": 3600, "refresh_token": 'lhNX9LSG8w1QuD6tSgc6CPfJ', }) _response = _srv.parse_response(refresh_response.to_json(), sformat="json") _srv.update_service_context(_response, key=_key) info = _srv.client_get("service_context").state.get_item(AccessTokenResponse, 'token_response', _key) assert '__expires_at' in info def test_2nd_refresh_token_parse_response(self): request_args = {'grant_type': 'client_credentials'} _srv = self.entity.client_get("service",'accesstoken') _request_info = _srv.get_request_parameters(request_args=request_args) response = AccessTokenResponse(**{ "access_token": "2YotnFZFEjr1zCsicMWpAA", "token_type": "example", "expires_in": 3600, "refresh_token": "tGzv3JOkF0XG5Qx2TlKWIA", "example_parameter": "example_value" }) _response = _srv.parse_response(response.to_json(), sformat="json") # since no state attribute is involved, a key is minted _key = rndstr(16) _srv.update_service_context(_response, key=_key) info = _srv.client_get("service_context").state.get_item(AccessTokenResponse, 'token_response', _key) assert '__expires_at' in info # Move from token to refresh token service _srv = self.entity.client_get("service",'refresh_token') _request_info = _srv.get_request_parameters(request_args=request_args, state=_key) refresh_response = AccessTokenResponse(**{ "access_token": 'wy4R01DmMoB5xkI65nNkVv1l', "token_type": "example", "expires_in": 3600, "refresh_token": 'lhNX9LSG8w1QuD6tSgc6CPfJ', }) _response = _srv.parse_response(refresh_response.to_json(), sformat="json") _srv.update_service_context(_response, key=_key) info = _srv.client_get("service_context").state.get_item(AccessTokenResponse, 'token_response', _key) assert '__expires_at' in info _request_info = _srv.get_request_parameters(request_args=request_args, state=_key) assert _request_info['headers'] == { 'Authorization': 'Bearer {}'.format(refresh_response["refresh_token"]), 'Content-Type': 'application/x-www-form-urlencoded' }

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py

Python

mapper/__init__.py

widal001/ejscreen-demo

e6f93b6d44730b3f0cb9a6bc18c1b679b293cffe

[ "MIT" ]

2

2021-03-22T19:29:33.000Z

2021-03-27T20:40:01.000Z

mapper/__init__.py

widal001/ejscreen-demo

e6f93b6d44730b3f0cb9a6bc18c1b679b293cffe

[ "MIT" ]

6

2021-03-08T01:54:37.000Z

2021-04-08T14:42:00.000Z

mapper/__init__.py

widal001/ejscreen-demo

e6f93b6d44730b3f0cb9a6bc18c1b679b293cffe

[ "MIT" ]

null

from mapper.server import create_app

18.5

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6b6ee9628ea3b3af8688389479d2871d15480f58

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py

Python

ARC051/ARC051a.py

VolgaKurvar/AtCoder

21acb489f1594bbb1cdc64fbf8421d876b5b476d

[ "Unlicense" ]

null

ARC051/ARC051a.py

VolgaKurvar/AtCoder

21acb489f1594bbb1cdc64fbf8421d876b5b476d

[ "Unlicense" ]

null

ARC051/ARC051a.py

VolgaKurvar/AtCoder

21acb489f1594bbb1cdc64fbf8421d876b5b476d

[ "Unlicense" ]

null

x,y,r=map(int,input().split()) x2,y2,x3,y3=map(int,input().split())

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32

py

Python

tests/conftest.py

vuonojenmustaturska/FanFicFare

0234c161175a10bb3420e446e76cbdc9f9a3cf8a

[ "Apache-2.0" ]

null

tests/conftest.py

vuonojenmustaturska/FanFicFare

0234c161175a10bb3420e446e76cbdc9f9a3cf8a

[ "Apache-2.0" ]

null

tests/conftest.py

vuonojenmustaturska/FanFicFare

0234c161175a10bb3420e446e76cbdc9f9a3cf8a

[ "Apache-2.0" ]

null

from fixtures_chireads import *

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6bce30e26502d803690a4f59ad3ee6fde6f73924

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py

Python

api/models/__init__.py

shaldengeki/mc-manager

dfb0920261e79c35c26e1b6bdf0d9d80a768a7a1

[ "MIT" ]

null

api/models/__init__.py

shaldengeki/mc-manager

dfb0920261e79c35c26e1b6bdf0d9d80a768a7a1

[ "MIT" ]

10

2020-12-21T01:59:16.000Z

2021-08-02T04:07:38.000Z

api/models/__init__.py

shaldengeki/mc-manager

dfb0920261e79c35c26e1b6bdf0d9d80a768a7a1

[ "MIT" ]

null

from .server import Server from .server_log import ServerLog from .server_backup import ServerBackup

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6

6bdb0b7d2abe14af30669696c8b4b8265d48b86a

17,690

py

Python

tests/unit/test_albums.py

movermeyer/openphoto-python

209a1da27c8d8c88dbcf4ea6c6f57031ea1bc44b

[ "Apache-2.0" ]

3

2015-02-11T10:48:28.000Z

2015-11-05T18:50:53.000Z

tests/unit/test_albums.py

movermeyer/openphoto-python

209a1da27c8d8c88dbcf4ea6c6f57031ea1bc44b

[ "Apache-2.0" ]

null

tests/unit/test_albums.py

movermeyer/openphoto-python

209a1da27c8d8c88dbcf4ea6c6f57031ea1bc44b

[ "Apache-2.0" ]

