xszheng2020/the_stack_dedup_python_hits_5 · Datasets at Hugging Face

4e40e56669e17e3ab0d16330e3eb1bb070b4f8b0

140

py

Python

idawilli/__init__.py

CrackerCat/idawilli

4cf895cf88144e9394a0aa21200cf35e84ca1cba

[ "Apache-2.0" ]

null

idawilli/__init__.py

CrackerCat/idawilli

4cf895cf88144e9394a0aa21200cf35e84ca1cba

[ "Apache-2.0" ]

null

idawilli/__init__.py

CrackerCat/idawilli

4cf895cf88144e9394a0aa21200cf35e84ca1cba

[ "Apache-2.0" ]

null

def align(value, alignment=0x1000): if value % alignment == 0: return value return value + (alignment - (value % alignment))

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py

Python

build/scripts-3.5/mooc_anon.py

acheamponge/mooc_anon

b06dec9c4c47011f69ff4f6e21a0f5862e2ffd5c

[ "MIT" ]

3

2019-07-08T01:16:57.000Z

2021-09-23T12:44:02.000Z

build/scripts-3.5/mooc_anon.py

acheamponge/mooc_anon

b06dec9c4c47011f69ff4f6e21a0f5862e2ffd5c

[ "MIT" ]

null

build/scripts-3.5/mooc_anon.py

acheamponge/mooc_anon

b06dec9c4c47011f69ff4f6e21a0f5862e2ffd5c

[ "MIT" ]

null

#!/usr/bin/env python print("hey there, this is my first pip package")

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9df56584ef8b053ce1af3875d1ad5df2cd5671e4

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py

Python

src/1.foundation/1.embedding_python/1.4.create_custom_module/assets/scripts/game.py

Chukobyte/learn-engine-dev

3f4437ed4abab9011d584bdc0ab4eff921393f00

[ "CC-BY-4.0", "CC0-1.0" ]

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2021-08-13T01:53:59.000Z

2022-01-23T18:50:17.000Z

src/1.foundation/1.embedding_python/1.4.create_custom_module/assets/scripts/game.py

Chukobyte/learn-engine-dev

3f4437ed4abab9011d584bdc0ab4eff921393f00

[ "CC-BY-4.0", "CC0-1.0" ]

null

src/1.foundation/1.embedding_python/1.4.create_custom_module/assets/scripts/game.py

Chukobyte/learn-engine-dev

3f4437ed4abab9011d584bdc0ab4eff921393f00

[ "CC-BY-4.0", "CC0-1.0" ]

null

import engine class Player: def talk(self, message: str) -> None: engine_version = engine.get_version() engine.print_log(message=f"Engine version = {engine_version}")

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py

Python

nfv/nfv-vim/nfv_vim/alarm/__init__.py

SidneyAn/nfv

5f0262a5b6ea4be59f977b9c587c483cbe0e373d

[ "Apache-2.0" ]

2

2020-02-07T19:01:36.000Z

2022-02-23T01:41:46.000Z

nfv/nfv-vim/nfv_vim/alarm/__init__.py

SidneyAn/nfv

5f0262a5b6ea4be59f977b9c587c483cbe0e373d

[ "Apache-2.0" ]

1

2021-01-14T12:02:25.000Z

nfv/nfv-vim/nfv_vim/alarm/__init__.py

SidneyAn/nfv

5f0262a5b6ea4be59f977b9c587c483cbe0e373d

[ "Apache-2.0" ]

2

2021-01-13T08:39:21.000Z

2022-02-09T00:21:55.000Z

# # Copyright (c) 2015-2016 Wind River Systems, Inc. # # SPDX-License-Identifier: Apache-2.0 # from nfv_common.alarm import * # noqa: F401,F403 from nfv_vim.alarm._general import clear_general_alarm # noqa: F401 from nfv_vim.alarm._general import raise_general_alarm # noqa: F401 from nfv_vim.alarm._host import host_clear_alarm # noqa: F401 from nfv_vim.alarm._host import host_raise_alarm # noqa: F401 from nfv_vim.alarm._instance import instance_clear_alarm # noqa: F401 from nfv_vim.alarm._instance import instance_manage_alarms # noqa: F401 from nfv_vim.alarm._instance import instance_raise_alarm # noqa: F401 from nfv_vim.alarm._instance_group import clear_instance_group_alarm # noqa: F401 from nfv_vim.alarm._instance_group import raise_instance_group_policy_alarm # noqa: F401 from nfv_vim.alarm._sw_update import clear_sw_update_alarm # noqa: F401 from nfv_vim.alarm._sw_update import raise_sw_update_alarm # noqa: F401

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py

Python

metrics/__init__.py

rizwan09/Tagger

7622f10561a0f6074abde0c9c26a4f25405b204b

[ "BSD-3-Clause" ]

null

metrics/__init__.py

rizwan09/Tagger

7622f10561a0f6074abde0c9c26a4f25405b204b

[ "BSD-3-Clause" ]

null

metrics/__init__.py

rizwan09/Tagger

7622f10561a0f6074abde0c9c26a4f25405b204b

[ "BSD-3-Clause" ]

null

# metrics/__init__.py # author: Playinf # email: playinf@stu.xmu.edu.cn from .metrics import create_tagger_evaluation_metrics

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d1ff7ff41f8abda906716eb125ac0014f5c4aa8f

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py

Python

graph_rl/global_algorithms/__init__.py

nicoguertler/graphrl

21a1cefc53e5c457745570460de0d99e68622e57

[ "MIT" ]

1

2022-01-04T15:21:55.000Z

graph_rl/global_algorithms/__init__.py

nicoguertler/graph_rl

21a1cefc53e5c457745570460de0d99e68622e57

[ "MIT" ]

null

graph_rl/global_algorithms/__init__.py

nicoguertler/graph_rl

21a1cefc53e5c457745570460de0d99e68622e57

[ "MIT" ]

null

from .global_hac import GlobalHAC

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5

ae074bc52a086a244bf599cb6b758a858b0ae56e

241

py

Python

cgn_framework/imagenet/models/__init__.py

anonymous-user-256/mlrc-cgn

64f43fcb89b3a13c0ae46db4f19060d9f204a6b1

[ "MIT" ]

78

2021-01-15T09:22:21.000Z

2022-03-06T12:15:36.000Z

cgn_framework/imagenet/models/__init__.py

anonymous-user-256/mlrc-cgn

64f43fcb89b3a13c0ae46db4f19060d9f204a6b1

[ "MIT" ]

3

2021-03-26T07:33:16.000Z

2022-01-17T14:49:51.000Z

cgn_framework/imagenet/models/__init__.py

anonymous-user-256/mlrc-cgn

64f43fcb89b3a13c0ae46db4f19060d9f204a6b1

[ "MIT" ]

14

2021-01-17T10:08:49.000Z

2022-01-14T06:32:11.000Z

from imagenet.models.biggan import BigGAN from imagenet.models.u2net import U2NET from imagenet.models.cgn import CGN from imagenet.models.classifier_ensemble import InvariantEnsemble __all__ = [ CGN, InvariantEnsemble, BigGAN, U2NET ]

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ae2e72786b0e755905085b12bef4f3ce69f9d8fc

34

py

Python

pycalc.py

erhuabushuo/pycalc

a46b85aaafe37ad7cca95ac0198d9bfea985b598

[ "MIT" ]

null

pycalc.py

erhuabushuo/pycalc

a46b85aaafe37ad7cca95ac0198d9bfea985b598

[ "MIT" ]

null

pycalc.py

erhuabushuo/pycalc

a46b85aaafe37ad7cca95ac0198d9bfea985b598

[ "MIT" ]

null

import calcpy calcpy.calculcate()

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ae3401171f8e9d1d9a120271065ad3caf42b8ad2

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py

Python

tests/__init__.py

masasin/latexipy

1f888a44f2077a5c0ef63216616cd24c279e44d0

[ "MIT" ]

144

2017-08-24T08:58:58.000Z

2021-04-18T10:38:44.000Z

tests/__init__.py

masasin/latexipy

1f888a44f2077a5c0ef63216616cd24c279e44d0

[ "MIT" ]

424

2017-09-04T16:21:10.000Z

2022-03-28T02:23:25.000Z

tests/__init__.py

masasin/latexipy

1f888a44f2077a5c0ef63216616cd24c279e44d0

[ "MIT" ]

15

2017-08-26T08:05:55.000Z

2019-05-13T22:29:44.000Z

'''Unit test package for latexipy.'''

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ae3a515565662ff474b5546eb89caaad693236c2

223

py

Python

leetcode_runner/models.py

fbjorn/leetcode-runner

38569e68a3ec2e420ed54aa509c236748f5d55dc

[ "MIT" ]

null

leetcode_runner/models.py

fbjorn/leetcode-runner

38569e68a3ec2e420ed54aa509c236748f5d55dc

[ "MIT" ]

null

leetcode_runner/models.py

fbjorn/leetcode-runner

38569e68a3ec2e420ed54aa509c236748f5d55dc

[ "MIT" ]

null

class Args: def __init__(self, *args, **kwargs): self.args = args self.kwargs = kwargs class TestCase: def __init__(self, args: Args, answer): self.args = args self.answer = answer

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ae7a6bf6cf0a8187540066ce63f57293b91d1b01

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py

Python

datamaps/__init__.py

fossabot/datamaps-1

c66c3f20e43bd41ec0874f40f39bd0eff89fd476

[ "MIT" ]

null

datamaps/__init__.py

fossabot/datamaps-1

c66c3f20e43bd41ec0874f40f39bd0eff89fd476

[ "MIT" ]

null

datamaps/__init__.py

fossabot/datamaps-1

c66c3f20e43bd41ec0874f40f39bd0eff89fd476

[ "MIT" ]

null

__version__ = "1.0.0b13"

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8821e5692a8f5f25d979cb717b556c74dc17abc9

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py

Python

pyfirebase/__init__.py

andela-cnnadi/python-fire

11868007a7ff7fec45ed87cec18466e351cdb5ab

[ "MIT" ]

14

2016-08-31T06:24:33.000Z

2019-12-12T11:23:21.000Z

pyfirebase/__init__.py

andela-cnnadi/python-fire

11868007a7ff7fec45ed87cec18466e351cdb5ab

[ "MIT" ]

2

2016-09-16T12:40:51.000Z

2016-12-27T06:26:39.000Z

pyfirebase/__init__.py

andela-cnnadi/python-fire

11868007a7ff7fec45ed87cec18466e351cdb5ab

[ "MIT" ]

5

2016-08-30T21:16:32.000Z

2020-11-05T20:39:52.000Z

from firebase import *

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88627cf7ecface35fcb049861351f30b77fd4c4c

173

py

Python

tfrec/utils/__init__.py

Praful932/Tf-Rec

fe0e08d3621da911149a95d8a701e434dfa61161

[ "MIT" ]

18

2020-12-22T04:16:54.000Z

2022-03-23T08:49:16.000Z

tfrec/utils/__init__.py

Praful932/Tf-Rec

fe0e08d3621da911149a95d8a701e434dfa61161

[ "MIT" ]

1

2021-05-11T12:28:07.000Z

2022-03-16T17:33:03.000Z

tfrec/utils/__init__.py

Praful932/Tf-Rec

fe0e08d3621da911149a95d8a701e434dfa61161

[ "MIT" ]

2

2021-04-26T10:29:44.000Z

2021-07-01T03:31:31.000Z

from tfrec.utils.model_utils import cross_validate from tfrec.utils.model_utils import preprocess_and_split __all__ = [ 'cross_validate', 'preprocess_and_split', ]

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889140ee18ea1e06b9b18606e947a9585cb410f1

145

py

Python

DSA/Python/src/dsa/lib/math/ds/tests/fixture.py

JackieMa000/problems

c521558830a0bbf67f94109af92d7be4397d0a43

[ "BSD-3-Clause" ]

null

DSA/Python/src/dsa/lib/math/ds/tests/fixture.py

JackieMa000/problems

c521558830a0bbf67f94109af92d7be4397d0a43

[ "BSD-3-Clause" ]

1

2020-10-23T04:06:56.000Z

DSA/Python/src/dsa/lib/math/ds/tests/fixture.py

JackieMa000/problems

c521558830a0bbf67f94109af92d7be4397d0a43

[ "BSD-3-Clause" ]

null

from dsa.lib.math.tests.fixture import MathTestCase class DsTestCase(MathTestCase): pass class ParenthesesTestCase(DsTestCase): pass

14.5

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88a8aa3a3b09f7b8f22914184124db2a1414e747

320

py

Python

src/sensors/__init__.py

ivanbukhtiyarov/elevators

e7ff582bbc9a26d22880bec61bede747427430c2

[ "MIT" ]

2

2021-03-22T16:12:56.000Z

2021-03-22T16:19:09.000Z

src/sensors/__init__.py

ivanbukhtiyarov/elevators

e7ff582bbc9a26d22880bec61bede747427430c2

[ "MIT" ]

46

2021-04-01T10:25:25.000Z

2021-12-26T23:43:46.000Z

src/sensors/__init__.py

ivanbukhtiyarov/elevators

e7ff582bbc9a26d22880bec61bede747427430c2

[ "MIT" ]

4

2021-04-01T10:22:46.000Z

2021-12-26T21:51:10.000Z

from src.sensors.door_block_sensor import DoorBlockSensor from src.sensors.door_state_sensor import DoorStateSensor from src.sensors.light_sensor import LightSensor from src.sensors.movement_sensor import MovementSensor from src.sensors.smoke_sensor import SmokeSensor from src.sensors.weight_sensor import WeightSensor

45.714286

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31f9305a21377f64bd0e727a4e26ba7424caa0ac

39

py

Python

tests/components/logbook/__init__.py

domwillcode/home-assistant

f170c80bea70c939c098b5c88320a1c789858958

[ "Apache-2.0" ]

30,023

2016-04-13T10:17:53.000Z

2020-03-02T12:56:31.000Z

tests/components/logbook/__init__.py

jagadeeshvenkatesh/core

1bd982668449815fee2105478569f8e4b5670add

[ "Apache-2.0" ]

31,101

2020-03-02T13:00:16.000Z

2022-03-31T23:57:36.000Z

tests/components/logbook/__init__.py

jagadeeshvenkatesh/core

1bd982668449815fee2105478569f8e4b5670add

[ "Apache-2.0" ]

11,956

2016-04-13T18:42:31.000Z

2020-03-02T09:32:12.000Z

"""Tests for the logbook component."""

19.5

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39

1

39

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true

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5

31fea9ffda59127cd7bda0c20fd0fcfb295048c1

142

py

Python

joga_moeda.py

lucaslk122/Programas-python

816bdaa128f2d279c255c588c1ff61cb4b834ccd

[ "MIT" ]

null

joga_moeda.py

lucaslk122/Programas-python

816bdaa128f2d279c255c588c1ff61cb4b834ccd

[ "MIT" ]

null

joga_moeda.py

lucaslk122/Programas-python

816bdaa128f2d279c255c588c1ff61cb4b834ccd

[ "MIT" ]

null

from random import random def joga_moeda(): if random() > 0.5: return "Coroa" else: return "Cara" print (joga_moeda())

20.285714

25

0.598592

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142

4.368421

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7

26

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true

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null

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1

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5

ee66be524d32778f359946d067c84065472b72da

94

py

Python

node-runner-cli/setup/__init__.py

stuartbain/node-runner

89d10986dbc79da06df402cb17f3edec736f3709

[ "Apache-2.0" ]

18

2018-11-26T13:22:10.000Z

2022-03-28T12:41:44.000Z

node-runner-cli/setup/__init__.py

stuartbain/node-runner

89d10986dbc79da06df402cb17f3edec736f3709

[ "Apache-2.0" ]

30

2018-09-12T06:40:03.000Z

2021-09-24T13:46:59.000Z

node-runner-cli/setup/__init__.py

stuartbain/node-runner

89d10986dbc79da06df402cb17f3edec736f3709

[ "Apache-2.0" ]

12

2018-09-24T01:57:02.000Z

2022-03-07T17:55:13.000Z

from setup.Base import Base from setup.Docker import Docker from setup.SystemD import SystemD

23.5

33

0.840426

15

94

5.266667

0.4

0.341772

0

0.12766

94

3

34

31.333333

0.963415

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

c9b4d11f803a768b9c496032b0ea1a63387421c9

133

py

Python

app/services/v1/healthcheck.py

rvmoura96/flask-template

d1383be7e17bff580e3ddf61ae580271c30201c4

[ "MIT" ]

2

2019-09-25T19:19:11.000Z

2019-10-08T01:05:35.000Z

app/services/v1/healthcheck.py

rvmoura96/flask-template

d1383be7e17bff580e3ddf61ae580271c30201c4

[ "MIT" ]

10

2019-09-13T23:41:42.000Z

2020-05-10T21:12:32.000Z

app/services/v1/healthcheck.py

rvmoura96/flask-template

d1383be7e17bff580e3ddf61ae580271c30201c4

[ "MIT" ]

9

2019-09-30T15:26:23.000Z

2020-09-28T23:36:25.000Z

from flask_restful import Resource import app from app.services.healthcheck import HealthApi class HealthApiV1(HealthApi): pass

19

46

0.827068

17

133

6.411765

0.705882

0

0.008696

0.135338

133

6

47

22.166667

0.93913

0

1

0

true

0.2

0.6

0

0.8

0

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0

null

0

1

0

null

0

1

0

5

c9f1e7cdebfd2710c6c2b7bf206e8cee0c794ff2

43

py

Python

test.py

Taraxa-project/taraxa-py

95aa0d8054bf4eba2c3200f3298421575b7bb5a0

[ "MIT" ]

null

test.py

Taraxa-project/taraxa-py

95aa0d8054bf4eba2c3200f3298421575b7bb5a0

[ "MIT" ]

1

2022-03-02T15:51:17.000Z

test.py

Taraxa-project/taraxa-py

95aa0d8054bf4eba2c3200f3298421575b7bb5a0

[ "MIT" ]

null

from pytaraxa.test import * blockNumber()

10.75

27

0.767442

5

43

6.6

1

0

0.139535

43

3

28

14.333333

0.891892

0

1

0

true

0

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

1

0

5

a013e70e32f34350be8bc00a3ce5fb9e45e8fb9c

4,912

py

Python

Day3.py

Swicano/AdventCode

3b6f425c773f05911bcc8d8d2f3cf5eb64bfdeff

[ "MIT" ]

null

Day3.py

Swicano/AdventCode

3b6f425c773f05911bcc8d8d2f3cf5eb64bfdeff

[ "MIT" ]

null

Day3.py

Swicano/AdventCode

3b6f425c773f05911bcc8d8d2f3cf5eb64bfdeff

[ "MIT" ]

null

input1str = 'R998,U367,R735,U926,R23,U457,R262,D473,L353,U242,L930,U895,R321,U683,L333,U623,R105,D527,R437,D473,L100,D251,L958,U384,R655,U543,L704,D759,R529,D176,R835,U797,R453,D650,L801,U437,L468,D841,R928,D747,L803,U677,R942,D851,R265,D684,L206,U763,L566,U774,L517,U337,L86,D585,R212,U656,L799,D953,L24,U388,L465,U656,L467,U649,R658,U519,L966,D290,L979,D819,R208,D907,R941,D458,L882,U408,R539,D939,R557,D771,L448,U460,L586,U148,R678,U360,R715,U312,L12,D746,L958,U216,R275,D278,L368,U663,L60,D543,L605,D991,L369,D599,R464,D387,L835,D876,L810,U377,L521,U113,L803,U680,L732,D449,R891,D558,L25,U249,L264,U643,L544,U504,R876,U403,R950,U19,L224,D287,R28,U914,R906,U970,R335,U295,R841,D810,R891,D596,R451,D79,R924,U823,L724,U968,R342,D349,R656,U373,R864,U374,L401,D102,L730,D886,R268,D188,R621,U258,L788,U408,L199,D422,R101,U368,L636,U543,R7,U722,L533,U242,L340,D195,R158,D291,L84,U936,L570,D937,L321,U947,L707,U32,L56,U650,L427,U490,L472,U258,R694,U87,L887,U575,R826,D398,R602,U794,R855,U225,R435,U591,L58,U281,L834,D400,R89,D201,L328,U278,L494,D70,L770,D182,L251,D44,R753,U431,R573,D71,R809,U983,L159,U26,R540,U516,R5,D23,L603,U65,L260,D187,R973,U877,R110,U49,L502,D68,R32,U153,R495,D315,R720,D439,R264,D603,R717,U586,R732,D111,R997,U578,L243,U256,R147,D425,L141,U758,R451,U779,R964,D219,L151,D789,L496,D484,R627,D431,R433,D761,R355,U975,L983,U364,L200,U578,L488,U668,L48,D774,R438,D456,L819,D927,R831,D598,L437,U979,R686,U930,L454,D553,L77,D955,L98,U201,L724,U211,R501,U492,L495,U732,L511' input2str = 'L998,U949,R912,D186,R359,D694,L878,U542,L446,D118,L927,U175,R434,U473,R147,D54,R896,U890,R300,D537,R254,D322,R758,D690,R231,U269,R288,U968,R638,U192,L732,D355,R879,U451,R336,D872,L141,D842,L126,U584,L973,D940,R890,D75,L104,U340,L821,D590,R577,U859,L948,D199,L872,D751,L368,U506,L308,U827,R181,U94,R670,U901,R739,D48,L985,D801,R722,D597,R654,D606,R183,U646,R939,U677,R32,U936,L541,D934,R316,U354,L415,D930,R572,U571,R147,D609,L534,D406,R872,D527,L816,D960,R652,D429,L402,D858,R374,D930,L81,U106,R977,U251,R917,U966,R353,U732,L613,U280,L713,D937,R481,U52,R746,U203,L500,D557,L209,U249,R89,D58,L149,U872,R331,D460,R343,D423,R392,D160,L876,U981,L399,D642,R525,U515,L537,U113,R886,D516,L301,D680,L236,U399,R460,D869,L942,D280,R669,U476,R683,D97,R199,D444,R137,D489,L704,D120,R753,D100,L737,U375,L495,D325,R48,D269,R575,U895,L184,D10,L502,D610,R618,D744,R585,U861,R695,D775,L942,U64,L819,U161,L332,U513,L461,D366,R273,D493,L197,D97,L6,U63,L564,U59,L699,U30,L68,U861,R35,U564,R540,U371,L115,D595,L412,D781,L185,D41,R207,D264,R999,D799,R421,D117,R377,D571,R268,D947,R77,D2,R712,D600,L516,U389,L868,D762,L996,U205,L178,D339,L844,D629,R67,D732,R109,D858,R630,U470,L121,D542,L751,U353,L61,U770,R952,U703,R264,D537,L569,U55,L795,U389,R836,U166,R585,U275,L734,U966,L130,D357,L260,U719,L647,D606,R547,U575,R791,U686,L597,D486,L774,U386,L163,U912,L234,D238,L948,U279,R789,U300,R117,D28,L833,U835,L340,U693,R343,D573,R882,D241,L731,U812,R600,D663,R902,U402,R831,D802,L577,U920,L947,D538,L192' #221 test0input1str = 'R8,U5,L5,D3' #6 #30 test0input2str = 'U7,R6,D4,L4' test1input1str = 'R75,D30,R83,U83,L12,D49,R71,U7,L72' #159 #610 test1input2str = 'U62,R66,U55,R34,D71,R55,D58,R83' test2input1str = 'R98,U47,R26,D63,R33,U87,L62,D20,R33,U53,R51' #135 #410 test2input2str = 'U98,R91,D20,R16,D67,R40,U7,R15,U6,R7' # step 0 convert string to list input1 = input1str.split(',') input2 = input2str.split(',') #input1 = test2input1str.split(',') #input2 = test2input2str.split(',') # step 1 make a function to generate a list of coordinates of all points a set of instructions passes through def wire_locs(incodes): curr_loc = [0,0] path = list() for inst in incodes: dir = inst[0] length = inst[1:] # im sure theres a better way to do this if dir == 'R': #Right for i in range(int(length)): curr_loc[0] += 1 path.append(tuple(curr_loc)) if dir == 'L': #Left for i in range(int(length)): curr_loc[0] -= 1 path.append(tuple(curr_loc)) if dir == 'U': #Up for i in range(int(length)): curr_loc[1] += 1 path.append(tuple(curr_loc)) if dir == 'D': #Down for i in range(int(length)): curr_loc[1] -= 1 path.append(tuple(curr_loc)) return path # step2 find the intersection between the two paths and calculate the manhatten distance path1 = wire_locs(input1) path2 = wire_locs(input2) intersects = set(path1) & set(path2) distances = [ abs(i[0])+abs(i[1]) for i in intersects] distances.sort() min_manhatten = distances[0] print(min_manhatten) # End Part 1 # Part 2: we have a new distance metric, the total path length distances2 = [path2.index(i)+path1.index(i)+2 for i in intersects] #+2 because of the index 0 distances2.sort() min_parttwo = distances2[0] print(min_parttwo)

72.235294

1,495

0.725366

896

4,912

3.958705

0.766741

0.017761

0.010149

0.012405

0.061742

0.060333

0

0.430251

0.099552

4,912

67

1,496

73.313433

0.371693

0.094666

0

0.181818

0

0.068182

0.704925

0.698599

0

1

0.022727

false

0

0.045455

0

null

0

1

0

1

0

1

0

1

null

0

5

a01a6cd80a71c68a6da168b3758e9d7078688990

100

py

Python

Pruebas.py

MacoChave/Server-Iniciales

035d98793a1c20738b7af885d455fd62197988bd

[ "Apache-2.0" ]

null

Pruebas.py

MacoChave/Server-Iniciales

035d98793a1c20738b7af885d455fd62197988bd

[ "Apache-2.0" ]

null

Pruebas.py

MacoChave/Server-Iniciales

035d98793a1c20738b7af885d455fd62197988bd

[ "Apache-2.0" ]

null

from datetime import date from datetime import datetime dateToday = date.today() print(dateToday)

14.285714

29

0.8

13

100

6.153846

0.538462

0.3

0.45

0

0.14

100

7

30

14.285714

0.930233

0

1

0

false

0

0.5

0

0.5

0.25

1

0

null

1

0

1

0

null

0

1

0

5

4e9e6122ed3109b35f3efe158b363d95df381cc6

10,892

py

Python

server/tree_pickler.py

michaelpeterswa/CPSC322Project-WildfireAnalysis

872727e8c59619fcfc11aaa70367762271207dbd

[ "MIT" ]

null

server/tree_pickler.py

michaelpeterswa/CPSC322Project-WildfireAnalysis

872727e8c59619fcfc11aaa70367762271207dbd

[ "MIT" ]

null

server/tree_pickler.py

michaelpeterswa/CPSC322Project-WildfireAnalysis

872727e8c59619fcfc11aaa70367762271207dbd

[ "MIT" ]

1

2021-04-16T21:21:25.000Z

import pickle best_trees = [ {'accuracy': 0.36416184971098264, 'tree': ['Attribute', 'att1', ['Value', 'Pend Oreille', ['Leaf', 2.0, 0, 69] ], ['Value', 'Okanogan', ['Leaf', 3.0, 0, 314] ], ['Value', 'Lincoln', ['Leaf', 5.0, 0, 55] ], ['Value', 'Grant', ['Leaf', 5.0, 0, 4] ], ['Value', 'Chelan', ['Leaf', 3.0, 0, 136]], ['Value', 'Stevens', ['Attribute', 'att2', ['Value', 'Recreation', ['Leaf', 2.0, 0, 18]], ['Value', 'Miscellaneou', ['Leaf', 2.0, 0, 83]], ['Value', 'Lightning', ['Leaf', 2.0, 0, 43]], ['Value', 'Under Invest', ['Leaf', 5.0, 0, 6]], ['Value', 'Debris Burn', ['Leaf', 3.0, 0, 120]], ['Value', 'Children', ['Leaf', 3.0, 0, 8]], ['Value', 'None', ['Leaf', 5.0, 1, 308]], ['Value', 'Smoker', ['Leaf', 2.0, 0, 7]], ['Value', 'Logging', ['Leaf', 3.0, 0, 8]], ['Value', 'Arson', ['Leaf', 2.0, 0, 5]], ['Value', 'Undetermined', ['Leaf', 9.0, 2, 308]], ['Value', 'Railroad', ['Leaf', 4.0, 0, 7]]]], ['Value', 'Clark', ['Leaf', 3.0, 0, 20]], ['Value', 'Yakima', ['Leaf', 3.0, 0, 97]], ['Value', 'Spokane', ['Attribute', 'att2', ['Value', 'Recreation', ['Leaf', 2.0, 0, 23]], ['Value', 'Miscellaneou', ['Leaf', 2.0, 0, 142]], ['Value', 'Lightning', ['Leaf', 3.0, 0, 24]], ['Value', 'Under Invest', ['Leaf', 3.0, 0, 4]], ['Value', 'Debris Burn', ['Leaf', 2.0, 0, 54]], ['Value', 'Children', ['Leaf', 3.0, 0, 20]], ['Value', 'None', ['Leaf', 3.0, 3, 326]], ['Value', 'Smoker', ['Leaf', 2.0, 0, 2]], ['Value', 'Logging', ['Leaf', 2.0, 0, 3]], ['Value', 'Arson', ['Leaf', 2.0, 0, 29]], ['Value', 'Undetermined', ['Leaf', 2.0, 0, 7]], ['Value', 'Railroad', ['Leaf', 2.0, 0, 15]]]], ['Value', 'Pierce', ['Leaf', 3.0, 0, 55]], ['Value', 'Skagit', ['Leaf', 3.0, 0, 34]], ['Value', 'Grays Harbor', ['Leaf', 3.0, 0, 52]], ['Value', 'Skamania', ['Leaf', 3.0, 0, 28]], ['Value', 'King', ['Leaf', 3.0, 0, 41]], ['Value', 'Island', ['Leaf', 3.0, 0, 7]], ['Value', 'Klickitat', ['Leaf', 3.0, 0, 180]], ['Value', 'Whitman', ['Leaf', 7.0, 0, 5]], ['Value', 'Cowlitz', ['Leaf', 3.0, 0, 68]], ['Value', 'Douglas', ['Leaf', 5.0, 0, 27]], ['Value', 'Ferry', ['Leaf', 3.0, 0, 72]], ['Value', 'Mason', ['Leaf', 3.0, 0, 66]], ['Value', 'Kittitas', ['Leaf', 3.0, 0, 99]], ['Value', 'Jefferson', ['Leaf', 3.0, 0, 30]], ['Value', 'Franklin', ['Leaf', 5.0, 3, 2503]], ['Value', 'Clallam', ['Leaf', 3.0, 0, 44]], ['Value', 'Pacific', ['Leaf', 3.0, 0, 51]], ['Value', 'Lewis', ['Leaf', 3.0, 0, 93]], ['Value', 'Thurston', ['Leaf', 2.0, 0, 59]], ['Value', 'Walla Walla', ['Leaf', 3.0, 0, 18]], ['Value', 'Snohomish', ['Leaf', 3.0, 0, 38]], ['Value', 'Asotin', ['Leaf', 4.0, 0, 23]], ['Value', 'Adams', ['Leaf', 5.0, 1, 2503]], ['Value', 'Whatcom', ['Leaf', 2.0, 0, 40]], ['Value', 'San Juan', ['Leaf', 3.0, 0, 7]], ['Value', 'Garfield', ['Leaf', 3.0, 0, 10]], ['Value', 'Columbia', ['Leaf', 3.0, 0, 14]], ['Value', 'Benton', ['Leaf', 7.0, 1, 2503]], ['Value', 'Wahkiakum', ['Leaf', 3.0, 5, 2503]], ['Value', 'No Data', ['Leaf', 4.0, 1, 2503]], ['Value', 'Kitsap', ['Leaf', 3.0, 0, 2]]]}, {'accuracy': 0.34375, 'tree': ['Attribute', 'att1', ['Value', 'Klickitat', ['Leaf', 2.0, 0, 150]], ['Value', 'Ferry', ['Leaf', 3.0, 0, 66]], ['Value', 'Okanogan', ['Leaf', 3.0, 0, 341]], ['Value', 'Clallam', ['Leaf', 3.0, 0, 53]], ['Value', 'Lewis', ['Leaf', 3.0, 0, 105]], ['Value', 'Kittitas', ['Leaf', 3.0, 0, 115]], ['Value', 'Spokane', ['Attribute', 'att2', ['Value', 'Recreation', ['Leaf', 2.0, 0, 31]], ['Value', 'Arson', ['Leaf', 2.0, 0, 37]], ['Value', 'Lightning', ['Leaf', 3.0, 0, 25]], ['Value', 'Miscellaneou', ['Leaf', 3.0, 0, 122]], ['Value', 'Logging', ['Leaf', 3.0, 1, 318]], ['Value', 'Under Invest', ['Leaf', 5.0, 4, 318]], ['Value', 'Debris Burn', ['Leaf', 3.0, 0, 51]], ['Value', 'Railroad', ['Leaf', 2.0, 0, 25]], ['Value', 'Children', ['Leaf', 4.0, 0, 12]], ['Value', 'Undetermined', ['Leaf', 5.0, 0, 5]], ['Value', 'Smoker', ['Leaf', 6.0, 0, 4]], ['Value', 'None', ['Leaf', 3.0, 1, 318]]]], ['Value', 'Chelan', ['Leaf', 3.0, 0, 142]], ['Value', 'Mason', ['Leaf', 3.0, 0, 69]], ['Value', 'Lincoln', ['Leaf', 3.0, 0, 79]], ['Value', 'Yakima', ['Leaf', 3.0, 0, 82]], ['Value', 'Jefferson', ['Leaf', 3.0, 0, 32]], ['Value', 'Pend Oreille', ['Leaf', 2.0, 0, 61]], ['Value', 'Stevens', ['Attribute', 'att2', ['Value', 'Recreation', ['Leaf', 2.0, 0, 15]], ['Value', 'Arson', ['Leaf', 2.0, 0, 11]], ['Value', 'Lightning', ['Leaf', 3.0, 0, 33]], ['Value', 'Miscellaneou', ['Leaf', 3.0, 0, 84]], ['Value', 'Logging', ['Leaf', 3.0, 4, 290]], ['Value', 'Under Invest', ['Leaf', 5.0, 0, 4]], ['Value', 'Debris Burn', ['Leaf', 2.0, 0, 117]], ['Value', 'Railroad', ['Leaf', 2.0, 0, 6]], ['Value', 'Children', ['Leaf', 2.0, 0, 4]], ['Value', 'Undetermined', ['Leaf', 9.0, 1, 290]], ['Value', 'Smoker', ['Leaf', 2.0, 0, 10]], ['Value', 'None', ['Leaf', 5.0, 1, 290]]]], ['Value', 'Cowlitz', ['Leaf', 3.0, 0, 77]], ['Value', 'Pierce', ['Leaf', 3.0, 0, 58]], ['Value', 'King', ['Leaf', 2.0, 0, 23]], ['Value', 'Walla Walla', ['Leaf', 3.0, 0, 24]], ['Value', 'Douglas', ['Leaf', 6.0, 0, 17]], ['Value', 'Island', ['Leaf', 3.0, 0, 9]], ['Value', 'Skamania', ['Leaf', 3.0, 0, 27]], ['Value', 'Thurston', ['Leaf', 2.0, 0, 52]], ['Value', 'Columbia', ['Leaf', 3.0, 0, 15]], ['Value', 'Snohomish', ['Leaf', 3.0, 0, 36]], ['Value', 'Skagit', ['Leaf', 3.0, 0, 47]], ['Value', 'Pacific', ['Leaf', 3.0, 0, 36]], ['Value', 'Grays Harbor', ['Leaf', 2.0, 0, 56]], ['Value', 'Whatcom', ['Leaf', 3.0, 0, 37]], ['Value', 'Clark', ['Leaf', 3.0, 0, 30]], ['Value', 'Kitsap', ['Leaf', 3.0, 2, 2503]], ['Value', 'San Juan', ['Leaf', 3.0, 0, 9]], ['Value', 'Asotin', ['Leaf', 4.0, 0, 20]], ['Value', 'Garfield', ['Leaf', 3.0, 0, 7]], ['Value', 'Adams', ['Leaf', 5.0, 2, 2503]], ['Value', 'Wahkiakum', ['Leaf', 2.0, 0, 7]], ['Value', 'Whitman', ['Leaf', 5.0, 0, 5]], ['Value', 'Grant', ['Leaf', 5.0, 1, 2503]], ['Value', 'No Data', ['Leaf', 4.0, 0, 2]], ['Value', 'Benton', ['Leaf', 7.0, 1, 2503]]]}, {'accuracy': 0.33568904593639576, 'tree': ['Attribute', 'att1', ['Value', 'Stevens', ['Attribute', 'att2', ['Value', 'Recreation', ['Leaf', 2.0, 0, 24]], ['Value', 'Debris Burn', ['Leaf', 2.0, 0, 105]], ['Value', 'Children', ['Leaf', 3.0, 0, 4]], ['Value', 'Miscellaneou', ['Leaf', 3.0, 0, 80]], ['Value', 'Railroad', ['Leaf', 2.0, 0, 6]], ['Value', 'Undetermined', ['Leaf', 9.0, 3, 300]], ['Value', 'Logging', ['Leaf', 3.0, 0, 9]], ['Value', 'Lightning', ['Leaf', 2.0, 0, 39]], ['Value', 'Smoker', ['Leaf', 2.0, 0, 8]], ['Value', 'None', ['Leaf', 5.0, 2, 300]], ['Value', 'Arson', ['Leaf', 3.0, 0, 15]], ['Value', 'Under Invest', ['Leaf', 3.0, 0, 5]]]], ['Value', 'Grays Harbor', ['Leaf', 2.0, 0, 49]], ['Value', 'Chelan', ['Leaf', 3.0, 0, 143]], ['Value', 'Okanogan', ['Leaf', 3.0, 0, 306]], ['Value', 'Spokane', ['Attribute', 'att2', ['Value', 'Recreation', ['Leaf', 2.0, 0, 27]], ['Value', 'Debris Burn', ['Leaf', 3.0, 0, 66]], ['Value', 'Children', ['Leaf', 2.0, 0, 10]], ['Value', 'Miscellaneou', ['Leaf', 3.0, 0, 152]], ['Value', 'Railroad', ['Leaf', 2.0, 0, 21]], ['Value', 'Undetermined', ['Leaf', 5.0, 0, 8]], ['Value', 'Logging', ['Leaf', 2.0, 0, 2]], ['Value', 'Lightning', ['Leaf', 3.0, 0, 25]], ['Value', 'Smoker', ['Leaf', 3.0, 0, 3]], ['Value', 'None', ['Leaf', 2.0, 0, 5]], ['Value', 'Arson', ['Leaf', 2.0, 0, 24]], ['Value', 'Under Invest', ['Leaf', 5.0, 2, 345]]]], ['Value', 'Cowlitz', ['Leaf', 3.0, 0, 74]], ['Value', 'Lincoln', ['Leaf', 3.0, 0, 66]], ['Value', 'Kittitas', ['Leaf', 3.0, 0, 122]], ['Value', 'Pacific', ['Leaf', 3.0, 0, 61]], ['Value', 'Skagit', ['Leaf', 3.0, 0, 57]], ['Value', 'Lewis', ['Leaf', 3.0, 0, 111]], ['Value', 'Island', ['Leaf', 3.0, 0, 8]], ['Value', 'Klickitat', ['Leaf', 2.0, 0, 193]], ['Value', 'Walla Walla', ['Leaf', 4.0, 0, 19]], ['Value', 'Jefferson', ['Leaf', 3.0, 0, 23]], ['Value', 'Garfield', ['Leaf', 7.0, 0, 6]], ['Value', 'Thurston', ['Leaf', 2.0, 0, 50]], ['Value', 'King', ['Leaf', 3.0, 0, 33]], ['Value', 'Douglas', ['Leaf', 6.0, 0, 28]], ['Value', 'Yakima', ['Leaf', 3.0, 0, 90]], ['Value', 'Mason', ['Leaf', 3.0, 0, 55]], ['Value', 'Snohomish', ['Leaf', 3.0, 0, 27]], ['Value', 'Pierce', ['Leaf', 3.0, 0, 44]], ['Value', 'Kitsap', ['Leaf', 3.0, 0, 6]], ['Value', 'Clark', ['Leaf', 3.0, 0, 18]], ['Value', 'Columbia', ['Leaf', 3.0, 0, 17]], ['Value', 'Pend Oreille', ['Leaf', 3.0, 0, 45]], ['Value', 'Skamania', ['Leaf', 3.0, 0, 27]], ['Value', 'Asotin', ['Leaf', 7.0, 0, 17]], ['Value', 'Whatcom', ['Leaf', 3.0, 0, 39]], ['Value', 'Ferry', ['Leaf', 3.0, 0, 72]], ['Value', 'Wahkiakum', ['Leaf', 3.0, 1, 2503]], ['Value', 'Clallam', ['Leaf', 3.0, 0, 38]], ['Value', 'Adams', ['Leaf', 5.0, 3, 2503]], ['Value', 'San Juan', ['Leaf', 2.0, 0, 3]], ['Value', 'Grant', ['Leaf', 6.0, 1, 2503]], ['Value', 'No Data', ['Leaf', 4.0, 0, 2]], ['Value', 'Whitman', ['Leaf', 5.0, 0, 4]]]}, {'accuracy': 0.33390705679862304, 'tree': ['Attribute', 'att1', ['Value', 'Spokane', ['Leaf', 3.0, 0, 364]], ['Value', 'Stevens', ['Leaf', 2.0, 0, 298]], ['Value', 'Klickitat', ['Leaf', 3.0, 0, 165]], ['Value', 'Okanogan', ['Leaf', 3.0, 0, 340]], ['Value', 'Yakima', ['Leaf', 5.0, 0, 88]], ['Value', 'Chelan', ['Leaf', 3.0, 0, 110]], ['Value', 'Cowlitz', ['Leaf', 3.0, 0, 84]], ['Value', 'Thurston', ['Leaf', 2.0, 0, 78]], ['Value', 'Pend Oreille', ['Leaf', 2.0, 0, 46]], ['Value', 'Pierce', ['Leaf', 3.0, 0, 45]], ['Value', 'Mason', ['Leaf', 3.0, 0, 69]], ['Value', 'Grays Harbor', ['Leaf', 2.0, 0, 58]], ['Value', 'Douglas', ['Leaf', 6.0, 0, 33]], ['Value', 'Ferry', ['Leaf', 3.0, 0, 77]], ['Value', 'Skagit', ['Leaf', 3.0, 0, 39]], ['Value', 'Clark', ['Leaf', 2.0, 0, 28]], ['Value', 'Kittitas', ['Leaf', 3.0, 0, 108]], ['Value', 'Lewis', ['Leaf', 3.0, 0, 106]], ['Value', 'Skamania', ['Leaf', 3.0, 0, 25]], ['Value', 'King', ['Leaf', 3.0, 0, 23]], ['Value', 'Asotin', ['Leaf', 3.0, 0, 24]], ['Value', 'Snohomish', ['Leaf', 3.0, 0, 26]], ['Value', 'Pacific', ['Leaf', 2.0, 0, 36]], ['Value', 'Jefferson', ['Leaf', 3.0, 0, 29]], ['Value', 'Clallam', ['Leaf', 3.0, 0, 44]], ['Value', 'Lincoln', ['Leaf', 3.0, 0, 56]], ['Value', 'Walla Walla', ['Leaf', 3.0, 0, 18]], ['Value', 'Island', ['Leaf', 3.0, 6, 2503]], ['Value', 'Whatcom', ['Leaf', 3.0, 0, 26]], ['Value', 'Benton', ['Leaf', 7.0, 1, 2503]], ['Value', 'Kitsap', ['Leaf', 3.0, 0, 8]], ['Value', 'San Juan', ['Leaf', 2.0, 0, 14]], ['Value', 'Columbia', ['Leaf', 3.0, 0, 16]], ['Value', 'Franklin', ['Leaf', 5.0, 1, 2503]], ['Value', 'Grant', ['Leaf', 5.0, 4, 2503]], ['Value', 'Garfield', ['Leaf', 3.0, 0, 5]], ['Value', 'Whitman', ['Leaf', 7.0, 2, 2503]], ['Value', 'Wahkiakum', ['Leaf', 2.0, 1, 2503]], ['Value', 'No Data', ['Leaf', 3.0, 1, 2503]], ['Value', 'Adams', ['Leaf', 5.0, 1, 2503]]]}] packaged_object = best_trees # pickle packaged_object outfile = open("trees.p", "wb") pickle.dump(packaged_object, outfile) outfile.close()

