xszheng2020/the_stack_dedup_python_hits_3 · Datasets at Hugging Face

ede81f243af3398e8f56425a8d4199584d3a742d

242

py

Python

test/appengine_api_tests.py

al3x/downforeveryoneorjustme

e1c47f78330b05753027b5d0f46b3d4f49261347

[ "Apache-2.0" ]

26

2015-01-13T23:41:29.000Z

2020-04-09T01:24:44.000Z

test/appengine_api_tests.py

ananthrk/downforeveryoneorjustme

e1c47f78330b05753027b5d0f46b3d4f49261347

[ "Apache-2.0" ]

2

2015-04-15T16:51:52.000Z

2017-09-04T10:00:49.000Z

test/appengine_api_tests.py

ananthrk/downforeveryoneorjustme

e1c47f78330b05753027b5d0f46b3d4f49261347

[ "Apache-2.0" ]

5

2015-05-25T11:34:01.000Z

2021-07-13T19:19:29.000Z

import unittest from google.appengine.api import urlfetch class AppEngineAPITest(unittest.TestCase): def test_urlfetch(self): response = urlfetch.fetch('http://www.google.com') self.assertEquals(0, response.content.find('<html>'))

30.25

57

0.760331

30

242

6.1

0.766667

0

0.00463

0.107438

242

7

58

34.571429

0.842593

0

0.11157

0

0.166667

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0.166667

false

0

0.333333

0

0.666667

0

null

0

1

0

null

0

1

0

1

0

3

ede90b07bb914c61617203da0d78fdef9c56ce61

1,515

py

Python

qcloudsdkvpc/CreateFlowLogRequest.py

f3n9/qcloudcli

b965a4f0e6cdd79c1245c1d0cd2ca9c460a56f19

[ "Apache-2.0" ]

null

qcloudsdkvpc/CreateFlowLogRequest.py

f3n9/qcloudcli

b965a4f0e6cdd79c1245c1d0cd2ca9c460a56f19

[ "Apache-2.0" ]

null

qcloudsdkvpc/CreateFlowLogRequest.py

f3n9/qcloudcli

b965a4f0e6cdd79c1245c1d0cd2ca9c460a56f19

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- from qcloudsdkcore.request import Request class CreateFlowLogRequest(Request): def __init__(self): super(CreateFlowLogRequest, self).__init__( 'vpc', 'qcloudcliV1', 'CreateFlowLog', 'vpc.api.qcloud.com') def get_cloudLogId(self): return self.get_params().get('cloudLogId') def set_cloudLogId(self, cloudLogId): self.add_param('cloudLogId', cloudLogId) def get_flowLogDescription(self): return self.get_params().get('flowLogDescription') def set_flowLogDescription(self, flowLogDescription): self.add_param('flowLogDescription', flowLogDescription) def get_flowLogName(self): return self.get_params().get('flowLogName') def set_flowLogName(self, flowLogName): self.add_param('flowLogName', flowLogName) def get_resourceId(self): return self.get_params().get('resourceId') def set_resourceId(self, resourceId): self.add_param('resourceId', resourceId) def get_resourceType(self): return self.get_params().get('resourceType') def set_resourceType(self, resourceType): self.add_param('resourceType', resourceType) def get_trafficType(self): return self.get_params().get('trafficType') def set_trafficType(self, trafficType): self.add_param('trafficType', trafficType) def get_vpcId(self): return self.get_params().get('vpcId') def set_vpcId(self, vpcId): self.add_param('vpcId', vpcId)

29.134615

72

0.687129

165

1,515

6.090909

0.193939

0.041791

0.097512

0.118408

0.181095

0

0.001634

0.192079

1,515

51

73

29.705882

0.819444

0.013861

0

0.133378

0

1

0.454545

false

0

0.030303

0.212121

0.727273

0

null

0

null

0

1

0

1

0

3

610c84993beb613ca5c83dcf688086e419e85c4c

1,219

py

Python

test/ofx_test_utils.py

myfreecomm/fixofx

6eb5c286a97c6af8e9c2fd502ee93ca097eaa91f

[ "Apache-2.0" ]

4

2015-09-25T03:45:42.000Z

2017-12-20T05:16:15.000Z

test/ofx_test_utils.py

myfreecomm/fixofx

6eb5c286a97c6af8e9c2fd502ee93ca097eaa91f

[ "Apache-2.0" ]

1

2015-09-17T21:52:21.000Z

2015-09-18T16:03:52.000Z

test/ofx_test_utils.py

myfreecomm/fixofx

6eb5c286a97c6af8e9c2fd502ee93ca097eaa91f

[ "Apache-2.0" ]

null

# Copyright 2005-2010 Wesabe, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os fixtures = os.path.join(os.path.dirname(__file__) or '.', "fixtures") def get_checking_stmt(): return _read_file("checking.ofx") def get_savings_stmt(): return _read_file("savings.ofx") def get_savings_with_self_closed_empty_tag_stmt(): return _read_file("savings_with_self_closed_empty_tag.ofx") def get_creditcard_stmt(): return _read_file("creditcard.ofx") def get_blank_memo_stmt(): return _read_file("blank_memo.ofx") def get_tag_with_line_break_stmt(): return _read_file("tag_with_line_break.ofx") def _read_file(filename): return open(os.path.join(fixtures, filename), 'rU').read()

29.731707

74

0.757998

188

1,219

4.659574

0.478723

0.063927

0.09589

0.123288

0.115297

0.06621

0

0.011505

0.144381

1,219

40

75

30.475

0.82838

0.454471

0

0.189522

0.093991

0

1

0.4375

false

0

0.0625

0.4375

0.9375

0

null

0

null

0

1

0

1

0

3

b6282638b7a5f8803c62e8ae2cb8e0ed33e66c5f

218

py

Python

tsadm/slave/urls.py

jctincan/tsadm-webapp

f67ac891c58b240434260692cdf0ed8b6b9dcef6

[ "BSD-3-Clause" ]

null

tsadm/slave/urls.py

jctincan/tsadm-webapp

f67ac891c58b240434260692cdf0ed8b6b9dcef6

[ "BSD-3-Clause" ]

null

tsadm/slave/urls.py

jctincan/tsadm-webapp

f67ac891c58b240434260692cdf0ed8b6b9dcef6

[ "BSD-3-Clause" ]

null

from django.conf.urls import patterns, include, url urlpatterns = patterns('', url(r'^(\d+)/$', 'tsadm.slave.views.dashboard', name='dashboard'), url(r'^admin/$', 'tsadm.slave.views.admin', name='admin'), )

24.222222

70

0.646789

28

218

5.035714

0.607143

0.056738

0.212766

0

0.123853

218

8

71

27.25

0.73822

0

0.368664

0.230415

0

1

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false

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0.2

0

0.2

0

1

0

null

0

1

0

1

0

null

0

3

b62e8682b1aa35938683234481acd17825241cdd

71

py

Python

main.py

Grimmaldi/supergoaltracker

e5cc7175816a0477b28117b6840972761c109270

[ "MIT" ]

null

main.py

Grimmaldi/supergoaltracker

e5cc7175816a0477b28117b6840972761c109270

[ "MIT" ]

3

2022-03-20T15:46:18.000Z

2022-03-27T21:26:39.000Z

main.py

Grimmaldi/supergoaltracker

e5cc7175816a0477b28117b6840972761c109270

[ "MIT" ]

null

from app import app if __name__ == '__main__': app = app.Session()

17.75

26

0.661972

10

71

3.9

0.7

0

0.211268

71

4

27

17.75

0.696429

0

0.111111

0

1

0

false

0

0.333333

0

0.333333

0

1

0

null

0

1

0

null

0

1

0

3

b639c388508053fbf6f6f4b27eeac43628c79a6c

306

py

Python

session-0/asym/std_asym.py

Ivan-Solovyev/data-analysis-tutorial

08bbfd1b907187f596da9d9460a2247562c28b88

[ "MIT" ]

1

2021-12-02T09:00:32.000Z

session-0/asym/std_asym.py

Ivan-Solovyev/data-analysis-tutorial

08bbfd1b907187f596da9d9460a2247562c28b88

[ "MIT" ]

null

session-0/asym/std_asym.py

Ivan-Solovyev/data-analysis-tutorial

08bbfd1b907187f596da9d9460a2247562c28b88

[ "MIT" ]

5

2020-07-29T03:54:43.000Z

2022-03-23T09:54:38.000Z

from math import sqrt, pow def std_asym_ostap(n1,n2): return (VE(n1,n1).asym(VE(n2,n2))).error() def std_asym_calc(n1,n2): return 2.*n1*sqrt(1./n1+1./n2) /( n2*pow(n1/n2+1.,2)) print("n=100") print(" ostap = " + str(std_asym_ostap(100,100))) print(" calc. = " + str(std_asym_calc (100,100)))

23.538462

57

0.627451

59

306

3.118644

0.355932

0.152174

0.108696

0

0.129278

0.140523

306

12

58

25.5

0.570342

0

0.081699

0

1

0.25

false

0

0.125

0.25

0.625

0.375

0

null

0

null

0

1

0

1

0

3

b643b4d400a7bca3427485fad0a4e08b11fcc707

6,376

py

Python

xdwlib/struct.py

linxsorg/xdwlib

47a5a568085f40cd101a0661aa3abb56749f9890

[ "ZPL-2.1" ]

null

xdwlib/struct.py

linxsorg/xdwlib

47a5a568085f40cd101a0661aa3abb56749f9890

[ "ZPL-2.1" ]

null

xdwlib/struct.py

linxsorg/xdwlib

47a5a568085f40cd101a0661aa3abb56749f9890

[ "ZPL-2.1" ]

null

#!/usr/bin/env python3 # vim: set fileencoding=utf-8 fileformat=unix expandtab : """struct.py -- Point and Rect Copyright (C) 2010 HAYASHI Hideki <hideki@hayasix.com> All rights reserved. This software is subject to the provisions of the Zope Public License, Version 2.1 (ZPL). A copy of the ZPL should accompany this distribution. THIS SOFTWARE IS PROVIDED "AS IS" AND ANY AND ALL EXPRESS OR IMPLIED WARRANTIES ARE DISCLAIMED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF TITLE, MERCHANTABILITY, AGAINST INFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE. """ from collections import namedtuple import math __all__ = ("Point", "Rect", "EPSILON") PI = math.pi EPSILON = 0.01 # mm _Point = namedtuple("Point", "x y") class Point(_Point): """Point represented by 2D coordinate. >>> p = Point(0, 10) >>> p Point(0.00, 10.00) >>> p + Point(5, 10) Point(5.00, 20.00) >>> p - Point(5, 10) Point(-5.00, 0.00) >>> -p Point(0.00, -10.00) >>> p * 2 Point(0.00, 20.00) >>> p / 2 Point(0.00, 5.00) >>> p.shift(Point(20, 30)) Point(20.00, 40.00) >>> p.shift([20, 30]) Point(20.00, 40.00) >>> p.shift(20) Point(20.00, 10.00) >>> list(p) [0, 10] >>> p == Point(0, 10) True >>> p != Point(0, 10) False >>> p == Point(5, 10) False >>> p != Point(5, 10) True >>> bool(p) True >>> bool(Point(0, 0)) False >>> p.rotate(30) Point(-5.00, 8.66) >>> p.rotate(30, origin=Point(10, 10)) Point(1.34, 5.00) """ def __str__(self): return f"({self.x:.2f}, {self.y:.2f})" def __repr__(self): return "Point" + self.__str__() def int(self): return Point(*map(int, self)) fix = int def floor(self): return Point(*map(math.floor, self)) def ceil(self): return Point(*map(math.ceil, self)) @staticmethod def _round(f, places=0): # Round a number in accordance with the traditional way, # while Python's round() rounds to the nearest even number. return math.floor(f * math.pow(10, places) + .5) / math.pow(10, places) def round(self, places=0): return Point(self._round(self.x, places), self._round(self.y, places)) def __bool__(self): return self != (0, 0) def __neg__(self): return Point(-self.x, -self.y) def __add__(self, pnt): return self.shift(pnt) def __sub__(self, pnt): return self.shift(-pnt) def __mul__(self, n): if not isinstance(n, (int, float)): raise NotImplementedError return Point(self.x * n, self.y * n) __rmul__ = __mul__ def __truediv__(self, n): if not isinstance(n, (int, float)): raise NotImplementedError return Point(self.x / n, self.y / n) def shift(self, pnt, _y = 0): if isinstance(pnt, (tuple, list)): return Point(self.x + pnt[0], self.y + pnt[1]) elif isinstance(pnt, (int, float)) and isinstance(_y, (int, float)): return Point(self.x + pnt, self.y + _y) else: raise NotImplementedError def rotate(self, degree, origin=None): p = Point(*self) if origin is not None: p -= origin rad = PI * degree / 180.0 sin, cos = math.sin(rad), math.cos(rad) p = Point(p.x * cos - p.y * sin, p.x * sin + p.y * cos) if origin is not None: p += origin return p _Rect = namedtuple("_Rect", "left top right bottom") class Rect(_Rect): """Half-open rectangular region. A region is represented by half-open coodinate intervals. Left-top coordinate is inclusive but right-bottom one is exclusive. >>> r = Rect(0, 10, 20, 30) >>> r Rect(0.00, 10.00, 20.00, 30.00) >>> r.position() Point(0.00, 10.00) >>> r.size() Point(20.00, 20.00) >>> r.shift(Point(15, 25)) Rect(15.00, 35.00, 35.00, 55.00) >>> r * 2 Rect(0.00, 10.00, 40.00, 50.00) >>> r / 2 Rect(0.00, 10.00, 10.00, 20.00) >>> list(r) [0, 10, 20, 30] >>> r == Rect(0, 10, 20, 30) True >>> r != Rect(0, 10, 20, 30) False >>> r.position() Point(0.00, 10.00) >>> r.size() Point(20.00, 20.00) >>> r.position_and_size() (Point(0.00, 10.00), Point(20.00, 20.00)) """ def __str__(self): return f"({', '.join(f'{x:.2f}' for x in self)})" def __repr__(self): return "Rect" + self.__str__() def half_open(self): """Get half-open version i.e. right-bottom is excluded.""" return Rect(self.left, self.top, self.right + EPSILON, self.bottom + EPSILON) def closed(self): """Get closed version i.e. rigit-bottom is included.""" return Rect(self.left, self.top, self.right - EPSILON, self.bottom - EPSILON) def int(self): """Special method to adapt to XDW_RECT.""" return Rect(*map(int, self)) fix = int def position(self): return Point(self.left, self.top) def size(self): return Point(self.right - self.left, self.bottom - self.top) def position_and_size(self): return (self.position(), self.size()) def __mul__(self, n): if not isinstance(n, (int, float)): raise NotImplementedError return Rect(self.left, self.top, self.left + (self.right - self.left) * n, self.top + (self.bottom - self.top) * n) __rmul__ = __mul__ def __truediv__(self, n): if not isinstance(n, (int, float)): raise NotImplementedError return Rect(self.left, self.top, self.left + (self.right - self.left) / n, self.top + (self.bottom - self.top) / n) def shift(self, pnt, _y=0): if isinstance(pnt, (tuple, list)): x, y = pnt elif isinstance(pnt, (int, float)) and isinstance(_y, (int, float)): x, y = pnt, _y else: raise NotImplementedError return Rect(self.left + x, self.top + y, self.right + x, self.bottom + y) def rotate(self, degree, origin=None): return Rect(p.rotate(degree, origin=origin) for p in self) if __name__ == "__main__": import doctest doctest.testmod()

26.131148

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0.558501

916

6,376

3.774017

0.20524

0.015042

0.017356

0.016199

0.430431

0.391091

0.318774

0.274805

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0

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6,376

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false

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0.161905

0.609524

0

null

0

null

0

1

0

1

0

3

b6465545c0f9db4b5071fac0297f0ee2d6686b31

336

py

Python

menpo3d/base.py

apapaion/menpo3d

09aaeb37fdd9435827041715dc8248e49e63a2d0

[ "BSD-3-Clause" ]

134

2015-03-14T22:53:45.000Z

2022-03-26T05:24:32.000Z

menpo3d/base.py

apapaion/menpo3d

09aaeb37fdd9435827041715dc8248e49e63a2d0

[ "BSD-3-Clause" ]

51

2015-02-02T12:48:53.000Z

2021-05-19T03:20:38.000Z

menpo3d/base.py

apapaion/menpo3d

09aaeb37fdd9435827041715dc8248e49e63a2d0

[ "BSD-3-Clause" ]

48

2015-02-02T16:48:52.000Z

2022-03-17T15:41:49.000Z

import os from pathlib import Path def menpo3d_src_dir_path(): r"""The path to the top of the menpo3d Python package. Useful for locating where the data folder is stored. Returns ------- path : str The full path to the top of the Menpo3d package """ return Path(os.path.abspath(__file__)).parent

21

58

0.669643

51

336

4.27451

0.607843

0.055046

0.082569

0.110092

0.220183

0

0.011952

0.252976

336

15

59

22.4

0.856574

0.547619

0

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0.2

true

0

0.4

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0.8

0

null

0

null

0

1

0

1

0

1

0

3

b69d04502a6f8d71ba5ee9a05c5959dfeda35999

1,093

py

Python

Auths/models.py

cool199966/AccountManager

7026e605482c59c53796be62125dc298bc3104ef

[ "MIT" ]

1

2022-03-29T13:54:42.000Z

Auths/models.py

cool-develope/AccountManager

7026e605482c59c53796be62125dc298bc3104ef

[ "MIT" ]

null

Auths/models.py

cool-develope/AccountManager

7026e605482c59c53796be62125dc298bc3104ef

[ "MIT" ]

null

from django.db import models import datetime from django.contrib.auth.models import ( BaseUserManager, AbstractBaseUser, Group, PermissionsMixin ) class MyUserManager(BaseUserManager): def create_user(self, username, password = None): user = self.model( username = username, ) user.set_password(password) user.save(using = self._db) return user def create_superuser(self, username, password): user = self.create_user(username, password) user.is_admin = True user.is_superuser = True user.save(using = self._db) return user class MyUser(AbstractBaseUser, PermissionsMixin): username = models.CharField(max_length = 20, unique = True) is_virtual = models.BooleanField(default = False) is_active = models.BooleanField(default=True) is_admin = models.BooleanField(default = False) objects = MyUserManager() USERNAME_FIELD = 'username' REQUIRED_FIELD = [] def __str__(self): return self.username def get_full_name(self): return self.username def get_short_name(self): return self.username @property def is_staff(self): return self.is_admin

23.76087

60

0.756633

138

1,093

5.818841

0.369565

0.07472

0.069738

0.082192

0.179328

0.141968

0.072229

0

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0.149131

1,093

46

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0.86129

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0.166667

false

0.111111

0.083333

0.111111

0.666667

0

null

0

null

0

1

0

1

0

3

fcc2c6d2b9ea0e61eef4b9a727178a5fce8e5eb2

117

py

Python

Turtles.py

Comp-Sci-Principles-2018-19/chapter-2-exercises-lanoflatfaceo

7c21318193017910b63fd6d9ba47903acc89a153

[ "MIT" ]

null

Turtles.py

Comp-Sci-Principles-2018-19/chapter-2-exercises-lanoflatfaceo

7c21318193017910b63fd6d9ba47903acc89a153

[ "MIT" ]

null

Turtles.py

Comp-Sci-Principles-2018-19/chapter-2-exercises-lanoflatfaceo

7c21318193017910b63fd6d9ba47903acc89a153

[ "MIT" ]

null

import turtle wn=turtle.Screen() alex=turtle.Turtle() alex.forward(50) alex.left(90) alex.forward(30) wn.mainloop()

13

20

0.752137

19

117

4.631579

0.578947

0.25

0

0.055556

0.076923

117

9

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0

1

0

false

0

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0

0.142857

0

1

0

null

1

0

1

0

null

0

3

fcc87ec4fa84a859d50e46156359c2558e44454c

3,040

py

Python

scoreboard_ocr/scoreboard_display.py

Nerdlytics/scoreboard_recognition

d423ce341c121f0ca93d734b264e986392134386

[ "MIT" ]

null

scoreboard_ocr/scoreboard_display.py

Nerdlytics/scoreboard_recognition

d423ce341c121f0ca93d734b264e986392134386

[ "MIT" ]

8

2020-01-19T16:04:36.000Z

2020-04-11T22:35:01.000Z

scoreboard_ocr/scoreboard_display.py

Nerdlytics/scoreboard_recognition

d423ce341c121f0ca93d734b264e986392134386

[ "MIT" ]

null

scoreboard_display = [['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25'], ['51', '50', '49', '48', '47', '46', '45', '44', '43', '42', '41', '40', '39', '38', '37', '36', '35', '34', '33', '32', '31', '30', '29', '28', '27', '26'], ['52', '53', '54', '55', '56', '57', '58', '59', '60', '61', '62', '63', '64', '65', '66', '67', '68', '69', '70', '71', '72', '73', '74', '75', '76', '77'], ['103', '102', '101', '100', '99', '98', '97', '96', '95', '94', '93', '92', '91', '90', '89', '88', '87', '86', '85', '84', '83', '82', '81', '80', '79', '78'], ['104', '105', '106', '107', '108', '109', '110', '111', '112', '113', '114', '115', '116', '117', '118', '119', '120', '121', '122', '123', '124', '125', '126', '127', '128', '129'], ['155', '154', '153', '152', '151', '150', '149', '148', '147', '146', '145', '144', '143', '142', '141', '140', '139', '138', '137', '136', '135', '134', '133', '132', '131', '130'], ['156', '157', '158', '159', '160', '161', '162', '163', '164', '165', '166', '167', '168', '169', '170', '171', '172', '173', '174', '175', '176', '177', '178', '179', '180', '181'], ['207', '206', '205', '204', '203', '202', '201', '200', '199', '198', '197', '196', '195', '194', '193', '192', '191', '190', '189', '188', '187', '186', '185', '184', '183', '182'], ['208', '209', '210', '211', '212', '213', '214', '215', '216', '217', '218', '219', '220', '221', '222', '223', '224', '225', '226', '227', '228', '229', '230', '231', '232', '233'], ['259', '258', '257', '256', '255', '254', '253', '252', '251', '250', '249', '248', '247', '246', '245', '244', '243', '242', '241', '240', '239', '238', '237', '236', '235', '234'], ['260', '261', '262', '263', '264', '265', '266', '267', '268', '269', '270', '271', '272', '273', '274', '275', '276', '277', '278', '279', '280', '281', '282', '283', '284', '285'], ['311', '310', '309', '308', '307', '306', '305', '304', '303', '302', '301', '300', '299', '298', '297', '296', '295', '294', '293', '292', '291', '290', '289', '288', '287', '286'], ['312', '313', '314', '315', '316', '317', '318', '319', '320', '321', '322', '323', '324', '325', '326', '327', '328', '329', '330', '331', '332', '333', '334', '335', '336', '337'], ['363', '362', '361', '360', '359', '358', '357', '356', '355', '354', '353', '352', '351', '350', '349', '348', '347', '346', '345', '344', '343', '342', '341', '340', '339', '338'], ['364', '365', '366', '367', '368', '369', '370', '371', '372', '373', '374', '375', '376', '377', '378', '379', '380', '381', '382', '383', '384', '385', '386', '387', '388', '389'], ['415', '414', '413', '412', '411', '410', '409', '408', '407', '406', '405', '404', '403', '402', '401', '400', '399', '398', '397', '396', '395', '394', '393', '392', '391', '390'], ['416', '417', '418', '419', '420', '421', '422', '423', '424', '425', '426', '427', '428', '429', '430', '431', '432', '433', '434', '435', '436', '437', '438', '439', '440', '441']]

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null

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1

0

1

0

null

0

3

fccd2a530ead0326ad7dd4cc03386520a9df637f

505

py

Python

differential/plugins/pterclub.py

funqc/Differential

738ebf9a2a54ea04498b3394f80d980aad083ea7

[ "MIT" ]

52

2021-10-12T11:23:45.000Z

2022-03-18T04:15:03.000Z

differential/plugins/pterclub.py

funqc/Differential

738ebf9a2a54ea04498b3394f80d980aad083ea7

[ "MIT" ]

4

2021-10-15T13:58:42.000Z

2022-03-15T12:42:35.000Z

differential/plugins/pterclub.py

funqc/Differential

738ebf9a2a54ea04498b3394f80d980aad083ea7

[ "MIT" ]

5

2021-11-18T05:41:23.000Z

2022-03-09T03:13:15.000Z

import argparse from differential.plugins.nexusphp import NexusPHP class PTerClub(NexusPHP): @classmethod def get_aliases(cls): return 'pter', @classmethod def get_help(cls): return 'PTerClub插件，适用于PTerClub' @classmethod def add_parser(cls, parser: argparse.ArgumentParser) -> argparse.ArgumentParser: return super().add_parser(parser) def __init__(self, **kwargs): super().__init__(upload_url="https://pterclub.com/upload.php", **kwargs)

22.954545

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505

6.109091

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null

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null

0

1

0

1

0

3

fcf5ffc9ef02cc09ed9ac38bacf1f6162d980844

114

py

Python

semanticeditor/utils/general.py

spookylukey/semanticeditor

82777fe63869c0f38530b9c20696de995d2fa874

[ "BSD-3-Clause" ]

null

semanticeditor/utils/general.py

spookylukey/semanticeditor

82777fe63869c0f38530b9c20696de995d2fa874

[ "BSD-3-Clause" ]

null

semanticeditor/utils/general.py

spookylukey/semanticeditor

82777fe63869c0f38530b9c20696de995d2fa874

[ "BSD-3-Clause" ]

null

""" Generic utilities """ def any(seq): for i in seq: if i: return True return False