5

2015-02-09T22:01:30.000Z

2018-03-04T21:53:28.000Z

from __future__ import unicode_literals import mock try: import unittest2 as unittest # Python2.6 except ImportError: import unittest import trovebox class TestAlbums(unittest.TestCase): test_host = "test.example.com" test_photos_dict = [{"id": "1a", "tags": ["tag1", "tag2"]}, {"id": "2b", "tags": ["tag3", "tag4"]}] test_albums_dict = [{"cover": {"id": "1a", "tags": ["tag1", "tag2"]}, "id": "1", "name": "Album 1", "photos": [test_photos_dict[0]], "totalRows": 2}, {"cover": {"id": "2b", "tags": ["tag3", "tag4"]}, "id": "2", "name": "Album 2", "photos": [test_photos_dict[1]], "totalRows": 2}] def setUp(self): self.client = trovebox.Trovebox(host=self.test_host) self.test_photos = [trovebox.objects.photo.Photo(self.client, photo) for photo in self.test_photos_dict] self.test_albums = [trovebox.objects.album.Album(self.client, album) for album in self.test_albums_dict] @staticmethod def _return_value(result, message="", code=200): return {"message": message, "code": code, "result": result} class TestAlbumsList(TestAlbums): @mock.patch.object(trovebox.Trovebox, 'get') def test_albums_list(self, mock_get): """Check that the album list is returned correctly""" mock_get.return_value = self._return_value(self.test_albums_dict) result = self.client.albums.list(foo="bar") mock_get.assert_called_with("/albums/list.json", foo="bar") self.assertEqual(len(result), 2) self.assertEqual(result[0].id, "1") self.assertEqual(result[0].name, "Album 1") self.assertEqual(result[1].id, "2") self.assertEqual(result[1].name, "Album 2") @mock.patch.object(trovebox.Trovebox, 'get') def test_empty_result(self, mock_get): """Check that an empty result is transformed into an empty list """ mock_get.return_value = self._return_value("") result = self.client.albums.list(foo="bar") mock_get.assert_called_with("/albums/list.json", foo="bar") self.assertEqual(result, []) @mock.patch.object(trovebox.Trovebox, 'get') def test_zero_rows(self, mock_get): """Check that totalRows=0 is transformed into an empty list """ mock_get.return_value = self._return_value([{"totalRows": 0}]) result = self.client.albums.list(foo="bar") mock_get.assert_called_with("/albums/list.json", foo="bar") self.assertEqual(result, []) @mock.patch.object(trovebox.Trovebox, 'get') def test_albums_list_returns_cover_photos(self, mock_get): """Check that the album list returns cover photo objects""" mock_get.return_value = self._return_value(self.test_albums_dict) result = self.client.albums.list(foo="bar") mock_get.assert_called_with("/albums/list.json", foo="bar") self.assertEqual(len(result), 2) self.assertEqual(result[0].id, "1") self.assertEqual(result[0].name, "Album 1") self.assertEqual(result[0].cover.id, "1a") self.assertEqual(result[0].cover.tags, ["tag1", "tag2"]) self.assertEqual(result[1].id, "2") self.assertEqual(result[1].name, "Album 2") self.assertEqual(result[1].cover.id, "2b") self.assertEqual(result[1].cover.tags, ["tag3", "tag4"]) class TestAlbumUpdateCover(TestAlbums): @mock.patch.object(trovebox.Trovebox, 'post') def test_album_cover_update(self, mock_post): """Check that an album cover can be updated""" mock_post.return_value = self._return_value(self.test_albums_dict[1]) result = self.client.album.cover_update(self.test_albums[0], self.test_photos[0], foo="bar") mock_post.assert_called_with("/album/1/cover/1a/update.json", foo="bar") self.assertEqual(result.id, "2") self.assertEqual(result.name, "Album 2") self.assertEqual(result.cover.id, "2b") self.assertEqual(result.cover.tags, ["tag3", "tag4"]) @mock.patch.object(trovebox.Trovebox, 'post') def test_album_cover_update_id(self, mock_post): """Check that an album cover can be updated using IDs""" mock_post.return_value = self._return_value(self.test_albums_dict[1]) result = self.client.album.cover_update("1", "1a", foo="bar") mock_post.assert_called_with("/album/1/cover/1a/update.json", foo="bar") self.assertEqual(result.id, "2") self.assertEqual(result.name, "Album 2") self.assertEqual(result.cover.id, "2b") self.assertEqual(result.cover.tags, ["tag3", "tag4"]) @mock.patch.object(trovebox.Trovebox, 'post') def test_album_object_cover_update(self, mock_post): """Check that an album cover can be updated using the album object directly""" mock_post.return_value = self._return_value(self.test_albums_dict[1]) album = self.test_albums[0] album.cover_update(self.test_photos[1], foo="bar") mock_post.assert_called_with("/album/1/cover/2b/update.json", foo="bar") self.assertEqual(album.id, "2") self.assertEqual(album.name, "Album 2") self.assertEqual(album.cover.id, "2b") self.assertEqual(album.cover.tags, ["tag3", "tag4"]) class TestAlbumCreate(TestAlbums): @mock.patch.object(trovebox.Trovebox, 'post') def test_album_create(self, mock_post): """Check that an album can be created""" mock_post.return_value = self._return_value(self.test_albums_dict[0]) result = self.client.album.create(name="Test", foo="bar") mock_post.assert_called_with("/album/create.json", name="Test", foo="bar") self.assertEqual(result.id, "1") self.assertEqual(result.name, "Album 1") self.assertEqual(result.cover.id, "1a") self.assertEqual(result.cover.tags, ["tag1", "tag2"]) class TestAlbumDelete(TestAlbums): @mock.patch.object(trovebox.Trovebox, 'post') def test_album_delete(self, mock_post): """Check that an album can be deleted""" mock_post.return_value = self._return_value(True) result = self.client.album.delete(self.test_albums[0], foo="bar") mock_post.assert_called_with("/album/1/delete.json", foo="bar") self.assertEqual(result, True) @mock.patch.object(trovebox.Trovebox, 'post') def test_album_delete_id(self, mock_post): """Check that an album can be deleted using its ID""" mock_post.return_value = self._return_value(True) result = self.client.album.delete("1", foo="bar") mock_post.assert_called_with("/album/1/delete.json", foo="bar") self.assertEqual(result, True) @mock.patch.object(trovebox.Trovebox, 'post') def test_album_object_delete(self, mock_post): """Check that an album can be deleted using the album object directly""" mock_post.return_value = self._return_value(True) album = self.test_albums[0] result = album.delete(foo="bar") mock_post.assert_called_with("/album/1/delete.json", foo="bar") self.assertEqual(result, True) self.assertEqual(album.get_fields(), {}) self.assertEqual(album.id, None) self.assertEqual(album.name, None) class TestAlbumAdd(TestAlbums): @mock.patch.object(trovebox.Trovebox, 'post') def test_album_add(self, mock_post): """ Check that photos can be added to an album """ mock_post.return_value = self._return_value(self.test_albums_dict[1]) result = self.client.album.add(self.test_albums[0], self.test_photos, foo="bar") mock_post.assert_called_with("/album/1/photo/add.json", ids=["1a", "2b"], foo="bar") self.assertEqual(result.id, self.test_albums[1].id) @mock.patch.object(trovebox.Trovebox, 'post') def test_album_add_id(self, mock_post): """ Check that photos can be added to an album using IDs """ mock_post.return_value = self._return_value(self.test_albums_dict[1]) result = self.client.album.add(self.test_albums[0].id, objects=["1a", "2b"], object_type="photo", foo="bar") mock_post.assert_called_with("/album/1/photo/add.json", ids=["1a", "2b"], foo="bar") self.assertEqual(result.id, self.test_albums[1].id) @mock.patch.object(trovebox.Trovebox, 