xszheng2020/the_stack_dedup_python_hits_4 · Datasets at Hugging Face

d05e84dc9aff4e779540be30d4ef0d0ace895b44

158

py

Python

nombredelproyecto/nombreapp/apps.py

engelpain/AprendiendoDjango

e0f04302fd79e8002910b8ccba916aa0ede29fb7

[ "MIT" ]

null

nombredelproyecto/nombreapp/apps.py

engelpain/AprendiendoDjango

e0f04302fd79e8002910b8ccba916aa0ede29fb7

[ "MIT" ]

null

nombredelproyecto/nombreapp/apps.py

engelpain/AprendiendoDjango

e0f04302fd79e8002910b8ccba916aa0ede29fb7

[ "MIT" ]

null

# -*- coding: utf-8 -*- from __future__ import unicode_literals from django.apps import AppConfig class NombreappConfig(AppConfig): name = 'nombreapp'

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6.222222

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d06e7c8dc134148cb706897bafa8bbbaa7f4791c

177

py

Python

packages/core/minos-microservice-networks/minos/networks/brokers/subscribers/queued/queues/database/__init__.py

minos-framework/minos-python

9a6ad6783361f3d8a497a088808b55ea7a938c6c

[ "MIT" ]

247

2022-01-24T14:55:30.000Z

2022-03-25T12:06:17.000Z

packages/core/minos-microservice-networks/minos/networks/brokers/subscribers/queued/queues/database/__init__.py

minos-framework/minos-python

9a6ad6783361f3d8a497a088808b55ea7a938c6c

[ "MIT" ]

168

2022-01-24T14:54:31.000Z

2022-03-31T09:31:09.000Z

packages/core/minos-microservice-networks/minos/networks/brokers/subscribers/queued/queues/database/__init__.py

minos-framework/minos-python

9a6ad6783361f3d8a497a088808b55ea7a938c6c

[ "MIT" ]

21

2022-02-06T17:25:58.000Z

2022-03-27T04:50:29.000Z

from .factories import ( BrokerSubscriberQueueDatabaseOperationFactory, ) from .impl import ( DatabaseBrokerSubscriberQueue, DatabaseBrokerSubscriberQueueBuilder, )

22.125

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0.80791

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177

15.888889

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25.285714

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0

1

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4

d08c9e38a3842963f3a335b7123900468483a24d

78

py

Python

benchmarks/Fibonacci/Fibonacci.py

Qlova/ilang

17188f6b2fd678928ad341cd218e807520279f1a

[ "Artistic-2.0" ]

6

2017-09-03T07:08:34.000Z

2018-08-09T14:14:49.000Z

benchmarks/Fibonacci/Fibonacci.py

qlova/ilang

17188f6b2fd678928ad341cd218e807520279f1a

[ "Artistic-2.0" ]

14

2016-07-20T12:25:14.000Z

2018-06-13T04:14:43.000Z

benchmarks/Fibonacci/Fibonacci.py

qlova/ilang

17188f6b2fd678928ad341cd218e807520279f1a

[ "Artistic-2.0" ]

1

2017-09-26T02:02:04.000Z

def fib(n): if n < 2: return n return fib(n-1) + fib(n-2) print(fib(30))

11.142857

27

0.564103

18

78

2.444444

0.5

0.272727

0

0.083333

0.230769

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1

0

1

0

null

0

1

0

4

d0a8ae299d5eb3533fa545c4fa09af8b902d9e90

747

py

Python

scripts/cosine.py

skoulouzis/E-COCO

d2657f283b9e715033c2aeb9a43958c43b2cc2b2

[ "Apache-2.0" ]

1

2017-12-19T16:27:26.000Z

scripts/cosine.py

skoulouzis/E-CO-2

d2657f283b9e715033c2aeb9a43958c43b2cc2b2

[ "Apache-2.0" ]

22

2016-07-01T12:27:01.000Z

2021-11-10T10:56:39.000Z

scripts/cosine.py

skoulouzis/E-COCO

d2657f283b9e715033c2aeb9a43958c43b2cc2b2

[ "Apache-2.0" ]

3

2017-09-18T09:56:53.000Z

2021-03-18T00:05:08.000Z

from scipy import spatial cv = [0.1523230959, 0.1723340427, 0.1436187823, 0.0937464038, 0.0770848655, 0.0296825692, 0.076575456, 0.1581296577, 0.1031274647, 0.0454623065, 0.1055528533, 0.0559519702, 0.0836470141, 0.2134543524, 0.1665261675, 0.1039377815, 0.0843013781, 0.0621802636, 0.0772685446, 0.1037510024, 0.0422574617, 0.2743938044, 0.1893437829] jobs = [0.2407257174, 0.0447829943, 0.2361261648, 0.0348669893, 0.1424850219, 0.0472701375, 0.0555587367, 0.2269497742, 0.0767076683, 0.0206122116, 0.0926937554, 0.0422161878, 0.0525557046, 0.1500645831, 0.2376042255, 0.0563189083, 0.0650523254, 0.0583425591, 0.1253678009, 0.0637731625, 0.0728895656, 0.0749859927, 0.0897501389] result = 1 - spatial.distance.cosine(cv, jobs) print result

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747

5.401869

0.542056

0

0.743025

0.088353

747

9

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83

0.105727

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0

1

0

null

0

1

0

4

d0d92d916115d67a5bbadfedd227bf8fcfa10392

51

py

Python

atcoder/abc186/a.py

sugitanishi/competitive-programming

51af65fdce514ece12f8afbf142b809d63eefb5d

[ "MIT" ]

null

atcoder/abc186/a.py

sugitanishi/competitive-programming

51af65fdce514ece12f8afbf142b809d63eefb5d

[ "MIT" ]

null

atcoder/abc186/a.py

sugitanishi/competitive-programming

51af65fdce514ece12f8afbf142b809d63eefb5d

[ "MIT" ]

null

print((lambda a,b:a//b)(*map(int,input().split())))

51

0.607843

10

51

3.1

0.8

0.129032

0

0.019608

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true

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null

0

1

0

null

0

1

0

1

0

4

190c0a796e69cd9a5fd4ca92bc039719abebbea4

1,050

py

Python

zilean/system/zilean_partitions.py

A-Hilaly/zilean

2b2e87969a0d8064e8b92b07c346a4006f93c795

[ "Apache-2.0" ]

null

zilean/system/zilean_partitions.py

A-Hilaly/zilean

2b2e87969a0d8064e8b92b07c346a4006f93c795

[ "Apache-2.0" ]

null

zilean/system/zilean_partitions.py

A-Hilaly/zilean

2b2e87969a0d8064e8b92b07c346a4006f93c795

[ "Apache-2.0" ]

null

from zilean.datasets.sys.machines import ZileanMachines from zilean.datasets.sys.linked import ZLinkedDatabases from .zilean_users import ZileanUsers class Partition(object): def __init__(self, mn, *args): self.front = '{0}_zdb'.format(mn) self.other = list(args) def dump(self): r = self.other + [self.front] return r class ZileanPartition(object): def __init__(object): pass def all_partitions(self): pass def all_databases(self): pass def all_linked(self): pass def new_database(self): pass def remove_database(self): pass def link_database(self): pass def add_machine_database(self): pass def remove_machine_database(self): pass def machine_partitions(self): pass def is_partition_of(self): pass def check_machine_partitions(self): pass def make_machine_partitions(self): pass def remove_machine_partitions(self): pass

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1

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1

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4

190d346bf20a93be207aa699647d7f3db2dfe246

88

py

Python

eb_anno/apps.py

kulits/ElephantBook

729aaf64b8f039cfc2a636e2d2745292aa4ea98e

[ "Apache-2.0" ]

null

eb_anno/apps.py

kulits/ElephantBook

729aaf64b8f039cfc2a636e2d2745292aa4ea98e

[ "Apache-2.0" ]

null

eb_anno/apps.py

kulits/ElephantBook

729aaf64b8f039cfc2a636e2d2745292aa4ea98e

[ "Apache-2.0" ]

2

2021-08-17T20:26:22.000Z

2021-09-18T11:44:36.000Z

from django.apps import AppConfig class EbAnnoConfig(AppConfig): name = 'eb_anno'

14.666667

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0.75

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88

5.909091

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0.170455

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4

ef85f11fa012293f928b263dc08ba7b1cab75a25

44

py

Python

mmdet/datasets/mmcocotools/__init__.py

VIRC-lab-csust/AGMNet

ead95466da343cf9436774138c642d2ca12da4e4

[ "Apache-2.0" ]

47

2020-06-07T16:53:02.000Z

2022-03-18T03:26:38.000Z

mmdet/datasets/pycoco/__init__.py

lh0515/cas-dc-template

5b0400ca5dc98d09beca36d46cc55bfabb9ce4e0

[ "Apache-2.0" ]

12

2020-06-25T15:59:03.000Z

2021-10-16T11:00:20.000Z

mmdet/datasets/pycoco/__init__.py

lh0515/cas-dc-template

5b0400ca5dc98d09beca36d46cc55bfabb9ce4e0

[ "Apache-2.0" ]

38

2020-05-24T11:27:36.000Z

2022-01-24T07:37:25.000Z

__author__ = 'tylin' __version__ = '12.0.2'

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1

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null

0

4

efa544d560acdebd0d00a774d0f671edb30aac31

67

py

Python

TermTk/TTkAbstract/__init__.py

ceccopierangiolieugenio/py-ttk

117d61844bb7344bbe22a7797b7e3763d5fe4de5

[ "MIT" ]

1

2022-02-28T16:33:25.000Z

TermTk/TTkAbstract/__init__.py

ceccopierangiolieugenio/py-ttk

117d61844bb7344bbe22a7797b7e3763d5fe4de5

[ "MIT" ]

null

TermTk/TTkAbstract/__init__.py

ceccopierangiolieugenio/py-ttk

117d61844bb7344bbe22a7797b7e3763d5fe4de5

[ "MIT" ]

null

from .abstractscrollarea import * from .abstractitemmodel import *

22.333333

33

0.820896

6

67

9.166667

0.666667

0

0.119403

67

2

34

33.5

0.932203

0

1

0

true

0

1

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1

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null

0

1

0

null

0

1

0

1

0

4

5602ddbae5a3c0103098a6fb7b4c6ecd67d11f13

167

py

Python

requests/requests-utils.urldefragauth.py

all3g/pieces

bc378fd22ddc700891fe7f34ab0d5b341141e434

[ "CNRI-Python" ]

34

2016-10-31T02:05:24.000Z

2018-11-08T14:33:13.000Z

requests/requests-utils.urldefragauth.py

join-us/python-programming

bc378fd22ddc700891fe7f34ab0d5b341141e434

[ "CNRI-Python" ]

2

2017-05-11T03:00:31.000Z

2017-11-01T23:37:37.000Z

requests/requests-utils.urldefragauth.py

join-us/python-programming

bc378fd22ddc700891fe7f34ab0d5b341141e434

[ "CNRI-Python" ]

21

2016-08-19T09:05:45.000Z

2018-11-08T14:33:16.000Z

#!/usr/bin/python # -*- coding: utf-8 -*- import requests url = "http://user:pass@demo.com/index.php?id=1&p=x" print(url) print(requests.utils.urldefragauth(url))

15.181818

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0.676647

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167

4.185185

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0.013333

0.101796

167

10

53

16.7

0.74

0.227545

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0.346457

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false

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0.25

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null

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null

0

1

0

1

0

4

ef092c0ff291b181f1d1a1ebb8f5907eb202c1be

624

py

Python

doc/source/image/contrast.py

ppawlak/pystacia

854053a2872c9374e2c121c4af549f6bba640116

[ "MIT" ]

9

2015-02-11T21:33:33.000Z

2021-06-14T14:55:24.000Z

doc/source/image/contrast.py

ppawlak/pystacia

854053a2872c9374e2c121c4af549f6bba640116

[ "MIT" ]

1

2016-08-01T12:31:17.000Z

doc/source/image/contrast.py

ppawlak/pystacia

854053a2872c9374e2c121c4af549f6bba640116

[ "MIT" ]

2

2015-08-21T08:23:25.000Z

2018-10-31T02:52:50.000Z

from os.path import dirname, join from pystacia import lena dest = join(dirname(__file__), '../_static/generated') image = lena(128) image.contrast(-1) image.write(join(dest, 'lena_contrast-1.jpg')) image.close() image = lena(128) image.contrast(-0.6) image.write(join(dest, 'lena_contrast-0.6.jpg')) image.close() image = lena(128) image.contrast(-0.25) image.write(join(dest, 'lena_contrast-0.25.jpg')) image.close() image = lena(128) image.contrast(0.25) image.write(join(dest, 'lena_contrast0.25.jpg')) image.close() image = lena(128) image.contrast(1) image.write(join(dest, 'lena_contrast1.jpg')) image.close()

20.129032

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0.725962

100

624

4.43

0.24

0.10158

0.13544

0.191874

0.735892

0.717833

0.62754

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0.061511

0.088141

624

30

55

20.8

0.717047

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1

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null

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4

ef1626154dd6387a915ea1ca609b4fe7d18ca37f

83

py

Python

mycelyso/misc/__init__.py

csachs/mycelyso

b7b383cd1fa55be7b084821e5b38b72bf9df7f59

[ "BSD-2-Clause" ]

2

2019-04-19T03:11:06.000Z

2021-10-06T03:11:22.000Z

mycelyso/misc/__init__.py

csachs/mycelyso

b7b383cd1fa55be7b084821e5b38b72bf9df7f59

[ "BSD-2-Clause" ]

1

2020-05-27T08:24:32.000Z

2020-06-09T07:53:46.000Z

mycelyso/misc/__init__.py

csachs/mycelyso

b7b383cd1fa55be7b084821e5b38b72bf9df7f59

[ "BSD-2-Clause" ]

3

2017-05-29T07:52:55.000Z

2021-01-15T19:44:45.000Z

# -*- coding: utf-8 -*- """ The misc package contains various helper functions. """

20.75

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0.650602

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5.4

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83

4

52

20.75

0.757143

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0

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true

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null

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1

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4

ef3a197c06a63860f1fdf3f65a8982ccbf755a35

116

py

Python

basic-concepts-and-functions/complex-type.py

jeantardelli/math-with-python

119bbbc62329c0d834d965232239bd3b39116cc1

[ "MIT" ]

1

2021-01-16T21:42:42.000Z

basic-concepts-and-functions/complex-type.py

jeantardelli/math-with-python

119bbbc62329c0d834d965232239bd3b39116cc1

[ "MIT" ]

null

basic-concepts-and-functions/complex-type.py

jeantardelli/math-with-python

119bbbc62329c0d834d965232239bd3b39116cc1

[ "MIT" ]

null

""" This module shows the complex Python numbers """ z = 1 + 1j print(z + 2) # 3 + 1j print(z.conjugate()) # 1 - 1j

16.571429

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20

116

3.5

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116

6

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19.333333

0.7

0.508621

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false

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0.666667

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null

0

1

0

1

0

null

0

1

0

4

ef4ac63f9bee0140eaf04088871051143d630c7c

1,314

py

Python

tests/test_tools.py

PTank/trashtalk

1fc539f1fbe02342fce8f18d5365cfad1902ead8

[ "MIT" ]

null

tests/test_tools.py

PTank/trashtalk

1fc539f1fbe02342fce8f18d5365cfad1902ead8

[ "MIT" ]

null

tests/test_tools.py

PTank/trashtalk

1fc539f1fbe02342fce8f18d5365cfad1902ead8

[ "MIT" ]

null

from trashtalk.tools import human_readable_from_bytes, print_files def test_human_readable_from_bytes(): b = human_readable_from_bytes(500) k = human_readable_from_bytes(1024) m = human_readable_from_bytes(1024**2) g = human_readable_from_bytes(1024**3) t = human_readable_from_bytes(1024**4) p = human_readable_from_bytes(1024**5) e = human_readable_from_bytes(1024**6) z = human_readable_from_bytes(1024**7) y = human_readable_from_bytes(1024**8) assert b == '500' assert k == "1K" assert m == "1M" assert g == "1G" assert t == "1T" assert p == "1P" assert e == "1E" assert z == "1Z" assert y == "1Y" assert "test error" == human_readable_from_bytes("test error") def test_print_files(capsys): l = [["un", 2, 3, 432], ["deux", 2, 3], ["trois", 2, 3, 4]] print_files(l, 4) out, err = capsys.readouterr() assert bool(err) == False s = out.split('\n') assert s[0] == "un 2 3 432" assert s[1] == "deux 2 3 " assert s[2] == "trois 2 3 4" print_files([], 4) out, err = capsys.readouterr() assert bool(err) == False assert bool(out) == False l = [[None, "error"]] print_files(l, 4) out, err = capsys.readouterr() assert bool(out) == False assert err == "error\n"

27.957447

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0

0.078921

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1,314

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67

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0

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0

null

1

0

null

0

1

0

4

ef4e92fc86e8906b500c1a08fea35b40a5b6ca6e

85

py

Python

utils/__init__.py

endymecy/NDIToolbox

f7a0a642b4a778d9d0c131871f4bfb9822ecb3da

[ "BSD-4-Clause" ]

5

2017-02-28T16:16:06.000Z

2020-07-13T06:49:34.000Z

utils/__init__.py

endymecy/NDIToolbox

f7a0a642b4a778d9d0c131871f4bfb9822ecb3da

[ "BSD-4-Clause" ]

1

2018-08-19T19:08:14.000Z

utils/__init__.py

endymecy/NDIToolbox

f7a0a642b4a778d9d0c131871f4bfb9822ecb3da

[ "BSD-4-Clause" ]

4

2017-10-25T20:17:15.000Z

2021-07-26T11:39:50.000Z

"""__init__.py - various utility functions Chris R. Coughlin (TRI/Austin, Inc.) """

17

42

0.694118

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85

5

1

0

0.141176

85

4

43

21.25

0.753425

0.905882

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

4

322f52a09af1b0d08c0ded63fce5a8eca4ddf88d

270

py

Python

examples/config.py

dhurley94/pritunl-api-python

ae1d24fccff2cf128fb54fd2449ff1378c587518

[ "MIT" ]

1

2021-09-22T10:04:22.000Z

examples/config.py

dhurley94/pritunl-api-python

ae1d24fccff2cf128fb54fd2449ff1378c587518

[ "MIT" ]

null

examples/config.py

dhurley94/pritunl-api-python

ae1d24fccff2cf128fb54fd2449ff1378c587518

[ "MIT" ]

null

from pritunl_api import * import os pritunl = Pritunl(url=os.getenv("PRITUNL_BASE_URL", "https://yoursite.com"), token=os.getenv("PRITUNL_API_TOKEN", "<your api token>"), secret=os.getenv("PRITUNL_API_SECRET", "<your api secret>"))

33.75

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4.742857

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0.216867

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38.571429

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null

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0

1

0

null

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1

0

4

3230502c01bb4ccede15e5922dc5a55238eb5eb6

140

py

Python

helpers/decorators.py

oasisvali/Cowin-Notification-Service

72d8851de469b529011b79c9672ddfb7f8f151bf

[ "MIT" ]

14

2021-05-07T13:09:03.000Z

2022-01-10T23:24:42.000Z

helpers/decorators.py

oasisvali/Cowin-Notification-Service

72d8851de469b529011b79c9672ddfb7f8f151bf

[ "MIT" ]

16

2021-05-10T16:41:21.000Z

2021-06-09T14:49:03.000Z

helpers/decorators.py

oasisvali/Cowin-Notification-Service

72d8851de469b529011b79c9672ddfb7f8f151bf

[ "MIT" ]

5

2021-05-09T12:14:03.000Z

2021-06-08T13:56:55.000Z

def validate_args(func, *args): def inner(*args, **kwargs): # Add validation here func(*args, **kwargs) return inner

28

31

0.6

17

140

4.882353

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140

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null

0

1

0

1

0

4

324a375ed88a3e083a2d73a7d7ff6b725f46d92f

4,000

py

Python

tests/TestSaving.py

manuSrep/easyScriptingPy

66fddb4b0bab8eb65b51c7cd0615ba2e68189dd2

[ "BSD-2-Clause" ]

null

tests/TestSaving.py

manuSrep/easyScriptingPy

66fddb4b0bab8eb65b51c7cd0615ba2e68189dd2

[ "BSD-2-Clause" ]

null

tests/TestSaving.py

manuSrep/easyScriptingPy

66fddb4b0bab8eb65b51c7cd0615ba2e68189dd2

[ "BSD-2-Clause" ]

null

import unittest import sys import os import shutil sys.path.append("../miscpy/") from miscpy import prepareSaving, extractFromFilename class TestExtractFromFilename(unittest.TestCase): def test_filename_including_path_name_ext(self): test_file = ["path/file.ext"] expected_name = ["file"] expected_path = ["path"] expected_ext = ["ext"] for f, file in enumerate(test_file): fname, path, ext = extractFromFilename(file) self.assertEqual(fname, expected_name[f]) self.assertEqual(path, expected_path[f]) self.assertEqual(ext, expected_ext[f]) def test_filename_including_extension(self): test_file = ["file.ext"] expected_name = ["file"] expected_path = [""] expected_ext = ["ext"] for f, file in enumerate(test_file): fname, path, ext = extractFromFilename(file) self.assertEqual(fname, expected_name[f]) self.assertEqual(path, expected_path[f]) self.assertEqual(ext, expected_ext[f]) def test_filename_including_path_only(self): test_file = ["path/"] expected_name = [""] expected_path = ["path"] expected_ext = [""] for f, file in enumerate(test_file): fname, path, ext = extractFromFilename(file) self.assertEqual(fname, expected_name[f]) self.assertEqual(path, expected_path[f]) self.assertEqual(ext, expected_ext[f]) class TestPrepareSaving(unittest.TestCase): def setUp(self): if not os.path.exists("delete_me"): os.makedirs("delete_me/") def test_filename_from_name_path_and_extension(self): test_name = ["file"] test_path = ["path", "path/path", "path//path"] test_ext = ["ext", ".ext"] for name in test_name: for path in test_path: for ext in test_ext: control = os.path.abspath(os.path.join("delete_me", os.path.join(path, "{n}.ext".format(n=name)))) new = prepareSaving(name, os.path.join("delete_me", path), ext) self.assertEqual(control, new) self.assertTrue(os.path.exists(os.path.abspath(os.path.join("delete_me", path)))) def test_filename_from_name_including_path_and_extension(self): test_name = ["path/file.ext"] test_path = ["path", "path/path", "path//path"] for name in test_name: for path in test_path: control = os.path.abspath(os.path.join("delete_me", os.path.join(path, "{n}".format(n=name)))) new = prepareSaving(name, os.path.join("delete_me", path)) self.assertEqual(control, new) self.assertTrue(os.path.exists(os.path.abspath(os.path.join("delete_me", path+"/path/")))) def test_filename_from_name_overwriting_extension(self): test_name = ["file.foo"] test_path = ["path", "path/path", "path//path"] test_ext = ["ext", ".ext"] for name in test_name: for path in test_path: for ext in test_ext: control = os.path.abspath(os.path.join("delete_me", os.path.join(path, "file.ext"))) new = prepareSaving(name, os.path.join("delete_me", path), ext) self.assertEqual(control, new) self.assertTrue(os.path.exists(os.path.abspath(os.path.join("delete_me", path)))) def test_filename_including_path_only(self): test_name = ["path/path/"] for name in test_name: control = os.path.abspath(os.path.join("delete_me",name)) + "/" new = prepareSaving(os.path.join("delete_me", name)) self.assertEqual(control, new) self.assertTrue(os.path.exists(os.path.abspath(os.path.join("delete_me", name)))) def doCleanups(self): shutil.rmtree("delete_me/") if __name__ == '__main__': unittest.main()

39.215686

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4,000

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0.7672

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4,000

102

119

39.215686

0.784187

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0.192771

0

null

0

1

0

1

0

null

0

4

326759e701a251ba0e385aed4d4ed208ec38cf10

133

py

Python

linprog_solver/simplex/exceptions.py

apirobot/django-linprog-solver-website

a90018c257b1d0a4c064baea1bb7c6e22bac1ab9

[ "MIT" ]