495.090909

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1

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null

0

5

14e2f68640f152f69f9e7b649672501b2bacc025

128

py

Python

demeter/admin/model/__load__.py

shemic/demeter

01f91aac43c325c48001dda86af17da43fb8d6fe

[ "MIT" ]

1

2017-12-05T08:17:53.000Z

demos/helloworld/model/__load__.py

shemic/demeter

01f91aac43c325c48001dda86af17da43fb8d6fe

[ "MIT" ]

null

demos/helloworld/model/__load__.py

shemic/demeter

01f91aac43c325c48001dda86af17da43fb8d6fe

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ demeter database name:__load__.py """ from demeter.model import * from demeter.core import *

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0.195313

128

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true

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null

0

1

0

1

0

5

14f1a8447efc963a4a6ad15b82d5aee9bf59542f

4,408

py

Python

tests/test_date_utils.py

rob-blackbourn/aiofix

2a07822e07414c1ea850708d7660c16a0564c21d

[ "Apache-2.0" ]

1

2021-03-25T21:52:36.000Z

tests/test_date_utils.py

rob-blackbourn/jetblack-fixengine

2a07822e07414c1ea850708d7660c16a0564c21d

[ "Apache-2.0" ]

null

tests/test_date_utils.py

rob-blackbourn/jetblack-fixengine

2a07822e07414c1ea850708d7660c16a0564c21d

[ "Apache-2.0" ]

null

"""Tests for date utils""" from datetime import time, datetime import pytz from jetblack_fixengine.utils.date_utils import ( is_dow_in_range, is_time_in_range, delay_for_time_period ) MONDAY = 0 TUESDAY = 1 WEDNESDAY = 2 THURSDAY = 3 FRIDAY = 4 SATURDAY = 5 SUNDAY = 6 def test_dow_range(): """Test day of week range""" assert is_dow_in_range(MONDAY, FRIDAY, MONDAY) assert is_dow_in_range(MONDAY, FRIDAY, WEDNESDAY) assert is_dow_in_range(MONDAY, FRIDAY, FRIDAY) assert not is_dow_in_range(MONDAY, FRIDAY, SATURDAY) assert not is_dow_in_range(TUESDAY, THURSDAY, MONDAY) assert not is_dow_in_range(TUESDAY, THURSDAY, FRIDAY) assert is_dow_in_range(WEDNESDAY, WEDNESDAY, WEDNESDAY) assert not is_dow_in_range(WEDNESDAY, WEDNESDAY, TUESDAY) assert not is_dow_in_range(WEDNESDAY, WEDNESDAY, THURSDAY) assert is_dow_in_range(FRIDAY, TUESDAY, FRIDAY) assert is_dow_in_range(FRIDAY, TUESDAY, SUNDAY) assert is_dow_in_range(FRIDAY, TUESDAY, TUESDAY) assert not is_dow_in_range(FRIDAY, TUESDAY, THURSDAY) assert not is_dow_in_range(SATURDAY, SUNDAY, MONDAY) def test_time_range(): """Test time range""" assert is_time_in_range(time(0, 0, 0), time(17, 30, 0), time(0, 0, 0)) assert is_time_in_range(time(0, 0, 0), time(17, 30, 0), time(12, 0, 0)) assert is_time_in_range(time(0, 0, 0), time(17, 30, 0), time(17, 30, 0)) assert not is_time_in_range(time(0, 0, 0), time(17, 30, 0), time(20, 0, 0)) assert not is_time_in_range(time(9, 30, 0), time(17, 30, 0), time(0, 0, 0)) def test_seconds_for_period(): """Test seconds in a period""" # now=6am, star=8am, end=4pm time_to_wait, end_datetime = delay_for_time_period( datetime(2019, 1, 1, 6, 0, 0), time(8, 0, 0), time(16, 0, 0)) assert time_to_wait.total_seconds() / 60 / 60 == 2 assert end_datetime == datetime(2019, 1, 1, 16, 0, 0) # now=10am, start=8am, end=4pm time_to_wait, end_datetime = delay_for_time_period( datetime(2019, 1, 1, 10, 0, 0), time(8, 0, 0), time(16, 0, 0)) assert time_to_wait.total_seconds() / 60 / 60 == 0 assert end_datetime == datetime(2019, 1, 1, 16, 0, 0) # now=6pm, start=8am, end=4pm time_to_wait, end_datetime = delay_for_time_period( datetime(2019, 1, 1, 18, 0, 0), time(8, 0, 0), time(16, 0, 0)) assert time_to_wait.total_seconds() / 60 / 60 == 14 assert end_datetime == datetime(2019, 1, 2, 16, 0, 0) # now=6pm,start=8pm, end=4am time_to_wait, end_datetime = delay_for_time_period( datetime(2019, 1, 1, 18, 0, 0), time(20, 0, 0), time(4, 0, 0)) assert time_to_wait.total_seconds() / 60 / 60 == 2 assert end_datetime == datetime(2019, 1, 2, 4, 0, 0) # now=10pm,start=8pm, end=4am time_to_wait, end_datetime = delay_for_time_period( datetime(2019, 1, 1, 22, 0, 0), time(20, 0, 0), time(4, 0, 0)) assert time_to_wait.total_seconds() / 60 / 60 == 0 assert end_datetime == datetime(2019, 1, 2, 4, 0, 0) # now=6am,start=8pm, end=4am time_to_wait, end_datetime = delay_for_time_period( datetime(2019, 1, 1, 6, 0, 0), time(20, 0, 0), time(4, 0, 0)) assert time_to_wait.total_seconds() / 60 / 60 == 14 assert end_datetime == datetime(2019, 1, 2, 4, 0, 0) london = pytz.timezone('Europe/London') # now=6pm,start=8pm, end=4am, London clocks forward. time_to_wait, end_datetime = delay_for_time_period( datetime(2019, 3, 31, 18, 0, 0, tzinfo=london), time(20, 0, 0), time(4, 0, 0)) assert time_to_wait.total_seconds() / 60 / 60 == 2 assert end_datetime == datetime(2019, 4, 1, 4, 0, 0, tzinfo=london) # now=10pm,start=8pm, end=4am time_to_wait, end_datetime = delay_for_time_period( datetime(2019, 3, 31, 22, 0, 0, tzinfo=london), time(20, 0, 0), time(4, 0, 0)) assert time_to_wait.total_seconds() / 60 / 60 == 0 assert end_datetime == datetime(2019, 4, 1, 4, 0, 0, tzinfo=london) # now=6am,start=8pm, end=4am time_to_wait, end_datetime = delay_for_time_period( datetime(2019, 3, 31, 6, 0, 0, tzinfo=london), time(20, 0, 0), time(4, 0, 0)) assert time_to_wait.total_seconds() / 60 / 60 == 14 assert end_datetime == datetime(2019, 4, 1, 4, 0, 0, tzinfo=london)

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null

0

1

0

5

095cddc3c05dd03a088f06ee8064ef92e069c45e

181

py

Python

face_recognition_final_year/face_recognizer_app/admin.py

chiragsaraswat/automated_authentication_system_using_face_recognition

9f63f582d79db44c9e10fec6f72d4f835af8ab3a

[ "MIT" ]

null

face_recognition_final_year/face_recognizer_app/admin.py

chiragsaraswat/automated_authentication_system_using_face_recognition

9f63f582d79db44c9e10fec6f72d4f835af8ab3a

[ "MIT" ]

null

face_recognition_final_year/face_recognizer_app/admin.py

chiragsaraswat/automated_authentication_system_using_face_recognition

9f63f582d79db44c9e10fec6f72d4f835af8ab3a

[ "MIT" ]

null

from django.contrib import admin # Register your models here. from user_manager_app.models import Attendance, Support admin.site.register(Attendance) admin.site.register(Support)

22.625

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0.828729

25

181

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0.22973

0

0.099448

181

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1

0

null

0

1

0

1

0

5

11791d8dd54ab856a076d61973de8d77641f2d2d

115

py

Python

app/route.py

Indexyz/pods-info-example

e36657d2e4448a9d42450fd5e41671bba11ee9b4

[ "Unlicense" ]

null

app/route.py

Indexyz/pods-info-example

e36657d2e4448a9d42450fd5e41671bba11ee9b4

[ "Unlicense" ]

null

app/route.py

Indexyz/pods-info-example

e36657d2e4448a9d42450fd5e41671bba11ee9b4

[ "Unlicense" ]

null

from flask_restful import Api import info def add_route(api: Api): api.add_resource(info.InfoRoute, '/')

19.166667

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115

5

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null

0

1

0

1

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1

0

5

119ba308325a28e7115e3336760ea5459e34bcae

31,721

py

Python

SRTG-Scheduler/SRTG-ResultAnalysis/scripts/aperiodic-jobs-resultAnalyzer.py

kiritigowda/RTG-scheduler

4aa3d66e011e6a0d16e19719f940c5cc0a6559ba

[ "MIT" ]

2

2021-10-15T12:00:51.000Z

2021-11-23T04:50:58.000Z

SRTG-Scheduler/SRTG-ResultAnalysis/scripts/aperiodic-jobs-resultAnalyzer.py

kiritigowda/RTG-scheduler

4aa3d66e011e6a0d16e19719f940c5cc0a6559ba

[ "MIT" ]

45

2018-01-24T15:38:11.000Z

2020-10-31T19:50:19.000Z

SRTG-Scheduler/SRTG-ResultAnalysis/scripts/aperiodic-jobs-resultAnalyzer.py

kiritigowda/RTG-scheduler

4aa3d66e011e6a0d16e19719f940c5cc0a6559ba

[ "MIT" ]