'post') def test_album_object_add(self, mock_post): """ Check that photos can be added to an album using the album object directly """ mock_post.return_value = self._return_value(self.test_albums_dict[1]) album = self.test_albums[0] album.add(self.test_photos, foo="bar") mock_post.assert_called_with("/album/1/photo/add.json", ids=["1a", "2b"], foo="bar") self.assertEqual(album.id, self.test_albums[1].id) @mock.patch.object(trovebox.Trovebox, 'post') def test_album_add_single(self, mock_post): """ Check that a single photo can be added to an album """ mock_post.return_value = self._return_value(self.test_albums_dict[1]) self.test_albums[0].add(self.test_photos[0], foo="bar") mock_post.assert_called_with("/album/1/photo/add.json", ids=["1a"], foo="bar") @mock.patch.object(trovebox.Trovebox, 'post') def test_album_add_invalid_type(self, _): """ Check that an exception is raised if an invalid object is added to an album. """ with self.assertRaises(AttributeError): self.test_albums[0].add([object()]) @mock.patch.object(trovebox.Trovebox, 'post') def test_album_add_multiple_types(self, _): """ Check that an exception is raised if multiple types are added to an album. """ with self.assertRaises(ValueError): self.test_albums[0].add(self.test_photos+self.test_albums) class TestAlbumRemovePhotos(TestAlbums): @mock.patch.object(trovebox.Trovebox, 'post') def test_album_remove(self, mock_post): """ Check that photos can be removed from an album """ mock_post.return_value = self._return_value(self.test_albums_dict[1]) result = self.client.album.remove(self.test_albums[0], self.test_photos, foo="bar") mock_post.assert_called_with("/album/1/photo/remove.json", ids=["1a", "2b"], foo="bar") self.assertEqual(result.id, self.test_albums[1].id) @mock.patch.object(trovebox.Trovebox, 'post') def test_album_remove_id(self, mock_post): """ Check that photos can be removed from an album using IDs """ mock_post.return_value = self._return_value(self.test_albums_dict[1]) result = self.client.album.remove(self.test_albums[0].id, objects=["1a", "2b"], object_type="photo", foo="bar") mock_post.assert_called_with("/album/1/photo/remove.json", ids=["1a", "2b"], foo="bar") self.assertEqual(result.id, self.test_albums[1].id) @mock.patch.object(trovebox.Trovebox, 'post') def test_album_object_remove(self, mock_post): """ Check that photos can be removed from an album using the album object directly """ mock_post.return_value = self._return_value(self.test_albums_dict[1]) album = self.test_albums[0] album.remove(self.test_photos, foo="bar") mock_post.assert_called_with("/album/1/photo/remove.json", ids=["1a", "2b"], foo="bar") self.assertEqual(album.id, self.test_albums[1].id) @mock.patch.object(trovebox.Trovebox, 'post') def test_album_remove_single(self, mock_post): """ Check that a single photo can be removed from an album """ mock_post.return_value = self._return_value(self.test_albums_dict[1]) self.test_albums[0].remove(self.test_photos[0], foo="bar") mock_post.assert_called_with("/album/1/photo/remove.json", ids=["1a"], foo="bar") @mock.patch.object(trovebox.Trovebox, 'post') def test_album_remove_invalid_type(self, _): """ Check that an exception is raised if an invalid object is removed from an album. """ with self.assertRaises(AttributeError): self.test_albums[0].remove([object()]) @mock.patch.object(trovebox.Trovebox, 'post') def test_album_remove_multiple_types(self, _): """ Check that an exception is raised if multiple types are removed from an album. """ with self.assertRaises(ValueError): self.test_albums[0].remove(self.test_photos+self.test_albums) class TestAlbumUpdate(TestAlbums): @mock.patch.object(trovebox.Trovebox, 'post') def test_album_update(self, mock_post): """Check that an album can be updated""" mock_post.return_value = self._return_value(self.test_albums_dict[1]) result = self.client.album.update(self.test_albums[0], name="Test") mock_post.assert_called_with("/album/1/update.json", name="Test") self.assertEqual(result.id, "2") self.assertEqual(result.name, "Album 2") self.assertEqual(result.cover.id, "2b") self.assertEqual(result.cover.tags, ["tag3", "tag4"]) @mock.patch.object(trovebox.Trovebox, 'post') def test_album_update_id(self, mock_post): """Check that an album can be updated using its ID""" mock_post.return_value = self._return_value(self.test_albums_dict[1]) result = self.client.album.update("1", name="Test") mock_post.assert_called_with("/album/1/update.json", name="Test") self.assertEqual(result.id, "2") self.assertEqual(result.name, "Album 2") self.assertEqual(result.cover.id, "2b") self.assertEqual(result.cover.tags, ["tag3", "tag4"]) @mock.patch.object(trovebox.Trovebox, 'post') def test_album_object_update(self, mock_post): """Check that an album can be updated using the album object directly""" mock_post.return_value = self._return_value(self.test_albums_dict[1]) album = self.test_albums[0] album.update(name="Test") mock_post.assert_called_with("/album/1/update.json", name="Test") self.assertEqual(album.id, "2") self.assertEqual(album.name, "Album 2") self.assertEqual(album.cover.id, "2b") self.assertEqual(album.cover.tags, ["tag3", "tag4"]) class TestAlbumView(TestAlbums): @mock.patch.object(trovebox.Trovebox, 'get') def test_album_view(self, mock_get): """Check that an album can be viewed""" mock_get.return_value = self._return_value(self.test_albums_dict[1]) result = self.client.album.view(self.test_albums[0], includeElements=True) mock_get.assert_called_with("/album/1/view.json", includeElements=True) self.assertEqual(result.id, "2") self.assertEqual(result.name, "Album 2") self.assertEqual(result.cover.id, "2b") self.assertEqual(result.cover.tags, ["tag3", "tag4"]) self.assertEqual(result.photos[0].id, self.test_photos[1].id) @mock.patch.object(trovebox.Trovebox, 'get') def test_album_view_id(self, mock_get): """Check that an album can be viewed using its ID""" mock_get.return_value = self._return_value(self.test_albums_dict[1]) result = self.client.album.view("1", includeElements=True) mock_get.assert_called_with("/album/1/view.json", includeElements=True) self.assertEqual(result.id, "2") self.assertEqual(result.name, "Album 2") self.assertEqual(result.cover.id, "2b") self.assertEqual(result.cover.tags, ["tag3", "tag4"]) self.assertEqual(result.photos[0].id, self.test_photos[1].id) @mock.patch.object(trovebox.Trovebox, 'get') def test_album_object_view(self, mock_get): """Check that an album can be viewed using the album object directly""" mock_get.return_value = self._return_value(self.test_albums_dict[1]) album = self.test_albums[0] album.view(includeElements=True) mock_get.assert_called_with("/album/1/view.json", includeElements=True) self.assertEqual(album.id, "2") self.assertEqual(album.name, "Album 2") self.assertEqual(album.cover.id, "2b") self.assertEqual(album.cover.tags, ["tag3", "tag4"]) self.assertEqual(album.photos[0].id, self.test_photos[1].id) class TestAlbumMisc(TestAlbums): def test_update_fields_with_no_cover(self): """Check that an album object can be updated with no cover""" album = self.test_albums[0] album.cover = None album.photos = None # Check that no exception is raised album._update_fields_with_objects()