2

2017-04-22T11:25:00.000Z

2020-04-05T20:22:41.000Z

linprog_solver/simplex/exceptions.py

apirobot/django-linprog-solver-website

a90018c257b1d0a4c064baea1bb7c6e22bac1ab9

[ "MIT" ]

null

linprog_solver/simplex/exceptions.py

apirobot/django-linprog-solver-website

a90018c257b1d0a4c064baea1bb7c6e22bac1ab9

[ "MIT" ]

null

class SimplexInitException(Exception): """ Error raised when a ``SimplexSolveForm`` can not be initialized. """ pass

22.166667

68

0.669173

13

133

6.846154

1

0

0.225564

133

5

69

26.6

0.864078

0.481203

0

1

0

true

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

4

326bd081192c10fc0ddb1821d78a545fe17e537d

223

py

Python

attendees/persons/serializers/registration_serializer.py

xjlin0/-attendees30

48a2f2cbec11ec471d7a40d24903b48890feebf9

[ "MIT" ]

null

attendees/persons/serializers/registration_serializer.py

xjlin0/-attendees30

48a2f2cbec11ec471d7a40d24903b48890feebf9

[ "MIT" ]

null

attendees/persons/serializers/registration_serializer.py

xjlin0/-attendees30

48a2f2cbec11ec471d7a40d24903b48890feebf9

[ "MIT" ]

null

from attendees.persons.models import Registration from rest_framework import serializers class RegistrationSerializer(serializers.ModelSerializer): class Meta: model = Registration fields = '__all__'

22.3

58

0.766816

21

223

7.904762

0.761905

0

0.183857

223

9

59

24.777778

0.912088

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0.03139

0

1

0

false

0

0.333333

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0.666667

0

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null

0

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0

null

0

1

0

1

0

4

32c728da267b9b16ceec95af6e5739382851d024

1,293

py

Python

src/firs/trecdata/TrecCollection/TrecCollection.py

guglielmof/pyRe

69a128678eddac8870ff9670411e8b5cdc0e7966

[ "MIT" ]

null

src/firs/trecdata/TrecCollection/TrecCollection.py

guglielmof/pyRe

69a128678eddac8870ff9670411e8b5cdc0e7966

[ "MIT" ]

null

src/firs/trecdata/TrecCollection/TrecCollection.py

guglielmof/pyRe

69a128678eddac8870ff9670411e8b5cdc0e7966

[ "MIT" ]

null

import pandas as pd from ... import configuration from ...utils import Logger class TrecCollection: def __init__(self, **kwargs): self.configs = configuration().get_config() self.logger = Logger().logger self.topics = None # the name of the collection is one of the predefined; it can be imported directly if 'collectionName' in kwargs and f"collections.{kwargs['collectionName']}" in self.configs.sections(): self._import_paths(kwargs['collectionName']) self.collection_name = kwargs['collectionName'] def get_name(self): return self.collection_name def get_paths(self): return self.cpaths def __str__(self): return f"collection: {self.collection_name}\n\t* topics: {len(self.qrel)}\n\t* runs: {len(self.runs)}" from .import_collection import import_collection, _import_collection, _import_runs, _import_qrels, _import_runs_list from .evaluate import evaluate, import_measures from .parallel_evaluate import parallel_evaluate from .misc import remove_shorter_runs from .get_topics import get_topics, _import_topics def _import_paths(self, collectionName): self.cpaths = dict(self.configs.items(f'collections.{collectionName}'))

30.785714

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1,293

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1,293

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121

31.536585

0.845029

0.061872

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0.041667

0.165289

0.094215

0

1

0.208333

false

0

0.416667

0.125

0.791667

0

null

0

null

0

1

0

1

0

4

08a9d56427015e0094e7d057f2ff8e668898ffad

42

py

Python

DEV ARDUINO/__libraries/teensy4_i2c-master/examples/test_harness/raspberry_pi/wire/multiple_slave_addresses.py

clockdiv/MechanicalTheatre

10964a9f25fbed7e4e85573867357e72a7166fb1

[ "MIT" ]

50

2019-11-25T19:46:04.000Z

2022-03-26T03:34:51.000Z

DEV ARDUINO/__libraries/teensy4_i2c-master/examples/test_harness/raspberry_pi/wire/multiple_slave_addresses.py

clockdiv/MechanicalTheatre

10964a9f25fbed7e4e85573867357e72a7166fb1

[ "MIT" ]

35

2020-12-31T19:59:45.000Z

2021-09-10T16:40:52.000Z

DEV ARDUINO/__libraries/teensy4_i2c-master/examples/test_harness/raspberry_pi/wire/multiple_slave_addresses.py

clockdiv/MechanicalTheatre

10964a9f25fbed7e4e85573867357e72a7166fb1

[ "MIT" ]

12

2020-01-13T19:06:19.000Z

2022-02-23T12:50:51.000Z

Use ../raw/raw_multiple_slave_addresses.py

42

0.857143

7

42

4.714286

0.857143

0

0.02381

42

1

42

0.804878

0

null

0

null

0

1

0

null

0

1

0

null

0

1

0

4

08ad922154aac5aebca8254dcf4ffe0d0a56dac6

590

py

Python

tests/test_848.py

sungho-joo/leetcode2github

ce7730ef40f6051df23681dd3c0e1e657abba620

[ "MIT" ]

null

tests/test_848.py

sungho-joo/leetcode2github

ce7730ef40f6051df23681dd3c0e1e657abba620

[ "MIT" ]

null

tests/test_848.py

sungho-joo/leetcode2github

ce7730ef40f6051df23681dd3c0e1e657abba620

[ "MIT" ]

null

#!/usr/bin/env python import pytest """ Test 848. Shifting Letters """ @pytest.fixture(scope="session") def init_variables_848(): from src.leetcode_848_shifting_letters import Solution solution = Solution() def _init_variables_848(): return solution yield _init_variables_848 class TestClass848: def test_solution_0(self, init_variables_848): assert init_variables_848().shiftingLetters("abc", [3, 5, 9]) == "rpl" def test_solution_1(self, init_variables_848): assert init_variables_848().shiftingLetters("aaa", [1, 2, 3]) == "gfd"

21.071429

78

0.701695

76

590

5.144737

0.486842

0.232737

0.286445

0.097187

0.29156

0

0.078512

0.179661

590

27

79

21.851852

0.729339

0.033898

0

0.035514

0

0.153846

1

0.307692

false

0

0.153846

0.076923

0.615385

0

null

1

0

null

0

1

0

1

0

4

08c98ef2ae881d53136576cf11ec30cde85d7dc2

52

py

Python

lib/muck/__main__.py

likebike/muck

b48c8b0b64c9a23a1e23511613174c641042f6ea

[ "MIT" ]

null

lib/muck/__main__.py

likebike/muck

b48c8b0b64c9a23a1e23511613174c641042f6ea

[ "MIT" ]

null

lib/muck/__main__.py

likebike/muck

b48c8b0b64c9a23a1e23511613174c641042f6ea

[ "MIT" ]

null

import muck if __name__ == '__main__': muck.main()

13

38

0.692308

7

52

4

0.714286

0

0.153846

52

3

39

17.333333

0.636364

0

0.153846

0

1

0

true

0

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

1

0

4

08da428500fe9792ec67b663b318b1a3524bcddd

1,746

py

Python

test/model/bii_type_test.py

Manu343726/biicode-common

91b32c6fd1e4a72ce5451183f1766d313cd0e420

[ "MIT" ]

17

2015-04-15T09:40:23.000Z

2017-05-17T20:34:49.000Z

test/model/bii_type_test.py

Manu343726/biicode-common

91b32c6fd1e4a72ce5451183f1766d313cd0e420

[ "MIT" ]

2

2015-04-22T11:29:36.000Z

2018-09-25T09:31:09.000Z

test/model/bii_type_test.py

bowlofstew/common

45e9ca902be7bbbdd73dafe3ab8957bc4a006020

[ "MIT" ]

22

2015-04-15T09:46:00.000Z

2020-09-29T17:03:31.000Z

import unittest from biicode.common.model.bii_type import BiiType, CPP, TEXT, XML, HTML, IMAGE, PYTHON, UNKNOWN, \ SOUND class BiiTypeTest(unittest.TestCase): def test_bii_types_from_name(self): self.assertEqual(CPP, BiiType.from_extension(".cpp")) self.assertEqual(CPP, BiiType.from_extension(".c")) self.assertEqual(CPP, BiiType.from_extension(".ino")) self.assertEqual(CPP, BiiType.from_extension(".h")) self.assertEqual(CPP, BiiType.from_extension(".hh")) self.assertEqual(CPP, BiiType.from_extension(".cc")) self.assertEqual(CPP, BiiType.from_extension(".inl")) self.assertEqual(CPP, BiiType.from_extension(".ipp")) self.assertEqual(TEXT, BiiType.from_extension(".txt")) self.assertEqual(XML, BiiType.from_extension(".xml")) self.assertEqual(HTML, BiiType.from_extension(".html")) self.assertEqual(HTML, BiiType.from_extension(".htm")) self.assertEqual(SOUND, BiiType.from_extension(".wav")) self.assertEqual(IMAGE, BiiType.from_extension(".jpg")) self.assertEqual(IMAGE, BiiType.from_extension(".gif")) self.assertEqual(IMAGE, BiiType.from_extension(".png")) self.assertEqual(IMAGE, BiiType.from_extension(".bmp")) self.assertEqual(PYTHON, BiiType.from_extension(".py")) self.assertEqual(TEXT, BiiType.from_extension(".bii")) self.assertEqual(UNKNOWN, BiiType.from_extension(".unknow")) def test_set_of_types(self): self.assertFalse(BiiType.from_extension(".cpp").is_binary()) self.assertTrue(BiiType.from_extension(".wav").is_binary()) self.assertTrue(BiiType.from_extension(".cpp") == CPP) if __name__ == "__main__": unittest.main()

47.189189

98

0.690149

203

1,746

5.73399

0.251232

0.217354

0.395189

0.171821

0.604811

0.072165

0

0.158076

1,746

36

99

48.5

0.791837

0

0.055556

0

0.741935

1

0.064516

false

0

0.064516

0

0.16129

0

null

1

0

null

0

1

0

4

08e5c7cf85774a8ae5b87b7d8e09b124fc7f92dd

490

py

Python

amnesia/modules/file/validation/file.py

silenius/amnesia

ba5e3ac79a89da599c22206ad1fd17541855f74c

[ "BSD-2-Clause" ]

4

2015-05-08T10:57:56.000Z

2021-05-17T04:32:11.000Z

amnesia/modules/file/validation/file.py

silenius/amnesia

ba5e3ac79a89da599c22206ad1fd17541855f74c

[ "BSD-2-Clause" ]

6

2019-12-26T16:43:41.000Z

2022-02-28T11:07:54.000Z

amnesia/modules/file/validation/file.py

silenius/amnesia

ba5e3ac79a89da599c22206ad1fd17541855f74c

[ "BSD-2-Clause" ]

1

2019-09-23T14:08:11.000Z

# -*- coding: utf-8 -*- from marshmallow.fields import String from marshmallow.fields import Integer from marshmallow.fields import Raw from marshmallow.fields import Float from amnesia.modules.content.validation import ContentSchema class FileSchema(ContentSchema): ''' Schema for the File model ''' content_id = Integer() mime_id = Integer(dump_only=True) original_name = String(dump_only=True) file_size = Float(dump_only=True) content = Raw(load_only=True)

25.789474

60

0.75102

64

490

5.625

0.5

0.166667

0.233333

0.3

0

0.002427

0.159184

490

18

61

27.222222

0.871359

0.1

0

1

0

false

0

0.454545

0

1

0

null

0

1

0

null

0

1

0

1

0

4

3ecc1e9cfa0e9f0d53b65b9067ce6c2b6d34664b

232

py

Python

main.py

sungkuk5420/python-web-scraper

ac0647a4ea759dbb9280dd76c12cb63b6a18d38f

[ "MIT" ]

null

main.py

sungkuk5420/python-web-scraper

ac0647a4ea759dbb9280dd76c12cb63b6a18d38f

[ "MIT" ]

null

main.py

sungkuk5420/python-web-scraper

ac0647a4ea759dbb9280dd76c12cb63b6a18d38f

[ "MIT" ]

null

from indeed import extract_indeed_pages, extract_indeed_jobs from save import save_to_file # jobs = [] # save_to_file(jobs) max_indeed_pages = extract_indeed_pages() jobs = extract_indeed_jobs(max_indeed_pages) save_to_file(jobs)

23.2

60

0.827586

37

232

4.702703

0.27027

0.298851

0.172414

0.241379

0

0.103448

232

9

61

25.777778

0.836538

0.12069

0

1

0

false

0

0.4

0

0.4

0

null

1

0

1

0

1

0

null

0

1

0

4

41081c0bbe0f47b636227bee0b90af344ee3f901

318

py

Python

p2c_backend/exercise/serializers.py

nvbr-m/p2c

902351a4604c964731ce93e674b7057f83bb85d7

[ "MIT" ]

2

2021-08-24T21:14:51.000Z

2021-09-17T06:45:22.000Z

p2c_backend/exercise/serializers.py

nvbr-m/p2c

902351a4604c964731ce93e674b7057f83bb85d7

[ "MIT" ]

null

p2c_backend/exercise/serializers.py

nvbr-m/p2c

902351a4604c964731ce93e674b7057f83bb85d7

[ "MIT" ]

null

from rest_framework import serializers from exercise.models import Task class TaskSerializer(serializers.ModelSerializer): class Meta: model = Task fields = ["id", "title"] class TaskDetailSerializer(serializers.ModelSerializer): class Meta: model = Task fields = "__all__"

21.2

56

0.694969

31

318

6.967742

0.580645

0.240741

0.287037

0.324074

0.462963

0

0.22956

318

14

57

22.714286

0.881633

0

0.4

0

0.044025

0

1

0

false

0

0.2

0

0.6

0

null

1

0

null

0

1

0

4

f5ae94d9f84c33c3e9e0b12e9bb1432136136418

736

py

Python

fpc_api/serializers.py

samwel2000/FiddyPolyClinic-backend

0be68076bebfa0d4eeb5dd5868de968d98bea4f4

[ "MIT" ]

1

2021-08-18T14:56:18.000Z

fpc_api/serializers.py

samwel2000/FiddyPolyClinic-backend

0be68076bebfa0d4eeb5dd5868de968d98bea4f4

[ "MIT" ]

null

fpc_api/serializers.py

samwel2000/FiddyPolyClinic-backend

0be68076bebfa0d4eeb5dd5868de968d98bea4f4

[ "MIT" ]

null

from django.db.models import fields from rest_framework import serializers from .models import * class NewsSerializer(serializers.ModelSerializer): class Meta: model = News fields = '__all__' class JobsSerializer(serializers.ModelSerializer): class Meta: model = Jobs fields = '__all__' class TeamMembersSerializer(serializers.ModelSerializer): class Meta: model = TeamMembers fields = '__all__' class ContactUsSerializer(serializers.ModelSerializer): class Meta: model = ContactUs exclude = ['created_date'] class SubscribersSerializer(serializers.ModelSerializer): class Meta: model = Subscribers exclude = ['created_date']

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736

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0.403226

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736

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58

22.30303

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null

1

0

null

0

1

0

4

f5f4c4cf1713c9e2679321c5c0dc48037f043968

312

py

Python

pytorch_pretrained_biggan/__init__.py

RicardoYangxx/pytorch-pretrained-BigGAN

51994885efb7c236c279cc7a812dbe6672b6a956

[ "MIT" ]

null

pytorch_pretrained_biggan/__init__.py

RicardoYangxx/pytorch-pretrained-BigGAN

51994885efb7c236c279cc7a812dbe6672b6a956

[ "MIT" ]

null

pytorch_pretrained_biggan/__init__.py

RicardoYangxx/pytorch-pretrained-BigGAN

51994885efb7c236c279cc7a812dbe6672b6a956

[ "MIT" ]

null

from .config import BigGANConfig from .model import BigGAN from .file_utils import PYTORCH_PRETRAINED_BIGGAN_CACHE, cached_path from .utils import (truncated_noise_sample, save_as_images, convert_to_images, display_in_terminal, one_hot_from_int, one_hot_from_names)

44.571429

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0.74359

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312

5.195122

0.682927

0.103286

0.093897

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0.217949

312

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0

1

0

true

0

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0.666667

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null

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1

0

null

0

1

0

1

0

1

0

4

f5fe7a8d40155a20b309b5d551ad57add5f334ff

238

py

Python

auth0/v2/authentication/__init__.py

maronnax/auth0-python

855e275da1f9fddc851f34df4a6b304eed8abb96

[ "MIT" ]

null

auth0/v2/authentication/__init__.py

maronnax/auth0-python

855e275da1f9fddc851f34df4a6b304eed8abb96

[ "MIT" ]

null

auth0/v2/authentication/__init__.py

maronnax/auth0-python

855e275da1f9fddc851f34df4a6b304eed8abb96

[ "MIT" ]

null

from .database import Database from .delegated import Delegated from .enterprise import Enterprise from .link import Link from .passwordless import Passwordless from .social import Social from .users import Users from .oauth import Oauth

26.444444

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0.831933

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238

6.1875

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238

8

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29.75

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true

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1

0

null

0

1

0

null

0

1

0

4

eb0145778d78b929d4ccc448aa784f37714106ab

1,996

py

Python

tests/utils.py

sys-git/certifiable

a3c33c0d4f3ac2c53be9eded3fae633fa5f697f8

[ "MIT" ]

null

tests/utils.py

sys-git/certifiable

a3c33c0d4f3ac2c53be9eded3fae633fa5f697f8

[ "MIT" ]

311

2017-09-14T22:34:21.000Z

2022-03-27T18:30:17.000Z

tests/utils.py

sys-git/certifiable

a3c33c0d4f3ac2c53be9eded3fae633fa5f697f8

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: latin-1 -*- # from collections import Iterable, Mapping, MutableMapping, MutableSequence, MutableSet, Sequence, \ Set class aIterable(Iterable): def __init__(self, i=None): self.iter = i or [] def __iter__(self): for i in self.iter: yield i class aSet(Set): def __init__(self, i=None): self.iter = i or [] def __contains__(self): pass def __iter__(self): return iter([]) def __len__(self): return len(self.iter) class mSet(MutableSet): def __init__(self, i=None): self.iter = i or [] def add(self, value): pass def discard(self, value): pass def __contains__(self): pass def __iter__(self): return iter([]) def __len__(self): return len(self.iter) class aSequence(Sequence): def __init__(self, i=None): self.iter = i or [] def __iter__(self): for i in self.iter: yield i def __getitem__(self, index): pass def __contains__(self, x): pass def __len__(self): pass class mSequence(MutableSequence): def __init__(self, i=None): self.iter = i or [] def __iter__(self): for i in self.iter: yield i def __getitem__(self, index): pass def __contains__(self, x): pass def __len__(self): pass def __delitem__(self): pass def __setitem__(self): pass def insert(self): pass class aMapping(Mapping): def __getitem__(self, index): pass def __iter__(self): return iter([]) def __len__(self): pass class mMapping(MutableMapping): def __getitem__(self, index): pass def __iter__(self): return iter([]) def __len__(self): pass def __delitem__(self, item): pass def __setitem__(self, item, value): pass

16.360656

99

0.561122

234

1,996

4.273504

0.209402

0.105

0.077

0.06

0.643

0.633

0.605

0

0.000751

0.333166

1,996

121

100

16.495868

0.750563

0.022044

0

0.794872

0

1

0.423077

false

0.24359

0.012821

0.076923

0.602564

0

null

0

1

0

1

0

null

0

1

0

1

0

4

eb3f12f7ff2554b586198fbf551a7ed12153cf5f

1,005

py

Python

insights/parsers/netconsole.py

mglantz/insights-core

6f20bbbe03f53ee786f483b2a28d256ff1ad0fd4

[ "Apache-2.0" ]

1

2020-02-19T06:36:22.000Z

insights/parsers/netconsole.py

mglantz/insights-core

6f20bbbe03f53ee786f483b2a28d256ff1ad0fd4

[ "Apache-2.0" ]

null

insights/parsers/netconsole.py

mglantz/insights-core

6f20bbbe03f53ee786f483b2a28d256ff1ad0fd4

[ "Apache-2.0" ]

null

''' NetConsole - file ``/etc/sysconfig/netconsole`` =============================================== This parser reads the ``/etc/sysconfig/netconsole`` file. It uses the ``SysconfigOptions`` parser class to convert the file into a dictionary of options. Sample data:: # This is the configuration file for the netconsole service. By starting # this service you allow a remote syslog daemon to record console output # from this system. # The local port number that the netconsole module will use LOCALPORT=6666 Examples: >>> config = shared[NetConsole] >>> 'LOCALPORT' in config.data True >>> 'DEV' in config # Direct access to options False ''' from .. import parser, SysconfigOptions, LegacyItemAccess from insights.specs import Specs @parser(Specs.netconsole) class NetConsole(SysconfigOptions, LegacyItemAccess): ''' Contents of the ``/etc/sysconfig/netconsole`` file. Uses the ``SysconfigOptions`` shared parser class. ''' pass

25.125

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116

1,005

5.844828

0.525862

0.061947

0.097345

0.073746

0.085546

0

0.004926

0.19204

1,005

39

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25.769231

0.830049

0.78408

0

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0

true

0.2

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0

null

0

null

0

1

0

1

0

4

de372c3f37f9f9823a6c076a318703d50ef500b3

161

py

Python

src/Helpers/CheckingValueHelpers/CheckingValueHelper.py

hirohio/Hello-World-ML

398b7b9f492d563226e9ba0374bb2844ad0dbf18

[ "MIT" ]

null

src/Helpers/CheckingValueHelpers/CheckingValueHelper.py

hirohio/Hello-World-ML

398b7b9f492d563226e9ba0374bb2844ad0dbf18

[ "MIT" ]

null

src/Helpers/CheckingValueHelpers/CheckingValueHelper.py

hirohio/Hello-World-ML

398b7b9f492d563226e9ba0374bb2844ad0dbf18

[ "MIT" ]

null

#class ValueChecker: # @classmethod # def is_num(val): # if val.isdigit() == True: # return True # else: # return False

17.888889

34

0.503106

16

161

5

0.8125

0

0.378882

161

8

35

20.125

0.8

0.906832

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

4

de5baa85239dcae789979f3c8769165cc7a8c4aa

222

py

Python

klab/git.py

Kortemme-Lab/klab

68f028a4d7f97b9009bff45799b5602824052dd1

[ "MIT" ]

2

2016-06-14T00:32:19.000Z

2021-07-04T01:56:17.000Z

klab/git.py

Kortemme-Lab/klab

68f028a4d7f97b9009bff45799b5602824052dd1

[ "MIT" ]

2

2019-01-17T18:52:17.000Z

2019-01-17T18:52:56.000Z

klab/git.py

Kortemme-Lab/klab

68f028a4d7f97b9009bff45799b5602824052dd1

[ "MIT" ]

null

#!/usr/bin/env python2 def get_git_root(): import shlex from . import process command = shlex.split('git rev-parse --show-toplevel') directory = process.check_output(command) return directory.strip()

22.2

58

0.693694

29

222

5.206897

0.793103

0

0.005556

0.189189

222

9

59

24.666667

0.833333

0.094595

0

0.145729

0

1

0.166667

false

0

0.333333

0

0.666667

0

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null

0

1

0

null

0

1

0

1

0

4

de630c2aad5fb84fd3c889e5a0e586ce3cd62c81

87

py

Python

david/scorer/apps.py

rising-entropy/Model-o-Department

42147e02209709ffd6450b04189890e9c57236aa

[ "MIT" ]

null

david/scorer/apps.py

rising-entropy/Model-o-Department

42147e02209709ffd6450b04189890e9c57236aa

[ "MIT" ]

null

david/scorer/apps.py

rising-entropy/Model-o-Department

42147e02209709ffd6450b04189890e9c57236aa

[ "MIT" ]

null

from django.apps import AppConfig class ScorerConfig(AppConfig): name = 'scorer'

14.5

33

0.747126

10

87

6.5

0.9

0

0.172414

87

5

34

17.4

0.902778

0

0.068966

0

1

0

false

0

0.333333

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

4

de6dd2505b20dc3982c3741cd0a5d91865739371

81

py

Python

zeus/god/apps.py

nightwarrior-xxx/Zeus

429fe0dcbdbcc4024d2d58a6d897108df1bbfffd

[ "MIT" ]

null

zeus/god/apps.py

nightwarrior-xxx/Zeus

429fe0dcbdbcc4024d2d58a6d897108df1bbfffd

[ "MIT" ]