2

2018-05-23T17:13:44.000Z

2020-09-18T15:06:17.000Z

# Copyright (c) 2017 - 2020 Kiriti Nagesh Gowda, Inc. All rights reserved. # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN # THE SOFTWARE. import collections import random import os import sys import argparse import csv from datetime import date __author__ = "Kiriti Nagesh Gowda" __copyright__ = "Copyright 2018 - 2020, Kiriti Nagesh Gowda - SRTG-Scheduler" __license__ = "MIT" __version__ = "1.0.1" __maintainer__ = "Kiriti Nagesh Gowda" __email__ = "Kiritigowda@gmail.com" __status__ = "Shipping" # import arguments parser = argparse.ArgumentParser() parser.add_argument('--input_directory', type=str, default='', help='Directory - RTGS_summary directory') parser.add_argument('--output_directory', type=str, default='', help='Directory - directory to save results') parser.add_argument('--results_filename', type=str, default='', help='Results File prefix - results .html file prefix') args = parser.parse_args() inputDirectory = args.input_directory outputDirectory = args.output_directory fileName = args.results_filename if inputDirectory == '' or outputDirectory == '' or fileName == '': print("ERROR - NO Arguments Passed, use --h option") exit() if not os.path.exists(inputDirectory): print("ERROR Invalid Input Directory") exit() if not os.path.exists(outputDirectory): os.makedirs(outputDirectory) row_count = 0 row_count_1 = 0 row_count_2 = 0 row_count_3 = 0 row_count_4 = 0 row_count_5 = 0 with open(inputDirectory+'/RTGS-Mode-1-Summary.csv') as mode1: reader_1 = csv.reader(mode1) next(reader_1) data_1 = [r for r in reader_1] row_count_1 = len(data_1) with open(inputDirectory+'/RTGS-Mode-2-Summary.csv') as mode2: reader_2 = csv.reader(mode2) next(reader_2) data_2 = [r for r in reader_2] row_count_2 = len(data_2) with open(inputDirectory+'/RTGS-Mode-3-Summary.csv') as mode3: reader_3 = csv.reader(mode3) next(reader_3) data_3 = [r for r in reader_3] row_count_3 = len(data_3) with open(inputDirectory+'/RTGS-Mode-4-Summary.csv') as mode4: reader_4 = csv.reader(mode4) next(reader_4) data_4 = [r for r in reader_4] row_count_4 = len(data_4) with open(inputDirectory+'/RTGS-Mode-5-Summary.csv') as mode5: reader_5 = csv.reader(mode5) next(reader_5) data_5 = [r for r in reader_5] row_count_5 = len(data_5) if row_count_1 != row_count_2 or row_count_2 != row_count_3 or row_count_3 != row_count_4 or row_count_4 != row_count_5: print("ERROR: Number of entries in Summary File are different") exit() else: row_count = row_count_1 # help print print("\nSRTG-ResultAnalysis - Aperiodic Job Result Accumulator and Analyzer V-"+__version__+"\n") # date today = date.today() dateCreated = today.strftime("%b-%d-%Y") # output accum file orig_stdout = sys.stdout result_accum_1 = outputDirectory+'/mode-1-accum-results.csv' result_accum_2 = outputDirectory+'/mode-2-accum-results.csv' result_accum_3 = outputDirectory+'/mode-3-accum-results.csv' result_accum_4 = outputDirectory+'/mode-4-accum-results.csv' result_accum_5 = outputDirectory+'/mode-5-accum-results.csv' if not os.path.isfile(result_accum_1): sys.stdout = open(result_accum_1, 'w+') print'AVG Lambda, AVG Jobs Released, AVG Jobs Accepted, AVG Jobs Accepted Percentage, \ Avg GCUs Requested - Accepted Jobs, Avg Exec Time - Accepted Jobs, Avg Response by Execution Time, \ Avg Response by Relative deadline, AVG Total GPU Usage Time - Accepted Jobs, AVG Total GPU Usage Time Requested - All Jobs, \ Avg Scheduler OverHead - Accepted Jobs, Avg Scheduler OverHead - All Jobs, Num Job Sets, '+dateCreated if not os.path.isfile(result_accum_2): sys.stdout = open(result_accum_2, 'w+') print'AVG Lambda, AVG Jobs Released, AVG Jobs Accepted, AVG Jobs Accepted Percentage, \ Avg GCUs Requested - Accepted Jobs, Avg Exec Time - Accepted Jobs, Avg Response by Execution Time, \ Avg Response by Relative deadline, AVG Total GPU Usage Time - Accepted Jobs, AVG Total GPU Usage Time Requested - All Jobs, \ Avg Scheduler OverHead - Accepted Jobs, Avg Scheduler OverHead - All Jobs, Num Job Sets, '+dateCreated if not os.path.isfile(result_accum_3): sys.stdout = open(result_accum_3, 'w+') print'AVG Lambda, AVG Jobs Released, AVG Jobs Accepted, AVG Jobs Accepted Percentage, \ Avg GCUs Requested - Accepted Jobs, Avg Exec Time - Accepted Jobs, Avg Response by Execution Time, \ Avg Response by Relative deadline, AVG Total GPU Usage Time - Accepted Jobs, AVG Total GPU Usage Time Requested - All Jobs, \ Avg Scheduler OverHead - Accepted Jobs, Avg Scheduler OverHead - All Jobs, Num Job Sets, '+dateCreated if not os.path.isfile(result_accum_4): sys.stdout = open(result_accum_4, 'w+') print'AVG Lambda, AVG Jobs Released, AVG Jobs Accepted, AVG Jobs Accepted Percentage, \ Avg GCUs Requested - Accepted Jobs, Avg Exec Time - Accepted Jobs, Avg Response by Execution Time, \ Avg Response by Relative deadline, AVG Total GPU Usage Time - Accepted Jobs, AVG Total GPU Usage Time Requested - All Jobs, \ Avg Scheduler OverHead - Accepted Jobs, Avg Scheduler OverHead - All Jobs, Num Job Sets, '+dateCreated if not os.path.isfile(result_accum_5): sys.stdout = open(result_accum_5, 'w+') print'AVG Lambda, AVG Jobs Released, AVG Jobs Accepted, AVG Jobs Accepted Percentage, \ Avg GCUs Requested - Accepted Jobs, Avg Exec Time - Accepted Jobs, Avg Response by Execution Time, \ Avg Response by Relative deadline, AVG Total GPU Usage Time - Accepted Jobs, AVG Total GPU Usage Time Requested - All Jobs, \ Avg Scheduler OverHead - Accepted Jobs, Avg Scheduler OverHead - All Jobs, Num Job Sets, '+dateCreated # HTML File html_output_file = outputDirectory+'/'+fileName+'-SchedulerResults.html' sys.stdout = open(html_output_file, 'w+') # HTML Header print"<html>" print"\t<head>" print"\t\t<script type=\"text/javascript\" src=\"https://www.gstatic.com/charts/loader.js\"></script>" print"\n" # Google Charts Script print"\t\t<script type=\"text/javascript\">" print"\n" # Jobs Accepted for GPU Schedule print"\t\t\tgoogle.charts.load('current', {packages: ['corechart', 'line']});" print"\t\t\tgoogle.charts.setOnLoadCallback(jobScheduledGraph);" print"\t\t\tfunction jobScheduledGraph() {" print"\t\t\tvar data = new google.visualization.DataTable();" print"\t\t\tdata.addColumn('number', 'X');" print"\t\t\tdata.addColumn('number', 'Mode 1');" print"\t\t\tdata.addColumn('number', 'Mode 2');" print"\t\t\tdata.addColumn('number', 'Mode 3');" print"\t\t\tdata.addColumn('number', 'Mode 4');" print"\t\t\tdata.addColumn('number', 'Mode 5');" print"\t\t\tdata.addRows([" for x in range(row_count): if(x < row_count-1): print '\t\t\t\t['+str(x)+','+str(data_1[x][2])+','+str(data_2[x][2])+','+str(data_3[x][2])+','+str(data_4[x][2])+','+str(data_5[x][2])+'],' else: print '\t\t\t\t['+str(x)+','+str(data_1[x][2])+','+str(data_2[x][2])+','+str(data_3[x][2])+','+str(data_4[x][2])+','+str(data_5[x][2])+']' print"\t\t\t]);" print"\t\t\tvar options = { title:'Average Jobs Accepted for GPU Schedule', \ titleTextStyle: { fontSize: 28, bold: true}, \ hAxis:{ title: 'JobSet ID', titleTextStyle: { fontSize: 24, bold: true}, marginTop: '5'}, \ vAxis:{ title: 'Number of Jobs Scheduled', titleTextStyle:{ fontSize: 24, bold: true} }, \ series:{ 0:{lineDashStyle: [1, 1]}, 1:{lineDashStyle: [2, 2]}, 2:{lineDashStyle: [4, 4]}, 3:{lineDashStyle: [5, 1, 3] }, 4:{ lineDashStyle: [5, 5]}}, \ legend:{ position: 'top', alignment: 'center', textStyle:{ fontSize: 26}}, \ width:1600, height:1000 };" print"\t\t\tvar chart = new google.visualization.LineChart(document.getElementById('jobScheduled_chart'));" print"\t\t\tchart.draw(data, options);}" print"\n\n\n" # Job Accepted Percentage for GPU Schedule print"\t\t\tgoogle.charts.load('current', {packages: ['corechart', 'line']});" print"\t\t\tgoogle.charts.setOnLoadCallback(jobScheduledPercentageGraph);" print"\t\t\tfunction jobScheduledPercentageGraph() {" print"\t\t\tvar data = new google.visualization.DataTable();" print"\t\t\tdata.addColumn('number', 'X');" print"\t\t\tdata.addColumn('number', 'Mode 1');" print"\t\t\tdata.addColumn('number', 'Mode 2');" print"\t\t\tdata.addColumn('number', 'Mode 3');" print"\t\t\tdata.addColumn('number', 'Mode 4');" print"\t\t\tdata.addColumn('number', 'Mode 5');" print"\t\t\tdata.addRows([" for x in range(row_count): if(x < row_count-1): print '\t\t\t\t['+str(x)+','+str(data_1[x][3])+','+str(data_2[x][3])+','+str(data_3[x][3])+','+str(data_4[x][3])+','+str(data_5[x][3])+'],' else: print '\t\t\t\t['+str(x)+','+str(data_1[x][3])+','+str(data_2[x][3])+','+str(data_3[x][3])+','+str(data_4[x][3])+','+str(data_5[x][3])+']' print"\t\t\t]);" print"\t\t\tvar options = { title:'Average Jobs Accepted Percentage for GPU Schedule', \ titleTextStyle: { fontSize: 28, bold: true}, \ hAxis:{ title: 'JobSet ID', titleTextStyle: { fontSize: 24, bold: true}, marginTop: '5'}, \ vAxis:{ title: 'Avg Jobs Scheduled %', titleTextStyle:{ fontSize: 24, bold: true}, minValue: 0, maxValue: 100 }, \ series:{ 0:{lineDashStyle: [1, 1]}, 1:{lineDashStyle: [2, 2]}, 2:{lineDashStyle: [4, 4]}, 3:{lineDashStyle: [5, 1, 3] }, 4:{ lineDashStyle: [5, 5]}}, \ legend:{ position: 'top', alignment: 'center', textStyle:{ fontSize: 26}}, \ width:1600, height:1000 };" print"\t\t\tvar chart = new google.visualization.LineChart(document.getElementById('JobScheduledPercentage_chart'));" print"\t\t\tchart.draw(data, options);}" print"\n\n\n" # Average Response by Execution Time print"\t\t\tgoogle.charts.load('current', {packages: ['corechart', 'line']});" print"\t\t\tgoogle.charts.setOnLoadCallback(avgResponseTimeGraph);" print"\t\t\tfunction avgResponseTimeGraph() {" print"\t\t\tvar data = new google.visualization.DataTable();" print"\t\t\tdata.addColumn('number', 'X');" print"\t\t\tdata.addColumn('number', 'Mode 1');" print"\t\t\tdata.addColumn('number', 'Mode 2');" print"\t\t\tdata.addColumn('number', 'Mode 3');" print"\t\t\tdata.addColumn('number', 'Mode 4');" print"\t\t\tdata.addColumn('number', 'Mode 5');" print"\t\t\tdata.addRows([" for x in range(row_count): if(x < row_count-1): print '\t\t\t\t['+str(x)+','+str(data_1[x][6])+','+str(data_2[x][6])+','+str(data_3[x][6])+','+str(data_4[x][6])+','+str(data_5[x][6])+'],' else: print '\t\t\t\t['+str(x)+','+str(data_1[x][6])+','+str(data_2[x][6])+','+str(data_3[x][6])+','+str(data_4[x][6])+','+str(data_5[x][6])+']' print"\t\t\t]);" print"\t\t\tvar options = { title:'Average Response by Execution Time', hAxis: { title: 'JobSet ID'}, vAxis: {title: 'Response by Execution Time'}, series: { 0.01: {curveType: 'function'} }, width:1600, height:1000 };" print"\t\t\tvar chart = new google.visualization.LineChart(document.getElementById('responseByExecTime_chart'));" print"\t\t\tchart.draw(data, options);}" print"\n\n\n" # Average Response by Relative Deadline print"\t\t\tgoogle.charts.load('current', {packages: ['corechart', 'line']});" print"\t\t\tgoogle.charts.setOnLoadCallback(avgResponseFactorGraph);" print"\t\t\tfunction avgResponseFactorGraph() {" print"\t\t\tvar data = new google.visualization.DataTable();" print"\t\t\tdata.addColumn('number', 'X');" print"\t\t\tdata.addColumn('number', 'Mode 1');" print"\t\t\tdata.addColumn('number', 'Mode 2');" print"\t\t\tdata.addColumn('number', 'Mode 3');" print"\t\t\tdata.addColumn('number', 'Mode 4');" print"\t\t\tdata.addColumn('number', 'Mode 5');" print"\t\t\tdata.addRows([" for x in range(row_count): if(x < row_count-1): print '\t\t\t\t['+str(x)+','+str(data_1[x][7])+','+str(data_2[x][7])+','+str(data_3[x][7])+','+str(data_4[x][7])+','+str(data_5[x][7])+'],' else: print '\t\t\t\t['+str(x)+','+str(data_1[x][7])+','+str(data_2[x][7])+','+str(data_3[x][7])+','+str(data_4[x][7])+','+str(data_5[x][7])+']' print"\t\t\t]);" print"\t\t\tvar options = { title:'Average Response by Relative Deadline', hAxis: { title: 'JobSet ID'}, vAxis: {title: 'Response by Relative Deadline'}, series: { 0.01: {curveType: 'function'} }, width:1600, height:1000 };" print"\t\t\tvar chart = new google.visualization.LineChart(document.getElementById('responseByRelativeDeadline_chart'));" print"\t\t\tchart.draw(data, options);}" print"\n\n\n" # GPU Usage Time for Jobs Accepted print"\t\t\tgoogle.charts.load('current', {packages: ['corechart', 'line']});" print"\t\t\tgoogle.charts.setOnLoadCallback(GPUUsagePercentageGraph);" print"\t\t\tfunction GPUUsagePercentageGraph() {" print"\t\t\tvar data = new google.visualization.DataTable();" print"\t\t\tdata.addColumn('number', 'X');" print"\t\t\tdata.addColumn('number', 'Mode 1');" print"\t\t\tdata.addColumn('number', 'Mode 2');" print"\t\t\tdata.addColumn('number', 'Mode 3');" print"\t\t\tdata.addColumn('number', 'Mode 4');" print"\t\t\tdata.addColumn('number', 'Mode 5');" print"\t\t\tdata.addRows([" for x in range(row_count): if(x < row_count-1): print '\t\t\t\t['+str(x)+','+str(data_1[x][8])+','+str(data_2[x][8])+','+str(data_3[x][8])+','+str(data_4[x][8])+','+str(data_5[x][8])+'],' else: print '\t\t\t\t['+str(x)+','+str(data_1[x][8])+','+str(data_2[x][8])+','+str(data_3[x][8])+','+str(data_4[x][8])+','+str(data_5[x][8])+']' print"\t\t\t]);" print"\t\t\tvar options = { title:'GPU Usage Jobs Accepted', hAxis: { title: 'JobSet ID'}, vAxis: {title: 'GPU Usage Jobs Accepted'}, series: { 0.01: {curveType: 'function'} }, width:1600, height:1000 };" print"\t\t\tvar chart = new google.visualization.LineChart(document.getElementById('GPUUsage_accepted_chart'));" print"\t\t\tchart.draw(data, options);}" print"\n\n\n" # GPU Usage Requested by all jobs print"\t\t\tgoogle.charts.load('current', {packages: ['corechart', 'line']});" print"\t\t\tgoogle.charts.setOnLoadCallback(GPUUsageGraph);" print"\t\t\tfunction GPUUsageGraph() {" print"\t\t\tvar data = new google.visualization.DataTable();" print"\t\t\tdata.addColumn('number', 'X');" print"\t\t\tdata.addColumn('number', 'Mode 1');" print"\t\t\tdata.addColumn('number', 'Mode 2');" print"\t\t\tdata.addColumn('number', 'Mode 3');" print"\t\t\tdata.addColumn('number', 'Mode 4');" print"\t\t\tdata.addColumn('number', 'Mode 5');" print"\t\t\tdata.addRows([" for x in range(row_count): if(x < row_count-1): print '\t\t\t\t['+str(x)+','+str(data_1[x][9])+','+str(data_2[x][9])+','+str(data_3[x][9])+','+str(data_4[x][9])+','+str(data_5[x][9])+'],' else: print '\t\t\t\t['+str(x)+','+str(data_1[x][9])+','+str(data_2[x][9])+','+str(data_3[x][9])+','+str(data_4[x][9])+','+str(data_5[x][9])+']' print"\t\t\t]);" print"\t\t\tvar options = { title:'Total GPU Usage Requested', hAxis: { title: 'JobSet ID'}, vAxis: {title: 'Total GPU Usage Requested'}, series: { 0.01: {curveType: 'function'} }, width:1600, height:1000 };" print"\t\t\tvar chart = new google.visualization.LineChart(document.getElementById('GPUUsage_requested_chart'));" print"\t\t\tchart.draw(data, options);}" print"\n\n\n" print"\t\t</script>" print"\t</head>" # Result Body print"\t<body>" # Summary of results print'\t\t<br><br><h1><center>SRTG-ResultAnalysis: A-Periodic Job Schedule Summary</center></h2><br>' print'\t\t<br><br><h3><center>Created on: '+dateCreated+'</center></h3><br>' print"\t\t<table align=\"center\" style=\"width: 95%\">" print"\t\t\t<tr>" print"\t\t\t\t<td><center></center></td>" print"\t\t\t\t<td><center>AVG Jobs Released</center></td>" print"\t\t\t\t<td><center>AVG Jobs Accepted</center></td>" print"\t\t\t\t<td><center>AVG Jobs Accepted Percentage</center></td>" print"\t\t\t\t<td><center>Avg GCUs Requested - Accepted Jobs</center></td>" print"\t\t\t\t<td><center>Avg Exec Time - Accepted Jobs</center></td>" print"\t\t\t\t<td><center>Avg Response by Execution Time</center></td>" print"\t\t\t\t<td><center>Avg Response by Relative deadline</center></td>" print"\t\t\t\t<td><center>AVG Total GPU Usage Time - Accepted Jobs</center></td>" print"\t\t\t\t<td><center>AVG Total GPU Usage Time Requested - All Jobs</center></td>" print"\t\t\t\t<td><center>Avg Scheduler OverHead - Accepted Jobs</center></td>" print"\t\t\t\t<td><center>Avg Scheduler OverHead - All Jobs</center></td>" print"\t\t\t</tr>" # Mode 1 avgJobsAccepted = 0 avgJobs = 0 avgProc = 0 avgExec = 0 totalGPUUsage = 0 avgResponseTime = 0 avgResponseFactor = 0 GPUUsagePercentage = 0 avgJobPercentage = 0 GPUScheduleOverhead = 0 AvgSchedulerOverhead = 0 avgReleaseLambda = 0 for x in range(row_count): avgJobs = avgJobs + int(data_1[x][0]) avgReleaseLambda = avgReleaseLambda + float(data_1[x][1]) avgJobsAccepted = avgJobsAccepted + float(data_1[x][2]) avgJobPercentage = avgJobPercentage + float(data_1[x][3]) avgProc = avgProc + float(data_1[x][4]) avgExec = avgExec + float(data_1[x][5]) avgResponseTime = avgResponseTime + float(data_1[x][6]) avgResponseFactor = avgResponseFactor + float(data_1[x][7]) GPUUsagePercentage = GPUUsagePercentage + float(data_1[x][8]) totalGPUUsage = totalGPUUsage + float(data_1[x][9]) GPUScheduleOverhead = GPUScheduleOverhead + float(data_1[x][10]) AvgSchedulerOverhead = AvgSchedulerOverhead + float(data_1[x][11]) avgJobsAccepted = float(avgJobsAccepted)/row_count avgJobs = float(avgJobs)/row_count avgProc = float(avgProc)/row_count avgExec = float(avgExec)/row_count totalGPUUsage = float(totalGPUUsage)/row_count avgResponseTime = float(avgResponseTime)/row_count avgResponseFactor = float(avgResponseFactor)/row_count GPUUsagePercentage = float(GPUUsagePercentage)/row_count avgJobPercentage = float(avgJobPercentage)/row_count GPUScheduleOverhead = float(GPUScheduleOverhead)/row_count AvgSchedulerOverhead = float(AvgSchedulerOverhead)/row_count avgReleaseLambda = float(avgReleaseLambda)/row_count # accum results sys.stdout = open(result_accum_1, 'a') print(str(avgReleaseLambda)+', '+str(avgJobs)+', '+str(avgJobsAccepted)+', '+str(avgJobPercentage)+', ' + str(avgProc)+', '+str(avgExec)+', ' + str(avgResponseTime)+', '+str(avgResponseFactor)+', ' + str(GPUUsagePercentage)+', '+str(totalGPUUsage)+', ' + str(GPUScheduleOverhead)+','+str(AvgSchedulerOverhead)+','+str(row_count)) sys.stdout = open(html_output_file, 'a') print"\t\t\t<tr>" print"\t\t\t\t<td><center>Mode 1</center></td>" print'\t\t\t\t<td><center>'+str(avgJobs)+'</center></td>' print'\t\t\t\t<td><center>'+str(avgJobsAccepted)+'</center></td>' print'\t\t\t\t<td><center>'+str(avgJobPercentage)+'</center></td>' print'\t\t\t\t<td><center>'+str(avgProc)+'</center></td>' print'\t\t\t\t<td><center>'+str(avgExec)+'</center></td>' print'\t\t\t\t<td><center>'+str(avgResponseTime)+'</center></td>' print'\t\t\t\t<td><center>'+str(avgResponseFactor)+'</center></td>' print'\t\t\t\t<td><center>'+str(GPUUsagePercentage)+'</center></td>' print'\t\t\t\t<td><center>'+str(totalGPUUsage)+'</center></td>' print'\t\t\t\t<td><center>'+str(GPUScheduleOverhead)+'</center></td>' print'\t\t\t\t<td><center>'+str(AvgSchedulerOverhead)+'</center></td>' print"\t\t\t</tr>" # Mode 2 avgJobsAccepted = 0 avgJobs = 0 avgProc = 0 avgExec = 0 totalGPUUsage = 0 avgResponseTime = 0 avgResponseFactor = 0 GPUUsagePercentage = 0 avgJobPercentage = 0 GPUScheduleOverhead = 0 AvgSchedulerOverhead = 0 for x in range(row_count): avgJobsAccepted = avgJobsAccepted + float(data_2[x][2]) avgJobPercentage = avgJobPercentage + float(data_2[x][3]) avgJobs = avgJobs + int(data_2[x][0]) avgProc = avgProc + float(data_2[x][4]) avgExec = avgExec + float(data_2[x][5]) avgResponseTime = avgResponseTime + float(data_2[x][6]) avgResponseFactor = avgResponseFactor + float(data_2[x][7]) GPUUsagePercentage = GPUUsagePercentage + float(data_2[x][8]) totalGPUUsage = totalGPUUsage + float(data_2[x][9]) GPUScheduleOverhead = GPUScheduleOverhead + float(data_2[x][10]) AvgSchedulerOverhead = AvgSchedulerOverhead + float(data_2[x][11]) avgJobsAccepted = float(avgJobsAccepted)/row_count avgJobs = float(avgJobs)/row_count avgProc = float(avgProc)/row_count avgExec = float(avgExec)/row_count totalGPUUsage = float(totalGPUUsage)/row_count avgResponseTime = float(avgResponseTime)/row_count avgResponseFactor = float(avgResponseFactor)/row_count GPUUsagePercentage = float(GPUUsagePercentage)/row_count avgJobPercentage = float(avgJobPercentage)/row_count GPUScheduleOverhead = float(GPUScheduleOverhead)/row_count AvgSchedulerOverhead = float(AvgSchedulerOverhead)/row_count # accum results sys.stdout = open(result_accum_2, 'a') print(str(avgReleaseLambda)+', '+str(avgJobs)+', '+str(avgJobsAccepted)+', '+str(avgJobPercentage)+', ' + str(avgProc)+', '+str(avgExec)+', ' + str(avgResponseTime)+', '+str(avgResponseFactor)+', ' + str(GPUUsagePercentage)+', '+str(totalGPUUsage)+', ' + str(GPUScheduleOverhead)+','+str(AvgSchedulerOverhead)+','+str(row_count)) sys.stdout = open(html_output_file, 'a') print"\t\t\t<tr>" print"\t\t\t\t<td><center>Mode 2</center></td>" print'\t\t\t\t<td><center>'+str(avgJobs)+'</center></td>' print'\t\t\t\t<td><center>'+str(avgJobsAccepted)+'</center></td>' print'\t\t\t\t<td><center>'+str(avgJobPercentage)+'</center></td>' print'\t\t\t\t<td><center>'+str(avgProc)+'</center></td>' print'\t\t\t\t<td><center>'+str(avgExec)+'</center></td>' print'\t\t\t\t<td><center>'+str(avgResponseTime)+'</center></td>' print'\t\t\t\t<td><center>'+str(avgResponseFactor)+'</center></td>' print'\t\t\t\t<td><center>'+str(GPUUsagePercentage)+'</center></td>' print'\t\t\t\t<td><center>'+str(totalGPUUsage)+'</center></td>' print'\t\t\t\t<td><center>'+str(GPUScheduleOverhead)+'</center></td>' print'\t\t\t\t<td><center>'+str(AvgSchedulerOverhead)+'</center></td>' print"\t\t\t</tr>" # Mode 3 avgJobsAccepted = 0 avgJobs = 0 avgProc = 0 avgExec = 0 totalGPUUsage = 0 avgResponseTime = 0 avgResponseFactor = 0 GPUUsagePercentage = 0 avgJobPercentage = 0 GPUScheduleOverhead = 0 AvgSchedulerOverhead = 0 for x in range(row_count): avgJobsAccepted = avgJobsAccepted + float(data_3[x][2]) avgJobs = avgJobs + int(data_3[x][0]) avgProc = avgProc + float(data_3[x][4]) avgExec = avgExec + float(data_3[x][5]) totalGPUUsage = totalGPUUsage + float(data_3[x][9]) avgResponseTime = avgResponseTime + float(data_3[x][6]) avgResponseFactor = avgResponseFactor + float(data_3[x][7]) GPUUsagePercentage = GPUUsagePercentage + float(data_3[x][8]) avgJobPercentage = avgJobPercentage + float(data_3[x][3]) GPUScheduleOverhead = GPUScheduleOverhead + float(data_3[x][10]) AvgSchedulerOverhead = AvgSchedulerOverhead + float(data_3[x][11]) avgJobsAccepted = float(avgJobsAccepted)/row_count avgJobs = float(avgJobs)/row_count avgProc = float(avgProc)/row_count avgExec = float(avgExec)/row_count totalGPUUsage = float(totalGPUUsage)/row_count avgResponseTime = float(avgResponseTime)/row_count avgResponseFactor = float(avgResponseFactor)/row_count GPUUsagePercentage = float(GPUUsagePercentage)/row_count avgJobPercentage = float(avgJobPercentage)/row_count GPUScheduleOverhead = float(GPUScheduleOverhead)/row_count AvgSchedulerOverhead = float(AvgSchedulerOverhead)/row_count # accum results sys.stdout = open(result_accum_3, 'a') print(str(avgReleaseLambda)+', '+str(avgJobs)+', '+str(avgJobsAccepted)+', '+str(avgJobPercentage)+', ' + str(avgProc)+', '+str(avgExec)+', ' + str(avgResponseTime)+', '+str(avgResponseFactor)+', ' + str(GPUUsagePercentage)+', '+str(totalGPUUsage)+', ' + str(GPUScheduleOverhead)+','+str(AvgSchedulerOverhead)+','+str(row_count)) sys.stdout = open(html_output_file, 'a') print"\t\t\t<tr>" print"\t\t\t\t<td><center>Mode 3</center></td>" print'\t\t\t\t<td><center>'+str(avgJobs)+'</center></td>' print'\t\t\t\t<td><center>'+str(avgJobsAccepted)+'</center></td>' print'\t\t\t\t<td><center>'+str(avgJobPercentage)+'</center></td>' print'\t\t\t\t<td><center>'+str(avgProc)+'</center></td>' print'\t\t\t\t<td><center>'+str(avgExec)+'</center></td>' print'\t\t\t\t<td><center>'+str(avgResponseTime)+'</center></td>' print'\t\t\t\t<td><center>'+str(avgResponseFactor)+'</center></td>' print'\t\t\t\t<td><center>'+str(GPUUsagePercentage)+'</center></td>' print'\t\t\t\t<td><center>'+str(totalGPUUsage)+'</center></td>' print'\t\t\t\t<td><center>'+str(GPUScheduleOverhead)+'</center></td>' print'\t\t\t\t<td><center>'+str(AvgSchedulerOverhead)+'</center></td>' print"\t\t\t</tr>" # Mode 4 avgJobsAccepted = 0 avgJobs = 0 avgProc = 0 avgExec = 0 totalGPUUsage = 0 avgResponseTime = 0 avgResponseFactor = 0 GPUUsagePercentage = 0 avgJobPercentage = 0 GPUScheduleOverhead = 0 AvgSchedulerOverhead = 0 for x in range(row_count): avgJobsAccepted = avgJobsAccepted + float(data_4[x][2]) avgJobs = avgJobs + int(data_4[x][0]) avgProc = avgProc + float(data_4[x][4]) avgExec = avgExec + float(data_4[x][5]) totalGPUUsage = totalGPUUsage + float(data_4[x][9]) avgResponseTime = avgResponseTime + float(data_4[x][6]) avgResponseFactor = avgResponseFactor + float(data_4[x][7]) GPUUsagePercentage = GPUUsagePercentage + float(data_4[x][8]) avgJobPercentage = avgJobPercentage + float(data_4[x][3]) GPUScheduleOverhead = GPUScheduleOverhead + float(data_4[x][10]) AvgSchedulerOverhead = AvgSchedulerOverhead + float(data_4[x][11]) avgJobsAccepted = float(avgJobsAccepted)/row_count avgJobs = float(avgJobs)/row_count avgProc = float(avgProc)/row_count avgExec = float(avgExec)/row_count totalGPUUsage = float(totalGPUUsage)/row_count avgResponseTime = float(avgResponseTime)/row_count avgResponseFactor = float(avgResponseFactor)/row_count GPUUsagePercentage = float(GPUUsagePercentage)/row_count avgJobPercentage = float(avgJobPercentage)/row_count GPUScheduleOverhead = float(GPUScheduleOverhead)/row_count AvgSchedulerOverhead = float(AvgSchedulerOverhead)/row_count # accum results sys.stdout = open(result_accum_4, 'a') print(str(avgReleaseLambda)+', '+str(avgJobs)+', '+str(avgJobsAccepted)+', '+str(avgJobPercentage)+', ' + str(avgProc)+', '+str(avgExec)+', ' + str(avgResponseTime)+', '+str(avgResponseFactor)+', ' + str(GPUUsagePercentage)+', '+str(totalGPUUsage)+', ' + str(GPUScheduleOverhead)+','+str(AvgSchedulerOverhead)+','+str(row_count)) sys.stdout = open(html_output_file, 'a') print"\t\t\t<tr>" print"\t\t\t\t<td><center>Mode 4</center></td>" print'\t\t\t\t<td><center>'+str(avgJobs)+'</center></td>' print'\t\t\t\t<td><center>'+str(avgJobsAccepted)+'</center></td>' print'\t\t\t\t<td><center>'+str(avgJobPercentage)+'</center></td>' print'\t\t\t\t<td><center>'+str(avgProc)+'</center></td>' print'\t\t\t\t<td><center>'+str(avgExec)+'</center></td>' print'\t\t\t\t<td><center>'+str(avgResponseTime)+'</center></td>' print'\t\t\t\t<td><center>'+str(avgResponseFactor)+'</center></td>' print'\t\t\t\t<td><center>'+str(GPUUsagePercentage)+'</center></td>' print'\t\t\t\t<td><center>'+str(totalGPUUsage)+'</center></td>' print'\t\t\t\t<td><center>'+str(GPUScheduleOverhead)+'</center></td>' print'\t\t\t\t<td><center>'+str(AvgSchedulerOverhead)+'</center></td>' print"\t\t\t</tr>" # Mode 5 avgJobsAccepted = 0 avgJobs = 0 avgProc = 0 avgExec = 0 totalGPUUsage = 0 avgResponseTime = 0 avgResponseFactor = 0 GPUUsagePercentage = 0 avgJobPercentage = 0 GPUScheduleOverhead = 0 AvgSchedulerOverhead = 0 for x in range(row_count): avgJobs = avgJobs + int(data_5[x][0]) avgJobsAccepted = avgJobsAccepted + float(data_5[x][2]) avgJobPercentage = avgJobPercentage + float(data_5[x][3]) avgProc = avgProc + float(data_5[x][4]) avgExec = avgExec + float(data_5[x][5]) avgResponseTime = avgResponseTime + float(data_5[x][6]) avgResponseFactor = avgResponseFactor + float(data_5[x][7]) GPUUsagePercentage = GPUUsagePercentage + float(data_5[x][8]) totalGPUUsage = totalGPUUsage + float(data_5[x][9]) GPUScheduleOverhead = GPUScheduleOverhead + float(data_5[x][10]) AvgSchedulerOverhead = AvgSchedulerOverhead + float(data_5[x][11]) avgJobsAccepted = float(avgJobsAccepted)/row_count avgJobs = float(avgJobs)/row_count avgProc = float(avgProc)/row_count avgExec = float(avgExec)/row_count totalGPUUsage = float(totalGPUUsage)/row_count avgResponseTime = float(avgResponseTime)/row_count avgResponseFactor = float(avgResponseFactor)/row_count GPUUsagePercentage = float(GPUUsagePercentage)/row_count avgJobPercentage = float(avgJobPercentage)/row_count GPUScheduleOverhead = float(GPUScheduleOverhead)/row_count AvgSchedulerOverhead = float(AvgSchedulerOverhead)/row_count # accum results sys.stdout = open(result_accum_5, 'a') print(str(avgReleaseLambda)+', '+str(avgJobs)+', '+str(avgJobsAccepted)+', '+str(avgJobPercentage)+', ' + str(avgProc)+', '+str(avgExec)+', ' + str(avgResponseTime)+', '+str(avgResponseFactor)+', ' + str(GPUUsagePercentage)+', '+str(totalGPUUsage)+', ' + str(GPUScheduleOverhead)+','+str(AvgSchedulerOverhead)+','+str(row_count)) sys.stdout = open(html_output_file, 'a') print"\t\t\t<tr>" print"\t\t\t\t<td><center>Mode 5</center></td>" print'\t\t\t\t<td><center>'+str(avgJobs)+'</center></td>' print'\t\t\t\t<td><center>'+str(avgJobsAccepted)+'</center></td>' print'\t\t\t\t<td><center>'+str(avgJobPercentage)+'</center></td>' print'\t\t\t\t<td><center>'+str(avgProc)+'</center></td>' print'\t\t\t\t<td><center>'+str(avgExec)+'</center></td>' print'\t\t\t\t<td><center>'+str(avgResponseTime)+'</center></td>' print'\t\t\t\t<td><center>'+str(avgResponseFactor)+'</center></td>' print'\t\t\t\t<td><center>'+str(GPUUsagePercentage)+'</center></td>' print'\t\t\t\t<td><center>'+str(totalGPUUsage)+'</center></td>' print'\t\t\t\t<td><center>'+str(GPUScheduleOverhead)+'</center></td>' print'\t\t\t\t<td><center>'+str(AvgSchedulerOverhead)+'</center></td>' print"\t\t\t</tr>" print"\t\t</table>" # Release time Lambda print'\t\t<br><br><h2><center> Avg Release Time Lambda:'+str(avgReleaseLambda)+'</center></h2><br>' # Google Charts print"\t\t<center><div id=\"JobScheduledPercentage_chart\" style=\"border: 1px solid #ccc\"></div></center>" print"\t\t<center><div id=\"jobScheduled_chart\" style=\"border: 1px solid #ccc\"></div></center>" print"\t\t<center><div id=\"GPUUsage_accepted_chart\" style=\"border: 1px solid #ccc\"></div></center>" print"\t\t<center><div id=\"GPUUsage_requested_chart\" style=\"border: 1px solid #ccc\"></div></center>" print"\t\t<center><div id=\"responseByExecTime_chart\" style=\"border: 1px solid #ccc\"></div></center>" print"\t\t<center><div id=\"responseByRelativeDeadline_chart\" style=\"border: 1px solid #ccc\"></div></center>" print"\t</body>" print"</html>"

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0.683511

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11b20ebad8eab479fb6fed2be3f7940e76f88665

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Python

lib/modeling/torchResNet.py

Min-Sheng/CA_FSIS_Cell

c24750d860a9417b30819c05613282cd74dc517f

[ "MIT" ]

null

lib/modeling/torchResNet.py

Min-Sheng/CA_FSIS_Cell

c24750d860a9417b30819c05613282cd74dc517f

[ "MIT" ]

1

2021-03-01T09:16:15.000Z

2021-03-01T09:34:49.000Z

lib/modeling/torchResNet.py

Min-Sheng/CA_FSIS_Cell

c24750d860a9417b30819c05613282cd74dc517f

[ "MIT" ]

null

import torch import torch.nn as nn import math import copy from collections import OrderedDict import torch.utils.model_zoo as model_zoo from core.config import cfg import utils.net as net_utils from deform.torch_deform_conv.layers import ConvOffset2D model_urls = { 'resnet50': 'https://s3.amazonaws.com/pytorch/models/resnet50-19c8e357.pth', 'resnet101': 'https://s3.amazonaws.com/pytorch/models/resnet101-5d3b4d8f.pth', 'resnet152': 'https://s3.amazonaws.com/pytorch/models/resnet152-b121ed2d.pth', } # ---------------------------------------------------------------------------- # # Helper functions # ---------------------------------------------------------------------------- # def weight_mapping(state_dict): state_dict_v2 = copy.deepcopy(state_dict) layer0_mapping = { 'conv1.weight': 'res1.conv1.weight', 'bn1.weight': 'res1.bn1.weight', 'bn1.bias': 'res1.bn1.bias', 'bn1.running_mean': 'res1.bn1.running_mean', 'bn1.running_var': 'res1.bn1.running_var', 'bn1.num_batches_tracked': 'res1.bn1.num_batches_tracked' } for key in state_dict: if key in layer0_mapping.keys(): new_key = layer0_mapping[key] state_dict_v2[new_key] = state_dict_v2.pop(key) if key.find('layer') != -1: layer_id = int(key[key.find('layer') + 5]) new_key = key.replace(f'layer{layer_id}', f'res{layer_id+1}') state_dict_v2[new_key] = state_dict_v2.pop(key) return state_dict_v2 # ---------------------------------------------------------------------------- # # Bits for specific architectures (ResNet50, ResNet101, ...) # ---------------------------------------------------------------------------- # def ResNet50_conv4_body(pretrained=True, model_path=None): """Constructs a ResNet-50 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet """ model_path = cfg.RESNETS.IMAGENET_PRETRAINED_WEIGHTS if model_path is None else model_path model = ResNet_convX_body((3, 4, 6, 3), 4) if pretrained: if model_path: print("Loading pretrained weights from %s" %(model_path)) state_dict = torch.load(model_path) state_dict = state_dict['state_dict'] state_dict_v2 = copy.deepcopy(state_dict) for key in state_dict: pre, post = key.split('module.') state_dict_v2[post] = state_dict_v2.pop(key) state_dict_v2 = weight_mapping(state_dict_v2) else: state_dict = model_zoo.load_url(model_urls['resnet50']) state_dict_v2 = weight_mapping(state_dict) model.load_state_dict(state_dict_v2, strict=False) return model def ResNet50_conv5_body(pretrained=True, model_path=None): """Constructs a ResNet-50 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet """ model_path = cfg.RESNETS.IMAGENET_PRETRAINED_WEIGHTS if model_path is None else model_path model = ResNet_convX_body((3, 4, 6, 3), 5) if pretrained: if model_path: print("Loading pretrained weights from %s" %(model_path)) state_dict = torch.load(model_path) state_dict = state_dict['state_dict'] state_dict_v2 = copy.deepcopy(state_dict) for key in state_dict: pre, post = key.split('module.') state_dict_v2[post] = state_dict_v2.pop(key) state_dict_v2 = weight_mapping(state_dict_v2) else: state_dict = model_zoo.load_url(model_urls['resnet50']) state_dict_v2 = weight_mapping(state_dict) model.load_state_dict(state_dict_v2, strict=False) return model def ResNet101_conv4_body(pretrained=True, model_path = None): """Constructs a ResNet-101 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet """ model_path = cfg.RESNETS.IMAGENET_PRETRAINED_WEIGHTS if model_path is None else model_path model = ResNet_convX_body((3, 4, 23, 3), 4) if pretrained: if model_path: print("Loading pretrained weights from %s" %(model_path)) state_dict = torch.load(model_path) state_dict = state_dict['state_dict'] state_dict_v2 = copy.deepcopy(state_dict) for key in state_dict: pre, post = key.split('module.') state_dict_v2[post] = state_dict_v2.pop(key) state_dict_v2 = weight_mapping(state_dict_v2) else: state_dict = model_zoo.load_url(model_urls['resnet101']) state_dict_v2 = weight_mapping(state_dict) model.load_state_dict(state_dict_v2, strict=False) return model def ResNet101_conv5_body(pretrained=True, model_path = None): """Constructs a ResNet-101 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet """ model_path = cfg.RESNETS.IMAGENET_PRETRAINED_WEIGHTS if model_path is None else model_path model = ResNet_convX_body((3, 4, 23, 3), 5) if pretrained: if model_path: print("Loading pretrained weights from %s" %(model_path)) state_dict = torch.load(model_path) state_dict = state_dict['state_dict'] state_dict_v2 = copy.deepcopy(state_dict) for key in state_dict: pre, post = key.split('module.') state_dict_v2[post] = state_dict_v2.pop(key) state_dict_v2 = weight_mapping(state_dict_v2) else: state_dict = model_zoo.load_url(model_urls['resnet101']) state_dict_v2 = weight_mapping(state_dict) model.load_state_dict(state_dict_v2, strict=False) return model def ResNet152_conv5_body(pretrained=True, model_path=None): """Constructs a ResNet-152 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet """ model_path = cfg.RESNETS.IMAGENET_PRETRAINED_WEIGHTS if model_path is None else model_path model = ResNet_convX_body((3, 8, 36, 3), 5) if pretrained: if model_path: print("Loading pretrained weights from %s" %(model_path)) state_dict = torch.load(model_path) state_dict = state_dict['state_dict'] state_dict_v2 = copy.deepcopy(state_dict) for key in state_dict: pre, post = key.split('module.') state_dict_v2[post] = state_dict_v2.pop(key) state_dict_v2 = weight_mapping(state_dict_v2) else: state_dict = model_zoo.load_url(model_urls['resnet152']) state_dict_v2 = weight_mapping(state_dict) model.load_state_dict(state_dict_v2, strict=False) return model # ---------------------------------------------------------------------------- # # Generic ResNet components # ---------------------------------------------------------------------------- # class ResNet_convX_body(nn.Module): def __init__(self, block_counts, convX): super().__init__() self.block_counts = block_counts self.convX = convX self.num_layers = (sum(block_counts) + 3 * (self.convX == 4)) * 3 + 2 self.res1 = globals()[cfg.RESNETS.STEM_FUNC]() dim_in = 64 dim_bottleneck = cfg.RESNETS.NUM_GROUPS * cfg.RESNETS.WIDTH_PER_GROUP #64 self.res2, dim_in = add_stage(dim_in, 256, dim_bottleneck, block_counts[0], dilation=1, stride_init=1) if cfg.MODEL.USE_DEFORM: self.res3, dim_in = add_stage(dim_in, 512, dim_bottleneck * 2, block_counts[1], dilation=1, stride_init=2, deform=True) self.res4, res4_dim_out = add_stage(dim_in, 1024, dim_bottleneck * 4, block_counts[2], dilation=1, stride_init=2, deform=True) else: self.res3, dim_in = add_stage(dim_in, 512, dim_bottleneck * 2, block_counts[1], dilation=1, stride_init=2) self.res4, res4_dim_out = add_stage(dim_in, 1024, dim_bottleneck * 4, block_counts[2], dilation=1, stride_init=2) stride_init = 2 if cfg.RESNETS.RES5_DILATION == 1 else 1 if cfg.MODEL.USE_DEFORM: self.res5, res5_dim_out = add_stage(res4_dim_out, 2048, dim_bottleneck * 8, block_counts[3], cfg.RESNETS.RES5_DILATION, stride_init, deform=True) else: self.res5, res5_dim_out = add_stage(res4_dim_out, 2048, dim_bottleneck * 8, block_counts[3], cfg.RESNETS.RES5_DILATION, stride_init) self.avgpool = nn.AvgPool2d(7) self.fc = nn.Linear(res5_dim_out, 1000) if self.convX == 5: self.spatial_scale = 1 / 32 * cfg.RESNETS.RES5_DILATION self.dim_out = res5_dim_out else: self.spatial_scale = 1 / 16 # final feature scale wrt. original image scale self.dim_out = res4_dim_out # Initial weights self.apply(self._init_weights) self._init_modules() def _init_weights(self, m): classname = m.__class__.__name__ if classname.find('Conv') != -1: n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels m.weight.data.normal_(0, math.sqrt(2. / n)) elif classname.find('BatchNorm') != -1: m.weight.data.fill_(1) m.bias.data.zero_() def _init_modules(self): assert cfg.RESNETS.FREEZE_AT in [0, 2, 3, 4, 5] assert cfg.RESNETS.FREEZE_AT <= self.convX for i in range(1, cfg.RESNETS.FREEZE_AT + 1): freeze_params(getattr(self, 'res%d' % i)) def set_bn_fix(m): classname = m.__class__.__name__ if classname.find('BatchNorm') != -1: for p in m.parameters(): p.requires_grad=False # Freeze all bn layers !!! self.apply(set_bn_fix) def train(self, mode=True): # Override self.training = mode for i in range(cfg.RESNETS.FREEZE_AT + 1, self.convX + 1): getattr(self, 'res%d' % i).train(mode) def set_bn_eval(m): classname = m.__class__.__name__ if classname.find('BatchNorm') != -1: m.eval() # Set all bn layers to eval self.apply(set_bn_eval) def forward(self, x): for i in range(self.convX): x = getattr(self, 'res%d' % (i + 1))(x) return x class ResNet_roi_conv5_head(nn.Module): def __init__(self, dim_in, roi_xform_func, spatial_scale): super().__init__() self.roi_xform = roi_xform_func self.spatial_scale = spatial_scale dim_bottleneck = cfg.RESNETS.NUM_GROUPS * cfg.RESNETS.WIDTH_PER_GROUP stride_init = cfg.FAST_RCNN.ROI_XFORM_RESOLUTION // 7 self.res5, self.dim_out = add_stage(dim_in, 2048, dim_bottleneck * 8, 3, dilation=1, stride_init=stride_init) self.avgpool = nn.AvgPool2d(7) self._init_modules() def _init_modules(self): model_path = cfg.RESNETS.IMAGENET_PRETRAINED_WEIGHTS if model_path is not None: state_dict = torch.load(model_path) state_dict = state_dict['state_dict'] state_dict_v2 = copy.deepcopy(state_dict) for key in state_dict: pre, post = key.split('module.') state_dict_v2[post] = state_dict_v2.pop(key) state_dict_v2 = weight_mapping(state_dict_v2) self.load_state_dict(state_dict_v2, strict=False) def set_bn_fix(m): classname = m.__class__.__name__ if classname.find('BatchNorm') != -1: for p in m.parameters(): p.requires_grad=False # Freeze all bn layers !!! self.apply(set_bn_fix) def forward(self, x, rpn_ret): x = self.roi_xform( x, rpn_ret, blob_rois='rois', method=cfg.FAST_RCNN.ROI_XFORM_METHOD, resolution=cfg.FAST_RCNN.ROI_XFORM_RESOLUTION, spatial_scale=self.spatial_scale, sampling_ratio=cfg.FAST_RCNN.ROI_XFORM_SAMPLING_RATIO ) res5_feat = self.res5(x) x = self.avgpool(res5_feat) if cfg.MODEL.SHARE_RES5 and self.training: return x, res5_feat else: return x class ResNet_roi_conv5_head_co(nn.Module): def __init__(self, dim_in, roi_xform_func, spatial_scale): super().__init__() self.roi_xform = roi_xform_func self.spatial_scale = spatial_scale dim_bottleneck = cfg.RESNETS.NUM_GROUPS * cfg.RESNETS.WIDTH_PER_GROUP stride_init = cfg.FAST_RCNN.ROI_XFORM_RESOLUTION // 7 self.res5, self.dim_out = add_stage(dim_in, 2048, dim_bottleneck * 8, 3, dilation=1, stride_init=stride_init) self.avgpool = nn.AvgPool2d(7) self._init_modules() def _init_modules(self): model_path = cfg.RESNETS.IMAGENET_PRETRAINED_WEIGHTS if model_path is not None: state_dict = torch.load(model_path) state_dict = state_dict['state_dict'] state_dict_v2 = copy.deepcopy(state_dict) for key in state_dict: pre, post = key.split('module.') state_dict_v2[post] = state_dict_v2.pop(key) state_dict_v2 = weight_mapping(state_dict_v2) self.load_state_dict(state_dict_v2, strict=False) # Freeze all bn layers !!! def set_bn_fix(m): classname = m.__class__.__name__ if classname.find('BatchNorm') != -1: for p in m.parameters(): p.requires_grad=False # Freeze all bn layers !!! self.apply(set_bn_fix) def forward(self, x, y, rpn_ret): x, y = self.roi_xform( x, rpn_ret, blob_rois='rois', method=cfg.FAST_RCNN.ROI_XFORM_METHOD, resolution=cfg.FAST_RCNN.ROI_XFORM_RESOLUTION, spatial_scale=self.spatial_scale, sampling_ratio=cfg.FAST_RCNN.ROI_XFORM_SAMPLING_RATIO, query_blobs_in=y ) res5_feat = self.res5(x) x = self.avgpool(res5_feat) query_res5_feat = self.res5(y) y = self.avgpool(query_res5_feat) if cfg.MODEL.SHARE_RES5 and self.training: return x, y, res5_feat, query_res5_feat else: return x, y def add_stage(inplanes, outplanes, innerplanes, nblocks, dilation=1, stride_init=2, deform=False): """Make a stage consist of `nblocks` residual blocks. Returns: - stage module: an nn.Sequentail module of residual blocks - final output dimension """ res_blocks = [] stride = stride_init for _ in range(nblocks): res_blocks.append(add_residual_block( inplanes, outplanes, innerplanes, dilation, stride, deform=deform) ) inplanes = outplanes stride = 1 return nn.Sequential(*res_blocks), outplanes def add_residual_block(inplanes, outplanes, innerplanes, dilation, stride, deform=False): """Return a residual block module, including residual connection, """ if stride != 1 or inplanes != outplanes: shortcut_func = globals()[cfg.RESNETS.SHORTCUT_FUNC] downsample = shortcut_func(inplanes, outplanes, stride) else: downsample = None trans_func = globals()[cfg.RESNETS.TRANS_FUNC] res_block = trans_func( inplanes, outplanes, innerplanes, stride, dilation=dilation, group=cfg.RESNETS.NUM_GROUPS, downsample=downsample, deform=deform) return res_block # ------------------------------------------------------------------------------ # various downsample shortcuts (may expand and may consider a new helper) # ------------------------------------------------------------------------------ def basic_bn_shortcut(inplanes, outplanes, stride): return nn.Sequential( nn.Conv2d(inplanes, outplanes, kernel_size=1, stride=stride, bias=False), nn.BatchNorm2d(outplanes), ) def basic_gn_shortcut(inplanes, outplanes, stride): return nn.Sequential( nn.Conv2d(inplanes, outplanes, kernel_size=1, stride=stride, bias=False), nn.GroupNorm(net_utils.get_group_gn(outplanes), outplanes, eps=cfg.GROUP_NORM.EPSILON) ) # ------------------------------------------------------------------------------ # various stems (may expand and may consider a new helper) # ------------------------------------------------------------------------------ def basic_bn_stem(): return nn.Sequential(OrderedDict([ ('conv1', nn.Conv2d(3, 64, 7, stride=2, padding=3, bias=False)), ('bn1', nn.BatchNorm2d(64)), ('relu', nn.ReLU(inplace=True)), ('maxpool', nn.MaxPool2d(kernel_size=3, stride=2, padding=0, ceil_mode=True))])) #('maxpool', nn.MaxPool2d(kernel_size=3, stride=2, padding=1))])) def basic_gn_stem(): return nn.Sequential(OrderedDict([ ('conv1', nn.Conv2d(3, 64, 7, stride=2, padding=3, bias=False)), ('gn1', nn.GroupNorm(net_utils.get_group_gn(64), 64, eps=cfg.GROUP_NORM.EPSILON)), ('relu', nn.ReLU(inplace=True)), ('maxpool', nn.MaxPool2d(kernel_size=3, stride=2, padding=1))])) # ------------------------------------------------------------------------------ # various transformations (may expand and may consider a new helper) # ------------------------------------------------------------------------------ class bottleneck_transformation(nn.Module): """ Bottleneck Residual Block """ def __init__(self, inplanes, outplanes, innerplanes, stride=1, dilation=1, group=1, downsample=None, deform=False): super().__init__() # In original resnet, stride=2 is on 1x1. # In fb.torch resnet, stride=2 is on 3x3. (str1x1, str3x3) = (stride, 1) if cfg.RESNETS.STRIDE_1X1 else (1, stride) self.stride = stride self.deform = deform if not self.deform: self.conv1 = nn.Conv2d( inplanes, innerplanes, kernel_size=1, stride=str1x1, bias=False) self.bn1 = nn.BatchNorm2d(innerplanes) self.conv2 = nn.Conv2d( innerplanes, innerplanes, kernel_size=3, stride=str3x3, bias=False, padding=1 * dilation, dilation=dilation, groups=group) self.bn2 = nn.BatchNorm2d(innerplanes) self.conv3 = nn.Conv2d( innerplanes, outplanes, kernel_size=1, stride=1, bias=False) self.bn3 = nn.BatchNorm2d(outplanes) self.downsample = downsample self.relu = nn.ReLU(inplace=True) else: self.offsets1 = ConvOffset2D(inplanes) self.conv1 = nn.Conv2d( inplanes, innerplanes, kernel_size=1, stride=str1x1, bias=False) self.bn1 = nn.BatchNorm2d(innerplanes) self.offsets2 = ConvOffset2D(innerplanes) self.conv2 = nn.Conv2d( innerplanes, innerplanes, kernel_size=3, stride=str3x3, bias=False, padding=1 * dilation, dilation=dilation, groups=group) self.bn2 = nn.BatchNorm2d(innerplanes) self.offsets3 = ConvOffset2D(innerplanes) self.conv3 = nn.Conv2d( innerplanes, outplanes, kernel_size=1, stride=1, bias=False) self.bn3 = nn.BatchNorm2d(outplanes) self.downsample = downsample self.relu = nn.ReLU(inplace=True) def forward(self, x): residual = x if not self.deform: out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = self.conv2(out) out = self.bn2(out) out = self.relu(out) out = self.conv3(out) out = self.bn3(out) else: out = self.offsets1(x) out = self.conv1(out) out = self.bn1(out) out = self.relu(out) out = self.offsets2(out) out = self.conv2(out) out = self.bn2(out) out = self.relu(out) out = self.offsets3(out) out = self.conv3(out) out = self.bn3(out) if self.downsample is not None: residual = self.downsample(x) out += residual out = self.relu(out) return out class bottleneck_gn_transformation(nn.Module): expansion = 4 def __init__(self, inplanes, outplanes, innerplanes, stride=1, dilation=1, group=1, downsample=None): super().__init__() # In original resnet, stride=2 is on 1x1. # In fb.torch resnet, stride=2 is on 3x3. (str1x1, str3x3) = (stride, 1) if cfg.RESNETS.STRIDE_1X1 else (1, stride) self.stride = stride self.conv1 = nn.Conv2d( inplanes, innerplanes, kernel_size=1, stride=str1x1, bias=False) self.gn1 = nn.GroupNorm(net_utils.get_group_gn(innerplanes), innerplanes, eps=cfg.GROUP_NORM.EPSILON) self.conv2 = nn.Conv2d( innerplanes, innerplanes, kernel_size=3, stride=str3x3, bias=False, padding=1 * dilation, dilation=dilation, groups=group) self.gn2 = nn.GroupNorm(net_utils.get_group_gn(innerplanes), innerplanes, eps=cfg.GROUP_NORM.EPSILON) self.conv3 = nn.Conv2d( innerplanes, outplanes, kernel_size=1, stride=1, bias=False) self.gn3 = nn.GroupNorm(net_utils.get_group_gn(outplanes), outplanes, eps=cfg.GROUP_NORM.EPSILON) self.downsample = downsample self.relu = nn.ReLU(inplace=True) def forward(self, x): residual = x out = self.conv1(x) out = self.gn1(out) out = self.relu(out) out = self.conv2(out) out = self.gn2(out) out = self.relu(out) out = self.conv3(out) out = self.gn3(out) if self.downsample is not None: residual = self.downsample(x) out += residual out = self.relu(out) return out def freeze_params(m): """Freeze all the weights by setting requires_grad to False """ for p in m.parameters(): p.requires_grad = False