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0

6

d41b3c9ca4833210b57d92c40bf9da6b7a300732

36

py

Python

example/mongo/__init__.py

estudio89/maestro-python

331079cb3f0c10de2e19210cbade793544510f33

[ "BSD-3-Clause" ]

null

example/mongo/__init__.py

estudio89/maestro-python

331079cb3f0c10de2e19210cbade793544510f33

[ "BSD-3-Clause" ]

null

example/mongo/__init__.py

estudio89/maestro-python

331079cb3f0c10de2e19210cbade793544510f33

[ "BSD-3-Clause" ]

null

from .factory import create_provider

36

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0

1

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1

0

1

0

6

d41f8a1db37dfdc8f0e21ec73d5c258264a9b5c0

14,135

py

Python

wall/views.py

viral85/test_wall_app

5487297e3dcd5971c4f8778fe0bc49e35efad587

[ "MIT" ]

null

wall/views.py

viral85/test_wall_app

5487297e3dcd5971c4f8778fe0bc49e35efad587

[ "MIT" ]

null

wall/views.py

viral85/test_wall_app

5487297e3dcd5971c4f8778fe0bc49e35efad587

[ "MIT" ]

null

import logging from django.core.paginator import Paginator from rest_framework.views import APIView from rest_framework import status from rest_framework.permissions import IsAuthenticated from drf_yasg.utils import swagger_auto_schema import constants from response_utils import ApiResponse, get_error_message from wall.models import Wall, Comment from wall.serializers import WallSerializer, CommentSerializer from wall.permissions import IsGetOrIsAuthenticated logger = logging.getLogger('django') class WallsList(APIView): """ Class is used for list all the wall or create new wall by a user. """ permission_classes = [IsGetOrIsAuthenticated] @swagger_auto_schema(operation_description="Api is used to get all wall details" "from the application", responses={200: WallSerializer()}) def get(self, request): """ Function is used to get all the Wall list. :param request: request header with required info. :return: Wall list """ page_number = self.request.query_params.get('page', 1) page_size = self.request.query_params.get('page_size', 10) sort_by = self.request.query_params.get('sort_by', 'created_on') order = self.request.query_params.get('order', 'desc') search = self.request.query_params.get('search', None) if order == 'desc': sort_by = '-' + sort_by if search: walls = Wall.objects.filter(title__icontains=search).order_by(sort_by) else: walls = Wall.objects.all().order_by(sort_by) paginator = Paginator(walls, page_size) count = paginator.count total_page = len(paginator.page_range) next = paginator.page(page_number).has_next() previous = paginator.page(page_number).has_previous() serializer = WallSerializer(paginator.page(page_number), many=True) api_response = ApiResponse(status=1, data=serializer.data, message=constants.WALLS_GET_SUCCESS, http_status=status.HTTP_200_OK, count=count, total_page=total_page, next=next, previous=previous) return api_response.create_response() @swagger_auto_schema(request_body=WallSerializer, operation_description="API is used to post the Wall detail " "and store data inside database") def post(self, request): """ Function is used to create new object or value in table and return status. :param request: request header with user info for creating new object. :return: wall info """ serializer = WallSerializer(data=request.data, context={'request': request}) if serializer.is_valid(): serializer.save() api_response = ApiResponse(status=1, data=serializer.data, message=constants.CREATE_WALL_SUCCESS, http_status=status.HTTP_201_CREATED) return api_response.create_response() api_response = ApiResponse(status=0, message=serializer.errors, http_status=status.HTTP_400_BAD_REQUEST) return api_response.create_response() class WallDetails(APIView): """ Class is used for retrieve, update or delete a wall instance. """ permission_classes = [IsGetOrIsAuthenticated, ] @swagger_auto_schema(operation_description="Api is used to get particular wall detail" "from the application", responses={200: WallSerializer()}) def get(self, request, pk): """ Function is used for get wall info with pk :param request: request header with required info. :param pk: primary key of a object. :return: wall info or send proper error status """ try: wall = Wall.objects.get(id=pk) except Wall.DoesNotExist as e: logger.exception(e) api_response = ApiResponse(status=0, message=constants.WALL_DOES_NOT_EXIST, http_status=status.HTTP_404_NOT_FOUND) return api_response.create_response() serializer = WallSerializer(wall) api_response = ApiResponse(status=1, data=serializer.data, message=constants.GET_WALL_SUCCESS, http_status=status.HTTP_200_OK) return api_response.create_response() @swagger_auto_schema(request_body=WallSerializer, operation_description="API is used to update the wall details " "and store data inside database") def put(self, request, pk): """ Function is used for modify wall info :param request: request header with required info. :param pk: primary key of a object. :return: wall info or send proper error status """ try: wall = Wall.objects.get(id=pk) except Wall.DoesNotExist as e: logger.exception(e) api_response = ApiResponse(status=0, message=constants.WALL_DOES_NOT_EXIST, http_status=status.HTTP_404_NOT_FOUND) return api_response.create_response() serializer = WallSerializer(wall, data=request.data, partial=True, context={'request': request}) if serializer.is_valid(): serializer.save() api_response = ApiResponse(status=1, data=serializer.data, message=constants.UPDATE_WALL_SUCCESS, http_status=status.HTTP_201_CREATED) return api_response.create_response() api_response = ApiResponse(status=0, message=get_error_message(serializer), http_status=status.HTTP_400_BAD_REQUEST) return api_response.create_response() @swagger_auto_schema(operation_description="API is used to delete the wall details " "from the database") def delete(self, request, pk): """ Function is used for deleting wall object :param request: request header with required info. :param pk: primary field to delete wall info. :return: 200 ok or error message """ try: wall = Wall.objects.get(id=pk) except Wall.DoesNotExist as e: logger.exception(e) api_response = ApiResponse(status=0, message=constants.WALL_DOES_NOT_EXIST, http_status=status.HTTP_404_NOT_FOUND) return api_response.create_response() wall.delete() api_response = ApiResponse(status=1, message=constants.DELETE_WALL_SUCCESS, http_status=status.HTTP_200_OK) return api_response.create_response() class CommentsList(APIView): """ Class is used for list all the Comments or create new Comments. """ permission_classes = [IsAuthenticated, ] @swagger_auto_schema(request_body=WallSerializer, operation_description="API is used to post the comment detail " "and store data inside database") def post(self, request): """ Function is used to create new object or value in table and return status. :param request: request header with user info for creating new object. :return: comment info """ serializer = CommentSerializer(data=request.data, context={'request': request}) if serializer.is_valid(): serializer.save() api_response = ApiResponse(status=1, data=serializer.data, message=constants.CREATE_COMMENT_SUCCESS, http_status=status.HTTP_201_CREATED) return api_response.create_response() api_response = ApiResponse(status=0, message=get_error_message(serializer), http_status=status.HTTP_400_BAD_REQUEST) return api_response.create_response() class CommentDetails(APIView): """ Class is used for retrieve, update or delete a comment instance. """ permission_classes = [IsAuthenticated, ] @swagger_auto_schema(operation_description="Api is used to get particular comment detail" "from the application", responses={200: CommentSerializer()}) def get(self, request, pk): """ Function is used for get comment info with pk :param request: request header with required info. :return: comment info or send proper error status """ try: comment = Comment.objects.get(id=pk) except Comment.DoesNotExist: api_response = ApiResponse(status=0, message=constants.COMMENT_DOES_NOT_EXIST, http_status=status.HTTP_404_NOT_FOUND) return api_response.create_response() serializer = CommentSerializer(comment) api_response = ApiResponse(status=1, data=serializer.data, message=constants.GET_COMMENT_SUCCESS, http_status=status.HTTP_200_OK) return api_response.create_response() @swagger_auto_schema(request_body=CommentSerializer, operation_description="API is used to update the comment details " "and store data inside database") def put(self, request, pk): """ Function is used for modify comment info :param request: request header with required info. :return: comment info or send proper error status """ try: comment = Comment.objects.get(id=pk) except Wall.DoesNotExist as e: logger.exception(e) api_response = ApiResponse(status=0, message=constants.COMMENT_DOES_NOT_EXIST, http_status=status.HTTP_404_NOT_FOUND) return api_response.create_response() serializer = CommentSerializer(comment, data=request.data, partial=True, context={'request': request}) if serializer.is_valid(): serializer.save() api_response = ApiResponse(status=1, data=serializer.data, message=constants.UPDATE_COMMENT_SUCCESS, http_status=status.HTTP_201_CREATED) return api_response.create_response() api_response = ApiResponse(status=0, message=get_error_message(serializer), http_status=status.HTTP_400_BAD_REQUEST) return api_response.create_response() @swagger_auto_schema(operation_description="API is used to delete the comment details " "from the database") def delete(self, request, pk): """ Function is used for deleting comment object :param request: request header with required info. :param pk: primary field to get comment info. :return: 200 ok or error message """ try: comment = Comment.objects.get(id=pk) except comment.DoesNotExist as e: logger.exception(e) api_response = ApiResponse(status=0, message=constants.COMMENT_DOES_NOT_EXIST, http_status=status.HTTP_404_NOT_FOUND) return api_response.create_response() comment.delete() api_response = ApiResponse(status=1, message=constants.DELETE_COMMENT_SUCCESS, http_status=status.HTTP_200_OK) return api_response.create_response() class LikeDetails(APIView): """ Class is used for create/remove Likes. """ permission_classes = [IsAuthenticated, ] def get(self, request, wall_pk): """ Function is used for get like info with pk :param request: request header with required info. :return: comment info or send proper error status """ try: wall = Wall.objects.get(id=wall_pk) if request.user in wall.likes.users.all(): wall.likes.users.remove(request.user) else: wall.likes.users.add(request.user) wall.dis_likes.users.remove(request.user) api_response = ApiResponse(status=1, message=constants.GET_LIKE_SUCCESS, http_status=status.HTTP_200_OK) return api_response.create_response() except Wall.DoesNotExist as e: logger.exception(e) api_response = ApiResponse(status=0, message=constants.WALL_DOES_NOT_EXIST, http_status=status.HTTP_404_NOT_FOUND) return api_response.create_response() class DislikeDetails(APIView): """ Class is used for create/Remove Dislikes. """ permission_classes = [IsAuthenticated, ] def get(self, request, wall_pk): """ Function is used for get dislikes info with pk :param request: request header with required info. :return: comment info or send proper error status """ try: wall = Wall.objects.get(id=wall_pk) if request.user in wall.dis_likes.users.all(): wall.dis_likes.users.remove(request.user) else: wall.dis_likes.users.add(request.user) wall.likes.users.remove(request.user) api_response = ApiResponse(status=1, message=constants.GET_DISLIKE_SUCCESS, http_status=status.HTTP_200_OK) return api_response.create_response() except Wall.DoesNotExist as e: logger.exception(e) api_response = ApiResponse(status=0, message=constants.WALL_DOES_NOT_EXIST, http_status=status.HTTP_404_NOT_FOUND) return api_response.create_response()

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0.105431

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null

0

1

0

null

0

6

d45b6363615e81f747b23fd0553b74bf97e36247

1,002

py

Python

sdk/python/kubeflow/tfjob/__init__.py

kuikuikuizzZ/tf-operator

6c58dcbb0fb5d2806058d71594422c8d52a38709

[ "Apache-2.0" ]