4

2020-06-06T00:42:50.000Z

2022-02-10T08:51:36.000Z

zeus/god/apps.py

nightwarrior-xxx/Zeus

429fe0dcbdbcc4024d2d58a6d897108df1bbfffd

[ "MIT" ]

null

from django.apps import AppConfig class GodConfig(AppConfig): name = 'god'

13.5

33

0.728395

10

81

5.9

0.9

0

0.185185

81

5

34

16.2

0.893939

0

0.037037

0

1

0

false

0

0.333333

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

4

de8f74c81d0dd2f908c6f3bafa7584972d70ab6c

239

py

Python

build/utils/common.py

arm61/fitbenchmarking

c745c684e3ca4895a666eb863426746d8f06636c

[ "BSD-3-Clause" ]

null

build/utils/common.py

arm61/fitbenchmarking

c745c684e3ca4895a666eb863426746d8f06636c

[ "BSD-3-Clause" ]

null

build/utils/common.py

arm61/fitbenchmarking

c745c684e3ca4895a666eb863426746d8f06636c

[ "BSD-3-Clause" ]

null

""" Script to hold common varibales used in building the project """ import os from build.utils.build_logger import BuildLogger ROOT_DIR = os.path.dirname(os.path.dirname(os.path.realpath(__file__))) BUILD_LOGGER = BuildLogger(ROOT_DIR)

23.9

71

0.790795

36

239

5.027778

0.638889

0.099448

0.198895

0.165746

0.176796

0

0.108787

239

9

72

26.555556

0.849765

0.251046

0

1

0

false

0

0.5

0

0.5

0

null

0

1

0

1

0

null

0

1

0

4

de9fe70cb30f02ec07532adafd3ce14bca4068f3

17,337

py

Python

ztlearn/objectives.py

jefkine/zeta-learn

04388f90093b52f5df2f334c898f3a1224f5a13f

[ "MIT" ]

30

2018-03-12T19:16:27.000Z

2021-12-16T05:32:38.000Z

ztlearn/objectives.py

jefkine/zeta-learn

04388f90093b52f5df2f334c898f3a1224f5a13f

[ "MIT" ]

4

2018-06-13T03:47:15.000Z

2018-11-05T21:33:34.000Z

ztlearn/objectives.py

jefkine/zeta-learn

04388f90093b52f5df2f334c898f3a1224f5a13f

[ "MIT" ]

4

2018-04-30T07:42:47.000Z

2022-01-31T11:35:53.000Z

# -*- coding: utf-8 -*- import math as mt import numpy as np class Objective(object): def clip(self, predictions, epsilon = 1e-15): clipped_predictions = np.clip(predictions, epsilon, 1 - epsilon) clipped_divisor = np.maximum(np.multiply(predictions, 1 - predictions), epsilon) return clipped_predictions, clipped_divisor def error(self, predictions, targets): error = targets - predictions abs_error = np.absolute(error) return error, abs_error def add_fuzz_factor(self, np_array, epsilon = 1e-05): return np.add(np_array, epsilon) @property def objective_name(self): return self.__class__.__name__ class MeanSquaredError: """ **Mean Squared error (MSE)** MSE measures the average squared difference between the predictions and the targets. The closer the predictions are to the targets the more efficient the estimator. References: [1] Mean Squared error * [Wikipedia Article] https://en.wikipedia.org/wiki/Mean_squared_error """ def loss(self, predictions, targets, np_type): """ Applies the MeanSquaredError Loss to prediction and targets provided Args: predictions (numpy.array): the predictions numpy array targets (numpy.array): the targets numpy array Returns: numpy.array: the output of MeanSquaredError Loss to prediction and targets """ return 0.5 * np.mean(np.sum(np.square(predictions - targets), axis = 1)) def derivative(self, predictions, targets, np_type): """ Applies the MeanSquaredError Derivative to prediction and targets provided Args: predictions (numpy.array): the predictions numpy array targets (numpy.array): the targets numpy array Returns: numpy.array: the output of MeanSquaredError Derivative to prediction and targets """ return predictions - targets def accuracy(self, predictions, targets, threshold = 0.5): return 0 @property def objective_name(self): return self.__class__.__name__ class HellingerDistance: """ **Hellinger Distance** Hellinger Distance is used to quantify the similarity between two probability distributions. References: [1] Hellinger Distance * [Wikipedia Article] https://en.wikipedia.org/wiki/Hellinger_distance """ SQRT_2 = np.sqrt(2) def sqrt_difference(self, predictions, targets): return np.sqrt(predictions) - np.sqrt(targets) def loss(self, predictions, targets, np_type): """ Applies the HellingerDistance Loss to prediction and targets provided Args: predictions (numpy.array): the predictions numpy array targets (numpy.array): the targets numpy array Returns: numpy.array: the output of HellingerDistance Loss to prediction and targets """ root_difference = self.sqrt_difference(predictions, targets) return np.mean(np.true_divide(np.sum(np.square(root_difference), axis = 1), HellingerDistance.SQRT_2)) def derivative(self, predictions, targets, np_type): """ Applies the HellingerDistance Derivative to prediction and targets provided Args: predictions (numpy.array): the predictions numpy array targets (numpy.array): the targets numpy array Returns: numpy.array: the output of HellingerDistance Derivative to prediction and targets """ root_difference = self.sqrt_difference(predictions, targets) return np.true_divide(root_difference, np.multiply(HellingerDistance.SQRT_2, np.sqrt(predictions))) def accuracy(self, predictions, targets, threshold = 0.5): return 0 @property def objective_name(self): return self.__class__.__name__ class HingeLoss: """ **Hinge Loss** Hinge Loss also known as SVM Loss is used "maximum-margin" classification, most notably for support vector machines (SVMs) References: [1] Hinge loss * [Wikipedia Article] https://en.wikipedia.org/wiki/Hinge_loss """ def loss(self, predictions, targets, np_type): """ Applies the Hinge-Loss to Loss prediction and targets provided Args: predictions (numpy.array): the predictions numpy array targets (numpy.array): the targets numpy array Returns: numpy.array: the output of Hinge-Loss Loss to prediction and targets """ correct_class = predictions[np.arange(predictions.shape[0]), np.argmax(targets, axis = 1)] margins = np.maximum(0, predictions - correct_class[:, np.newaxis] + 1.0) # delta = 1.0 margins[np.arange(predictions.shape[0]), np.argmax(targets, axis = 1)] = 0 return np.mean(np.sum(margins, axis = 0)) def derivative(self, predictions, targets, np_type): """ Applies the Hinge-Loss Derivative to prediction and targets provided Args: predictions (numpy.array): the predictions numpy array targets (numpy.array): the targets numpy array Returns: numpy.array: the output of Hinge-Loss Derivative to prediction and targets """ correct_class = predictions[np.arange(predictions.shape[0]), np.argmax(targets, axis = 1)] binary = np.maximum(0, predictions - correct_class[:, np.newaxis] + 1.0) # delta = 1.0 binary[binary > 0] = 1 incorrect_class = np.sum(binary, axis = 1) binary[np.arange(predictions.shape[0]), np.argmax(targets, axis = 1)] = -incorrect_class return binary def accuracy(self, predictions, targets, threshold = 0.5): """ Calculates the Hinge-Loss Accuracy Score given prediction and targets Args: predictions (numpy.array): the predictions numpy array targets (numpy.array): the targets numpy array Returns: numpy.float32: the output of Hinge-Loss Accuracy Score """ return np.mean(np.argmax(predictions, axis = 1) == np.argmax(targets, axis = 1)) @property def objective_name(self): return self.__class__.__name__ class BinaryCrossEntropy(Objective): """ **Binary Cross Entropy** Binary CrossEntropy measures the performance of a classification model whose output is a probability value between 0 & 1. 'Binary' is meant for discrete classification tasks in which the classes are independent and not mutually exclusive. Targets here could be either 0 or 1 scalar References: [1] Cross Entropy * [Wikipedia Article] https://en.wikipedia.org/wiki/Cross_entropy """ def loss(self, predictions, targets, np_type): """ Applies the BinaryCrossEntropy Loss to prediction and targets provided Args: predictions (numpy.array): the predictions numpy array targets (numpy.array): the targets numpy array Returns: numpy.array: the output of BinaryCrossEntropy Loss to prediction and targets """ clipped_predictions, _ = super(BinaryCrossEntropy, self).clip(predictions) return np.mean( -np.sum( ( np.multiply(targets, np.log(clipped_predictions)), np.multiply((1 - targets), np.log(1 - clipped_predictions)) ), axis = 1 ) ) def derivative(self, predictions, targets, np_type): """ Applies the BinaryCrossEntropy Derivative to prediction and targets provided Args: predictions (numpy.array): the predictions numpy array targets (numpy.array): the targets numpy array Returns: numpy.array: the output of BinaryCrossEntropy Derivative to prediction and targets """ clipped_predictions, clipped_divisor = super(BinaryCrossEntropy, self).clip(predictions) return np.true_divide((clipped_predictions - targets), clipped_divisor) def accuracy(self, predictions, targets, threshold = 0.5): """ Calculates the BinaryCrossEntropy Accuracy Score given prediction and targets Args: predictions (numpy.array) : the predictions numpy array targets (numpy.array) : the targets numpy array threshold (numpy.float32): the threshold value Returns: numpy.float32: the output of BinaryCrossEntropy Accuracy Score """ return 1 - np.true_divide(np.count_nonzero((predictions > threshold) == targets), float(targets.size)) @property def objective_name(self): return self.__class__.__name__ class CategoricalCrossEntropy(Objective): """ **Categorical Cross Entropy** Categorical Cross Entropy measures the performance of a classification model whose output is a probability value between 0 and 1. 'Categorical' is meant for discrete classification tasks in which the classes are mutually exclusive. References: [1] Cross Entropy * [Wikipedia Article] https://en.wikipedia.org/wiki/Cross_entropy """ def loss(self, predictions, targets, np_type): """ Applies the CategoricalCrossEntropy Loss to prediction and targets provided Args: predictions (numpy.array): the predictions numpy array targets (numpy.array): the targets numpy array Returns: numpy.array: the output of CategoricalCrossEntropy Loss to prediction and targets """ clipped_predictions, _ = super(CategoricalCrossEntropy, self).clip(predictions) return np.mean(-np.sum(np.multiply(targets, np.log(clipped_predictions)), axis = 1)) def derivative(self, predictions, targets, np_type): """ Applies the CategoricalCrossEntropy Derivative to prediction and targets provided Args: predictions (numpy.array): the predictions numpy array targets (numpy.array): the targets numpy array Returns: numpy.array: the output of CategoricalCrossEntropy Derivative to prediction and targets """ clipped_predictions, _ = super(CategoricalCrossEntropy, self).clip(predictions) return clipped_predictions - targets def accuracy(self, predictions, targets): """ Calculates the CategoricalCrossEntropy Accuracy Score given prediction and targets Args: predictions (numpy.array): the predictions numpy array targets (numpy.array): the targets numpy array Returns: numpy.float32: the output of CategoricalCrossEntropy Accuracy Score """ return np.mean(np.argmax(predictions, axis = 1) == np.argmax(targets, axis = 1)) @property def objective_name(self): return self.__class__.__name__ class KLDivergence(Objective): """ **KL Divergence** Kullback–Leibler divergence (also called relative entropy) is a measure of divergence between two probability distributions. """ def loss(self, predictions, targets, np_type): """ Applies the KLDivergence Loss to prediction and targets provided Args: predictions (numpy.array): the predictions numpy array targets (numpy.array): the targets numpy array Returns: numpy.array: the output of KLDivergence Loss to prediction and targets """ targets = super(KLDivergence, self).add_fuzz_factor(targets) predictions = super(KLDivergence, self).add_fuzz_factor(predictions) return np.sum(np.multiply(targets, np.log(np.true_divide(targets, predictions))), axis = 1) def derivative(self, predictions, targets, np_type): """ Applies the KLDivergence Derivative to prediction and targets provided Args: predictions (numpy.array): the predictions numpy array targets (numpy.array): the targets numpy array Returns: numpy.array: the output of KLDivergence Derivative to prediction and targets """ targets = super(KLDivergence, self).add_fuzz_factor(targets) predictions = super(KLDivergence, self).add_fuzz_factor(predictions) d_log_diff = np.multiply((predictions - targets), (np.log(np.true_divide(targets, predictions)))) return np.multiply((1 + np.log(np.true_divide(targets, predictions))), d_log_diff) def accuracy(self, predictions, targets): """ Calculates the KLDivergence Accuracy Score given prediction and targets Args: predictions (numpy.array): the predictions numpy array targets (numpy.array): the targets numpy array Returns: numpy.float32: the output of KLDivergence Accuracy Score """ return np.mean(np.argmax(predictions, axis = 1) == np.argmax(targets, axis = 1)) @property def objective_name(self): return self.__class__.__name__ class HuberLoss(Objective): """ **Huber Loss** Huber Loss: is a loss function used in robust regression where it is found to be less sensitive to outliers in data than the squared error loss. References: [1] Huber Loss * [Wikipedia Article] https://en.wikipedia.org/wiki/Huber_loss [2] Huber loss * [Wikivisually Article] https://wikivisually.com/wiki/Huber_loss """ def loss(self, predictions, targets, np_type, delta = 1.): """ Applies the HuberLoss Loss to prediction and targets provided Args: predictions (numpy.array): the predictions numpy array targets (numpy.array): the targets numpy array Returns: numpy.array: the output of KLDivergence Loss to prediction and targets """ error, abs_error = super(HuberLoss, self).error(predictions, targets) return np.sum(np.where(abs_error < delta, 0.5 * (np.square(error)), delta * abs_error - 0.5 * (mt.pow(delta, 2)))) def derivative(self, predictions, targets, np_type, delta = 1.): """ Applies the HuberLoss Derivative to prediction and targets provided Args: predictions (numpy.array): the predictions numpy array targets (numpy.array): the targets numpy array Returns: numpy.array: the output of KLDivergence Derivative to prediction and targets """ error, abs_error = super(HuberLoss, self).error(predictions, targets) return np.sum(np.where(abs_error > delta, delta * np.sign(error), error)) def accuracy(self, predictions, targets): """ Calculates the HuberLoss Accuracy Score given prediction and targets Args: predictions (numpy.array): the predictions numpy array targets (numpy.array): the targets numpy array Returns: numpy.float32: the output of KLDivergence Accuracy Score """ return np.mean(predictions - targets) @property def objective_name(self): return self.__class__.__name__ class ObjectiveFunction: _functions = { 'svm' : HingeLoss, 'hinge' : HingeLoss, 'hinge_loss' : HingeLoss, 'huber' : HuberLoss, 'huber_loss' : HuberLoss, 'kld' : KLDivergence, 'kullback_leibler_divergence' : KLDivergence, 'mse' : MeanSquaredError, 'mean_squared_error' : MeanSquaredError, 'hld' : HellingerDistance, 'hellinger_distance' : HellingerDistance, 'bce' : BinaryCrossEntropy, 'binary_crossentropy' : BinaryCrossEntropy, 'cce' : CategoricalCrossEntropy, 'categorical_crossentropy' : CategoricalCrossEntropy } def __init__(self, name): if name not in self._functions.keys(): raise Exception('Objective function must be either one of the following: {}.'.format(', '.join(self._functions.keys()))) self.objective_func = self._functions[name]() @property def name(self): return self.objective_func.objective_name def forward(self, predictions, targets, np_type = np.float32): return self.objective_func.loss(predictions, targets, np_type) def backward(self, predictions, targets, np_type = np.float32): return self.objective_func.derivative(predictions, targets, np_type) def accuracy(self, predictions, targets): return self.objective_func.accuracy(predictions, targets)

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720b99e812e5823ff1bc46d04176a6e1f6d8b396

86

py

Python

backend/wsgi.py

arontaupe/KommunikationsKrake

145bf9a2b4b3d70635987d18a6a0d4d8438bfb96

[ "MIT" ]

null

backend/wsgi.py

arontaupe/KommunikationsKrake

145bf9a2b4b3d70635987d18a6a0d4d8438bfb96

[ "MIT" ]

null

backend/wsgi.py

arontaupe/KommunikationsKrake

145bf9a2b4b3d70635987d18a6a0d4d8438bfb96

[ "MIT" ]

null

#!/usr/bin/python3 from webhook import app if __name__ == "__main__": app.run()

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171

py

Python

tests/conftest.py

ahmedhindi/datapipe

96bd764814b285d2744d7577f24fa9f5db5a9b25

[ "MIT" ]

1

2021-04-26T14:32:13.000Z

tests/conftest.py

ahmedhindi/dukto

96bd764814b285d2744d7577f24fa9f5db5a9b25

[ "MIT" ]

null

tests/conftest.py

ahmedhindi/dukto

96bd764814b285d2744d7577f24fa9f5db5a9b25

[ "MIT" ]

null

import pandas as pd import pytest @pytest.fixture(scope="module") def simple_data(): rng = list(range(1, 10)) return pd.DataFrame({"first": rng, "second": rng})

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py

Python

app/authentication.py

CrowdClick/CrowdLink

beb4b7822b787d53b104138d4b2612cf3a6713d1

[ "BSD-3-Clause" ]

2

2021-12-16T19:43:57.000Z

2021-12-18T08:15:39.000Z

app/authentication.py

CrowdClick/CrowdLink

beb4b7822b787d53b104138d4b2612cf3a6713d1

[ "BSD-3-Clause" ]

5

2020-06-25T20:44:25.000Z

2021-09-22T19:01:54.000Z

app/authentication.py

CrowdClick/CrowdLink

beb4b7822b787d53b104138d4b2612cf3a6713d1

[ "BSD-3-Clause" ]

null

from rest_framework.authentication import SessionAuthentication class CustomSessionAuthentication(SessionAuthentication): def enforce_csrf(self, *args): pass

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a0d2b3b8622367e6ebfa52e5e99ad5368a7ed924

2,720

py

Python

eos_pure.py

pysg/pyther

6a47fc41533cc50bc64134e42ddd3ed8d54d75c7

[ "MIT" ]

9

2017-07-10T19:21:35.000Z

2022-01-24T16:41:34.000Z

eos_pure.py

NERD-cpu/pyther

6a47fc41533cc50bc64134e42ddd3ed8d54d75c7

[ "MIT" ]

1

2017-05-28T01:45:00.000Z

2018-01-08T14:54:31.000Z

eos_pure.py

NERD-cpu/pyther

6a47fc41533cc50bc64134e42ddd3ed8d54d75c7

[ "MIT" ]

3

2017-08-18T18:47:21.000Z

2021-03-01T02:25:24.000Z

import numpy as np def func_zc_d1(del1): d1 = (1 + del1 ** 2) / (1 + del1) y = 1 + (2 * (1 + del1)) ** (1.0 / 3) + (4 / (1 + del1)) ** (1.0 / 3) Zc = y / (3 * y + d1 - 1.0) return Zc def get_del_1(Zcin, del1): # del1 = del_1_init # d1 = (1 + del1 ** 2) / (1 + del1) # y = 1 + (2 * (1 + del1)) ** (1.0 / 3) + (4 / (1 + del1)) ** (1.0 / 3) # Zc = y / (3 * y + d1 - 1.0) # dold = del_1_init # Zc = func_zc_d1(del_1_init) # if Zc > Zcin: # del1 = 1.01 * del1 # else: # del1 = 0.99 * del1 # error_Z_critico = abs(Zc - Zcin) while True: Zc = func_zc_d1(del1) aux = del1 del1 = del1 - (Zc - Zcin) * (del1 - dold) / (Zc - Zold) dold = aux error_Z_critico = abs(Zc - Zcin) if error_Z_critico <= 1e-6: break while True: d1 = (1 + del1 ** 2) / (1 + del1) y = 1 + (2 * (1 + del1)) ** (1.0 / 3) + (4 / (1 + del1)) ** (1.0 / 3) Zold = Zc Zc = y / (3 * y + d1 - 1.0) aux = del1 del1 = del1 - (Zc - Zcin) * (del1 - dold) / (Zc - Zold) dold = aux error_Z_critico = abs(Zc - Zcin) if error_Z_critico <= 1e-6: break return del1, error_Z_critico def getdel1(Zcin, del_1_init): del1 = del_1_init d1 = (1 + del1 ** 2) / (1 + del1) y = 1 + (2 * (1 + del1)) ** (1.0 / 3) + (4 / (1 + del1)) ** (1.0 / 3) Zc = y / (3 * y + d1 - 1.0) dold = del_1_init if Zc > Zcin: del1 = 1.01 * del1 else: del1 = 0.99 * del1 error_Z_critico = abs(Zc - Zcin) while True: d1 = (1 + del1 ** 2) / (1 + del1) y = 1 + (2 * (1 + del1)) ** (1.0 / 3) + (4 / (1 + del1)) ** (1.0 / 3) Zold = Zc Zc = y / (3 * y + d1 - 1.0) aux = del1 del1 = del1 - (Zc - Zcin) * (del1 - dold) / (Zc - Zold) dold = aux error_Z_critico = abs(Zc - Zcin) if error_Z_critico <= 1e-6: break return del1, error_Z_critico def acentric_factor_cal(*arg): al, be, ga = arg[0], arg[1], arg[2] try: OM = 0.5 * (- be + np.sqrt(be ** 2 - 4 * al * ga)) / (2 * al) except RuntimeWarning: raise RuntimeWarning else: OM = 0 return OM def compressibility_factor_cal(del1): d1 = (1 + del1 ** 2) / (1 + del1) y = 1 + (2 * (1 + del1)) ** (1.0 / 3) + (4 / (1 + del1)) ** (1.0 / 3) # numerator_OMa = (3 * y * y + 3 * y * d1 + d1 ** 2 + d1 - 1.0) numerator_OMa = (3 * y **2 + 3 * y * d1 + d1 ** 2 + d1 - 1.0) denominator_OMa = (3 * y + d1 - 1.0) ** 2 OMa = numerator_OMa / denominator_OMa OMb = 1 / (3 * y + d1 - 1.0) Zc = y / (3 * y + d1 - 1.0) return Zc, OMa, OMb

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null

0

1

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1

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null

0

4

a0e7544e69192fd9372b3a7e0f454cfcf7d7e67f

798

py

Python

src/marmo/report/blueprints/paragraph.py

SINTEF/simapy

650b8c2f15503dad98e2bfc0d0788509593822c7

[ "MIT" ]

null

src/marmo/report/blueprints/paragraph.py

SINTEF/simapy

650b8c2f15503dad98e2bfc0d0788509593822c7

[ "MIT" ]

null

src/marmo/report/blueprints/paragraph.py

SINTEF/simapy

650b8c2f15503dad98e2bfc0d0788509593822c7

[ "MIT" ]

null

# # Generated with ParagraphBlueprint from dmt.blueprint import Blueprint from dmt.dimension import Dimension from dmt.attribute import Attribute from dmt.enum_attribute import EnumAttribute from dmt.blueprint_attribute import BlueprintAttribute from .reportitem import ReportItemBlueprint class ParagraphBlueprint(ReportItemBlueprint): """""" def __init__(self, name="Paragraph", package_path="marmo/report", description=""): super().__init__(name,package_path,description) self.attributes.append(Attribute("name","string","",default="")) self.attributes.append(Attribute("description","string","",default="")) self.attributes.append(Attribute("text","string","",default="")) self.attributes.append(Attribute("markup","boolean","",default=False))

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null

0

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1

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a0f05e0bc909a6549d803ac271d4a527da38475a

156

py

Python

awardApp/apps.py

Kerrykogei24/K-Awards

67d02ce19970c6ad8066f7e460159c5f79e39ffb

[ "MIT" ]

null

awardApp/apps.py

Kerrykogei24/K-Awards

67d02ce19970c6ad8066f7e460159c5f79e39ffb

[ "MIT" ]

null

awardApp/apps.py

Kerrykogei24/K-Awards

67d02ce19970c6ad8066f7e460159c5f79e39ffb

[ "MIT" ]

null

# -*- coding: utf-8 -*- from __future__ import unicode_literals from django.apps import AppConfig class AwardappConfig(AppConfig): name = 'awardApp'

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9d4c8b38f4f94cc6d7519999e49af7735718a857

4,016

py

Python

mod_safe/consts.py

Guymer/fortranlib

30e27b010cf4bc5acf0f3a63d50f11789640e0e3

[ "Apache-2.0" ]