11.4

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0

1

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null

0

1

0

3

fcfc1dfc919229f8945327e93c8aabcb984030c5

1,031

py

Python

tests/mocks/meetup.py

PyColorado/boulderpython.org

fbe6ba581f213523fd7cde0816c6f31edbb4804a

[ "MIT" ]

5

2018-01-18T16:47:53.000Z

2018-05-19T14:42:18.000Z

tests/mocks/meetup.py

PyColorado/boulderpython.org

fbe6ba581f213523fd7cde0816c6f31edbb4804a

[ "MIT" ]

28

2017-08-11T16:03:17.000Z

2018-12-02T17:30:19.000Z

tests/mocks/meetup.py

PyColorado/boulderpython.org

fbe6ba581f213523fd7cde0816c6f31edbb4804a

[ "MIT" ]

7

2017-08-11T04:46:05.000Z

2019-07-11T15:16:45.000Z

# -*- coding: utf-8 -*- """ meetup.py ~~~~~~~~~ a mock for the Meetup APi client """ class MockMeetupGroup: def __init__(self, *args, **kwargs): self.name = "Mock Meetup Group" self.link = "https://www.meetup.com/MeetupGroup/" self.next_event = { "id": 0, "name": "Monthly Meetup", "venue": "Galvanize", "yes_rsvp_count": 9, "time": 1518571800000, # February 13, 2018 6:30PM "utc_offset": -25200000, } class MockMeetupEvents: def __init__(self, *args, **kwargs): self.results = [MockMeetupGroup().next_event] + [self.events(_) for _ in range(1, 6)] def events(self, idx): return {k: idx for k in ["id", "venue", "time", "utc_offset"]} class MockMeetup: api_key = "" def __init__(self, *args, **kwargs): return def GetGroup(self, *args, **kwargs): return MockMeetupGroup() def GetEvents(self, *args, **kwargs): return MockMeetupEvents()

24.547619

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0.553831

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0.047749

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1,031

41

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0

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0.16

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0

null

0

null

0

1

0

1

0

3

fcff9cc3e23b570d8492cf00e65943351af677a0

1,264

py

Python

accounts/views.py

manuggz/memes_telegram_bot

2ed73aac099923d08c89616ec35c965204cac119

[ "Apache-2.0" ]

null

accounts/views.py

manuggz/memes_telegram_bot

2ed73aac099923d08c89616ec35c965204cac119

[ "Apache-2.0" ]

null

accounts/views.py

manuggz/memes_telegram_bot

2ed73aac099923d08c89616ec35c965204cac119

[ "Apache-2.0" ]

null

from django.contrib.auth import authenticate, login from django.contrib.auth.models import User from django.shortcuts import render, HttpResponseRedirect from django.contrib.auth.forms import UserCreationForm from django.contrib.auth import views as auth_views def crear_cuenta(request): if request.user.is_authenticated(): return HttpResponseRedirect('/chat/') if request.method == "POST": form = UserCreationForm(request.POST) if form.is_valid(): user = User.objects.create_user(username=form.cleaned_data['username'], password=form.cleaned_data['password1']) user_autenticado = authenticate(username=user.username, password=form.cleaned_data['password1']) login(request, user_autenticado) return HttpResponseRedirect('/chat/') else: form = UserCreationForm() return render(request, 'registration/signup.html', {'form': form}) def login_check(request): # Todos los usuarios autenticados tienen permiso de chatear # Por lo que no hace falta que se autentique con otra cuenta if request.user.is_authenticated(): return HttpResponseRedirect('/BotTelegram/') return auth_views.login(request)

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1,264

6.177305

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35

109

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0.086957

0.217391

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null

0

null

0

1

0

1

0

1

0

3

1e08bf6c174e80d317e171840bcccc9978e86d5e

3,798

py

Python

tests/_event/test_mouse_up_interface.py

ynsnf/apysc

b10ffaf76ec6beb187477d0a744fca00e3efc3fb

[ "MIT" ]

16

2021-04-16T02:01:29.000Z

2022-01-01T08:53:49.000Z

tests/_event/test_mouse_up_interface.py

ynsnf/apysc

b10ffaf76ec6beb187477d0a744fca00e3efc3fb

[ "MIT" ]

613

2021-03-24T03:37:38.000Z

2022-03-26T10:58:37.000Z

tests/_event/test_mouse_up_interface.py

simon-ritchie/apyscript

c319f8ab2f1f5f7fad8d2a8b4fc06e7195476279

[ "MIT" ]

2

2021-06-20T07:32:58.000Z

2021-12-26T08:22:11.000Z

from random import randint from typing import Any from typing import Dict from retrying import retry import apysc as ap from apysc._event.mouse_up_interface import MouseUpInterface from apysc._expression import expression_data_util from apysc._type.variable_name_interface import VariableNameInterface class _TestMouseUp(MouseUpInterface, VariableNameInterface): def __init__(self) -> None: """Test class for mouse up interface. """ self.variable_name = 'test_mouse_up' class TestMouseUpInterface: def on_mouse_up_1( self, e: ap.MouseEvent, options: Dict[str, Any]) -> None: """ Test handler for mouse up event. Parameters ---------- e : MouseEvent Created event instance. options : dict Optional arguments dictionary. """ def on_mouse_up_2( self, e: ap.MouseEvent, options: Dict[str, Any]) -> None: """ Test handler for mouse up event. Parameters ---------- e : MouseEvent Created event instance. options : dict Optional arguments dictionary. """ @retry(stop_max_attempt_number=15, wait_fixed=randint(10, 3000)) def test__initialize_mouse_up_handlers_if_not_initialized(self) -> None: interface_1: MouseUpInterface = MouseUpInterface() interface_1._initialize_mouse_up_handlers_if_not_initialized() assert interface_1._mouse_up_handlers == {} interface_1._initialize_mouse_up_handlers_if_not_initialized() assert interface_1._mouse_up_handlers == {} @retry(stop_max_attempt_number=15, wait_fixed=randint(10, 3000)) def test_mouseup(self) -> None: expression_data_util.empty_expression() interface_1: _TestMouseUp = _TestMouseUp() name: str = interface_1.mouseup( handler=self.on_mouse_up_1, options={'msg': 'Hello!'}) assert name in interface_1._mouse_up_handlers expression: str = \ expression_data_util.get_current_event_handler_scope_expression() expected: str = f'function {name}(' assert expected in expression expression = expression_data_util.get_current_expression() expected = ( f'{interface_1.variable_name}.mouseup({name});' ) assert expected in expression @retry(stop_max_attempt_number=15, wait_fixed=randint(10, 3000)) def test_unbind_mouseup(self) -> None: expression_data_util.empty_expression() interface_1: _TestMouseUp = _TestMouseUp() name: str = interface_1.mouseup(handler=self.on_mouse_up_1) interface_1.unbind_mouseup(handler=self.on_mouse_up_1) assert interface_1._mouse_up_handlers == {} expression: str = expression_data_util.get_current_expression() expected: str = ( f'{interface_1.variable_name}.off(' f'"{ap.MouseEventType.MOUSEUP.value}", {name});' ) assert expected in expression @retry(stop_max_attempt_number=15, wait_fixed=randint(10, 3000)) def test_unbind_mouseup_all(self) -> None: expression_data_util.empty_expression() interface_1: _TestMouseUp = _TestMouseUp() interface_1.mouseup(handler=self.on_mouse_up_1) interface_1.mouseup(handler=self.on_mouse_up_2) interface_1.unbind_mouseup_all() interface_1._mouse_up_handlers == {} expression: str = expression_data_util.get_current_expression() expected: str = ( f'{interface_1.variable_name}.off(' f'"{ap.MouseEventType.MOUSEUP.value}");' ) assert expected in expression

36.519231

78

0.651659

428

3,798

5.415888

0.182243

0.060397

0.062123

0.02157

0.72692

0.704055

0.701467

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0.640207

0

0.021019

0.260927

3,798

103

79

36.873786

0.804774

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0

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0.257576

0

null

0

1

0

1

0

null

0

3

1e0a84eff400caa1b64f0d51ecb416d92de0482f

378

py

Python

margen/segment02/pitch.py

DaviRaubach/la_otra_margen

5f7f2745d11a4dc6cd824236ad2af9af150289b2

[ "CC0-1.0" ]

null

margen/segment02/pitch.py

DaviRaubach/la_otra_margen

5f7f2745d11a4dc6cd824236ad2af9af150289b2

[ "CC0-1.0" ]

null

margen/segment02/pitch.py

DaviRaubach/la_otra_margen

5f7f2745d11a4dc6cd824236ad2af9af150289b2

[ "CC0-1.0" ]

null

import abjad I_pitches = { "matA": abjad.PitchSegment([1, 6, 11, -6, -1, 4]), "matB": abjad.PitchSegment([1, 6, 11]), } II_pitches = { "matA": abjad.PitchSegment([4, -1, -6, -10, -4, 1]), "matB": abjad.PitchSegment([4, -1, -6]) } III_pitches = { "matA": abjad.PitchSegment([1, -4, -10, 11, 6, 1]), "matB": abjad.PitchSegment([1, -4, -10]) }

25.2

56

0.529101

53

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3.716981

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0.365482

0.426396

0.741117

0

0.112245

0.222222

378

14

57

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0.557823

0

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0

1

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false

0

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0

0.076923

0

null

1

0

null

0

3

1e0bccc81b0c212a8ab41b2597bdffc455ed2eb3

105

py

Python

app/schema/token.py

jburckel/fastapi-login-example

f2e2574dd8f4fce31cae3237bb92d90315350137

[ "MIT" ]

null

app/schema/token.py

jburckel/fastapi-login-example

f2e2574dd8f4fce31cae3237bb92d90315350137

[ "MIT" ]

null

app/schema/token.py

jburckel/fastapi-login-example

f2e2574dd8f4fce31cae3237bb92d90315350137

[ "MIT" ]

1

2020-09-21T12:44:43.000Z

from .mixin import AppSchemaBase class Token(AppSchemaBase): access_token: str token_type: str

15

32

0.752381

13

105

5.923077

0.692308

0

0.190476

105

6

33

17.5

0.905882

0

1

0

true

0

0.25

0

1

0

1

0

null

0

1

0

null

0

1

0

3

1e20d14aa5cadf888513af055af81b5931b4e301

3,036

py

Python

ExpSettings/Dataset/SyntheticImages/Dataset.py

gokhangg/Uncertainix

feb86dc9a8152bc133f99c56d8f15bf760754218

[ "Apache-2.0" ]

null

ExpSettings/Dataset/SyntheticImages/Dataset.py

gokhangg/Uncertainix

feb86dc9a8152bc133f99c56d8f15bf760754218

[ "Apache-2.0" ]

null

ExpSettings/Dataset/SyntheticImages/Dataset.py

gokhangg/Uncertainix

feb86dc9a8152bc133f99c56d8f15bf760754218

[ "Apache-2.0" ]

null

# *========================================================================= # * # * Copyright Erasmus MC Rotterdam and contributors # * This software is licensed under the Apache 2 license, quoted below. # * Copyright 2019 Erasmus MC Rotterdam. # * Copyright 2019 Gokhan Gunay <g.gunay@erasmsumc.nl> # * Licensed under the Apache License, Version 2.0 (the "License"); you may not # * use this file except in compliance with the License. You may obtain a copy of # * the License at # * http: //www.apache.org/licenses/LICENSE-2.0 # * Unless required by applicable law or agreed to in writing, software # * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # * License for the specific language governing permissions and limitations under # * the License. # *========================================================================= from Parameter.Parameter import Parameter as Par from ExpSettings.DatasetBase import DatasetBase from ExpSettings.Dataset.SyntheticImages.Environment import Environment import os __selfPath = os.path.dirname(os.path.realpath(__file__)) def GetParameters(): mapFunct = lambda a : pow(2, a) """Simulated Dataset""" par = [] """Simulated Dataset""" par1 = Par("Metric1Weight", "Gauss", 4.12, 2.65) par1.SetMapFunct(mapFunct) """Simulated Dataset""" par2 = Par("FinalGridSpacingInPhysicalUnits", "Gauss", 4.37, 0.55) par2.SetMapFunct(mapFunct) par.append(par1) par.append(par2) return par """ @brief: Used to generate weights file from PCE executable for registration sampling locations . @return: NA. """ __DATASET_SIZE = 30 def GetFixedImage(ind: int): return __selfPath + "/Images/ImFlatN.mhd" def GetFixedImageSegmentation(ind: int): return __selfPath + "/Images/ImFlat.mhd" def GetMovingImage(ind: int): return __selfPath + "/Images/Im" + str(ind) + "N.mhd" def GetMovingImageSegmentation(ind: int): return __selfPath + "/Images/Im" + str(ind) + ".mhd" def GetDataset(ind: int): retVal = {} retVal.update({"fixedIm": GetFixedImage(ind)}) retVal.update({"movingIm": GetMovingImage(ind)}) retVal.update({"fixedSeg": GetFixedImageSegmentation(ind)}) retVal.update({"movingSeg": GetMovingImageSegmentation(ind)}) return retVal def GetPceSettingsFile(): return __selfPath + "/PceSettings.json" class Dataset(DatasetBase): def __init__(self): pass def GetDatasetSize(self): return __DATASET_SIZE def GetDatasetWithIndex(self, ind:int): return GetDataset(ind) def GetMethodExtensionParams(self, ind:int): return {"commandlineParameters": {}} def GetModeExtensionParams(self, ind:int): return {"sampleSize": 100, "batchSize":50, "isVector": True} def GetParameters(self, datasetIndex): return GetParameters() def GetEnvironment(self, rootDir): return Environment(rootDir)

30.666667

96

0.665349

335

3,036

5.952239

0.471642

0.024072

0.042126

0.04012

0.060181

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0.18083

3,036

98

97

30.979592

0.785686

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0

1

0.291667

false

0.020833

0.083333

0.229167

0.666667

0

null

0

null

0

1

0

1

0

3

1e2c70f4123e227934c612aca4b3f6707598fb24

198

py

Python

accounts/forms/__init__.py

BloodLagbe/blood_lagbe

597fcabea3523c9932381c591f65c0a91cc5f74c

[ "Apache-2.0" ]

3

2021-04-24T16:30:09.000Z

2021-06-19T08:02:22.000Z

accounts/forms/__init__.py

BloodLagbe/blood_lagbe

597fcabea3523c9932381c591f65c0a91cc5f74c

[ "Apache-2.0" ]

16

2021-04-24T07:44:34.000Z

2021-04-28T17:12:25.000Z

accounts/forms/__init__.py

BloodLagbe/blood_lagbe

597fcabea3523c9932381c591f65c0a91cc5f74c

[ "Apache-2.0" ]

4

2021-04-24T23:42:51.000Z

2021-06-20T16:53:00.000Z

from .forms import( LoginForm, RegistrationForm, ProfileForm ) from .search_doner import SearchDoner __all__ = [ LoginForm, RegistrationForm, ProfileForm, SearchDoner ]

14.142857

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0.69697

16

198

8.3125

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0

0.242424

198

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0

null

1

0

null

0

3

1e33bfecf12d884b056149d9e05862e880469b84

307

py

Python

bh_modules/erlangcase.py

jfcherng-sublime/ST-BracketHighlighter

223ffd4ceafd58686503e3328934c039e959a88c

[ "Unlicense", "MIT" ]

1,047

2015-01-01T16:11:42.000Z

2022-03-12T08:29:13.000Z

bh_modules/erlangcase.py

jfcherng-sublime/ST-BracketHighlighter

223ffd4ceafd58686503e3328934c039e959a88c

[ "Unlicense", "MIT" ]

374

2015-01-07T02:47:55.000Z

2022-03-24T12:59:09.000Z

bh_modules/erlangcase.py

jfcherng-sublime/ST-BracketHighlighter

223ffd4ceafd58686503e3328934c039e959a88c

[ "Unlicense", "MIT" ]

223

2015-01-11T04:21:06.000Z

2021-10-05T15:00:32.000Z

""" BracketHighlighter. Copyright (c) 2013 - 2016 Isaac Muse <isaacmuse@gmail.com> License: MIT """ from BracketHighlighter.bh_plugin import import_module lowercase = import_module("bh_modules.lowercase") def validate(*args): """Check if bracket is lowercase.""" return lowercase.validate(*args)

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null

0

1

0

1

0

1

0

3

1e35c32ef63c48b1ca0a41c93b46faa23eaeb61c

275

py

Python

tests/projects/flask1/main.py

mblackgeo/lambdarado_py

9a1e8538b569bbfdf0b0de11b42e97290812c826

[ "MIT" ]

4

2021-05-11T03:50:57.000Z

2022-01-20T14:20:44.000Z

tests/projects/flask1/main.py

mblackgeo/lambdarado_py

9a1e8538b569bbfdf0b0de11b42e97290812c826

[ "MIT" ]

1

2022-01-20T14:21:56.000Z

2022-01-20T14:22:54.000Z

tests/projects/flask1/main.py

mblackgeo/lambdarado_py

9a1e8538b569bbfdf0b0de11b42e97290812c826

[ "MIT" ]

1

2022-02-28T10:06:35.000Z

from flask import Flask from lambdarado import start def get_app(): app = Flask(__name__) @app.route('/a') def get_a(): return 'AAA' @app.route('/b') def get_b(): return 'BBB' return app print("RUNNING main.py") start(get_app)

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null

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1

0

1

0

3

1e3c1dd9c86f7c8cf4c2d93e5bb0dcb96cc42bb8

7,801

py

Python

src/tests/test_inputCheck.py

retsilagracias/horoscope-cli

fcbaf11a4db21fcf609b480f9800943c8cc37815

[ "MIT" ]

null

src/tests/test_inputCheck.py

retsilagracias/horoscope-cli

fcbaf11a4db21fcf609b480f9800943c8cc37815

[ "MIT" ]

null

src/tests/test_inputCheck.py

retsilagracias/horoscope-cli

fcbaf11a4db21fcf609b480f9800943c8cc37815

[ "MIT" ]

null

from horoscopecli.inputCheck import validInputCategoryOption, validInputSign, validInputDateOption def test_belier_validInputSign(): resultWithoutAccent = validInputSign("belier") resultWithAccent = validInputSign("bélier") assert resultWithAccent[0] == True assert resultWithAccent[1] == "aries" assert resultWithAccent[2] == "belier" assert resultWithoutAccent[0] == True assert resultWithoutAccent[1] == "aries" assert resultWithoutAccent[2] == "belier" def test_Taureau_validInputSign(): result = validInputSign("Taureau") assert result[0] == True assert result[1] == "taurus" assert result[2] == "taureau" def test_gemeaux_validInputSign(): resultWithoutAccent = validInputSign("gemeaux") resultWithAccent = validInputSign("gémeaux") assert resultWithAccent[0] == True assert resultWithAccent[1] == "gemini" assert resultWithAccent[2] == "gemeaux" assert resultWithoutAccent[0] == True assert resultWithoutAccent[1] == "gemini" assert resultWithoutAccent[2] == "gemeaux" def test_CaNcer_validInputSign(): result = validInputSign("CaNcer") assert result[0] == True assert result[1] == "cancer" assert result[2] == "cancer" def test_lion_validInputSign(): result = validInputSign("lion") assert result[0] == True assert result[1] == "leo" assert result[2] == "lion" def test_viergE_validInputSign(): result = validInputSign("viergE") assert result[0] == True assert result[1] == "virgo" assert result[2] == "vierge" def test_balance_validInputSign(): result = validInputSign("balance") assert result[0] == True assert result[1] == "libra" assert result[2] == "balance" def test_Scorpion_validInputSign(): result = validInputSign("Scorpion") assert result[0] == True assert result[1] == "scorpio" assert result[2] == "scorpion" def test_sagiTtaire_validInputSign(): result = validInputSign("sagiTtaire") assert result[0] == True assert result[1] == "sagittarius" assert result[2] == "sagittaire" def test_capricorne_validInputSign(): result = validInputSign("capricorne") assert result[0] == True assert result[1] == "capricorn" assert result[2] == "capricorne" def test_Verseau_validInputSign(): result = validInputSign("Verseau") assert result[0] == True assert result[1] == "aquarius" assert result[2] == "verseau" def test_PoIsSoNs_validInputSign(): result = validInputSign("PoIsSoNs") assert result[0] == True assert result[1] == "pisces" assert result[2] == "poissons" def test_Aries_validInputSign(): result = validInputSign("Aries") assert result[0] == True assert result[1] == ("aries") assert result[2] == ("belier") def test_taurus_validInputSign(): result = validInputSign("taurus") assert result[0] == True assert result[1] == ("taurus") assert result[2] == ("taureau") def test_GeMiNi_validInputSign(): result = validInputSign("GeMiNi") assert result[0] == True assert result[1] == ("gemini") assert result[2] == ("gemeaux") def test_cancer_validInputSign(): result = validInputSign("cancer") assert result[0] == True assert result[1] == ("cancer") assert result[2] == ("cancer") def test_leo_validInputSign(): result = validInputSign("leo") assert result[0] == True assert result[1] == ("leo") assert result[2] == ("lion") def test_viergO_validInputSign(): result = validInputSign("virgO") assert result[0] == True assert result[1] == ("virgo") assert result[2] == ("vierge") def test_libra_validInputSign(): result = validInputSign("libra") assert result[0] == True assert result[1] == ("libra") assert result[2] == ("balance") def test_Scorpio_validInputSign(): result = validInputSign("scorpio") assert result[0] == True assert result[1] == ("scorpio") assert result[2] == ("scorpion") def test_sagittarius_validInputSign(): result = validInputSign("sagittarius") assert result[0] == True assert result[1] == ("sagittarius") assert result[2] == ("sagittaire") def test_CapriCorn_validInputSign(): result = validInputSign("CapriCorn") assert result[0] == True assert result[1] == ("capricorn") assert result[2] == ("capricorne") def test_aquarius_validInputSign(): result = validInputSign("aquarius") assert result[0] == True assert result[1] == ("aquarius") assert result[2] == ("verseau") def test_Pisces_validInputSign(): result = validInputSign("Pisces") assert result[0] == True assert result[1] == ("pisces") assert result[2] == ("poissons") def test_InvalidSign_validInputSign(): result = validInputSign("InvalidSign") assert result[0] == False def test_gemeau_validInputSign(): result = validInputSign("gemeau") assert result[0] == False def test_noOption_validInputDateOption(): result = validInputDateOption(False, False, False, False) assert result[0] == True assert result[1] == "today" def test_today_validInputDateOption(): result = validInputDateOption(True, False, False, False) assert result[0] == True assert result[1] == "today" def test_week_validInputDateOption(): result = validInputDateOption(False, True, False, False) assert result[0] == True assert result[1] == "week" def test_month_validInputDateOption(): result = validInputDateOption(False, False, True, False) assert result[0] == True assert result[1] == "month" def test_year_validInputDateOption(): result = validInputDateOption(False, False, False, True) assert result[0] == True assert result[1] == "year" def test_twoOptions_validInputDateOption(): result = validInputDateOption(False, True, True, False) assert result[0] == False def test_threeOptions_validInputDateOption(): result = validInputDateOption(True, True, True, False) assert result[0] == False def test_fourOptions_validInputDateOption(): result = validInputDateOption(True, True, True, True) assert result[0] == False def test_noOption_validInputCategoryOption(): result = validInputCategoryOption(False, False, False, False, False) assert result[0] == True assert result[1] == "self" def test_love_validInputCategoryOption(): result = validInputCategoryOption(True, False, False, False, False) assert result[0] == True assert result[1] == "love" def test_work_validInputCategoryOption(): result = validInputCategoryOption(False, True, False, False, False) assert result[0] == True assert result[1] == "work" def test_finance_validInputCategoryOption(): result = validInputCategoryOption(False, False, True, False, False) assert result[0] == True assert result[1] == "finance" def test_self_validInputCategoryOption(): result = validInputCategoryOption(False, False, False, True, False) assert result[0] == True assert result[1] == "self" def test_family_validInputCategoryOption(): result = validInputCategoryOption(False, False, False, False, True) assert result[0] == True assert result[1] == "family" def test_twoOptions_validInputCategoryOption(): result = validInputCategoryOption(False, True, False, True, False) assert result[0] == False def test_threeOptions_validInputCategoryOption(): result = validInputCategoryOption(True, True, False, True, False) assert result[0] == False def test_fourOptions_validInputCategoryOption(): result = validInputCategoryOption(False, True, True, True, True) assert result[0] == False def test_fiveOptions_validInputCategoryOption(): result = validInputCategoryOption(True, True, True, True, True) assert result[0] == False

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null

1

0

null

0

1

0

1

0

3

1e4bd3839eab345fc45832ae908f28c4ea53ff3c

1,052

py

Python

edmunds/profiler/drivers/basedriver.py

LowieHuyghe/edmunds-python

236d087746cb8802a8854b2706b8d3ff009e9209

[ "Apache-2.0" ]

4

2017-09-07T13:39:50.000Z

2018-05-31T16:14:50.000Z

edmunds/profiler/drivers/basedriver.py

LowieHuyghe/edmunds-python

236d087746cb8802a8854b2706b8d3ff009e9209

[ "Apache-2.0" ]