2

2020-03-16T15:57:47.000Z

2020-09-27T09:39:20.000Z

sdk/python/kubeflow/tfjob/__init__.py

kuikuikuizzZ/tf-operator

6c58dcbb0fb5d2806058d71594422c8d52a38709

[ "Apache-2.0" ]

195

2021-01-25T10:23:13.000Z

2022-03-25T15:07:01.000Z

sdk/python/kubeflow/tfjob/__init__.py

kuikuikuizzZ/tf-operator

6c58dcbb0fb5d2806058d71594422c8d52a38709

[ "Apache-2.0" ]

3

2021-02-01T08:18:47.000Z

2021-11-08T07:30:54.000Z

# coding: utf-8 # flake8: noqa """ tfjob Python SDK for TF-Operator # noqa: E501 OpenAPI spec version: v0.1 Generated by: https://github.com/swagger-api/swagger-codegen.git """ from __future__ import absolute_import # import utils and constants from kubeflow.tfjob.utils import utils from kubeflow.tfjob.constants import constants # import ApiClient from kubeflow.tfjob.api_client import ApiClient from kubeflow.tfjob.configuration import Configuration from kubeflow.tfjob.api.tf_job_client import TFJobClient # import models into sdk package from kubeflow.tfjob.models.v1_job_condition import V1JobCondition from kubeflow.tfjob.models.v1_job_status import V1JobStatus from kubeflow.tfjob.models.v1_replica_spec import V1ReplicaSpec from kubeflow.tfjob.models.v1_replica_status import V1ReplicaStatus from kubeflow.tfjob.models.v1_tf_job import V1TFJob from kubeflow.tfjob.models.v1_tf_job_list import V1TFJobList from kubeflow.tfjob.models.v1_tf_job_spec import V1TFJobSpec

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1,002

5.601399

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0.179775

0.254682

0.200999

0.342072

0.262172

0.11236

0

0.023702

0.115768

1,002

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null

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1

0

1

0

1

0

6

d4856f76dd135ab2e972d2c7e43de17e7a879b79

30,355

py

Python

src/models/ConvRNN.py

PatBall1/DeepForestcast

f9444490d71b89aa7823e830cf7fbe6752c74d9a

[ "MIT" ]

null

src/models/ConvRNN.py

PatBall1/DeepForestcast

f9444490d71b89aa7823e830cf7fbe6752c74d9a

[ "MIT" ]

1

2022-02-05T10:35:48.000Z

src/models/ConvRNN.py

PatBall1/DeepForestcast

f9444490d71b89aa7823e830cf7fbe6752c74d9a

[ "MIT" ]