3

2020-05-28T02:05:59.000Z

2021-10-16T16:50:21.000Z

mod_safe/consts.py

Guymer/fortranlib

30e27b010cf4bc5acf0f3a63d50f11789640e0e3

[ "Apache-2.0" ]

2

2019-06-17T16:49:20.000Z

2022-02-11T18:47:36.000Z

mod_safe/consts.py

Guymer/fortranlib

30e27b010cf4bc5acf0f3a63d50f11789640e0e3

[ "Apache-2.0" ]

1

2019-09-11T04:51:33.000Z

#!/usr/bin/env python3 # Use the proper idiom in the main module ... # NOTE: See https://docs.python.org/3.8/library/multiprocessing.html#multiprocessing-programming if __name__ == "__main__": # Import standard modules ... import math # Import special modules ... try: import scipy import scipy.constants except: raise Exception("\"scipy\" is not installed; run \"pip install --user scipy\"") from None # Open output file ... with open("consts.f90", "wt", encoding = "utf-8") as fobj: # Write documentation ... fobj.write("!> @cite scipy\n") fobj.write("!>\n") fobj.write("\n") # Write declarations ... fobj.write("REAL(kind = REAL64), PARAMETER :: const_1sigma = {:.15e}_REAL64\n".format(math.erf(1.0 / math.sqrt(2.0)))) fobj.write("REAL(kind = REAL64), PARAMETER :: const_2sigma = {:.15e}_REAL64\n".format(math.erf(2.0 / math.sqrt(2.0)))) fobj.write("REAL(kind = REAL64), PARAMETER :: const_3sigma = {:.15e}_REAL64\n".format(math.erf(3.0 / math.sqrt(2.0)))) fobj.write("REAL(kind = REAL64), PARAMETER :: const_4sigma = {:.15e}_REAL64\n".format(math.erf(4.0 / math.sqrt(2.0)))) fobj.write("REAL(kind = REAL64), PARAMETER :: const_5sigma = {:.15e}_REAL64\n".format(math.erf(5.0 / math.sqrt(2.0)))) fobj.write("REAL(kind = REAL64), PARAMETER :: const_6sigma = {:.15e}_REAL64\n".format(math.erf(6.0 / math.sqrt(2.0)))) fobj.write("REAL(kind = REAL64), PARAMETER :: const_amu = {:.15e}_REAL64\n".format(scipy.constants.atomic_mass)) fobj.write("REAL(kind = REAL64), PARAMETER :: const_c = {:.15e}_REAL64\n".format(scipy.constants.c)) fobj.write("REAL(kind = REAL64), PARAMETER :: const_e = {:.15e}_REAL64\n".format(scipy.constants.e)) fobj.write("REAL(kind = REAL64), PARAMETER :: const_eps0 = {:.15e}_REAL64\n".format(scipy.constants.epsilon_0)) fobj.write("REAL(kind = REAL64), PARAMETER :: const_g = {:.15e}_REAL64\n".format(scipy.constants.g)) fobj.write("REAL(kind = REAL64), PARAMETER :: const_h = {:.15e}_REAL64\n".format(scipy.constants.h)) fobj.write("REAL(kind = REAL64), PARAMETER :: const_kb = {:.15e}_REAL64\n".format(scipy.constants.k)) fobj.write("REAL(kind = REAL64), PARAMETER :: const_me = {:.15e}_REAL64\n".format(scipy.constants.m_e)) fobj.write("REAL(kind = REAL64), PARAMETER :: const_mn = {:.15e}_REAL64\n".format(scipy.constants.m_n)) fobj.write("REAL(kind = REAL64), PARAMETER :: const_mp = {:.15e}_REAL64\n".format(scipy.constants.m_p)) fobj.write("REAL(kind = REAL64), PARAMETER :: const_mu0 = {:.15e}_REAL64\n".format(scipy.constants.mu_0)) fobj.write("REAL(kind = REAL64), PARAMETER :: const_na = {:.15e}_REAL64\n".format(scipy.constants.N_A)) fobj.write("REAL(kind = REAL64), PARAMETER :: const_pi = {:.15e}_REAL64\n".format(scipy.constants.pi)) fobj.write("REAL(kind = REAL64), PARAMETER :: const_sig = {:.15e}_REAL64\n".format(scipy.constants.sigma))

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1

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null

0

1

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4

9d52028a42707c5461dfa999e00bb04b39daa9ed

300

py

Python

nbtest/nbtest.py

StevenBorg/nbtest

1c3a77207b4de45306e8bfd9749ae0ba6eb2f319

[ "Apache-2.0" ]

null

nbtest/nbtest.py

StevenBorg/nbtest

1c3a77207b4de45306e8bfd9749ae0ba6eb2f319

[ "Apache-2.0" ]

4

2020-09-23T17:25:08.000Z

2022-02-26T08:56:30.000Z

nbtest/nbtest.py

StevenBorg/nbtest

1c3a77207b4de45306e8bfd9749ae0ba6eb2f319

[ "Apache-2.0" ]

null

# AUTOGENERATED! DO NOT EDIT! File to edit: 01_nbtest.ipynb (unless otherwise specified). __all__ = ['say_hello', 'do_something'] # Cell def say_hello(name): "Say Hello generically" print('Hello!') # Cell def do_something(): "Do something" print('Doing something!') do_something()

20

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1

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null

0

1

0

4

19e38da55b1426036c544fef27e67c7cf09be63a

205

py

Python

python_crash_course/chapter_05/5-03_alien_colors_2.py

valdsonmota/python-studies

1abf5ba4337006f77b2a162b37f341b116414f59

[ "MIT" ]

null

python_crash_course/chapter_05/5-03_alien_colors_2.py

valdsonmota/python-studies

1abf5ba4337006f77b2a162b37f341b116414f59

[ "MIT" ]

null

python_crash_course/chapter_05/5-03_alien_colors_2.py

valdsonmota/python-studies

1abf5ba4337006f77b2a162b37f341b116414f59

[ "MIT" ]

null

#Exercise 5-3 Alien Colors - 2 alien_color = 'yellow' if alien_color == 'green': print('You have earned 5 points.') if alien_color == 'yellow': print('\n') if alien_color == 'red': print('\n')

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4

c20f3be8879b22de458d3ac814b81a309d2cb9d8

2,162

py

Python

mlcomp/parallelm/extra/sagemaker/monitor/job_monitor_transformer.py

mlpiper/mlpiper

0fd2b6773f970c831038db47bf4920ada21a5f51

[ "Apache-2.0" ]

7

2019-04-08T02:31:55.000Z

2021-11-15T14:40:49.000Z

mlcomp/parallelm/extra/sagemaker/monitor/job_monitor_transformer.py

mlpiper/mlpiper

0fd2b6773f970c831038db47bf4920ada21a5f51

[ "Apache-2.0" ]

31

2019-02-22T22:23:26.000Z

2021-08-02T17:17:06.000Z

mlcomp/parallelm/extra/sagemaker/monitor/job_monitor_transformer.py

mlpiper/mlpiper

0fd2b6773f970c831038db47bf4920ada21a5f51

[ "Apache-2.0" ]

8

2019-03-15T23:46:08.000Z

2020-02-06T09:16:02.000Z

from parallelm.common.cached_property import cached_property from parallelm.extra.sagemaker.monitor.job_monitor_base import JobMonitorBase from parallelm.extra.sagemaker.monitor.sm_api_constants import SMApiConstants class JobMonitorTransformer(JobMonitorBase): def __init__(self, sagemaker_client, job_name, logger): super(self.__class__, self).__init__(sagemaker_client, job_name, logger) def _describe_job(self): return self._sagemaker_client.describe_transform_job(TransformJobName=self._job_name) def _job_status(self, describe_response): return describe_response[SMApiConstants.Transformer.JOB_STATUS] def _job_start_time(self, describe_response): return describe_response.get(SMApiConstants.Transformer.START_TIME) def _job_end_time(self, describe_response): return describe_response.get(SMApiConstants.Transformer.END_TIME) @cached_property def _host_metrics_defs(self): return [ JobMonitorBase.MetricMeta('cpuavg_{}', SMApiConstants.METRIC_CPU_UTILIZATION, SMApiConstants.STAT_AVG), JobMonitorBase.MetricMeta('cpumin_{}', SMApiConstants.METRIC_CPU_UTILIZATION, SMApiConstants.STAT_MIN), JobMonitorBase.MetricMeta('cpumax_{}', SMApiConstants.METRIC_CPU_UTILIZATION, SMApiConstants.STAT_MAX), JobMonitorBase.MetricMeta('memavg_{}', SMApiConstants.METRIC_MEMORY_UTILIZATION, SMApiConstants.STAT_AVG), JobMonitorBase.MetricMeta('memmin_{}', SMApiConstants.METRIC_MEMORY_UTILIZATION, SMApiConstants.STAT_MIN), JobMonitorBase.MetricMeta('memmax_{}', SMApiConstants.METRIC_MEMORY_UTILIZATION, SMApiConstants.STAT_MAX), ] def _metrics_namespace(self): return SMApiConstants.Transformer.NAMESPACE def _report_extended_online_metrics(self, describe_response): pass def _report_extended_final_metrics(self, describe_response): pass

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c20f3cdea33ba0009e09a954a5111ba43eb0cd9d

173

py

Python

sparkdq/profiling/ProfilerSuite.py

PasaLab/SparkDQ

16d50210747ef7de03cf36d689ce26ff7445f63a

[ "Apache-2.0" ]

1

2021-02-08T07:49:54.000Z

sparkdq/profiling/ProfilerSuite.py

PasaLab/SparkDQ

16d50210747ef7de03cf36d689ce26ff7445f63a

[ "Apache-2.0" ]

null

sparkdq/profiling/ProfilerSuite.py

PasaLab/SparkDQ

16d50210747ef7de03cf36d689ce26ff7445f63a

[ "Apache-2.0" ]

null

from sparkdq.profiling.ProfilerRunBuilder import ProfilerRunBuilder class ProfilerSuite: @staticmethod def on_data(data): return ProfilerRunBuilder(data)

19.222222

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dfadb8238ef58dc6bbf139bf4e4863b52edfdfd0

153

py

Python

old/python.py

naeimnb/pythonexersices

94761d5a954c5f6a710baf4ea5f2be57f110c13e

[ "Apache-2.0" ]

null

old/python.py

naeimnb/pythonexersices

94761d5a954c5f6a710baf4ea5f2be57f110c13e

[ "Apache-2.0" ]

null

old/python.py

naeimnb/pythonexersices

94761d5a954c5f6a710baf4ea5f2be57f110c13e

[ "Apache-2.0" ]

null

name = input('what is your name? ') if name == 'naeim': print('hey nimi') elif name == 'jadi': print('Hey jadi') else : print('hey gharibeh')

21.857143

35

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153

4.090909

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4

dfafe91697ce152bd5531fd9278988ebcb74b6dc

204

py

Python

python_exercises/Curso_em_video/ex007.py

Matheus-IT/lang-python-related

dd2e5d9b9f16d3838ba1670fdfcba1fa3fe305e9

[ "MIT" ]

null

python_exercises/Curso_em_video/ex007.py

Matheus-IT/lang-python-related

dd2e5d9b9f16d3838ba1670fdfcba1fa3fe305e9

[ "MIT" ]

null

python_exercises/Curso_em_video/ex007.py

Matheus-IT/lang-python-related

dd2e5d9b9f16d3838ba1670fdfcba1fa3fe305e9

[ "MIT" ]

null

n1 = float(input('\033[1;32mDigite a primeira nota: \033[m')) n2 = float(input('\033[1;31mDigite a segunda nota: \033[m')) print('\033[1;33mA média entre as duas notas é {}\033[m'.format((n1 + n2)/2))

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null

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dfb2d220b1401238e378c18e70e92c345da09741

839

py

Python

webapp/migrations/0001_initial.py

mchrh/hungry

9ba450249d0ef1c7e0b3ba360d19ef604f0b7c92

[ "MIT" ]

null

webapp/migrations/0001_initial.py

mchrh/hungry

9ba450249d0ef1c7e0b3ba360d19ef604f0b7c92

[ "MIT" ]

3

2021-03-30T12:50:06.000Z

2021-06-04T22:33:43.000Z

webapp/migrations/0001_initial.py

mchrh/hungry

9ba450249d0ef1c7e0b3ba360d19ef604f0b7c92

[ "MIT" ]

null

# Generated by Django 3.0.3 on 2020-02-22 19:06 from django.db import migrations, models class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='results', fields=[ ('id', models.IntegerField(primary_key=True, serialize=False)), ('name', models.CharField(max_length=100)), ('ING1', models.CharField(max_length=100)), ('ING2', models.CharField(max_length=100)), ('ING3', models.CharField(max_length=100)), ('ING4', models.CharField(max_length=100)), ('ING5', models.CharField(max_length=100)), ], options={ 'verbose_name_plural': 'recipes', }, ), ]

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dfbc9d88ff5818d0cf1335cd7e19f0f3a9422e3b

190

py

Python

DesignPatterns/03_decorator/2_decorator/cars/luxury.py

eduardormonteiro/PythonPersonalLibrary

561733bb8305c4e25a08f99c28b60ec77251ad67

[ "MIT" ]

null

DesignPatterns/03_decorator/2_decorator/cars/luxury.py

eduardormonteiro/PythonPersonalLibrary

561733bb8305c4e25a08f99c28b60ec77251ad67

[ "MIT" ]

null

DesignPatterns/03_decorator/2_decorator/cars/luxury.py

eduardormonteiro/PythonPersonalLibrary

561733bb8305c4e25a08f99c28b60ec77251ad67

[ "MIT" ]

null

from .abstract_car import AbstractCar class Luxury(AbstractCar): @property def description(self): return 'Luxury' @property def cost(self): return 18000.00

17.272727

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null

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dfbeac91f89647e20b6ddba347ec4e22b2e3a5fb

106

py

Python

metadata-ingestion/src/datahub/metadata/com/linkedin/pegasus2avro/mxe/__init__.py

john-bodley/datahub

28c008f939f709eb8b401c26a954be529a52752f

[ "Apache-2.0" ]

null

metadata-ingestion/src/datahub/metadata/com/linkedin/pegasus2avro/mxe/__init__.py

john-bodley/datahub

28c008f939f709eb8b401c26a954be529a52752f

[ "Apache-2.0" ]

3

2022-02-14T13:39:45.000Z

2022-02-27T17:32:49.000Z

metadata-ingestion/src/datahub/metadata/com/linkedin/pegasus2avro/mxe/__init__.py

john-bodley/datahub

28c008f939f709eb8b401c26a954be529a52752f

[ "Apache-2.0" ]

null

from .....schema_classes import MetadataChangeEventClass MetadataChangeEvent = MetadataChangeEventClass

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null

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dfd80291378fd3b7d704ee5652a1c41e8c96d72a

116

py

Python

comparing_lists/target_list.py

William-Lake/comparing_lists

d9d53c89d4a36b1843bc536655cf8831afd4a2d4

[ "MIT" ]

null

comparing_lists/target_list.py

William-Lake/comparing_lists

d9d53c89d4a36b1843bc536655cf8831afd4a2d4

[ "MIT" ]

1

2018-10-25T22:38:47.000Z

comparing_lists/target_list.py

William-Lake/comparing_lists

d9d53c89d4a36b1843bc536655cf8831afd4a2d4

[ "MIT" ]

null

class Target_List(object): def __init__(self,name,items): self.name = name self.items = items

16.571429

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1

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null

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4

dfda441071f6c8e910568e68d8201a4a49dea163

194

py

Python

main.py

Blackth01/Panasonic-Viera-Voice-Contro

6c5d30544c718ba6865c4f072b66793bf43431f8

[ "MIT" ]

2

2020-10-22T23:55:02.000Z

2020-11-21T16:58:32.000Z

main.py

Blackth01/Panasonic-Viera-Voice-Contro

6c5d30544c718ba6865c4f072b66793bf43431f8

[ "MIT" ]

null

main.py

Blackth01/Panasonic-Viera-Voice-Contro

6c5d30544c718ba6865c4f072b66793bf43431f8

[ "MIT" ]

null

from app import app if __name__ == "__main__": #app.run(host='0.0.0.0', port='5050', ssl_context=('server.crt', 'server.key')) app.run(host='0.0.0.0', port='5050', ssl_context='adhoc')

32.333333

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5

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null

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dfeed89df1a35828195ea860e982cfe190caa03a

253

py

Python

schemas/__init__.py

donovan-PNW/dwellinglybackend

448df61f6ea81f00dde7dab751f8b2106f0eb7b1

[ "MIT" ]

null

schemas/__init__.py

donovan-PNW/dwellinglybackend

448df61f6ea81f00dde7dab751f8b2106f0eb7b1

[ "MIT" ]

56

2021-08-05T02:49:38.000Z

2022-03-31T19:35:13.000Z

schemas/__init__.py

donovan-PNW/dwellinglybackend

448df61f6ea81f00dde7dab751f8b2106f0eb7b1

[ "MIT" ]

null

from .lease import LeaseSchema from .tenant import TenantSchema from .property import PropertySchema from .property_assignment import PropertyAssignSchema from .user import * from .staff_tenants import StaffTenantSchema from .ticket import TicketSchema

31.625

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true

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4

5f04925ee1767c7d6437f6b0e2bc3cf10a05b20b

252

py

Python

Chapter14/ABQ_Data_Entry/abq_data_entry/images/__init__.py

JTamarit/Tkinter_libro

1d0672235d10ad9011d2f7526f9fef363197b8da

[ "MIT" ]

173

2018-07-26T00:46:28.000Z

2022-03-09T13:54:30.000Z

Chapter14/ABQ_Data_Entry/abq_data_entry/images/__init__.py

my01chap/Python-GUI-Programming-with-Tkinter

1d0672235d10ad9011d2f7526f9fef363197b8da

[ "MIT" ]

1

2021-03-06T12:29:33.000Z

2021-03-06T15:08:24.000Z

Chapter14/ABQ_Data_Entry/abq_data_entry/images/__init__.py

my01chap/Python-GUI-Programming-with-Tkinter

1d0672235d10ad9011d2f7526f9fef363197b8da

[ "MIT" ]

105

2018-05-15T02:47:48.000Z

2022-03-17T05:52:08.000Z

from os import path IMAGE_DIRECTORY = path.dirname(__file__) ABQ_LOGO_16 = path.join(IMAGE_DIRECTORY, 'abq_logo-16x10.png') ABQ_LOGO_32 = path.join(IMAGE_DIRECTORY, 'abq_logo-32x20.png') ABQ_LOGO_64 = path.join(IMAGE_DIRECTORY, 'abq_logo-64x40.png')

31.5

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a027dbd60785b7bfb61b2488dc2836c9157991d8

21

py

Python

deep_classifier/__init__.py

joonilahn/Deep-Classifier

1f764bf3e5038d337bd862fb2a2cb735a3edfef8

[ "MIT" ]

null

deep_classifier/__init__.py

joonilahn/Deep-Classifier

1f764bf3e5038d337bd862fb2a2cb735a3edfef8

[ "MIT" ]

null

deep_classifier/__init__.py

joonilahn/Deep-Classifier

1f764bf3e5038d337bd862fb2a2cb735a3edfef8

[ "MIT" ]

null

"""deep_classifier"""

21

0.714286

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true

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null

0

1

0

4

a03fb3ffb9cb6e969c900ae7f7d0d1b271343d00

1,674

py

Python

djorm_pgfulltext/tests/models.py

uk-gov-mirror/ministryofjustice.djorm-ext-pgfulltext

a6dbbd8e57a376a02f59123d4180da2e336302c7

[ "BSD-3-Clause" ]

1

2016-07-23T14:56:17.000Z

djorm_pgfulltext/tests/models.py

Fry-IT/djorm-ext-pgfulltext

7ff0327a0dcb433cce89108d6fedf84d96b7c820

[ "BSD-3-Clause" ]

null

djorm_pgfulltext/tests/models.py

Fry-IT/djorm-ext-pgfulltext

7ff0327a0dcb433cce89108d6fedf84d96b7c820

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- from django.db import models from ..fields import VectorField from ..models import SearchManager class Person(models.Model): name = models.CharField(max_length=32) description = models.TextField() search_index = VectorField() objects = SearchManager( fields=('name', 'description'), search_field = 'search_index', config = 'names', ) def __unicode__(self): return self.name def save(self, *args, **kwargs): super(Person, self).save(*args, **kwargs) self.update_search_field() class Person2(models.Model): name = models.CharField(max_length=32) description = models.TextField() search_index = VectorField() objects = SearchManager( fields=(('name', 'A'), ('description', 'B')), search_field = 'search_index', config = 'names', ) def __unicode__(self): return self.name class Person3(models.Model): name = models.CharField(max_length=32) description = models.TextField() search_index = VectorField() objects = SearchManager( fields=('name', 'description'), search_field = 'search_index', auto_update_search_field = True, config = 'names' ) def __unicode__(self): return self.name class Book(models.Model): author = models.ForeignKey(Person) name = models.CharField(max_length=32) search_index = VectorField() objects = SearchManager( fields=('name',), search_field = 'search_index', auto_update_search_field = True, config = 'names' ) def __unicode__(self): return self.name

23.577465

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a04c5be23ff85e54c6b16b41281f30ceae8c59c2

157

py

Python

conda/run_test.py

0just0/ibench

72b5c42d202b71ba11a09e1f3323186cc6aa5275

[ "MIT" ]

null

conda/run_test.py

0just0/ibench

72b5c42d202b71ba11a09e1f3323186cc6aa5275

[ "MIT" ]

null

conda/run_test.py

0just0/ibench

72b5c42d202b71ba11a09e1f3323186cc6aa5275

[ "MIT" ]

1

2021-11-24T04:24:00.000Z

19.625

64

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7

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null

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null

0

1

0

1

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4

a0590e973355185cc588f0bb6823f35a8dfb4961

6,998

py

Python

tests/test_client.py

bachya/aioridwell

2eba18727ec7ce3ff4e0b6a1317aa0e6afdd05a6

[ "MIT" ]

2

2021-12-20T20:21:34.000Z

2021-12-20T20:21:42.000Z

tests/test_client.py

bachya/aioridwell

2eba18727ec7ce3ff4e0b6a1317aa0e6afdd05a6

[ "MIT" ]