103

2017-03-19T15:58:21.000Z

2018-07-11T20:36:17.000Z

edmunds/profiler/drivers/basedriver.py

LowieHuyghe/edmunds-python

236d087746cb8802a8854b2706b8d3ff009e9209

[ "Apache-2.0" ]

2

2017-10-14T15:20:11.000Z

2018-04-20T09:55:44.000Z

from edmunds.globals import abc, ABC class BaseDriver(ABC): """ The base driver for profiler-drivers """ def __init__(self, app): """ Initiate the instance :param app: The application :type app: Edmunds.Application """ self._app = app @abc.abstractmethod def process(self, profiler, start, end, environment, suggestive_file_name): """ Process the results :param profiler: The profiler :type profiler: cProfile.Profile :param start: Start of profiling :type start: int :param end: End of profiling :type end: int :param environment: The environment :type environment: Environment :param suggestive_file_name: A suggestive file name :type suggestive_file_name: str """ pass

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0.442015

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0

null

0

1

0

1

0

1

0

3

1e5c414c9596bddab6137c7c0d788734d49dba78

2,653

py

Python

web/tracker/models.py

webisteme/punkmoney

79253f8a37c80789e22c5c63eb6c88ccade61286

[ "MIT" ]

1

2018-10-01T11:41:57.000Z

web/tracker/models.py

webisteme/punkmoney

79253f8a37c80789e22c5c63eb6c88ccade61286

[ "MIT" ]

null

web/tracker/models.py

webisteme/punkmoney

79253f8a37c80789e22c5c63eb6c88ccade61286

[ "MIT" ]

null

from django.db import models class events(models.Model): id = models.AutoField(primary_key=True) note_id = models.BigIntegerField(null=True, blank=True) tweet_id = models.BigIntegerField() type = models.IntegerField(null=True, blank=True) timestamp = models.DateTimeField() from_user = models.CharField(max_length=90) to_user = models.CharField(max_length=90) class Meta: db_table = u'tracker_events' class notes(models.Model): id = models.BigIntegerField(max_length=30, primary_key=True) issuer = models.CharField(max_length=90, blank=True) bearer = models.CharField(max_length=90, blank=True) promise = models.CharField(max_length=420, blank=True) created = models.DateTimeField(null=True, blank=True) expiry = models.DateTimeField(null=True, blank=True) status = models.IntegerField(null=True, blank=True) transferable = models.IntegerField(null=True, blank=True) type = models.IntegerField(null=True, blank=True) conditional = models.CharField(max_length=420, null=True, blank=True) class Meta: db_table = u'tracker_notes' class trustlist(models.Model): id = models.AutoField(primary_key=True) user = models.CharField(max_length=90, blank=True) trusted = models.CharField(max_length=90, blank=True) class Meta: db_table = u'tracker_trust_list' class tags(models.Model): id = models.AutoField(primary_key=True) tag = models.CharField(max_length=30) class Meta: db_table = u'tracker_tags' class tweets(models.Model): id = models.AutoField(primary_key=True) timestamp = models.DateTimeField(null=True, blank=True) tweet_id = models.BigIntegerField(null=True, blank=True) author = models.CharField(max_length=90, blank=True) content = models.CharField(max_length=420, blank=True) reply_to_id = models.BigIntegerField(null=True, blank=True) parsed = models.CharField(max_length=1, null=True, blank=True) url = models.CharField(max_length=420, null=True, blank=True) display_url = models.CharField(max_length=420, null=True, blank=True) img_url = models.CharField(max_length=420, null=True, blank=True) tag_1 = models.IntegerField(null=True, blank=True) tag_2 = models.IntegerField(null=True, blank=True) tag_3 = models.IntegerField(null=True, blank=True) class Meta: db_table = u'tracker_tweets' class users(models.Model): id = models.AutoField(primary_key=True) username = models.CharField(max_length=90, blank=True) karma = models.IntegerField(null=True, blank=True) class Meta: db_table = u'tracker_users'

42.111111

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1

0

1

0

null

0

1

0

3

1e7c67e0a9f238cd366a07d35c3544885629f78b

372

py

Python

flashtext/flashtextDemo.py

polarbear0330/i_like_demos

e713b9833da4d2126657fe7605537fd4aaee11ef

[ "Apache-2.0" ]

1

2017-05-09T09:28:35.000Z

flashtext/flashtextDemo.py

polarbear0330/i_like_demos

e713b9833da4d2126657fe7605537fd4aaee11ef

[ "Apache-2.0" ]

null

flashtext/flashtextDemo.py

polarbear0330/i_like_demos

e713b9833da4d2126657fe7605537fd4aaee11ef

[ "Apache-2.0" ]

null

from flashtext import KeywordProcessor keywordProcessor = KeywordProcessor() keywordProcessor.add_keyword_from_file("keywords.txt") keywordProcessor.add_keyword("orange", "watermelon") print(" ") print(keywordProcessor.get_all_keywords()) print(keywordProcessor.extract_keywords("I like apple and Banana!")) print(keywordProcessor.replace_keywords("I like orange!"))

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372

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0

null

1

0

null

0

1

0

3

1e7c883cf25b8c576632aac93552052d4d021b8f

1,606

py

Python

scheduling/create_scheduling_data/agent.py

CORE-Robotics-Lab/Personalized_Neural_Trees

3e8dd12fe4fc850be65c96c847eb143ef3bcdc2e

[ "MIT" ]

3

2021-05-22T19:25:01.000Z

2021-12-01T07:59:56.000Z

scheduling/create_scheduling_data/agent.py

CORE-Robotics-Lab/Personalized_Neural_Trees

3e8dd12fe4fc850be65c96c847eb143ef3bcdc2e

[ "MIT" ]

null

scheduling/create_scheduling_data/agent.py

CORE-Robotics-Lab/Personalized_Neural_Trees

3e8dd12fe4fc850be65c96c847eb143ef3bcdc2e

[ "MIT" ]

null

import random import numpy as np from scheduling.create_scheduling_data.constants import * class Agent: def __init__(self, v = None, z = None, name = ""): if v == None: self.v = random.randint(0,10) # velocity else: self.v = v if z == None: self.z = (random.randint(0, grid_size_x-1),random.randint(0,grid_size_y-1)) self.orig_location = self.z else: self.z = z self.orig_location = z self.isBusy = False self.name = name self.curr_finish_time = 0 self.curr_task = None self.task_list = [] self.orientation = np.random.uniform(0, np.pi) self.task_event_dict = {} # task_num: [start_time, expected_finish_time] def set_orientation(self, new_orientation): self.orientation = new_orientation def getv(self): return self.v def getz(self): return self.z def getisBusy(self): return self.isBusy def changebusy(self,b): self.isBusy = b def updateAgentLocation(self, new_location): self.z = new_location def getOrientation(self): return self.orientation def getName(self): return self.name def setFinishTime(self, finish_time): self.curr_finish_time = finish_time def getFinishTime(self): return self.curr_finish_time def setCurrTask(self, task): self.curr_task = task self.task_list.append(task) def getCurrTask(self): return self.curr_task # TODO: add method to print all traits

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0.103814

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0.04661

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0.829636

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null

0

null

0

1

0

1

0

1

0

3

1e8b5e801fb33480ceaca4540478760d9fe24dde

1,480

py

Python

SVassembly/__init__.py

AV321/SVPackage

c9c625af7f5047ddb43ae79f8beb2ce9aadf7697

[ "MIT" ]

null

SVassembly/__init__.py

AV321/SVPackage

c9c625af7f5047ddb43ae79f8beb2ce9aadf7697

[ "MIT" ]

null

SVassembly/__init__.py

AV321/SVPackage

c9c625af7f5047ddb43ae79f8beb2ce9aadf7697

[ "MIT" ]

1

2019-01-22T19:16:24.000Z

from SVassembly import bedpe2window_f from bedpe2window_f import bedpe2window from SVassembly import get_shared_bcs_f from get_shared_bcs_f import get_shared_bcs from SVassembly import assign_sv_haps_f from assign_sv_haps_f import assign_sv_haps from SVassembly import count_bcs_f from count_bcs_f import count_bcs #can't have "-" from SVassembly import map_to_genome_f from map_to_genome_f import map_to_genome from SVassembly import extract_reads_2_0_new from extract_reads_2_0_new import extract_readsv2_0_new #LR v2.0 from SVassembly import extract_reads_2_0_old from extract_reads_2_0_old import extract_readsv2_0_old #LR v2.0 from SVassembly import extract_reads_by_barcode_2_1_new #uncomment these from extract_reads_by_barcode_2_1_new import extract_readsv2_1_new #LR v2.1 from SVassembly import extract_reads_by_barcode_2_1_old #uncomment these from extract_reads_by_barcode_2_1_old import extract_readsv2_1_old #LR v2.1 from SVassembly import InterestingContigs from InterestingContigs import interestingContigs from SVassembly import filt_svs from filt_svs import filter_svs from SVassembly import align from align import mappyAlign from SVassembly import mappy_contig_eval from mappy_contig_eval import interesting_contigs_mappy #from SVassembly import phase_svs #from phase_svs import phase #from SVassembly import plot_bcs_across_bkpts #this is an R file from SVassembly import plot_bcs_across_bkpts from plot_bcs_across_bkpts import plot_bcs_bkpt

31.489362

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4.719368

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0.090452

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0

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1,480

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true

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1

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null

0

1

0

null

0

1

0

1

0

3

1eb0616d9ab54adba50432c69e2f0d3d29ad3b8e

2,208

py

Python

experiments/compile_scripts.py

uiuc-arc/DeepJ

1c0493511b12394ca6f9a0098d3401cdcab50806

[ "MIT" ]

2

2022-01-20T15:46:19.000Z

2022-01-29T16:51:37.000Z

experiments/compile_scripts.py

uiuc-arc/DeepJ

1c0493511b12394ca6f9a0098d3401cdcab50806

[ "MIT" ]

null

experiments/compile_scripts.py

uiuc-arc/DeepJ

1c0493511b12394ca6f9a0098d3401cdcab50806

[ "MIT" ]

1

2021-10-31T02:02:43.000Z

import os is_fpsound = False if is_fpsound: sound = '-D SOUND' else: sound = '' print('Compiling ConvBig_Classify') os.system(f'g++ -std=c++17 -O2 -fopenmp convbig_classify.cpp -o convbig_classify') print('Compiling ConvBig') os.system(f'g++ {sound} -std=c++17 -O2 -fopenmp convbig.cpp -o convbig') print('Compiling ConvBig_Splitting') os.system(f'g++ {sound} -std=c++17 -O2 -fopenmp convbig_splitting.cpp -o convbig_splitting') print('Compiling ConvBig_Compose') os.system(f'g++ {sound} -std=c++17 -O2 -fopenmp convbig_compose.cpp -o convbig_compose') print('Compiling ConvBig_Compose_Splitting') os.system(f'g++ {sound} -std=c++17 -O2 -fopenmp convbig_compose_splitting.cpp -o convbig_compose_splitting') print('Compiling ConvMed_Classify') os.system(f'g++ -std=c++17 -O2 -fopenmp convmed_classify.cpp -o convmed_classify') print('Compiling ConvMed') os.system(f'g++ {sound} -std=c++17 -O2 -fopenmp convmed.cpp -o convmed') print('Compiling ConvMed_Splitting') os.system(f'g++ {sound} -std=c++17 -O2 -fopenmp convmed_splitting.cpp -o convmed_splitting') print('Compiling ConvMed_Compose') os.system(f'g++ {sound} -std=c++17 -O2 -fopenmp convmed_compose.cpp -o convmed_compose') print('Compiling ConvMed_Compose_Splitting') os.system(f'g++ {sound} -std=c++17 -O2 -fopenmp convmed_compose_splitting.cpp -o convmed_compose_splitting') print('Compiling FFNN_Classify') os.system(f'g++ -std=c++17 -O2 -fopenmp ffnn_classify.cpp -o ffnn_classify') print('Compiling FFNN') os.system(f'g++ {sound} -std=c++17 -O2 -fopenmp ffnn.cpp -o ffnn') print('Compiling FFNN_Splitting') os.system(f'g++ {sound} -std=c++17 -O2 -fopenmp ffnn_splitting.cpp -o ffnn_splitting') print('Compiling FFNN_Compose') os.system(f'g++ {sound} -std=c++17 -O2 -fopenmp ffnn_compose.cpp -o ffnn_compose') print('Compiling FFNN_Compose_Splitting') os.system(f'g++ {sound} -std=c++17 -O2 -fopenmp ffnn_compose_splitting.cpp -o ffnn_compose_splitting') print('Compiling Perturb Baseline') os.system(f'g++ {sound} -std=c++17 -O2 -fopenmp perturb_baseline.cpp -o perturb_baseline') print('Compiling Perturb_Compose Baseline') os.system(f'g++ {sound} -std=c++17 -O2 -fopenmp perturb_compose_baseline.cpp -o perturb_compose_baseline')

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0

1

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1ec1e4359d56546f46a7816e9ba4d788788f4cbc

13,587

py

Python

smi_analysis/integrate1D.py

NSLS-II-SMI/smi-analysis

d3e1237da86fac42014c0b27f49962e85a04277c

[ "BSD-3-Clause" ]

1

2019-08-19T22:53:22.000Z

smi_analysis/integrate1D.py

NSLS-II-SMI/SMI_analysis

d3e1237da86fac42014c0b27f49962e85a04277c

[ "BSD-3-Clause" ]

1

2020-04-21T15:19:12.000Z

smi_analysis/integrate1D.py

NSLS-II-SMI/smi-analysis

d3e1237da86fac42014c0b27f49962e85a04277c

[ "BSD-3-Clause" ]

1

2020-04-21T15:17:14.000Z

import numpy as np from pyFAI.multi_geometry import MultiGeometry from pyFAI.ext import splitBBox def inpaint_saxs(imgs, ais, masks): """ Inpaint the 2D image collected by the pixel detector to remove artifacts in later data reduction Parameters: ----------- :param imgs: List of 2D image in pixel :type imgs: ndarray :param ais: List of AzimuthalIntegrator/Transform generated using pyGIX/pyFAI which contain the information about the experiment geometry :type ais: list of AzimuthalIntegrator / TransformIntegrator :param masks: List of 2D image (same dimension as imgs) :type masks: ndarray """ inpaints, mask_inpaints = [], [] for i, (img, ai, mask) in enumerate(zip(imgs, ais, masks)): inpaints.append(ai.inpainting(img.copy(order='C'), mask)) mask_inpaints.append(np.logical_not(np.ones_like(mask))) return inpaints, mask_inpaints def cake_saxs(inpaints, ais, masks, radial_range=(0, 60), azimuth_range=(-90, 90), npt_rad=250, npt_azim=250): """ Unwrapp the stitched image from q-space to 2theta-Chi space (Radial-Azimuthal angle) Parameters: ----------- :param inpaints: List of 2D inpainted images :type inpaints: List of ndarray :param ais: List of AzimuthalIntegrator/Transform generated using pyGIX/pyFAI which contain the information about the experiment geometry :type ais: list of AzimuthalIntegrator / TransformIntegrator :param masks: List of 2D image (same dimension as inpaints) :type masks: List of ndarray :param radial_range: minimum and maximum of the radial range in degree :type radial_range: Tuple :param azimuth_range: minimum and maximum of the 2th range in degree :type azimuth_range: Tuple :param npt_rad: number of point in the radial range :type npt_rad: int :param npt_azim: number of point in the azimuthal range :type npt_azim: int """ mg = MultiGeometry(ais, unit='q_A^-1', radial_range=radial_range, azimuth_range=azimuth_range, wavelength=None, empty=0.0, chi_disc=180) cake, q, chi = mg.integrate2d(lst_data=inpaints, npt_rad=npt_rad, npt_azim=npt_azim, correctSolidAngle=True, lst_mask=masks) return cake, q, chi[::-1] def integrate_rad_saxs(inpaints, ais, masks, radial_range=(0, 40), azimuth_range=(0, 90), npt=2000): """ Radial integration of transmission data using the pyFAI multigeometry module Parameters: ----------- :param inpaints: List of 2D inpainted images :type inpaints: List of ndarray :param ais: List of AzimuthalIntegrator/Transform generated using pyGIX/pyFAI which contain the information about the experiment geometry :type ais: list of AzimuthalIntegrator / TransformIntegrator :param masks: List of 2D image (same dimension as inpaints) :type masks: List of ndarray :param radial_range: minimum and maximum of the radial range in degree :type radial_range: Tuple :param azimuth_range: minimum and maximum of the 2th range in degree :type azimuth_range: Tuple :param npt: number of point of the final 1D profile :type npt: int """ mg = MultiGeometry(ais, unit='q_A^-1', radial_range=radial_range, azimuth_range=azimuth_range, wavelength=None, empty=-1, chi_disc=180) q, i_rad = mg.integrate1d(lst_data=inpaints, npt=npt, correctSolidAngle=True, lst_mask=masks) return q, i_rad def integrate_azi_saxs(cake, q_array, chi_array, radial_range=(0, 10), azimuth_range=(-90, 0)): """ Azimuthal integration of transmission data using masked array on a caked images (image in 2-theta_chi space) Parameters: ----------- :param cake: 2D array unwrapped in 2th-chi space :type cake: ndarray (same dimension as tth_array and chiarray) :param q_array: 2D array containing 2th angles of each pixel :type q_array: ndarray (same dimension as cake and chiarray) :param chi_array: 2D array containing chi angles of each pixel :type chi_array: ndarray (same dimension as cake and tth_array) :param radial_range: minimum and maximum of the radial range in degree :type radial_range: Tuple :param azimuth_range: minimum and maximum of the 2th range in degree :type azimuth_range: Tuple """ q_mesh, chi_mesh = np.meshgrid(q_array, chi_array) cake_mask = np.ma.masked_array(cake) cake_mask = np.ma.masked_where(q_mesh < radial_range[0], cake_mask) cake_mask = np.ma.masked_where(q_mesh > radial_range[1], cake_mask) cake_mask = np.ma.masked_where(azimuth_range[0] > chi_mesh, cake_mask) cake_mask = np.ma.masked_where(azimuth_range[1] < chi_mesh, cake_mask) i_azi = cake_mask.mean(axis=1) return chi_array, i_azi def integrate_rad_gisaxs(img, q_par, q_per, bins=1000, radial_range=None, azimuth_range=None): """ Radial integration of Grazing incidence data using the pyFAI multigeometry module Parameters: ----------- :param q_par: minimum and maximum q_par (in A-1) of the input image :type q_par: Tuple :param q_per: minimum and maximum of q_par in A-1 :type q_per: Tuple :param bins: number of point of the final 1D profile :type bins: int :param img: 2D array containing the stitched intensity :type img: ndarray :param radial_range: q_par range (in A-1) at the which the integration will be done :type radial_range: Tuple :param azimuth_range: q_per range (in A-1) at the which the integration will be done :type azimuth_range: Tuple """ # recalculate the q-range of the input array q_h = np.linspace(q_par[0], q_par[-1], np.shape(img)[1]) q_v = np.linspace(q_per[0], q_per[-1], np.shape(img)[0])[::-1] if radial_range is None: radial_range = (0, q_h.max()) if azimuth_range is None: azimuth_range = (0, q_v.max()) q_h_te, q_v_te = np.meshgrid(q_h, q_v) tth_array = np.sqrt(q_h_te ** 2 + q_v_te ** 2) chi_array = np.rad2deg(np.arctan2(q_h_te, q_v_te)) # Mask the remeshed array img_mask = np.ma.masked_array(img, mask=img == 0) img_mask = np.ma.masked_where(img < 1E-5, img_mask) img_mask = np.ma.masked_where(tth_array < radial_range[0], img_mask) img_mask = np.ma.masked_where(tth_array > radial_range[1], img_mask) img_mask = np.ma.masked_where(chi_array < np.min(azimuth_range), img_mask) img_mask = np.ma.masked_where(chi_array > np.max(azimuth_range), img_mask) q_rad, i_rad, _, _ = splitBBox.histoBBox1d(img_mask, pos0=tth_array, delta_pos0=np.ones_like(img_mask) * (q_par[1] - q_par[0])/np.shape( img_mask)[1], pos1=q_v_te, delta_pos1=np.ones_like(img_mask) * (q_per[1] - q_per[0])/np.shape( img_mask)[0], bins=bins, pos0Range=np.array([np.min(tth_array), np.max(tth_array)]), pos1Range=q_per, dummy=None, delta_dummy=None, mask=img_mask.mask ) return q_rad, i_rad def integrate_qpar(img, q_par, q_per, q_par_range=None, q_per_range=None): """ Horizontal integration of a 2D array using masked array Parameters: ----------- :param q_par: minimum and maximum q_par (in A-1) of the input image :type q_par: Tuple :param q_per: minimum and maximum of q_par in A-1 :type q_per: Tuple :param img: 2D array containing intensity :type img: ndarray :param q_par_range: q_par range (in A-1) at the which the integration will be done :type q_par_range: Tuple :param q_per_range: q_per range (in A-1) at the which the integration will be done :type q_per_range: Tuple """ if q_par_range is None: q_par_range = (np.asarray(q_par).min(), np.asarray(q_par).max()) if q_per_range is None: q_per_range = (np.asarray(q_per).min(), np.asarray(q_per).max()) q_par = np.linspace(q_par[0], q_par[1], np.shape(img)[1]) q_per = np.linspace(q_per[0], q_per[1], np.shape(img)[0])[::-1] qpar_mesh, qper_mesh = np.meshgrid(q_par, q_per) img_mask = np.ma.masked_array(img, mask=img == 0) img_mask = np.ma.masked_where(qper_mesh < q_per_range[0], img_mask) img_mask = np.ma.masked_where(qper_mesh > q_per_range[1], img_mask) img_mask = np.ma.masked_where(q_par_range[0] > qpar_mesh, img_mask) img_mask = np.ma.masked_where(q_par_range[1] < qpar_mesh, img_mask) i_par = np.mean(img_mask, axis=0) return q_par, i_par def integrate_qper(img, q_par, q_per, q_par_range=None, q_per_range=None): """ Vertical integration of a 2D array using masked array Parameters: ----------- :param q_par: minimum and maximum q_par (in A-1) of the input image :type q_par: Tuple :param q_per: minimum and maximum of q_par in A-1 :type q_per: Tuple :param img: 2D array containing intensity :type img: ndarray :param q_par_range: q_par range (in A-1) at the which the integration will be done :type q_par_range: Tuple :param q_per_range: q_per range (in A-1) at the which the integration will be done :type q_per_range: Tuple """ if q_par_range is None: q_par_range = (np.asarray(q_par).min(), np.asarray(q_par).max()) if q_per_range is None: q_per_range = (np.asarray(q_per).min(), np.asarray(q_per).max()) q_par = np.linspace(q_par[0], q_par[1], np.shape(img)[1]) q_per = np.linspace(q_per[0], q_per[1], np.shape(img)[0])[::-1] q_par_mesh, q_per_mesh = np.meshgrid(q_par, q_per) img_mask = np.ma.masked_array(img, mask=img == 0) img_mask = np.ma.masked_where(q_per_mesh < q_per_range[0], img_mask) img_mask = np.ma.masked_where(q_per_mesh > q_per_range[1], img_mask) img_mask = np.ma.masked_where(q_par_mesh < q_par_range[0], img_mask) img_mask = np.ma.masked_where(q_par_mesh > q_par_range[1], img_mask) i_per = np.mean(img_mask, axis=1) return q_per, i_per # TODO: Implement azimuthal integration for GI def cake_gisaxs(img, q_par, q_per, bins=None, radial_range=None, azimuth_range=None): """ Unwrap the stitched image from q-space to 2theta-Chi space (Radial-Azimuthal angle) Parameters: ----------- :param img: List of 2D images :type img: List of ndarray :param q_par: minimum and maximum q_par (in A-1) of the input image :type q_par: Tuple :param q_per: minimum and maximum of q_par in A-1 :type q_per: Tuple :param bins: number of point in both x and y direction of the final cake :type bins: Tuple :param radial_range: minimum and maximum of the radial range in degree :type radial_range: Tuple :param azimuth_range: minimum and maximum of the 2th range in degree :type azimuth_range: Tuple """ if bins is None: bins = tuple(reversed(img.shape)) if radial_range is None: radial_range = (0, q_par[-1]) if azimuth_range is None: azimuth_range = (-180, 180) azimuth_range = np.deg2rad(azimuth_range) # recalculate the q-range of the input array q_h = np.linspace(q_par[0], q_par[-1], bins[0]) q_v = np.linspace(q_per[0], q_per[-1], bins[1])[::-1] q_h_te, q_v_te = np.meshgrid(q_h, q_v) tth_array = np.sqrt(q_h_te**2 + q_v_te**2) chi_array = -np.arctan2(q_h_te, q_v_te) # Mask the remeshed array img_mask = np.ma.masked_array(img, mask=img == 0) img_mask = np.ma.masked_where(tth_array < radial_range[0], img_mask) img_mask = np.ma.masked_where(tth_array > radial_range[1], img_mask) img_mask = np.ma.masked_where(chi_array < np.min(azimuth_range), img_mask) img_mask = np.ma.masked_where(chi_array > np.max(azimuth_range), img_mask) cake, q, chi, _, _ = splitBBox.histoBBox2d(weights=img_mask, pos0=tth_array, delta_pos0=np.ones_like(img_mask) * (q_par[1] - q_par[0])/bins[1], pos1=chi_array, delta_pos1=np.ones_like(img_mask) * (q_per[1] - q_per[0])/bins[1], bins=bins, pos0Range=np.array([np.min(radial_range), np.max(radial_range)]), pos1Range=np.array([np.min(azimuth_range), np.max(azimuth_range)]), dummy=None, delta_dummy=None, mask=img_mask.mask) return cake, q, np.rad2deg(chi)[::-1]