38.484848

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0.580971

2,796

22,860

4.506795

0.10372

0.082136

0.046266

0.041425

0.776526

0.751686

0.737957

0.728434

0.718118

0.69923

0

0.030928

0.281496

22,860

594

105

38.484848

0.736256

0.109405

0

0.654462

0

0.044131

0.003566

0

0.004577

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false

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0.020595

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0.011442

0

null

0

1

0

null

0

5

11d5570c1f5104f2732b1bf852cd1144b65ea155

61

py

Python

fastISM/__init__.py

kundajelab/fastISM

1573feccba1ad5d9f1cee508f5bb03c4aa09bb2b

[ "MIT" ]

12

2020-09-20T17:03:48.000Z

2022-03-16T06:51:52.000Z

fastISM/__init__.py

kundajelab/fastISM

1573feccba1ad5d9f1cee508f5bb03c4aa09bb2b

[ "MIT" ]

5

2020-10-24T20:43:45.000Z

2022-02-25T19:40:47.000Z

fastISM/__init__.py

kundajelab/fastISM

1573feccba1ad5d9f1cee508f5bb03c4aa09bb2b

[ "MIT" ]

2

2020-10-14T05:18:55.000Z

2022-02-21T07:34:14.000Z

from .fast_ism import FastISM from .ism_base import NaiveISM

20.333333

30

0.836066

10

61

4.9

0.7

0

0.131148

61

2

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30.5

0.924528

0

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true

0

1

0

1

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null

0

1

0

1

0

1

0

5

eea59cf9926de3446a108f54259fdcc310099f7a

172

py

Python

api/utils/get_earnings.py

syth0le/REST-API_YANDEX

7a693430973e4d0ae428860d17fc33504dc25fb2

[ "MIT" ]

null

api/utils/get_earnings.py

syth0le/REST-API_YANDEX

7a693430973e4d0ae428860d17fc33504dc25fb2

[ "MIT" ]

null

api/utils/get_earnings.py

syth0le/REST-API_YANDEX

7a693430973e4d0ae428860d17fc33504dc25fb2

[ "MIT" ]

null

def get_salary(courier_type, completed_orders): DATA = {"foot": 2, "bike": 5, "car": 9} salary = 500 * DATA[str(courier_type)] * completed_orders return salary

34.4

61

0.668605

24

172

4.583333

0.708333

0.2

0.363636

0.472727

0

0.042857

0.186047

172

4

62

43

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null

0

1

0

1

0

null

0

1

0

5

eeaeecc00f80638bdeeeac780d5b87b92462f522

464

py

Python

dummyGPIO.py

yasokada/python-151127-7segLed_IPadrDisplay

eb97f17685ac2477e6a3a7321159d6463f736dd2

[ "MIT" ]

1

2017-01-13T23:57:21.000Z

toLearn/dummyGPIO.py

yasokada/python-151113-lineMonitor

224342d5855d8ee6792fad6ad36399d95fce1b09

[ "MIT" ]

2

2015-12-08T23:40:12.000Z

2015-12-24T22:09:07.000Z

dummyGPIO.py

yasokada/python-151127-7segLed_IPadrDisplay

eb97f17685ac2477e6a3a7321159d6463f736dd2

[ "MIT" ]

null

''' v0.1 2015/11/26 - add output() - add setmode() - add setup() ''' class CDummyGPIO: def __init__(self): self.BOARD = 0; self.OUT = 1; # do nothing return def setmode(self, board): # do nothing return def setup(self, pinnum, inout): # do nothing return def output(self, pinnum, onoff): # do nothing return # Usage ''' from dummyGPIO import CDummyGPIO GPIO = CDummyGPIO() GPIO.setmode(GPIO.BOARD) GPIO.setup(10, GPIO.OUT) '''

12.888889

33

0.642241

64

464

4.59375

0.453125

0.122449

0.204082

0.183673

0

0.038567

0.217672

464

35

34

13.257143

0.77135

0.252155

0

0.363636

0

1

0.363636

false

0

0.272727

0.818182

0

null

0

1

0

null

0

1

0

1

0

5

e11a8e425c834148530d1f4e74a6a8f4d690673a

146

py

Python

Curso Python/ex009.py

sandro-fidelis/Cursos

cee1960181b1309be93034694cab8cf2878e2194

[ "MIT" ]

null

Curso Python/ex009.py

sandro-fidelis/Cursos

cee1960181b1309be93034694cab8cf2878e2194

[ "MIT" ]

null

Curso Python/ex009.py

sandro-fidelis/Cursos

cee1960181b1309be93034694cab8cf2878e2194

[ "MIT" ]

null

n = int(input('Qual tabuada deseja ver: ')) c=1 print(11*'=') while c <= 10: print('{} x {:2} = {}'.format(n,c,c*n)) c += 1 print(11*'=')

18.25

43

0.493151

26

146

2.769231

0.615385

0.055556

0.194444

0.25

0

0.077586

0.205479

146

7

44

20.857143

0.543103

0

0.285714

0

0.280822

0

1

0

false

0

0.428571

1

0

null

0

1

0

1

0

null

0

1

0

5

0165f80525bcd690617df14c805c36b82363c9f9

119

py

Python

experiments/localization.py

seba-1511/cervix.kaggle

5bf956a85481a961fb9af237aba2d2254cf6921a

[ "Apache-2.0" ]

null

experiments/localization.py

seba-1511/cervix.kaggle

5bf956a85481a961fb9af237aba2d2254cf6921a

[ "Apache-2.0" ]

null

experiments/localization.py

seba-1511/cervix.kaggle

5bf956a85481a961fb9af237aba2d2254cf6921a

[ "Apache-2.0" ]

null

#!/usr/bin/env python def get_localization(args): # TODO: Implement the localization fitting the centers pass

19.833333

58

0.731092

16

119

5.375

0.875

0

0.184874

119

5

59

23.8

0.886598

0.613445

0

0.2

0

1

0.5

false

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

0170d25b5b5c179dc15a428fac48dd41cba9b842

700

py

Python

terrascript/resource/hashicorp/ad.py

mjuenema/python-terrascript

6d8bb0273a14bfeb8ff8e950fe36f97f7c6e7b1d

[ "BSD-2-Clause" ]

507

2017-07-26T02:58:38.000Z

2022-01-21T12:35:13.000Z

terrascript/resource/hashicorp/ad.py

mjuenema/python-terrascript

6d8bb0273a14bfeb8ff8e950fe36f97f7c6e7b1d

[ "BSD-2-Clause" ]

135

2017-07-20T12:01:59.000Z

2021-10-04T22:25:40.000Z

terrascript/resource/hashicorp/ad.py

mjuenema/python-terrascript

6d8bb0273a14bfeb8ff8e950fe36f97f7c6e7b1d

[ "BSD-2-Clause" ]

81

2018-02-20T17:55:28.000Z

2022-01-31T07:08:40.000Z

# terrascript/resource/hashicorp/ad.py # Automatically generated by tools/makecode.py (24-Sep-2021 15:10:57 UTC) import terrascript class ad_computer(terrascript.Resource): pass class ad_gplink(terrascript.Resource): pass class ad_gpo(terrascript.Resource): pass class ad_gpo_security(terrascript.Resource): pass class ad_group(terrascript.Resource): pass class ad_group_membership(terrascript.Resource): pass class ad_ou(terrascript.Resource): pass class ad_user(terrascript.Resource): pass __all__ = [ "ad_computer", "ad_gplink", "ad_gpo", "ad_gpo_security", "ad_group", "ad_group_membership", "ad_ou", "ad_user", ]

14.583333

73

0.71

89

700

5.314607

0.325843

0.361522

0.389006

0.414376

0.477801

0.287526

0

0.021053

0.185714

700

47

74

14.893617

0.808772

0.154286

0

0.296296

1

0

0.135823

0

1

0

false

0.296296

0.037037

0

0.333333

0

null

1

0

1

0

null

0

1

0

5

6d7de936a991106b4fb0cef936e0e2db3b670915

192

py

Python

visionlib/face/__init__.py

sumeshmn/Visionlib

c543ee038d6d1dcf9d88a8d7a782addd998e6036

[ "MIT" ]

null

visionlib/face/__init__.py

sumeshmn/Visionlib

c543ee038d6d1dcf9d88a8d7a782addd998e6036

[ "MIT" ]

null

visionlib/face/__init__.py

sumeshmn/Visionlib

c543ee038d6d1dcf9d88a8d7a782addd998e6036

[ "MIT" ]

null

from .detection import FDetector from .haar_detector import HaarDetector from .hog_detector import Hog_detector from .mtcnn_detector import MTCNNDetector from .dnn_detector import DnnDetector

32

41

0.869792

25

192

6.48

0.48

0.345679

0

0.104167

192

5

42

38.4

0.94186

0

1

0

true

0

1

0

1

0

1

0

null

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null

0

1

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1

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5

6d8fdc15326338e43a53a51f9d3225823820ab40

74,548

py

Python

appengine/components/components/prpc/discovery/service_prpc_pb2.py

stefb965/luci-py

e0a8a5640c4104e5c90781d833168aa8a8d1f24d

[ "Apache-2.0" ]

null

appengine/components/components/prpc/discovery/service_prpc_pb2.py

stefb965/luci-py

e0a8a5640c4104e5c90781d833168aa8a8d1f24d

[ "Apache-2.0" ]

null

appengine/components/components/prpc/discovery/service_prpc_pb2.py

stefb965/luci-py

e0a8a5640c4104e5c90781d833168aa8a8d1f24d

[ "Apache-2.0" ]

1

2020-07-05T19:54:40.000Z

# Generated by the pRPC protocol buffer compiler plugin. DO NOT EDIT! # source: service.proto import base64 from google.protobuf import descriptor_pb2 # Includes description of the service.proto and all of its transitive # dependencies. Includes source code info. FILE_DESCRIPTOR_SET = descriptor_pb2.FileDescriptorSet() FILE_DESCRIPTOR_SET.ParseFromString(base64.b64decode( 'Co8JCg1zZXJ2aWNlLnByb3RvEglkaXNjb3ZlcnkaIGdvb2dsZS9wcm90b2J1Zi9kZXNjcmlwdG' '9yLnByb3RvIgYKBFZvaWQidAoQRGVzY3JpYmVSZXNwb25zZRJECgtkZXNjcmlwdGlvbhgBIAEo' 'CzIiLmdvb2dsZS5wcm90b2J1Zi5GaWxlRGVzY3JpcHRvclNldFILZGVzY3JpcHRpb24SGgoIc2' 'VydmljZXMYAiADKAlSCHNlcnZpY2VzMkcKCURpc2NvdmVyeRI6CghEZXNjcmliZRIPLmRpc2Nv' 'dmVyeS5Wb2lkGhsuZGlzY292ZXJ5LkRlc2NyaWJlUmVzcG9uc2UiAEqBBwoIEAQQABAbEAEKtQ' 'EIDBAEEAAQEjKqASBDb3B5cmlnaHQgMjAxNiBUaGUgTFVDSSBBdXRob3JzLiBBbGwgcmlnaHRz' 'IHJlc2VydmVkLgogVXNlIG9mIHRoaXMgc291cmNlIGNvZGUgaXMgZ292ZXJuZWQgdW5kZXIgdG' 'hlIEFwYWNoZSBMaWNlbnNlLCBWZXJzaW9uIDIuMAogdGhhdCBjYW4gYmUgZm91bmQgaW4gdGhl' 'IExJQ0VOU0UgZmlsZS4KCggIAhAGEAgQEQoKCAMIABAIEAcQKQotCAYIABALEAAQDxABGh8gRG' 'lzY292ZXJ5IGRlc2NyaWJlcyBzZXJ2aWNlcy4KCgwIBggACAEQCxAIEBEKbwgGCAAIAggAEA4Q' 'AhAzGl8gRGVzY3JpYmUgcmV0dXJucyBhIGxpc3Qgb2Ygc2VydmljZXMgYW5kIGEgZGVzY3JpcH' 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'IG9uZSBwYXN0CiB0aGUgbGFzdCByZWxldmFudCBieXRlIChzbyB0aGUgbGVuZ3RoIG9mIHRoZS' 'B0ZXh0ID0gZW5kIC0gYmVnaW4pLgoKFQgECBMIAwgACAIIAwgEELcGEAQQDAoVCAQIEwgDCAAI' 'AggDCAUQtwYQDRASChUIBAgTCAMIAAgCCAMIARC3BhATEBYKFQgECBMIAwgACAIIAwgDELcGEB' 'kQGg==')) _INDEX = { f.name: { 'descriptor': f, 'services': {s.name: s for s in f.service}, } for f in FILE_DESCRIPTOR_SET.file } DiscoveryServiceDescription = { 'file_descriptor_set': FILE_DESCRIPTOR_SET, 'file_descriptor': _INDEX[u'service.proto']['descriptor'], 'service_descriptor': _INDEX[u'service.proto']['services'][u'Discovery'], }

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1

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5

6da810a7e416553569ccec2032142f91db2446a4

4,161

py

Python

xoa_driver/internals/core/commands/px_commands.py

xenadevel/xena-open-automation-python-api

b17e512aa14eee7c51677004b4c91712005edcd0

[ "Apache-2.0" ]

1

2022-03-18T17:17:59.000Z

xoa_driver/internals/core/commands/px_commands.py

xenadevel/xena-open-automation-python-api

b17e512aa14eee7c51677004b4c91712005edcd0

[ "Apache-2.0" ]

null

xoa_driver/internals/core/commands/px_commands.py

xenadevel/xena-open-automation-python-api

b17e512aa14eee7c51677004b4c91712005edcd0

[ "Apache-2.0" ]

null

#: L23 Port Transceiver Commands from dataclasses import dataclass import typing from ..protocol.command_builders import ( build_get_request, build_set_request ) from .. import interfaces from ..transporter.token import Token from ..protocol.fields.data_types import * from ..protocol.fields.field import XmpField from ..registry import register_command from .enums import * @register_command @dataclass class PX_RW: """ Provides access to the register interface supported by the port transceiver. It is possible to both read and write register values. """ code: typing.ClassVar[int] = 501 pushed: typing.ClassVar[bool] = False _connection: "interfaces.IConnection" _module: int _port: int _page_xindex: int _register_xaddress: int @dataclass(frozen=True) class SetDataAttr: value: XmpField[XmpHex4] = XmpField(XmpHex4) # 4 hex bytes, register value of the port transceiver @dataclass(frozen=True) class GetDataAttr: value: XmpField[XmpHex4] = XmpField(XmpHex4) # 4 hex bytes, register value of the port transceiver def get(self) -> "Token[GetDataAttr]": """Get the register value of a transceiver. :return: the register value of a transceiver :rtype: PX_RW.GetDataAttr """ return Token(self._connection, build_get_request(self, module=self._module, port=self._port, indices=[self._page_xindex, self._register_xaddress])) def set(self, value: str) -> "Token": """Set the register value of a transceiver. :param value: register value of a transceiver :type value: str """ return Token(self._connection, build_set_request(self, module=self._module, port=self._port, indices=[self._page_xindex, self._register_xaddress], value=value)) @register_command @dataclass class PX_MII: """Provides access to the register interface supported by the media-independent interface (MII) transceiver. It is possible to both read and write register values.""" code: typing.ClassVar[int] = 537 pushed: typing.ClassVar[bool] = False _connection: "interfaces.IConnection" _module: int _port: int _register_xaddress: int @dataclass(frozen=True) class SetDataAttr: value: XmpField[XmpHex2] = XmpField(XmpHex2) # 2 hex bytes, register value of the transceiver @dataclass(frozen=True) class GetDataAttr: value: XmpField[XmpHex2] = XmpField(XmpHex2) # 2 hex bytes, register value of the transceiver def get(self) -> "Token[GetDataAttr]": """Get the register value of a transceiver. :return: the register value of a transceiver :rtype: PX_MII.GetDataAttr """ return Token(self._connection, build_get_request(self, module=self._module, port=self._port, indices=[self._register_xaddress])) def set(self, value: str) -> "Token": """Set the register value of a transceiver. :param value: register value of a transceiver :type value: str """ return Token(self._connection, build_set_request(self, module=self._module, port=self._port, indices=[self._register_xaddress], value=value)) @register_command @dataclass class PX_TEMPERATURE: """ Transceiver temperature in degrees Celsius. """ code: typing.ClassVar[int] = 538 pushed: typing.ClassVar[bool] = True _connection: "interfaces.IConnection" _module: int _port: int @dataclass(frozen=True) class GetDataAttr: temperature_msb: XmpField[XmpByte] = XmpField(XmpByte) # byte, temperature value before the decimal digit. temperature_decimal_fraction: XmpField[XmpByte] = XmpField(XmpByte) # byte, 1/256th of a degree Celsius after the decimal digit. def get(self) -> "Token[GetDataAttr]": """Get transceiver temperature in degrees Celsius. :return: temperature value before the decimal digit, and 1/256th of a degree Celsius after the decimal digit. :rtype: PX_TEMPERATURE.GetDataAttr """ return Token(self._connection, build_get_request(self, module=self._module, port=self._port))

33.02381

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4,161

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0.194882

0.054891

0.063336

0.045039

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null

0

1

0

1

0

null

0

5

6dada164e1de575c5db21cda78d63fcb6436eab8

33

py

Python

kick/device2/elektra/actions/constants.py

CiscoDevNet/firepower-kickstart

37a36856fcdc661e8c51edaa694e48f74cc6fcb5

[ "Apache-2.0" ]

2

2020-02-10T23:36:57.000Z

2020-03-25T15:46:05.000Z

kick/device2/elektra/actions/constants.py

CiscoDevNet/firepower-kickstart

37a36856fcdc661e8c51edaa694e48f74cc6fcb5

[ "Apache-2.0" ]

1

2020-08-07T13:01:32.000Z

kick/device2/elektra/actions/constants.py

CiscoDevNet/firepower-kickstart

37a36856fcdc661e8c51edaa694e48f74cc6fcb5

[ "Apache-2.0" ]

1

2020-02-19T13:58:35.000Z

class ElektraConstants: pass

11

23

0.757576

3

33

8.333333

1

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0.212121

33

2

24

16.5

0.961538

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null

0

1

0

null

0

1

0

5

6dd35f03ff9bc671f79e8585b1d7db025e32de94

115

py

Python

tests/conftest.py

techjacker/sitemapgenerator

512d6ea6f36ac661d3e0b1275a055c381b0ce455

[ "MIT" ]

null

tests/conftest.py

techjacker/sitemapgenerator

512d6ea6f36ac661d3e0b1275a055c381b0ce455

[ "MIT" ]

null

tests/conftest.py

techjacker/sitemapgenerator

512d6ea6f36ac661d3e0b1275a055c381b0ce455

[ "MIT" ]

null

import pytest from pytest_httpbin.plugin import httpbin_ca_bundle pytest.fixture(autouse=True)(httpbin_ca_bundle)

23

51

0.869565

17

115

5.588235

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0.069565

115

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null

0

1

0

1

0

5

6dd57794c6789b5f5554c5238dd5b5fff9f2b1a6

138

py

Python

Exercise 10/exercise_code/util/__init__.py

CornellLenard/Deep-Learning-Course-Exercises

db32f2b9ab93a50580e93e9dd83be1db7c4c4a19

[ "MIT" ]

null

Exercise 10/exercise_code/util/__init__.py

CornellLenard/Deep-Learning-Course-Exercises

db32f2b9ab93a50580e93e9dd83be1db7c4c4a19

[ "MIT" ]

null

Exercise 10/exercise_code/util/__init__.py

CornellLenard/Deep-Learning-Course-Exercises

db32f2b9ab93a50580e93e9dd83be1db7c4c4a19

[ "MIT" ]

null

"""Util functions""" from .vis_utils import visualizer from .save_model import save_model from .Util import checkParams, checkSize, test

23

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0.797101

19

138

5.631579

0.631579

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0.123188

138

5

47

27.6

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0.101449

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true

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null

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1

0

null

0

1

0

1

0

1

0

5

6dddd5e456a7ccaaa8659db73eee87ff70f0dccb

91

py

Python

02-array-seq/2_13.py

393562632/example-code

4a5da5726408284aed9e01f93a25a0d8dd348fb5

[ "MIT" ]

null

02-array-seq/2_13.py

393562632/example-code

4a5da5726408284aed9e01f93a25a0d8dd348fb5

[ "MIT" ]

null

02-array-seq/2_13.py

393562632/example-code

4a5da5726408284aed9e01f93a25a0d8dd348fb5

[ "MIT" ]

null

weird_board = [['_'] * 3] *3 print(weird_board) weird_board[0][2] = 'X' print(weird_board)

18.2

28

0.659341

15

91

3.666667

0.466667

0.727273

0.545455

0

0.05

0.120879

91

5

29

18.2

0.6375

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0.021739

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null

1

0

1

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null

0

1

0

5

6de4cb3c7c7e948bd05ee2500418fd79816b080a

438

py

Python

rubin_sim/maf/mafContrib/LSSObsStrategy/__init__.py

RileyWClarke/flarubin

eb7b1ee21c828523f8a5374fe4510fe6e5ec2a2a

[ "MIT" ]

null

rubin_sim/maf/mafContrib/LSSObsStrategy/__init__.py

RileyWClarke/flarubin

eb7b1ee21c828523f8a5374fe4510fe6e5ec2a2a

[ "MIT" ]

null

rubin_sim/maf/mafContrib/LSSObsStrategy/__init__.py

RileyWClarke/flarubin

eb7b1ee21c828523f8a5374fe4510fe6e5ec2a2a

[ "MIT" ]

null

from .newDitherStackers import * from .newDitherStackers import * from .maskingAlgorithmGeneralized import * from .saveBundleData_npzFormat import * from .numObsMetric import * from .galaxyCountsMetric_extended import * from .galaxyCounts_withPixelCalibration import * from .artificialStructureCalculation import * from .almPlots import * from .coaddM5Analysis import * from .constantsForPipeline import * from .os_bias_analysis import *

33.692308

48

0.835616

41

438

8.804878

0.439024

0.304709

0.149584

0.171745

0

0.002564

0.109589

438

12

49

36.5

0.923077

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1

0

true

0

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null

1

0

1

0

null

0

1

0

1

0

1

0

5

09b5a2bb038f2cac57634bfef33f5cb085b77a89

48

py

Python

tests/__init__.py

jebabi/controllerx

bc68cdd69e416880e6394b3ecf92522b3871e959

[ "MIT" ]

1

2020-02-28T17:26:36.000Z

tests/__init__.py

jebabi/controllerx

bc68cdd69e416880e6394b3ecf92522b3871e959

[ "MIT" ]

null

tests/__init__.py

jebabi/controllerx

bc68cdd69e416880e6394b3ecf92522b3871e959

[ "MIT" ]

null

import sys sys.path.append("apps/controllerx")

12

35

0.770833

7

48

5.285714

0.857143

0

0.083333

48

3

36

16

0.840909

0

0.333333

0

1

0

true

0

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

1

0

5

09bf7c1ce0c20840d83284f246ccdbf099539181

4,970

py

Python

intvalpy/linear/system_properties.py

SShary/intvalpy

42f4c8f6b23e6481f4032b0a0f7cc0d798fda3be

[ "MIT" ]

null

intvalpy/linear/system_properties.py

SShary/intvalpy

42f4c8f6b23e6481f4032b0a0f7cc0d798fda3be

[ "MIT" ]

null

intvalpy/linear/system_properties.py

SShary/intvalpy

42f4c8f6b23e6481f4032b0a0f7cc0d798fda3be

[ "MIT" ]

null

import numpy as np from scipy.optimize import minimize from intvalpy.MyClass import Interval from intvalpy.intoper import zeros def Uni(A, b, x=None, maxQ=False, x0=None, tol=1e-12, maxiter=1e3): """ Вычисление распознающего функционала Uni. В случае, если maxQ=True то находится максимум функционала. Parameters: A: Interval Матрица ИСЛАУ. b: Interval Вектор правой части ИСЛАУ. Optional Parameters: x: float, array_like Точка в которой вычисляется распознающий функционал. По умолчанию x равен массиву из нулей. maxQ: bool Если значение параметра равно True, то производится максимизация функционала. x0: float, array_like Первоначальная догадка. tol: float Погрешность для прекращения оптимизационного процесса. maxiter: int Максимальное количество итераций. Returns: out: float, tuple Возвращается значение распознающего функционала в точке x. В случае, если maxQ=True, то возвращается кортеж, где первый элемент -- корректность завершения оптимизации, второй элемент -- точка оптимума, третий элемент -- значение функции в этой точке. """ __uni = lambda x: min(b.rad - (b.mid - A @ x).mig) __minus_uni = lambda x: -__uni(x) if maxQ==False: if x is None: x = np.zeros(A.shape[1]) return __uni(x) else: from scipy.optimize import minimize if x0 is None: x0 = np.zeros(A.shape[1])+1 maximize = minimize(__minus_uni, x0, method='Nelder-Mead', tol=tol, options={'maxiter': maxiter}) return maximize.success, maximize.x, -maximize.fun def Tol(A, b, x=None, maxQ=False, x0=None, tol=1e-12, maxiter=1e3): """ Вычисление распознающего функционала Tol. В случае, если maxQ=True то находится максимум функционала. Parameters: A: Interval Матрица ИСЛАУ. b: Interval Вектор правой части ИСЛАУ. Optional Parameters: x: float, array_like Точка в которой вычисляется распознающий функционал. По умолчанию x равен массиву из нулей. maxQ: bool Если значение параметра равно True, то производится максимизация функционала. x0: float, array_like Первоначальная догадка. tol: float Погрешность для прекращения оптимизационного процесса. maxiter: int Максимальное количество итераций. Returns: out: float, tuple Возвращается значение распознающего функционала в точке x. В случае, если maxQ=True, то возвращается кортеж, где первый элемент -- корректность завершения оптимизации, второй элемент -- точка оптимума, третий элемент -- значение функции в этой точке. """ __tol = lambda x: min(b.rad - abs(b.mid - A @ x)) __minus_tol = lambda x: -__tol(x) if maxQ==False: if x is None: x = np.zeros(A.shape[1]) return __tol(x) else: from scipy.optimize import minimize if x0 is None: x0 = np.zeros(A.shape[1])+1 maximize = minimize(__minus_tol, x0, method='Nelder-Mead', tol=tol, options={'maxiter': maxiter}) return maximize.success, maximize.x, -maximize.fun def ive(A, b, N=40): """ Вычисление меры вариабельности оценки параметров. Parameters: A: Interval Матрица ИСЛАУ. b: Interval Вектор правой части ИСЛАУ. Optional Parameters: N: int Количество угловых матриц для которых вычисляется обусловленность. Returns: out: float Возвращается мера вариабельности IVE. """ success, _arg_max, _max = Tol(A, b, maxQ=True) if not success: print('Оптимизация функционала Tol завершена некорректно!') _inf = A.a _sup = A.b cond = float('inf') angle_A = np.zeros(A.shape, dtype='float64') for _ in range(N): for k in range(A.shape[0]): for l in range(A.shape[1]): angle_A[k, l] = np.random.choice([_inf[k,l], _sup[k,l]]) tmp = np.linalg.cond(angle_A) cond = tmp if tmp<cond else cond return np.sqrt(A.shape[1]) * _max * cond * \ (np.linalg.norm(_arg_max, ord=2)/np.sqrt(sum(abs(b)**2)))

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0

5

09c1b09e1644ac2346f010a7494eb66da023d9f8

88

py

Python

yad2/encoders/__init__.py

odcinek/yad2

5ecf5073a7eb9651944837e33c083c4a1e7945bc

[ "MIT" ]

null

yad2/encoders/__init__.py

odcinek/yad2

5ecf5073a7eb9651944837e33c083c4a1e7945bc

[ "MIT" ]

null

yad2/encoders/__init__.py

odcinek/yad2

5ecf5073a7eb9651944837e33c083c4a1e7945bc

[ "MIT" ]

1

2021-10-17T15:46:50.000Z

from format2 import Format2 from format40 import Format40 from format80 import Format80

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09ca172ccac0fbc44db1af59fb9a28884f053bb0

162

py

Python

remote_control/apps.py

adrienemery/auv-control-api

44d04c070879be3a52633369886534657b2d67ca

[ "MIT" ]

null

remote_control/apps.py

adrienemery/auv-control-api

44d04c070879be3a52633369886534657b2d67ca

[ "MIT" ]

2

2016-08-03T00:37:37.000Z

2016-08-03T00:46:12.000Z

remote_control/apps.py

adrienemery/auv-control

44d04c070879be3a52633369886534657b2d67ca

[ "MIT" ]

null

from django.apps import AppConfig class RemoteControlConfig(AppConfig): name = 'remote_control' def ready(self): import remote_control.signals

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113341028baadbdf6860b5c685deb7e0ad58a04a

186

py

Python

utils/DiceRatio.py

jasonxingqi/3D-Unet--Tensorflow

d925d3c16d3f02c6cb9cd0e059e30f4455ff299e

[ "MIT" ]

2

2019-04-30T09:09:11.000Z

2019-05-05T01:50:15.000Z

utils/DiceRatio.py

seanbefore/3D-Unet--Tensorflow

36a24c38041ad88d74b5d5ab09ded3c3894b00b3

[ "MIT" ]

null

utils/DiceRatio.py

seanbefore/3D-Unet--Tensorflow

36a24c38041ad88d74b5d5ab09ded3c3894b00b3

[ "MIT" ]

null

import numpy as np def dice_ratio(pred, label): '''Note: pred & label should only contain 0 or 1. ''' return np.sum(pred[label==1])*2.0 / (np.sum(pred) + np.sum(label))

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1148006841dace7c2d15cf681638c79c776c650b

270

py

Python

pytext/data/sources/__init__.py

shruti-bh/pytext

ae84a5493a5331ac07699d3dfa5b9de521ea85ea

[ "BSD-3-Clause" ]

1

2020-10-20T09:14:15.000Z

pytext/data/sources/__init__.py

shruti-bh/pytext

ae84a5493a5331ac07699d3dfa5b9de521ea85ea

[ "BSD-3-Clause" ]

null

pytext/data/sources/__init__.py

shruti-bh/pytext

ae84a5493a5331ac07699d3dfa5b9de521ea85ea

[ "BSD-3-Clause" ]

null

#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved from .data_source import DataSchema, DataSchemaConfig, DataSource from .tsv import TSVDataSource __all__ = ["DataSchema", "DataSchemaConfig", "DataSource", "TSVDataSource"]

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5

11649ccd701bc4417bcc78c7dc346d299411f6ad

102

py

Python

keras/legacy_tf_layers/__init__.py

tsheaff/keras

ee227dda766d769b7499a5549e8ed77b5e88105b

[ "Apache-2.0" ]

37,222

2017-12-13T00:52:55.000Z

2022-03-31T22:34:35.000Z

keras/legacy_tf_layers/__init__.py

amirsadafi/keras

f1e9c76675981ee6683f54a3ce569212d551d12d

[ "Apache-2.0" ]

7,624

2017-12-13T01:03:40.000Z

2022-03-31T23:57:24.000Z

keras/legacy_tf_layers/__init__.py

amirsadafi/keras

f1e9c76675981ee6683f54a3ce569212d551d12d

[ "Apache-2.0" ]

14,914

2017-12-13T02:30:46.000Z

2022-03-30T14:49:16.000Z

"""Init file.""" from keras.legacy_tf_layers import migration_utils # pylint: disable=unused-import

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5

feccebf8b7f5ab31a62544c1a696cbcf12f4d112

1,264

py

Python

DelibeRating/DelibeRating/env/Lib/site-packages/tests/test_widgets.py

Severose/DelibeRating

5d227f35c071477ce3fd6fbf3ab13a44d13f6e08

[ "MIT" ]