null

import torch from spp_layer import * device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") import torch.nn as nn # Initially from https://github.com/ndrplz/ConvLSTM_pytorch/blob/master/convlstm.py # Updated at https://github.com/TUM-LMF/MTLCC-pytorch/blob/master/src/models/convlstm/convlstm.py class ConvLSTMCell(nn.Module): def __init__(self, input_size, input_dim, hidden_dim, kernel_size, bias): """ Initialize ConvLSTM cell. Parameters ---------- input_size: (int, int) Height and width of input tensor as (height, width). input_dim: int Number of channels of input tensor. hidden_dim: int Number of channels of hidden state. kernel_size: (int, int) Size of the convolutional kernel. bias: bool Whether or not to add the bias. """ super(ConvLSTMCell, self).__init__() self.height, self.width = input_size self.input_dim = input_dim self.hidden_dim = hidden_dim self.kernel_size = kernel_size self.padding = kernel_size[0] // 2, kernel_size[1] // 2 self.bias = bias self.conv = nn.Conv2d( in_channels=self.input_dim + self.hidden_dim, out_channels=4 * self.hidden_dim, # does 4 represent years kernel_size=self.kernel_size, padding=self.padding, bias=self.bias, ) def forward(self, input_tensor, cur_state): h_cur, c_cur = cur_state combined = torch.cat( [input_tensor, h_cur], dim=1 ) # concatenate along channel axis combined_conv = self.conv(combined) cc_i, cc_f, cc_o, cc_g = torch.split(combined_conv, self.hidden_dim, dim=1) i = torch.sigmoid(cc_i) f = torch.sigmoid(cc_f) o = torch.sigmoid(cc_o) g = torch.tanh(cc_g) c_next = f * c_cur + i * g h_next = o * torch.tanh(c_next) return h_next, c_next def init_hidden(self, batch_size): # return (Variable(torch.zeros(batch_size, self.hidden_dim, self.height, self.width)).cuda(), # Variable(torch.zeros(batch_size, self.hidden_dim, self.height, self.width)).cuda()) return ( torch.zeros(batch_size, self.hidden_dim, self.height, self.width).data, torch.zeros(batch_size, self.hidden_dim, self.height, self.width).data, ) class ConvLSTM(nn.Module): def __init__( self, input_size=(21, 21), input_dim=5, hidden_dim=(16, 32), kernel_size=((3, 3),), num_layers=2, bias=True, return_all_layers=False, ): super(ConvLSTM, self).__init__() self._check_kernel_size_consistency(kernel_size) # Make sure that both `kernel_size` and `hidden_dim` are lists having len == num_layers if not len(kernel_size) == num_layers: kernel_size = self._extend_for_multilayer(kernel_size, num_layers) if not len(hidden_dim) == num_layers: hidden_dim = self._extend_for_multilayer(hidden_dim, num_layers) self.height, self.width = input_size self.input_size = input_size self.input_dim = input_dim self.hidden_dim = hidden_dim self.kernel_size = kernel_size self.num_layers = num_layers self.bias = bias self.return_all_layers = return_all_layers cell_list = [] for i in range(0, self.num_layers): cur_input_dim = self.input_dim if i == 0 else self.hidden_dim[i - 1] cell_list.append( ConvLSTMCell( input_size=self.input_size, input_dim=cur_input_dim, hidden_dim=self.hidden_dim[i], kernel_size=self.kernel_size[i], bias=self.bias, ) ) self.cell_list = nn.ModuleList(cell_list) def forward(self, input_tensor, hidden_state=None): """ Parameters ---------- Returns ------- last_state_list, layer_output """ # Implement stateful ConvLSTM if hidden_state is not None: raise NotImplementedError() else: hidden_state = self._init_hidden(batch_size=input_tensor.size(0)) # layer_output_list = [] last_state_list = [] seq_len = input_tensor.size(2) # Number of years worth of dynamic tensors cur_layer_input = input_tensor for layer_idx in range(self.num_layers): h, c = hidden_state[layer_idx] output_inner = [] for t in range(seq_len): h, c = self.cell_list[layer_idx]( input_tensor=cur_layer_input[:, :, t, :, :], cur_state=[h, c] ) output_inner.append(h) layer_output = torch.stack(output_inner, dim=2) cur_layer_input = layer_output # returns all [layer_1(h_1,h_2,...h_t),layer_2(h_1,h_2,...h_t),layer_3(h_1,h_2,...h_t)...] # dont need it if not tracking individual loss # layer_output_list.append(layer_output) last_state_list.append([h, c]) if not self.return_all_layers: # layer_output_list = layer_output_list[-1:] last_state_list = last_state_list[-1:] # return layer_output_list, last_state_list return last_state_list[0] def _init_hidden(self, batch_size): init_states = [] for i in range(self.num_layers): init_states.append(self.cell_list[i].init_hidden(batch_size)) return init_states @staticmethod def _check_kernel_size_consistency(kernel_size): if not ( isinstance(kernel_size, tuple) or ( isinstance(kernel_size, list) and all([isinstance(elem, tuple) for elem in kernel_size]) ) ): raise ValueError("`kernel_size` must be tuple or list of tuples") @staticmethod def _extend_for_multilayer(param, num_layers): if not isinstance(param, list): param = [param[0]] * num_layers return param # Adapted from https://github.com/TUM-LMF/MTLCC-pytorch/blob/master/src/models/sequenceencoder.py class LSTMSequentialEncoder(torch.nn.Module): def __init__( self, height=21, width=21, input_dim=(2, 5), hidden_dim=(16, 16, 64, 8), kernel_size=((3, 3), (1, 3, 3), (3, 3), (3, 3)), levels=(13,), dropout=0.2, bias=True, ): super(LSTMSequentialEncoder, self).__init__() self.levels = levels self.hidden_dim = hidden_dim self.conv = nn.Sequential( nn.Conv2d(input_dim[0], hidden_dim[0], kernel_size=kernel_size[0]), nn.ReLU(), nn.BatchNorm2d(hidden_dim[0]), nn.Conv2d(hidden_dim[0], hidden_dim[0], kernel_size=kernel_size[0]), nn.ReLU(), nn.BatchNorm2d(hidden_dim[0]), nn.Conv2d(hidden_dim[0], hidden_dim[0], kernel_size=kernel_size[0]), nn.ReLU(), nn.BatchNorm2d(hidden_dim[0]), ) self.inconv = nn.Sequential( torch.nn.Conv3d(input_dim[1], hidden_dim[1], kernel_size[1]), nn.ReLU(), nn.BatchNorm3d(hidden_dim[1]), torch.nn.Conv3d(hidden_dim[1], hidden_dim[1], kernel_size[1]), nn.ReLU(), nn.BatchNorm3d(hidden_dim[1]), torch.nn.Conv3d(hidden_dim[1], hidden_dim[1], kernel_size[1]), nn.ReLU(), nn.BatchNorm3d(hidden_dim[1]), ) cell_input_size = height - 3 * (kernel_size[1][-1] - 1) self.cell = ConvLSTMCell( input_size=(cell_input_size, cell_input_size), input_dim=hidden_dim[1], hidden_dim=hidden_dim[2], kernel_size=kernel_size[2], bias=bias, ) self.final = nn.Sequential( torch.nn.Conv2d( hidden_dim[2] + hidden_dim[0], hidden_dim[3], kernel_size[3] ), torch.nn.ReLU(), nn.BatchNorm2d(hidden_dim[3]), ) ln_in = 0 for i in levels: ln_in += hidden_dim[3] * i * i self.ln = torch.nn.Sequential( torch.nn.Linear(ln_in, 100), torch.nn.ReLU(), torch.nn.BatchNorm1d(100), torch.nn.Dropout(dropout), torch.nn.Linear(100, 1), ) self.sig = torch.nn.Sigmoid() def forward(self, data, sigmoid=True): # Split into static (z) and dynamic tensors (x) to be fed into different branches z, x = data # 2D convolutions over the static tensor z = self.conv.forward(z) # x = self.inconv.forward(x) # bands, channels, time, height, width b, c, t, h, w = x.shape hidden = torch.zeros((b, self.hidden_dim[2], h, w)) state = torch.zeros((b, self.hidden_dim[2], h, w)) for iter in range(t): hidden, state = self.cell.forward(x[:, :, iter, :, :], (hidden, state)) x = hidden # Join dynamic and static branches x = torch.cat((x, z), dim=1) x = self.final.forward(x) x = spp_layer(x, self.levels) x = self.ln(x) if sigmoid: x = self.sig(x) return x.flatten() class DeepLSTMSequentialEncoder(torch.nn.Module): """ DeepLSTMSequentialEncoder with the option to add multiple ConvLSTM layers """ def __init__( self, height=21, width=21, input_dim=(2, 5), hidden_dim=(16, 16, (16, 16), 8), kernel_size=((3, 3), (1, 3, 3), ((3, 3),), (3, 3)), num_layers=2, levels=(13,), dropout=0.2, bias=True, return_all_layers=False, ): super(DeepLSTMSequentialEncoder, self).