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null

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1

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null

0

3

1eceacebfd4dbe82e0eb8410fe4b6fe74dbc1b33

5,235

py

Python

pyroc/compare.py

noudald/pyroc

9f73df0c0b0718918d9cbbb532d0eeea73a421ae

[ "MIT" ]

1

2022-01-01T03:42:05.000Z

pyroc/compare.py

noudald/pyroc

9f73df0c0b0718918d9cbbb532d0eeea73a421ae

[ "MIT" ]

null

pyroc/compare.py

noudald/pyroc

9f73df0c0b0718918d9cbbb532d0eeea73a421ae

[ "MIT" ]

null

"""Tools for comparing ROC curves with AUC.""" from math import erf from typing import Optional, Tuple import numpy as np from pyroc import bootstrap_roc, ROC def gaussian_cdf(x: float) -> float: """Gaussian cummulative distribution function for N(0, 1). Parameters ---------- x Quantile for which to compute the cummulative distribution. Returns ------- Cummulative distribution for quantile x for Gaussian distribution N(0, 1). """ return (1.0 + erf(x / 2.0**.5)) / 2.0 def compare_bootstrap( roc1: ROC, roc2: ROC, alt_hypothesis: float = 0.05, seed: Optional[int] = None) -> Tuple[bool, float]: """Compute roc1 < roc2 with alternative hypothesis using DeLong bootstrapping. The idea behind the this algorithm is to bootstrap roc1 and roc2, and compute the AUC (Area Under the Curve) for each of the bootstraps for roc1 and roc2. For each bootstraps of roc1 and roc2 we compute the difference of the AUCs of ROC curves. Let aucs_diff = [auc11 - auc21, auc12 - auc22, ..., auc1n - auc2n], where auc1i is the AUC of ith bootstrap of roc1, and auc2i is the AUC of the ith bootstrap of roc2. We define a new stochast by Z = mean(aucs_diff) / std(aucs_diff). We assume that Z ~ N(0, 1), i.e. Z is drawn from a Gaussian distribution centered around 0 with standard deviation 1. Our zero hypothesis is that roc1 >= roc2, or in other words that P(Z) < 1 - alt_hypothesis. So that our alternative hypothesis is that roc1 < roc2. We reject the zero hypothesis if P(Z) > 1 - alt_hypothesis. Parameters ---------- roc1 The "to be assumed" smaller ROC curve than roc2. roc2 The "to be assumed" larger ROC curve than roc1. alt_hypothesis The density for which we reject the zero hypothesis, and for which we therefore accept roc1 < roc2. seed Seed used for DeLong bootstrapping. If no seed is given a random seed will be used, resulting in non-deterministic results. Raises ------ ValueError If alt_hypothesis is not between 0 and 1. Returns ------- Tuple of a boolean and the p-value. I.e. the boolean represents if we can accept the alternative hypothesis roc1 < roc2, and the p-value represents the strength with which we accept the alternative hypothesis roc1 < roc2. """ if not 0 <= alt_hypothesis <= 1: raise ValueError('Alternative hypothesis must be between 0 and 1.') bootstrap_auc1 = np.array(list(roc.auc for roc in bootstrap_roc(roc1, seed=seed))) bootstrap_auc2 = np.array(list(roc.auc for roc in bootstrap_roc(roc2, seed=seed))) aucs = bootstrap_auc2 - bootstrap_auc1 sample = np.mean(aucs) if np.std(aucs) > 0: sample /= np.std(aucs) p_value = 1 - gaussian_cdf(sample) return p_value < alt_hypothesis, p_value def compare_binary( roc1: ROC, roc2: ROC, alt_hypothesis: float = 0.05, seed: Optional[int] = None) -> Tuple[bool, float]: """Compute roc1 < roc2 using binary comparison with bootstrapping. The idea behind the this algorithm is to bootstrap roc1 and roc2, and compute the AUC (Area Under the Curve) for each of the bootstraps for roc1 and roc2. For each bootstraps of roc1 and roc2 we compute the difference of the AUCs of ROC curves. Let aucs_diff = [auc11 - auc21, auc12 - auc22, ..., auc1n - auc2n], where auc1i is the AUC of ith bootstrap of roc1, and auc2i is the AUC of the ith bootstrap of roc2. We define the statistical strength, i.e. p-value, for which we can reject the zero hypothesis roc1 > roc2 as p_value = sum(aucs_diff > 0) / n. If p_value is smaller than alt_hypothesis we accept the alternative hypothesis roc1 < roc2. Parameters ---------- roc1 The "to be assumed" smaller ROC curve than roc2. roc2 The "to be assumed" larger ROC curve than roc1. alt_hypothesis The density for which we reject the zero hypothesis, and for which we therefore accept roc1 < roc2. seed Seed used for DeLong bootstrapping. If no seed is given a random seed will be used, resulting in non-deterministic results. Raises ------ ValueError If alt_hypothesis is not between 0 and 1. Returns ------- Tuple of a boolean and the p-value. I.e. the boolean represents if we can accept the alternative hypothesis roc1 < roc2, and the p-value represents the strength with which we accept the alternative hypothesis roc1 < roc2. """ if not 0 <= alt_hypothesis <= 1: raise ValueError('Alternative hypothesis must be between 0 and 1.') bootstrap_auc1 = np.array(list(roc.auc for roc in bootstrap_roc(roc1, seed=seed))) bootstrap_auc2 = np.array(list(roc.auc for roc in bootstrap_roc(roc2, seed=seed))) aucs = bootstrap_auc2 - bootstrap_auc1 p_value = sum(aucs <= 0) / aucs.size return p_value < alt_hypothesis, p_value

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null

0

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1

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null

0

3

1ed5ca41b8d66ded4bdeb1cf3ea0b3e35e8d7ee3

988

py

Python

PDFSegmenter/util/StorageUtil.py

MBAigner/PDFSegmenter

a559809355f7d3607111b55934dd5f24b674b365

[ "MIT" ]

11

2020-09-20T16:49:49.000Z

2022-01-20T01:52:55.000Z

GraphConverter/util/StorageUtil.py

MBAigner/GraphConverter

243a4b24928ce3b6b570a16a31280cc322e713fc

[ "MIT" ]

null

GraphConverter/util/StorageUtil.py

MBAigner/GraphConverter

243a4b24928ce3b6b570a16a31280cc322e713fc

[ "MIT" ]

1

2020-10-29T12:35:59.000Z

import pickle def save_object(obj, path, name): """ :param obj: :param path: :param name: :return: """ with open(path + name + '.pkl', 'wb') as f: pickle.dump(obj, f, pickle.HIGHEST_PROTOCOL) def load_object(path, name): """ :param path: :param name: :return: """ with open(path + name + '.pkl', 'rb') as f: return pickle.load(f) def get_file_name(path): """ :param path: :return: """ parts = path.split("/") return parts[len(parts) - 1] def replace_file_type(file_name, new_type): """ :param file_name: :param new_type: :return: """ file_name_parts = file_name.split(".") return file_name.replace(file_name_parts[len(file_name_parts)-1], new_type) def cut_file_type(file_name): """ :param file_name: :return: """ file_name_parts = file_name.split(".") return file_name.replace("." + file_name_parts[len(file_name_parts)-1], "")

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0.21978

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null

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null

0

1

0

1

0

3

1ed7990a7bbc4e7457fa21480a6253094958d11c

198

py

Python

src/eazyserver/rpc/__init__.py

MacherLabs/eazyserver

ce207f06971f8e81a9282f58de74d71b07ba7118

[ "MIT" ]

4

2019-02-23T13:24:36.000Z

2021-03-25T07:55:09.000Z

src/eazyserver/rpc/__init__.py

MacherLabs/eazyserver

ce207f06971f8e81a9282f58de74d71b07ba7118

[ "MIT" ]

1

2019-03-07T13:01:14.000Z

src/eazyserver/rpc/__init__.py

MacherLabs/eazyserver

ce207f06971f8e81a9282f58de74d71b07ba7118

[ "MIT" ]

2

2019-05-09T15:07:21.000Z

2020-09-22T16:14:56.000Z

import logging logger = logging.getLogger(__name__) logger.debug("Loaded " + __name__) from jsonrpcserver import methods from .exceptions import * from .influxdb_api import * from .meta import *

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1edc4333fc4f6d215d68beba2d0a07189e353a24

1,101

py

Python

typings/bpy_extras/wm_utils/progress_report.py

Argmaster/PyR3

6786bcb6a101fe4bd4cc50fe43767b8178504b15

[ "MIT" ]

2

2021-12-12T18:51:52.000Z

2022-02-23T09:49:16.000Z

src/blender/blender_autocomplete-master/2.92/bpy_extras/wm_utils/progress_report.py

JonasWard/ClayAdventures

a716445ac690e4792e70658319aa1d5299f9c9e9

[ "MIT" ]

2

2021-11-08T12:09:02.000Z

2021-12-12T23:01:12.000Z

src/blender/blender_autocomplete-master/2.92/bpy_extras/wm_utils/progress_report.py

JonasWard/ClayAdventures

a716445ac690e4792e70658319aa1d5299f9c9e9

[ "MIT" ]

null

import sys import typing class ProgressReport: curr_step = None ''' ''' running = None ''' ''' start_time = None ''' ''' steps = None ''' ''' wm = None ''' ''' def enter_substeps(self, nbr, msg): ''' ''' pass def finalize(self): ''' ''' pass def initialize(self, wm): ''' ''' pass def leave_substeps(self, msg): ''' ''' pass def start(self): ''' ''' pass def step(self, msg, nbr): ''' ''' pass def update(self, msg): ''' ''' pass class ProgressReportSubstep: final_msg = None ''' ''' level = None ''' ''' msg = None ''' ''' nbr = None ''' ''' progress = None ''' ''' def enter_substeps(self, nbr, msg): ''' ''' pass def leave_substeps(self, msg): ''' ''' pass def step(self, msg, nbr): ''' ''' pass

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3

9498a8c6ee04d55e9083c6decd5b3cafed1a96b6

1,194

py

Python

impor.py

raotnameh/FAKE_NEWS_LIAR-PLUS-dataset

fbf2a953c16fc111c4afb876eb0857a8d4bb7cb5

[ "Apache-2.0" ]

2

2019-08-11T21:15:09.000Z

2019-10-30T16:54:00.000Z

impor.py

raotnameh/FAKE_NEWS_LIAR-PLUS-dataset

fbf2a953c16fc111c4afb876eb0857a8d4bb7cb5

[ "Apache-2.0" ]

null

impor.py

raotnameh/FAKE_NEWS_LIAR-PLUS-dataset

fbf2a953c16fc111c4afb876eb0857a8d4bb7cb5

[ "Apache-2.0" ]

null

import pandas as pd import matplotlib.pyplot as plt import seaborn as sns import numpy as np import folium import json import re import glob import os import string import random import requests import scipy from matplotlib.colors import * import seaborn as sn from dateutil.parser import parse import datetime as dt pd.options.mode.chained_assignment = None # default='warn' # Sklearn imports from sklearn.model_selection import * from sklearn.preprocessing import * from sklearn.linear_model import * from sklearn.ensemble import * from sklearn.metrics import * from sklearn.utils import shuffle from sklearn.utils.class_weight import * from sklearn.svm import * from sklearn.externals import * from scipy.stats import * # For sentiment analysis import vaderSentiment from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer import nltk from nltk.sentiment.vader import SentimentIntensityAnalyzer nltk.download('vader_lexicon') from google.cloud import language from tqdm import tqdm # Import for WordCloud import wordcloud # Text classifier - TextBlob from textblob.classifiers import NaiveBayesClassifier # Local imports from helpers import * import cleaningtool as ct

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0.100815

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0.132328

1,194

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null

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0

1

0

3

94d92d7bf931d71ef42fd53ff7d0ec216ee8563b

1,189

py

Python

exercises/en/test_02_07.py

hfboyce/MCL-DSCI-571-machine-learning

25757369491ac547daa94ff1143ca7389d433a6e

[ "MIT" ]

1

2020-11-23T03:19:18.000Z

exercises/en/test_02_07.py

hfboyce/MCL-DSCI-571-machine-learning

25757369491ac547daa94ff1143ca7389d433a6e

[ "MIT" ]

13

2020-10-02T16:48:24.000Z

2020-12-09T18:58:21.000Z

exercises/en/test_02_07.py

hfboyce/MCL-DSCI-571-machine-learning

25757369491ac547daa94ff1143ca7389d433a6e

[ "MIT" ]

2

2020-10-28T19:43:42.000Z

2021-03-30T22:57:47.000Z

def test(): # Here we can either check objects created in the solution code, or the # string value of the solution, available as __solution__. A helper for # printing formatted messages is available as __msg__. See the testTemplate # in the meta.json for details. # If an assertion fails, the message will be displayed assert 'DecisionTreeClassifier' in __solution__, "Make sure you are specifying a 'DecisionTreeClassifier'." assert model.get_params()['random_state'] == 1, "Make sure you are settting the model's 'random_state' to 1." assert 'model.fit' in __solution__, "Make sure you are using the '.fit()' function to fit 'X' and 'y'." assert 'model.predict(X)' in __solution__, "Make sure you are using the model to predict on 'X'." assert list(predicted).count('Canada') == 6, "Your predicted values are incorrect. Are you fitting the model properly?" assert list(predicted).count('Both') == 8, "Your predicted values are incorrect. Are you fitting the model properly?" assert list(predicted).count('America') == 11, "Your predicted values are incorrect. Are you fitting the model properly?" __msg__.good("Nice work, well done!")

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1,189

4.820809

0.479769

0.047962

0.052758

0.067146

0.378897

0.35012

0.273381

0

0.006148

0.179142

1,189

15

126

79.266667

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0.052928

0

0.777778

1

0.111111

true

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0.111111

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null

0

1

0

null

0

1

0

1

0

3

94e3747bade02f542649433d315425e5214cbff5

1,028

py

Python

tests/transformers/test_date_parser.py

GoC-Spending/fuzzy-tribble

c6c7b82faad577025a799af84c64686e903499e6

[ "MIT" ]

6

2017-09-20T16:28:27.000Z

2018-10-08T18:41:05.000Z

tests/transformers/test_date_parser.py

GoC-Spending/fuzzy-tribble

c6c7b82faad577025a799af84c64686e903499e6

[ "MIT" ]

13

2017-12-02T01:35:10.000Z

2018-02-28T14:06:31.000Z

tests/transformers/test_date_parser.py

GoC-Spending/fuzzy-tribble

c6c7b82faad577025a799af84c64686e903499e6

[ "MIT" ]

2

2017-12-19T03:50:46.000Z

2018-02-20T04:47:14.000Z

import datetime import pandas as pd from tribble.transformers import date_parser def test_apply() -> None: data = pd.DataFrame([{'id': 1, 'data': '2017-01-01'}]) output = date_parser.DateParser('data').apply(data) assert output.to_dict('records') == [ {'id': 1, 'data': datetime.date(2017, 1, 1)} ] def test_reversed_month_day() -> None: data = pd.DataFrame([{'id': 1, 'data': '2017-13-01'}]) output = date_parser.DateParser('data').apply(data) assert output.to_dict('records') == [ {'id': 1, 'data': datetime.date(2017, 1, 13)} ] def test_non_date() -> None: data = pd.DataFrame([{'id': 1, 'data': 'foo'}]) output = date_parser.DateParser('data').apply(data) assert output.to_dict('records') == [ {'id': 1, 'data': None} ] def test_bad_date() -> None: data = pd.DataFrame([{'id': 1, 'data': '2017-01-32'}]) output = date_parser.DateParser('data').apply(data) assert output.to_dict('records') == [ {'id': 1, 'data': None} ]

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0

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0.200389

1,028

35

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0.259259

0

null

0

1

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null

0

3

94f21e95ced1386a941e623940a6426863cc4545

1,113

py

Python

battleships/migrations/0002_auto_20181202_1829.py

ArturAdamczyk/Battleships

748e4fa87ed0c17c57abbdf5a0a2bca3c91dff24

[ "MIT" ]

null

battleships/migrations/0002_auto_20181202_1829.py

ArturAdamczyk/Battleships

748e4fa87ed0c17c57abbdf5a0a2bca3c91dff24

[ "MIT" ]

null

battleships/migrations/0002_auto_20181202_1829.py

ArturAdamczyk/Battleships

748e4fa87ed0c17c57abbdf5a0a2bca3c91dff24

[ "MIT" ]

null

# Generated by Django 2.1.3 on 2018-12-02 17:29 import battleships.models.ship from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('battleships', '0001_initial'), ] operations = [ migrations.AlterField( model_name='carrier', name='experience', field=models.CharField(default=battleships.models.ship.Experience('RECRUIT'), max_length=20), ), migrations.AlterField( model_name='destroyer', name='experience', field=models.CharField(default=battleships.models.ship.Experience('RECRUIT'), max_length=20), ), migrations.AlterField( model_name='frigate', name='experience', field=models.CharField(default=battleships.models.ship.Experience('RECRUIT'), max_length=20), ), migrations.AlterField( model_name='submarine', name='experience', field=models.CharField(default=battleships.models.ship.Experience('RECRUIT'), max_length=20), ), ]

31.8

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1,113

6.35514

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0.125

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0.170588

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1,113

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1

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1

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1

0

1

0

null

0

3

a20882b19e76633a9de6527854d316da9b23d890

28,958

py

Python

data_split/prepareSingleTests.py

UKPLab/linspector

46a7cca6ad34dc673feb47c4d452f1248d5e635b

[ "Apache-2.0" ]

21

2019-03-21T12:10:09.000Z

2022-03-01T04:42:34.000Z

data_split/prepareSingleTests.py

UKPLab/linspector

46a7cca6ad34dc673feb47c4d452f1248d5e635b

[ "Apache-2.0" ]

1

2019-03-25T17:27:40.000Z

2019-04-04T07:01:08.000Z

data_split/prepareSingleTests.py

UKPLab/linspector

46a7cca6ad34dc673feb47c4d452f1248d5e635b

[ "Apache-2.0" ]