1

2018-11-01T15:05:12.000Z

DelibeRating/DelibeRating/env/Lib/site-packages/tests/test_widgets.py

Severose/DelibeRating

5d227f35c071477ce3fd6fbf3ab13a44d13f6e08

[ "MIT" ]

null

DelibeRating/DelibeRating/env/Lib/site-packages/tests/test_widgets.py

Severose/DelibeRating

5d227f35c071477ce3fd6fbf3ab13a44d13f6e08

[ "MIT" ]

null

import pytest from tempus_dominus import widgets def test_datepicker_format_localized(settings): settings.TEMPUS_DOMINUS_LOCALIZE = True widget = widgets.DatePicker() assert widget.get_js_format() == 'L' def test_datepicker_format_nonlocalized(settings): settings.TEMPUS_DOMINUS_LOCALIZE = False widget = widgets.DatePicker() assert widget.get_js_format() == 'YYYY-MM-DD' def test_timepicker_format_localized(settings): settings.TEMPUS_DOMINUS_LOCALIZE = True widget = widgets.TimePicker() assert widget.get_js_format() == 'LTS' def test_timepicker_format_nonlocalized(settings): settings.TEMPUS_DOMINUS_LOCALIZE = False widget = widgets.TimePicker() assert widget.get_js_format() == 'HH:mm:ss' def test_datetimepicker_format_localized(settings): settings.TEMPUS_DOMINUS_LOCALIZE = True widget = widgets.DateTimePicker() assert widget.get_js_format() == 'L LTS' def test_datetimepicker_format_nonlocalized(settings): settings.TEMPUS_DOMINUS_LOCALIZE = False widget = widgets.DateTimePicker() assert widget.get_js_format() == 'YYYY-MM-DD HH:mm:ss' def test_get_js_format_error(): with pytest.raises(NotImplementedError): widgets.TempusDominusMixin().get_js_format()

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null

0

1

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5

fecfe168fd1f83e2b06ca1bb819712b3c0b0b0b9

293

py

Python

src/songbook/console/_update.py

kipyin/-

5d372c7d987e6a1da380197c1b990def0d240298

[ "MIT" ]

1

2021-01-03T10:40:28.000Z

src/songbook/console/_update.py

kipyin/-

5d372c7d987e6a1da380197c1b990def0d240298

[ "MIT" ]

null

src/songbook/console/_update.py

kipyin/-

5d372c7d987e6a1da380197c1b990def0d240298

[ "MIT" ]

1

2021-01-03T10:40:29.000Z

import click @click.group() def update(): pass @update.command("song") def _update_song(): pass @update.command("arrangement") def _update_arrangement(): pass @update.command("worship") def _update_worship(): pass @update.command("hymn") def _update_hymn(): pass

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5

fef0f2eca41493ff175b1ce22f370a3502ed826a

50

py

Python

rubin_sim/scheduler/features/__init__.py

RileyWClarke/flarubin

eb7b1ee21c828523f8a5374fe4510fe6e5ec2a2a

[ "MIT" ]

null

rubin_sim/scheduler/features/__init__.py

RileyWClarke/flarubin

eb7b1ee21c828523f8a5374fe4510fe6e5ec2a2a

[ "MIT" ]

null

rubin_sim/scheduler/features/__init__.py

RileyWClarke/flarubin

eb7b1ee21c828523f8a5374fe4510fe6e5ec2a2a

[ "MIT" ]

null

from .features import * from .conditions import *

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25

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5

fefb10e3bc54bf078e079e6dd58a9eee22dea396

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py

Python

vdp/pipeline/v1alpha/pipeline_service_pb2.py

instill-ai/protogen-python

6e118d34566b8d59e8bcd40e0ae28e0fc1a5d50f

[ "Apache-2.0" ]

1

2022-03-22T09:09:46.000Z

vdp/pipeline/v1alpha/pipeline_service_pb2.py

instill-ai/protogen-python

6e118d34566b8d59e8bcd40e0ae28e0fc1a5d50f

[ "Apache-2.0" ]

4

2022-03-16T12:36:12.000Z

2022-03-22T10:53:12.000Z

vdp/pipeline/v1alpha/pipeline_service_pb2.py

instill-ai/protogen-python

6e118d34566b8d59e8bcd40e0ae28e0fc1a5d50f

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # Generated by the protocol buffer compiler. DO NOT EDIT! # source: vdp/pipeline/v1alpha/pipeline_service.proto """Generated protocol buffer code.""" from google.protobuf import descriptor as _descriptor from google.protobuf import descriptor_pool as _descriptor_pool from google.protobuf import message as _message from google.protobuf import reflection as _reflection from google.protobuf import symbol_database as _symbol_database # @@protoc_insertion_point(imports) _sym_db = _symbol_database.Default() from google.api import annotations_pb2 as google_dot_api_dot_annotations__pb2 from google.api import client_pb2 as google_dot_api_dot_client__pb2 from vdp.pipeline.v1alpha import healthcheck_pb2 as vdp_dot_pipeline_dot_v1alpha_dot_healthcheck__pb2 from vdp.pipeline.v1alpha import pipeline_pb2 as vdp_dot_pipeline_dot_v1alpha_dot_pipeline__pb2 DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n+vdp/pipeline/v1alpha/pipeline_service.proto\x12\x14vdp.pipeline.v1alpha\x1a\x1cgoogle/api/annotations.proto\x1a\x17google/api/client.proto\x1a&vdp/pipeline/v1alpha/healthcheck.proto\x1a#vdp/pipeline/v1alpha/pipeline.proto2\xcc\x10\n\x0fPipelineService\x12\x92\x01\n\x08Liveness\x12%.vdp.pipeline.v1alpha.LivenessRequest\x1a&.vdp.pipeline.v1alpha.LivenessResponse\"7\x82\xd3\xe4\x93\x02\x31Z\x1a\x12\x18/v1alpha/health/pipeline\x12\x13/v1alpha/__liveness\x12z\n\tReadiness\x12&.vdp.pipeline.v1alpha.ReadinessRequest\x1a\'.vdp.pipeline.v1alpha.ReadinessResponse\"\x1c\x82\xd3\xe4\x93\x02\x16\x12\x14/v1alpha/__readiness\x12\x9c\x01\n\x0e\x43reatePipeline\x12+.vdp.pipeline.v1alpha.CreatePipelineRequest\x1a,.vdp.pipeline.v1alpha.CreatePipelineResponse\"/\xda\x41\x08pipeline\x82\xd3\xe4\x93\x02\x1e:\x08pipeline\"\x12/v1alpha/pipelines\x12\x81\x01\n\x0cListPipeline\x12).vdp.pipeline.v1alpha.ListPipelineRequest\x1a*.vdp.pipeline.v1alpha.ListPipelineResponse\"\x1a\x82\xd3\xe4\x93\x02\x14\x12\x12/v1alpha/pipelines\x12\x8e\x01\n\x0bGetPipeline\x12(.vdp.pipeline.v1alpha.GetPipelineRequest\x1a).vdp.pipeline.v1alpha.GetPipelineResponse\"*\xda\x41\x04name\x82\xd3\xe4\x93\x02\x1d\x12\x1b/v1alpha/{name=pipelines/*}\x12\xba\x01\n\x0eUpdatePipeline\x12+.vdp.pipeline.v1alpha.UpdatePipelineRequest\x1a,.vdp.pipeline.v1alpha.UpdatePipelineResponse\"M\xda\x41\x14pipeline,update_mask\x82\xd3\xe4\x93\x02\x30:\x08pipeline2$/v1alpha/{pipeline.name=pipelines/*}\x12\x97\x01\n\x0e\x44\x65letePipeline\x12+.vdp.pipeline.v1alpha.DeletePipelineRequest\x1a,.vdp.pipeline.v1alpha.DeletePipelineResponse\"*\xda\x41\x04name\x82\xd3\xe4\x93\x02\x1d*\x1b/v1alpha/{name=pipelines/*}\x12\xa8\x01\n\x0eLookUpPipeline\x12+.vdp.pipeline.v1alpha.LookUpPipelineRequest\x1a,.vdp.pipeline.v1alpha.LookUpPipelineResponse\";\xda\x41\tpermalink\x82\xd3\xe4\x93\x02)\x12\'/v1alpha/{permalink=pipelines/*}:lookUp\x12\xa9\x01\n\x10\x41\x63tivatePipeline\x12-.vdp.pipeline.v1alpha.ActivatePipelineRequest\x1a..vdp.pipeline.v1alpha.ActivatePipelineResponse\"6\xda\x41\x04name\x82\xd3\xe4\x93\x02):\x01*\"$/v1alpha/{name=pipelines/*}:activate\x12\xb1\x01\n\x12\x44\x65\x61\x63tivatePipeline\x12/.vdp.pipeline.v1alpha.DeactivatePipelineRequest\x1a\x30.vdp.pipeline.v1alpha.DeactivatePipelineResponse\"8\xda\x41\x04name\x82\xd3\xe4\x93\x02+:\x01*\"&/v1alpha/{name=pipelines/*}:deactivate\x12\xb1\x01\n\x0eRenamePipeline\x12+.vdp.pipeline.v1alpha.RenamePipelineRequest\x1a,.vdp.pipeline.v1alpha.RenamePipelineResponse\"D\xda\x41\x14name,new_pipeline_id\x82\xd3\xe4\x93\x02\':\x01*\"\"/v1alpha/{name=pipelines/*}:rename\x12\xac\x01\n\x0fTriggerPipeline\x12,.vdp.pipeline.v1alpha.TriggerPipelineRequest\x1a-.vdp.pipeline.v1alpha.TriggerPipelineResponse\"<\xda\x41\x0bname,inputs\x82\xd3\xe4\x93\x02(:\x01*\"#/v1alpha/{name=pipelines/*}:trigger\x12\xae\x01\n\x1fTriggerPipelineBinaryFileUpload\x12<.vdp.pipeline.v1alpha.TriggerPipelineBinaryFileUploadRequest\x1a=.vdp.pipeline.v1alpha.TriggerPipelineBinaryFileUploadResponse\"\x0c\xda\x41\tname,file(\x01\x42\xea\x01\n\x18\x63om.vdp.pipeline.v1alphaB\x14PipelineServiceProtoP\x01ZFgithub.com/instill-ai/protogen-go/vdp/pipeline/v1alpha;pipelinev1alpha\xa2\x02\x03VPX\xaa\x02\x14Vdp.Pipeline.V1alpha\xca\x02\x14Vdp\\Pipeline\\V1alpha\xe2\x02 Vdp\\Pipeline\\V1alpha\\GPBMetadata\xea\x02\x16Vdp::Pipeline::V1alphab\x06proto3') _PIPELINESERVICE = DESCRIPTOR.services_by_name['PipelineService'] if _descriptor._USE_C_DESCRIPTORS == False: DESCRIPTOR._options = None DESCRIPTOR._serialized_options = b'\n\030com.vdp.pipeline.v1alphaB\024PipelineServiceProtoP\001ZFgithub.com/instill-ai/protogen-go/vdp/pipeline/v1alpha;pipelinev1alpha\242\002\003VPX\252\002\024Vdp.Pipeline.V1alpha\312\002\024Vdp\\Pipeline\\V1alpha\342\002 Vdp\\Pipeline\\V1alpha\\GPBMetadata\352\002\026Vdp::Pipeline::V1alpha' _PIPELINESERVICE.methods_by_name['Liveness']._options = None _PIPELINESERVICE.methods_by_name['Liveness']._serialized_options = b'\202\323\344\223\0021Z\032\022\030/v1alpha/health/pipeline\022\023/v1alpha/__liveness' _PIPELINESERVICE.methods_by_name['Readiness']._options = None _PIPELINESERVICE.methods_by_name['Readiness']._serialized_options = b'\202\323\344\223\002\026\022\024/v1alpha/__readiness' _PIPELINESERVICE.methods_by_name['CreatePipeline']._options = None _PIPELINESERVICE.methods_by_name['CreatePipeline']._serialized_options = b'\332A\010pipeline\202\323\344\223\002\036:\010pipeline\"\022/v1alpha/pipelines' _PIPELINESERVICE.methods_by_name['ListPipeline']._options = None _PIPELINESERVICE.methods_by_name['ListPipeline']._serialized_options = b'\202\323\344\223\002\024\022\022/v1alpha/pipelines' _PIPELINESERVICE.methods_by_name['GetPipeline']._options = None _PIPELINESERVICE.methods_by_name['GetPipeline']._serialized_options = b'\332A\004name\202\323\344\223\002\035\022\033/v1alpha/{name=pipelines/*}' _PIPELINESERVICE.methods_by_name['UpdatePipeline']._options = None _PIPELINESERVICE.methods_by_name['UpdatePipeline']._serialized_options = b'\332A\024pipeline,update_mask\202\323\344\223\0020:\010pipeline2$/v1alpha/{pipeline.name=pipelines/*}' _PIPELINESERVICE.methods_by_name['DeletePipeline']._options = None _PIPELINESERVICE.methods_by_name['DeletePipeline']._serialized_options = b'\332A\004name\202\323\344\223\002\035*\033/v1alpha/{name=pipelines/*}' _PIPELINESERVICE.methods_by_name['LookUpPipeline']._options = None _PIPELINESERVICE.methods_by_name['LookUpPipeline']._serialized_options = b'\332A\tpermalink\202\323\344\223\002)\022\'/v1alpha/{permalink=pipelines/*}:lookUp' _PIPELINESERVICE.methods_by_name['ActivatePipeline']._options = None _PIPELINESERVICE.methods_by_name['ActivatePipeline']._serialized_options = b'\332A\004name\202\323\344\223\002):\001*\"$/v1alpha/{name=pipelines/*}:activate' _PIPELINESERVICE.methods_by_name['DeactivatePipeline']._options = None _PIPELINESERVICE.methods_by_name['DeactivatePipeline']._serialized_options = b'\332A\004name\202\323\344\223\002+:\001*\"&/v1alpha/{name=pipelines/*}:deactivate' _PIPELINESERVICE.methods_by_name['RenamePipeline']._options = None _PIPELINESERVICE.methods_by_name['RenamePipeline']._serialized_options = b'\332A\024name,new_pipeline_id\202\323\344\223\002\':\001*\"\"/v1alpha/{name=pipelines/*}:rename' _PIPELINESERVICE.methods_by_name['TriggerPipeline']._options = None _PIPELINESERVICE.methods_by_name['TriggerPipeline']._serialized_options = b'\332A\013name,inputs\202\323\344\223\002(:\001*\"#/v1alpha/{name=pipelines/*}:trigger' _PIPELINESERVICE.methods_by_name['TriggerPipelineBinaryFileUpload']._options = None _PIPELINESERVICE.methods_by_name['TriggerPipelineBinaryFileUpload']._serialized_options = b'\332A\tname,file' _PIPELINESERVICE._serialized_start=202 _PIPELINESERVICE._serialized_end=2326 # @@protoc_insertion_point(module_scope)

131.389831

3,390

0.821723

1,014

7,752

6.083826

0.2357

0.102124

0.105041

0.118009

0.438969

0.281407

0.163722

0.143621

0.106014

0.053493

0

0.110385

0.032379

7,752

58

3,391

133.655172

0.712038

0.030444

0

1

0.325581

0.474154

0.427258

0

1

0

false

0

0.209302

0

0.209302

0

null

0

1

0

1

0

1

null

0

5

fefbae820a9ce01089538fc58c0ca13a3a6231eb

119

py

Python

slash/__init__.py

SilentJungle399/dpy-appcommands

d383ebd3414457aaaf1f65ff048604accb7bb1bc

[ "MIT" ]

2

2021-09-02T13:06:46.000Z

2021-09-03T07:19:54.000Z

slash/__init__.py

SilentJungle399/dpy-appcommands

d383ebd3414457aaaf1f65ff048604accb7bb1bc

[ "MIT" ]

null

slash/__init__.py

SilentJungle399/dpy-appcommands

d383ebd3414457aaaf1f65ff048604accb7bb1bc

[ "MIT" ]

1

2021-08-14T03:38:42.000Z

__author__ = "SilentJungle399" __version__ = "1.0.0" from .client import * from .models import * from .enums import *

17

30

0.722689

15

119

5.2

0.666667

0.25641

0

0.06

0.159664

119

6

31

19.833333

0.72

0

0.168067

0

1

0

false

0

0.6

0

0.6

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

3a16438d4a6793d41974ba3f9e345b3deca9076f

296

py

Python

portfolio/admin.py

jokimies/django-pj-portfolio

ce32882fa3f5cc3206b2a61eb5cd88c0cdf243ec

[ "BSD-3-Clause" ]

3

2017-02-02T19:58:57.000Z

2021-08-10T14:43:37.000Z

portfolio/admin.py

jokimies/django-pj-portfolio

ce32882fa3f5cc3206b2a61eb5cd88c0cdf243ec

[ "BSD-3-Clause" ]

4

2016-01-15T14:18:37.000Z

2016-03-06T15:06:31.000Z

portfolio/admin.py

jokimies/django-pj-portfolio

ce32882fa3f5cc3206b2a61eb5cd88c0cdf243ec

[ "BSD-3-Clause" ]

2

2019-10-12T02:05:49.000Z

2022-03-08T16:25:17.000Z

from portfolio.models import Transaction, Security, Price, Account from portfolio.models import PriceTracker from django.contrib import admin admin.site.register(Transaction) admin.site.register(Security) admin.site.register(Price) admin.site.register(PriceTracker) admin.site.register(Account)

29.6

66

0.841216

38

296

6.552632

0.368421

0.180723

0.341365

0.200803

0

0.070946

296

9

67

32.888889

0.905455

0

1

0

true

0

0.375

0

0.375

0

null

0

1

0

1

0

null

0

1

0

1

0

5

3a16fcd29e32261f583e0fe17a97b6df4dbfd030

391

py

Python

OpticsLab/components.py

AzizAlqasem/OpticsLab

a68c12edc9998f0709bae3da2fa0f85778e19bf0

[ "MIT" ]

null

OpticsLab/components.py

AzizAlqasem/OpticsLab

a68c12edc9998f0709bae3da2fa0f85778e19bf0

[ "MIT" ]

null

OpticsLab/components.py

AzizAlqasem/OpticsLab

a68c12edc9998f0709bae3da2fa0f85778e19bf0

[ "MIT" ]

null

""" The components module has all optical components that are used in optics """ class Mirror: def __init__(self,): pass class Lense: def __init__(self,): pass class Mediam: def __init__(self,): pass class BeamSpliter: def __init__(self,): pass class Waveplate: def __init__(self,): pass

11.848485

76

0.557545

42

391

4.714286

0.5

0.176768

0.277778

0.378788

0.40404

0

0.363171

391

33

77

11.848485

0.795181

0.184143

0

0.666667

0

1

0.333333

false

0.333333

0

0.666667

0

null

0

1

0

null

0

1

0

1

0

1

0

5

28a941e336c661de4e3bc64a26dac8f5e03e398f

58

py

Python

Python/Tests/TestData/AddImport/ImportFunctionFromExistingFromImportAsName.py

techkey/PTVS

8355e67eedd8e915ca49bd38a2f36172696fd903

[ "Apache-2.0" ]

404

2019-05-07T02:21:57.000Z

2022-03-31T17:03:04.000Z

Python/Tests/TestData/AddImport/ImportFunctionFromExistingFromImportAsName.py

techkey/PTVS

8355e67eedd8e915ca49bd38a2f36172696fd903

[ "Apache-2.0" ]

1,672

2019-05-06T21:09:38.000Z

2022-03-31T23:16:04.000Z

Python/Tests/TestData/AddImport/ImportFunctionFromExistingFromImportAsName.py

techkey/PTVS

8355e67eedd8e915ca49bd38a2f36172696fd903

[ "Apache-2.0" ]

186

2019-05-13T03:17:37.000Z

2022-03-31T16:24:05.000Z

from test_module import module_func_2 as oar module_func()

29

44

0.862069

11

58

4.181818

0.727273

0.434783

0

0.019231

0.103448

58

2

45

29

0.865385

0

1

0

true

0

0.5

0

0.5

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

28d45ba4d24a1ed53683bb58cb50eef96ffa6861

209

py

Python

winaudio/exceptions.py

Pixelsuft/winaudio

b66109771811548339905208f9034cb492768337

[ "MIT" ]

1

2021-12-15T10:17:27.000Z

winaudio/exceptions.py

Pixelsuft/winaudio

b66109771811548339905208f9034cb492768337

[ "MIT" ]

1

2022-03-17T14:27:18.000Z

2022-03-17T14:27:29.000Z

winaudio/exceptions.py

Pixelsuft/winaudio

b66109771811548339905208f9034cb492768337

[ "MIT" ]

null

class SoundError(Exception): pass class WavePlayError(Exception): pass class ArgumentError(Exception): pass class PlayerError(Exception): pass class PlayerMciError(Exception): pass

11

32

0.722488

20

209

7.55

0.4

0.430464

0.476821

0

0.205742

209

18

33

11.611111

0.909639

0

0.5

0

1

0

true

0.5

0

0.5

0

1

0

null

1

0

null

0

1

0

5

28f476813b8879c19e3170513ebfab33d088e25a

93

py

Python

ror/number_utils.py

jakub-tomczak/ror

cf9ab38a2d66f4816a1289b9726911960059fce7

[ "MIT" ]

null

ror/number_utils.py

jakub-tomczak/ror

cf9ab38a2d66f4816a1289b9726911960059fce7

[ "MIT" ]

null

ror/number_utils.py

jakub-tomczak/ror

cf9ab38a2d66f4816a1289b9726911960059fce7

[ "MIT" ]

null

def format_number(number: float, precision: int) -> str: return f'{number:.{precision}f}'

46.5

56

0.698925

13

93

4.923077

0.692308

0

0.129032

93

2

57

46.5

0.790123

0

0.234043

0

1

0.5

false

0

0.5

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

3a5d52f7066df721bcc6a4454c0e49f976cabd83

39

py

Python

kfdata/__main__.py

kylef-archive/KFData.py

685d58255c9f8518834e395d94d3b75d3dd3eceb

[ "BSD-3-Clause" ]

1

2015-11-08T13:23:39.000Z

kfdata/__main__.py

kylef/KFData.py

685d58255c9f8518834e395d94d3b75d3dd3eceb

[ "BSD-3-Clause" ]

null

kfdata/__main__.py

kylef/KFData.py

685d58255c9f8518834e395d94d3b75d3dd3eceb

[ "BSD-3-Clause" ]

null

from kfdata.manage import main main()

9.75

30

0.769231

6

39

5

0.833333

0

0.153846

39

3

31

13

0.909091

0

1

0

true

0

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

1

0

5

3a5f95c4dd3189822a688ab6608502a352c54b4b

167

py

Python

slackforms/handlers/__init__.py

Albatrous/django-slack-forms

baee37942085bf2f9e35beb9a4a4aa767b319b35

[ "MIT" ]

1

2019-06-20T00:11:58.000Z

slackforms/handlers/__init__.py

Albatrous/django-slack-forms

baee37942085bf2f9e35beb9a4a4aa767b319b35

[ "MIT" ]

3

2020-02-11T23:46:14.000Z

2021-06-10T21:10:37.000Z

slackforms/handlers/__init__.py

Albatrous/django-slack-forms

baee37942085bf2f9e35beb9a4a4aa767b319b35

[ "MIT" ]

3

2019-12-13T06:53:18.000Z

2021-06-04T07:12:56.000Z

# flake8: noqa from .form import FormHandler from .slash import SlashHandler from .manual import ManualHandler from .interactions import ActionHandler, MessageHandler

27.833333

55

0.838323

19

167

7.368421

0.684211

0

0.006803

0.11976

167

5

56

33.4

0.945578

0.071856

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

3a84202d32e5e1c571adc31fc572e8596c4a5a08

87

py

Python

rura/pipeline/__init__.py

fdabek1/rura

6779733149d7e4181be54ecb72fbd4de6d71c678

[ "MIT" ]

null

rura/pipeline/__init__.py

fdabek1/rura

6779733149d7e4181be54ecb72fbd4de6d71c678

[ "MIT" ]

null

rura/pipeline/__init__.py

fdabek1/rura

6779733149d7e4181be54ecb72fbd4de6d71c678

[ "MIT" ]

null

from .dataset import Dataset from .model import Model from .transform import Transform

21.75

32

0.827586

12

87

6

0.416667

0

0.137931

87

3

33

29

0.96

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

3aa041de8b903df622c3ee51ddf1f6842ee18d8c

59

py

Python

perception/navigator_vision/navigator_vision/__init__.py

czk100/NaviGator

c078c68768c1df4ad48c4c9a60a8c0bf4bdab63a

[ "MIT" ]

null

perception/navigator_vision/navigator_vision/__init__.py

czk100/NaviGator

c078c68768c1df4ad48c4c9a60a8c0bf4bdab63a

[ "MIT" ]

null

perception/navigator_vision/navigator_vision/__init__.py

czk100/NaviGator

c078c68768c1df4ad48c4c9a60a8c0bf4bdab63a

[ "MIT" ]

null

from scan_the_code_classifier import ScanTheCodeClassifier

29.5

58

0.932203

7

59

7.428571

1

0

0.067797

59

1

59

0.945455

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

3aa64d8b8830c4c3c052d815f3baf34b10969969

168

py

Python

core/admin.py

yasminfarza/country-state-address-api

39c8d349095dcca4f2411f7097497d6a8f39c1e1

[ "MIT" ]

4

2021-06-06T14:16:33.000Z

2021-06-09T03:42:11.000Z

core/admin.py

yasminfarza/country-state-address-api

39c8d349095dcca4f2411f7097497d6a8f39c1e1

[ "MIT" ]

null

core/admin.py

yasminfarza/country-state-address-api

39c8d349095dcca4f2411f7097497d6a8f39c1e1

[ "MIT" ]

null

from django.contrib import admin from core.models import Country, State, Address admin.site.register(Country) admin.site.register(State) admin.site.register(Address)

21

47

0.815476

24

168

5.708333

0.5

0.19708

0.372263

0

0.089286

168

7

48

24

0.895425

0

1

0

true

0

0.4

0

0.4

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

5

3adf36991cec5979dbe14a96fcb6614f4fd9f191

12,144

py

Python

team_9/cocos/utest/test_euclid.py

Donnyvdm/dojo19

3cf043a84e3ad6d3c4d59cd9c50b160e1ff03400

[ "BSD-3-Clause" ]

1

2019-09-15T18:59:49.000Z

team_9/cocos/utest/test_euclid.py

Donnyvdm/dojo19

3cf043a84e3ad6d3c4d59cd9c50b160e1ff03400

[ "BSD-3-Clause" ]

null

team_9/cocos/utest/test_euclid.py

Donnyvdm/dojo19

3cf043a84e3ad6d3c4d59cd9c50b160e1ff03400

[ "BSD-3-Clause" ]

null

from __future__ import division, print_function, unicode_literals import cocos.euclid as eu import unittest import copy try: import cPickle as pickle except Exception: import pickle import io class Test_Vector2(unittest.TestCase): def test_instantiate(self): xy = (1.0, 2.0) v2 = eu.Vector2(*xy) self.assertEqual(repr(v2), "Vector2(%.2f, %.2f)" % xy) def test_instantiate_default(self): v2 = eu.Vector2() self.assertEqual(repr(v2), "Vector2(%.2f, %.2f)" % (0, 0)) def test_copy(self): xy = (1.0, 2.0) v2 = eu.Vector2(*xy) copied = v2.__copy__() self.assertEqual(repr(v2), repr(copied)) self.assertFalse(copied is v2) def test_deepcopy(self): xy = (1.0, 2.0) v2 = eu.Vector2(*xy) copied = copy.deepcopy(v2) self.assertEqual(repr(v2), repr(copied)) self.assertFalse(copied is v2) self.assertFalse(hasattr(copied, '__dict__')) # they need __getstate__ and __setstate__ implemented def test_pickle_lower_protocols(self): xy = (1.0, 2.0) v2 = eu.Vector2(*xy) s = pickle.dumps(v2, 0) copied = pickle.loads(s) self.assertEqual(repr(v2), repr(copied)) self.assertFalse(copied is v2) self.assertFalse(hasattr(copied, '__dict__')) s = pickle.dumps(v2, 1) copied = pickle.loads(s) self.assertEqual(repr(v2), repr(copied)) self.assertFalse(copied is v2) self.assertFalse(hasattr(copied, '__dict__')) # don't need __getstate__ / __setstate__ implemented def test_pickle_protocol_2(self): xy = (1.0, 2.0) v2 = eu.Vector2(*xy) s = pickle.dumps(v2, 2) copied = pickle.loads(s) self.assertEqual(repr(v2), repr(copied)) self.assertFalse(copied is v2) self.assertFalse(hasattr(copied, '__dict__')) def test_eq_v2(self): xy = (1.0, 2.0) self.assertTrue(eu.Vector2(*xy), eu.Vector2(*xy)) other = (1.0, 3.0) self.assertTrue( eu.Vector2(*xy) != eu.Vector2(*other)) def test_eq_tuple(self): xy = (1.0, 2.0) self.assertEqual(eu.Vector2(*xy), xy) other = (1.0, 2.0, 3.0) self.assertRaises( AssertionError, lambda a, b: a == b, eu.Vector2(*xy), other) other = 1.0 self.assertRaises( AssertionError, lambda a, b: a == b, eu.Vector2(*xy), other) def test_len(self): xy = (1.0, 2.0) self.assertEqual(len(eu.Vector2(*xy)), 2) def test_index_access__get(self): xy = (1.0, 2.0) v2 = eu.Vector2(*xy) self.assertEqual( v2[0], xy[0]) self.assertEqual(v2[1], xy[1]) self.assertRaises(IndexError, lambda a: v2[a], 2) def test_index_access__set(self): xy = (1.0, 2.0) v2 = eu.Vector2(*xy) v2[0] = 7.0 self.assertEqual(repr(v2), "Vector2(%.2f, %.2f)" % (7.0, 2.0)) v2[1] = 8.0 self.assertEqual(repr(v2), "Vector2(%.2f, %.2f)" % (7.0, 8.0)) def f(): v2[2] = 9.0 self.assertRaises(IndexError, f) def test_iter(self): xy = [1.0, 2.0] v2 = eu.Vector2(*xy) sequence = [e for e in v2] self.assertEqual(sequence, xy) def test_swizzle_get(self): xy = (1.0, 2.0) v2 = eu.Vector2(*xy) self.assertEqual(v2.x, xy[0]) self.assertEqual(v2.y, xy[1]) self.assertEqual(v2.xy, xy) self.assertEqual(v2.yx, (xy[1], xy[0])) exception = None try: v2.z == 11.0 except Exception as a: exception = a assert isinstance(exception, AttributeError) def test_sub__v2_v2(self): a = (3.0, 7.0) b = (1.0, 2.0) va = eu.Vector2(*a) vb = eu.Vector2(*b) self.assertEqual(va-vb, eu.Vector2(2.0, 5.0)) def test_sub__v2_t2(self): a = (3.0, 7.0) b = (1.0, 2.0) va = eu.Vector2(*a) vb = eu.Vector2(*b) self.assertEqual(va-b, eu.Vector2(2.0, 5.0)) def test_rsub__t2_v2(self): a = (3.0, 7.0) b = (1.0, 2.0) va = eu.Vector2(*a) vb = eu.Vector2(*b) self.assertEqual(a-vb, eu.Vector2(2.0, 5.0)) # in py3 or py2 with 'from __future__ import division' # else the integer division is used, as in old euclid.py def test_default_div(self): xy = (4, 7) v2 = eu.Vector2(*xy) c = v2 / 3 self.assertTrue(c.x == 4.0 / 3, c.y == 7.0 / 3) def test_integer_division(self): xy = (4, 7) v2 = eu.Vector2(*xy) c = v2 // 3 self.assertTrue(c.x == 4 // 3, c.y == 7 // 3) def test_add(self): a = (3.0, 7.0) b = (1.0, 2.0) va = eu.Vector2(*a) vb = eu.Vector2(*b) self.assertTrue(isinstance(va+vb, eu.Vector2)) self.assertEqual(repr(va+vb), 'Vector2(%.2f, %.2f)' % (4.0, 9.0)) c = (11.0, 17.0) pc = eu.Point2(*c) d = (13.0, 23.0) pd = eu.Point2(*d) self.assertTrue(isinstance(va+pc, eu.Point2)) self.assertTrue(isinstance(pc+pd, eu.Vector2)) self.assertTrue(isinstance(va + b, eu.Vector2)) self.assertEqual(va + vb, va + b) def test_inplace_add(self): a = (3.0, 7.0) b = (1.0, 2.0) va = eu.Vector2(*a) vb = eu.Vector2(*b) va += b self.assertEqual((va.x, va.y) , (4.0, 9.0)) va = eu.Vector2(*a) va += b self.assertEqual((va.x, va.y) , (4.0, 9.0)) class Test_Vector3(unittest.TestCase): def test_instantiate(self): xyz = (1.0, 2.0, 3.0) v3 = eu.Vector3(*xyz) self.assertEqual(repr(v3), "Vector3(%.2f, %.2f, %.2f)" % xyz) def test_instantiate_default(self): v3 = eu.Vector3() self.assertEqual(repr(v3), "Vector3(%.2f, %.2f, %.2f)" % (0, 0, 0)) def test_copy(self): xyz = (1.0, 2.0, 3.0) v3 = eu.Vector3(*xyz) copied = v3.__copy__() self.assertEqual(repr(v3), repr(copied)) self.assertFalse(copied is v3) def test_deepcopy(self): xyz = (1.0, 2.0, 3.0) v3 = eu.Vector3(*xyz) copied = copy.deepcopy(v3) self.assertEqual(repr(v3), repr(copied)) self.assertFalse(copied is v3) # they need __getstate__ and __setstate__ implemented def test_pickle_lower_protocols(self): xyz = (1.0, 2.0, 3.0) v3 = eu.Vector3(*xyz) s = pickle.dumps(v3, 0) copied = pickle.loads(s) self.assertEqual(repr(v3), repr(copied)) self.assertFalse(copied is v3) self.assertFalse(hasattr(copied, '__dict__')) s = pickle.dumps(v3, 1) copied = pickle.loads(s) self.assertEqual(repr(v3), repr(copied)) self.assertFalse(copied is v3) self.assertFalse(hasattr(copied, '__dict__')) # no need for __getstate__ and __setstate__ def test_pickle_protocol_2(self): xyz = (1.0, 2.0, 3.0) v3 = eu.Vector3(*xyz) s = pickle.dumps(v3, 2) copied = pickle.loads(s) self.assertEqual(repr(v3), repr(copied)) self.assertFalse(copied is v3) def test_eq_v3(self): xyz = (1.0, 2.0, 3.0) self.assertTrue(eu.Vector3(*xyz), eu.Vector3(*xyz)) other = (1.0, 3.0, 7.0) self.assertTrue( eu.Vector3(*xyz) != eu.Vector3(*other)) def test_eq_tuple(self): xyz = (1.0, 2.0, 3.0) self.assertEqual(eu.Vector3(*xyz), xyz) other = (1.0, 2.0, 3.0, 4.0) self.assertRaises( AssertionError, lambda a, b: a == b, eu.Vector3(*xyz), other) other = 1.0 self.assertRaises( AssertionError, lambda a, b: a == b, eu.Vector3(*xyz), other) def test_len(self): xyz = (1.0, 2.0, 3.0) self.assertEqual(len(eu.Vector3(*xyz)), 3) def test_index_access__get(self): xyz = (1.0, 2.0, 3.0) v3 = eu.Vector3(*xyz) self.assertEqual( v3[0], xyz[0]) self.assertEqual(v3[1], xyz[1]) self.assertEqual(v3[2], xyz[2]) self.assertRaises(IndexError, lambda a: v3[a], 3) def test_index_access__set(self): xyz = (1.0, 2.0, 3.0) v3 = eu.Vector3(*xyz) v3[0] = 7.0 self.assertEqual(repr(v3), "Vector3(%.2f, %.2f, %.2f)" % (7.0, 2.0, 3.0)) v3[1] = 8.0 self.assertEqual(repr(v3), "Vector3(%.2f, %.2f, %.2f)" % (7.0, 8.0, 3.0)) v3[2] = 9.0 self.assertEqual(repr(v3), "Vector3(%.2f, %.2f, %.2f)" % (7.0, 8.0, 9.0)) def f(): v3[3] = 9.0 self.assertRaises(IndexError, f) def test_iter(self): xyz = [1.0, 2.0, 3.0] v3 = eu.Vector3(*xyz) sequence = [e for e in v3] self.assertEqual(sequence, xyz) def test_swizzle_get(self): xyz = (1.0, 2.0, 3.0) v3 = eu.Vector3(*xyz) self.assertEqual(v3.x, xyz[0]) self.assertEqual(v3.y, xyz[1]) self.assertEqual(v3.z, xyz[2]) self.assertEqual(v3.xy, (xyz[0], xyz[1])) self.assertEqual(v3.xz, (xyz[0], xyz[2])) self.assertEqual(v3.yz, (xyz[1], xyz[2])) self.assertEqual(v3.yx, (xyz[1], xyz[0])) self.assertEqual(v3.zx, (xyz[2], xyz[0])) self.assertEqual(v3.zy, (xyz[2], xyz[1])) self.assertEqual(v3.xyz, xyz) self.assertEqual(v3.xzy, (xyz[0], xyz[2], xyz[1]) ) self.assertEqual(v3.zyx, (xyz[2], xyz[1], xyz[0]) ) self.assertEqual(v3.zxy, (xyz[2], xyz[0], xyz[1]) ) self.assertEqual(v3.yxz, (xyz[1], xyz[0], xyz[2]) ) self.assertEqual(v3.yzx, (xyz[1], xyz[2], xyz[0]) ) exception = None try: v3.u == 11.0 except Exception as a: exception = a assert isinstance(exception, AttributeError) def test_sub__v3_v3(self): a = (3.0, 7.0, 9.0) b = (1.0, 2.0, 3.0) va = eu.Vector3(*a) vb = eu.Vector3(*b) self.assertEqual(va-vb, eu.Vector3(2.0, 5.0, 6.0)) def test_sub__v3_t3(self): a = (3.0, 7.0, 9.0) b = (1.0, 2.0, 3.0) va = eu.Vector3(*a) vb = eu.Vector3(*b) self.assertEqual(va-b, eu.Vector3(2.0, 5.0, 6.0)) def test_rsub__t3_v3(self): a = (3.0, 7.0, 9.0) b = (1.0, 2.0, 3.0) va = eu.Vector3(*a) vb = eu.Vector3(*b) self.assertEqual(a-vb, eu.Vector3(2.0, 5.0, 6.0)) class Test_Point2(unittest.TestCase): def test_swizzle_get(self): xy = (1.0, 2.0) v2 = eu.Point2(*xy) self.assertEqual(v2.x, xy[0]) self.assertEqual(v2.y, xy[1]) self.assertEqual(v2.xy, xy) self.assertEqual(v2.yx, (xy[1], xy[0])) exception = None try: v2.z == 11.0 except Exception as a: exception = a assert isinstance(exception, AttributeError) class Test_Point3(unittest.TestCase): def test_swizzle_get(self): xyz = (1.0, 2.0, 3.0) v3 = eu.Point3(*xyz) self.assertEqual(v3.x, xyz[0]) self.assertEqual(v3.y, xyz[1]) self.assertEqual(v3.z, xyz[2]) self.assertEqual(v3.xy, (xyz[0], xyz[1])) self.assertEqual(v3.xz, (xyz[0], xyz[2])) self.assertEqual(v3.yz, (xyz[1], xyz[2])) self.assertEqual(v3.yx, (xyz[1], xyz[0])) self.assertEqual(v3.zx, (xyz[2], xyz[0])) self.assertEqual(v3.zy, (xyz[2], xyz[1])) self.assertEqual(v3.xyz, xyz) self.assertEqual(v3.xzy, (xyz[0], xyz[2], xyz[1]) ) self.assertEqual(v3.zyx, (xyz[2], xyz[1], xyz[0]) ) self.assertEqual(v3.zxy, (xyz[2], xyz[0], xyz[1]) ) self.assertEqual(v3.yxz, (xyz[1], xyz[0], xyz[2]) ) self.assertEqual(v3.yzx, (xyz[1], xyz[2], xyz[0]) ) if __name__ == '__main__': unittest.main()