20

2021-10-12T21:10:33.000Z

2022-03-20T14:59:25.000Z

tests/test_client.py

bachya/aioridwell

2eba18727ec7ce3ff4e0b6a1317aa0e6afdd05a6

[ "MIT" ]

null

"""Define tests for the client.""" import logging from time import time import aiohttp import pytest from aioridwell import async_get_client from aioridwell.errors import InvalidCredentialsError, RequestError from .common import generate_jwt @pytest.mark.asyncio async def test_expired_token_successful( aresponses, authentication_response, caplog, token_expired_response ): """Test that getting a new access token successfully retries the request.""" caplog.set_level(logging.INFO) aresponses.add( "api.ridwell.com", "/", "post", response=aiohttp.web_response.json_response( authentication_response, status=200 ), ) aresponses.add( "api.ridwell.com", "/", "post", response=aiohttp.web_response.json_response(token_expired_response, status=200), ) # Simulate a JWT that's generated at some point in the future from the original one: authentication_response["data"]["createAuthentication"][ "authenticationToken" ] = generate_jwt(issued_at=round(time()) + 1000) aresponses.add( "api.ridwell.com", "/", "post", response=aiohttp.web_response.json_response( authentication_response, status=200 ), ) aresponses.add( "api.ridwell.com", "/", "post", response=aiohttp.web_response.json_response({}, status=200), ) async with aiohttp.ClientSession() as session: client = await async_get_client( "user", "password", session=session, # We set this parameter low so that this test doesn't take longer than # necessary: request_retry_delay=0, ) # Perform a fake request that has an expired token: await client.async_request() assert any( "Token failed; refreshing and trying again" in e.message for e in caplog.records ) # Verify that the token actually changed between retries: token_1 = aresponses.history[1].request.headers["Authorization"] token_2 = aresponses.history[3].request.headers["Authorization"] assert token_1 != token_2 aresponses.assert_plan_strictly_followed() @pytest.mark.asyncio async def test_expired_token_failure( aresponses, authentication_response, token_expired_response ): """Test that failing to get a new access token is handled correctly.""" aresponses.add( "api.ridwell.com", "/", "post", response=aiohttp.web_response.json_response( authentication_response, status=200 ), ) aresponses.add( "api.ridwell.com", "/", "post", response=aiohttp.web_response.json_response(token_expired_response, status=200), ) aresponses.add( "api.ridwell.com", "/", "post", response=aiohttp.web_response.json_response( authentication_response, status=200 ), ) aresponses.add( "api.ridwell.com", "/", "post", response=aiohttp.web_response.json_response(token_expired_response, status=200), ) aresponses.add( "api.ridwell.com", "/", "post", response=aiohttp.web_response.json_response( authentication_response, status=200 ), ) aresponses.add( "api.ridwell.com", "/", "post", response=aiohttp.web_response.json_response(token_expired_response, status=200), ) aresponses.add( "api.ridwell.com", "/", "post", response=aiohttp.web_response.json_response( authentication_response, status=200 ), ) async with aiohttp.ClientSession() as session: client = await async_get_client( "user", "password", session=session, # We set this parameter low so that this test doesn't take longer than # necessary: request_retry_delay=0, ) # Perform a fake request that has an expired token: with pytest.raises(InvalidCredentialsError): await client.async_request() aresponses.assert_plan_strictly_followed() @pytest.mark.asyncio async def test_http_error(aresponses, authentication_response): """Test that a repeated HTTP error is handled.""" aresponses.add( "api.ridwell.com", "/", "post", response=aiohttp.web_response.json_response( authentication_response, status=200 ), ) aresponses.add( "api.ridwell.com", "/", "post", response=aresponses.Response(text="Not Found", status=404), ) async with aiohttp.ClientSession() as session: client = await async_get_client( "user", "password", session=session, # We set this parameter low so that this test doesn't take longer than # necessary: request_retry_delay=0, ) # Perform a fake request that has an expired token: with pytest.raises(RequestError): await client.async_request() aresponses.assert_plan_strictly_followed() @pytest.mark.asyncio async def test_invalid_credentials(aresponses, invalid_credentials_response): """Test that invalid credentials on login are dealt with immediately (no retry).""" aresponses.add( "api.ridwell.com", "/", "post", response=aiohttp.web_response.json_response( invalid_credentials_response, status=200 ), ) async with aiohttp.ClientSession() as session: with pytest.raises(InvalidCredentialsError): await async_get_client("user", "password", session=session) aresponses.assert_plan_strictly_followed() @pytest.mark.asyncio async def test_create_client(aresponses, authentication_response): """Test the successful creation of a client.""" aresponses.add( "api.ridwell.com", "/", "post", response=aiohttp.web_response.json_response( authentication_response, status=200 ), ) async with aiohttp.ClientSession() as session: client = await async_get_client("user", "password", session=session) assert client.user_id == "userId1" aresponses.assert_plan_strictly_followed() @pytest.mark.asyncio async def test_create_client_no_session(aresponses, authentication_response): """Test the successful creation of a client without an explicit ClientSession.""" aresponses.add( "api.ridwell.com", "/", "post", response=aiohttp.web_response.json_response( authentication_response, status=200 ), ) client = await async_get_client("user", "password") assert client.user_id == "userId1" aresponses.assert_plan_strictly_followed()

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null

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4

a085486a97ce5a97cd97744bff9c46f3b5d03d4c

2,365

py

Python

tests/three/test_tv_episodes.py

Cologler/ezapi-tmdb

6a8a1a0a3da99cb18d11f47f1b40bbffb2a16be6

[ "MIT" ]

4

2017-05-16T02:30:52.000Z

2021-07-01T13:21:27.000Z

tests/three/test_tv_episodes.py

Cologler/ezapi-tmdb

6a8a1a0a3da99cb18d11f47f1b40bbffb2a16be6

[ "MIT" ]

4

2020-09-03T03:19:49.000Z

2021-12-21T05:24:04.000Z

tests/three/test_tv_episodes.py

Cologler/ezapi-tmdb

6a8a1a0a3da99cb18d11f47f1b40bbffb2a16be6

[ "MIT" ]

3

2021-02-15T18:13:08.000Z

2021-04-10T03:53:58.000Z

import pytest from . import polite tv_id = 1418 # The Big Bang Theory season_number = 12 episode_number = 1 @polite def test_get_tv_episode_details(tmdb): assert tmdb.get_tv_episode_details(tv_id, season_number, episode_number) is not None @polite def test_get_tv_episode_changes(tmdb): episode_id = tmdb.get_tv_episode_details(tv_id, season_number, episode_number).get( "id" ) assert tmdb.get_tv_episode_changes(episode_id) is not None @polite @pytest.mark.parametrize("with_guest_session", [True, False]) def test_post_tv_episode_rating(tmdb, with_guest_session): rating = 10 if with_guest_session: guest_session_id = tmdb.create_guest_session().get("guest_session_id") assert tmdb.post_tv_episode_rating( tv_id, season_number, episode_number, rating, guest_session_id=guest_session_id, ) else: with pytest.raises(RuntimeError): assert tmdb.post_tv_episode_rating( tv_id, season_number, episode_number, rating ) @polite @pytest.mark.parametrize("with_guest_session", [True, False]) def test_delete_tv_episode_rating(tmdb, with_guest_session): if with_guest_session: guest_session_id = tmdb.create_guest_session().get("guest_session_id") assert tmdb.delete_tv_episode_rating( tv_id, season_number, episode_number, guest_session_id=guest_session_id ) else: with pytest.raises(RuntimeError): assert tmdb.delete_tv_episode_rating(tv_id, season_number, episode_number) @polite def test_get_tv_episode_credits(tmdb): assert tmdb.get_tv_episode_credits(tv_id, season_number, episode_number) is not None @polite def test_get_tv_episode_external_ids(tmdb): assert ( tmdb.get_tv_episode_external_ids(tv_id, season_number, episode_number) is not None ) @polite def test_get_tv_episode_images(tmdb): assert tmdb.get_tv_episode_images(tv_id, season_number, episode_number) is not None @polite def test_get_tv_episode_translations(tmdb): assert ( tmdb.get_tv_episode_translations(tv_id, season_number, episode_number) is not None ) @polite def test_get_tv_episode_videos(tmdb): assert tmdb.get_tv_episode_videos(tv_id, season_number, episode_number) is not None

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null

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null

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4

a0a009ca1e7ab186ff8520ca9887c616e454ce41

46,226

py

Python

optimization/pre_optimization/no_ma_cuts/ma_files/Output/Histos/MadAnalysis5job_0/selection_7.py

sheride/axion_pheno

7d3fc08f5ae5b17a3500eba19a2e43f87f076ce5

[ "MIT" ]

null

optimization/pre_optimization/no_ma_cuts/ma_files/Output/Histos/MadAnalysis5job_0/selection_7.py

sheride/axion_pheno

7d3fc08f5ae5b17a3500eba19a2e43f87f076ce5

[ "MIT" ]

null

optimization/pre_optimization/no_ma_cuts/ma_files/Output/Histos/MadAnalysis5job_0/selection_7.py

sheride/axion_pheno

7d3fc08f5ae5b17a3500eba19a2e43f87f076ce5

[ "MIT" ]