2

2019-04-17T07:36:12.000Z

2019-10-10T09:15:29.000Z

# -*- coding: utf-8 -*- import sys sys.path.append('../') import argparse import pickle import random from data_split.util import * from data_util.reader import * from data_util.schema import * def ensure_dir(file_path): directory = os.path.dirname(file_path) if not os.path.exists(directory): os.makedirs(directory) def reverse_dict_list(orig_dict): rev_dict = dict() for test in orig_dict: for lang in orig_dict[test]: if lang not in rev_dict: rev_dict[lang] = [test] else: rev_dict[lang].append(test) return rev_dict def get_mixed_surface(feat, lang, vocab, threshold): """ Get cnt number of frequent surface and lemma which DOES not contain the 'feat' None of the surface forms should have the feat :param feat: morphological feature :param lang: language :param vocab: list of frequent words :param cnt: number of desired nonsense labels :return: list of word, label tuple """ freq_surf = [] rare_surf = [] schema = UnimorphSchema() data = load_ds("unimorph", lang) forbid_vocab = dict() # make a vocabulary of forbidden words for x in data[lang]: # exclude lemmas with space if ' ' in x["form"]: continue x_feats = schema.decode_msd(x["msd"])[0] if feat in x_feats: forbid_vocab[x["form"]] = 1 # include each surface form once surf_cnt = dict() for x in data[lang]: # exclude lemmas with space if ' ' in x["form"]: continue # if any of the surface forms have the feat, exclude them if x["form"] in forbid_vocab: continue # exclude x, if the surface form is already in the data if x["form"] in surf_cnt: continue else: surf_cnt[x['form']] = 1 x_feats = schema.decode_msd(x["msd"])[0] ## Exceptions: drop V.PTCP from the case and gender tests - russian if (feat in ['Case', 'Gender']) and (lang == 'russian') and (x_feats['Part of Speech'] == 'Participle'): continue ## Exceptions: If it is a gender test and the noun does not have a gender feature, ignore if (feat == 'Gender') and (lang == 'russian') and (x_feats['Part of Speech'] == 'Noun') and ( 'Gender' not in x_feats): continue if feat not in x_feats: # flag x x["flag"] = 1 if x["form"].lower() in vocab: freq_surf.append(x) else: rare_surf.append(x) """ else: if feat == 'Number' and (x_feats['Part of Speech'] != 'Noun') and (feat in x_feats): x["flag"] = 1 if x["form"].lower() in vocab: freq_surf.append(x) else: rare_surf.append(x) """ # Try to sample 80%-20% if possible if (len(freq_surf) >= int(threshold * 0.8)) and (len(rare_surf) >= int(threshold * 0.2)): shuffled_frequent = random.sample(freq_surf, int(threshold * 0.8)) shuffled_rare = random.sample(rare_surf, int(threshold * 0.2)) instances = shuffled_frequent + shuffled_rare # else get all the frequent ones, and sample the rest from the rare ones elif (len(freq_surf) + len(rare_surf)) >= threshold: shuffled_frequent = random.sample(freq_surf, len(freq_surf)) shuffled_rare = random.sample(rare_surf, int(threshold - len(freq_surf))) instances = shuffled_frequent + shuffled_rare else: print("Not enough instances are left") return [] return instances def split_for_morph_test_mixed(feat, lang, vocab, nonlabelratio, savedir, threshold=10000): """ Splits unimorph data into training, dev an test for the given feature and language. Precheck 1: We check if the feature can have more than one label beforehand This function eliminates cases where the form has space - e.g., "anlıyor musun" This function eliminates cases where the feature is very sparse (seen less than 5 times) This function eliminates ambiguous forms :param feat: Case, Valency... :param lang: turkish, russian, english... :param vocab: frequent word list from wikipedia :param savedir: folder to save the splits :param threshold: fixed to 10K :return: Default output directory is ./output/feature/lang/train-dev-test.txt """ freq_surf = [] rare_surf = [] schema = UnimorphSchema() data = load_ds("unimorph", lang) # make a label dictionary for noisy labels label_cnt = dict() # make a surface form dictionary for ambiguous surf_cnt = dict() nonlabel_cnt = threshold * nonlabelratio reallabel_cnt = threshold * (1. - nonlabelratio) for x in data[lang]: # exclude lemmas with space if ' ' in x["form"]: continue x_feats = schema.decode_msd(x["msd"])[0] if feat in x_feats: # There is a bug with Number: 'Part of Speech' # Don't include verbs/verb like words to singular/plural test # if feat=='Number' and (x_feats['Part of Speech']!='Noun'): # continue ## Exceptions: drop V.PTCP from the case and gender tests - russian if (feat in ['Case', 'Gender']) and (lang == 'russian') and (x_feats['Part of Speech'] == 'Participle'): continue ## Exceptions: If it is a gender test and the noun does not have a gender feature, ignore if (feat == 'Gender') and (lang == 'russian') and (x_feats['Part of Speech'] == 'Noun') and ( 'Gender' not in x_feats): continue if x["form"].lower() in vocab: freq_surf.append(x) # rare surface and frequent lemma else: rare_surf.append(x) # for sparse labels if x_feats[feat] not in label_cnt: label_cnt[x_feats[feat]] = 1 else: label_cnt[x_feats[feat]] += 1 # for amb. forms if x['form'] not in surf_cnt: surf_cnt[x['form']] = 1 else: surf_cnt[x['form']] += 1 # if there is any (very) sparse label, exclude those forbid_labs = [] for label in label_cnt: if (label_cnt[label]) < 5: forbid_labs.append(label) # if there are any surface forms with multiple values, exclude those amb_form_dict = dict() for surf, cnt in surf_cnt.items(): if cnt > 1: amb_form_dict[surf] = 1 # check here if we don't have enough instances or labels already if ((len(label_cnt) - len(forbid_labs)) < 2) or len(surf_cnt) < reallabel_cnt: print("Not enough instances or labels are left") return False # Exclude the noisy labels, ambiguous forms and rare words if ((len(forbid_labs) > 0) or (len(amb_form_dict) > 0)): freq_surf = [] rare_surf = [] for x in data[lang]: # exclude lemmas with space if ' ' in x["form"]: continue # exclude amb. forms if x["form"] in amb_form_dict: continue x_feats = schema.decode_msd(x["msd"])[0] if 'Part of Speech' not in x_feats: # probably a mistake in unimorph, just pass continue # exclude non nominal forms which has plurality tag # if feat=='Number' and (x_feats['Part of Speech']!='Noun'): # continue ## Exceptions: drop V.PTCP from the case and gender tests - russian if (feat in ['Case', 'Gender']) and (lang == 'russian') and x_feats['Part of Speech'] == 'Participle': continue ## Exceptions: If it is a gender test and the noun does not have a gender feature, ignore if (feat == 'Gender') and (lang == 'russian') and (x_feats['Part of Speech'] == 'Noun') and ( 'Gender' not in x_feats): continue if (feat in x_feats) and (x_feats[feat] not in forbid_labs): # instances.append(x) # if frequent surface if x["form"].lower() in vocab: freq_surf.append(x) # rare surface else: rare_surf.append(x) # Try to sample 80%-20% if possible if (len(freq_surf) >= int(reallabel_cnt * 0.8)) and (len(rare_surf) >= int(reallabel_cnt * 0.2)): shuffled_frequent = random.sample(freq_surf, int(reallabel_cnt * 0.8)) shuffled_rare = random.sample(rare_surf, int(reallabel_cnt * 0.2)) instances = shuffled_frequent + shuffled_rare # else get all the frequent ones, and sample the rest from the rare ones elif (len(freq_surf) + len(rare_surf)) >= reallabel_cnt: shuffled_frequent = random.sample(freq_surf, len(freq_surf)) shuffled_rare = random.sample(rare_surf, int(reallabel_cnt - len(freq_surf))) instances = shuffled_frequent + shuffled_rare else: print("Not enough instances are left") return False # get the nonlabel instances non_instances = get_mixed_surface(feat, lang, vocab, nonlabel_cnt) if len(non_instances) == 0: return False all_instances = instances + non_instances shuffled_instances = random.sample(all_instances, threshold) train_inst = shuffled_instances[:int(threshold * 0.7)] dev_inst = shuffled_instances[int(threshold * 0.7):int(threshold * 0.9)] test_inst = shuffled_instances[int(threshold * 0.9):] train_path = os.path.join(savedir, feat, lang, "train.txt") ensure_dir(train_path) dev_path = os.path.join(savedir, feat, lang, "dev.txt") ensure_dir(dev_path) test_path = os.path.join(savedir, feat, lang, "test.txt") ensure_dir(test_path) # Write file with open(train_path, 'w') as fout: for inst in train_inst: x_feats = schema.decode_msd(inst["msd"])[0] if "flag" not in inst: if feat == 'Person': x_feats[feat] = x_feats[feat] + " " + x_feats['Number'] fout.write("\t".join([inst["form"], x_feats[feat]]) + "\n") else: fout.write("\t".join([inst["form"], "None"]) + "\n") fout.close() with open(dev_path, 'w') as fout: for inst in dev_inst: x_feats = schema.decode_msd(inst["msd"])[0] if "flag" not in inst: if feat == 'Person': x_feats[feat] = x_feats[feat] + " " + x_feats['Number'] fout.write("\t".join([inst["form"], x_feats[feat]]) + "\n") else: fout.write("\t".join([inst["form"], "None"]) + "\n") fout.close() with open(test_path, 'w') as fout: for inst in test_inst: x_feats = schema.decode_msd(inst["msd"])[0] if "flag" not in inst: if feat == 'Person': x_feats[feat] = x_feats[feat] + " " + x_feats['Number'] fout.write("\t".join([inst["form"], x_feats[feat]]) + "\n") else: fout.write("\t".join([inst["form"], "None"]) + "\n") fout.close() return True def split_for_morph_test(feat, lang, vocab, savedir, threshold=10000): """ Splits unimorph data into training, dev an test for the given feature and language. Precheck 1: We check if the feature can have more than one label beforehand This function eliminates cases where the form has space - e.g., "anlıyor musun" This function eliminates cases where the feature is very sparse (seen less than 5 times) This function eliminates ambiguous forms :param feat: Case, Valency... :param lang: turkish, russian, english... :param vocab: frequent word list from wikipedia :param savedir: folder to save the splits :param threshold: fixed to 10K :return: Default output directory is ./output/feature/lang/train-dev-test.txt """ freq_surf = [] rare_surf = [] schema = UnimorphSchema() data = load_ds("unimorph", lang) # make a label dictionary for noisy labels label_cnt = dict() # make a surface form dictionary for ambiguous surf_cnt = dict() for x in data[lang]: # exclude lemmas with space if ' ' in x["form"]: continue x_feats = schema.decode_msd(x["msd"])[0] if feat in x_feats: # There is a bug with Number: 'Part of Speech' # Don't include verbs/verb like words to singular/plural test # if feat=='Number' and (x_feats['Part of Speech']!='Noun'): # continue # instances.append(x) if x["form"].lower() in vocab: freq_surf.append(x) # rare surface else: rare_surf.append(x) # for sparse labels if x_feats[feat] not in label_cnt: label_cnt[x_feats[feat]] = 1 else: label_cnt[x_feats[feat]] += 1 # for amb. forms if x['form'] not in surf_cnt: surf_cnt[x['form']] = 1 else: surf_cnt[x['form']] += 1 # if there is any (very) sparse label, exclude those forbid_labs = [] for label in label_cnt: if (label_cnt[label]) < 5: forbid_labs.append(label) # if there are any surface forms with multiple values, exclude those amb_form_dict = dict() for surf, cnt in surf_cnt.items(): if cnt > 1: amb_form_dict[surf] = 1 # check here if we don't have enough instances or labels already if ((len(label_cnt) - len(forbid_labs)) < 2) or len(surf_cnt) < threshold: print("Not enough instances or labels are left") return False # Exclude the noisy labels, ambiguous forms and rare words if ((len(forbid_labs) > 0) or (len(amb_form_dict) > 0)): freq_surf = [] rare_surf = [] for x in data[lang]: # exclude lemmas with space if ' ' in x["form"]: continue # exclude amb. forms if x["form"] in amb_form_dict: continue x_feats = schema.decode_msd(x["msd"])[0] if 'Part of Speech' not in x_feats: # probably a mistake in unimorph, just pass continue # exclude non nominal forms which has plurality tag # if feat=='Number' and (x_feats['Part of Speech']!='Noun'): # continue if (feat in x_feats) and (x_feats[feat] not in forbid_labs): # instances.append(x) # if frequent surface if x["form"].lower() in vocab: freq_surf.append(x) # rare surface else: rare_surf.append(x) # Try to sample 80%-20% if possible if (len(freq_surf) >= int(threshold * 0.8)) and (len(rare_surf) >= int(threshold * 0.2)): shuffled_frequent = random.sample(freq_surf, int(threshold * 0.8)) shuffled_rare = random.sample(rare_surf, int(threshold * 0.2)) instances = shuffled_frequent + shuffled_rare # else get all the frequent ones, and sample the rest from the rare ones elif (len(freq_surf) + len(rare_surf)) >= threshold: shuffled_frequent = random.sample(freq_surf, len(freq_surf)) shuffled_rare = random.sample(rare_surf, int(threshold - len(freq_surf))) instances = shuffled_frequent + shuffled_rare else: print("Not enough instances are left") return False shuffled_instances = random.sample(instances, threshold) train_inst = shuffled_instances[:int(threshold * 0.7)] dev_inst = shuffled_instances[int(threshold * 0.7):int(threshold * 0.9)] test_inst = shuffled_instances[int(threshold * 0.9):] train_path = os.path.join(savedir, feat, lang, "train.txt") ensure_dir(train_path) dev_path = os.path.join(savedir, feat, lang, "dev.txt") ensure_dir(dev_path) test_path = os.path.join(savedir, feat, lang, "test.txt") ensure_dir(test_path) # Write file with open(train_path, 'w') as fout: for inst in train_inst: x_feats = schema.decode_msd(inst["msd"])[0] if feat == 'Person': x_feats[feat] = x_feats[feat] + " " + x_feats['Number'] fout.write("\t".join([inst["form"], x_feats[feat]]) + "\n") fout.close() with open(dev_path, 'w') as fout: for inst in dev_inst: x_feats = schema.decode_msd(inst["msd"])[0] if feat == 'Person': x_feats[feat] = x_feats[feat] + " " + x_feats['Number'] fout.write("\t".join([inst["form"], x_feats[feat]]) + "\n") fout.close() with open(test_path, 'w') as fout: for inst in test_inst: x_feats = schema.decode_msd(inst["msd"])[0] if feat == 'Person': x_feats[feat] = x_feats[feat] + " " + x_feats['Number'] fout.write("\t".join([inst["form"], x_feats[feat]]) + "\n") fout.close() return True def split_for_number_test(lang, vocab, savedir, threshold=10000): """ Create train, dev, test splits for 'number of characters' and 'number of morphemes' tests :param lang: turkish, russian, english... :param vocab: frequent word list from wikipedia :param savedir: folder to save the splits :param threshold: fixed to 10K :return: Default output directory is ./output/CharacterCount/lang/train-dev-test.txt and ./output/TagCount/lang/train-dev-test.txt """ # instances = [] freq_surf = [] rare_surf = [] schema = UnimorphSchema() data = load_ds("unimorph", lang) surf_dict = dict() for x in data[lang]: # exclude lemmas with space if ' ' in x["form"]: continue # exclude duplicates if x['form'] in surf_dict: continue # exclude rare words # if x[raretype].lower() not in vocab: # continue # else: surf_dict[x['form']] = 1 x["num_chars"] = str(len(x["form"])) x["num_morph_tags"] = str(len(schema.decode_msd(x["msd"])[0])) if x["form"].lower() in vocab: freq_surf.append(x) # rare surface and frequent lemma else: rare_surf.append(x) # instances.append(x) # Try to sample 80%-20% if possible if (len(freq_surf) >= int(threshold * 0.8)) and (len(rare_surf) >= int(threshold * 0.2)): shuffled_frequent = random.sample(freq_surf, int(threshold * 0.8)) shuffled_rare = random.sample(rare_surf, int(threshold * 0.2)) instances = shuffled_frequent + shuffled_rare # else get all the frequent ones, and sample the rest from the rare ones elif (len(freq_surf) + len(rare_surf)) >= threshold: shuffled_frequent = random.sample(freq_surf, len(freq_surf)) shuffled_rare = random.sample(rare_surf, int(threshold - len(freq_surf))) instances = shuffled_frequent + shuffled_rare else: print("Not enough instances are left") return False shuffled_instances = random.sample(instances, threshold) train_inst = shuffled_instances[:int(threshold * 0.7)] dev_inst = shuffled_instances[int(threshold * 0.7):int(threshold * 0.9)] test_inst = shuffled_instances[int(threshold * 0.9):] feat = "CharacterCount" train_path = os.path.join(savedir, feat, lang, "train.txt") ensure_dir(train_path) dev_path = os.path.join(savedir, feat, lang, "dev.txt") ensure_dir(dev_path) test_path = os.path.join(savedir, feat, lang, "test.txt") ensure_dir(test_path) # Write file with open(train_path, 'w') as fout: for inst in train_inst: fout.write("\t".join([inst["form"], inst["num_chars"]]) + "\n") fout.close() with open(dev_path, 'w') as fout: for inst in dev_inst: fout.write("\t".join([inst["form"], inst["num_chars"]]) + "\n") fout.close() with open(test_path, 'w') as fout: for inst in test_inst: fout.write("\t".join([inst["form"], inst["num_chars"]]) + "\n") fout.close() feat = "TagCount" train_path = os.path.join(savedir, feat, lang, "train.txt") ensure_dir(train_path) dev_path = os.path.join(savedir, feat, lang, "dev.txt") ensure_dir(dev_path) test_path = os.path.join(savedir, feat, lang, "test.txt") ensure_dir(test_path) # Write file with open(train_path, 'w') as fout: for inst in train_inst: fout.write("\t".join([inst["form"], inst["num_morph_tags"]]) + "\n") fout.close() with open(dev_path, 'w') as fout: for inst in dev_inst: fout.write("\t".join([inst["form"], inst["num_morph_tags"]]) + "\n") fout.close() with open(test_path, 'w') as fout: for inst in test_inst: fout.write("\t".join([inst["form"], inst["num_morph_tags"]]) + "\n") fout.close() return True def split_for_nonsense(lang, pseudodir, savedir, type="ort", threshold=10000): """ Create splits in two different formats: Binary: given the word, guess if it is pseduo or not Old20: given the pseudo word, guess its level of nonsense - approximately Probably binary one makes more sense, but there are more options available :param lang: any supported-prcessed wuggy language under generated folder :param pseudodir: folder of pseudo files generated by wuggy :param savedir: folder to save the splits :param type: ort or phon :param threshold: :return: """ instances = [] words = [] fin_path = os.path.join(pseudodir, (type + "_" + lang)) # Read file i = 0 with open(fin_path) as fin: for line in fin: if i == 0: i += 1 continue x = {} all_cols = line.rstrip().split("\t") x["word"] = all_cols[0] words.append(x["word"]) x["non_sense"] = all_cols[1] instances.append(x) fin.close() # make a vocab word_vocab = list(set(words)) if len(instances) < threshold: print("Not enough instances") return False if len(word_vocab) < (threshold / 2): print("Not enough words") return False # shuffle is an in-place operation random.shuffle(word_vocab) shuffled_instances = random.sample(instances, threshold) shuffled_labels = np.random.choice([0, 1], size=(threshold,), p=[1. / 2, 1. / 2]) train_inst = shuffled_instances[:int(threshold * 0.7)] train_labels = shuffled_labels[:int(threshold * 0.7)] dev_inst = shuffled_instances[int(threshold * 0.7):int(threshold * 0.9)] dev_labels = shuffled_labels[int(threshold * 0.7):int(threshold * 0.9)] test_inst = shuffled_instances[int(threshold * 0.9):] test_labels = shuffled_labels[int(threshold * 0.9):] feat = "NonSense_Binary" train_path = os.path.join(savedir, feat, lang, "train.txt") ensure_dir(train_path) dev_path = os.path.join(savedir, feat, lang, "dev.txt") ensure_dir(dev_path) test_path = os.path.join(savedir, feat, lang, "test.txt") ensure_dir(test_path) # Write file wi = 0 with open(train_path, 'w') as fout: for inst, label in zip(train_inst, train_labels): if label == 0: fout.write("\t".join([inst["non_sense"], str(label)]) + "\n") elif label == 1: fout.write("\t".join([word_vocab[wi], str(label)]) + "\n") wi += 1 fout.close() with open(dev_path, 'w') as fout: for inst, label in zip(dev_inst, dev_labels): if label == 0: fout.write("\t".join([inst["non_sense"], str(label)]) + "\n") elif label == 1: fout.write("\t".join([word_vocab[wi], str(label)]) + "\n") wi += 1 fout.close() with open(test_path, 'w') as fout: for inst, label in zip(test_inst, test_labels): if label == 0: fout.write("\t".join([inst["non_sense"], str(label)]) + "\n") elif label == 1: fout.write("\t".join([word_vocab[wi], str(label)]) + "\n") wi += 1 fout.close() return True def main(args): langs = {'portuguese': 'pt', 'french': 'fr', 'serbo-croatian': 'sh', 'polish': 'pl', 'czech': 'cs', 'modern-greek': 'el', 'catalan': 'ca', 'bulgarian': 'bg', 'danish': 'da', 'estonian': 'et', 'quechua': 'qu', 'swedish': 'sv', 'armenian': 'hy', 'macedonian': 'mk', 'arabic': 'ar', 'dutch': 'nl', 'hungarian': 'hu', 'italian': 'it', 'romanian': 'ro', 'ukranian': 'uk', 'german': 'de', 'finnish': 'fi', 'russian': 'ru', 'turkish': 'tr', 'spanish': 'es' } # Language specific vocabulary sizes # wiki vocabulary sizes: de: 2275234, es: 985668, fi: 730484, tr: 416052, ru: 1888424, # pt: 592109, fr: 1152450, sh: 454675, pl: 1032578, cs:627842, el: 306450, ca:490566, bg: 334079, da: 312957 # et: 329988, qu: 23074, sv: 1143274, hy: 332673, mk: 176948, ar: 610978, nl: 871023, hu: 793867, it: 871054, # ro: 354325, uk: 912459 langs_vocab = {'german': 750000, 'finnish': 500000, 'russian': 750000, 'turkish': 500000, 'spanish': 500000, \ 'portuguese': 500000, 'french': 750000, 'serbo-croatian': 500000, 'polish': 750000, 'czech': 500000, \ 'modern-greek': 500000, 'catalan': 500000, 'bulgarian': 500000, 'danish': 500000, 'estonian': 500000, \ 'quechua': 500000, 'swedish': 750000, 'armenian': 500000, 'macedonian': 500000, 'arabic': 500000, \ 'dutch': 600000, 'hungarian': 600000, 'italian': 600000, 'romanian': 500000, 'ukranian': 750000} with open('test_vs_lang_feat_over_10K.pkl', 'rb') as handle: test_vs_lang = pickle.load(handle) lang_vs_test = reverse_dict_list(test_vs_lang) # Load preprocessed statistics with open('supported_languages_over_10K.pkl', 'rb') as handle: supported_lang_list = pickle.load(handle) if args.feat == 1: for lang in lang_vs_test: if lang in langs: embfile = os.path.join('..', "embeddings", "wiki." + langs[lang] + ".vec") print("Reading vocabulary for lang " + lang) vocab = load_dict(embfile, maxvoc=langs_vocab[lang]) for test_name in lang_vs_test[lang]: print("Preparing " + lang + " - " + test_name) split_for_morph_test_mixed(test_name, lang, vocab, args.nonlabelratio, args.savedir) if args.common == 1: # General tests for all supported languages for lang in supported_lang_list: if lang in langs: # get the vocabulary first embfile = os.path.join('..', "embeddings", "wiki." + langs[lang] + ".vec") print("Reading vocabulary for lang " + lang) vocab = load_dict(embfile, maxvoc=langs_vocab[lang]) print("Preparing Character and Tag Count Tests- " + lang) split_for_number_test(lang, vocab, args.savedir) print("Preparing POS Test- " + lang) split_for_morph_test("Part of Speech", lang, vocab, args.savedir) if args.pseudo == 1: # Pseudo word tests only for languages with wuggy support # Orthographic pseudo ort_lang_lst = ["turkish", "german", "spanish", "english", 'dutch', 'french', 'serbian_latin', 'basque', 'vietnamese'] for lang in ort_lang_lst: print("Processing orthographic " + lang) split_for_nonsense(lang, args.pseudodir, args.savedir, type="ort") if __name__ == '__main__': parser = argparse.ArgumentParser() # Prepare feature tests parser.add_argument('--nonlabelratio', type=float, default=0.3) parser.add_argument('--savedir', type=str, default='./probing_datasets_2') parser.add_argument('--feat', type=int, default=0) parser.add_argument('--common', type=int, default=1) parser.add_argument('--pseudo', type=int, default=1) parser.add_argument('--pseudodir', type=str, default='./generated_wuggy_files') args = parser.parse_args() main(args)

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a21fc7c205150998371a4d620812a958d885dc65

336

py

Python

locale/pot/api/core/_autosummary/pyvista-UniformGrid-1.py

tkoyama010/pyvista-doc-translations

23bb813387b7f8bfe17e86c2244d5dd2243990db

[ "MIT" ]

4

2020-08-07T08:19:19.000Z

2020-12-04T09:51:11.000Z

locale/pot/api/core/_autosummary/pyvista-UniformGrid-1.py

tkoyama010/pyvista-doc-translations

23bb813387b7f8bfe17e86c2244d5dd2243990db

[ "MIT" ]

19

2020-08-06T00:24:30.000Z

2022-03-30T19:22:24.000Z

locale/pot/api/core/_autosummary/pyvista-UniformGrid-1.py

tkoyama010/pyvista-doc-translations

23bb813387b7f8bfe17e86c2244d5dd2243990db

[ "MIT" ]

1

2021-03-09T07:50:40.000Z

import pyvista import vtk import numpy as np # grid = pyvista.UniformGrid() # vtkgrid = vtk.vtkImageData() grid = pyvista.UniformGrid(vtkgrid) # dims = (10, 10, 10) grid = pyvista.UniformGrid(dims) # spacing = (2, 1, 5) grid = pyvista.UniformGrid(dims, spacing) # origin = (10, 35, 50) grid = pyvista.UniformGrid(dims, spacing, origin)

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a22664719551bd9b619aac10e37bfaca231b7de4

1,277

py

Python

app/barber/forms.py

avb76/barbershop

975b501b0c53600909910619e248dff627acaa22

[ "MIT" ]

null

app/barber/forms.py

avb76/barbershop

975b501b0c53600909910619e248dff627acaa22

[ "MIT" ]

null

app/barber/forms.py

avb76/barbershop

975b501b0c53600909910619e248dff627acaa22

[ "MIT" ]

null

from flask_wtf import FlaskForm from wtforms import StringField, PasswordField, SubmitField, TextAreaField, IntegerField from wtforms.validators import DataRequired, EqualTo, ValidationError, Length from app.models import Barber class NewBarberForm(FlaskForm): first_name = StringField('First Name', validators=[DataRequired()]) last_name = StringField('Last Name', validators=[DataRequired()]) username = StringField('Username', validators=[DataRequired()]) password = PasswordField('Password', validators=[DataRequired()]) password2 = PasswordField('Repeat Password', validators=[DataRequired(), EqualTo('password')]) submit = SubmitField('Add barber') def validate_username(self, username): user = Barber.query.filter_by(username=username.data).first() if user is not None: raise ValidationError('Please use a different username.') class NewServiceForm(FlaskForm): name = StringField('Name', validators=[DataRequired()]) description = TextAreaField('Description', validators=[DataRequired(), Length(min=0, max=300)]) duration = IntegerField('Duration (minutes)', validators=[DataRequired()]) price = IntegerField('Price (RON)', validators=[DataRequired()]) submit = SubmitField('Add service')

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3

bf87f1499d3b717a9457ce2ddb8d7a572916001a

41,840

py

Python

tests/util/answers.py

selectel/python-selvpcclient

99955064215c2be18b568e5e9b34f17087ec304f

[ "Apache-2.0" ]

7

2017-07-15T12:44:23.000Z

2020-03-24T09:45:11.000Z

tests/util/answers.py

selectel/python-selvpcclient

99955064215c2be18b568e5e9b34f17087ec304f

[ "Apache-2.0" ]

13

2017-07-05T09:34:09.000Z

2021-04-20T08:18:46.000Z

tests/util/answers.py

selectel/python-selvpcclient

99955064215c2be18b568e5e9b34f17087ec304f

[ "Apache-2.0" ]