30.512563

81

0.525115

1,786

12,144

3.472564

0.077828

0.188649

0.018381

0.023218

0.844566

0.790874

0.74605

0.725411

0.675427

0.669623

0

0.083373

0.307642

12,144

397

82

30.589421

0.654258

0.025609

0

0.67619

0

0.023338

0

0.365079

1

0.126984

false

0

0.022222

0

0.161905

0.003175

0

null

0

1

0

1

0

null

0

5

3af126e2b1c1da6fe4b8a9b65f8ca9e789c79dde

191

py

Python

apps/access/admin.py

usdigitalresponse/rtovid-encampments

b9d0b6ff27c0b47e31b5db5b0f2f92a1da446f86

[ "MIT" ]

1

2021-06-22T10:11:10.000Z

apps/access/admin.py

usdigitalresponse/rtovid-encampments

b9d0b6ff27c0b47e31b5db5b0f2f92a1da446f86

[ "MIT" ]

23

2020-05-28T01:00:01.000Z

2020-06-23T12:49:55.000Z

apps/access/admin.py

RTCovid/encampments

b9d0b6ff27c0b47e31b5db5b0f2f92a1da446f86

[ "MIT" ]

null

from django.contrib.gis import admin from apps.access.models import InvitedEmail class InvitedEmailAdmin(admin.ModelAdmin): pass admin.site.register(InvitedEmail, InvitedEmailAdmin)

17.363636

52

0.816754

22

191

7.090909

0.727273

0

0.115183

191

10

53

19.1

0.923077

0

1

0

true

0.2

0.4

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0.6

0

1

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null

0

null

0

1

0

1

0

5

aaee23ba02e2df2083e1a2d6aa2430790b04b2a3

35

py

Python

src/utils/__init__.py

Columbine21/Hierarchical-Attention-Networks

623840970cb302c7f74515ffff1560c0131b975e

[ "MIT" ]

1

2021-03-15T02:45:28.000Z

src/utils/__init__.py

Columbine21/Hierarchical-Attention-Networks

623840970cb302c7f74515ffff1560c0131b975e

[ "MIT" ]

null

src/utils/__init__.py

Columbine21/Hierarchical-Attention-Networks

623840970cb302c7f74515ffff1560c0131b975e

[ "MIT" ]

null

from .vocab import gloveVocabulary

17.5

34

0.857143

4

35

7.5

1

0

0.114286

35

1

35

0.967742

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

c94170821cd5e437201c56213668e61ba65bc8e5

21,018

py

Python

methcomp/regression.py

daneishdespot/methcomp

767d85aa56a8fda372847585decca8879ec2ac98

[ "MIT" ]

null

methcomp/regression.py

daneishdespot/methcomp

767d85aa56a8fda372847585decca8879ec2ac98

[ "MIT" ]

null

methcomp/regression.py

daneishdespot/methcomp

767d85aa56a8fda372847585decca8879ec2ac98

[ "MIT" ]

null

import matplotlib.pyplot as plt import pandas as pd import scipy.stats as st import statsmodels.api as sm import math import numpy as np __all__ = ["deming", "passingbablok", "linear"] class _Deming(object): """Internal class for drawing a Deming regression plot""" def __init__(self, method1, method2, vr, sdr, bootstrap, x_label, y_label, title, CI, line_reference, line_CI, legend, color_points, color_deming, point_kws): self.method1: np.array = np.asarray(method1) self.method2: np.array = np.asarray(method2) self.vr = vr self.sdr = sdr self.bootstrap = bootstrap self.x_title = x_label self.y_title = y_label self.graph_title = title self.color_points = color_points self.color_deming = color_deming self.CI = CI self.line_reference = line_reference self.line_CI = line_CI self.legend = legend self.point_kws = {} if point_kws is None else point_kws.copy() self._check_params() self._derive_params() def _check_params(self): if len(self.method1) != len(self.method2): raise ValueError('Length of method 1 and method 2 are not equal.') if self.bootstrap is not None and not isinstance(self.bootstrap, int): raise ValueError('Bootstrap argument should either be None or an integer.') if self.CI is not None and (self.CI > 1 or self.CI < 0): raise ValueError('Confidence interval must be between 0 and 1.') if any([not isinstance(x, str) for x in [self.x_title, self.y_title]]): raise ValueError('Axes labels arguments should be provided as a str.') def _derive_params(self): def _deming(x, y, lamb): ssdx = np.var(x, ddof=1) * (self.n - 1) ssdy = np.var(y, ddof=1) * (self.n - 1) spdxy = np.cov(x, y)[1][1] * (self.n - 1) beta = (ssdy - lamb * ssdx + math.sqrt((ssdy - lamb * ssdx) ** 2 + 4 * lamb * (ssdy ** 2))) / ( 2 * spdxy) alpha = y.mean() - beta * x.mean() ksi = (lamb * x + beta * (y - alpha)) / (lamb + beta ** 2) sigmax = lamb * sum((x - ksi) ** 2) + sum((y - alpha - beta * ksi) ** 2) / ( (self.n - 2) * beta) sigmay = math.sqrt(lamb * sigmax) sigmax = math.sqrt(sigmax) return alpha, beta, sigmax, sigmay self.n = len(self.method1) if self.vr is not None: _lambda = self.vr elif self.sdr is not None: _lambda = self.sdr else: _lambda = 1 params = _deming(self.method1, self.method2, _lambda) if self.bootstrap is None: self.alpha = params[0] self.beta = params[1] self.sigmax = params[2] self.sigmay = params[3] else: _params = np.zeros([self.bootstrap, 4]) for i in range(self.bootstrap): idx = np.random.choice(range(self.n), self.n, replace=True) _params[i] = _deming(np.take(self.method1, idx), np.take(self.method2, idx), _lambda) _paramsdf = pd.DataFrame(_params, columns=['alpha', 'beta', 'sigmax', 'sigmay']) se = np.sqrt(np.diag(np.cov(_paramsdf.cov()))) t = np.transpose( np.apply_along_axis(np.quantile, 0, _params, [0.5, (1 - self.CI) / 2, 1 - (1 - self.CI) / 2])) self.alpha = [t[0][0], se[0], t[0][1], t[0][2]] self.beta = [t[1][0], se[1], t[0][1], t[0][2]] self.sigmax = [t[2][0], se[2], t[0][1], t[0][2]] self.sigmay = [t[3][0], se[3], t[0][1], t[0][2]] def plot(self, ax): # plot individual points ax.scatter(self.method1, self.method2, s=20, alpha=0.6, color=self.color_points) # plot reference line if self.line_reference: ax.plot([0, 1], [0, 1], label='Reference', color='grey', linestyle='--', transform=ax.transAxes) # plot Deming-line _xvals = np.array(ax.get_xlim()) if self.bootstrap is None: _yvals = self.alpha + self.beta * _xvals ax.plot(_xvals, _yvals, label=f'{self.alpha:.2f} + {self.beta:.2f} * Method 1', color=self.color_deming, linestyle='-') else: _yvals = [self.alpha[s] + self.beta[0] * _xvals for s in range(0, 4)] ax.plot(_xvals, _yvals[0], label=f'{self.alpha[0]:.2f} + {self.beta[0]:.2f} * Method 1', color=self.color_deming, linestyle='-') ax.fill_between(_xvals, _yvals[2], _yvals[3], color=self.color_deming, alpha=0.2) if self.line_CI: ax.plot(_xvals, _yvals[2], linestyle='--') ax.plot(_xvals, _yvals[3], linestyle='--') if self.legend: ax.legend(loc='upper left', frameon=False) ax.set_ylabel(self.y_title) ax.set_xlabel(self.x_title) if self.graph_title is not None: ax.set_title(self.graph_title) def deming(method1, method2, vr=None, sdr=None, bootstrap=1000, x_label='Method 1', y_label='Method 2', title=None, CI=0.95, line_reference=True, line_CI=False, legend=True, color_points='#000000', color_deming='#008bff', point_kws=None, square=False, ax=None): """Provide a method comparison using Deming regression. This is an Axis-level function which will draw the Deming plot onto the current active Axis object unless ``ax`` is provided. Parameters ---------- method1, method2 : array, or list Values obtained from both methods, preferably provided in a np.array. vr : float The assumed known ratio of the (residual) variance of the ys relative to that of the xs. Defaults to 1. sdr : float The assumed known standard deviations. Parameter vr takes precedence if both are given. Defaults to 1. bootstrap : int or None Amount of bootstrap estimates that should be performed to acquire standard errors (and confidence intervals). If None, no bootstrapping is performed. Defaults to 1000. x_label : str, optional The label which is added to the X-axis. If None is provided, a standard label will be added. y_label : str, optional The label which is added to the Y-axis. If None is provided, a standard label will be added. title : str, optional Title of the plot. If None is provided, no title will be plotted. CI : float, optional The confidence interval employed in Deming line. Defaults to 0.95. line_reference : bool, optional If True, a grey reference line at y=x will be plotted in the plot. Defaults to true. line_CI : bool, optional If True, dashed lines will be plotted at the boundaries of the confidence intervals. Defaults to false. legend : bool, optional If True, will provide a legend containing the computed Deming equation. Defaults to true. color_points : str, optional Color of the individual differences that will be plotted. Color should be provided in format compatible with matplotlib. color_deming : str, optional Color of the mean difference line that will be plotted. Color should be provided in format compatible with matplotlib. square : bool, optional If True, set the Axes aspect to "equal" so each cell will be square-shaped. point_kws : dict of key, value mappings, optional Additional keyword arguments for `plt.scatter`. ax : matplotlib Axes, optional Axes in which to draw the plot, otherwise use the currently-active Axes. Returns ------- ax : matplotlib Axes Axes object with the Deming plot. See Also ------- Koopmans, T. C. (1937). Linear regression analysis of economic time series. DeErven F. Bohn, Haarlem, Netherlands. Deming, W. E. (1943). Statistical adjustment of data. Wiley, NY (Dover Publications edition, 1985). """ plotter: _Deming = _Deming(method1, method2, vr, sdr, bootstrap, x_label, y_label, title, CI, line_reference, line_CI, legend, color_points, color_deming, point_kws) # Draw the plot and return the Axes if ax is None: ax = plt.gca() if square: ax.set_aspect('equal') plotter.plot(ax) return ax class _PassingBablok(object): """Internal class for drawing a Passing-Bablok regression plot""" def __init__(self, method1, method2, x_label, y_label, title, CI, line_reference, line_CI, legend, color_points, color_paba, point_kws): self.method1: np.array = np.asarray(method1) self.method2: np.array = np.asarray(method2) self.x_title = x_label self.y_title = y_label self.graph_title = title self.CI = CI self.color_points = color_points self.color_paba = color_paba self.line_reference = line_reference self.line_CI = line_CI self.legend = legend self.point_kws = {} if point_kws is None else point_kws.copy() self._check_params() self._derive_params() def _check_params(self): if len(self.method1) != len(self.method2): raise ValueError('Length of method 1 and method 2 are not equal.') if self.CI is not None and (self.CI > 1 or self.CI < 0): raise ValueError('Confidence interval must be between 0 and 1.') if any([not isinstance(x, str) for x in [self.x_title, self.y_title]]): raise ValueError('Axes labels arguments should be provided as a str.') def _derive_params(self): self.n = len(self.method1) self.sv = [] for i in range(self.n - 1): for j in range(i + 1, self.n): self.sv.append((self.method2[i] - self.method2[j]) / (self.method1[i] - self.method1[j])) self.sv.sort() n = len(self.sv) k = math.floor(len([a for a in self.sv if a < 0]) / 2) if n % 2 == 1: self.slope = self.sv[int((n + 1) / k + 2)] else: self.slope = math.sqrt(self.sv[int(n / 2 + k)] * self.sv[int(n / 2 + k + 1)]) _ci = st.norm.ppf(1 - (1 - self.CI) / 2) * math.sqrt((self.n * (self.n - 1) * (2 * self.n + 5)) / 18) _m1 = int(round((n - _ci) / 2)) _m2 = n - _m1 - 1 self.slope = [self.slope, self.sv[k + _m1], self.sv[k + _m2]] self.intercept = [np.median(self.method2 - self.slope[0] * self.method1), np.median(self.method2 - self.slope[1] * self.method1), np.median(self.method2 - self.slope[2] * self.method1)] def plot(self, ax): # plot individual points ax.scatter(self.method1, self.method2, s=20, alpha=0.6, color=self.color_points, **self.point_kws) # plot reference line if self.line_reference: ax.plot([0, 1], [0, 1], label='Reference', color='grey', linestyle='--', transform=ax.transAxes) # plot PaBa-line _xvals = np.array(ax.get_xlim()) _yvals = [self.intercept[s] + self.slope[s] * _xvals for s in range(0, 3)] ax.plot(_xvals, _yvals[0], label=f'{self.intercept[0]:.2f} + {self.slope[0]:.2f} * Method 1', color=self.color_paba, linestyle='-') ax.fill_between(_xvals, _yvals[1], _yvals[2], color=self.color_paba, alpha=0.2) if self.line_CI: ax.plot(_xvals, _yvals[1], linestyle='--') ax.plot(_xvals, _yvals[2], linestyle='--') if self.legend: ax.legend(loc='upper left', frameon=False) ax.set_ylabel(self.y_title) ax.set_xlabel(self.x_title) if self.graph_title is not None: ax.set_title(self.graph_title) def passingbablok(method1, method2, x_label='Method 1', y_label='Method 2', title=None, CI=0.95, line_reference=True, line_CI=False, legend=True, color_points='#000000', color_paba='#008bff', point_kws=None, square=False, ax=None): """Provide a method comparison using Passing-Bablok regression. This is an Axis-level function which will draw the Passing-Bablok plot onto the current active Axis object unless ``ax`` is provided. Parameters ---------- method1, method2 : array, or list Values obtained from both methods, preferably provided in a np.array. x_label : str, optional The label which is added to the X-axis. If None is provided, a standard label will be added. y_label : str, optional The label which is added to the Y-axis. If None is provided, a standard label will be added. title : str, optional Title of the Passing-Bablok plot. If None is provided, no title will be plotted. CI : float, optional The confidence interval employed in the passing-bablok line. Defaults to 0.95. line_reference : bool, optional If True, a grey reference line at y=x will be plotted in the plot. Defaults to true. line_CI : bool, optional If True, dashed lines will be plotted at the boundaries of the confidence intervals. Defaults to false. legend : bool, optional If True, will provide a legend containing the computed Passing-Bablok equation. Defaults to true. color_points : str, optional Color of the individual differences that will be plotted. Color should be provided in format compatible with matplotlib. color_paba : str, optional Color of the mean difference line that will be plotted. Color should be provided in format compatible with matplotlib. square : bool, optional If True, set the Axes aspect to "equal" so each cell will be square-shaped. point_kws : dict of key, value mappings, optional Additional keyword arguments for `plt.scatter`. ax : matplotlib Axes, optional Axes in which to draw the plot, otherwise use the currently-active Axes. Returns ------- ax : matplotlib Axes Axes object with the Passing-Bablok plot. See Also ------- Passing H and Bablok W. J Clin Chem Clin Biochem, vol. 21, no. 11, 1983, pp. 709 - 720 """ plotter: _PassingBablok = _PassingBablok(method1, method2, x_label, y_label, title, CI, line_reference, line_CI, legend, color_points, color_paba, point_kws) # Draw the plot and return the Axes if ax is None: ax = plt.gca() if square: ax.set_aspect('equal') plotter.plot(ax) return ax class _Linear(object): """Internal class for drawing a simple, linear regression plot""" def __init__(self, method1, method2, x_label, y_label, title, CI, line_reference, line_CI, legend, color_points, color_regr, point_kws): self.method1: np.array = np.asarray(method1) self.method2: np.array = np.asarray(method2) self.x_title = x_label self.y_title = y_label self.graph_title = title self.CI = CI self.color_points = color_points self.color_regr = color_regr self.line_reference = line_reference self.line_CI = line_CI self.legend = legend self.point_kws = {} if point_kws is None else point_kws.copy() self._check_params() self._derive_params() def _check_params(self): if len(self.method1) != len(self.method2): raise ValueError('Length of method 1 and method 2 are not equal.') if self.CI is not None and (self.CI > 1 or self.CI < 0): raise ValueError('Confidence interval must be between 0 and 1.') if any([not isinstance(x, str) for x in [self.x_title, self.y_title]]): raise ValueError('Axes labels arguments should be provided as a str.') def _derive_params(self): self.n = len(self.method1) _model = sm.OLS(self.method1, sm.add_constant(self.method2)).fit() _params = _model.params _confint = _model.conf_int(alpha=self.CI) self.intercept = [_confint[0][0], _params[0], _confint[0][1]] self.slope = [_confint[1][0], _params[1], _confint[1][1]] def plot(self, ax): # plot individual points ax.scatter(self.method1, self.method2, s=20, alpha=0.6, color=self.color_points, **self.point_kws) # plot reference line if self.line_reference: ax.plot([0, 1], [0, 1], label='Reference', color='grey', linestyle='--', transform=ax.transAxes) # plot linear regression _xvals = np.array(ax.get_xlim()) _yvals = [self.intercept[s] + self.slope[s] * _xvals for s in range(0, 3)] ax.plot(_xvals, _yvals[0], label=f'{self.intercept[0]:.2f} + {self.slope[0]:.2f} * Method 1', color=self.color_regr, linestyle='-') ax.fill_between(_xvals, _yvals[1], _yvals[2], color=self.color_regr, alpha=0.2) if self.line_CI: ax.plot(_xvals, _yvals[1], linestyle='--') ax.plot(_xvals, _yvals[2], linestyle='--') if self.legend: ax.legend(loc='upper left', frameon=False) ax.set_ylabel(self.y_title) ax.set_xlabel(self.x_title) if self.graph_title is not None: ax.set_title(self.graph_title) def linear(method1, method2, x_label='Method 1', y_label='Method 2', title=None, CI=0.95, line_reference=True, line_CI=False, legend=True, color_points='#000000', color_regr='#008bff', point_kws=None, square=False, ax=None): """Provide a method comparison using simple, linear regression. This is an Axis-level function which will draw the linear regression plot onto the current active Axis object unless ``ax`` is provided. Parameters ---------- method1, method2 : array, or list Values obtained from both methods, preferably provided in a np.array. x_label : str, optional The label which is added to the X-axis. If None is provided, a standard label will be added. y_label : str, optional The label which is added to the Y-axis. If None is provided, a standard label will be added. title : str, optional Title of the linear regression plot. If None is provided, no title will be plotted. CI : float, optional The confidence interval employed in the linear regression line. Defaults to 0.95. line_reference : bool, optional If True, a grey reference line at y=x will be plotted in the plot. Defaults to true. line_CI : bool, optional If True, dashed lines will be plotted at the boundaries of the confidence intervals. Defaults to false. legend : bool, optional If True, will provide a legend containing the computed Linear regression equation. Defaults to true. color_points : str, optional Color of the individual differences that will be plotted. Color should be provided in format compatible with matplotlib. color_paba : str, optional Color of the mean difference line that will be plotted. Color should be provided in format compatible with matplotlib. square : bool, optional If True, set the Axes aspect to "equal" so each cell will be square-shaped. point_kws : dict of key, value mappings, optional Additional keyword arguments for `plt.scatter`. ax : matplotlib Axes, optional Axes in which to draw the plot, otherwise use the currently-active Axes. Returns ------- ax : matplotlib Axes Axes object with the linear regression plot. See Also ------- .............. """ plotter: _Linear = _Linear(method1, method2, x_label, y_label, title, CI, line_reference, line_CI, legend, color_points, color_regr, point_kws) # Draw the plot and return the Axes if ax is None: ax = plt.gca() if square: ax.set_aspect('equal') plotter.plot(ax) return ax

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Python

run.py

Tokisaki-Kurumi001/ASMART-34

04ffbabe4a1c18f8ed68a2ee883145985fc5ec7f

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2021-04-17T08:34:08.000Z

2021-04-17T08:57:23.000Z

run.py

Tokisaki-Kurumi001/ASMART-34

04ffbabe4a1c18f8ed68a2ee883145985fc5ec7f

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null

run.py

Tokisaki-Kurumi001/ASMART-34

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[ "MIT" ]

null

import os os.system('python function_18351015.py > log.txt')

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Python

social/backends/ubuntu.py

raccoongang/python-social-auth

81c0a542d158772bd3486d31834c10af5d5f08b0

[ "BSD-3-Clause" ]

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2015-01-01T16:12:45.000Z

2022-03-29T14:24:25.000Z

social/backends/ubuntu.py

raccoongang/python-social-auth

81c0a542d158772bd3486d31834c10af5d5f08b0

[ "BSD-3-Clause" ]

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2015-01-01T22:55:25.000Z

2022-03-10T15:07:51.000Z

virtual/lib/python3.6/site-packages/social/backends/ubuntu.py

dennismwaniki67/awards

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[ "MIT" ]

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2015-01-01T16:27:26.000Z

2022-03-22T21:18:33.000Z

from social_core.backends.ubuntu import UbuntuOpenId

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py

Python

derender/networks.py

tonyman1008/RADAR

b2fc944230c2fd445528a9827eea42e1a94957b8

[ "CC0-1.0" ]

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2021-08-19T18:07:49.000Z

2022-02-28T10:41:29.000Z

derender/networks.py

tonyman1008/RADAR

b2fc944230c2fd445528a9827eea42e1a94957b8

[ "CC0-1.0" ]

1

2021-10-30T14:43:18.000Z

2021-11-13T01:18:53.000Z

derender/networks.py

tonyman1008/RADAR

b2fc944230c2fd445528a9827eea42e1a94957b8

[ "CC0-1.0" ]

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2021-08-20T05:12:42.000Z

2022-01-13T06:14:27.000Z

import numpy as np import torch import torch.nn as nn import torchvision EPS = 1e-7 class Encoder(nn.Module): def __init__(self, cin, cout, in_size=64, nf=64, activation=nn.Tanh): super(Encoder, self).__init__() network = [ nn.Conv2d(cin, nf, kernel_size=4, stride=2, padding=1, bias=False), # 64x64 -> 32x32 nn.ReLU(inplace=True), nn.Conv2d(nf, nf*2, kernel_size=4, stride=2, padding=1, bias=False), # 32x32 -> 16x16 nn.ReLU(inplace=True), nn.Conv2d(nf*2, nf*4, kernel_size=4, stride=2, padding=1, bias=False), # 16x16 -> 8x8 nn.ReLU(inplace=True), nn.Conv2d(nf*4, nf*8, kernel_size=4, stride=2, padding=1, bias=False), # 8x8 -> 4x4 nn.ReLU(inplace=True), ] add_downsample = int(np.log2(in_size//64)) if add_downsample > 0: for _ in range(add_downsample): network += [ nn.Conv2d(nf*8, nf*8, kernel_size=4, stride=2, padding=1, bias=False), # 8x8 -> 4x4 nn.ReLU(inplace=True), ] network += [ nn.Conv2d(nf*8, nf*8, kernel_size=4, stride=1, padding=0, bias=False), # 4x4 -> 1x1 nn.ReLU(inplace=True), nn.Conv2d(nf*8, cout, kernel_size=1, stride=1, padding=0, bias=False) ] if activation is not None: network += [activation()] self.network = nn.Sequential(*network) def forward(self, input): return self.network(input).reshape(input.size(0),-1) class SoRNet(nn.Module): def __init__(self, cin, cout2=5, in_size=64, out_size=32, zdim=128, nf=64, activation=nn.Tanh): super(SoRNet, self).__init__() encoder = [ nn.Conv2d(cin, nf, kernel_size=4, stride=2, padding=1, bias=False), # 64x64 -> 32x32 nn.ReLU(inplace=True), nn.Conv2d(nf, nf*2, kernel_size=4, stride=2, padding=1, bias=False), # 32x32 -> 16x16 nn.ReLU(inplace=True), nn.Conv2d(nf*2, nf*4, kernel_size=4, stride=2, padding=1, bias=False), # 16x16 -> 8x8 nn.ReLU(inplace=True), nn.Conv2d(nf*4, nf*8, kernel_size=4, stride=2, padding=1, bias=False), # 8x8 -> 4x4 nn.ReLU(inplace=True), ] add_downsample = int(np.log2(in_size//64)) if add_downsample > 0: for _ in range(add_downsample): encoder += [ nn.Conv2d(nf*8, nf*8, kernel_size=4, stride=2, padding=1, bias=False), # 8x8 -> 4x4 nn.ReLU(inplace=True), ] encoder += [ nn.Conv2d(nf*8, nf*8, kernel_size=4, stride=1, padding=0, bias=False), # 4x4 -> 1x1 nn.ReLU(inplace=True), nn.Conv2d(nf*8, zdim, kernel_size=1, stride=1, padding=0, bias=False), nn.ReLU(inplace=True), ] self.encoder = nn.Sequential(*encoder) out_net1 = [] add_upsample = int(np.log2(out_size//2)) if add_upsample > 0: for _ in range(add_upsample): out_net1 += [ nn.Upsample(scale_factor=(2,1), mode='nearest'), # 1x1 -> 2x1 nn.Conv2d(zdim, zdim, kernel_size=(3,1), stride=(1,1), padding=(1,0), bias=False, padding_mode='replicate'), nn.ReLU(inplace=True), ] out_net1 += [ nn.Upsample(scale_factor=(2,1), mode='nearest'), # 16x1 -> 32x1 nn.Conv2d(zdim, 1, kernel_size=(3,1), stride=(1,1), padding=(1,0), bias=False, padding_mode='replicate'), ] if activation is not None: out_net1 += [activation()] self.out_net1 = nn.Sequential(*out_net1) out_net2 = [ nn.Linear(zdim, zdim), nn.ReLU(inplace=True), nn.Linear(zdim, cout2), nn.Sigmoid(), # nn.Tanh(), ] self.out_net2 = nn.Sequential(*out_net2) def forward(self, input): z = self.encoder(input) out1 = self.out_net1(z).view(input.size(0), -1) out2 = self.out_net2(z.view(input.size(0), -1)) # /2+0.5 return out1, out2 class EnvMapNet(nn.Module): def __init__(self, cin, cout, cout2=None, in_size=64, out_size=16, zdim=128, nf=64, activation=nn.Tanh): super(EnvMapNet, self).__init__() ## downsampling encoder = [ nn.Conv2d(cin, nf, kernel_size=4, stride=2, padding=1, bias=False), # 64x64 -> 32x32 nn.GroupNorm(16, nf), # nn.BatchNorm2d(nf), nn.LeakyReLU(0.2, inplace=True), nn.Conv2d(nf, nf*2, kernel_size=4, stride=2, padding=1, bias=False), # 32x32 -> 16x16 nn.GroupNorm(16*2, nf*2), # nn.BatchNorm2d(nf*2), nn.LeakyReLU(0.2, inplace=True), nn.Conv2d(nf*2, nf*4, kernel_size=4, stride=2, padding=1, bias=False), # 16x16 -> 8x8 nn.GroupNorm(16*4, nf*4), # nn.BatchNorm2d(nf*4), nn.LeakyReLU(0.2, inplace=True), nn.Conv2d(nf*4, nf*8, kernel_size=4, stride=2, padding=1, bias=False), # 16x16 -> 8x8 nn.GroupNorm(16*8, nf*8), # nn.BatchNorm2d(nf*4), nn.LeakyReLU(0.2, inplace=True), ] add_downsample = int(np.log2(in_size//128)) if add_downsample > 0: for _ in range(add_downsample): encoder += [ nn.Conv2d(nf*8, nf*8, kernel_size=4, stride=2, padding=1, bias=False), # 16x16 -> 8x8 nn.GroupNorm(16*8, nf*8), # nn.BatchNorm2d(nf*8), nn.LeakyReLU(0.2, inplace=True), ] encoder += [ nn.Conv2d(nf*8, nf*8, kernel_size=4, stride=2, padding=1, bias=False), # 8x8 -> 4x4 nn.LeakyReLU(0.2, inplace=True), nn.Conv2d(nf*8, zdim, kernel_size=4, stride=1, padding=0, bias=False), # 4x4 -> 1x1 nn.ReLU(inplace=True) ] self.encoder = nn.Sequential(*encoder) ## upsampling decoder_envmap = [ nn.ConvTranspose2d(zdim, nf*8, kernel_size=(2,6), stride=1, padding=0, bias=False), # 1x1 -> 4x4 nn.ReLU(inplace=True), ] add_upsample = int(np.log2(out_size//16)) if add_upsample > 0: for _ in range(add_upsample): decoder_envmap += [ nn.Upsample(scale_factor=2, mode='nearest'), nn.Conv2d(nf*8, nf*8, kernel_size=3, stride=1, padding=1, bias=False, padding_mode='replicate'), nn.GroupNorm(16*8, nf*8), nn.ReLU(inplace=True), ] decoder_envmap += [ nn.Upsample(scale_factor=2, mode='nearest'), nn.Conv2d(nf*8, nf*4, kernel_size=3, stride=1, padding=1, bias=False, padding_mode='replicate'), nn.GroupNorm(16*4, nf*4), nn.ReLU(inplace=True), nn.Upsample(scale_factor=2, mode='nearest'), nn.Conv2d(nf*4, nf*2, kernel_size=3, stride=1, padding=1, bias=False, padding_mode='replicate'), nn.GroupNorm(16*2, nf*2), nn.ReLU(inplace=True), nn.Upsample(scale_factor=2, mode='nearest'), nn.Conv2d(nf*2, nf, kernel_size=3, stride=1, padding=1, bias=False, padding_mode='replicate'), nn.GroupNorm(16, nf), nn.ReLU(inplace=True), nn.Conv2d(nf, cout, kernel_size=5, stride=1, padding=2, bias=False, padding_mode='replicate') ] self.decoder_envmap = nn.Sequential(*decoder_envmap) if activation is not None: self.act = activation() else: self.act = None if cout2 is not None: decoder_light_param = [ nn.Linear(zdim, zdim), nn.ReLU(inplace=True), nn.Linear(zdim, cout2), nn.Sigmoid() ] self.decoder_light_param = nn.Sequential(*decoder_light_param) else: self.decoder_light_param = None def forward(self, input): z = self.encoder(input) env_map = self.decoder_envmap(z) env_map = env_map - 2 # initial sigmoid(-2) # env_map = env_map - 3 # initial sigmoid(-3), for 32x96 env_map if self.act is not None: env_map = self.act(env_map) if self.decoder_light_param is not None: light_param = self.decoder_light_param(z.view(*z.shape[:2])) return env_map, light_param else: return env_map class DiscNet(nn.Module): def __init__(self, cin, cout, nf=64, norm=nn.InstanceNorm2d, activation=None): super(DiscNet, self).__init__() network = [ nn.Conv2d(cin, nf, kernel_size=4, stride=2, padding=1, bias=False), # 64x64 -> 32x32 norm(nf), nn.LeakyReLU(0.2, inplace=True), nn.Conv2d(nf, nf*2, kernel_size=4, stride=2, padding=1, bias=False), # 32x32 -> 16x16 norm(nf*2), nn.LeakyReLU(0.2, inplace=True), nn.Conv2d(nf*2, nf*4, kernel_size=4, stride=2, padding=1, bias=False), # 16x16 -> 8x8 norm(nf*4), nn.LeakyReLU(0.2, inplace=True), nn.Conv2d(nf*4, nf*8, kernel_size=4, stride=2, padding=1, bias=False), # 8x8 -> 4x4 # norm(nf*8), nn.LeakyReLU(0.2, inplace=True), nn.Conv2d(nf*8, cout, kernel_size=4, stride=1, padding=0, bias=False), # 4x4 -> 1x1 ] if activation is not None: network += [activation()] self.network = nn.Sequential(*network) def forward(self, input): return self.network(input).reshape(input.size(0),-1)

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py

Python

tools/inject_pydoc/idp.py

fengjixuchui/src

0c5a6cd8057717f73b1373f8d85eb9b19e1934e1

[ "BSD-3-Clause" ]

1,160

2015-05-02T15:13:20.000Z

2022-03-31T20:04:28.000Z

tools/inject_pydoc/idp.py

fengjixuchui/src

0c5a6cd8057717f73b1373f8d85eb9b19e1934e1

[ "BSD-3-Clause" ]

19

2015-04-20T13:47:00.000Z

2021-07-07T13:00:42.000Z

tools/inject_pydoc/idp.py

fengjixuchui/src

0c5a6cd8057717f73b1373f8d85eb9b19e1934e1

[ "BSD-3-Clause" ]