null

def selection_7(): # Library import import numpy import matplotlib import matplotlib.pyplot as plt import matplotlib.gridspec as gridspec # Library version matplotlib_version = matplotlib.__version__ numpy_version = numpy.__version__ # Histo binning xBinning = numpy.linspace(0.0,8000.0,161,endpoint=True) # Creating data sequence: middle of each bin xData = numpy.array([25.0,75.0,125.0,175.0,225.0,275.0,325.0,375.0,425.0,475.0,525.0,575.0,625.0,675.0,725.0,775.0,825.0,875.0,925.0,975.0,1025.0,1075.0,1125.0,1175.0,1225.0,1275.0,1325.0,1375.0,1425.0,1475.0,1525.0,1575.0,1625.0,1675.0,1725.0,1775.0,1825.0,1875.0,1925.0,1975.0,2025.0,2075.0,2125.0,2175.0,2225.0,2275.0,2325.0,2375.0,2425.0,2475.0,2525.0,2575.0,2625.0,2675.0,2725.0,2775.0,2825.0,2875.0,2925.0,2975.0,3025.0,3075.0,3125.0,3175.0,3225.0,3275.0,3325.0,3375.0,3425.0,3475.0,3525.0,3575.0,3625.0,3675.0,3725.0,3775.0,3825.0,3875.0,3925.0,3975.0,4025.0,4075.0,4125.0,4175.0,4225.0,4275.0,4325.0,4375.0,4425.0,4475.0,4525.0,4575.0,4625.0,4675.0,4725.0,4775.0,4825.0,4875.0,4925.0,4975.0,5025.0,5075.0,5125.0,5175.0,5225.0,5275.0,5325.0,5375.0,5425.0,5475.0,5525.0,5575.0,5625.0,5675.0,5725.0,5775.0,5825.0,5875.0,5925.0,5975.0,6025.0,6075.0,6125.0,6175.0,6225.0,6275.0,6325.0,6375.0,6425.0,6475.0,6525.0,6575.0,6625.0,6675.0,6725.0,6775.0,6825.0,6875.0,6925.0,6975.0,7025.0,7075.0,7125.0,7175.0,7225.0,7275.0,7325.0,7375.0,7425.0,7475.0,7525.0,7575.0,7625.0,7675.0,7725.0,7775.0,7825.0,7875.0,7925.0,7975.0]) # Creating weights for histo: y8_M_0 y8_M_0_weights = numpy.array([0.0,0.0,3.15653883439,12.0325134577,23.2462076328,36.1221003516,50.492355761,63.9905439346,74.9094543679,85.6236849806,94.3563573295,102.495390198,109.348904194,115.166579097,119.485855312,122.093773027,123.760051567,123.94021141,125.401770129,124.722170724,121.925893174,120.574854358,117.885056715,115.588258727,113.213700808,109.267024265,105.16474786,101.160711368,97.9100342159,94.7329969995,89.8282812968,86.0822045789,81.6442084673,77.9718116848,74.7538945042,70.5369781989,67.097941212,62.5780651721,59.4010679556,56.7726702585,52.3346741469,48.7605572783,46.684839097,43.7739616474,40.5068845098,37.5673150853,35.491612904,33.5264546257,31.4548484408,28.9206066612,27.0864602681,24.9411581478,23.803003145,21.9647607555,20.4663260684,18.5953277077,17.7437604538,16.5851334689,15.1562987208,13.9812957503,12.8677047259,11.9711015115,11.1768502074,10.3785029069,9.42048774622,8.39287264657,8.09809690484,7.5822453568,6.6037582141,6.18616257998,5.67439902836,5.29774335837,4.69181988925,4.49120806502,3.77474549275,3.62326442547,3.52091211515,3.09922168461,2.67343685766,2.44826225495,2.4318858693,2.32953355897,1.99791304952,1.71951529344,1.83824358942,1.5475638441,1.40017677323,1.2650720916,1.1381554028,1.18319016335,1.04808548172,0.855663650309,0.859757646722,0.790158107701,0.720558568681,0.69190019379,0.573171897814,0.491289969555,0.425784826948,0.462631594664,0.487195973142,0.360279364341,0.393032055644,0.311150367385,0.274303639668,0.2210805263,0.270209523255,0.18013968217,0.196516027822,0.192421951409,0.139198838041,0.126916608802,0.131010685215,0.155575183693,0.106446186737,0.102352110324,0.0900698410851,0.118728415976,0.0695994190203,0.0573171897814,0.0655053426074,0.0695994190203,0.0409408441296,0.0409408441296,0.0409408441296,0.0245645024778,0.0286585868907,0.0204704180648,0.0122822532389,0.0122822532389,0.0122822532389,0.00818816882592,0.0204704180648,0.0163763336518,0.00818816882592,0.0122822532389,0.00818816882592,0.00409408441296,0.0122822532389,0.0286585868907,0.00818816882592,0.00409408441296,0.00818816882592,0.0,0.0122822532389,0.00409408441296,0.00818816882592,0.0,0.0,0.0,0.00818816882592,0.0122822532389,0.0122822532389,0.00409408441296,0.0,0.0,0.00409408441296,0.0,0.0,0.0]) # Creating weights for histo: y8_M_1 y8_M_1_weights = numpy.array([267.83091033,7530.97135106,853.176362185,554.158001598,438.435098903,352.091334661,292.489066756,240.245983785,205.719341418,174.753964541,148.792048311,131.962739585,113.202626199,100.529382316,87.1302340709,76.85196987,66.2192454715,56.9381884823,50.6823204968,44.3007551332,39.4904871102,34.3889801044,32.575424901,28.7717132828,24.3992350481,21.7157562495,18.7256968189,16.7208356535,16.5981785656,14.169351119,12.6615033154,11.1062045513,8.6281997079,7.07265659893,6.26907862765,5.56606256459,5.73535746556,5.01735625617,4.75228219617,3.99824213045,3.72962386904,3.57263032717,2.87953906953,3.01368236621,2.50337344236,2.16263500838,2.00513955824,2.0056530829,1.91982596773,1.84681613971,1.48236985032,1.61569519274,1.40922903746,1.28787819288,1.14228874392,1.16646766407,0.984174420457,0.959565293219,0.789930712912,0.814151291849,0.486153236485,0.66844567897,0.631789952139,0.558899092,0.570715366569,0.522381560184,0.449392962122,0.425067835653,0.473901146915,0.255509442857,0.303559809265,0.170192006922,0.25523453492,0.255162753626,0.243287115286,0.218735108654,0.206661030285,0.0850923983735,0.121557215801,0.133799611692,0.0970914508696,0.133603534998,0.145920436027,0.133686772459,0.0606814708255,0.109398578106,0.0363869154965,0.0487009244443,0.0486194494707,0.0121482870889,0.0364733694963,0.0243191820811,0.0242483621433,0.0242720515727,0.0,0.0,0.0485322864704,0.0243438769293,0.0121541834096,0.0,0.0,0.0607646682297,0.0,0.0,0.0121482870889,0.0121216535049,0.0121482870889,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0121636527723,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0121874623713,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y8_M_2 y8_M_2_weights = numpy.array([6.73631230172,655.847885019,771.548096302,935.937740541,808.433754886,699.377557049,610.563307514,530.137646634,468.796531128,408.764838734,362.46682088,317.016421957,285.9524877,256.374954661,229.29032125,208.242901501,186.422984511,170.179096817,154.415690425,140.262731075,125.841845964,115.650956578,106.617594082,99.1459197773,90.3930033244,83.1215708825,76.8249435978,69.398557176,63.5830310081,60.2913539435,52.8838925678,50.9141588965,47.4000092379,43.4946317708,39.3366627881,35.1414222082,31.0926399979,27.0990709897,24.6184519806,19.7978708552,18.2719295144,17.7103886876,15.9946142591,13.3938955853,12.9606938428,11.8067164317,11.2243787147,10.6323718694,9.8394868167,8.72535943708,8.20261908684,6.84751967401,6.59620084705,6.12480604759,5.13046429258,4.75883093142,4.7993958173,3.93555630416,3.32310462414,3.01215909251,3.12251649057,2.84132253629,2.42939204973,2.44956085975,2.23889575896,2.07850558024,1.73750724072,1.80718859924,1.647050479,1.47578459695,1.47552634032,1.4858549528,1.30534141723,0.893691500674,1.10451279508,1.10456609925,0.793187522896,0.893567950701,0.873431371106,0.692702966017,0.672964308243,0.652716161782,0.51209769769,0.552210531162,0.471797679811,0.532081389358,0.462100039934,0.461945912375,0.431858808008,0.391732379907,0.341455391776,0.361382556232,0.361468751966,0.250997225138,0.291146916996,0.230909941569,0.210960918272,0.331240040321,0.21085377276,0.21089777969,0.180769767472,0.120483454698,0.080279508286,0.160568850985,0.120490190031,0.0802931442362,0.140578547947,0.130570710862,0.100405674973,0.0903324679696,0.070289604542,0.100370386787,0.0200760277115,0.0703055544716,0.070211466415,0.0301293675489,0.0602513964772,0.0200668709643,0.0602766023246,0.0602232568345,0.0501861644384,0.0301488297687,0.0200793292643,0.0501891808759,0.0401948970993,0.0200843539053,0.0200817672069,0.0,0.0100330883852,0.0200897876249,0.0100459020463,0.0,0.0,0.0,0.0201029277224,0.0,0.040178881056,0.0100534183474,0.0100698393372,0.0,0.0301235949966,0.0100155310663,0.0100704591531,0.0,0.0100548232635,0.0,0.0100273282293,0.0,0.0,0.0,0.0,0.0,0.0100155310663,0.0,0.0,0.0200557266741,0.0,0.0,0.0,0.0]) # Creating weights for histo: y8_M_3 y8_M_3_weights = numpy.array([0.385018944747,50.4219555325,10.599199331,35.9829376697,208.551322405,304.306749052,337.847203639,343.275318483,318.500619561,290.025533398,266.156867701,244.885602875,224.49818967,204.580490218,189.344612437,171.69561173,159.488985469,148.292759725,135.423163497,124.642619394,115.607353173,105.712935727,99.2430979907,91.5091931852,84.402507775,79.5910496579,72.9294588585,66.3720322232,63.698417635,59.3822268053,54.1831187452,50.1367634763,47.6932119386,45.4172005774,42.7773865292,38.7575638717,35.9167503623,33.2513502804,31.5065355305,29.3445969292,27.7345699577,25.9301858187,24.4114812436,23.1009303065,21.6165747177,19.7513008559,18.4820824541,17.3698821858,16.1377712518,15.3016263312,14.6357110051,13.3115626632,12.826098345,11.5175786904,10.9179928615,9.71386487454,9.44378149723,8.48759184631,8.06983423479,7.64067300358,6.75992112034,5.85868984742,5.45112114868,4.86210611367,4.7738306409,4.62067600663,3.87221870565,3.58099658439,3.12944208918,2.57429462649,2.4478387625,2.25501602575,2.12878400909,2.08447970754,1.78210218931,1.78224762913,1.56756991823,1.38053105513,1.3363563494,1.1549449512,1.23208127727,1.14387771189,1.08349296595,0.940452899514,0.945857735861,0.753688665801,0.670954937811,0.65448814349,0.759022407262,0.599519609072,0.659983168778,0.572059920381,0.483988794622,0.467603657856,0.423600392367,0.46747934337,0.401529492891,0.429052760723,0.341020107454,0.363129479419,0.302449466256,0.291604327371,0.242012881981,0.214576146782,0.231051716243,0.186899598376,0.142981728002,0.148494425446,0.154002085311,0.120986067228,0.0880650725375,0.142969987189,0.120994232983,0.154076714628,0.0605041853545,0.0990183162746,0.0384792781758,0.0880580849259,0.0604757067745,0.0660383331421,0.0495184410981,0.0604602284022,0.0384825891662,0.054998272393,0.0385421423052,0.0495469196781,0.0440159406911,0.0275112832655,0.0219813280449,0.0219860649956,0.0164966460476,0.0550408886988,0.0110186957203,0.0330224044353,0.0274859247354,0.0219985858206,0.0385004847646,0.0164807939193,0.0109727724866,0.010982656707,0.016526623714,0.0110185535305,0.0055097114597,0.0110137597039,0.0219946166947,0.0,0.0109727724866,0.0109987810271,0.00551628468972,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0109941903288,0.0,0.0,0.0]) # Creating weights for histo: y8_M_4 y8_M_4_weights = numpy.array([0.00296148985659,2.84410811646,0.387818414842,0.48556466238,0.905995676435,1.51078109762,2.56777672151,5.91308236158,28.5749591588,43.0020125596,49.9172117907,52.3432493947,49.0885310568,45.3870450174,43.1575354989,40.7946690683,37.9192543445,36.0258186746,33.4983704942,31.6324559713,29.4555156113,27.9167804679,26.2393172592,24.6388739804,23.2718674269,21.7283423372,20.1517044897,18.8291782985,17.8352343999,16.4727332016,15.6129158132,14.7722341597,13.6774889497,12.9721042367,12.3948616074,11.3105620877,10.8711256093,10.2453662363,9.57747537828,8.85702738678,8.40213882252,7.91960886162,7.40546525581,6.8952818733,6.54883448431,6.28832955261,5.78193804382,5.44932337813,5.23024847479,4.88675521999,4.66487849876,4.33519792563,4.0351749357,3.76680847974,3.58217589499,3.3671101165,3.15314823021,3.01896359932,2.68123551575,2.43660354382,2.22631528573,2.11787491212,1.93917464567,1.86219600152,1.8029549891,1.54546311392,1.55135013817,1.48227470484,1.33126072824,1.26519153729,1.16351641154,1.11127753888,1.06381418245,0.99584103799,0.943392129196,0.861501685822,0.76481892659,0.751941787546,0.643417239146,0.654320679857,0.563569437265,0.520058688327,0.505241921697,0.467714797601,0.421417061557,0.400640599775,0.397681094439,0.375970533024,0.333551851736,0.287170942987,0.285203577804,0.267430752996,0.242763892904,0.19638194199,0.204313692062,0.19738197854,0.175636825295,0.16776123183,0.171698407273,0.138172030624,0.140136108982,0.138169264881,0.120370546306,0.114477790151,0.100669677965,0.087808532131,0.0779601221641,0.0917724831721,0.0818959347765,0.0592266225398,0.0730181001841,0.0582059832088,0.063158426829,0.0601990016597,0.049343300078,0.0384869571646,0.035532786907,0.0355321095003,0.0306010943758,0.0197423745267,0.0256413676323,0.0256504064011,0.0256555410631,0.019739240018,0.0207324864684,0.0187557618512,0.00986137689239,0.0108594934554,0.0147995949737,0.0108605195862,0.00789217990611,0.0167743595208,0.0128346067267,0.00790004423619,0.0069055712389,0.0128299731051,0.0128323500408,0.010852318557,0.00789612810444,0.00888522193202,0.00295480838268,0.00098629882383,0.00887323704572,0.00197196753926,0.00296166502031,0.000986374981971,0.0,0.00691087825879,0.00296224622717,0.0,0.00493845858067,0.00296126659299,0.0,0.0,0.0,0.0,0.000986802670054,0.00295690233071,0.0,0.000985643863823]) # Creating weights for histo: y8_M_5 y8_M_5_weights = numpy.array([0.0,0.172418129509,0.0370535696054,0.0458799088969,0.0715933093876,0.113677759436,0.177712639164,0.245014487011,0.327958724175,0.459279207732,0.718428511144,1.51447342155,6.83352634743,10.3662602078,11.9708771273,12.8463816929,11.6817776154,10.7657470252,10.2143946422,9.67066014348,9.09538984409,8.66258278453,8.21311160292,7.76236810659,7.40678414901,6.95685285359,6.58696535787,6.27500819186,5.86262218367,5.54624392405,5.25861277534,4.9414663257,4.69501736386,4.36726028957,4.20387587464,3.93485332898,3.69799113853,3.60171092256,3.40330224401,3.16056019911,2.97641824959,2.79859426288,2.6523820957,2.49563252198,2.35541783835,2.20943212718,2.07030331215,2.01007641437,1.88733085191,1.77227599184,1.63878297088,1.56393605668,1.46436622699,1.39674349977,1.2943745777,1.2220874967,1.15098670914,1.08190041989,0.993390926103,0.952349974175,0.900651586014,0.855533225494,0.799561780345,0.744877453579,0.710887845878,0.664715624675,0.630229494154,0.564647671773,0.546733640542,0.523591916183,0.491039463978,0.448726597428,0.424028488081,0.395259851844,0.364750865113,0.369289667836,0.306030620019,0.29595320726,0.280314698905,0.271505759917,0.252318453727,0.245029730782,0.214255278385,0.201664043805,0.189805750527,0.182006461287,0.177968142365,0.158067379694,0.148478727836,0.145944580987,0.123513072308,0.117725520694,0.116209665731,0.104608836139,0.0899989822319,0.0932662704314,0.0857046799893,0.080164349528,0.0703271561907,0.0710851036773,0.0564614863658,0.0599975210426,0.0549531532631,0.0456267262694,0.0461232090799,0.0418455909643,0.0342866131685,0.0342828402352,0.0332723902894,0.0312526186768,0.0239461313373,0.0254630225569,0.0211765182432,0.0181538145444,0.0191580389503,0.0138627611055,0.0184042284871,0.0168915543106,0.0136135954716,0.0131082864779,0.0108440824023,0.00907853767552,0.0105871948584,0.0113438060144,0.0083183096248,0.00932460254235,0.00730947207781,0.00806697945554,0.00655365711872,0.00554361127284,0.00680537538398,0.00378331864522,0.00579946656144,0.0047894847314,0.00605200903058,0.00327664531403,0.00277244059348,0.00201775711428,0.00201706094207,0.00226745768044,0.00226796060484,0.00126152005077,0.00176456088342,0.000252040304924,0.00201700252762,0.00226813944908,0.00100770086582,0.000504528400614,0.000252009497304,0.000252077834207,0.000251636365005,0.000252212107421,0.00075509358065,0.000252350061546,0.00151265457164,0.000252441284111,0.0,0.000252178219039,0.00050424673094,0.0]) # Creating weights for histo: y8_M_6 y8_M_6_weights = numpy.array([0.0,0.0168984400821,0.00687219517873,0.00831114936776,0.0143085790557,0.0234689654798,0.0355188323991,0.045533762099,0.0518195230435,0.0669750179344,0.0967594144035,0.116820403358,0.143726975421,0.183537317894,0.267691281793,0.526822135858,2.21724704915,3.40355200935,4.16067297326,4.41691499661,4.63771791081,4.82043760304,4.76836907355,4.71213494173,4.37561585367,4.10678405456,3.87657729562,3.65238351915,3.48320628303,3.25264864182,3.10932573082,2.98436066024,2.83010941601,2.6644629952,2.53838929659,2.41265848294,2.2667004561,2.17823713811,2.07541863671,1.92207108927,1.86441343509,1.77264822499,1.65491872666,1.5603294641,1.48626631444,1.36725724544,1.3000328012,1.21788736338,1.20028926806,1.12467663829,1.05503900374,0.977389957254,0.937176450055,0.872525542314,0.838325317484,0.792226984815,0.739314538479,0.732857804901,0.663942927845,0.629830473566,0.601862057782,0.571422771048,0.508836670504,0.486205863798,0.472662008155,0.460110419578,0.387985419612,0.38016634314,0.374773052745,0.332379077157,0.317545354302,0.30035242303,0.276511422379,0.271459817339,0.249025744623,0.249635540019,0.219316113068,0.207579750959,0.202504853734,0.182351415799,0.157740573452,0.166048585872,0.146558725287,0.135969878153,0.131374719962,0.118509836505,0.113604882259,0.1099225178,0.0961775096494,0.0838908621768,0.0839287394679,0.080463862035,0.0807336115262,0.0655626918108,0.0638522057287,0.0586968954851,0.0578315858217,0.0463726306343,0.0486606129497,0.0486765575998,0.0418027839507,0.0377761150152,0.0392342757599,0.0283598944309,0.0334873940058,0.0323437677256,0.0286264649888,0.0248971862702,0.0260224587086,0.0229178703882,0.0220534404296,0.018605857194,0.0171680296269,0.0160298015356,0.0154619320723,0.0103145791588,0.0123100496205,0.0080224282421,0.00772660150057,0.0083051503806,0.00830043696209,0.00686764670487,0.00801431696366,0.00801623232101,0.00601087417696,0.00487151946359,0.00544337358991,0.00572330566456,0.00429545075139,0.00457448912591,0.00400743338959,0.00400385658971,0.00372335770524,0.00342957027947,0.0022939892999,0.00143265130384,0.00343798045761,0.000855258935763,0.00229207094356,0.00257508598379,0.00171731579063,0.000861006407319,0.000572159523846,0.00200202726165,0.000570381620384,0.000860475485459,0.000859723137893,0.000858643200244,0.000572915070338,0.00057356505225,0.00057356505225,0.0,0.0,0.000283554259302,0.00028617058145,0.000287115464415,0.000286437092028,0.000858461561189,0.0,0.0]) # Creating weights for histo: y8_M_7 y8_M_7_weights = numpy.array([0.0,0.000648125465779,0.000194369788245,0.000366939718819,0.000756012685422,0.00120945676579,0.00254892435981,0.00293715222125,0.00354131922632,0.00410439815269,0.00513968949304,0.00519868550324,0.00708396363119,0.00762138178673,0.00839786014073,0.0107316663641,0.0117473893184,0.0136239963698,0.0161109399244,0.0192601668138,0.0240975965749,0.029384853626,0.0433431034439,0.0869525837016,0.377149083786,0.607702910011,0.731939027556,0.80283440351,0.844751452227,0.876990338953,0.884933447436,0.886046144794,0.794257204215,0.726774938349,0.6937167208,0.648392848421,0.611649947192,0.58998901953,0.55608506817,0.525337321632,0.501119914164,0.474624725099,0.44918062642,0.427977769647,0.407747967762,0.38566115571,0.365274025542,0.344657566564,0.330056714115,0.311020075837,0.29574251061,0.280591931184,0.265250076777,0.250739371673,0.239369239146,0.226013308543,0.212758715123,0.200360081434,0.193136768698,0.177463410107,0.170653283181,0.159652499122,0.151533496445,0.145162203947,0.135642459885,0.125317843779,0.118051950986,0.111408833438,0.102774260032,0.101685995915,0.0941800873874,0.0899373367386,0.0853814801779,0.0794303723766,0.0727419925631,0.0698431959209,0.0653630279262,0.0632577877612,0.0581222808368,0.054394220819,0.0507421846432,0.0482582082815,0.0463009505787,0.0431284429592,0.0390452795146,0.0339011308502,0.032414994657,0.0302974959618,0.0278971164897,0.0267401710764,0.0241400383306,0.0247053807893,0.0221542779735,0.0202096559864,0.0197098138931,0.0183982726977,0.0171707097685,0.0159322252222,0.015332889964,0.0121600512594,0.0115516425934,0.0120718443242,0.0111887146619,0.00965135248072,0.00982168109096,0.00915738609996,0.00842402005376,0.00784264301841,0.0070807282174,0.00622038475293,0.00630587176124,0.00650254049539,0.00518219830397,0.00440698980797,0.00427661352885,0.00414756075189,0.00362712474687,0.00375678311617,0.00308713125224,0.00308772008079,0.00276536264566,0.00280655046899,0.00220010526322,0.00224422800919,0.0021811927609,0.00159850898718,0.00166234386883,0.00144702289039,0.00131607287014,0.00127523541027,0.00151056272912,0.00110162319031,0.00094884376619,0.000884159378879,0.00114455654452,0.000583367736834,0.000756140928507,0.000691239449959,0.000691306505167,0.000454020755153,0.000410416387855,0.000323955779833,0.000259358899568,0.000453633092232,0.000302462993105,0.000345290022904,0.000237592318043,0.00017284623994,0.000151176301734,0.000194554357705,0.000172760535002,0.000215996392023,0.000108063952025,8.64731808738e-05,0.000194468862315,0.00010796362067,8.64285891605e-05,6.48603234136e-05,0.0,0.000151259953106]) # Creating weights for histo: y8_M_8 y8_M_8_weights = numpy.array([0.0,0.0,0.0,2.8370686899e-05,0.000113474402088,8.52134690097e-05,0.000113356956145,0.00031244674808,0.000340859340713,0.000426073507657,0.000482336336384,0.000595195368656,0.000593898845039,0.00116144474497,0.000906109483561,0.00113271630012,0.00127962247916,0.00107555165168,0.00124988871247,0.00149849529697,0.00169901171038,0.00237754900113,0.00203897086484,0.00249287144312,0.00292309721208,0.00326346621709,0.00323247349642,0.00441960413988,0.00585979225574,0.00651577588632,0.0104038974111,0.0201581282359,0.0960970504598,0.155871214407,0.189802385345,0.207602224586,0.22095132441,0.23063523171,0.238495898034,0.237312677305,0.240444321626,0.2365840046,0.233809197555,0.236297257709,0.228145389597,0.221901851001,0.214098652853,0.212011384961,0.199703840132,0.197518416002,0.187068563355,0.177487415686,0.17550275893,0.169530226587,0.163628824106,0.153618521328,0.147431411431,0.138380307445,0.133394802154,0.129456227432,0.12040279205,0.118148554606,0.111659090829,0.105669303184,0.100109606177,0.0956027647523,0.0897188848668,0.0866596771768,0.0815517661564,0.0769339829392,0.0738706321948,0.0716862029925,0.0671139191267,0.0613705467027,0.0599727632694,0.0535337586287,0.0546045822978,0.047135531148,0.0473017433671,0.0434184836203,0.0400475222158,0.0384313151669,0.035828927498,0.0350723225865,0.0335948707396,0.0290316303153,0.0293518334851,0.02868780134,0.0253998049733,0.0237866866532,0.022284034937,0.0219584116422,0.0191948606378,0.0173504291991,0.0171813806954,0.0160012189963,0.0154286607429,0.0144317616399,0.0136607124664,0.0121660721406,0.0117064782036,0.0108741593422,0.00964568576668,0.00963589390237,0.00797100522051,0.00857623717584,0.00731290848363,0.00773785519926,0.00679897370904,0.00627245384952,0.00536454874535,0.0044467285247,0.00442463223399,0.00442019218632,0.00456728992594,0.0039394716412,0.0032018000434,0.00343304782349,0.00277582573146,0.00266738610583,0.00229703118694,0.00218147560532,0.00218070342312,0.00186932540491,0.00147192450739,0.00147618502269,0.00136093846245,0.00130570634846,0.00127280232835,0.000879293526128,0.00110236879708,0.000623352992666,0.00096334585585,0.000708347384675,0.000566163545295,0.000509159421653,0.000877839299145,0.000453035140232,0.000620745986753,0.000423707808682,0.000426082268955,0.000283627423321,0.000453900578285,0.000511072800053,0.000282431431887,0.000226896533382,0.000142022349352,0.000141935196711,0.000170185541983,8.47584309477e-05,0.000141579859251,8.52821932257e-05,0.000170187026949,0.000142122421195,8.49620346049e-05,0.000170287262139,5.67041160068e-05,5.67227374776e-05,0.0,0.000198719159327]) # Creating weights for histo: y8_M_9 y8_M_9_weights = numpy.array([263613.774479,1429376.51294,563738.176175,216174.237787,100909.486142,53316.9867832,30200.0405353,18019.1866252,11380.1378045,7618.93658001,4804.06976578,3307.71767667,2231.18927393,1684.04589358,1146.86854232,834.103725808,620.395501519,471.742552865,346.729603565,242.36919047,182.416169515,135.530305378,99.0132181995,104.251862667,57.3034753289,52.139389587,33.8885210475,20.8545061398,15.6411708799,15.6327190966,18.2533102513,15.6315001634,7.8257361059,7.81884932524,7.80639082756,13.0185686776,5.21991057812,2.60341880751,5.2083710869,0.0,0.0,2.61208515383,0.0,0.0,2.60341880751,0.0,0.0,0.0,2.61294148329,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y8_M_10 y8_M_10_weights = numpy.array([15935.4170009,72221.6124727,385509.980289,280766.803178,135684.567411,72520.0329886,42689.3703131,26929.1310799,17629.9422644,11920.5991212,8391.26478798,6248.93560814,4455.25747336,3346.18970726,2498.24758784,1863.10322844,1400.77920711,1078.54007067,882.583293104,659.37005686,482.396225396,433.953295223,323.294343077,280.173540422,220.144729982,188.515695089,149.543499357,122.166264247,88.4770658542,69.517320832,53.7184246762,52.6839951285,45.2939319411,38.9722342085,43.1932146473,25.2821814956,11.5918055837,6.31554925426,13.6908068441,10.5256794371,7.37203329391,8.42535075176,11.5905320253,6.31779625763,5.2644403237,3.16008742461,4.2174329438,5.26728370639,3.15636909574,3.15892660112,1.05322703905,4.21418171461,2.10784883618,0.0,0.0,0.0,2.10384116716,0.0,1.05322703905,0.0,1.05409890714,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.05314700879,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y8_M_11 y8_M_11_weights = numpy.array([766.753736097,2839.40325198,4201.07779211,10882.9867448,48161.7723606,46775.1992915,36196.0313893,26753.5060591,16898.4092494,11349.9076469,8017.72131349,5828.80937287,4395.2295287,3338.9276598,2527.03796754,1987.99198151,1592.72970743,1247.48388695,1000.12581409,797.928288141,669.121501855,533.430455399,459.282397171,384.428123048,301.511153417,257.517438068,205.005406532,172.755085574,146.02254698,124.153292298,105.712642782,96.7296380725,76.0060054318,68.6233212896,55.2806437533,51.3706744546,37.7707391433,32.7033019723,31.3283979874,23.0353869555,21.1947916066,19.3469919333,15.8945298863,14.5118029658,11.9747242835,10.3665923071,7.3732512803,8.52273484006,7.37306300729,4.60759763938,3.45456763091,4.60775517394,4.14597911999,1.84197896345,3.45638388909,2.07232714639,2.76429077488,0.459414572507,0.921726243954,2.99377828121,2.07326351643,0.92188877351,1.6118451812,0.920890926567,0.460782817775,0.460084670723,0.230527695492,0.230498955041,0.0,0.461037946913,0.0,0.0,0.460495413265,0.22999665034,0.23081494631,0.229679813764,0.0,0.460333652171,0.230162714818,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.23081494631,0.0,0.23072864811,0.0,0.0,0.230341189945,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y8_M_12 y8_M_12_weights = numpy.array([3.68283786851,118.548712143,147.261208916,171.129223014,219.47236964,261.126441529,346.940579056,725.430926552,3795.28692746,4487.6212243,4104.88000907,3476.28295237,2353.78749379,1669.17519246,1271.31660908,986.29641603,778.668530305,625.601282538,516.521940535,418.304636257,350.572979275,288.957402804,242.404872753,203.861566283,168.969934244,146.927199525,122.645193828,104.311917702,90.6345755273,76.7067455446,66.3730751295,57.9294315945,50.811207496,42.0903769461,38.3803066556,32.5906258776,30.3783272549,23.7603555079,22.2360308245,18.6347501858,16.724822009,14.8992848712,13.348249439,11.7680141534,11.186779336,8.77755674751,7.67132087532,7.03274431665,6.06454796264,5.23297922346,4.23689104628,4.68025177289,3.51697000513,3.04630260658,2.68672699316,2.10426801325,2.38175472104,2.1317047188,1.91074838875,1.68918536346,1.41223148567,0.830778534973,1.19039108101,0.609248979872,1.02469518515,0.664601745607,0.775558880559,0.415400808931,0.443287630795,0.498713877058,0.360060623369,0.36001803544,0.221452612141,0.332241126595,0.276862815796,0.276993811187,0.166051026097,0.138279849502,0.110728999074,0.110729037546,0.0830820084172,0.0830896257702,0.0554073952375,0.110799209525,0.0276650376283,0.0829973711607,0.0277335399459,0.0276724433882,0.0,0.0,0.0276925101123,0.027641354585,0.0,0.027641354585,0.0276409737174,0.0276605249236,0.027723379628,0.0276901556577,0.0277086912169,0.0276859430306,0.0276936488681,0.0,0.0276262506819,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0276409737174,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y8_M_13 y8_M_13_weights = numpy.array([0.0,9.57745849872,17.0470174622,19.2266201224,20.8602389247,25.2760736937,26.9885258826,28.4705037412,32.1709032251,37.5649552803,53.4855916529,121.996289154,691.673005423,900.556903469,887.104071899,810.432823293,560.11464093,408.162028938,318.060037402,259.137860925,210.835708568,175.324638815,147.109374169,121.659255339,103.141753393,88.509060737,75.5702755213,66.9442973446,56.3092238203,48.242306705,42.9280711023,36.5975395819,32.1714857826,28.704042567,24.6808030397,21.5951838194,18.9837482392,16.786541418,14.2453647056,12.9247674247,11.7354640515,10.0416294309,8.92264560855,8.36795372884,7.56193074483,6.55343260264,5.45461316028,4.87934304197,4.38531724904,3.88108941702,3.44844880533,3.08534007812,2.38958128105,2.48050395054,2.13729927292,1.71428178375,1.54283691752,1.40142532029,1.19999451788,1.189508482,1.13920038981,0.836907029748,0.82685427104,0.917319390115,0.766498881626,0.594828699136,0.494082410249,0.453465648359,0.393125490397,0.423333830059,0.443668911558,0.302442094291,0.363097379477,0.262123953829,0.201567157278,0.191567981729,0.232049784452,0.252108333165,0.211650075475,0.191618591416,0.141113279228,0.141271722745,0.0907781223909,0.100839801511,0.100880337807,0.0704537211136,0.060508365386,0.0504474569411,0.0503439073368,0.0807049741693,0.0503856633628,0.070658526502,0.0201427768248,0.0302693504283,0.0302264535605,0.0201284980965,0.0,0.0201631724073,0.0201653569981,0.0101024278068,0.0503901539106,0.020173318618,0.0100987746855,0.0,0.0,0.0201365568093,0.0,0.0201573832417,0.0100787796113,0.0,0.0,0.0100786036304,0.0100787796113,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y8_M_14 y8_M_14_weights = numpy.array([0.0,1.11174586198,3.50519751325,4.21001040631,4.6457685906,4.99932676071,5.16887078249,5.58211774362,5.62437389882,5.97805903361,6.36300825992,6.90874094483,7.729175189,9.63917033273,14.2958424965,32.8411390944,191.036334132,260.553961488,272.069735252,260.331696343,244.949809484,226.146224397,203.997697007,179.920191328,140.029041917,112.474281344,94.3191779987,78.8691919002,67.5458273427,58.0432902467,49.6551568068,43.5836286815,37.6480528005,33.2847729365,29.6779986746,25.960303448,22.8313203756,19.9540197805,18.0273191623,16.0217311539,14.1399183226,12.6519114661,11.5740005381,10.0344575622,8.9686082792,7.99857501085,7.40974743433,6.52957823017,5.93000482451,5.28223100801,4.61712843588,4.20950639498,3.63828843537,3.28750694442,3.08101427596,2.76984153419,2.52083185581,2.22079737948,1.91256059983,1.83615479212,1.58727130894,1.46554641921,1.37500867271,1.15726231636,1.03840490478,0.797823757696,0.789384453641,0.746874369072,0.763998057495,0.650742096915,0.560157027214,0.472480987736,0.472424045999,0.393183463162,0.367884210958,0.322541121079,0.294283053106,0.319590961992,0.223468731588,0.251804632759,0.183900841957,0.178272574474,0.158434881276,0.186771898198,0.138571718193,0.124473713606,0.141509834871,0.132977615753,0.118824092972,0.0849248686772,0.0848685810007,0.059448558451,0.0622360873199,0.0565857741514,0.0537433426754,0.01979692984,0.0565881210743,0.0226221749147,0.0226384918003,0.0282886780041,0.0197986496343,0.0113217601856,0.0254695387764,0.0198234231373,0.0254745173309,0.0113217486434,0.0113298589935,0.00566534501753,0.00849217137971,0.0084776281523,0.00283039137661,0.00564846640942,0.0113323636604,0.0,0.00283439114889,0.00283014668104,0.00282798289503,0.00283100696296,0.00566127830023,0.00565841582372,0.0028300989731,0.00283014668104,0.00564986686835,0.00565940460929,0.0,0.00282876776763,0.0,0.0,0.00283011859491,0.0028292871686,0.0,0.0,0.00283620558964,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.00283095579234,0.0,0.0,0.0028292871686,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y8_M_15 y8_M_15_weights = numpy.array([0.0,0.0259825854279,0.144752947926,0.255908707027,0.254294582397,0.271285075668,0.357925992009,0.353495066287,0.328960726415,0.335091445898,0.347366473762,0.360954189071,0.396051237627,0.388263263537,0.411528518599,0.358203678164,0.385298402551,0.462946186888,0.533129294719,0.655299504703,0.738637964592,1.0466701354,1.60404875765,4.09290604518,25.949830278,37.7943618863,40.2470750712,41.3672137552,40.5238040953,38.969754207,35.8741325826,33.5015348075,25.8781636171,21.2483909761,17.9114541746,15.4263166999,13.5888378704,11.6677601469,10.1501970377,8.99203661998,7.99264887428,7.04644216304,6.46413784068,5.71796914573,5.12036372541,4.53786797685,4.10818351028,3.81122120942,3.31481289553,3.110168834,2.70426139216,2.41437713415,2.23136895985,2.03370131985,1.79908369657,1.63762751284,1.51746030627,1.35572525474,1.21425183938,1.12117108569,0.972025379124,0.904828638186,0.78120146212,0.78578576513,0.738638437249,0.621535940513,0.605001443359,0.539376878716,0.452437006643,0.393042419514,0.376363880051,0.383929941546,0.3275608337,0.274391261183,0.280376874879,0.234642437789,0.201143679843,0.164501987278,0.16743718902,0.173557391879,0.162880300132,0.156938407234,0.129433416323,0.118829822785,0.109733143387,0.103672873472,0.0807533199484,0.0746530514106,0.0821685267592,0.0655598342272,0.0670754334454,0.0593471205212,0.0441263861075,0.0487343810642,0.0350138845069,0.0365717747357,0.03657172747,0.0167314061797,0.0273961852039,0.0243489991348,0.0274695179817,0.0121587896209,0.0152248110828,0.0121596167711,0.00455374807999,0.0212869007284,0.00761697022913,0.0106546417071,0.0106827459092,0.00758521120448,0.00456303106908,0.0045645163946,0.00611834886061,0.00305150733832,0.00153596956556,0.00303111808467,0.00455404585408,0.00612734707368,0.00455955940132,0.00305183347184,0.00306720428676,0.00304453446171,0.00304112542104,0.00459040147083,0.0,0.0,0.00152160551066,0.0,0.0,0.00153120636177,0.0,0.0,0.0,0.0,0.00152776423509,0.0,0.00302948269046,0.0,0.0,0.0,0.0,0.00154508239802,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y8_M_16 y8_M_16_weights = numpy.array([0.0,0.000180614037165,0.0122783293619,0.0213020238349,0.0202251163832,0.0242037514719,0.0269018663287,0.0288945248495,0.0310566045778,0.0312387349754,0.0330407826027,0.033225920842,0.0315896318646,0.033042242234,0.0301520876625,0.0319555833673,0.0334023012939,0.0377340670082,0.0307024110018,0.0420758922392,0.0355634528662,0.0393590525089,0.0483809985054,0.0572467370009,0.0695098731037,0.0740175147536,0.0796482636871,0.103450227591,0.135602321997,0.20585160038,0.32301462052,0.860942494007,5.39138096492,8.24408193806,9.30286916562,9.59857350806,9.63868448247,9.37501885306,9.04877009026,8.67838577615,8.15266049681,7.69095487447,7.25237612696,6.7813927958,6.36440811776,5.97222946816,5.5740620941,5.09686666484,4.78844542969,4.45125906081,4.10101303621,3.78401887461,3.59551732147,3.24648829811,3.02133575129,2.79797982181,2.56124458123,2.38919526732,2.21814343227,2.03509643247,1.92480376946,1.75344383283,1.60208585076,1.47863994618,1.34684988056,1.2684734602,1.15215895181,1.07270263541,0.997024799753,0.916187803145,0.850068819003,0.781638687886,0.695307469857,0.655451064373,0.607071413812,0.561545169007,0.516228818403,0.478298047792,0.459375604144,0.401015002907,0.39055803529,0.348121125025,0.3154412141,0.303343143419,0.272842126866,0.25098828925,0.242864960024,0.197878855801,0.188877899224,0.173331132023,0.16684332152,0.155117129445,0.130908625506,0.11953520974,0.117909242166,0.103646565323,0.0942557447103,0.0830550969053,0.0814388730433,0.0688014320338,0.0642786163811,0.0622878180235,0.0586837997943,0.0480187095603,0.049113548524,0.0433391087162,0.0408064752179,0.0339427268698,0.0339442519727,0.0315974075782,0.0310651466941,0.019497626933,0.0231147664383,0.0193229602962,0.0211233711339,0.0185970806768,0.0126373524303,0.0135410990916,0.0097496626715,0.0106510793147,0.00975099135956,0.0101098411208,0.00794428754715,0.00541555538512,0.00523548541684,0.00613883154614,0.00433405721987,0.00379327655683,0.00379211770976,0.00360996381942,0.00379246355379,0.00288942514094,0.00324885297774,0.00288929342751,0.00162540528907,0.00162530823708,0.00144453233104,0.00162545805147,0.00144435016599,0.00180611341276,0.00162595216938,0.000903742039831,0.000902174958171,0.00126351109913,0.000542116283388,0.00162293315902,0.000541613692686,0.00108366257885,0.000902798478743,0.000721144098082,0.000361716993024,0.000180614037165,0.000722044910075,0.000541377224723,0.000181235555077,0.000180221323189,0.0,0.000180752990977,0.000180269772162,0.000180822005731]) # Creating a new Canvas fig = plt.figure(figsize=(12,6),dpi=80) frame = gridspec.GridSpec(1,1,right=0.7) pad = fig.add_subplot(frame[0]) # Creating a new Stack pad.hist(x=xData, bins=xBinning, weights=y8_M_0_weights+y8_M_1_weights+y8_M_2_weights+y8_M_3_weights+y8_M_4_weights+y8_M_5_weights+y8_M_6_weights+y8_M_7_weights+y8_M_8_weights+y8_M_9_weights+y8_M_10_weights+y8_M_11_weights+y8_M_12_weights+y8_M_13_weights+y8_M_14_weights+y8_M_15_weights+y8_M_16_weights,\ label="$bg\_dip\_1600\_inf$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#e5e5e5", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y8_M_0_weights+y8_M_1_weights+y8_M_2_weights+y8_M_3_weights+y8_M_4_weights+y8_M_5_weights+y8_M_6_weights+y8_M_7_weights+y8_M_8_weights+y8_M_9_weights+y8_M_10_weights+y8_M_11_weights+y8_M_12_weights+y8_M_13_weights+y8_M_14_weights+y8_M_15_weights,\ label="$bg\_dip\_1200\_1600$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#f2f2f2", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y8_M_0_weights+y8_M_1_weights+y8_M_2_weights+y8_M_3_weights+y8_M_4_weights+y8_M_5_weights+y8_M_6_weights+y8_M_7_weights+y8_M_8_weights+y8_M_9_weights+y8_M_10_weights+y8_M_11_weights+y8_M_12_weights+y8_M_13_weights+y8_M_14_weights,\ label="$bg\_dip\_800\_1200$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#ccc6aa", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y8_M_0_weights+y8_M_1_weights+y8_M_2_weights+y8_M_3_weights+y8_M_4_weights+y8_M_5_weights+y8_M_6_weights+y8_M_7_weights+y8_M_8_weights+y8_M_9_weights+y8_M_10_weights+y8_M_11_weights+y8_M_12_weights+y8_M_13_weights,\ label="$bg\_dip\_600\_800$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#ccc6aa", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y8_M_0_weights+y8_M_1_weights+y8_M_2_weights+y8_M_3_weights+y8_M_4_weights+y8_M_5_weights+y8_M_6_weights+y8_M_7_weights+y8_M_8_weights+y8_M_9_weights+y8_M_10_weights+y8_M_11_weights+y8_M_12_weights,\ label="$bg\_dip\_400\_600$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#c1bfa8", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y8_M_0_weights+y8_M_1_weights+y8_M_2_weights+y8_M_3_weights+y8_M_4_weights+y8_M_5_weights+y8_M_6_weights+y8_M_7_weights+y8_M_8_weights+y8_M_9_weights+y8_M_10_weights+y8_M_11_weights,\ label="$bg\_dip\_200\_400$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#bab5a3", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y8_M_0_weights+y8_M_1_weights+y8_M_2_weights+y8_M_3_weights+y8_M_4_weights+y8_M_5_weights+y8_M_6_weights+y8_M_7_weights+y8_M_8_weights+y8_M_9_weights+y8_M_10_weights,\ label="$bg\_dip\_100\_200$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#b2a596", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y8_M_0_weights+y8_M_1_weights+y8_M_2_weights+y8_M_3_weights+y8_M_4_weights+y8_M_5_weights+y8_M_6_weights+y8_M_7_weights+y8_M_8_weights+y8_M_9_weights,\ label="$bg\_dip\_0\_100$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#b7a39b", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y8_M_0_weights+y8_M_1_weights+y8_M_2_weights+y8_M_3_weights+y8_M_4_weights+y8_M_5_weights+y8_M_6_weights+y8_M_7_weights+y8_M_8_weights,\ label="$bg\_vbf\_1600\_inf$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#ad998c", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y8_M_0_weights+y8_M_1_weights+y8_M_2_weights+y8_M_3_weights+y8_M_4_weights+y8_M_5_weights+y8_M_6_weights+y8_M_7_weights,\ label="$bg\_vbf\_1200\_1600$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#9b8e82", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y8_M_0_weights+y8_M_1_weights+y8_M_2_weights+y8_M_3_weights+y8_M_4_weights+y8_M_5_weights+y8_M_6_weights,\ label="$bg\_vbf\_800\_1200$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#876656", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y8_M_0_weights+y8_M_1_weights+y8_M_2_weights+y8_M_3_weights+y8_M_4_weights+y8_M_5_weights,\ label="$bg\_vbf\_600\_800$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#afcec6", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y8_M_0_weights+y8_M_1_weights+y8_M_2_weights+y8_M_3_weights+y8_M_4_weights,\ label="$bg\_vbf\_400\_600$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#84c1a3", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y8_M_0_weights+y8_M_1_weights+y8_M_2_weights+y8_M_3_weights,\ label="$bg\_vbf\_200\_400$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#89a8a0", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y8_M_0_weights+y8_M_1_weights+y8_M_2_weights,\ label="$bg\_vbf\_100\_200$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#829e8c", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y8_M_0_weights+y8_M_1_weights,\ label="$bg\_vbf\_0\_100$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#adbcc6", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y8_M_0_weights,\ label="$signal$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#7a8e99", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") # Axis plt.rc('text',usetex=False) plt.xlabel(r"M [ j_{1} , j_{2} ] ( GeV ) ",\ fontsize=16,color="black") plt.ylabel(r"$\mathrm{Events}$ $(\mathcal{L}_{\mathrm{int}} = 40.0\ \mathrm{fb}^{-1})$ ",\ fontsize=16,color="black") # Boundary of y-axis ymax=(y8_M_0_weights+y8_M_1_weights+y8_M_2_weights+y8_M_3_weights+y8_M_4_weights+y8_M_5_weights+y8_M_6_weights+y8_M_7_weights+y8_M_8_weights+y8_M_9_weights+y8_M_10_weights+y8_M_11_weights+y8_M_12_weights+y8_M_13_weights+y8_M_14_weights+y8_M_15_weights+y8_M_16_weights).max()*1.1 ymin=0 # linear scale #ymin=min([x for x in (y8_M_0_weights+y8_M_1_weights+y8_M_2_weights+y8_M_3_weights+y8_M_4_weights+y8_M_5_weights+y8_M_6_weights+y8_M_7_weights+y8_M_8_weights+y8_M_9_weights+y8_M_10_weights+y8_M_11_weights+y8_M_12_weights+y8_M_13_weights+y8_M_14_weights+y8_M_15_weights+y8_M_16_weights) if x])/100. # log scale plt.gca().set_ylim(ymin,ymax) # Log/Linear scale for X-axis plt.gca().set_xscale("linear") #plt.gca().set_xscale("log",nonposx="clip") # Log/Linear scale for Y-axis plt.gca().set_yscale("linear") #plt.gca().set_yscale("log",nonposy="clip") # Legend plt.legend(bbox_to_anchor=(1.05,1), loc=2, borderaxespad=0.) # Saving the image plt.savefig('../../HTML/MadAnalysis5job_0/selection_7.png') plt.savefig('../../PDF/MadAnalysis5job_0/selection_7.png') plt.savefig('../../DVI/MadAnalysis5job_0/selection_7.eps') # Running! if __name__ == '__main__': selection_7()