9

2017-06-29T13:51:35.000Z

2021-06-26T21:00:49.000Z

from tests.util.params import LOGO_BASE64 PROJECTS_LIST = { 'projects': [{ "id": "15c578ea47a5466db2aeb57dc8443676", "name": "pr1", "url": "http://11111.selvpc.ru", "enabled": True, "theme": { "color": "", "logo": "", "brand_color": "", } }, { "id": "2c578ea47a5466db2aeb57dc8443676", "name": "pr2", "url": "http://11111.selvpc.ru", "enabled": True, "theme": { "color": "", "logo": "", "brand_color": "", } }] } PROJECTS_CREATE = { 'project': { "id": "15c578ea47a5466db2aeb57dc8443676", "name": "project1", "url": "http://11111.selvpc.ru", "enabled": True, "custom_url": "", "theme": { "color": "", "logo": "", "brand_color": "", } } } PROJECTS_CREATE_WITH_AUTO_QUOTAS = { 'project': { "id": "15c578ea47a5466db2aeb57dc8443676", "name": "project1", "url": "http://11111.selvpc.ru", "enabled": True, "quotas": { "compute_cores": [ { "region": "ru-1", "used": 0, "zone": "ru-1a", "value": 10, }, ], }, } } PROJECTS_SET = { 'project': { "id": "15c578ea47a5466db2aeb57dc8443676", "name": "project1", "url": "http://11111.selvpc.ru", "enabled": True, "custom_url": "www.customhost.no", "theme": { "color": "", "logo": "", "brand_color": "", } } } PROJECTS_SET_WITHOUT_CNAME = PROJECTS_CREATE PROJECTS_SHOW = { 'project': { "id": "f5c578ea47a5466db2aeb57dc8443676", "name": "pr1", "url": "http://11111.selvpc.ru", "enabled": True, "quotas": { "compute_cores": [ { "region": "ru-1", "zone": "ru-1a", "value": 10, "used": 1 }, { "region": "ru-1", "zone": "ru-1b", "value": 10, "used": 0 } ], "compute_ram": [ { "region": "ru-1", "zone": "ru-1a", "value": 1024, "used": 512 }, { "region": "ru-1", "zone": "ru-1b", "value": 2048, "used": 0 } ] }, "theme": { "color": "", "logo": "", "brand_color": "", } } } PROJECTS_SHOW_ROLES = { 'roles': [{ "project_id": "1_7354286c9ebf464d86efc16fb56d4fa3", "user_id": "5900efc62db34decae9f2dbc04a8ce0f" }, { "project_id": "2_7354286c9ebf464d86efc16fb56d4fa3", "user_id": "5900efc62db34decae9f2dbc04a8ce0f" }] } PROJECT_CUSTOMIZE = { 'theme': { "color": "00ffee", "logo": LOGO_BASE64, "brand_color": "00ffee", } } CUSTOMIZATION_CREATE = PROJECT_CUSTOMIZE CUSTOMIZATION_SHOW = PROJECT_CUSTOMIZE CUSTOMIZATION_UPDATE = { 'theme': { "color": "00eeff", "logo": LOGO_BASE64, "brand_color": "00ffee", } } CUSTOMIZATION_NO_THEME = { "theme": {"color": "", "logo": "", "brand_color": ""} } LIMITS_SHOW = { 'quotas': { "compute_cores": [ { "region": "ru-2", "zone": "ru-2a", "value": 10 }, { "region": "ru-1", "zone": "ru-1a", "value": 10 }, { "region": "ru-3", "zone": "ru-3c", "value": 10 }, { "region": "ru-3", "zone": "ru-3a", "value": 10 }, { "region": "ru-3", "zone": "ru-3z", "value": 10 } ], "compute_ram": [ { "region": "ru-1", "zone": "ru-1a", "value": 1024 }, { "region": "ru-1", "zone": "ru-1b", "value": 2048 } ], "volume_gigabytes_fast": [ { "region": "ru-1", "zone": "ru-1a", "value": 100 }, { "region": "ru-1", "zone": "ru-1b", "value": 100 } ], "volume_gigabytes_universal": [ { "region": "ru-1", "zone": "ru-1a", "value": 100 }, { "region": "ru-1", "zone": "ru-1b", "value": 100 } ], "volume_gigabytes_basic": [ { "region": "ru-1", "zone": "ru-1a", "value": 100 }, { "region": "ru-1", "zone": "ru-1b", "value": 100 } ], "image_gigabytes": [ { "region": "ru-2", "zone": None, "value": 10 }, { "region": "ru-1", "zone": None, "value": 10 }, ], "network_floatingips": [ { "region": "ru-1", "zone": None, "value": 5 } ], "network_subnets_29": [ { "region": "ru-1", "zone": None, "value": 1 } ], "license_windows_2012_standard": [ { "region": "ru-1", "zone": None, "value": 1 } ] } } LIMITS_SHOW_FREE = { 'quotas': { "compute_cores": [ { "region": "ru-1", "zone": "ru-1a", "value": 10 }, { "region": "ru-1", "zone": "ru-1b", "value": 10 } ], "compute_ram": [ { "region": "ru-1", "zone": "ru-1a", "value": 1024 }, { "region": "ru-1", "zone": "ru-1b", "value": 2048 } ], "volume_gigabytes_fast": [ { "region": "ru-1", "zone": "ru-1a", "value": 100 }, { "region": "ru-1", "zone": "ru-1b", "value": 100 } ], "volume_gigabytes_universal": [ { "region": "ru-1", "zone": "ru-1a", "value": 100 }, { "region": "ru-1", "zone": "ru-1b", "value": 100 } ], "volume_gigabytes_basic": [ { "region": "ru-1", "zone": "ru-1a", "value": 100 }, { "region": "ru-1", "zone": "ru-1b", "value": 100 } ], "image_gigabytes": [ { "region": "ru-1", "zone": None, "value": 10 } ], "network_floatingips": [ { "region": "ru-1", "zone": None, "value": 5 } ], "network_subnets_29": [ { "region": "ru-1", "zone": None, "value": 1 } ], "license_windows_2012_standard": [ { "region": "ru-1", "zone": None, "value": 1 } ] } } QUOTAS_OPTIMIZE_ALL_USING = { 'quotas': { "compute_cores": [ { "region": "ru-1", "zone": "ru-1a", "value": 10, "used": 10 }, { "region": "ru-1", "zone": "ru-1b", "value": 10, "used": 10 } ], "compute_ram": [ { "region": "ru-1", "zone": "ru-1a", "value": 1024, "used": 1024 }, { "region": "ru-1", "zone": "ru-1b", "value": 2048, "used": 2048 } ], "volume_gigabytes_fast": [ { "region": "ru-1", "zone": "ru-1a", "value": 100, "used": 100 }, { "region": "ru-1", "zone": "ru-1b", "value": 100, "used": 100 } ] } } QUOTAS_LIST = { "quotas": { "30bde559615740d28bb63ee626fd0f25": { "compute_cores": [ { "region": "ru1", "used": 0, "zone": "ru1b", "value": 36 }, { "region": "ru1", "used": 0, "zone": "ru1a", "value": 14 }, { "region": "ru2", "used": 0, "zone": "ru2a", "value": 66 } ], "volume_gigabytes_basic": [ { "region": "ru1", "used": 0, "zone": "ru1b", "value": 44 }, { "region": "ru1", "used": 0, "zone": "ru1a", "value": 25 }, { "region": "ru1", "used": 0, "zone": "ru1c", "value": 0 }, { "region": "ru2", "used": 0, "zone": "ru2c", "value": 0 }, { "region": "ru2", "used": 0, "zone": "ru2b", "value": 0 }, { "region": "ru2", "used": 0, "zone": "ru2a", "value": 81 } ], "compute_ram": [ { "region": "ru1", "used": 0, "zone": "ru1b", "value": 9728 }, { "region": "ru1", "used": 0, "zone": "ru1a", "value": 4608 }, { "region": "ru2", "used": 0, "zone": "ru2a", "value": 12800 } ], "volume_gigabytes_fast": [ { "region": "ru1", "used": 0, "zone": "ru1b", "value": 47 }, { "region": "ru1", "used": 0, "zone": "ru1a", "value": 26 }, { "region": "ru1", "used": 0, "zone": "ru1c", "value": 0 }, { "region": "ru2", "used": 0, "zone": "ru2b", "value": 0 }, { "region": "ru2", "used": 0, "zone": "ru2c", "value": 0 }, { "region": "ru2", "used": 0, "zone": "ru2a", "value": 26 } ], "image_gigabytes": [ { "region": "ru1", "used": 0, "zone": None, "value": 46 }, { "region": "ru2", "used": 0, "zone": None, "value": 44 } ] }, "efae8856aa67477a97847ad595306628": { "compute_cores": [ { "region": "ru1", "used": 0, "zone": "ru1b", "value": 11 }, { "region": "ru1", "used": 0, "zone": "ru1a", "value": 0 }, { "region": "ru2", "used": 0, "zone": "ru2a", "value": 0 } ], "volume_gigabytes_basic": [ { "region": "ru1", "used": 0, "zone": "ru1b", "value": 13 }, { "region": "ru1", "used": 0, "zone": "ru1a", "value": 0 }, { "region": "ru1", "used": 0, "zone": "ru1c", "value": 0 }, { "region": "ru2", "used": 0, "zone": "ru2c", "value": 0 }, { "region": "ru2", "used": 0, "zone": "ru2b", "value": 0 }, { "region": "ru2", "used": 0, "zone": "ru2a", "value": 0 } ], "compute_ram": [ { "region": "ru1", "used": 0, "zone": "ru1b", "value": 3840 }, { "region": "ru1", "used": 0, "zone": "ru1a", "value": 0 }, { "region": "ru2", "used": 0, "zone": "ru2a", "value": 0 } ], "volume_gigabytes_fast": [ { "region": "ru1", "used": 0, "zone": "ru1b", "value": 12 }, { "region": "ru1", "used": 0, "zone": "ru1a", "value": 0 }, { "region": "ru1", "used": 0, "zone": "ru1c", "value": 0 }, { "region": "ru2", "used": 0, "zone": "ru2b", "value": 0 }, { "region": "ru2", "used": 0, "zone": "ru2c", "value": 0 }, { "region": "ru2", "used": 0, "zone": "ru2a", "value": 0 } ], "image_gigabytes": [ { "region": "ru1", "used": 0, "zone": None, "value": 13 }, { "region": "ru2", "used": 0, "zone": None, "value": 0 } ] } } } QUOTAS_SET = { "quotas": { "volume_gigabytes_basic": [ { "region": "ru-1", "used": 0, "zone": "ru-1b", "value": 0 }, { "region": "ru-1", "used": 0, "zone": "ru-1a", "value": 0 }, { "region": "ru-2", "used": 0, "zone": "ru-2a", "value": 0 } ], "compute_cores": [ { "region": "ru-1", "used": 0, "zone": "ru-1b", "value": 1 }, { "region": "ru-1", "used": 2, "zone": "ru-1a", "value": 2 }, { "region": "ru-2", "used": 0, "zone": "ru-2a", "value": 1 } ], "volume_gigabytes_universal": [ { "region": "ru-1", "used": 0, "zone": "ru-1b", "value": 0 }, { "region": "ru-1", "used": 0, "zone": "ru-1a", "value": 0 }, { "region": "ru-2", "used": 0, "zone": "ru-2a", "value": 0 } ], "compute_ram": [ { "region": "ru-1", "used": 0, "zone": "ru-1b", "value": 512 }, { "region": "ru-1", "used": 1536, "zone": "ru-1a", "value": 1536 }, { "region": "ru-2", "used": 0, "zone": "ru-2a", "value": 0 } ], "volume_gigabytes_fast": [ { "region": "ru-1", "used": 5, "zone": "ru-1b", "value": 5 }, { "region": "ru-1", "used": 20, "zone": "ru-1a", "value": 20 }, { "region": "ru-2", "used": 0, "zone": "ru-2a", "value": 0 } ], "image_gigabytes": [ { "region": "ru-1", "used": 0, "zone": None, "value": 0 }, { "region": "ru-2", "used": 0, "zone": None, "value": 0 } ] } } QUOTAS_SHOW = { 'quotas': { "compute_cores": [ { "region": "ru-1", "zone": "ru-1a", "value": 10, "used": 0 }, { "region": "ru-1", "zone": "ru-1b", "value": 10, "used": 0 } ], "compute_ram": [ { "region": "ru-1", "zone": "ru-1a", "value": 1024, "used": 0 }, { "region": "ru-1", "zone": "ru-1b", "value": 2048, "used": 0 } ], "volume_gigabytes_fast": [ { "region": "ru-1", "zone": "ru-1a", "value": 100, "used": 0 }, { "region": "ru-1", "zone": "ru-1b", "value": 100, "used": 0 } ], "network_subnets_29_vrrp": [ { "region": None, "used": 0, "value": 0, "zone": None } ], } } USERS_LIST = { 'users': [{ "id": "f9fd1d3167ba4641a3190b4848382216", "name": "user1", "enabled": True }, { "id": "1d3161d317ba4641a3190b4848382216", "name": "user2", "enabled": True }] } USERS_CREATE = { 'user': { "id": "f9fd1d3167ba4641a3190b4848382216", "name": "user", "enabled": True } } USERS_ROLE_SHOW = { 'roles': [{ "project_id": "1_7354286c9ebf464d86efc16fb56d4fa3", "user_id": "5900efc62db34decae9f2dbc04a8ce0f" }, { "project_id": "1_7354286c9ebf464d86efc16fb56d4fa3", "user_id": "5900efc62db34decae9f2dbc04a8ce0f" }] } USERS_EMPTY = { "field": "user_id", "error": "invalid_id" } USERS_SET = USERS_CREATE USERS_SHOW = USERS_CREATE TOKENS_CREATE = { 'token': { 'id': "a9a81014462d499d9d55d3402991f224" } } LICENSES_LIST = { 'licenses': [{ "id": 0, "region": "ru-1", "type": "license_windows_2012_standard", "project_id": "e7081cb46966421fb8b3f3fd9b4db75b", "servers": [ { "id": "177b0416-2830-4557-898a-581c1147f0ff", "updated": "2016-01-01T00:00:00Z", "status": "PAUSED", "name": "s1" } ], "status": "ACTIVE" }, { "id": 1, "region": "ru-2", "type": "license_windows_2012_standard", "project_id": "xxxx1cb46966421fb8b3f3fd9b4db75b", "servers": [ { "id": "177b0416-2830-4557-898a-581c1147f0ff", "updated": "2016-01-01T00:00:00Z", "status": "PAUSED", "name": "s1" } ], "status": "ACTIVE" }] } LICENSES_SHOW = { "license": { "id": 420, "region": "ru-1", "type": "license_windows_2012_standard", "project_id": "e7081cb46966421fb8b3f3fd9b4db75b", "servers": [ { "id": "177b0416-2830-4557-898a-581c1147f0ff", "updated": "2016-01-01T00:00:00Z", "status": "PAUSED", "name": "s1" } ], "status": "ACTIVE" } } LICENSES_CREATE = { 'licenses': [{ "id": 1, "region": "ru-1", "type": "license_windows_2012_standard", "project_id": "e7081cb46966421fb8b3f3fd9b4db75b", "servers": [ { "id": "177b0416-2830-4557-898a-581c1147f0ff", "updated": "2016-01-01T00:00:00Z", "status": "PAUSED", "name": "s1" } ], "status": "ACTIVE" }, { "id": 2, "region": "ru-2", "type": "license_windows_2012_standard", "project_id": "e7081cb46966421fb8b3f3fd9b4db75b", "servers": [ { "id": "177b0416-2830-4557-898a-581c1147f0ff", "updated": "2016-01-01T00:00:00Z", "status": "PAUSED", "name": "s1" } ], "status": "ACTIVE" }] } ROLES_LIST = { 'roles': [{ "project_id": "1_7354286c9ebf464d86efc16fb56d4fa3", "user_id": "1900efc62db34decae9f2dbc04a8ce0f" }, { "project_id": "1_7354286c9ebf464d86efc16fb56d4fa3", "user_id": "5900efc62db34decae9f2dbc04a8ce0f" }] } ROLES_ADD = { 'role': { "project_id": "1_7354286c9ebf464d86efc16fb56d4fa3", "user_id": "5900efc62db34decae9f2dbc04a8ce0f" } } FLOATINGIP_LIST = { "floatingips": [ { "status": "ACTIVE", "tenant_id": "a2e6dd715ca24681b9b335d247b83d16", "servers": [ { "status": "ACTIVE", "updated": "2016-01-01T00:00:00Z", "id": "dc113178-b573-4459-bdde-272ec18140f3", "name": "Raya" } ], "fixed_ip_address": "192.168.0.4", "floating_ip_address": "12.34.56.78", "project_id": "a2e6dd715ca24681b9b335d247b83d16", "port_id": "dc801110-94f2-4fdd-b71a-74e2d3d8bfd0", "id": "0d987b46-bad5-41b7-97e3-bac9974aa97a", "region": "ru-1" }, { "status": "ACTIVE", "tenant_id": "xxxxdd715ca24681b9b335d247b83d16", "servers": [ { "status": "ACTIVE", "updated": "2016-01-01T00:00:00Z", "id": "dc113178-b573-4459-bdde-272ec18140f3", "name": "Raya" } ], "fixed_ip_address": "192.168.0.4", "floating_ip_address": "12.34.56.78", "project_id": "xxxxdd715ca24681b9b335d247b83d16", "port_id": "dc801110-94f2-4fdd-b71a-74e2d3d8bfd0", "id": "0d987b46-bad5-41b7-97e3-bac9974aa97a", "region": "ru-2" } ] } FLOATINGIP_ADD = { "floatingips": [ { "status": "ACTIVE", "tenant_id": "a2e6dd715ca24681b9b335d247b83d16", "servers": [ { "status": "ACTIVE", "updated": "2016-01-01T00:00:00Z", "id": "dc113178-b573-4459-bdde-272ec18140f3", "name": "Raya" } ], "fixed_ip_address": "192.168.0.4", "floating_ip_address": "12.34.56.78", "project_id": "a2e6dd715ca24681b9b335d247b83d16", "port_id": "dc801110-94f2-4fdd-b71a-74e2d3d8bfd0", "id": "0d987b46-bad5-41b7-97e3-bac9974aa97a", "region": "ru-1" } ] } FLOATINGIP_SHOW = { "floatingip": { "status": "ACTIVE", "servers": [ { "status": "ACTIVE", "updated": "2016-01-01T00:00:00Z", "id": "dc113178-b573-4459-bdde-272ec18140f3", "name": "Raya" } ], "floating_ip_address": "12.34.56.78", "project_id": "a2e6dd715ca24681b9b335d247b83d16", "id": "0d987b46-bad5-41b7-97e3-bac9974aa97a", "region": "ru-1" } } SUBNET_LIST = { "subnets": [ { "id": 20, "region": "ru-1", "cidr": "192.168.5.32/29", "enabled": True, "network_id": "70e73ef1-bade-4377-a52c-4a8cff843170", "project_id": "e7081cb46966421fb8b3f3fd9b4db75b", "status": "ACTIVE", "subnet_id": "61053c51-93f4-4d64-9a94-d4f88d1ee88f", "servers": [ { "id": "177b0416-2830-4557-898a-581c1147f0ff", "updated": "2016-01-01T00:00:00Z", "status": "PAUSED", "name": "s1" } ] }, { "id": 21, "region": "ru-2", "cidr": "192.168.5.32/29", "enabled": True, "network_id": "70e73ef1-bade-4377-a52c-4a8cff843170", "project_id": "xxxxcb46966421fb8b3f3fd9b4db75b", "status": "ACTIVE", "subnet_id": "61053c51-93f4-4d64-9a94-d4f88d1ee88f", "servers": [ { "id": "177b0416-2830-4557-898a-581c1147f0ff", "updated": "2016-01-01T00:00:00Z", "status": "PAUSED", "name": "s1" } ] } ] } SUBNET_ADD = { "subnets": [ { "id": 20, "region": "ru-1", "cidr": "192.168.5.32/29", "enabled": True, "network_id": "70e73ef1-bade-4377-a52c-4a8cff843170", "project_id": "e7081cb46966421fb8b3f3fd9b4db75b", "status": "ACTIVE", "subnet_id": "61053c51-93f4-4d64-9a94-d4f88d1ee88f", "servers": [ { "id": "177b0416-2830-4557-898a-581c1147f0ff", "updated": "2016-01-01T00:00:00Z", "status": "PAUSED", "name": "s1" } ] }, { "id": 21, "region": "ru-1", "cidr": "192.168.5.32/29", "enabled": True, "network_id": "70e73ef1-bade-4377-a52c-4a8cff843170", "project_id": "e7081cb46966421fb8b3f3fd9b4db75b", "status": "ACTIVE", "subnet_id": "61053c51-93f4-4d64-9a94-d4f88d1ee88f", "servers": [ { "id": "177b0416-2830-4557-898a-581c1147f0ff", "updated": "2016-01-01T00:00:00Z", "status": "PAUSED", "name": "s1" } ] } ] } SUBNET_SHOW = { "subnet": { "status": "ACTIVE", "subnet_id": "6145fba6-dbe2-47af-bad2-6d1dcese5996", "region": "ru1", "servers": [ { "id": "177b0416-2830-4557-898a-581c1147f0ff", "updated": "2016-01-01T00:00:00Z", "status": "PAUSED", "name": "s1" } ], "network_id": "47e4a3e8-a2c0-400c-a20c-2b3bf2f8b681", "cidr": "192.168.5.0/29", "project_id": "7810f45ae1be4a1f8ab3e95aef2e3ddd", "id": 420, } } CAPABILITIES_LIST = { "capabilities": { "licenses": [ { "availability": [ "ru-1", "ru-2" ], "type": "license_windows_2012_standard" } ], "regions": [ { "description": "Moscow", "is_default": True, "name": "ru-2", "zones": [ { "description": "Berzarina-1 (ru-2a)", "enabled": True, "is_default": True, "is_private": False, "name": "ru-2a" } ] }, { "description": "Saint Petersburg", "is_default": False, "name": "ru-1", "zones": [ { "description": "Dubrovka-1 (ru-1a)", "enabled": True, "is_default": False, "is_private": False, "name": "ru-1a" }, { "description": "Dubrovka-2 (ru-1b)", "enabled": True, "is_default": True, "is_private": False, "name": "ru-1b" } ] } ], "resources": [ { "name": "network_floatingips", "quota_scope": None, "quotable": False, "unbillable": False, }, { "name": "volume_gigabytes_universal", "quota_scope": "zone", "quotable": True, "unbillable": True, }, { "name": "volume_gigabytes_basic", "quota_scope": "zone", "quotable": True, "unbillable": False, }, { "name": "compute_ram", "quota_scope": "zone", "quotable": True, "unbillable": False, }, { "name": "volume_gigabytes_fast", "quota_scope": "zone", "quotable": True, "unbillable": False, }, { "name": "license_windows_2012_standard", "quota_scope": None, "quotable": False, "unbillable": False, }, { "name": "image_gigabytes", "quota_scope": "region", "quotable": True, "unbillable": False, }, { "name": "network_subnets_29", "quota_scope": None, "quotable": False, "unbillable": False, }, { "name": "compute_cores", "quota_scope": "zone", "quotable": True, "unbillable": False, }, { "name": "network_subnets_25", "quota_scope": None, "quotable": False, "unbillable": True, } ], "subnets": [ { "availability": [ "ru-1", "ru-2" ], "prefix_length": "29", "type": "ipv4" } ], "traffic": { "granularities": [ { "granularity": 3600, "timespan": 96 }, { "granularity": 1, "timespan": 32 }, { "granularity": 86400, "timespan": 1825 } ] } } } VRRP_ADD = { "vrrp_subnets": [{ "status": "DOWN", "cidr": "78.155.195.8/29", "project_id": "b63ab68796e34858befb8fa2a8b1e12a", "id": 6, "subnets": [ { "network_id": "827fe85f-a379-4f28-a426-2ddf7ddab6a2", "subnet_id": "6595e66c-b14e-4167-9a48-6be6fb407c63", "region": "ru-1" }, { "network_id": "68b6a3e0-d016-4248-b8de-03cb20cacb2c", "subnet_id": "9e8cf4bb-a385-401d-bda4-395f3985ead1", "region": "ru-2" } ], }] } VRRP_SHOW = { "vrrp_subnet": { "status": "DOWN", "subnets": [ { "network_id": "1eb0e13d-0ce6-4c00-99e8-45e4787766fd", "subnet_id": "053f7817-6804-4fad-8f6b-0d1edef074ed", "region": "ru-1" }, { "network_id": "74694b81-4203-4599-ae71-029182f9cef9", "subnet_id": "cc1d50b9-4890-4173-a750-4537c1f747a2", "region": "ru-2" } ], "servers": [], "cidr": "78.155.195.0/29", "project_id": "b63ab68796e34858befb8fa2a8b1e12a", "id": 2, "master_region": "ru-1", "slave_region": "ru-2" } } VRRP_LIST = { "vrrp_subnets": [ { "status": "DOWN", "subnets": [], "servers": [], "cidr": "78.155.196.0/29", "project_id": "x63ab68796e34858befb8fa2a8b1e12a", "id": 3, "master_region": "ru-1", "slave_region": "ru-2" }, { "status": "DOWN", "subnets": [], "servers": [], "cidr": "78.155.195.0/29", "project_id": "b63ab68796e34858befb8fa2a8b1e12a", "id": 2, "master_region": "ru-1", "slave_region": "ru-2" } ] } QUOTAS_PARTIAL = { "quotas": { "fail": [ { "region": "ru-1", "resource": "compute_ram", "zone": "ru-1b", "value": 2048 } ], "ok": [ { "region": "ru-1", "resource": "image_gigabytes", "zone": None, "used": 0, "value": 400 } ], "error": "multi_status" } } QUOTAS_PARTIAL_RESULT = { "quotas": { 'image_gigabytes': [ { "zone": None, "region": "ru-1", "value": 400, "used": 0} ] } } FLOATING_IPS_PARTIAL = { "floatingips": { "fail": [ { "region": "ru-2", "quantity": 1 } ], "ok": [ { "status": "DOWN", "floating_ip_address": "12.34.56.77", "project_id": "a2e6dd715ca24681b9b335d247b83d16", "id": "0d987b46-bad5-41b7-97e3-bac9974aa97a", "region": "ru-1" }, { "status": "DOWN", "floating_ip_address": "12.34.56.78", "project_id": "a2e6dd715ca24681b9b335d247b83d16", "id": "0d987b46-bad5-41b7-97e3-bac9974aa97b", "region": "ru-1" } ], "error": "multi_status" } } FLOATING_IPS_PARTIAL_RESULT = [ { "status": "DOWN", "floating_ip_address": "12.34.56.77", "project_id": "a2e6dd715ca24681b9b335d247b83d16", "id": "0d987b46-bad5-41b7-97e3-bac9974aa97a", "region": "ru-1" }, { "status": "DOWN", "floating_ip_address": "12.34.56.78", "project_id": "a2e6dd715ca24681b9b335d247b83d16", "id": "0d987b46-bad5-41b7-97e3-bac9974aa97b", "region": "ru-1" } ] LICENSES_PARTIAL = { 'licenses': { "fail": [ { "quantity": 1, "region": "ru-2", "type": "license_windows_2012_standard" } ], "ok": [ { "id": 1, "region": "ru-1", "type": "license_windows_2012_standard", "project_id": "e7081cb46966421fb8b3f3fd9b4db75b", "status": "DOWN" } ], "error": "multi_status" } } LICENSES_PARTIAL_RESULT = [ { "id": 1, "region": "ru-1", "type": "license_windows_2012_standard", "project_id": "e7081cb46966421fb8b3f3fd9b4db75b", "status": "DOWN" } ] ROLES_PARTIAL = { 'roles': { "fail": [{ "project_id": "1_7354286c9ebf464d86efc16fb56d4fa3", "user_id": "1900efc62db34decae9f2dbc04a8ce0f" }], "ok": [ { "project_id": "1_7354286c9ebf464d86efc16fb56d4fa3", "user_id": "5900efc62db34decae9f2dbc04a8ce0f" } ], "error": "multi_status" } } ROLES_PARTIAL_RESULT = [ { "project_id": "1_7354286c9ebf464d86efc16fb56d4fa3", "user_id": "5900efc62db34decae9f2dbc04a8ce0f" } ] SUBNETS_PARTIAL = { "subnets": { "fail": [ { "region": "ru-2", "prefix_length": 29, "quantity": 1, "type": "ipv4" } ], "ok": [ { "status": "DOWN", "subnet_id": "6145fba6-dbe2-47af-bad2-6d1dcese5996", "region": "ru-1", "network_id": "47e4a3e8-a2c0-400c-a20c-2b3bf2f8b681", "cidr": "192.168.5.0/29", "project_id": "7810f45ae1be4a1f8ab3e95aef2e3ddd", "id": 420 } ], "error": "multi_status" } } SUBNETS_PARTIAL_RESULT = [ { "status": "DOWN", "subnet_id": "6145fba6-dbe2-47af-bad2-6d1dcese5996", "region": "ru-1", "network_id": "47e4a3e8-a2c0-400c-a20c-2b3bf2f8b681", "cidr": "192.168.5.0/29", "project_id": "7810f45ae1be4a1f8ab3e95aef2e3ddd", "id": 420 } ] KEYPAIR_LIST = { "keypairs": [ { "name": "User_1", "public_key": "ssh-rsa ... user@name", "regions": [ "ru-1" ], "user_id": "88ad5569d8c64f828ac3d2efa4e552dd" }, { "name": "User_2", "public_key": "ssh-rsa ... user@name", "regions": [ "ru-2" ], "user_id": "88ad5569d8c64f828ac3d2efa4e552dd" } ] } KEYPAIR_ADD = { "keypair": [ { "name": "MOSCOW_KEY", "region": "ru-1", "user_id": "88ad5569d8c64f828ac3d2efa4e552dd" }, { "name": "MOSCOW_KEY", "region": "ru-2", "user_id": "88ad5569d8c64f828ac3d2efa4e552dd" } ] }