257

2015-04-01T21:42:33.000Z

2022-03-10T11:57:51.000Z

{ "ev_get_bg_color" : { "repl_text" : ("(self, color, ea) -> int", "(self, ea) -> int or None"), } }

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a3842c6138c7e752e05c72628b0129a00a3d511f

1,617

py

Python

tests/test_reduce_sum.py

gavinuhma/tf-encrypted

4e18d78a151bbe91489a1773fb839b889ff5b460

[ "Apache-2.0" ]

3

2018-10-18T19:36:02.000Z

2020-07-05T19:46:23.000Z

tests/test_reduce_sum.py

dropoutlabs/tf-encrypted

48c9dc7419163425e736ad05bb19980d134fc851

[ "Apache-2.0" ]

null

tests/test_reduce_sum.py

dropoutlabs/tf-encrypted

48c9dc7419163425e736ad05bb19980d134fc851

[ "Apache-2.0" ]

null

# pylint: disable=missing-docstring import unittest import numpy as np import tensorflow as tf import tf_encrypted as tfe class TestReduceSum(unittest.TestCase): def setUp(self): tf.reset_default_graph() def test_reduce_sum_1d(self): t = [1, 2] with tf.Session() as sess: out = tf.reduce_sum(t) actual = sess.run(out) with tfe.protocol.Pond() as prot: b = prot.define_private_variable(tf.constant(t)) out = prot.reduce_sum(b) with tfe.Session() as sess: sess.run(tf.global_variables_initializer()) final = sess.run(out.reveal()) np.testing.assert_array_equal(final, actual) def test_reduce_sum_2d(self): t = [[1, 2], [1, 3]] with tf.Session() as sess: out = tf.reduce_sum(t, axis=1) actual = sess.run(out) with tfe.protocol.Pond() as prot: b = prot.define_private_variable(tf.constant(t)) out = prot.reduce_sum(b, axis=1) with tfe.Session() as sess: sess.run(tf.global_variables_initializer()) final = sess.run(out.reveal()) np.testing.assert_array_equal(final, actual) def test_reduce_sum_huge_vector(self): t = [1] * 2**13 with tf.Session() as sess: out = tf.reduce_sum(t) actual = sess.run(out) with tfe.protocol.Pond() as prot: b = prot.define_private_variable(tf.constant(t)) out = prot.reduce_sum(b) with tfe.Session() as sess: sess.run(tf.global_variables_initializer()) final = sess.run(out.reveal()) np.testing.assert_array_equal(final, actual) if __name__ == '__main__': unittest.main()

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a3a5bb350e05522589702afb78e2a9430fe6a8c4

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py

Python

test.py

vinsmokemau/NQueens

7c9291f655b8e4f0ce4c6c5d07a80440f8f2c0a8

[ "MIT" ]

null

test.py

vinsmokemau/NQueens

7c9291f655b8e4f0ce4c6c5d07a80440f8f2c0a8

[ "MIT" ]

null

test.py

vinsmokemau/NQueens

7c9291f655b8e4f0ce4c6c5d07a80440f8f2c0a8

[ "MIT" ]

null

import unittest from algorithm import NQueens class TestNQueens(unittest.TestCase): def test_1_queen(self): self.assertEqual(NQueens(1).solutions, 1) def test_2_queen(self): self.assertEqual(NQueens(2).solutions, 0) def test_3_queen(self): self.assertEqual(NQueens(3).solutions, 0) def test_4_queen(self): self.assertEqual(NQueens(4).solutions, 2) def test_5_queen(self): self.assertEqual(NQueens(5).solutions, 10) def test_6_queen(self): self.assertEqual(NQueens(6).solutions, 4) def test_7_queen(self): self.assertEqual(NQueens(7).solutions, 40) def test_8_queen(self): self.assertEqual(NQueens(8).solutions, 92) def test_9_queen(self): self.assertEqual(NQueens(9).solutions, 352) def test_10_queen(self): self.assertEqual(NQueens(10).solutions, 724) def test_float_size(self): n_queen = NQueens(8.5) self.assertEqual(n_queen.solutions, 0) self.assertEqual(n_queen.error, "The size isn't a digit")

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1

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5

6e7654580b77f1dbecf04a37ead830e9b06ecf31

198

py

Python

mwptoolkit/module/Encoder/__init__.py

ShubhamAnandJain/MWP-CS229

ce86233504fdb37e104a3944fd81d4606fbfa621

[ "MIT" ]

71

2021-03-08T06:06:15.000Z

2022-03-30T11:59:37.000Z

mwptoolkit/module/Encoder/__init__.py

ShubhamAnandJain/MWP-CS229

ce86233504fdb37e104a3944fd81d4606fbfa621

[ "MIT" ]

13

2021-09-07T12:38:23.000Z

2022-03-22T15:08:16.000Z

mwptoolkit/module/Encoder/__init__.py

ShubhamAnandJain/MWP-CS229

ce86233504fdb37e104a3944fd81d4606fbfa621

[ "MIT" ]

21

2021-02-16T07:46:36.000Z

2022-03-23T13:41:33.000Z

from __future__ import absolute_import from __future__ import print_function from __future__ import division from mwptoolkit.module.Encoder import graph_based_encoder,rnn_encoder,transformer_encoder

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6e91c4809b083bd8e190189c7a4286818bc08e69

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py

Python

deprecated.py

thu-fit/DCGAN-anime

da549bd45a6ca3c4c5a8894945d3242c59f823a0

[ "MIT" ]

null

deprecated.py

thu-fit/DCGAN-anime

da549bd45a6ca3c4c5a8894945d3242c59f823a0

[ "MIT" ]

null

deprecated.py

thu-fit/DCGAN-anime

da549bd45a6ca3c4c5a8894945d3242c59f823a0

[ "MIT" ]

null

def sampler(self, z, y=None): '''generate iamge given z''' with tf.variable_scope("generator") as scope: # we hope the weights defined in generator to be reused scope.reuse_variables() if not self.y_dim: s_h, s_w = self.output_height, self.output_width s_h2, s_w2 = conv_out_size_same(s_h, 2), conv_out_size_same(s_w, 2) s_h4, s_w4 = conv_out_size_same(s_h2, 2), conv_out_size_same(s_w2, 2) s_h8, s_w8 = conv_out_size_same(s_h4, 2), conv_out_size_same(s_w4, 2) s_h16, s_w16 = conv_out_size_same(s_h8, 2), conv_out_size_same(s_w8, 2) # project `z` and reshape h0 = tf.reshape( linear(z, self.gf_dim*8*s_h16*s_w16, 'g_h0_lin'), [-1, s_h16, s_w16, self.gf_dim * 8]) h0 = tf.nn.relu(self.g_bn0(h0, train=False)) h1 = deconv2d(h0, [batch_size, s_h8, s_w8, self.gf_dim*4], name='g_h1') h1 = tf.nn.relu(self.g_bn1(h1, train=False)) h2 = deconv2d(h1, [batch_size, s_h4, s_w4, self.gf_dim*2], name='g_h2') h2 = tf.nn.relu(self.g_bn2(h2, train=False)) h3 = deconv2d(h2, [batch_size, s_h2, s_w2, self.gf_dim*1], name='g_h3') h3 = tf.nn.relu(self.g_bn3(h3, train=False)) h4 = deconv2d(h3, [batch_size, s_h, s_w, self.c_dim], name='g_h4') return tf.nn.tanh(h4) else: s_h, s_w = self.output_height, self.output_width s_h2, s_h4 = int(s_h/2), int(s_h/4) s_w2, s_w4 = int(s_w/2), int(s_w/4) # yb = tf.reshape(y, [-1, 1, 1, self.y_dim]) yb = tf.reshape(y, [batch_size, 1, 1, self.y_dim]) z = concat([z, y], 1) h0 = tf.nn.relu(self.g_bn0(linear(z, self.gfc_dim, 'g_h0_lin'), train=False)) h0 = concat([h0, y], 1) h1 = tf.nn.relu(self.g_bn1( linear(h0, self.gf_dim*2*s_h4*s_w4, 'g_h1_lin'), train=False)) h1 = tf.reshape(h1, [batch_size, s_h4, s_w4, self.gf_dim * 2]) h1 = conv_cond_concat(h1, yb) h2 = tf.nn.relu(self.g_bn2( deconv2d(h1, [batch_size, s_h2, s_w2, self.gf_dim * 2], name='g_h2'), train=False)) h2 = conv_cond_concat(h2, yb) return tf.nn.sigmoid(deconv2d(h2, [batch_size, s_h, s_w, self.c_dim], name='g_h3')) def sampler1(self, z, y=None, reuse=True): '''Generate a given number of samples using z. The first dimension of z is the number of samples''' with tf.variable_scope("generator") as scope: # we hope the weights defined in generator to be reused if reuse: scope.reuse_variables() num_samples = z.get_shape().as_list()[0] s_h, s_w = self.output_height, self.output_width s_h2, s_w2 = conv_out_size_same(s_h, 2), conv_out_size_same(s_w, 2) s_h4, s_w4 = conv_out_size_same(s_h2, 2), conv_out_size_same(s_w2, 2) s_h8, s_w8 = conv_out_size_same(s_h4, 2), conv_out_size_same(s_w4, 2) s_h16, s_w16 = conv_out_size_same(s_h8, 2), conv_out_size_same(s_w8, 2) # project `z` and reshape h0 = tf.reshape( linear(z, self.gf_dim*8*s_h16*s_w16, 'g_h0_lin'), [-1, s_h16, s_w16, self.gf_dim * 8]) h0 = tf.nn.relu(self.g_bn0(h0, train=False)) h1 = deconv2d(h0, [num_samples, s_h8, s_w8, self.gf_dim*4], name='g_h1') h1 = tf.nn.relu(self.g_bn1(h1, train=False)) h2 = deconv2d(h1, [num_samples, s_h4, s_w4, self.gf_dim*2], name='g_h2') h2 = tf.nn.relu(self.g_bn2(h2, train=False)) h3 = deconv2d(h2, [num_samples, s_h2, s_w2, self.gf_dim*1], name='g_h3') h3 = tf.nn.relu(self.g_bn3(h3, train=False)) h4 = deconv2d(h3, [num_samples, s_h, s_w, self.c_dim], name='g_h4') return tf.nn.tanh(h4)

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null

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5

6ea56221c4382d050ea20b187d845407bd8d039d

90

py

Python

renormalizer/mps/tdh/__init__.py

liwt31/Renormalizer

123a9d53f4f5f32c0088c255475f0ee60d02c745

[ "Apache-2.0" ]

null

renormalizer/mps/tdh/__init__.py

liwt31/Renormalizer

123a9d53f4f5f32c0088c255475f0ee60d02c745

[ "Apache-2.0" ]

null

renormalizer/mps/tdh/__init__.py

liwt31/Renormalizer

123a9d53f4f5f32c0088c255475f0ee60d02c745

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- from renormalizer.mps.tdh.propagation import unitary_propagation

22.5

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0

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0

5

6eaaaf9c78bb564348f5f92937368a9dbc35cca5

66

py

Python

src/clusto/drivers/devices/networkswitches/__init__.py

rongoro/clusto

d6425433e5132e8778feeb9db4b8dd80b933b030

[ "BSD-3-Clause" ]

5

2015-07-19T08:28:01.000Z

2021-07-08T14:49:27.000Z

src/clusto/drivers/devices/networkswitches/__init__.py

wt/clusto

c114ce7c42dcfa33c1e79f4d3b49313115fea06b

[ "BSD-3-Clause" ]

null

src/clusto/drivers/devices/networkswitches/__init__.py

wt/clusto

c114ce7c42dcfa33c1e79f4d3b49313115fea06b

[ "BSD-3-Clause" ]

5

2015-01-06T07:57:07.000Z

2021-11-10T18:01:33.000Z

from basicnetworkswitch import * from cisconetworkswitch import *

22

32

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66

9.333333

0.666667

0

0.121212

66

2

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5

42b8718fafc7a5efe59718792e559a9ba4afb7ac

38

py

Python

jetbrains-academy/Zookeeper/Problems/Print an integer/task.py

robinpatra/ML-Study-3

6f401706a8da4cac5e63304ce09ff6ff62756d0b

[ "MIT" ]

null

jetbrains-academy/Zookeeper/Problems/Print an integer/task.py

robinpatra/ML-Study-3

6f401706a8da4cac5e63304ce09ff6ff62756d0b

[ "MIT" ]

null

jetbrains-academy/Zookeeper/Problems/Print an integer/task.py

robinpatra/ML-Study-3

6f401706a8da4cac5e63304ce09ff6ff62756d0b

[ "MIT" ]

null

# put your python code here print(10)

12.666667

27

0.736842

7

38

4

1

0

0.064516

0.184211

38

2

28

19

0.83871

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0

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1

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null

0

1

0

1

0

5

42d1b2020952de616b4d4ac7d2ca23c0bbc1bae9

144

py

Python

tests/test_sass-director.py

Sass-Director/Sass-Director_Sublime

57dff551213b4884c603cb69700fa2583f646202

[ "MIT" ]

4

2015-07-08T14:25:24.000Z

2021-01-20T22:11:09.000Z

tests/test_sass-director.py

Sass-Director/Sass-Director_Sublime

57dff551213b4884c603cb69700fa2583f646202

[ "MIT" ]

4

2015-06-16T19:48:59.000Z

2020-06-23T17:17:38.000Z

tests/test_sass-director.py

Sass-Director/Sass-Director_Sublime

57dff551213b4884c603cb69700fa2583f646202

[ "MIT" ]

2

2015-01-24T17:38:48.000Z

2017-04-18T13:23:46.000Z

# Load in test framework from sublime_plugin_tests import framework class TestExample(framework.TestCase): def sampleTest(): pass

18

42

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144

6.235294

0.882353

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0.194444

144

7

43

20.571429

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0

1

0.25

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0.25

0

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0

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null

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1

0

null

0

1

0

5

42e887c8fdf1e23d81a9463a69d52200b7a5826e

67

py

Python

pyrallest/__init__.py

ivancrneto/pyrallest

158780c418ae276935fb155e82b18db242cd98e5

[ "MIT" ]

null

pyrallest/__init__.py

ivancrneto/pyrallest

158780c418ae276935fb155e82b18db242cd98e5

[ "MIT" ]

null

pyrallest/__init__.py

ivancrneto/pyrallest

158780c418ae276935fb155e82b18db242cd98e5

[ "MIT" ]

null

def main(): print('This is the very beginning of pyrallest')

13.4

52

0.671642

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67

4.5

1

0

0.223881

67

4

53

16.75

0.865385

0

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1

0.5

true

0

0.5

1

0

null

0

1

0

null

0

1

0

1

0

5

6e1de2b972d3bacd17bc4fe230cc40342951d8ec

130

py

Python

code/helpers/__init__.py

briandesilva/discovery-of-physics-from-data

b79c34317f049c9b47aaf2cc4c54c5ec7219f3d7

[ "MIT" ]

11

2020-07-02T01:48:27.000Z

2022-03-29T18:23:32.000Z

code/helpers/__init__.py

briandesilva/discovery-of-physics-from-data

b79c34317f049c9b47aaf2cc4c54c5ec7219f3d7

[ "MIT" ]

null

code/helpers/__init__.py

briandesilva/discovery-of-physics-from-data

b79c34317f049c9b47aaf2cc4c54c5ec7219f3d7

[ "MIT" ]

3

2020-11-21T09:11:21.000Z

2022-03-29T18:23:58.000Z

from .library import * from .differentiation import * from .sindy_ball import SINDyBall from .tests import * from .utils import *

21.666667

33

0.776923

17

130

5.882353

0.529412

0.3

0

0.153846

130

5

34

26

0.909091

0

1

0

true

0

1

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1

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1

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null

1

0

1

0

null

0

1

0

1

0

5

287d866f9124af9905e3876a7fc982e255ffcb59

157

py

Python

npt/pipelines/__init__.py

chbrandt/npt

7d58db9987c8f4d93c4e61e1fc98cce38733d06e

[ "MIT" ]

null

npt/pipelines/__init__.py

chbrandt/npt

7d58db9987c8f4d93c4e61e1fc98cce38733d06e

[ "MIT" ]

2

2022-02-18T16:38:13.000Z

2022-02-18T16:56:33.000Z

npt/pipelines/__init__.py

chbrandt/npt

7d58db9987c8f4d93c4e61e1fc98cce38733d06e

[ "MIT" ]

1

2022-03-15T09:03:51.000Z

from npt import log from . import search as Search from . import download as Download from . import processing as Processing from . import mosaic as Mosaic

22.428571

38

0.789809

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157

5.166667

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0.322581

0

0.184713

157

6

39

26.166667

0.96875

0

1

0

true

0

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null

0

1

0

1

0

5

2895a62d74a6cf74dd272cfa08d6a6029b8f3434

48

py

Python

starfish/__main__.py

haoxusci/starfish

d7bd856024c75f2ce41504406f2a663566c3814b

[ "MIT" ]

164

2018-03-21T21:52:56.000Z

2022-03-23T17:14:39.000Z

starfish/__main__.py

lbgbox/starfish

0e879d995d5c49b6f5a842e201e3be04c91afc7e

[ "MIT" ]

1,728

2018-03-15T23:16:09.000Z

2022-03-12T00:09:18.000Z

starfish/__main__.py

lbgbox/starfish

0e879d995d5c49b6f5a842e201e3be04c91afc7e

[ "MIT" ]

66

2018-03-25T17:21:15.000Z

2022-01-16T09:17:11.000Z

from .core.starfish import starfish starfish()

12

35

0.791667

6

48

6.333333

0.666667

0

0.125

48

3

36

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py

Python

demo/deep_learning/base/second_stage_bounding_box_prediction/dcn_feature_calibration.py

jihuacao/Putil

b753fc94bea4cbda00f483681c55f0e9f54adef2

[ "Apache-2.0" ]

1

2018-12-09T06:09:29.000Z

demo/deep_learning/base/second_stage_bounding_box_prediction/dcn_feature_calibration.py

jihuacao/Putil

b753fc94bea4cbda00f483681c55f0e9f54adef2

[ "Apache-2.0" ]

null

demo/deep_learning/base/second_stage_bounding_box_prediction/dcn_feature_calibration.py

jihuacao/Putil

b753fc94bea4cbda00f483681c55f0e9f54adef2

[ "Apache-2.0" ]

null

# coding=utf-8 import torch class RotateRectangleDCNFeatureCalibration(torch.nn.Module): def __init__(self): torch.nn.Module.__init__(self) pass

20.875

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9547e7b57fef282a81e3052edbdb2d34bb2cd61a

222

py

Python

src/honey.py

terror/golf

9d38f8376c2ddbbb34360a3353ec6f4289736bd4

[ "Unlicense" ]

null

src/honey.py

terror/golf

9d38f8376c2ddbbb34360a3353ec6f4289736bd4

[ "Unlicense" ]

null

src/honey.py

terror/golf

9d38f8376c2ddbbb34360a3353ec6f4289736bd4

[ "Unlicense" ]

null

# https://open.kattis.com/problems/honey print(*(lambda x: [x[int(input())] for _ in range(int(input()))])([1, 0, 6, 12, 90, 360, 2040, 10080, 54810, 290640, 1588356, 8676360, 47977776, 266378112, 1488801600]), sep="\n")

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95a163ba2b23c18ae5bb7535ab4caa4e069308b6

144

py

Python

bolt/core/exceptions.py

ph7vc/CL4M-B0T

e992cf63b1215ea7c241cab94edc251653dbaed7

[ "MIT" ]

9

2019-02-17T06:33:14.000Z

2021-10-05T02:19:00.000Z

bolt/core/exceptions.py

ns-phennessy/Bolt

e992cf63b1215ea7c241cab94edc251653dbaed7

[ "MIT" ]

28

2019-02-10T07:48:05.000Z

2021-12-20T00:15:37.000Z

bolt/core/exceptions.py

ph7vc/CL4M-B0T

e992cf63b1215ea7c241cab94edc251653dbaed7

[ "MIT" ]

4

2015-03-13T03:58:55.000Z

2015-05-27T08:29:46.000Z

class InvalidConfigurationError(Exception): pass class InvalidBotToken(Exception): pass class InvalidBotPlugin(Exception): pass

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null

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95b525d705b0f34eba83af30d5fc61bd4affc2f0

48

pyw

Python

seemee.pyw

gaming32/SeeMee

a99655efdd9e1aea218474bcdbd1370954a366d2

[ "MIT" ]

null

seemee.pyw

gaming32/SeeMee

a99655efdd9e1aea218474bcdbd1370954a366d2

[ "MIT" ]

null

seemee.pyw

gaming32/SeeMee

a99655efdd9e1aea218474bcdbd1370954a366d2

[ "MIT" ]

null

import runpy runpy._run_module_as_main('SeeMee')

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null

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5

95cae2c1de14d040a592e9ed57f23f978ae86e71

150

py

Python

test_cases/conftest.py

majdukovic/pybooker

b9a373d556be0481c93a528f731407ca7a47b11f

[ "MIT" ]

null

test_cases/conftest.py

majdukovic/pybooker

b9a373d556be0481c93a528f731407ca7a47b11f

[ "MIT" ]

null

test_cases/conftest.py

majdukovic/pybooker

b9a373d556be0481c93a528f731407ca7a47b11f

[ "MIT" ]

null

import pytest from framework.services.booker_client import BookerClient booker_client = BookerClient() @pytest.fixture() def clear_env(): pass

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252147a24fb71425db336b4bd835e50e021bad1a

1,649

py

Python

acme/agents/jax/ail/__init__.py

Tsaousis/acme

14278693bcc5fef0839ac60792d452d3d80acfd7

[ "Apache-2.0" ]

2,650

2020-06-01T16:31:25.000Z

2022-03-31T07:32:41.000Z

acme/agents/jax/ail/__init__.py

Tsaousis/acme

14278693bcc5fef0839ac60792d452d3d80acfd7

[ "Apache-2.0" ]

199

2020-06-02T01:09:09.000Z

2022-03-31T17:11:20.000Z

acme/agents/jax/ail/__init__.py

Tsaousis/acme

14278693bcc5fef0839ac60792d452d3d80acfd7

[ "Apache-2.0" ]

344

2020-06-01T16:45:21.000Z

2022-03-30T11:15:09.000Z

# Copyright 2018 DeepMind Technologies Limited. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Implementations of a AIL agent.""" from acme.agents.jax.ail import losses from acme.agents.jax.ail import rewards from acme.agents.jax.ail.agents import AIL from acme.agents.jax.ail.agents import DistributedAIL from acme.agents.jax.ail.builder import AILBuilder from acme.agents.jax.ail.config import AILConfig from acme.agents.jax.ail.dac_agents import DAC from acme.agents.jax.ail.dac_agents import DACConfig from acme.agents.jax.ail.dac_agents import DistributedDAC from acme.agents.jax.ail.gail_agents import DistributedGAIL from acme.agents.jax.ail.gail_agents import GAIL from acme.agents.jax.ail.gail_agents import GAILConfig from acme.agents.jax.ail.learning import AILLearner from acme.agents.jax.ail.networks import AILNetworks from acme.agents.jax.ail.networks import AIRLModule from acme.agents.jax.ail.networks import compute_ail_reward from acme.agents.jax.ail.networks import DiscriminatorMLP from acme.agents.jax.ail.networks import DiscriminatorModule from acme.agents.jax.ail.networks import make_discriminator

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2530a05e38dc4778931bafbbddc794641c581d85

28,045

py

Python

tests/test_subnetlaplace.py

georgezefko/Laplace

c488f7bf739297bab5d771f65635352a07716ca0

[ "MIT" ]

null

tests/test_subnetlaplace.py

georgezefko/Laplace

c488f7bf739297bab5d771f65635352a07716ca0

[ "MIT" ]

null

tests/test_subnetlaplace.py

georgezefko/Laplace

c488f7bf739297bab5d771f65635352a07716ca0

[ "MIT" ]