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null

0

4

a0ad6e56e90608421833f0eb9a2f2deb7aece724

176

py

Python

Linked_Lists/Reverse_a_doubly_lined_list.py

NikolayVaklinov10/Interview_Preparation_Kit

517c4c7e83a7bcc99a4f570dff6959b5229b1a29

[ "MIT" ]

null

Linked_Lists/Reverse_a_doubly_lined_list.py

NikolayVaklinov10/Interview_Preparation_Kit

517c4c7e83a7bcc99a4f570dff6959b5229b1a29

[ "MIT" ]

null

Linked_Lists/Reverse_a_doubly_lined_list.py

NikolayVaklinov10/Interview_Preparation_Kit

517c4c7e83a7bcc99a4f570dff6959b5229b1a29

[ "MIT" ]

null

def Reverse(head): if not head: return head head.next, head.prev = head.prev, head.next if not head.prev: return head return Reverse(head.prev)

22

47

0.619318

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1

0

null

0

1

0

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a0b1807059bab4d3e653994341175fe3e5efa35d

1,850

py

Python

smp_manifold_learning/data/synthetic/synthetic_noisy_quaternion_dataset_generator.py

gsutanto/smp_manifold_learning

60ef8278942c784c8d3bcd0a09031475f80d96fb

[ "MIT" ]

11

2020-09-26T12:13:01.000Z

2022-03-23T07:34:14.000Z

smp_manifold_learning/data/synthetic/synthetic_noisy_quaternion_dataset_generator.py

gsutanto/smp_manifold_learning

60ef8278942c784c8d3bcd0a09031475f80d96fb

[ "MIT" ]

1

2021-04-10T10:42:28.000Z

2021-04-16T07:04:26.000Z

smp_manifold_learning/data/synthetic/synthetic_noisy_quaternion_dataset_generator.py

gsutanto/smp_manifold_learning

60ef8278942c784c8d3bcd0a09031475f80d96fb

[ "MIT" ]

5

2020-09-24T18:52:46.000Z

2022-03-23T07:26:15.000Z

import numpy as np def generate_synth_noisy_quat_dataset(N_data=1000000, rand_seed=38, sampling_magnitude=50000.0, dataset_save_path_prefix_name='quaternion_random'): """ Generate a (noisy) dataset on a unit quaternion: """ np.random.seed(rand_seed) random_4d_dataset = np.random.uniform(low=-sampling_magnitude, high=sampling_magnitude, size=(N_data, 4)) random_quaternion_dataset = (random_4d_dataset/ np.expand_dims( np.linalg.norm(random_4d_dataset, axis=1), axis=1)) norm_random_quaternion_dataset = np.linalg.norm(random_quaternion_dataset, axis=1) np.save(dataset_save_path_prefix_name+'.npy', random_quaternion_dataset) noisy_random_quaternion_dataset = (random_quaternion_dataset * np.expand_dims( np.fabs(np.random.normal(1.0, 0.5, N_data)), axis=1)) norm_noisy_random_quaternion_dataset = np.linalg.norm( noisy_random_quaternion_dataset, axis=1) np.save(dataset_save_path_prefix_name+'_noisy.npy', noisy_random_quaternion_dataset) np.save(dataset_save_path_prefix_name+'_diff.npy', random_quaternion_dataset - noisy_random_quaternion_dataset) return None if __name__ == '__main__': generate_synth_noisy_quat_dataset()

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260684df0c67042c1c9c18118063a6f29765e982

238

py

Python

programme/tasks.py

darkismus/kompassi

35dea2c7af2857a69cae5c5982b48f01ba56da1f

[ "CC-BY-3.0" ]

13

2015-11-29T12:19:12.000Z

2021-02-21T15:42:11.000Z

programme/tasks.py

darkismus/kompassi

35dea2c7af2857a69cae5c5982b48f01ba56da1f

[ "CC-BY-3.0" ]

23

2015-04-29T19:43:34.000Z

2021-02-10T05:50:17.000Z

programme/tasks.py

darkismus/kompassi

35dea2c7af2857a69cae5c5982b48f01ba56da1f

[ "CC-BY-3.0" ]

11

2015-09-20T18:59:00.000Z

2020-02-07T08:47:34.000Z

from celery import shared_task @shared_task(ignore_result=True) def programme_apply_state_async(programme_pk): from .models import Programme programme = Programme.objects.get(pk=programme_pk) programme._apply_state_async()

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1

0

null

0

1

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4

26530bf4dc728709bda11542b41eb6b09fba9d2c

58

py

Python

view/modes/__init__.py

jlchamaa/wf-cli

bd4271ffa457fa9fbbbdfd7f908f8580b0a7283d

[ "MIT" ]

null

view/modes/__init__.py

jlchamaa/wf-cli

bd4271ffa457fa9fbbbdfd7f908f8580b0a7283d

[ "MIT" ]

null

view/modes/__init__.py

jlchamaa/wf-cli

bd4271ffa457fa9fbbbdfd7f908f8580b0a7283d

[ "MIT" ]

null

from .normal import NormalMode from .edit import EditMode

19.333333

30

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null

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null

0

1

0

1

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4

26653179eb20a92cd71527de72ec803a6e29d370

72

py

Python

mysign_app/routes/__init__.py

mindhashnl/roomsignage

7508a14f0d350bad874f69ae75044baa87267cbe

[ "MIT" ]

null

mysign_app/routes/__init__.py

mindhashnl/roomsignage

7508a14f0d350bad874f69ae75044baa87267cbe

[ "MIT" ]

11

2020-06-06T00:27:01.000Z

2022-02-10T09:38:26.000Z

mysign_app/routes/__init__.py

mindhashnl/roomsignage

7508a14f0d350bad874f69ae75044baa87267cbe

[ "MIT" ]

null

from .screen import index as screen_index __all__ = ['screen_index', ]

18

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4.8

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null

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1

0

null

0

1

0

4

2677ca5a2aa26f2fca3bc248b22e0ae047142210

19

py

Python

boneless/__init__.py

jakeelkins/boneless

7b94c1ed15687ea219fad1c1bbab0a956e1cec44

[ "MIT" ]

2

2019-09-09T18:38:11.000Z

2019-09-10T01:36:05.000Z

boneless/__init__.py

jakeelkins/boneless

7b94c1ed15687ea219fad1c1bbab0a956e1cec44

[ "MIT" ]

null

boneless/__init__.py

jakeelkins/boneless

7b94c1ed15687ea219fad1c1bbab0a956e1cec44

[ "MIT" ]

null

name = 'boneless'

9.5

18

0.631579

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4.333333

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0.75

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null

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1

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null

0

1

0

4

26785d0d535b6a776418ab7c4cee741f7d86ee16

92

py

Python

cms_perf/__main__.py

maxfischer2781/cms_perf

193128dbf46d334485a02bdeec2638764afae4c7

[ "MIT" ]

null

cms_perf/__main__.py

maxfischer2781/cms_perf

193128dbf46d334485a02bdeec2638764afae4c7

[ "MIT" ]

8

2020-07-14T14:01:27.000Z

2020-08-24T15:25:04.000Z

cms_perf/__main__.py

maxfischer2781/cms_perf

193128dbf46d334485a02bdeec2638764afae4c7

[ "MIT" ]

null

"""This is executed by `python -m cms_perf` and similar""" from .report import main main()

18.4

58

0.706522

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92

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null

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null

0

1

0

1

0

4

cd0276b8bc3e4d504a5bf937baf723ce3104465d

598

py

Python

tests/unit/assets/fund.py

amaas-fintech/amaas-core-sdk-python

bd77884de6e5ab05d864638addeb4bb338a51183

[ "Apache-2.0" ]

null

tests/unit/assets/fund.py

amaas-fintech/amaas-core-sdk-python

bd77884de6e5ab05d864638addeb4bb338a51183

[ "Apache-2.0" ]

8

2017-06-06T09:42:41.000Z

2018-01-16T10:16:16.000Z

tests/unit/assets/fund.py

amaas-fintech/amaas-core-sdk-python

bd77884de6e5ab05d864638addeb4bb338a51183

[ "Apache-2.0" ]

8

2017-01-18T04:14:01.000Z

2017-12-01T08:03:10.000Z

from __future__ import absolute_import, division, print_function, unicode_literals from decimal import Decimal import unittest from amaascore.assets.fund import Fund from amaascore.tools.generate_asset import generate_fund class FundTest(unittest.TestCase): def setUp(self): self.longMessage = True # Print complete error message on failure self.fund = generate_fund() self.asset_id = self.fund.asset_id def tearDown(self): pass def test_Fund(self): self.assertEqual(type(self.fund), Fund) if __name__ == '__main__': unittest.main()

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0

1

0

1

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4

cd48d49ce60fc7f8ae1188a53304e6b1649236b6

186

py

Python

venv/bin/django-admin.py

Maxcutex/waitressappv2

ecc237015f152c61208fc1355195afe183a6e7bb

[ "MIT" ]

null

venv/bin/django-admin.py

Maxcutex/waitressappv2

ecc237015f152c61208fc1355195afe183a6e7bb

[ "MIT" ]

null

venv/bin/django-admin.py

Maxcutex/waitressappv2

ecc237015f152c61208fc1355195afe183a6e7bb

[ "MIT" ]

null

#!/Users/andeladeveloper/Projects/Python/AndelaEats/waitress/venv/bin/python2.7 from django.core import management if __name__ == "__main__": management.execute_from_command_line()

31

79

0.806452

23

186

6.043478

0.913043

0

0.011696

0.080645

186

5

80

37.2

0.80117

0.419355

0

0.074766

0

1

0

true

0

0.333333

0

0.333333

0

1

0

null

0

1

0

null

0

1

0

1

0

4

cd651230f141a4db606dbb166e86752e274cee10

157

py

Python

frequentlyasked/urls.py

Pesenin-Team/pesenin-2.0

883468e6b6d7e3a24bc2ee60bbc7063117745424

[ "MIT" ]

null

frequentlyasked/urls.py

Pesenin-Team/pesenin-2.0

883468e6b6d7e3a24bc2ee60bbc7063117745424

[ "MIT" ]

null

frequentlyasked/urls.py

Pesenin-Team/pesenin-2.0

883468e6b6d7e3a24bc2ee60bbc7063117745424

[ "MIT" ]

null

from django.urls import path from . import views app_name = 'frequentlyasked' urlpatterns = [ path('', views.frequentlyasked, name='frequentlyasked') ]

19.625

59

0.738854

17

157

6.764706

0.588235

0.330435

0

0.146497

157

8

60

19.625

0.858209

0

0.189873

0

1

0

false

0

0.333333

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0.333333

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null

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1

0

null

0

1

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4

cd7003a472e195bbd5ef3165324086a2be4ccb89

298

py

Python

pagral/graph/attribute.py

mazzalab/pagral

c824ca453591a135716b59958d4f8b5f985b77cb

[ "MIT" ]

null

pagral/graph/attribute.py

mazzalab/pagral

c824ca453591a135716b59958d4f8b5f985b77cb

[ "MIT" ]

null

pagral/graph/attribute.py

mazzalab/pagral

c824ca453591a135716b59958d4f8b5f985b77cb

[ "MIT" ]

null

class Attribute: def __init__(self): self.__attr = {} def __getitem__(self, attr_key): return self.__attr[attr_key] def __setitem__(self, attr_key, attr_value): self.__attr[attr_key] = attr_value def get_attributes_names(self): self.__attr.keys()

22.923077

48

0.651007

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4.368421

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0.289157

0.144578

0.180723

0

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298

12

49

24.833333

0.737778

0

1

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false

0

0.111111

0.666667

0

null

1

0

1

0

null

0

1

0

1

0

4

cd7aae402e42c02dc864055bc9334ee038fdb42b

217

py

Python

Zad_Strategia/PodatekPolska.py

Paarzivall/Wzorce-Projektowe

aa4136f140ad02c0fc0de45709b5a01ca42b417f

[ "MIT" ]

null

Zad_Strategia/PodatekPolska.py

Paarzivall/Wzorce-Projektowe

aa4136f140ad02c0fc0de45709b5a01ca42b417f

[ "MIT" ]

null

Zad_Strategia/PodatekPolska.py

Paarzivall/Wzorce-Projektowe

aa4136f140ad02c0fc0de45709b5a01ca42b417f

[ "MIT" ]

null

from ObliczPodatek import ObliczPodatek class PodatekPolska(ObliczPodatek): def __init__(self): self.VAT = 0.23 def kwotaPodatku(self, ilosc, cena): return ilosc * (cena + (cena * self.VAT))

24.111111

49

0.672811

25

217

5.68

0.6

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0.017857

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217

9

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24.111111

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null

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null

0

1

0

1

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4

cd8807692e564a264a96e5af10e5127f8cbb1935

108

py

Python

py_tdlib/constructors/supergroup_members_filter_restricted.py

Mr-TelegramBot/python-tdlib

2e2d21a742ebcd439971a32357f2d0abd0ce61eb

[ "MIT" ]

24

2018-10-05T13:04:30.000Z

2020-05-12T08:45:34.000Z

py_tdlib/constructors/supergroup_members_filter_restricted.py

MrMahdi313/python-tdlib

2e2d21a742ebcd439971a32357f2d0abd0ce61eb

[ "MIT" ]

3

2019-06-26T07:20:20.000Z

2021-05-24T13:06:56.000Z

py_tdlib/constructors/supergroup_members_filter_restricted.py

MrMahdi313/python-tdlib

2e2d21a742ebcd439971a32357f2d0abd0ce61eb

[ "MIT" ]

5

2018-10-05T14:29:28.000Z

2020-08-11T15:04:10.000Z

from ..factory import Type class supergroupMembersFilterRestricted(Type): query = None # type: "string"

18

46

0.759259

11

108

7.454545

0.818182

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108

5

47

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false

0

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1

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null

0

1

0

1

0

4

26a5c7fb940145c44e6ed3c54956b00c82b7966f

274

py

Python

Ekeopara_Praise/Phase 1/Python Basic 1/Day10 Tasks/Task7.py

CodedLadiesInnovateTech/-python-challenge-solutions

430cd3eb84a2905a286819eef384ee484d8eb9e7

[ "MIT" ]

6

2020-05-23T19:53:25.000Z

2021-05-08T20:21:30.000Z

Ekeopara_Praise/Phase 1/Python Basic 1/Day10 Tasks/Task7.py

CodedLadiesInnovateTech/-python-challenge-solutions

430cd3eb84a2905a286819eef384ee484d8eb9e7

[ "MIT" ]

8

2020-05-14T18:53:12.000Z

2020-07-03T00:06:20.000Z

Ekeopara_Praise/Phase 1/Python Basic 1/Day10 Tasks/Task7.py

CodedLadiesInnovateTech/-python-challenge-solutions

430cd3eb84a2905a286819eef384ee484d8eb9e7

[ "MIT" ]

39

2020-05-10T20:55:02.000Z

2020-09-12T17:40:59.000Z

'''7. Write a Python program to get the size of a file.''' import os file_size = os.path.getsize('c:/Users/Sir_Praise/Documents/python-challenges-solutions/Ekeopara_Praise/Phase 1/Python Basic 1/Day10 Tasks/Task3.py') print('\nThe size of abc.txt is : ', file_size, 'Bytes')

68.5

148

0.751825

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274

4.208333

0.75

0.059406

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0.02449

0.105839

274

4

149

68.5

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0.189781

0

0.333333

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false

0

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null

0

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null

0

1

0

4

26aefa271f54640c4c088758474d573cb1eabc66

296

py

Python

db/default_data/dados_buddies.py

LeandroLFE/capmon

9d1200301628ea4fec0e8ed09d5e9b67a426d923

[ "MIT" ]

null

db/default_data/dados_buddies.py

LeandroLFE/capmon

9d1200301628ea4fec0e8ed09d5e9b67a426d923

[ "MIT" ]

null

db/default_data/dados_buddies.py

LeandroLFE/capmon

9d1200301628ea4fec0e8ed09d5e9b67a426d923

[ "MIT" ]

null

from db.default_data.default_create_table_with_underline_structure import default_create_table_structure_buddies script_create_table_buddies = lambda dados = {} : f""" DROP TABLE IF EXISTS Buddies; CREATE TABLE Buddies ( {default_create_table_structure_buddies()} ); """

32.888889

112

0.763514

36

296

5.805556

0.5

0.263158

0.258373

0.325359

0

0.165541

296

9

113

32.888889

0.846154

0

0.420875

0.141414

0

1

0

false

0

0.142857

0

0.142857

0

null

1

0

1

0

null

0

4

26c3026bfbcfcf7ef95bbb2b496e25f6e33094a8

305

py

Python

app/api/v1/api.py

VangelisTsiatouras/311-chicago-incidents-nosql

f03ba8c3bacfc66a29ba8cfac7f7db537b22d1f3

[ "MIT" ]