26.199123

73

0.328489

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41,840

4.959259

0.115556

0.062733

0.054444

0.041748

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null

0

1

0

1

0

null

0

3

bfbd4ea24f52b1f2ee0d10cbddf9da3af3c314fa

1,181

py

Python

pinolo/tasks.py

piger/pinolo

9fca121adccb759c7f50c8862813c279c17b6d48

[ "BSD-3-Clause" ]

2

2016-04-13T07:12:28.000Z

2018-04-10T15:14:25.000Z

pinolo/tasks.py

piger/pinolo

9fca121adccb759c7f50c8862813c279c17b6d48

[ "BSD-3-Clause" ]

null

pinolo/tasks.py

piger/pinolo

9fca121adccb759c7f50c8862813c279c17b6d48

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- """ pinolo.tasks ~~~~~~~~~~~~ A Task object is a `threading.Thread` instance that will be executed without blocking the main thread. This is useful to perform potentially blocking actions like fecthing resources via HTTP. :copyright: (c) 2013 Daniel Kertesz :license: BSD, see LICENSE for more details. """ import threading class Task(threading.Thread): """A task is an execution unit that will be run in a separate thread that should not block tha main thread (handling irc connections). """ def __init__(self, event, *args, **kwargs): self.event = event super(Task, self).__init__(*args, **kwargs) @property def queue(self): return self.event.client.bot.coda @property def reply(self): return self.event.reply def run(self): raise RuntimeError("Must be implemented!") def put_results(self, *data): """Task output will be sent to the main thread via the configured queue; data should be a string containing the full output, that will later be splitted on newlines.""" self.queue.put(tuple(data))

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1,181

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0.026455

0.050265

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0.253175

1,181

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0.6

0

null

0

null

0

1

0

1

0

3

449c69aeb84e825bf01bd5371b20267c504112a5

134

py

Python

example/cat/urls.py

govindsinghr3/s3directmul

a8e28d4af491dfbfaf3bc18c44efb3467e9b1603

[ "MIT" ]

1

2020-02-28T12:27:01.000Z

example/cat/urls.py

govindsinghr3/s3directmul

a8e28d4af491dfbfaf3bc18c44efb3467e9b1603

[ "MIT" ]

null

example/cat/urls.py

govindsinghr3/s3directmul

a8e28d4af491dfbfaf3bc18c44efb3467e9b1603

[ "MIT" ]

null

from django.conf.urls import patterns, url from .views import MyView urlpatterns = [ url('', MyView.as_view(), name='form'), ]

14.888889

43

0.679104

18

134

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0.777778

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0.171642

134

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null

0

1

0

null

0

1

0

3

44c197ce3c0345e83eea3cd5f823cbdd5413b901

23

py

Python

boxuegu/boxuegu/apps/users/constants.py

1111111111122222222223333333333/Django_boxuegu

cec4451bcc55f04013efd4e4cb7c4098a0fd5056

[ "MIT" ]

null

boxuegu/boxuegu/apps/users/constants.py

1111111111122222222223333333333/Django_boxuegu

cec4451bcc55f04013efd4e4cb7c4098a0fd5056

[ "MIT" ]

6

2021-02-08T20:30:13.000Z

2022-03-11T23:50:00.000Z

boxuegu/boxuegu/apps/users/constants.py

1111111111122222222223333333333/Django_boxuegu

cec4451bcc55f04013efd4e4cb7c4098a0fd5056

[ "MIT" ]

null

EMAIL_EXPIRES = 60 * 2

11.5

22

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23

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1

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0.666667

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1

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null

0

1

0

null

0

3

44cfdb489562c4d561d19a7518f0065fa73d7dde

9,573

py

Python

pyaff4/rdfvalue.py

rmddcr/pyaff4

398941ac51754900d9619c954d216bf14b9811ea

[ "Apache-2.0" ]

null

pyaff4/rdfvalue.py

rmddcr/pyaff4

398941ac51754900d9619c954d216bf14b9811ea

[ "Apache-2.0" ]

null

pyaff4/rdfvalue.py

rmddcr/pyaff4

398941ac51754900d9619c954d216bf14b9811ea

[ "Apache-2.0" ]

null

# Copyright 2014 Google Inc. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); you may not # use this file except in compliance with the License. You may obtain a copy of # the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations under # the License. """RDF Values are responsible for serialization.""" from __future__ import unicode_literals from future import standard_library standard_library.install_aliases() from builtins import str from builtins import object import functools import urllib.parse import urllib.request, urllib.parse, urllib.error import binascii import posixpath import rdflib from pyaff4 import registry from pyaff4 import utils # pylint: disable=protected-access class Memoize(object): def __call__(self, f): f.memo_pad = {} @functools.wraps(f) def Wrapped(self, *args): key = tuple(args) if len(f.memo_pad) > 100: f.memo_pad.clear() if key not in f.memo_pad: f.memo_pad[key] = f(self, *args) return f.memo_pad[key] return Wrapped class RDFValue(object): datatype = "" def __init__(self, initializer=None): self.Set(initializer) def GetRaptorTerm(self): return rdflib.Literal(self.SerializeToString(), datatype=self.datatype) def SerializeToString(self): """Serializes to a sequence of bytes.""" return "" def UnSerializeFromString(self, string): """Unserializes from bytes.""" raise NotImplementedError def Set(self, string): raise NotImplementedError def __bytes__(self): return self.SerializeToString() def __eq__(self, other): return utils.SmartStr(self) == utils.SmartStr(other) def __req__(self, other): return utils.SmartStr(self) == utils.SmartStr(other) def __hash__(self): return hash(self.SerializeToString()) class RDFBytes(RDFValue): value = b"" datatype = rdflib.XSD.hexBinary def SerializeToString(self): return binascii.hexlify(self.value) def UnSerializeFromString(self, string): self.Set(binascii.unhexlify(string)) def Set(self, data): self.value = data def __eq__(self, other): if isinstance(other, RDFBytes): return self.value == other.value class XSDString(RDFValue): """A unicode string.""" datatype = rdflib.XSD.string def SerializeToString(self): return utils.SmartStr(self.value) def UnSerializeFromString(self, string): self.Set(utils.SmartUnicode(string)) def Set(self, data): self.value = utils.SmartUnicode(data) def __str__(self): return self.value @functools.total_ordering class XSDInteger(RDFValue): datatype = rdflib.XSD.integer def SerializeToString(self): return utils.SmartStr(self.value) def UnSerializeFromString(self, string): self.Set(int(string)) def Set(self, data): self.value = int(data) def __eq__(self, other): if isinstance(other, XSDInteger): return self.value == other.value return self.value == other def __int__(self): return self.value def __long__(self): return int(self.value) def __cmp__(self, o): return self.value - o def __add__(self, o): return self.value + o def __lt__(self, o): return self.value < o def __str__(self): return str(self.value) class RDFHash(XSDString): # value is the hex encoded digest. def __eq__(self, other): if isinstance(other, RDFHash): if self.datatype == other.datatype: return self.value == other.value return utils.SmartStr(self.value) == utils.SmartStr(other) def __ne__(self, other): return not self == other def digest(self): return binascii.unhexlify(self.value) class SHA512Hash(RDFHash): datatype = rdflib.URIRef("http://aff4.org/Schema#SHA512") class SHA256Hash(RDFHash): datatype = rdflib.URIRef("http://aff4.org/Schema#SHA256") class SHA1Hash(RDFHash): datatype = rdflib.URIRef("http://aff4.org/Schema#SHA1") class Blake2bHash(RDFHash): datatype = rdflib.URIRef("http://aff4.org/Schema#Blake2b") class MD5Hash(RDFHash): datatype = rdflib.URIRef("http://aff4.org/Schema#MD5") class SHA512BlockMapHash(RDFHash): datatype = rdflib.URIRef("http://aff4.org/Schema#blockMapHashSHA512") class URN(RDFValue): """Represent a URN. According to RFC1738 URLs must be encoded in ASCII. Therefore the internal representation of a URN is bytes. When creating the URN from other forms (e.g. filenames, we assume UTF8 encoding if the filename is a unicode string. """ # The encoded URN as a unicode string. value = None original_filename = None @classmethod def FromFileName(cls, filename): """Parse the URN from filename. Filename may be a unicode string, in which case it will be UTF8 encoded into the URN. URNs are always ASCII. """ result = cls("file:%s" % urllib.request.pathname2url(filename)) result.original_filename = filename return result @classmethod def NewURNFromFilename(cls, filename): return cls.FromFileName(filename) def ToFilename(self): # For file: urls we exactly reverse the conversion applied in # FromFileName. if self.value.startswith("file:"): return urllib.request.url2pathname(self.value[5:]) components = self.Parse() if components.scheme == "file": return components.path def GetRaptorTerm(self): return rdflib.URIRef(self.value) def SerializeToString(self): components = self.Parse() return utils.SmartStr(urllib.parse.urlunparse(components)) def UnSerializeFromString(self, string): utils.AssertStr(string) self.Set(utils.SmartUnicode(string)) return self def Set(self, data): if data is None: return elif isinstance(data, URN): self.value = data.value else: utils.AssertUnicode(data) self.value = data def Parse(self): return self._Parse(self.value) # URL parsing seems to be slow in Python so we cache it as much as possible. @Memoize() def _Parse(self, value): components = urllib.parse.urlparse(value) # dont normalise path for http URI's if components.scheme and not components.scheme == "http": normalized_path = posixpath.normpath(components.path) if normalized_path == ".": normalized_path = "" components = components._replace(path=normalized_path) if not components.scheme: # For file:// URNs, we need to parse them from a filename. components = components._replace( netloc="", path=urllib.request.pathname2url(value), scheme="file") self.original_filename = value return components def Scheme(self): components = self.Parse() return components.scheme def Append(self, component, quote=True): components = self.Parse() if quote: component = urllib.parse.quote(component) # Work around usual posixpath.join bug. component = component.lstrip("/") new_path = posixpath.normpath(posixpath.join( "/", components.path, component)) components = components._replace(path=new_path) return URN(urllib.parse.urlunparse(components)) def RelativePath(self, urn): urn_value = str(urn) if urn_value.startswith(self.value): return urn_value[len(self.value):] def __str__(self): return self.value def __lt__(self, other): return self.value < utils.SmartUnicode(other) def __repr__(self): return "<%s>" % self.value def AssertURN(urn): if not isinstance(urn, URN): raise TypeError("Expecting a URN.") def AssertURN(urn): if not isinstance(urn, URN): raise TypeError("Expecting a URN.") registry.RDF_TYPE_MAP.update({ rdflib.XSD.hexBinary: RDFBytes, rdflib.XSD.string: XSDString, rdflib.XSD.integer: XSDInteger, rdflib.XSD.int: XSDInteger, rdflib.XSD.long: XSDInteger, rdflib.URIRef("http://aff4.org/Schema#SHA512"): SHA512Hash, rdflib.URIRef("http://aff4.org/Schema#SHA256"): SHA256Hash, rdflib.URIRef("http://aff4.org/Schema#SHA1"): SHA1Hash, rdflib.URIRef("http://aff4.org/Schema#MD5"): MD5Hash, rdflib.URIRef("http://aff4.org/Schema#Blake2b"): Blake2bHash, rdflib.URIRef("http://aff4.org/Schema#blockMapHashSHA512"): SHA512BlockMapHash, rdflib.URIRef("http://afflib.org/2009/aff4#SHA512"): SHA512Hash, rdflib.URIRef("http://afflib.org/2009/aff4#SHA256"): SHA256Hash, rdflib.URIRef("http://afflib.org/2009/aff4#SHA1"): SHA1Hash, rdflib.URIRef("http://afflib.org/2009/aff4#MD5"): MD5Hash, rdflib.URIRef("http://afflib.org/2009/aff4#blockMapHashSHA512"): SHA512BlockMapHash })

27.587896

87

0.651311

1,131

9,573

5.412025

0.240495

0.045581

0.044437

0.039209

0.319392

0.258781

0.231825

0.131024

0.06829

0

0.017936

0.242871

9,573

346

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0.826573

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0

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0

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0

0.018868

1

0.231132

false

0

0.056604

0.108491

0.603774

0

null

0

null

0

1

0

1

0

3

44d4d050ac098be9e394e56783387233beb62b57

190

py

Python

LittlePerformance/module/_tmp/test.py

TatsuyaOGth/PepperScript

bf2ef38976afbbd679b9d81b36d1035b392521d9

[ "CC0-1.0" ]

null

LittlePerformance/module/_tmp/test.py

TatsuyaOGth/PepperScript

bf2ef38976afbbd679b9d81b36d1035b392521d9

[ "CC0-1.0" ]

null

LittlePerformance/module/_tmp/test.py

TatsuyaOGth/PepperScript

bf2ef38976afbbd679b9d81b36d1035b392521d9

[ "CC0-1.0" ]

null

# -*- encoding: UTF-8 -*- from naoqi import ALProxy IP = '127.0.0.1' PORT = 49340 motion_proxy = ALProxy("ALMotion",IP,PORT) motion_proxy.openHand('LHand') motion_proxy.openHand('RHand')

17.272727

42

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190

4.678571

0.678571

0.251908

0.290076

0

0.071856

0.121053

190

10

43

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false

0

0.166667

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0.166667

0

1

0

null

1

0

null

0

3

44e32735f8a27f27f94b6e9b157e83a3efe9a0ed

1,874

py

Python

qcloudsdkapigateway/RunApiRequest.py

f3n9/qcloudcli

b965a4f0e6cdd79c1245c1d0cd2ca9c460a56f19

[ "Apache-2.0" ]

null

qcloudsdkapigateway/RunApiRequest.py

f3n9/qcloudcli

b965a4f0e6cdd79c1245c1d0cd2ca9c460a56f19

[ "Apache-2.0" ]

null

qcloudsdkapigateway/RunApiRequest.py

f3n9/qcloudcli

b965a4f0e6cdd79c1245c1d0cd2ca9c460a56f19

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- from qcloudsdkcore.request import Request class RunApiRequest(Request): def __init__(self): super(RunApiRequest, self).__init__( 'apigateway', 'qcloudcliV1', 'RunApi', 'apigateway.api.qcloud.com') def get_apiId(self): return self.get_params().get('apiId') def set_apiId(self, apiId): self.add_param('apiId', apiId) def get_contentType(self): return self.get_params().get('contentType') def set_contentType(self, contentType): self.add_param('contentType', contentType) def get_requestBody(self): return self.get_params().get('requestBody') def set_requestBody(self, requestBody): self.add_param('requestBody', requestBody) def get_requestBodyDict(self): return self.get_params().get('requestBodyDict') def set_requestBodyDict(self, requestBodyDict): self.add_param('requestBodyDict', requestBodyDict) def get_requestHeader(self): return self.get_params().get('requestHeader') def set_requestHeader(self, requestHeader): self.add_param('requestHeader', requestHeader) def get_requestMethod(self): return self.get_params().get('requestMethod') def set_requestMethod(self, requestMethod): self.add_param('requestMethod', requestMethod) def get_requestPath(self): return self.get_params().get('requestPath') def set_requestPath(self, requestPath): self.add_param('requestPath', requestPath) def get_requestQuery(self): return self.get_params().get('requestQuery') def set_requestQuery(self, requestQuery): self.add_param('requestQuery', requestQuery) def get_serviceId(self): return self.get_params().get('serviceId') def set_serviceId(self, serviceId): self.add_param('serviceId', serviceId)

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1,874

6.068293

0.165854

0.043408

0.101286

0.12299

0.188103

0

0.001322

0.192636

1,874

63

80

29.746032

0.820886

0.011206

0

0.136143

0.013506

0

1

0.463415

false

0

0.02439

0.219512

0.731707

0

null

0

null

0

1

0

1

0

3

7818bb22a2a0106d2b35255ef5c676f48223ad0c

138

py

Python

precedent/models/bad_query.py

lewisjared/precedent

6447634c6961f44b058a0379b782c8d46a21560e

[ "MIT" ]

null

precedent/models/bad_query.py

lewisjared/precedent

6447634c6961f44b058a0379b782c8d46a21560e

[ "MIT" ]

null

precedent/models/bad_query.py

lewisjared/precedent

6447634c6961f44b058a0379b782c8d46a21560e

[ "MIT" ]

null

from django.db import models class BadQuery(models.Model): date = models.DateTimeField(auto_now=True) query = models.TextField()

23

46

0.746377

18

138

5.666667

0.833333

0

0.152174

138

6

47

23

0.871795

0

1

0

false

0

0.25

0

1

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1

0

null

0

1

0

null

0

1

0

3

7832c8a18523ffb201793709a64fdbf272669118

1,738

py

Python

addons/Sprytile-6b68d00/rx/linq/observable/transduce.py

trisadmeslek/V-Sekai-Blender-tools

0d8747387c58584b50c69c61ba50a881319114f8

[ "MIT" ]

733

2017-08-22T09:47:54.000Z

2022-03-27T23:56:52.000Z

rx/linq/observable/transduce.py

asheraryam/Sprytile

c63be50d14b07192ff134ceab256f0d69b9c4c92

[ "MIT" ]

74

2017-08-16T09:13:05.000Z

2022-03-15T02:31:49.000Z

rx/linq/observable/transduce.py

asheraryam/Sprytile

c63be50d14b07192ff134ceab256f0d69b9c4c92

[ "MIT" ]

77

2017-09-14T16:56:11.000Z

2022-03-27T13:55:16.000Z

"""Transducers for RxPY. There are several different implementations of transducers in Python. This implementation is currently targeted for: - http://code.sixty-north.com/python-transducers You should also read the excellent article series "Understanding Transducers through Python" at: - http://sixty-north.com/blog/series/understanding-transducers-through-python Other implementations of transducers in Python are: - https://github.com/cognitect-labs/transducers-python """ from rx.core import Observable, AnonymousObservable from rx.internal import extensionmethod class Observing(object): """An observing transducer.""" def __init__(self, observer): self.observer = observer def initial(self): return self.observer def step(self, obs, input): return obs.on_next(input) def complete(self, obs): return obs.on_completed() def __call__(self, result, item): return self.step(result, item) @extensionmethod(Observable) def transduce(self, transducer): """Execute a transducer to transform the observable sequence. Keyword arguments: :param Transducer transducer: A transducer to execute. :returns: An Observable sequence containing the results from the transducer. :rtype: Observable """ source = self def subscribe(observer): xform = transducer(Observing(observer)) def on_next(v): try: xform.step(observer, v) except Exception as e: observer.on_error(e) def on_completed(): xform.complete(observer) return source.subscribe(on_next, observer.on_error, on_completed) return AnonymousObservable(subscribe)

25.558824

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0.695627

200

1,738

5.965

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0.030176

0.04694

0.050293

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1,738

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0

null

0

null

0

1

0

1

0

3

783c6e3dcbf1023809e12a0e989e924850829a01

281

py

Python

management_utils/management_utils.py

singparvi/Photo-Transform-Application

9f93aff236794f0f23b0826b65dcf91c7ddbc0b3

[ "MIT" ]

null

management_utils/management_utils.py

singparvi/Photo-Transform-Application

9f93aff236794f0f23b0826b65dcf91c7ddbc0b3

[ "MIT" ]

null

management_utils/management_utils.py

singparvi/Photo-Transform-Application

9f93aff236794f0f23b0826b65dcf91c7ddbc0b3

[ "MIT" ]

null

""" cpu_data_loader.py - Loads all data in the ../../data/transcribed_stories directory """ import pathlib import os.path as path class CPUDataLoader(): def __init__(self): self.data_path = path.join(path.dirname(__file__), "..", "..", "data", "transcribed_stories")

25.545455

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281

5

0.666667

0.166667

0.244444

0

0.149466

281

10

102

28.1

0.753138

0.295374

0

0.142105

0

1

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0.4

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0.8

0

null

0

1

0

null

0

1

0

1

0

3

78518dbb827250b7b706626c909295a7bb16606b

125

py

Python

StatisticsFunctions/standardDeviation.py

mkm99/TeamProject_StatsCalculator

81085c1af47f38d3e49b43d667e312016c44ad10

[ "MIT" ]

null

StatisticsFunctions/standardDeviation.py

mkm99/TeamProject_StatsCalculator

81085c1af47f38d3e49b43d667e312016c44ad10

[ "MIT" ]

7

2020-03-03T21:37:57.000Z

2020-03-06T04:11:42.000Z

StatisticsFunctions/standardDeviation.py

mkm99/TeamProject_StatsCalculator

81085c1af47f38d3e49b43d667e312016c44ad10

[ "MIT" ]

null

import numpy as np class StandardDeviation(): @staticmethod def standardDeviation(data): return np.std(data)

20.833333

32

0.704

14

125

6.285714

0.785714

0

0.216

125

6

33

20.833333

0.897959

0

1

0.2

false

0

0.2

0.8

0

1

0

null

0

1

0

null

0

1

0

3

78614f22d935f3d142bf7ca036e4178dacad3150

1,359

bzl

Python

source/bazel/rules/tree_hcp/string_tree_to_static_tree_parser.bzl

luxe/CodeLang-compiler

78837d90bdd09c4b5aabbf0586a5d8f8f0c1e76a

[ "MIT" ]

33

2019-05-30T07:43:32.000Z

2021-12-30T13:12:32.000Z

source/bazel/rules/tree_hcp/string_tree_to_static_tree_parser.bzl

luxe/CodeLang-compiler

78837d90bdd09c4b5aabbf0586a5d8f8f0c1e76a

[ "MIT" ]

371

2019-05-16T15:23:50.000Z

2021-09-04T15:45:27.000Z

source/bazel/rules/tree_hcp/string_tree_to_static_tree_parser.bzl

UniLang/compiler

c338ee92994600af801033a37dfb2f1a0c9ca897

[ "MIT" ]

6

2019-08-22T17:37:36.000Z

2020-11-07T07:15:32.000Z

load("//bazel/rules/cpp:object.bzl", "cpp_object") load("//bazel/rules/hcp:hcp.bzl", "hcp") load("//bazel/rules/hcp:hcp_hdrs_derive.bzl", "hcp_hdrs_derive") def string_tree_to_static_tree_parser(name): #the file names to use target_name = name + "_string_tree_parser_dat" in_file = name + ".dat" outfile = name + "_string_tree_parser.hcp" #converting hcp to hpp/cpp native.genrule( name = target_name, srcs = [in_file], outs = [outfile], tools = ["//code/programs/transcompilers/tree_hcp/string_tree_to_static_tree_parser:string_tree_to_static_tree_parser"], cmd = "$(location //code/programs/transcompilers/tree_hcp/string_tree_to_static_tree_parser:string_tree_to_static_tree_parser) -i $(SRCS) -o $@", ) #compile hcp file #unique dep (TODO: dynamically decide) static_struct_dep = "//code/utilities/code:concept_static_tree_structs" deps = [ "//code/utilities/data_structures/tree/generic:string_tree", "//code/utilities/data_structures/tree/generic:string_to_string_tree", "//code/utilities/types/strings/transformers/appending:lib", "//code/utilities/data_structures/tree/generic/tokens:tree_token", "//code/utilities/types/vectors/observers:lib", static_struct_dep, ] hcp(name + "_string_tree_parser", deps)

39.970588

153

0.701987

179

1,359

4.988827

0.340782

0.111982

0.067189

0.100784

0.416573

0.371781

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0.199328

0

0.164827

1,359

33

154

41.181818

0.786784

0.072848

0

0.041667

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0.547771

0

0.030303

0

1

0.041667

false

0

0.041667

0

null

0

1

null

0

1

0

3

78669a47b4d2056f5795f7757d17b963b58901f7

2,926

py

Python

cogs/utils/db.py

NextChai/FURYBot

074ef86950167cf26a6b3afc3acc9a0a5ce91c9e

[ "MIT" ]

null

cogs/utils/db.py

NextChai/FURYBot

074ef86950167cf26a6b3afc3acc9a0a5ce91c9e

[ "MIT" ]