__init__() self.levels = levels self.hidden_dim = hidden_dim self.conv = nn.Sequential( nn.Conv2d(input_dim[0], hidden_dim[0], kernel_size=kernel_size[0]), nn.ReLU(), nn.BatchNorm2d(hidden_dim[0]), nn.Conv2d(hidden_dim[0], hidden_dim[0], kernel_size=kernel_size[0]), nn.ReLU(), nn.BatchNorm2d(hidden_dim[0]), nn.Conv2d(hidden_dim[0], hidden_dim[0], kernel_size=kernel_size[0]), nn.ReLU(), nn.BatchNorm2d(hidden_dim[0]), ) self.inconv = nn.Sequential( torch.nn.Conv3d(input_dim[1], hidden_dim[1], kernel_size[1]), nn.ReLU(), nn.BatchNorm3d(hidden_dim[1]), torch.nn.Conv3d(hidden_dim[1], hidden_dim[1], kernel_size[1]), nn.ReLU(), nn.BatchNorm3d(hidden_dim[1]), torch.nn.Conv3d(hidden_dim[1], hidden_dim[1], kernel_size[1]), nn.ReLU(), nn.BatchNorm3d(hidden_dim[1]), ) cell_input_size = height - 3 * (kernel_size[1][1] - 1) self.cell = ConvLSTM( input_size=(cell_input_size, cell_input_size), input_dim=hidden_dim[1], hidden_dim=hidden_dim[2], kernel_size=kernel_size[2], num_layers=num_layers, bias=bias, return_all_layers=return_all_layers, ) self.final = nn.Sequential( torch.nn.Conv2d( hidden_dim[2][-1] + hidden_dim[0], hidden_dim[3], kernel_size[3] ), torch.nn.ReLU(), nn.BatchNorm2d(hidden_dim[3]), ) ln_in = 0 for i in levels: ln_in += hidden_dim[3] * i * i self.ln = torch.nn.Sequential( torch.nn.Linear(ln_in, 100), torch.nn.ReLU(), torch.nn.BatchNorm1d(100), torch.nn.Dropout(dropout), torch.nn.Linear(100, 1), ) self.sig = torch.nn.Sigmoid() def forward(self, data, sigmoid=True): z, x = data z = self.conv.forward(z) x = self.inconv.forward(x) hidden, state = self.cell.forward(x) x = hidden # Join dynamic and static branches x = torch.cat((x, z), dim=1) x = self.final.forward(x) x = spp_layer(x, self.levels) x = self.ln(x) if sigmoid: x = self.sig(x) return x.flatten() class Conv_3D(torch.nn.Module): """ Making deforestation predictions with 3D convolutions (space + time) """ def __init__( self, input_dim=(2, 8), hidden_dim=(16, 32, 32), kernel_size=((5, 5), (2, 5, 5), (5, 5)), levels=(13,), dropout=0.2, start_year=14, end_year=17, ): super(Conv_3D, self).__init__() self.levels = levels self.hidden_dim = hidden_dim self.conv_2D = torch.nn.Sequential( torch.nn.Conv2d(input_dim[0], hidden_dim[0], kernel_size=kernel_size[0]), torch.nn.ReLU(), torch.nn.BatchNorm2d(hidden_dim[0]), torch.nn.Conv2d(hidden_dim[0], hidden_dim[0], kernel_size=kernel_size[0]), torch.nn.ReLU(), torch.nn.BatchNorm2d(hidden_dim[0]), ) self.conv_3D = torch.nn.Sequential( torch.nn.Conv3d( in_channels=input_dim[1], out_channels=hidden_dim[1], kernel_size=kernel_size[1], ), torch.nn.ReLU(), torch.nn.BatchNorm3d(hidden_dim[1]), # This second 3d conv layer is troublesome # Kernel size needs to be tweaked by year torch.nn.Conv3d( in_channels=hidden_dim[1], out_channels=hidden_dim[1], kernel_size=( kernel_size[1][0] + (end_year - start_year - 2), kernel_size[1][1], kernel_size[1][2], ), ), torch.nn.ReLU(), torch.nn.BatchNorm3d(hidden_dim[1]), ) self.final = torch.nn.Sequential( torch.nn.Conv2d( hidden_dim[0] + hidden_dim[1], hidden_dim[2], kernel_size[2] ), torch.nn.ReLU(), torch.nn.BatchNorm2d(hidden_dim[2]), torch.nn.Conv2d(hidden_dim[2], hidden_dim[2], kernel_size[2]), torch.nn.ReLU(), torch.nn.BatchNorm2d(hidden_dim[2]), torch.nn.Conv2d(hidden_dim[2], hidden_dim[2], kernel_size[2]), torch.nn.ReLU(), torch.nn.BatchNorm2d(hidden_dim[2]), torch.nn.Conv2d(hidden_dim[2], hidden_dim[2], kernel_size[2]), torch.nn.ReLU(), torch.nn.BatchNorm2d(hidden_dim[2]), torch.nn.Conv2d(hidden_dim[2], hidden_dim[2], kernel_size[2]), torch.nn.ReLU(), torch.nn.BatchNorm2d(hidden_dim[2]), torch.nn.Conv2d(hidden_dim[2], hidden_dim[2], kernel_size[2]), torch.nn.ReLU(), torch.nn.BatchNorm2d(hidden_dim[2]), ) ln_in = 0 for i in levels: ln_in += hidden_dim[2] * i * i self.ln = torch.nn.Sequential( torch.nn.Linear(ln_in, 100), torch.nn.ReLU(), torch.nn.BatchNorm1d(100), torch.nn.Dropout(dropout), torch.nn.Linear(100, 1), ) self.sig = torch.nn.Sigmoid() def forward(self, data, sigmoid=True): z, x = data z = self.conv_2D.forward(z) x = self.conv_3D.forward(x) x = x.squeeze(dim=2) # print("x shape post squeeze:", x.shape) x = torch.cat((x, z), dim=1) x = self.final.forward(x) x = spp_layer(x, self.levels) x = self.ln(x) if sigmoid: x = self.sig(x) return x.flatten() # Kernel size needs to be different depending on how many years of data are being handled # This model is for an even number of training years (e.g. start_date = 14, end_date = 17) class Conv_3Deven(torch.nn.Module): def __init__( self, input_dim=(2, 8), hidden_dim=(16, 32, 32), kernel_size=((5, 5), (2, 5, 5), (5, 5)), levels=(13,), dropout=0.2, ): super(Conv_3Deven, self).__init__() self.levels = levels self.hidden_dim = hidden_dim self.conv_2D = torch.nn.Sequential( torch.nn.Conv2d(input_dim[0], hidden_dim[0], kernel_size=kernel_size[0]), torch.nn.ReLU(), torch.nn.BatchNorm2d(hidden_dim[0]), torch.nn.Conv2d(hidden_dim[0], hidden_dim[0], kernel_size=kernel_size[0]), torch.nn.ReLU(), torch.nn.BatchNorm2d(hidden_dim[0]), ) self.conv_3D = torch.nn.Sequential( torch.nn.Conv3d( in_channels=input_dim[1], out_channels=hidden_dim[1], kernel_size=kernel_size[1], ), torch.nn.ReLU(), torch.nn.BatchNorm3d(hidden_dim[1]), torch.nn.Conv3d( in_channels=hidden_dim[1], out_channels=hidden_dim[1], # DEPENDING ON NUMBER OF YEARS, NEED TO SWITCH BETWEEN KERNEL SIZE # # This one for odd num of years# # kernel_size = kernel_size[1]), # This one for even num of years# kernel_size=( kernel_size[1][0] + 1, kernel_size[1][1], kernel_size[1][2], ), ), torch.nn.ReLU(), torch.nn.BatchNorm3d(hidden_dim[1]), ) self.final = torch.nn.Sequential( torch.nn.Conv2d( hidden_dim[0] + hidden_dim[1], hidden_dim[2], kernel_size[2] ), torch.nn.ReLU(), torch.nn.BatchNorm2d(hidden_dim[2]), torch.nn.Conv2d(hidden_dim[2], hidden_dim[2], kernel_size[2]), torch.nn.ReLU(), torch.nn.BatchNorm2d(hidden_dim[2]), torch.nn.Conv2d(hidden_dim[2], hidden_dim[2], kernel_size[2]), torch.nn.ReLU(), torch.nn.BatchNorm2d(hidden_dim[2]), torch.nn.Conv2d(hidden_dim[2], hidden_dim[2], kernel_size[2]), torch.nn.ReLU(), torch.nn.BatchNorm2d(hidden_dim[2]), torch.nn.Conv2d(hidden_dim[2], hidden_dim[2], kernel_size[2]), torch.nn.ReLU(), torch.nn.BatchNorm2d(hidden_dim[2]), torch.nn.Conv2d(hidden_dim[2], hidden_dim[2], kernel_size[2]), torch.nn.ReLU(), torch.nn.BatchNorm2d(hidden_dim[2]), ) ln_in = 0 for i in levels: ln_in += hidden_dim[2] * i * i self.ln = torch.nn.Sequential( torch.nn.Linear(ln_in, 100), torch.nn.ReLU(), torch.nn.BatchNorm1d(100), torch.nn.Dropout(dropout), torch.nn.Linear(100, 1), ) self.sig = torch.nn.Sigmoid() def forward(self, data, sigmoid=True): z, x = data # print("z shape start:", z.shape) # print("x shape start:", x.shape) z = self.conv_2D.forward(z) x = self.conv_3D.forward(x) # print("z shape post conv2d:", z.shape) # print("x shape post conv3d:", x.shape) x = x.squeeze(dim=2) # print("x shape post squeeze:", x.shape) x = torch.cat((x, z), dim=1) x = self.final.forward(x) x = spp_layer(x, self.levels) x = self.ln(x) if sigmoid: x = self.sig(x) return x.flatten() # Kernel size needs to be different depending on how many years of data are being handled # This model is for an odd number of training years (e.g. start_date = 14, end_date = 16) class Conv_3Dodd(torch.nn.Module): def __init__( self, input_dim=(2, 8), hidden_dim=(16, 32, 32), kernel_size=((5, 5), (2, 5, 5), (5, 5)), levels=(13,), dropout=0.2, ): super(Conv_3Dodd, self).