2

2020-12-31T16:55:20.000Z

2021-01-10T21:02:03.000Z

app/api/v1/api.py

VangelisTsiatouras/311-chicago-incidents-nosql

f03ba8c3bacfc66a29ba8cfac7f7db537b22d1f3

[ "MIT" ]

null

app/api/v1/api.py

VangelisTsiatouras/311-chicago-incidents-nosql

f03ba8c3bacfc66a29ba8cfac7f7db537b22d1f3

[ "MIT" ]

null

from fastapi import APIRouter from app.api.v1.endpoints import queries, incidents, citizens api_router = APIRouter() api_router.include_router(queries.router, tags=['queries']) api_router.include_router(incidents.router, tags=['incidents']) api_router.include_router(citizens.router, tags=['citizens'])

33.888889

63

0.806557

40

305

5.975

0.35

0.150628

0.200837

0.276151

0

0.003521

0.068852

305

8

64

38.125

0.838028

0

0.078689

0

1

0

false

0

0.333333

0

0.333333

0

null

0

1

0

1

0

null

0

1

0

4

26e0467192d5959f8fe801cc55601b0aab877f08

212

py

Python

neural-architecture-search/utils.py

volodymyrlut/masters-project

d725f5d0f4d623e024bd0835f1c611b719fef93f

[ "MIT" ]

null

neural-architecture-search/utils.py

volodymyrlut/masters-project

d725f5d0f4d623e024bd0835f1c611b719fef93f

[ "MIT" ]

8

2020-09-26T01:06:18.000Z

2022-03-12T00:30:45.000Z

neural-architecture-search/utils.py

volodymyrlut/masters-project

d725f5d0f4d623e024bd0835f1c611b719fef93f

[ "MIT" ]

null

import hashlib import json from datetime import datetime def timestamp(): now = datetime.now() timestamp = datetime.timestamp(now) return timestamp def calculate_hash(cell): return hash(json.dumps(cell))

19.272727

36

0.768868

28

212

5.785714

0.464286

0.148148

0

0.141509

212

11

37

19.272727

0.89011

0

1

0.222222

false

0

0.333333

0.111111

0.777778

0

1

0

null

0

null

0

1

0

1

0

4

26fdd9b049e69b6d399a686bc86dbf96ff862862

1,000

py

Python

src/015-lattice-paths/python/test/test_solver.py

xfbs/ProjectEulerRust

e26768c56ff87b029cb2a02f56dc5cd32e1f7c87

[ "MIT" ]

1

2018-01-26T21:18:12.000Z

src/015-lattice-paths/python/test/test_solver.py

xfbs/ProjectEulerRust

e26768c56ff87b029cb2a02f56dc5cd32e1f7c87

[ "MIT" ]

3

2017-12-09T14:49:30.000Z

2017-12-09T14:59:39.000Z

src/015-lattice-paths/python/test/test_solver.py

xfbs/ProjectEulerRust

e26768c56ff87b029cb2a02f56dc5cd32e1f7c87

[ "MIT" ]

null

import unittest import solver class TestSolution(unittest.TestCase): def test_solve(self): self.assertEqual(solver.solve(4), 70) def test_collatz(self): c = solver.LatticePaths(12, 12) self.assertEqual(c.count(0, 1), 1) self.assertEqual(c.count(0, 5), 1) self.assertEqual(c.count(9, 0), 1) self.assertEqual(c.count(7, 0), 1) self.assertEqual(c.count(2, 0), 1) self.assertEqual(c.count(1, 1), 2) self.assertEqual(c.count(1, 5), 6) self.assertEqual(c.count(9, 1), 10) self.assertEqual(c.count(7, 1), 8) self.assertEqual(c.count(2, 1), 3) self.assertEqual(c.count(2, 2), 6) self.assertEqual(c.count(2, 3), 10) self.assertEqual(c.count(3, 2), 10) self.assertEqual(c.count(5, 3), 56) self.assertEqual(c.count(4, 4), 70) self.assertEqual(c.count(4, 6), 210) self.assertEqual(c.count(5, 5), 252) self.assertEqual(c.count(8, 4), 495) self.assertEqual(c.count(5, 7), 792)

31.25

43

0.616

160

1,000

3.8375

0.20625

0.488599

0.495114

0.649837

0.669381

0.112378

0

0.097468

0.21

1,000

31

44

32.258065

0.679747

0

0.769231

1

0.076923

false

0

0.076923

0

0.192308

0

null

1

0

null

0

1

0

4

f8446517c6a1ce85a2ff418ae078cfdac7da8c1b

56

py

Python

pathfinder/__init__.py

MichaelCurrin/path-finder

fd79ef00fb1f71007ab79d76cf651e9157f84fbb

[ "MIT" ]

1

2019-08-16T03:13:41.000Z

pathfinder/__init__.py

MichaelCurrin/path-finder

fd79ef00fb1f71007ab79d76cf651e9157f84fbb

[ "MIT" ]

3

2018-01-18T11:35:01.000Z

2018-01-18T11:35:19.000Z

pathfinder/__init__.py

MichaelCurrin/path-finder

fd79ef00fb1f71007ab79d76cf651e9157f84fbb

[ "MIT" ]

null

"""Initialisation file for pathfinder app directory."""

28

55

0.767857

6

56

7.166667

1

0

0.107143

56

1

56

0.86

0.875

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

4

f84836e1a1238ef0226dc690ff5d94f11790af01

371

py

Python

src/dictionaries.py

getsadzeg/python-codes

cd31412e34648c8bdb52299a770c41bf95bc2bf8

[ "MIT" ]

2

2016-01-18T20:54:21.000Z

2016-09-29T18:33:38.000Z

src/dictionaries.py

getsadzeg/python-codes

cd31412e34648c8bdb52299a770c41bf95bc2bf8

[ "MIT" ]

null

src/dictionaries.py

getsadzeg/python-codes

cd31412e34648c8bdb52299a770c41bf95bc2bf8

[ "MIT" ]

null

personinfo = {'Name': 'John', 'Surname': 'Reese', 'Occupation': 'Killer'}; personinfo['Occupation'] = "Agent"; # Update personinfo['Employer'] = "Harold Finch"; # Add print personinfo['Name'] print "personinfo keys:", personinfo.keys(); print "personinfo values:", personinfo.values(); personinfo.clear(); print "personinfo's length after clearing:", len(personinfo);

33.727273

74

0.703504

39

371

6.692308

0.564103

0.229885

0.199234

0

0.105121

371

10

75

37.1

0.786145

0.02965

0

0.405634

0

null

0

null

0.5

0

null

1

0

null

0

1

0

1

0

4

f8804113ee4ec8984da421d69e845bbfc8c19f07

72

py

Python

decrement or increment.py

Tanuka-Mondal/Competi

b244ade867862b4e33e63dabd2cdd136340c0bf8

[ "MIT" ]

1

2021-09-08T05:36:48.000Z

decrement or increment.py

Tanuka-Mondal/Competi

b244ade867862b4e33e63dabd2cdd136340c0bf8

[ "MIT" ]

null

decrement or increment.py

Tanuka-Mondal/Competi

b244ade867862b4e33e63dabd2cdd136340c0bf8

[ "MIT" ]

null

n = int(input()) if(n%4 == 0): print(n+1) else: print(n-1)

10.285714

16

0.444444

14

72

2.285714

0.642857

0.375

0.4375

0

0.08

0.305556

72

6

17

12

0.56

0

1

0

false

0

0.4

1

0

null

1

0

1

0

null

0

4

f8b9ad3e904a1ce6ecd69e77772a514226e6e47b

40

py

Python

ploo/real_book_tunes.py

MRGRAVITY817/ploo

4cbb754a5600747f391c82732a1983d39de6bac9

[ "MIT" ]

null

ploo/real_book_tunes.py

MRGRAVITY817/ploo

4cbb754a5600747f391c82732a1983d39de6bac9

[ "MIT" ]

null

ploo/real_book_tunes.py

MRGRAVITY817/ploo

4cbb754a5600747f391c82732a1983d39de6bac9

[ "MIT" ]

null

import json alice_in_wonder_land = { }

8

24

0.75

6

40

4.5

1

0

0.175

40

5

25

8

0.818182

0

1

0

false

0

0.333333

0

0.333333

0

1

0

null

0

1

0

null

0

1

0

4

f8bb5c057a6d69df3d3303cc9fdea1b6d1cb80ba

1,350

py

Python

seven23/models/stats/migrations/0001_initial.py

niwo/seven23_server

f97c26e38908a6a7900024d1ea8af3422858ed30

[ "MIT" ]

null

seven23/models/stats/migrations/0001_initial.py

niwo/seven23_server

f97c26e38908a6a7900024d1ea8af3422858ed30

[ "MIT" ]

1

2019-07-30T08:23:15.000Z

2019-07-30T17:02:49.000Z

seven23/models/stats/migrations/0001_initial.py

niwo/seven23_server

f97c26e38908a6a7900024d1ea8af3422858ed30

[ "MIT" ]

null

# Generated by Django 2.1 on 2019-03-09 06:20 from django.db import migrations, models class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='DailyActiveUser', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('year', models.IntegerField(default=2019, editable=False, verbose_name='Year')), ('month', models.IntegerField(default=3, editable=False, verbose_name='Month')), ('day', models.IntegerField(default=9, editable=False, verbose_name='Day')), ('counter', models.IntegerField(default=0, editable=False, verbose_name='Counter')), ], ), migrations.CreateModel( name='MonthlyActiveUser', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('year', models.IntegerField(default=2019, editable=False, verbose_name='Year')), ('month', models.IntegerField(default=3, editable=False, verbose_name='Month')), ('counter', models.IntegerField(default=0, editable=False, verbose_name='Counter')), ], ), ]

39.705882

114

0.601481

133

1,350

6.007519

0.345865

0.135169

0.180225

0.210263

0.660826

0

0.026946

0.257778

1,350

33

115

40.909091

0.770459

0.031852

0

0.615385

1

0

0.084291

0

1

0

false

0

0.038462

0

0.192308

0

null

0

1

0

1

0

1

0

null

0

4

f8bb81be402cb2ebf693b83f8031b0d7cc48d6fd

296

py

Python

Incident-Response/Tools/dfirtrack/dfirtrack_artifacts/admin.py

sn0b4ll/Incident-Playbook

cf519f58fcd4255674662b3620ea97c1091c1efb

[ "MIT" ]

1

2021-07-24T17:22:50.000Z

Incident-Response/Tools/dfirtrack/dfirtrack_artifacts/admin.py

sn0b4ll/Incident-Playbook

cf519f58fcd4255674662b3620ea97c1091c1efb

[ "MIT" ]

2

2022-02-28T03:40:31.000Z

2022-02-28T03:40:52.000Z

Incident-Response/Tools/dfirtrack/dfirtrack_artifacts/admin.py

sn0b4ll/Incident-Playbook

cf519f58fcd4255674662b3620ea97c1091c1efb

[ "MIT" ]

2

2022-02-25T08:34:51.000Z

2022-03-16T17:29:44.000Z

from django.contrib import admin from dfirtrack_artifacts.models import Artifact, Artifactpriority, Artifactstatus, Artifacttype # Register your models here. admin.site.register(Artifact) admin.site.register(Artifactpriority) admin.site.register(Artifactstatus) admin.site.register(Artifacttype)

37

95

0.85473

34

296

7.411765

0.470588

0.142857

0.269841

0

0.067568

296

7

96

42.285714

0.913043

0.087838

0

1

0

true

0

0.333333

0

0.333333

0

null

0

1

0

1

0

null

0

1

0

1

0

4

3e3363a28c884cd59d2992204ba2bbb19ce35376

54

py

Python

tests/models.py

JamesRitchie/django-rest-framework-session-endpoint

a968129be88a1981d9904c3679e5fdd9490e890d

[ "BSD-2-Clause" ]

21

2015-03-04T09:25:47.000Z

2019-11-08T14:19:24.000Z

tests/models.py

JamesRitchie/django-rest-framework-session-endpoint

a968129be88a1981d9904c3679e5fdd9490e890d

[ "BSD-2-Clause" ]

1

2015-03-04T09:26:17.000Z

2015-03-11T13:10:20.000Z

tests/models.py

JamesRitchie/django-rest-framework-session-endpoint

a968129be88a1981d9904c3679e5fdd9490e890d

[ "BSD-2-Clause" ]

1

2020-05-17T04:16:27.000Z

"""Blank file required to run tests on Django 1.4."""

27

53

0.685185

10

54

3.7

1

0

0.044444

0.166667

54

1

54

0.777778

0.87037

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

4

3e69d1eaf9701ceeff1155290dacd214bc48ac73

134

py

Python

CodeArena/hello.py

SaberSz/WeByte

3f88d572990b8342d2f28065fbb1d092449bf0ea

[ "MIT" ]

null

CodeArena/hello.py

SaberSz/WeByte

3f88d572990b8342d2f28065fbb1d092449bf0ea

[ "MIT" ]

null

CodeArena/hello.py

SaberSz/WeByte

3f88d572990b8342d2f28065fbb1d092449bf0ea

[ "MIT" ]

null

a = input().split() print(a) for i in a: print(i) print("\nlook heres the output\n") b = input().split() for i in b: print(i)

14.888889

34

0.589552

26

134

3.038462

0.5

0.253165

0.151899

0

0.216418

134

8

35

16.75

0.752381

0

0.25

0

0.186567

0

1

0

false

0

0.5

1

0

null

1

0

1

0

null

0

1

0

4

e44937e13d8b45578c75da5bfa7eee47e5fcbb88

91

py

Python

agro_site/agroblog/apps.py

LukoninDmitryPy/agro_site-2

eab7694d42104774e5ce6db05a79f11215db6ae3

[ "MIT" ]

null

agro_site/agroblog/apps.py

LukoninDmitryPy/agro_site-2

eab7694d42104774e5ce6db05a79f11215db6ae3

[ "MIT" ]

null

agro_site/agroblog/apps.py

LukoninDmitryPy/agro_site-2

eab7694d42104774e5ce6db05a79f11215db6ae3

[ "MIT" ]

1

2022-03-13T11:32:48.000Z

from django.apps import AppConfig class AgroblogConfig(AppConfig): name = 'agroblog'

15.166667

33

0.758242

10

91

6.9

0.9

0

0.164835

91

5

34

18.2

0.907895

0

0.087912

0

1

0

false

0

0.333333

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

4

e4727f0e6870efbd102956d5a4279b733fe867a0

225

py

Python

example/example/routes.py

yashpokar/mvc

f524973739bfd63a85dfa06bdfc7fd62472c19dc

[ "MIT" ]

null

example/example/routes.py

yashpokar/mvc

f524973739bfd63a85dfa06bdfc7fd62472c19dc

[ "MIT" ]

null

example/example/routes.py

yashpokar/mvc

f524973739bfd63a85dfa06bdfc7fd62472c19dc

[ "MIT" ]

null

from mvc.router import Router Router.get('/', 'HomeController@index') Router.get('/profile/<username>', 'ProfileController@show') Router.get('/contact', 'Contact@index') Router.get('/auth/signup', 'RegisterController@form')

32.142857

59

0.742222

26

225

6.423077

0.615385

0.215569

0.167665

0

0.057778

225

6

60

37.5

0.787736

0

0.524444

0.2

0

1

0

true

0

0.2

0

0.2

0

1

0

null

1

0

1

0

null

0

1

0

4

e4bdbeef9824e566949f7f6d4bd53af264666231

409

py

Python

bandits/normal.py

XiaoMutt/ucbc

f8aeb65dc5a11ecd82fd969d120f3a848d61c064

[ "MIT" ]

null

bandits/normal.py

XiaoMutt/ucbc

f8aeb65dc5a11ecd82fd969d120f3a848d61c064

[ "MIT" ]

null

bandits/normal.py

XiaoMutt/ucbc

f8aeb65dc5a11ecd82fd969d120f3a848d61c064

[ "MIT" ]

null

from .basis import Bandit import numpy as np class NormalBandit(Bandit): def __init__(self, thetas: list): self.thetas = thetas self._narms = len(self.thetas) self._qstar = max(theta[0] for theta in self.thetas) def reward(self, action): return np.random.normal(*self.thetas[action]) def regret(self, action): return self.Qstar - self.thetas[action][0]

25.5625

60

0.655257

56

409

4.678571

0.5

0.229008

0.122137

0

0.006369

0.232274

409

15

61

27.266667

0.828025

0

1

0.272727

false

0

0.181818

0.727273

0

null

1

0

null

0

1

0

1

0

4

e4dea4f52d0e175b433830b3901e62afd1973263

180

py

Python

src/test/datascience/serverConfigFiles/remoteNoAuth.py

ChaseKnowlden/vscode-jupyter

9bdaf87f0b6dcd717c508e9023350499a6093f97

[ "MIT" ]

615

2020-11-11T22:55:28.000Z

2022-03-30T21:48:08.000Z

src/test/datascience/serverConfigFiles/remoteNoAuth.py

ChaseKnowlden/vscode-jupyter

9bdaf87f0b6dcd717c508e9023350499a6093f97

[ "MIT" ]

8,428

2020-11-11T22:06:43.000Z

2022-03-31T23:42:34.000Z

src/test/datascience/serverConfigFiles/remoteNoAuth.py

ChaseKnowlden/vscode-jupyter

9bdaf87f0b6dcd717c508e9023350499a6093f97

[ "MIT" ]

158

2020-11-12T07:49:02.000Z

2022-03-27T20:50:20.000Z

# With these settings you can connect to a server with no token and no password c.NotebookApp.token = '' c.NotebookApp.open_browser = False c.NotebookApp.disable_check_xsrf = True

45

79

0.794444

29

180

4.827586

0.758621

0.257143

0

0.138889

180

4

80

45

0.903226

0.427778

0

1

0

true

0

1

0

null

1

0

1

0

null

0

1

0

4

901d839400134a929cc48b1941f993e9eac06d6d

850

py

Python

specs/merge/suffix.py

ultratwo/eth2.0-specs

e4b5be67dd362619eb68e8173cf37183e7fe04e4

[ "CC0-1.0" ]

null

specs/merge/suffix.py

ultratwo/eth2.0-specs

e4b5be67dd362619eb68e8173cf37183e7fe04e4

[ "CC0-1.0" ]

null

specs/merge/suffix.py

ultratwo/eth2.0-specs

e4b5be67dd362619eb68e8173cf37183e7fe04e4

[ "CC0-1.0" ]

null

ExecutionState = Any def get_pow_block(hash: Bytes32) -> PowBlock: return PowBlock(block_hash=hash, is_valid=True, is_processed=True, total_difficulty=uint256(0), difficulty=uint256(0)) def get_execution_state(execution_state_root: Bytes32) -> ExecutionState: pass def get_pow_chain_head() -> PowBlock: pass class NoopExecutionEngine(ExecutionEngine): def on_payload(self, execution_payload: ExecutionPayload) -> bool: return True def set_head(self, block_hash: Hash32) -> bool: return True def finalize_block(self, block_hash: Hash32) -> bool: return True def assemble_block(self, block_hash: Hash32, timestamp: uint64, random: Bytes32) -> ExecutionPayload: raise NotImplementedError("no default block production") EXECUTION_ENGINE = NoopExecutionEngine()

26.5625

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0.721176

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850

6.030612

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0.071066

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0.190588

850

31

106

27.419355

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0.388889

false

0.111111

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0.222222

0.666667

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null

0

null

0

1

0

1

0

1

0

4

5f6de640ac9c7ffd73a9508d7679ae20329d3133

820

py

Python

informatiom/day07_macro.py

wangcan-code/information11

e4f494eefbb6f5365f941512ec6c68fb715c61c9

[ "MIT" ]

null

informatiom/day07_macro.py

wangcan-code/information11

e4f494eefbb6f5365f941512ec6c68fb715c61c9

[ "MIT" ]

null

informatiom/day07_macro.py

wangcan-code/information11

e4f494eefbb6f5365f941512ec6c68fb715c61c9

[ "MIT" ]

null

# !/usr/bin/env python # -*- coding: UTF-8 -*- from flask import Flask, render_template app = Flask(__name__) @app.route('/') def index(): return "hello python!!!" @app.route("/demo1") # 宏 def demo1(): my_srt="宏的使用！" return render_template('day07_macro.html',my_srt=my_srt) # 继承 @app.route('/demo2') def demo2(): my_srt="继承的使用！" return render_template('day08_extend.html',my_srt=my_srt) # 抽取模版 @app.route('/news_index') def demo3(): return render_template('index.html') # 抽取模版 @app.route('/news_detail') def demo4(): return render_template('detail.html') # 包含 @app.route('/demo5') def demo5(): my_srt="包含的使用" return render_template('day09_include.html',my_srt=my_srt) if __name__ == '__main__': # app.run(host='192.168.1.6',port='5000',debug=True) app.run(debug=True)

19.52381

62

0.665854

122

820

4.213115

0.442623

0.087549

0.194553

0.064202

0.081712

0

0.038516

0.145122

820

42

63

19.52381

0.694722

0.135366

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0.24

false

0

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0

null

0

1

0

null

0

1

0

1

0

4

5fb885b56fa9143e796acd262bd8db1935ae1ae9

99

py

Python

plugin/src/test/resources/refactoring/extractmethod/DuplicateSingleLine.after.py

consulo/consulo-python

586c3eaee3f9c2cc87fb088dc81fb12ffa4b3a9d

[ "Apache-2.0" ]

null

plugin/src/test/resources/refactoring/extractmethod/DuplicateSingleLine.after.py

consulo/consulo-python

586c3eaee3f9c2cc87fb088dc81fb12ffa4b3a9d

[ "Apache-2.0" ]

11

2017-02-27T22:35:32.000Z

2021-12-24T08:07:40.000Z

plugin/src/test/resources/refactoring/extractmethod/DuplicateSingleLine.after.py

consulo/consulo-python

586c3eaee3f9c2cc87fb088dc81fb12ffa4b3a9d

[ "Apache-2.0" ]

null

def foo(): a = 1 return a def bar(): a = foo() print a a = foo() print a

9

13

0.414141

16

99

2.5625

0.4375

0.195122

0.439024

0.487805

0

0.018519

0.454545

99

10

14

9.9

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0.5

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1

0

null

0

1

0

null

0

1

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4

397e34ebc62565273dd1980523d12a63c9a9d0ac

183

py

Python

tests/unit/output/test_ssh.py

kaiogu/dvc

ffa8fe5888dbbb3d37b3874562f99fd77d4bbcb7

[ "Apache-2.0" ]

3

2020-01-31T05:33:14.000Z

2021-05-20T08:19:25.000Z

tests/unit/output/test_ssh.py

kaiogu/dvc

ffa8fe5888dbbb3d37b3874562f99fd77d4bbcb7

[ "Apache-2.0" ]

null

tests/unit/output/test_ssh.py

kaiogu/dvc

ffa8fe5888dbbb3d37b3874562f99fd77d4bbcb7

[ "Apache-2.0" ]

1

2019-12-01T07:43:48.000Z

from dvc.output.ssh import OutputSSH from tests.unit.output.test_local import TestOutputLOCAL class TestOutputSSH(TestOutputLOCAL): def _get_cls(self): return OutputSSH

22.875

56

0.786885

23

183

6.130435

0.782609

0

0.153005

183

7

57

26.142857

0.909677

0

1

0.2

false

0

0.4

0.2

1

0

1

0

null

0

1

0

null

0

1

0

4

399b3164aa89ed08f15e1338945cb29f85e82b62

158

py

Python

examples/miniapps/bundles/bundles/users/entities.py

kinow/python-dependency-injector

ebd98bebe9a8fc0b57e68cfc12c4979833baa6a5

[ "BSD-3-Clause" ]

null

examples/miniapps/bundles/bundles/users/entities.py

kinow/python-dependency-injector

ebd98bebe9a8fc0b57e68cfc12c4979833baa6a5

[ "BSD-3-Clause" ]

null

examples/miniapps/bundles/bundles/users/entities.py

kinow/python-dependency-injector

ebd98bebe9a8fc0b57e68cfc12c4979833baa6a5

[ "BSD-3-Clause" ]

null

"""Users bundle entities module.""" class User: """User entity.""" def __init__(self, id): """Initialize instance.""" self.id = id

15.8

35

0.550633

17

158

4.882353

0.764706

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0

0.272152

158

9

36

17.555556

0.721739

0.398734

0

1

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1

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