null

import pytest from itertools import product import torch from torch import nn from torch.nn.utils import parameters_to_vector from torch.utils.data import DataLoader, TensorDataset from torchvision.models import wide_resnet50_2 from laplace import Laplace, SubnetLaplace, FullSubnetLaplace, DiagSubnetLaplace from laplace.baselaplace import DiagLaplace from laplace.utils import (SubnetMask, RandomSubnetMask, LargestMagnitudeSubnetMask, LargestVarianceDiagLaplaceSubnetMask, LargestVarianceSWAGSubnetMask, ParamNameSubnetMask, ModuleNameSubnetMask, LastLayerSubnetMask) torch.manual_seed(240) torch.set_default_tensor_type(torch.DoubleTensor) score_based_subnet_masks = [RandomSubnetMask, LargestMagnitudeSubnetMask, LargestVarianceDiagLaplaceSubnetMask, LargestVarianceSWAGSubnetMask] layer_subnet_masks = [ParamNameSubnetMask, ModuleNameSubnetMask, LastLayerSubnetMask] all_subnet_masks = score_based_subnet_masks + layer_subnet_masks likelihoods = ['classification', 'regression'] hessian_structures = ['full', 'diag'] @pytest.fixture def model(): model = torch.nn.Sequential(nn.Linear(3, 20), nn.Linear(20, 2)) model_params = list(model.parameters()) setattr(model, 'n_params', len(parameters_to_vector(model_params))) return model @pytest.fixture def large_model(): model = wide_resnet50_2() return model @pytest.fixture def class_loader(): X = torch.randn(10, 3) y = torch.randint(2, (10,)) return DataLoader(TensorDataset(X, y), batch_size=3) @pytest.fixture def reg_loader(): X = torch.randn(10, 3) y = torch.randn(10, 2) return DataLoader(TensorDataset(X, y), batch_size=3) @pytest.mark.parametrize('likelihood', likelihoods) def test_subnet_laplace_init(model, likelihood): # use random subnet mask for this test subnetwork_mask = RandomSubnetMask subnetmask_kwargs = dict(model=model, n_params_subnet=10) subnetmask = subnetwork_mask(**subnetmask_kwargs) subnetmask.select() # subnet Laplace with full Hessian should work hessian_structure = 'full' lap = Laplace(model, likelihood=likelihood, subset_of_weights='subnetwork', subnetwork_indices=subnetmask.indices, hessian_structure=hessian_structure) assert isinstance(lap, FullSubnetLaplace) # subnet Laplace with diagonal Hessian should work hessian_structure = 'diag' lap = Laplace(model, likelihood=likelihood, subset_of_weights='subnetwork', subnetwork_indices=subnetmask.indices, hessian_structure=hessian_structure) assert isinstance(lap, DiagSubnetLaplace) # subnet Laplace without specifying subnetwork indices should raise an error with pytest.raises(TypeError): lap = Laplace(model, likelihood=likelihood, subset_of_weights='subnetwork', hessian_structure=hessian_structure) # subnet Laplace with kron or lowrank Hessians should raise errors hessian_structure = 'kron' with pytest.raises(ValueError): lap = Laplace(model, likelihood=likelihood, subset_of_weights='subnetwork', subnetwork_indices=subnetmask.indices, hessian_structure=hessian_structure) hessian_structure = 'lowrank' with pytest.raises(ValueError): lap = Laplace(model, likelihood=likelihood, subset_of_weights='subnetwork', subnetwork_indices=subnetmask.indices, hessian_structure=hessian_structure) @pytest.mark.parametrize('likelihood,hessian_structure', product(likelihoods, hessian_structures)) def test_subnet_laplace_large_init(large_model, likelihood, hessian_structure): # use random subnet mask for this test subnetwork_mask = RandomSubnetMask n_param_subnet = 10 subnetmask_kwargs = dict(model=large_model, n_params_subnet=n_param_subnet) subnetmask = subnetwork_mask(**subnetmask_kwargs) subnetmask.select() lap = Laplace(large_model, likelihood=likelihood, subset_of_weights='subnetwork', subnetwork_indices=subnetmask.indices, hessian_structure=hessian_structure) assert lap.n_params_subnet == n_param_subnet if hessian_structure == 'full': assert lap.H.shape == (lap.n_params_subnet, lap.n_params_subnet) else: assert lap.H.shape == (lap.n_params_subnet,) H = lap.H.clone() lap._init_H() assert torch.allclose(H, lap.H) @pytest.mark.parametrize('likelihood,hessian_structure', product(likelihoods, hessian_structures)) def test_custom_subnetwork_indices(model, likelihood, class_loader, reg_loader, hessian_structure): loader = class_loader if likelihood == 'classification' else reg_loader # subnetwork indices that are None should raise an error subnetwork_indices = None with pytest.raises(ValueError): lap = Laplace(model, likelihood=likelihood, subset_of_weights='subnetwork', subnetwork_indices=subnetwork_indices, hessian_structure=hessian_structure) # subnetwork indices that are not PyTorch tensors should raise an error subnetwork_indices = [0, 5, 11, 42] with pytest.raises(ValueError): lap = Laplace(model, likelihood=likelihood, subset_of_weights='subnetwork', subnetwork_indices=subnetwork_indices, hessian_structure=hessian_structure) # subnetwork indices that are empty tensors should raise an error subnetwork_indices = torch.LongTensor([]) with pytest.raises(ValueError): lap = Laplace(model, likelihood=likelihood, subset_of_weights='subnetwork', subnetwork_indices=subnetwork_indices, hessian_structure=hessian_structure) # subnetwork indices that are scalar tensors should raise an error subnetwork_indices = torch.LongTensor(11) with pytest.raises(ValueError): lap = Laplace(model, likelihood=likelihood, subset_of_weights='subnetwork', subnetwork_indices=subnetwork_indices, hessian_structure=hessian_structure) # subnetwork indices that are not 1D PyTorch tensors should raise an error subnetwork_indices = torch.LongTensor([[0, 5], [11, 42]]) with pytest.raises(ValueError): lap = Laplace(model, likelihood=likelihood, subset_of_weights='subnetwork', subnetwork_indices=subnetwork_indices, hessian_structure=hessian_structure) # subnetwork indices that are double tensors should raise an error subnetwork_indices = torch.DoubleTensor([0.0, 5.0, 11.0, 42.0]) with pytest.raises(ValueError): lap = Laplace(model, likelihood=likelihood, subset_of_weights='subnetwork', subnetwork_indices=subnetwork_indices, hessian_structure=hessian_structure) # subnetwork indices that are float tensors should raise an error subnetwork_indices = torch.FloatTensor([0.0, 5.0, 11.0, 42.0]) with pytest.raises(ValueError): lap = Laplace(model, likelihood=likelihood, subset_of_weights='subnetwork', subnetwork_indices=subnetwork_indices, hessian_structure=hessian_structure) # subnetwork indices that are half tensors should raise an error subnetwork_indices = torch.HalfTensor([0.0, 5.0, 11.0, 42.0]) with pytest.raises(ValueError): lap = Laplace(model, likelihood=likelihood, subset_of_weights='subnetwork', subnetwork_indices=subnetwork_indices, hessian_structure=hessian_structure) # subnetwork indices that are int tensors should raise an error subnetwork_indices = torch.IntTensor([0, 5, 11, 42]) with pytest.raises(ValueError): lap = Laplace(model, likelihood=likelihood, subset_of_weights='subnetwork', subnetwork_indices=subnetwork_indices, hessian_structure=hessian_structure) # subnetwork indices that are short tensors should raise an error subnetwork_indices = torch.ShortTensor([0, 5, 11, 42]) with pytest.raises(ValueError): lap = Laplace(model, likelihood=likelihood, subset_of_weights='subnetwork', subnetwork_indices=subnetwork_indices, hessian_structure=hessian_structure) # subnetwork indices that are char tensors should raise an error subnetwork_indices = torch.CharTensor([0, 5, 11, 42]) with pytest.raises(ValueError): lap = Laplace(model, likelihood=likelihood, subset_of_weights='subnetwork', subnetwork_indices=subnetwork_indices, hessian_structure=hessian_structure) # subnetwork indices that are bool tensors should raise an error subnetwork_indices = torch.BoolTensor([0, 5, 11, 42]) with pytest.raises(ValueError): lap = Laplace(model, likelihood=likelihood, subset_of_weights='subnetwork', subnetwork_indices=subnetwork_indices, hessian_structure=hessian_structure) # subnetwork indices that contain elements smaller than zero should raise an error subnetwork_indices = torch.LongTensor([0, -1, -11]) with pytest.raises(ValueError): lap = Laplace(model, likelihood=likelihood, subset_of_weights='subnetwork', subnetwork_indices=subnetwork_indices, hessian_structure=hessian_structure) # subnetwork indices that contain elements larger than n_params should raise an error subnetwork_indices = torch.LongTensor([model.n_params + 1, model.n_params + 42]) with pytest.raises(ValueError): lap = Laplace(model, likelihood=likelihood, subset_of_weights='subnetwork', subnetwork_indices=subnetwork_indices, hessian_structure=hessian_structure) # subnetwork indices that contain duplicate entries should raise an error subnetwork_indices = torch.LongTensor([0, 0, 5, 11, 11, 42]) with pytest.raises(ValueError): lap = Laplace(model, likelihood=likelihood, subset_of_weights='subnetwork', subnetwork_indices=subnetwork_indices, hessian_structure=hessian_structure) # Non-empty, 1-dimensional torch.LongTensor with valid entries should work subnetwork_indices = torch.LongTensor([0, 5, 11, 42]) lap = Laplace(model, likelihood=likelihood, subset_of_weights='subnetwork', subnetwork_indices=subnetwork_indices, hessian_structure=hessian_structure) lap.fit(loader) assert isinstance(lap, SubnetLaplace) assert lap.n_params_subnet == 4 if hessian_structure == 'full': assert lap.H.shape == (4, 4) else: assert lap.H.shape == (4,) assert lap.backend.subnetwork_indices.equal(subnetwork_indices) @pytest.mark.parametrize('subnetwork_mask,likelihood,hessian_structure', product(score_based_subnet_masks, likelihoods, hessian_structures)) def test_score_based_subnet_masks(model, likelihood, subnetwork_mask, class_loader, reg_loader, hessian_structure): loader = class_loader if likelihood == 'classification' else reg_loader model_params = parameters_to_vector(model.parameters()) # set subnetwork mask arguments if subnetwork_mask == LargestVarianceDiagLaplaceSubnetMask: diag_laplace_model = DiagLaplace(model, likelihood) subnetmask_kwargs = dict(model=model, diag_laplace_model=diag_laplace_model) elif subnetwork_mask == LargestVarianceSWAGSubnetMask: subnetmask_kwargs = dict(model=model, likelihood=likelihood) else: subnetmask_kwargs = dict(model=model) # should raise error if we don't pass number of subnet parameters within the subnetmask_kwargs with pytest.raises(TypeError): subnetmask = subnetwork_mask(**subnetmask_kwargs) subnetmask.select(loader) # should raise error if we set number of subnet parameters to None subnetmask_kwargs.update(n_params_subnet=None) with pytest.raises(ValueError): subnetmask = subnetwork_mask(**subnetmask_kwargs) subnetmask.select(loader) # should raise error if number of subnet parameters is larger than number of model parameters subnetmask_kwargs.update(n_params_subnet=99999) with pytest.raises(ValueError): subnetmask = subnetwork_mask(**subnetmask_kwargs) subnetmask.select(loader) # define subnetwork mask n_params_subnet = 32 subnetmask_kwargs.update(n_params_subnet=n_params_subnet) subnetmask = subnetwork_mask(**subnetmask_kwargs) # should raise error if we try to access the subnet indices before the subnet has been selected with pytest.raises(AttributeError): subnetmask.indices # select subnet mask subnetmask.select(loader) # should raise error if we try to select the subnet again with pytest.raises(ValueError): subnetmask.select(loader) # define valid subnet Laplace model lap = Laplace(model, likelihood=likelihood, subset_of_weights='subnetwork', subnetwork_indices=subnetmask.indices, hessian_structure=hessian_structure) assert isinstance(lap, SubnetLaplace) # fit Laplace model lap.fit(loader) # check some parameters assert subnetmask.indices.equal(lap.backend.subnetwork_indices) assert subnetmask.n_params_subnet == n_params_subnet assert lap.n_params_subnet == n_params_subnet assert parameters_to_vector(model.parameters()).equal(model_params) # check that Hessian and prior precision is of correct shape if hessian_structure == 'full': assert lap.H.shape == (n_params_subnet, n_params_subnet) else: assert lap.H.shape == (n_params_subnet,) assert lap.prior_precision_diag.shape == (n_params_subnet,) @pytest.mark.parametrize('subnetwork_mask,likelihood,hessian_structure', product(layer_subnet_masks, likelihoods, hessian_structures)) def test_layer_subnet_masks(model, likelihood, subnetwork_mask, class_loader, reg_loader, hessian_structure): loader = class_loader if likelihood == 'classification' else reg_loader subnetmask_kwargs = dict(model=model) # fit last-layer Laplace model lllap = Laplace(model, likelihood=likelihood, subset_of_weights='last_layer', hessian_structure=hessian_structure) lllap.fit(loader) # should raise error if we pass number of subnet parameters subnetmask_kwargs.update(n_params_subnet=32) with pytest.raises(TypeError): subnetmask = subnetwork_mask(**subnetmask_kwargs) subnetmask.select(loader) subnetmask_kwargs = dict(model=model) if subnetwork_mask == ParamNameSubnetMask: # should raise error if we pass no parameter name list subnetmask_kwargs.update() with pytest.raises(TypeError): subnetmask = subnetwork_mask(**subnetmask_kwargs) subnetmask.select(loader) # should raise error if we pass an empty parameter name list subnetmask_kwargs.update(parameter_names=[]) with pytest.raises(ValueError): subnetmask = subnetwork_mask(**subnetmask_kwargs) subnetmask.select(loader) # should raise error if we pass a parameter name list with invalid parameter names subnetmask_kwargs.update(parameter_names=['123']) with pytest.raises(ValueError): subnetmask = subnetwork_mask(**subnetmask_kwargs) subnetmask.select(loader) # define last-layer Laplace model by parameter names and check that # Hessian is identical to that of a full LLLaplace model subnetmask_kwargs.update(parameter_names=['1.weight', '1.bias']) subnetmask = subnetwork_mask(**subnetmask_kwargs) subnetmask.select(loader) lap = Laplace(model, likelihood=likelihood, subset_of_weights='subnetwork', subnetwork_indices=subnetmask.indices, hessian_structure=hessian_structure) lap.fit(loader) assert lllap.H.equal(lap.H) # define valid parameter name subnet mask subnetmask_kwargs.update(parameter_names=['0.weight', '1.bias']) subnetmask = subnetwork_mask(**subnetmask_kwargs) # should raise error if we access number of subnet parameters before selecting the subnet n_params_subnet = 62 with pytest.raises(AttributeError): n_params_subnet = subnetmask.n_params_subnet # select subnet mask and fit Laplace model subnetmask.select(loader) lap = Laplace(model, likelihood=likelihood, subset_of_weights='subnetwork', subnetwork_indices=subnetmask.indices, hessian_structure=hessian_structure) lap.fit(loader) assert isinstance(lap, SubnetLaplace) elif subnetwork_mask == ModuleNameSubnetMask: # should raise error if we pass no module name list subnetmask_kwargs.update() with pytest.raises(TypeError): subnetmask = subnetwork_mask(**subnetmask_kwargs) subnetmask.select(loader) # should raise error if we pass an empty module name list subnetmask_kwargs.update(module_names=[]) with pytest.raises(ValueError): subnetmask = subnetwork_mask(**subnetmask_kwargs) subnetmask.select(loader) # should raise error if we pass a module name list with invalid module names subnetmask_kwargs.update(module_names=['123']) with pytest.raises(ValueError): subnetmask = subnetwork_mask(**subnetmask_kwargs) subnetmask.select(loader) # define last-layer Laplace model by module name and check that # Hessian is identical to that of a full LLLaplace model subnetmask_kwargs.update(module_names=['1']) subnetmask = subnetwork_mask(**subnetmask_kwargs) subnetmask.select(loader) lap = Laplace(model, likelihood=likelihood, subset_of_weights='subnetwork', subnetwork_indices=subnetmask.indices, hessian_structure=hessian_structure) lap.fit(loader) assert lllap.H.equal(lap.H) # define valid parameter name subnet mask subnetmask_kwargs.update(module_names=['0']) subnetmask = subnetwork_mask(**subnetmask_kwargs) # should raise error if we access number of subnet parameters before selecting the subnet n_params_subnet = 80 with pytest.raises(AttributeError): n_params_subnet = subnetmask.n_params_subnet # select subnet mask and fit Laplace model subnetmask.select(loader) lap = Laplace(model, likelihood=likelihood, subset_of_weights='subnetwork', subnetwork_indices=subnetmask.indices, hessian_structure=hessian_structure) lap.fit(loader) assert isinstance(lap, SubnetLaplace) elif subnetwork_mask == LastLayerSubnetMask: # should raise error if we pass invalid last-layer name subnetmask_kwargs.update(last_layer_name='123') with pytest.raises(KeyError): subnetmask = subnetwork_mask(**subnetmask_kwargs) subnetmask.select(loader) # define valid last-layer subnet mask (without passing the last-layer name) subnetmask_kwargs = dict(model=model) subnetmask = subnetwork_mask(**subnetmask_kwargs) # should raise error if we access number of subnet parameters before selecting the subnet with pytest.raises(AttributeError): n_params_subnet = subnetmask.n_params_subnet # select subnet mask and fit Laplace model subnetmask.select(loader) lap = Laplace(model, likelihood=likelihood, subset_of_weights='subnetwork', subnetwork_indices=subnetmask.indices, hessian_structure=hessian_structure) lap.fit(loader) assert isinstance(lap, SubnetLaplace) # check that Hessian is identical to that of a full LLLaplace model assert lllap.H.equal(lap.H) # define valid last-layer subnet mask (with passing the last-layer name) subnetmask_kwargs.update(last_layer_name='1') subnetmask = subnetwork_mask(**subnetmask_kwargs) # should raise error if we access number of subnet parameters before selecting the subnet n_params_subnet = 42 with pytest.raises(AttributeError): n_params_subnet = subnetmask.n_params_subnet # select subnet mask and fit Laplace model subnetmask.select(loader) lap = Laplace(model, likelihood=likelihood, subset_of_weights='subnetwork', subnetwork_indices=subnetmask.indices, hessian_structure=hessian_structure) lap.fit(loader) assert isinstance(lap, SubnetLaplace) # check that Hessian is identical to that of a full LLLaplace model assert lllap.H.equal(lap.H) # check some parameters assert subnetmask.indices.equal(lap.backend.subnetwork_indices) assert subnetmask.n_params_subnet == n_params_subnet assert lap.n_params_subnet == n_params_subnet # check that Hessian and prior precision is of correct shape if hessian_structure == 'full': assert lap.H.shape == (n_params_subnet, n_params_subnet) else: assert lap.H.shape == (n_params_subnet,) assert lap.prior_precision_diag.shape == (n_params_subnet,) @pytest.mark.parametrize('likelihood,hessian_structure', product(likelihoods, hessian_structures)) def test_full_subnet_mask(model, likelihood, class_loader, reg_loader, hessian_structure): loader = class_loader if likelihood == 'classification' else reg_loader # define full model 'subnet' mask class (i.e. where all parameters are part of the subnet) class FullSubnetMask(SubnetMask): def get_subnet_mask(self, train_loader): return torch.ones(model.n_params).byte() # define and fit valid subnet Laplace model over all weights subnetwork_mask = FullSubnetMask subnetmask = subnetwork_mask(model=model) subnetmask.select(loader) lap = Laplace(model, likelihood=likelihood, subset_of_weights='subnetwork', subnetwork_indices=subnetmask.indices, hessian_structure=hessian_structure) lap.fit(loader) assert isinstance(lap, SubnetLaplace) # check some parameters assert subnetmask.indices.equal(torch.tensor(list(range(model.n_params)))) assert subnetmask.n_params_subnet == model.n_params assert lap.n_params_subnet == model.n_params # check that the Hessian is identical to that of an all-weights Laplace model full_lap = Laplace(model, likelihood=likelihood, subset_of_weights='all', hessian_structure=hessian_structure) full_lap.fit(loader) assert full_lap.H.equal(lap.H) @pytest.mark.parametrize('subnetwork_mask,hessian_structure', product(all_subnet_masks, hessian_structures)) def test_regression_predictive(model, reg_loader, subnetwork_mask, hessian_structure): subnetmask_kwargs = dict(model=model) if subnetwork_mask in score_based_subnet_masks: subnetmask_kwargs.update(n_params_subnet=32) if subnetwork_mask == LargestVarianceSWAGSubnetMask: subnetmask_kwargs.update(likelihood='regression') elif subnetwork_mask == LargestVarianceDiagLaplaceSubnetMask: diag_laplace_model = DiagLaplace(model, 'regression') subnetmask_kwargs.update(diag_laplace_model=diag_laplace_model) elif subnetwork_mask == ParamNameSubnetMask: subnetmask_kwargs.update(parameter_names=['0.weight', '1.bias']) elif subnetwork_mask == ModuleNameSubnetMask: subnetmask_kwargs.update(module_names=['0']) subnetmask = subnetwork_mask(**subnetmask_kwargs) subnetmask.select(reg_loader) lap = Laplace(model, likelihood='regression', subset_of_weights='subnetwork', subnetwork_indices=subnetmask.indices, hessian_structure=hessian_structure) assert isinstance(lap, SubnetLaplace) lap.fit(reg_loader) X, _ = reg_loader.dataset.tensors f = model(X) # error with pytest.raises(ValueError): lap(X, pred_type='linear') # GLM predictive f_mu, f_var = lap(X, pred_type='glm') assert torch.allclose(f_mu, f) assert f_var.shape == torch.Size([f_mu.shape[0], f_mu.shape[1], f_mu.shape[1]]) assert len(f_mu) == len(X) # NN predictive (only diagonal variance estimation) f_mu, f_var = lap(X, pred_type='nn') assert f_mu.shape == f_var.shape assert f_var.shape == torch.Size([f_mu.shape[0], f_mu.shape[1]]) assert len(f_mu) == len(X) @pytest.mark.parametrize('subnetwork_mask,hessian_structure', product(all_subnet_masks, hessian_structures)) def test_classification_predictive(model, class_loader, subnetwork_mask, hessian_structure): subnetmask_kwargs = dict(model=model) if subnetwork_mask in score_based_subnet_masks: subnetmask_kwargs.update(n_params_subnet=32) if subnetwork_mask == LargestVarianceSWAGSubnetMask: subnetmask_kwargs.update(likelihood='classification') elif subnetwork_mask == LargestVarianceDiagLaplaceSubnetMask: diag_laplace_model = DiagLaplace(model, 'classification') subnetmask_kwargs.update(diag_laplace_model=diag_laplace_model) elif subnetwork_mask == ParamNameSubnetMask: subnetmask_kwargs.update(parameter_names=['0.weight', '1.bias']) elif subnetwork_mask == ModuleNameSubnetMask: subnetmask_kwargs.update(module_names=['0']) subnetmask = subnetwork_mask(**subnetmask_kwargs) subnetmask.select(class_loader) lap = Laplace(model, likelihood='classification', subset_of_weights='subnetwork', subnetwork_indices=subnetmask.indices, hessian_structure=hessian_structure) assert isinstance(lap, SubnetLaplace) lap.fit(class_loader) X, _ = class_loader.dataset.tensors f = torch.softmax(model(X), dim=-1) # error with pytest.raises(ValueError): lap(X, pred_type='linear') # GLM predictive f_pred = lap(X, pred_type='glm', link_approx='mc', n_samples=100) assert f_pred.shape == f.shape assert torch.allclose(f_pred.sum(), torch.tensor(len(f_pred), dtype=torch.double)) # sum up to 1 f_pred = lap(X, pred_type='glm', link_approx='probit') assert f_pred.shape == f.shape assert torch.allclose(f_pred.sum(), torch.tensor(len(f_pred), dtype=torch.double)) # sum up to 1 f_pred = lap(X, pred_type='glm', link_approx='bridge') assert f_pred.shape == f.shape assert torch.allclose(f_pred.sum(), torch.tensor(len(f_pred), dtype=torch.double)) # sum up to 1 # NN predictive f_pred = lap(X, pred_type='nn', n_samples=100) assert f_pred.shape == f.shape assert torch.allclose(f_pred.sum(), torch.tensor(len(f_pred), dtype=torch.double)) # sum up to 1 @pytest.mark.parametrize('subnetwork_mask,likelihood,hessian_structure', product(all_subnet_masks, likelihoods, hessian_structures)) def test_subnet_marginal_likelihood(model, subnetwork_mask, likelihood, hessian_structure, class_loader, reg_loader): subnetmask_kwargs = dict(model=model) if subnetwork_mask in score_based_subnet_masks: subnetmask_kwargs.update(n_params_subnet=32) if subnetwork_mask == LargestVarianceSWAGSubnetMask: subnetmask_kwargs.update(likelihood=likelihood) elif subnetwork_mask == LargestVarianceDiagLaplaceSubnetMask: diag_laplace_model = DiagLaplace(model, likelihood) subnetmask_kwargs.update(diag_laplace_model=diag_laplace_model) elif subnetwork_mask == ParamNameSubnetMask: subnetmask_kwargs.update(parameter_names=['0.weight', '1.bias']) elif subnetwork_mask == ModuleNameSubnetMask: subnetmask_kwargs.update(module_names=['0']) subnetmask = subnetwork_mask(**subnetmask_kwargs) loader = class_loader if likelihood == 'classification' else reg_loader subnetmask.select(loader) lap = Laplace(model, likelihood=likelihood, subset_of_weights='subnetwork', subnetwork_indices=subnetmask.indices, hessian_structure=hessian_structure) assert isinstance(lap, SubnetLaplace) lap.fit(loader) lap.log_marginal_likelihood()

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cu-swe4s-fall-2020/version-control-rezgarshakeri

859f863a71dbab5714a1f24e54933a0b4398790b

[ "MIT" ]

null

math_lib.py

cu-swe4s-fall-2020/version-control-rezgarshakeri

859f863a71dbab5714a1f24e54933a0b4398790b

[ "MIT" ]

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math_lib.py

cu-swe4s-fall-2020/version-control-rezgarshakeri

859f863a71dbab5714a1f24e54933a0b4398790b

[ "MIT" ]

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import numpy as np def div(a, b): if b == 0: print("denominator iz zero!!!") return np.inf else: return a/b def add(a,b): return (a+b)

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quantdsl/infrastructure/event_sourced_repos/contract_specification_repo.py

johnbywater/quantdsl

81c1c69f27e094a6ed0542b28cf1ac8fcce5494a

[ "BSD-3-Clause" ]

269

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2022-03-30T17:09:46.000Z

quantdsl/infrastructure/event_sourced_repos/contract_specification_repo.py

johnbywater/quantdsl

81c1c69f27e094a6ed0542b28cf1ac8fcce5494a

[ "BSD-3-Clause" ]

22

2017-04-01T13:44:56.000Z

2018-09-10T11:48:56.000Z

quantdsl/infrastructure/event_sourced_repos/contract_specification_repo.py

johnbywater/quantdsl

81c1c69f27e094a6ed0542b28cf1ac8fcce5494a

[ "BSD-3-Clause" ]

59

2015-01-09T00:56:50.000Z

2022-03-13T23:52:27.000Z

from eventsourcing.infrastructure.event_sourced_repo import EventSourcedRepository from quantdsl.domain.model.contract_specification import ContractSpecification, ContractSpecificationRepository class ContractSpecificationRepo(ContractSpecificationRepository, EventSourcedRepository): domain_class = ContractSpecification

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Code/YOLO/darkflow/darkflow/net/build.py

kalvin-osoro/ml_project

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Code/YOLO/darkflow/darkflow/net/build.py

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bf0bdc5719f2712682dd070045a5f1edf933a0c4

[ "Apache-2.0" ]

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Code/YOLO/darkflow/darkflow/net/build.py

kalvin-osoro/ml_project

bf0bdc5719f2712682dd070045a5f1edf933a0c4

[ "Apache-2.0" ]

null

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customers/tests/test_gemsutils.py

dcopm999/sibdev

9dc01ed5d172869d4870c847f01d168602f31be8

[ "MIT" ]

null

customers/tests/test_gemsutils.py

dcopm999/sibdev

9dc01ed5d172869d4870c847f01d168602f31be8

[ "MIT" ]

null

customers/tests/test_gemsutils.py

dcopm999/sibdev

9dc01ed5d172869d4870c847f01d168602f31be8

[ "MIT" ]

null

from django.test import TestCase from customers.gems_utils import Gems class GemUtilsCase(TestCase): def setUp(self): self.gems = Gems() pass

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tests/0400_i18n/08_update_catalogs.py

sveetch/Optimus

983aebeccd2ada7a5a0ab96f9296d4bba1112022

[ "MIT" ]

2

2019-05-31T00:23:15.000Z

2021-04-26T07:26:16.000Z

tests/0400_i18n/08_update_catalogs.py

sveetch/Optimus

983aebeccd2ada7a5a0ab96f9296d4bba1112022

[ "MIT" ]

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2015-04-21T14:43:26.000Z

2022-01-29T00:42:53.000Z

tests/0400_i18n/08_update_catalogs.py

sveetch/Optimus

983aebeccd2ada7a5a0ab96f9296d4bba1112022

[ "MIT" ]

1

2017-05-21T17:32:28.000Z

import os import logging import shutil from optimus.i18n.manager import I18NManager def test_update_catalogs_all( minimal_i18n_settings, caplog, temp_builds_dir, fixtures_settings ): """ Update every catalogs """ basepath = temp_builds_dir.join("i18n_update_catalogs_all") # Copy sample project to temporary dir samplename = "minimal_i18n" samplepath = os.path.join(fixtures_settings.fixtures_path, samplename) destination = os.path.join(basepath.strpath, samplename) shutil.copytree(samplepath, destination) # Get manager with settings settings = minimal_i18n_settings(destination) manager = I18NManager(settings) updated = manager.update_catalogs() assert updated == ["en_US", "fr_FR"] assert caplog.record_tuples == [ ( "optimus", logging.INFO, "Updating catalog (PO) for language 'en_US' to {}".format( manager.get_po_filepath("en_US") ), ), ( "optimus", logging.INFO, "Updating catalog (PO) for language 'fr_FR' to {}".format( manager.get_po_filepath("fr_FR") ), ), ] def test_update_catalogs_one( minimal_i18n_settings, caplog, temp_builds_dir, fixtures_settings ): """ Update only default locale catalog """ basepath = temp_builds_dir.join("i18n_update_catalogs_one") # Copy sample project to temporary dir samplename = "minimal_i18n" samplepath = os.path.join(fixtures_settings.fixtures_path, samplename) destination = os.path.join(basepath.strpath, samplename) shutil.copytree(samplepath, destination) # Get manager with settings settings = minimal_i18n_settings(destination) manager = I18NManager(settings) updated = manager.update_catalogs([settings.LANGUAGE_CODE]) assert updated == [settings.LANGUAGE_CODE] assert os.path.exists(manager.get_po_filepath(settings.LANGUAGE_CODE)) is True assert caplog.record_tuples == [ ( "optimus", logging.INFO, "Updating catalog (PO) for language 'en_US' to {}".format( manager.get_po_filepath(settings.LANGUAGE_CODE) ), ), ]

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tests/acceptance/__init__.py

datphan/moviecrab

e3bcff700b994388f1ded68d268a960b10d57a81

[ "BSD-3-Clause" ]

null

tests/acceptance/__init__.py

datphan/moviecrab

e3bcff700b994388f1ded68d268a960b10d57a81

[ "BSD-3-Clause" ]

null

tests/acceptance/__init__.py

datphan/moviecrab

e3bcff700b994388f1ded68d268a960b10d57a81

[ "BSD-3-Clause" ]

null

"""acceptance tests""" import unittest from nose.plugins.attrib import attr @attr('acc') class AcceptanceTestCase(unittest.TestCase): """Base AcceptanceTestCase""" pass

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OC/network.py

Xin-Ye-1/HIEM

6764f579eef6ec92dd85a005af27419f630df7da

[ "Apache-2.0" ]

2

2021-04-12T02:41:00.000Z

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OC/network.py

Xin-Ye-1/HIEM

6764f579eef6ec92dd85a005af27419f630df7da

[ "Apache-2.0" ]

null

OC/network.py

Xin-Ye-1/HIEM

6764f579eef6ec92dd85a005af27419f630df7da

[ "Apache-2.0" ]

null

#! /usr/bin/env python import tensorflow as tf import tensorflow.contrib.slim as slim seed = 0 def fc2d(inputs, num_outputs, activation_fn, scope, ): with tf.variable_scope(scope, reuse=tf.AUTO_REUSE) as s: n0, n1, n2 = inputs.get_shape().as_list() weights = tf.get_variable(name='weights', shape=[n2, num_outputs], initializer=tf.contrib.layers.xavier_initializer(seed=seed), trainable=True) wx = tf.einsum('ijk,kl->ijl', inputs, weights) biases = tf.get_variable(name='biases', shape=[num_outputs], initializer=tf.zeros_initializer(), trainable=True) wx_b = wx + biases result = wx_b if activation_fn is None else activation_fn(wx_b, name=s.name) return result def conv3d(scope_name, input, filter_size): with tf.variable_scope(scope_name, reuse=tf.AUTO_REUSE) as scope: conv_filter = tf.get_variable(name='weights', shape=filter_size, initializer=tf.contrib.layers.xavier_initializer(seed=seed), trainable=True) conv = tf.nn.conv3d(input=input, filter=conv_filter, strides=[1, 1, 1, 1, 1], padding='VALID') biases = tf.get_variable(name='biases', shape=[filter_size[-1]], initializer=tf.zeros_initializer(), trainable=True) bias = tf.nn.bias_add(conv, biases) result = tf.nn.relu(bias, name=scope.name) return result class OC_Network(): def __init__(self, window_size, num_labels, num_options, action_size, history_steps, scope ): with tf.variable_scope(scope): self.visions = tf.placeholder(shape=[None, history_steps * window_size * window_size, num_labels], dtype=tf.float32) self.depths = tf.placeholder(shape=[None, history_steps * window_size * window_size, 1], dtype=tf.float32) self.targets = tf.placeholder(shape=[None, num_labels], dtype=tf.float32) related_visions = fc2d(inputs=self.visions, num_outputs=1, activation_fn=None, scope='vision_preprocess') related_visions = slim.flatten(related_visions) depths = slim.flatten(self.depths) hidden_visions = slim.fully_connected(inputs=related_visions, num_outputs=256, activation_fn=tf.nn.relu, weights_initializer=tf.contrib.layers.xavier_initializer(seed=seed), biases_initializer=tf.zeros_initializer(), scope='vision_hidden') hidden_depths = slim.fully_connected(inputs=depths, num_outputs=256, activation_fn=tf.nn.relu, weights_initializer=tf.contrib.layers.xavier_initializer(seed=seed), biases_initializer=tf.zeros_initializer(), scope='depth_hidden') hidden_targets = slim.fully_connected(inputs=self.targets, num_outputs=256, activation_fn=tf.nn.relu, weights_initializer=tf.contrib.layers.xavier_initializer(seed=seed), biases_initializer=tf.zeros_initializer(), scope='target_hidden') vision_depth_feature = tf.concat([hidden_visions, hidden_depths, hidden_targets], -1) embed_feature = slim.fully_connected(inputs=vision_depth_feature, num_outputs=256, activation_fn=tf.nn.relu, weights_initializer=tf.contrib.layers.xavier_initializer(seed=seed), biases_initializer=tf.zeros_initializer(), scope='embed') option_qvalues = slim.fully_connected(inputs=embed_feature, num_outputs=num_options, activation_fn=None, weights_initializer=tf.contrib.layers.xavier_initializer(seed=seed), biases_initializer=tf.zeros_initializer(), scope='option_qvalue') self.option_qvalues = option_qvalues action_policy = slim.fully_connected(inputs=embed_feature, num_outputs=num_options*action_size, activation_fn=None, weights_initializer=tf.contrib.layers.xavier_initializer(seed=seed), biases_initializer=tf.zeros_initializer(), scope='action_policy') self.action_policy = tf.nn.softmax(tf.reshape(action_policy, [-1, num_options, action_size]), axis=-1) terminations = slim.fully_connected(inputs=embed_feature, num_outputs=num_options, activation_fn=None, weights_initializer=tf.contrib.layers.xavier_initializer(seed=seed), biases_initializer=tf.zeros_initializer(), scope='termination') self.terminations = tf.sigmoid(terminations) # highlevel training if not scope.startswith('global'): self.chosen_options = tf.placeholder(shape=[None], dtype=tf.int32) self.target_option_qvalues = tf.placeholder(shape=[None], dtype=tf.float32) self.chosen_actions = tf.placeholder(shape=[None], dtype=tf.int32) self.lr = tf.placeholder(dtype=tf.float32) self.termination_reg = tf.placeholder(dtype=tf.float32) options_onehot = tf.one_hot(self.chosen_options, num_options, dtype=tf.float32) qvalues_for_chosen_options = tf.reduce_sum(self.option_qvalues*options_onehot, axis=1) option_td_error = tf.square(self.target_option_qvalues - qvalues_for_chosen_options) self.option_qvalue_loss = 0.5*tf.reduce_mean(option_td_error) option_onehot_expanded = tf.tile(tf.expand_dims(options_onehot, 2), [1, 1, action_size]) pi_for_chosen_options = tf.reduce_sum(self.action_policy * option_onehot_expanded, axis=1) logpi_for_chosen_options = tf.log(tf.clip_by_value(pi_for_chosen_options, 0.000001, 0.999999)) action_onehot = tf.one_hot(self.chosen_actions, action_size, dtype=tf.float32) logpi_for_chosen_actions = tf.reduce_sum(logpi_for_chosen_options * action_onehot, axis=-1) advantage = self.target_option_qvalues - qvalues_for_chosen_options self.action_policy_loss = -tf.reduce_mean(logpi_for_chosen_actions * tf.stop_gradient(advantage)) self.entropy_loss = -tf.reduce_mean( tf.reduce_sum(pi_for_chosen_options * (-logpi_for_chosen_options), axis=-1)) chosen_terminations = tf.reduce_sum(self.terminations * options_onehot, axis=1) self.termination_loss = tf.reduce_mean(chosen_terminations * tf.stop_gradient( qvalues_for_chosen_options - tf.reduce_max(self.option_qvalues, axis=-1) + self.termination_reg)) # factor = tf.stop_gradient(qvalues_for_chosen_options - tf.reduce_max(self.option_qvalues, axis=-1) + self.termination_reg) # sign = tf.stop_gradient(tf.where(tf.greater_equal(factor, 0.0), tf.ones_like(factor), tf.zeros_like(factor))) # self.termination_loss = tf.reduce_mean(sign*chosen_terminations*factor + # (1-sign)*(1-chosen_terminations)*(-factor)) # self.loss = self.option_qvalue_loss + self.action_policy_loss + 0 * self.entropy_loss + self.termination_loss trainer = tf.train.RMSPropOptimizer(learning_rate=self.lr) params = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope) global_params = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, 'global/main') gradients = tf.gradients(self.option_qvalue_loss, params) norm_gradients, _ = tf.clip_by_global_norm(gradients, 40.0) self.option_update = trainer.apply_gradients(zip(norm_gradients, global_params)) gradients = tf.gradients(self.action_policy_loss + 0.01*self.entropy_loss, params) norm_gradients, _ = tf.clip_by_global_norm(gradients, 40.0) self.action_update = trainer.apply_gradients(zip(norm_gradients, global_params)) gradients = tf.gradients(self.termination_loss, params) norm_gradients, _ = tf.clip_by_global_norm(gradients, 40.0) self.term_update = trainer.apply_gradients(zip(norm_gradients, global_params)) class Lowlevel_Network(): def __init__(self, window_size, num_labels, action_size, history_steps, scope ): with tf.variable_scope('lowlevel'): with tf.variable_scope(scope): self.visions = tf.placeholder( shape=[None, history_steps * window_size * window_size, num_labels], dtype=tf.float32) self.depths = tf.placeholder(shape=[None, history_steps * window_size * window_size, 1], dtype=tf.float32) self.subtargets = tf.placeholder(shape=[None, num_labels], dtype=tf.float32) subtargets_expanded = tf.tile(tf.expand_dims(self.subtargets, 1), [1, history_steps * window_size * window_size, 1]) masked_visions = tf.reduce_sum(self.visions * subtargets_expanded, axis=-1) masked_visions = slim.flatten(masked_visions) depths = slim.flatten(self.depths) hidden_visions = slim.fully_connected(inputs=masked_visions, num_outputs=256, activation_fn=tf.nn.relu, weights_initializer=tf.contrib.layers.xavier_initializer(seed=seed), biases_initializer=tf.zeros_initializer(), scope='vision_hidden') hidden_depths = slim.fully_connected(inputs=depths, num_outputs=256, activation_fn=tf.nn.relu, weights_initializer=tf.contrib.layers.xavier_initializer(seed=seed), biases_initializer=tf.zeros_initializer(), scope='depth_hidden') vision_depth_feature = tf.concat([hidden_visions, hidden_depths], 1) embed_feature = slim.fully_connected(inputs=vision_depth_feature, num_outputs=256, activation_fn=tf.nn.relu, weights_initializer=tf.contrib.layers.xavier_initializer(seed=seed), biases_initializer=tf.zeros_initializer(), scope='embed') # policy estimation hidden_policy = slim.fully_connected(inputs=embed_feature, num_outputs=20, activation_fn=tf.nn.relu, weights_initializer=tf.contrib.layers.xavier_initializer(seed=seed), biases_initializer=tf.zeros_initializer(), scope='policy_hidden') self.policy = slim.fully_connected(inputs=hidden_policy, num_outputs=action_size, activation_fn=tf.nn.softmax, weights_initializer=tf.contrib.layers.xavier_initializer(seed=seed), biases_initializer=tf.zeros_initializer(), scope='policy') # value estimation hidden_value = slim.fully_connected(inputs=embed_feature, num_outputs=20, activation_fn=tf.nn.relu, weights_initializer=tf.contrib.layers.xavier_initializer(seed=seed), biases_initializer=tf.zeros_initializer(), scope='value_hidden') self.value = slim.fully_connected(inputs=hidden_value, num_outputs=1, activation_fn=None, weights_initializer=tf.contrib.layers.xavier_initializer(seed=seed), biases_initializer=tf.zeros_initializer(), scope='value') # Lowlevel training if not scope.startswith('global'): self.chosen_actions = tf.placeholder(shape=[None], dtype=tf.int32) self.advantages = tf.placeholder(shape=[None], dtype=tf.float32) self.target_values = tf.placeholder(shape=[None], dtype=tf.float32) self.lowlevel_lr = tf.placeholder(dtype=tf.float32) self.er = tf.placeholder(dtype=tf.float32) actions_onehot = tf.one_hot(self.chosen_actions, action_size, dtype=tf.float32) log_policy = tf.log(tf.clip_by_value(self.policy, 0.000001, 0.999999)) log_pi_for_action = tf.reduce_sum(tf.multiply(log_policy, actions_onehot), axis=1) self.value_loss = 0.5 * tf.reduce_mean(tf.square(self.target_values - self.value)) self.policy_loss = -tf.reduce_mean(log_pi_for_action * self.advantages) self.entropy_loss = -tf.reduce_mean(tf.reduce_sum(self.policy * (-log_policy), axis=1)) self.lowlevel_loss = self.value_loss + self.policy_loss + self.er * self.entropy_loss local_lowlevel_params = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, 'lowlevel/%s'%scope) gradients = tf.gradients(self.lowlevel_loss, local_lowlevel_params) norm_gradients, _ = tf.clip_by_global_norm(gradients, 40.0) lowlevel_trainer = tf.train.RMSPropOptimizer(learning_rate=self.lowlevel_lr) global_lowlevel_params = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, 'lowlevel/global') self.lowlevel_update = lowlevel_trainer.apply_gradients(zip(norm_gradients, global_lowlevel_params)) class Lowlevel_Network_ex(): def __init__(self, window_size, num_labels, action_size, history_steps, scope ): with tf.variable_scope('lowlevel'): with tf.variable_scope(scope): self.visions = tf.placeholder( shape=[None, history_steps * window_size * window_size, num_labels], dtype=tf.float32) self.depths = tf.placeholder(shape=[None, history_steps * window_size * window_size, 1], dtype=tf.float32) self.subtargets = tf.placeholder(shape=[None, num_labels], dtype=tf.float32) self.targets = tf.placeholder(shape=[None, num_labels], dtype=tf.float32) subtargets_expanded = tf.tile(tf.expand_dims(self.subtargets, 1), [1, history_steps * window_size * window_size, 1]) masked_visions = tf.reduce_sum(self.visions * subtargets_expanded, axis=-1) masked_visions = slim.flatten(masked_visions) depths = slim.flatten(self.depths) hidden_visions = slim.fully_connected(inputs=masked_visions, num_outputs=256, activation_fn=tf.nn.relu, weights_initializer=tf.contrib.layers.xavier_initializer(seed=seed), biases_initializer=tf.zeros_initializer(), scope='vision_hidden') hidden_depths = slim.fully_connected(inputs=depths, num_outputs=256, activation_fn=tf.nn.relu, weights_initializer=tf.contrib.layers.xavier_initializer(seed=seed), biases_initializer=tf.zeros_initializer(), scope='depth_hidden') hidden_targets = slim.fully_connected(inputs=depths, num_outputs=256, activation_fn=tf.nn.relu, weights_initializer=tf.contrib.layers.xavier_initializer(seed=seed), biases_initializer=tf.zeros_initializer(), scope='target_hidden') vision_depth_feature = tf.concat([hidden_visions, hidden_depths, hidden_targets], 1) embed_feature = slim.fully_connected(inputs=vision_depth_feature, num_outputs=256, activation_fn=tf.nn.relu, weights_initializer=tf.contrib.layers.xavier_initializer(seed=seed), biases_initializer=tf.zeros_initializer(), scope='embed') self.qvalues = slim.fully_connected(inputs=embed_feature, num_outputs=action_size, activation_fn=None, weights_initializer=tf.contrib.layers.xavier_initializer(seed=seed), biases_initializer=tf.zeros_initializer(), scope='qvalue') # Lowlevel training if not scope.startswith('global'): self.chosen_actions = tf.placeholder(shape=[None], dtype=tf.int32) self.target_q_values = tf.placeholder(shape=[None], dtype=tf.float32) self.lowlevel_lr = tf.placeholder(dtype=tf.float32) actions_onehot = tf.one_hot(self.chosen_actions, action_size, dtype=tf.float32) q_values_for_chosen_actions = tf.reduce_sum(self.qvalues*actions_onehot, axis=1) td_error = tf.square(self.target_q_values - q_values_for_chosen_actions) self.qvalue_loss = 0.5*tf.reduce_mean(td_error) lowlevel_trainer = tf.train.RMSPropOptimizer(learning_rate=self.lowlevel_lr) lowlevel_params = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, 'lowlevel/%s' % scope) gradients = tf.gradients(self.qvalue_loss, lowlevel_params) norm_gradients, _ = tf.clip_by_global_norm(gradients, 40.0) global_lowlevel_params = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, 'lowlevel/global/ex/main') self.lowlevel_update = lowlevel_trainer.apply_gradients(zip(norm_gradients, global_lowlevel_params))

55.6775

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5

6c27c7aa14b6a3a742020fc655ba28804b70f883

98

py

Python

rover-stub/accelsensor.py

GamesCreatorsClub/GCC-Rover

25a69f62a1bb01fc421924ec39f180f50d6a640b

[ "MIT" ]

3

2018-02-13T21:39:55.000Z

2018-04-26T18:17:39.000Z

rover-stub/accelsensor.py

GamesCreatorsClub/GCC-Rover

25a69f62a1bb01fc421924ec39f180f50d6a640b

[ "MIT" ]

null

rover-stub/accelsensor.py

GamesCreatorsClub/GCC-Rover

25a69f62a1bb01fc421924ec39f180f50d6a640b

[ "MIT" ]

null

10.888889

41

0.744898

12

98

6

1

0

0.1

0.183673

98

8

42

12.25

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0.520408

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true

0

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null

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