__init__() self.levels = levels self.hidden_dim = hidden_dim self.conv_2D = torch.nn.Sequential( torch.nn.Conv2d(input_dim[0], hidden_dim[0], kernel_size=kernel_size[0]), torch.nn.ReLU(), torch.nn.BatchNorm2d(hidden_dim[0]), torch.nn.Conv2d(hidden_dim[0], hidden_dim[0], kernel_size=kernel_size[0]), torch.nn.ReLU(), torch.nn.BatchNorm2d(hidden_dim[0]), ) self.conv_3D = torch.nn.Sequential( torch.nn.Conv3d( in_channels=input_dim[1], out_channels=hidden_dim[1], kernel_size=kernel_size[1], ), torch.nn.ReLU(), torch.nn.BatchNorm3d(hidden_dim[1]), torch.nn.Conv3d( in_channels=hidden_dim[1], out_channels=hidden_dim[1], # DEPENDING ON NUMBER OF YEARS, NEED TO SWITCH BETWEEN KERNEL SIZE # # This one for odd num of years# # kernel_size=kernel_size[1], # This one for even num of years# kernel_size=( kernel_size[1][0] + 2, kernel_size[1][1], kernel_size[1][2], ) # ), ), torch.nn.ReLU(), torch.nn.BatchNorm3d(hidden_dim[1]), ) self.final = torch.nn.Sequential( torch.nn.Conv2d( hidden_dim[0] + hidden_dim[1], hidden_dim[2], kernel_size[2] ), torch.nn.ReLU(), torch.nn.BatchNorm2d(hidden_dim[2]), torch.nn.Conv2d(hidden_dim[2], hidden_dim[2], kernel_size[2]), torch.nn.ReLU(), torch.nn.BatchNorm2d(hidden_dim[2]), torch.nn.Conv2d(hidden_dim[2], hidden_dim[2], kernel_size[2]), torch.nn.ReLU(), torch.nn.BatchNorm2d(hidden_dim[2]), torch.nn.Conv2d(hidden_dim[2], hidden_dim[2], kernel_size[2]), torch.nn.ReLU(), torch.nn.BatchNorm2d(hidden_dim[2]), torch.nn.Conv2d(hidden_dim[2], hidden_dim[2], kernel_size[2]), torch.nn.ReLU(), torch.nn.BatchNorm2d(hidden_dim[2]), torch.nn.Conv2d(hidden_dim[2], hidden_dim[2], kernel_size[2]), torch.nn.ReLU(), torch.nn.BatchNorm2d(hidden_dim[2]), ) ln_in = 0 for i in levels: ln_in += hidden_dim[2] * i * i self.ln = torch.nn.Sequential( torch.nn.Linear(ln_in, 100), torch.nn.ReLU(), torch.nn.BatchNorm1d(100), torch.nn.Dropout(dropout), torch.nn.Linear(100, 1), ) self.sig = torch.nn.Sigmoid() def forward(self, data, sigmoid=True): z, x = data print("z shape start:", z.shape) print("x shape start:", x.shape) z = self.conv_2D.forward(z) x = self.conv_3D.forward(x) print("z shape post conv2d:", z.shape) print("x shape post conv3d:", x.shape) x = x.squeeze(dim=2) print("x shape post squeeze:", x.shape) x = torch.cat((x, z), dim=1) # Problem with dimensions here x = self.final.forward(x) x = spp_layer(x, self.levels) x = self.ln(x) if sigmoid: x = self.sig(x) return x.flatten() # Updated to change how labels are handled - 2 labels instead of one class Conv_3DoddT(torch.nn.Module): def __init__( self, input_dim=(2, 8), hidden_dim=(16, 32, 32), kernel_size=((5, 5), (2, 5, 5), (5, 5)), levels=(13,), dropout=0.2, ): super(Conv_3DoddT, self).__init__() self.levels = levels self.hidden_dim = hidden_dim self.conv_2D = torch.nn.Sequential( torch.nn.Conv2d(input_dim[0], hidden_dim[0], kernel_size=kernel_size[0]), torch.nn.ReLU(), torch.nn.BatchNorm2d(hidden_dim[0]), torch.nn.Conv2d(hidden_dim[0], hidden_dim[0], kernel_size=kernel_size[0]), torch.nn.ReLU(), torch.nn.BatchNorm2d(hidden_dim[0]), ) self.conv_3D = torch.nn.Sequential( torch.nn.Conv3d( in_channels=input_dim[1], out_channels=hidden_dim[1], kernel_size=kernel_size[1], ), torch.nn.ReLU(), torch.nn.BatchNorm3d(hidden_dim[1]), torch.nn.Conv3d( in_channels=hidden_dim[1], out_channels=hidden_dim[1], # DEPENDING ON NUMBER OF YEARS, NEED TO SWITCH BETWEEN KERNEL SIZE # # This one for odd num of years# kernel_size=kernel_size[1], ), # This one for even num of years# # kernel_size = (kernel_size[1][0]+1,kernel_size[1][1],kernel_size[1][2])), torch.nn.ReLU(), torch.nn.BatchNorm3d(hidden_dim[1]), ) self.final = torch.nn.Sequential( torch.nn.Conv2d( hidden_dim[0] + hidden_dim[1], hidden_dim[2], kernel_size[2] ), torch.nn.ReLU(), torch.nn.BatchNorm2d(hidden_dim[2]), torch.nn.Conv2d(hidden_dim[2], hidden_dim[2], kernel_size[2]), torch.nn.ReLU(), torch.nn.BatchNorm2d(hidden_dim[2]), torch.nn.Conv2d(hidden_dim[2], hidden_dim[2], kernel_size[2]), torch.nn.ReLU(), torch.nn.BatchNorm2d(hidden_dim[2]), torch.nn.Conv2d(hidden_dim[2], hidden_dim[2], kernel_size[2]), torch.nn.ReLU(), torch.nn.BatchNorm2d(hidden_dim[2]), torch.nn.Conv2d(hidden_dim[2], hidden_dim[2], kernel_size[2]), torch.nn.ReLU(), torch.nn.BatchNorm2d(hidden_dim[2]), torch.nn.Conv2d(hidden_dim[2], hidden_dim[2], kernel_size[2]), torch.nn.ReLU(), torch.nn.BatchNorm2d(hidden_dim[2]), ) ln_in = 0 for i in levels: ln_in += hidden_dim[2] * i * i self.ln = torch.nn.Sequential( torch.nn.Linear(ln_in, 100), torch.nn.ReLU(), torch.nn.BatchNorm1d(100), torch.nn.Dropout(dropout), torch.nn.Linear(100, 2), ) # changed to 2 # self.sig = torch.nn.Sigmoid() # self.sfmx = torch.nn.Softmax(dim=1) def forward(self, data, sigmoid=True): z, x = data z = self.conv_2D.forward(z) x = self.conv_3D.forward(x) x = x.squeeze(dim=2) x = torch.cat((x, z), dim=1) x = self.final.forward(x) x = spp_layer(x, self.levels) x = self.ln(x) # if sigmoid: # x = self.sig(x) # x = self.sfmx(x) # need this? return x # Model graveyard # class Conv_3D(torch.nn.Module): # def __init__(self, input_dim=(2,5), # hidden_dim=(16,16,64), # kernel_size=((3,3),(2,3,3),(3,3)), # levels=(12,), # dropout = 0.2): # super(Conv_3D, self).__init__() # self.levels = levels # self.hidden_dim = hidden_dim # self.conv_2D = nn.Sequential( # nn.Conv2d(input_dim[0],hidden_dim[0],kernel_size = kernel_size[0]), # nn.ReLU(), # nn.BatchNorm2d(hidden_dim[0]), # nn.Conv2d(hidden_dim[0],hidden_dim[0],kernel_size = kernel_size[0]), # nn.ReLU(), # nn.BatchNorm2d(hidden_dim[0])) # self.conv_3D = nn.Sequential( # torch.nn.Conv3d(in_channels = input_dim[1], # out_channels = hidden_dim[1], # kernel_size = kernel_size[1]), # nn.ReLU(), # nn.BatchNorm3d(hidden_dim[1]), # torch.nn.Conv3d(in_channels = hidden_dim[1], # out_channels = hidden_dim[1], # kernel_size = kernel_size[1]), # nn.ReLU(), # nn.BatchNorm3d(hidden_dim[1])) # self.final = nn.Sequential( # torch.nn.Conv2d(hidden_dim[0]+hidden_dim[1], hidden_dim[2], kernel_size[2]), # nn.ReLU(), # nn.BatchNorm2d(hidden_dim[2]), # torch.nn.Conv2d(hidden_dim[2], hidden_dim[2], kernel_size[2]), # nn.ReLU(), # nn.BatchNorm2d(hidden_dim[2]), # torch.nn.Conv2d(hidden_dim[2], hidden_dim[2], kernel_size[2]), # nn.ReLU(), # nn.BatchNorm2d(hidden_dim[2]), # torch.nn.Conv2d(hidden_dim[2], hidden_dim[2], kernel_size[2]), # nn.ReLU(), # nn.BatchNorm2d(hidden_dim[2])) # ln_in = 0 # for i in levels: # ln_in += hidden_dim[2]*i*i # self.ln = torch.nn.Sequential( # torch.nn.Linear(ln_in,100), # torch.nn.ReLU(), # torch.nn.BatchNorm1d(100), # torch.nn.Dropout(dropout), # torch.nn.Linear(100, 1)) # self.sig = torch.nn.Sigmoid() # def forward(self, data , sigmoid = True ): # z , x = data # z = self.conv_2D.forward(z) # x = self.conv_3D.forward(x) # x = x.squeeze(dim = 2 ) # x = torch.cat((x,z),dim = 1) # print("Before final CNN: ",x.shape) # x = self.final.forward(x) # print("After final CNN: ",x.shape) # x = spp_layer(x, self.levels) # # print(x.shape) # x= self.ln(x) # # print(x.shape) # if sigmoid: # x = self.sig(x) # return x.flatten()

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