3

2021-12-31T07:03:24.000Z

2021-12-31T07:16:21.000Z

cogs/utils/db.py

NextChai/Fury-Bot

074ef86950167cf26a6b3afc3acc9a0a5ce91c9e

[ "MIT" ]

null

""" The MIT License (MIT) Copyright (c) 2020-present NextChai Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. """ from __future__ import annotations from typing import ClassVar, List from functools import cached_property class Row: """Used to represent a Row to a database. Attributes ---------- name: :class:`str` The key's name. value: :class:`str` The key's value. """ __slots__ = ('name', 'value', '_original_args') def __init__(self, name: str, type: str, *args) -> None: self.name = name self.value = type self._original_args = args # EXAMPLE:: PRIMARY KEY NOT NULL def __str__(self) -> str: return f'{self.name} {self.value} {" ".join(self._original_args)}' def __repr__(self) -> str: return f'<Row :: name: {self.name}:: value: {self.value}>' class TableMeta(type): __table_name__: str def __new__(cls, *args, **kwargs): name, bases, attrs = args attrs['__table_name__'] = kwargs.pop('name', name) new_cls = super().__new__(cls, name, bases, attrs, **kwargs) return new_cls @classmethod def qualified_name(cls) -> str: return cls.__table_name__ class Table(metaclass=TableMeta): __table_name__: ClassVar[str] def __init__(self, *, keys: List[Row]) -> None: self.keys: List[Row] = keys @cached_property def qualified_name(self) -> str: return self.__table_name__ def create_string(self) -> str: """:class:`str`: Returns a string that can be used to create the table.""" return 'CREATE TABLE IF NOT EXISTS {0} ({1});'.format( self.qualified_name, ', '.join([str(key) for key in self.keys]) ) async def create(self, connection) -> None: """Creates the table.""" await connection.execute(self.create_string())

32.511111

82

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395

2,926

4.744304

0.402532

0.046958

0.020811

0.014941

0.016009

0

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0.237184

2,926

89

83

32.876404

0.836918

0.452837

0

0.120656

0.018361

0

1

0.216216

false

0

0.081081

0.108108

0.621622

0

null

0

null

0

1

0

1

0

3

786f50385bc4363183c9ed20db0b507df0526042

4,865

py

Python

raco/myrial/exceptions.py

uwescience/raco

1f2bedbef71bacf715340289f4973d85a3c1dc97

[ "BSD-3-Clause" ]

61

2015-02-09T17:27:40.000Z

2022-03-28T14:37:53.000Z

raco/myrial/exceptions.py

uwescience/raco

1f2bedbef71bacf715340289f4973d85a3c1dc97

[ "BSD-3-Clause" ]

201

2015-01-03T02:46:19.000Z

2017-09-19T02:16:36.000Z

raco/myrial/exceptions.py

uwescience/raco

1f2bedbef71bacf715340289f4973d85a3c1dc97

[ "BSD-3-Clause" ]

17

2015-06-03T12:01:30.000Z

2021-11-27T15:49:21.000Z

class MyrialCompileException(Exception): pass class MyrialUnexpectedEndOfFileException(MyrialCompileException): def __str__(self): return "Unexpected end-of-file" class MyrialParseException(MyrialCompileException): def __init__(self, token): self.token = token def __str__(self): return 'Parse error at token %s on line %d' % (self.token.value, self.token.lineno) class MyrialScanException(MyrialCompileException): def __init__(self, token): self.token = token def __str__(self): return 'Illegal token string %s on line %d' % (self.token.value, self.token.lineno) class DuplicateFunctionDefinitionException(MyrialCompileException): def __init__(self, funcname, lineno): self.funcname = funcname self.lineno = lineno def __str__(self): return 'Duplicate function definition for %s on line %d' % (self.funcname, # noqa self.lineno) # noqa class NoSuchFunctionException(MyrialCompileException): def __init__(self, funcname, lineno): self.funcname = funcname self.lineno = lineno def __str__(self): return 'No such function definition for %s on line %d' % (self.funcname, # noqa self.lineno) # noqa class ReservedTokenException(MyrialCompileException): def __init__(self, token, lineno): self.token = token self.lineno = lineno def __str__(self): return 'The token "%s" on line %d is reserved.' % (self.token, self.lineno) # noqa class InvalidArgumentList(MyrialCompileException): def __init__(self, funcname, expected_args, lineno): self.funcname = funcname self.expected_args = expected_args self.lineno = lineno def __str__(self): return "Incorrect number of arguments for %s(%s) on line %d" % ( self.funcname, ','.join(self.expected_args), self.lineno) class UndefinedVariableException(MyrialCompileException): def __init__(self, funcname, var, lineno): self.funcname = funcname self.var = var self.lineno = lineno def __str__(self): return "Undefined variable %s in function %s at line %d" % ( self.var, self.funcname, self.lineno) class DuplicateVariableException(MyrialCompileException): def __init__(self, funcname, lineno): self.funcname = funcname self.lineno = lineno def __str__(self): return "Duplicately defined in function %s at line %d" % ( self.funcname, self.lineno) class BadApplyDefinitionException(MyrialCompileException): def __init__(self, funcname, lineno): self.funcname = funcname self.lineno = lineno def __str__(self): return "Bad apply definition for in function %s at line %d" % ( self.funcname, self.lineno) class UnnamedStateVariableException(MyrialCompileException): def __init__(self, funcname, lineno): self.funcname = funcname self.lineno = lineno def __str__(self): return "Unnamed state variable in function %s at line %d" % ( self.funcname, self.lineno) class IllegalWildcardException(MyrialCompileException): def __init__(self, funcname, lineno): self.funcname = funcname self.lineno = lineno def __str__(self): return "Illegal use of wildcard in function %s at line %d" % ( self.funcname, self.lineno) class NestedTupleExpressionException(MyrialCompileException): def __init__(self, lineno): self.lineno = lineno def __str__(self): return "Illegal use of tuple expression on line %d" % self.lineno class InvalidEmitList(MyrialCompileException): def __init__(self, function, lineno): self.function = function self.lineno = lineno def __str__(self): return "Wrong number of emit arguments in %s at line %d" % ( self.function, self.lineno) class IllegalColumnNamesException(MyrialCompileException): def __init__(self, lineno): self.lineno = lineno def __str__(self): return "Invalid column names on line %d" % self.lineno class ColumnIndexOutOfBounds(Exception): pass class SchemaMismatchException(MyrialCompileException): def __init__(self, op_name): self.op_name = op_name def __str__(self): return "Incompatible input schemas for %s operation" % self.op_name class NoSuchRelationException(MyrialCompileException): def __init__(self, relname): self.relname = relname def __str__(self): return "No such relation: %s" % self.relname

29.484848

90

0.642754

499

4,865

5.985972

0.174349

0.087044

0.056913

0.091061

0.593907

0.51222

0.481419

0.431202

0

0.272765

4,865

164

91

29.664634

0.844262

0.004933

0

0.54955

0

0.143566

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0.297297

false

0.018018

0

0.153153

0.621622

0

null

0

null

0

1

0

1

0

3

788f71f9cc159c688647d0021e6480432be1fe82

226

py

Python

tutorial/client.py

kangjunseo/GraphQL

dde0381ddde80c4716d5d0233e95710d93a15a70

[ "MIT" ]

null

tutorial/client.py

kangjunseo/GraphQL

dde0381ddde80c4716d5d0233e95710d93a15a70

[ "MIT" ]

null

tutorial/client.py

kangjunseo/GraphQL

dde0381ddde80c4716d5d0233e95710d93a15a70

[ "MIT" ]

null

from gql import Client from gql.transport.requests import RequestsHTTPTransport transport = RequestsHTTPTransport(url='http://localhost:8080/v1/graphql') client = Client(transport=transport, fetch_schema_from_transport=True)

37.666667

73

0.840708

27

226

6.925926

0.592593

0.074866

0

0.02381

0.070796

226

6

74

37.666667

0.866667

0

0.140969

0

1

0

false

0

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

3

78b1088a5138ebe9e271cee17d9f041b05a1e072

122

py

Python

pyeccodes/defs/mars/grib_oper_ea_def.py

ecmwf/pyeccodes

dce2c72d3adcc0cb801731366be53327ce13a00b

[ "Apache-2.0" ]

7

2020-04-14T09:41:17.000Z

2021-08-06T09:38:19.000Z

pyeccodes/defs/mars/grib_oper_ea_def.py

ecmwf/pyeccodes

dce2c72d3adcc0cb801731366be53327ce13a00b

[ "Apache-2.0" ]

null

pyeccodes/defs/mars/grib_oper_ea_def.py

ecmwf/pyeccodes

dce2c72d3adcc0cb801731366be53327ce13a00b

[ "Apache-2.0" ]

3

2020-04-30T12:44:48.000Z

2020-12-15T08:40:26.000Z

import pyeccodes.accessors as _ def load(h): if (h.get_l('class') == 8): h.alias('mars.origin', 'centre')

13.555556

40

0.581967

18

122

3.833333

0.888889

0

0.010638

0.229508

122

8

41

15.25

0.723404

0

0.181818

0

1

0.25

false

0

0.25

0

0.5

0

1

0

null

0

1

0

null

0

1

0

3

152faa8abbce27b3e2233aadc2f9c27068ddad3e

3,073

py

Python

exploration/similarity.py

webmasterraj/gitrecommender

aa32ced2110fd15191bf0b7fdb46ab16bdcff709

[ "MIT" ]

1

2017-06-24T15:40:03.000Z

exploration/similarity.py

webmasterraj/gitrecommender

aa32ced2110fd15191bf0b7fdb46ab16bdcff709

[ "MIT" ]

null

exploration/similarity.py

webmasterraj/gitrecommender

aa32ced2110fd15191bf0b7fdb46ab16bdcff709

[ "MIT" ]

null

def similar_users_query(user): # Pass user object, return query to get users who starred same things as them query = """ select subq.user_id , sum(1/log(subq.stargazers_count+1)) `score` , count(*) `count` from (select others.user_id , others.starred_repo_id , repo.repo_name , repo.description , repo.last_modified , repo.language , repo.stargazers_count , repo.forks_count , repo.from_hacker_news from (select user_id , starred_repo_id from github_user_starred_repos where user_id != {0}) others join (select starred_repo_id from github_user_starred_repos where user_id = {0}) usr on others.starred_repo_id=usr.starred_repo_id join (select id , repo_name , description , last_modified , language , stargazers_count , forks_count , from_hacker_news from github_repos) repo on others.starred_repo_id=repo.id) subq group by subq.user_id order by 2 desc """.format(user.id) return query def similar_repos_query(user): # Pass user object, return query to get users who starred same things as them query = """ select other_repos.user_id , other_repos.starred_repo_id , repo.repo_name , repo.description , repo.last_modified , repo.language , repo.stargazers_count , repo.forks_count , repo.from_hacker_news , hn.added_at , hn.submission_time , hn.title , hn.url from (select user_id , starred_repo_id from github_user_starred_repos where user_id != {0}) other_repos join (select distinct(others.user_id) `user_id` from (select user_id , starred_repo_id from github_user_starred_repos where user_id != {0}) others join (select starred_repo_id from github_user_starred_repos where user_id = {0}) usr on others.starred_repo_id = usr.starred_repo_id) others on other_repos.user_id=others.user_id join (select id , repo_name , description , last_modified , language , stargazers_count , forks_count , from_hacker_news from github_repos) repo on other_repos.starred_repo_id=repo.id join (select added_at , submission_time , title , url , github_repo_name from hacker_news) hn on repo.repo_name = hn.github_repo_name """.format(user.id) return query

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null

0

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0

1

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null

0

3

153bb6e2b74499852615242fd0c43386ac956fe1

911

py

Python

175 _labs/d_linked_node.py

lzeeorno/Python-practice175

e2998830114b304f9857b8f7d89dafec7ae02080

[ "Apache-2.0" ]

null

175 _labs/d_linked_node.py

lzeeorno/Python-practice175

e2998830114b304f9857b8f7d89dafec7ae02080

[ "Apache-2.0" ]

null

175 _labs/d_linked_node.py

lzeeorno/Python-practice175

e2998830114b304f9857b8f7d89dafec7ae02080

[ "Apache-2.0" ]

1

2019-03-09T07:41:12.000Z

class d_linked_node: def __init__(self, initData, initNext, initPrevious): # constructs a new node and initializes it to contain # the given object (initData) and links to the given next # and previous nodes. self.__data = initData self.__next = initNext self.__previous = initPrevious if (initPrevious != None): initPrevious.__next = self if (initNext != None): initNext.__previous = self def getData(self): return self.__data def getNext(self): return self.__next def getPrevious(self): return self.__previous def setData(self, newData): self.__data = newData def setNext(self, newNext): self.__next= newNext def setPrevious(self, newPrevious): self.__previous= newPrevious

29.387097

67

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92

911

5.456522

0.413043

0.047809

0.083665

0

0.349067

911

31

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null

0

null

0

1

0

1

0

3

153d7fb61c38c41553a4e473fb3e5c0de21d4d4c

2,184

py

Python

app/tests/test_parser_cve_json.py

stanleyshuang/cvedata

b90afb3e4f90a4ed24d93bd044181c836b4d3747

[ "Apache-2.0" ]

null

app/tests/test_parser_cve_json.py

stanleyshuang/cvedata

b90afb3e4f90a4ed24d93bd044181c836b4d3747

[ "Apache-2.0" ]

null

app/tests/test_parser_cve_json.py

stanleyshuang/cvedata

b90afb3e4f90a4ed24d93bd044181c836b4d3747

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # # Author: Stanley Huang # Project: crawler 1.0 # Date: 2021-07-10 # import unittest from pkg.util.parser_cve_json import is_cve_json_filename from pkg.util.parser_cve_json import extract_cveid from pkg.util.parser_cve_json import splitcveid class IsCveJsonFilenameTestCase(unittest.TestCase): def setUp(self): pass def tearDown(self): pass def test_is_cve_json_filename_10(self): self.assertTrue(is_cve_json_filename('CVE-2021-28809')) def test_is_cve_json_filename_20(self): self.assertTrue(is_cve_json_filename('CVE-2021-3660')) def test_is_cve_json_filename_30(self): self.assertFalse(is_cve_json_filename('CVE-2021-3660.json')) def test_is_cve_json_filename_40(self): self.assertFalse(is_cve_json_filename('openpgp-encrypted-message')) class ExtractCveidTestCase(unittest.TestCase): def setUp(self): pass def tearDown(self): pass def test_extract_cveid_10(self): self.assertTrue('CVE-2021-28491'==extract_cveid('CVE-2021-28491. - SQLite heap overflow')) def test_extract_cveid_20(self): self.assertTrue(None==extract_cveid('TYPO3 Form Designer backend module of the Form Framework is vulnerable to cross-site scripting')) def test_extract_cveid_30(self): self.assertTrue('CVE-2020-11575'==extract_cveid('Display and loop C codes, CVE-2020-11575, are vulnerable to heap based buffer overflow')) class ExtractCveidTestCase(unittest.TestCase): def setUp(self): pass def tearDown(self): pass def test_splitcveid_10(self): self.assertTrue('2021', '28491'==splitcveid('CVE-2021-28491')) def test_splitcveid_20(self): self.assertTrue((None, None)==splitcveid('TYPO3 Form Designer backend module of the Form Framework is vulnerable to cross-site scripting')) def test_splitcveid_30(self): self.assertTrue('2020', '11575'==splitcveid('Display and loop C codes, CVE-2020-11575, are vulnerable to heap based buffer overflow'))

33.6

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null

0

null

0

1

0

1

0

1

0

3

156821be7ed14dd901dce4ac81ab7755213aa310

165

py

Python

electricitylci/canadian_imports.py

gschivley/ElectricityLCI

1c1c1b69705d3ffab1e1e844aaf7379e4f51198e

[ "CC0-1.0" ]

1

2019-04-15T18:11:16.000Z

electricitylci/canadian_imports.py

gschivley/ElectricityLCI

1c1c1b69705d3ffab1e1e844aaf7379e4f51198e

[ "CC0-1.0" ]

3

2019-05-07T19:04:22.000Z

2019-09-30T21:29:59.000Z

electricitylci/canadian_imports.py

gschivley/ElectricityLCI

1c1c1b69705d3ffab1e1e844aaf7379e4f51198e

[ "CC0-1.0" ]

null

# -*- coding: utf-8 -*- """ Created on Wed Feb 20 15:10:09 2019 @author: cooneyg """ import pandas as pd ca_tech_mix = pd.read_csv('data/canadian_imports.csv')

12.692308

54

0.666667

28

165

3.785714

0.928571

0

0.094891

0.169697

165

12

55

13.75

0.678832

0.460606

0

0.320513

0

1

0

false

0

1

0

1

0

1

0

null

0

null

0

1

0

1

0

3

159a295855895460560feb94935eaba5b7b885ee

196

py

Python

main/experiments/init.py

resuly/Traffic-3DResnets

cb48c6b7a4921d8593368c262c0d3c0406a6cc32

[ "MIT" ]

2

2021-05-13T07:46:04.000Z

2021-09-11T11:42:15.000Z

main/experiments/init.py

resuly/Traffic-3DResnets

cb48c6b7a4921d8593368c262c0d3c0406a6cc32

[ "MIT" ]

null

main/experiments/init.py

resuly/Traffic-3DResnets

cb48c6b7a4921d8593368c262c0d3c0406a6cc32

[ "MIT" ]

null

import os,sys,glob,shutil lists = glob.glob('./*/*.log') lists += glob.glob('./*/*.tar') lists += glob.glob('./*/*.pk') lists += glob.glob('./*/*weights.json') for f in lists: os.remove(f)

17.818182

39

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196

3.862069

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0.464286

0

0.132653

196

10

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0

0.142857

0

1

0

null

1

0

null

0

3

159a92f6efa3a811dff4d2c2b32d80f32fd49fbb

175

py

Python

examples/sim_taxi/config/env.py

CPFL/AMS

bb685024b1c061e7144dc2ef93e09d6d6c830af8

[ "Apache-2.0" ]

26

2018-02-16T10:49:19.000Z

2022-03-23T16:42:48.000Z

examples/sim_taxi/config/env.py

CPFL/Autoware-Management-System

bb685024b1c061e7144dc2ef93e09d6d6c830af8

[ "Apache-2.0" ]

10

2018-11-13T08:16:49.000Z

2019-01-09T04:59:24.000Z

examples/graph/config/env.py

CPFL/AMS

bb685024b1c061e7144dc2ef93e09d6d6c830af8

[ "Apache-2.0" ]

19

2018-03-28T07:38:45.000Z

2022-01-27T05:18:21.000Z

#!/usr/bin/env python # coding: utf-8 import os from dotenv import load_dotenv load_dotenv(os.path.abspath(os.path.dirname(__file__))+'/sample.env') env = dict(os.environ)

17.5

69

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175

4.275862

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175

9

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0.5

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null

0

1

0

null

0

1

0

3

15a9b7abe9ab116968a744dad3510257e70aa315

111

py

Python

src/txtwrpr/__init__.py

hendrikdutoit/TxtWrpr

7800254ca76d17b97c77702fcf185fe93ea8bcd6

[ "MIT" ]

null

src/txtwrpr/__init__.py

hendrikdutoit/TxtWrpr

7800254ca76d17b97c77702fcf185fe93ea8bcd6

[ "MIT" ]

null

src/txtwrpr/__init__.py

hendrikdutoit/TxtWrpr

7800254ca76d17b97c77702fcf185fe93ea8bcd6

[ "MIT" ]

null

"""Application Utilities for Bright Edge eServices developments""" from .txtwrpr import * _version = "2.2.2"

18.5

66

0.738739

14

111

5.785714

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0.031579

0.144144

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1

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0

0.5

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1

0

null

0

1

0

null

0

1

0

3

15bbc0ef07fd0f3c49fdc9253453c03bbf9b0a1d

977

py

Python

rf_pymods/smwrand.py

rfinnie/rf-pymods

14c73ba1a8614c9579a7957de97d7b3c7058891e

[ "Unlicense" ]

null

rf_pymods/smwrand.py

rfinnie/rf-pymods

14c73ba1a8614c9579a7957de97d7b3c7058891e

[ "Unlicense" ]

null

rf_pymods/smwrand.py

rfinnie/rf-pymods

14c73ba1a8614c9579a7957de97d7b3c7058891e

[ "Unlicense" ]

null

# SPDX-FileCopyrightText: Copyright (C) 2019-2021 Ryan Finnie # SPDX-License-Identifier: MIT class SMWRand: """Super Mario World random number generator Based on deconstruction by Retro Game Mechanics Explained https://www.youtube.com/watch?v=q15yNrJHOak """ # SPDX-SnippetComment: Originally from https://github.com/rfinnie/rf-pymods # SPDX-SnippetCopyrightText: Copyright (C) 2019-2021 Ryan Finnie # SPDX-LicenseInfoInSnippet: MIT seed_1 = 0 seed_2 = 0 def __enter__(self): return self def __exit__(self, exc_type, exc_value, traceback): pass def _rand(self): self.seed_1 = (self.seed_1 + (self.seed_1 << 2) + 1) & 0xFF self.seed_2 = ( (self.seed_2 << 1) + int((self.seed_2 & 0x90) in (0x90, 0)) ) & 0xFF return self.seed_1 ^ self.seed_2 def rand(self): output_2 = self._rand() output_1 = self._rand() return (output_1, output_2)

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0

null

0

null

0

1

0

1

0

1

0

3

ec698900a476a259034508e51008d66c5de32af5

371

py

Python

simple_page/urls.py

aprild-c/WiCS_Website_v2

7e38a6c650efb07d224ba9914d08c8e4a3667101

[ "MIT" ]

null

simple_page/urls.py

aprild-c/WiCS_Website_v2

7e38a6c650efb07d224ba9914d08c8e4a3667101

[ "MIT" ]

null

simple_page/urls.py

aprild-c/WiCS_Website_v2

7e38a6c650efb07d224ba9914d08c8e4a3667101

[ "MIT" ]

null

from django.urls import path from simple_page import views urlpatterns = [ path('', views.home, name='simple_page'), path('calendar/', views.calendar, name='simple_page'), path('contact/', views.contact, name='simple_page'), path('eboard/', views.eboard, name='simple_page'), path('alumni-speaker-series/',views.speaker_series, name='simple_page'), ]

37.1

76

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48

371

5.270833

0.354167

0.237154

0.27668

0.284585

0

0.123989

371

10

77

37.1

0.778462

0

0.271505

0.05914

0

1

0

false

0

0.222222

0

0.222222

0

null

1

0

null

0

3

ec8728c7e7cb1908b2335c86c881a88dcdeb4b26

668

py

Python

backpack/extensions/secondorder/hbp/custom_module.py

pitmonticone/backpack

b5bdb7e1171a1c0ace187915b9e54c2087d61d3a

[ "MIT" ]

null

backpack/extensions/secondorder/hbp/custom_module.py

pitmonticone/backpack

b5bdb7e1171a1c0ace187915b9e54c2087d61d3a

[ "MIT" ]

null

backpack/extensions/secondorder/hbp/custom_module.py

pitmonticone/backpack

b5bdb7e1171a1c0ace187915b9e54c2087d61d3a

[ "MIT" ]

null

"""Module extensions for custom properties of HBPBaseModule.""" from backpack.core.derivatives.scale_module import ScaleModuleDerivatives from backpack.core.derivatives.sum_module import SumModuleDerivatives from backpack.extensions.secondorder.hbp.hbpbase import HBPBaseModule class HBPScaleModule(HBPBaseModule): """HBP extension for ScaleModule.""" def __init__(self): """Initialization.""" super().__init__(derivatives=ScaleModuleDerivatives()) class HBPSumModule(HBPBaseModule): """HBP extension for SumModule.""" def __init__(self): """Initialization.""" super().__init__(derivatives=SumModuleDerivatives())

31.809524

73

0.751497

61

668

7.934426

0.491803

0.07438

0.066116

0.11157

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0

0.139222

668

20

74

33.4

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0

0.222222

0

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0.222222

false

0

0.333333

0

0.777778

0

null

0

null

0

1

0

1

0

1

0

3

eca6a81e5b7d21c327f69df655f7f4493dd305bd

1,161

py

Python

application/utils/__init__.py

fajaragungpramana/backend-warungku-deprecated

e1378781550c61b8946908eefbab6e9ffec61195

[ "Apache-2.0" ]

1

2021-01-18T07:39:44.000Z

application/utils/__init__.py

fajaragungpramana/backend-warungku-deprecated

e1378781550c61b8946908eefbab6e9ffec61195

[ "Apache-2.0" ]

null

application/utils/__init__.py

fajaragungpramana/backend-warungku-deprecated

e1378781550c61b8946908eefbab6e9ffec61195

[ "Apache-2.0" ]

null

import os import uuid import requests from datetime import datetime from dotenv import load_dotenv from flask import jsonify, make_response, request # get .env path and set it load_dotenv('../backend-warungku/.env') # This function to get .env variable configuration # @params var - fill with the same variable name in .env configuration def get_env(var: str): return str(os.environ.get(var)) # This function to get date time now with custom format def date_now(pattern: str = '%d %b %Y %H:%M:%S'): return datetime.now().strftime(pattern) # This function to get user ip address def get_ip_address(): return requests.get('https://ipinfo.io/').json()['ip'] # This to generate unique id def get_unique_id(): return str(uuid.uuid4()) # This to make json response def json_response(response: dict, http_code: int): return make_response(jsonify(response)), http_code # This to verify value is none or not def is_none(value): return isinstance(value, type(None)) # This to get body or form data def get_post(var: str): return request.form.get(var) # This to get parameter def get_param(var: str): return request.args.get(var)

26.386364

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0.430851

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0.05036

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0

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0.956522

0

null

0

null

0

1

0

1

0

3

ece03d3e786976781d7eace1e7e8c722f3bd7272

350

py

Python

PythonExercicios/ex025.py

gabjohann/python_3

380cb622669ed82d6b22fdd09d41f02f1ad50a73

[ "MIT" ]

null

PythonExercicios/ex025.py

gabjohann/python_3

380cb622669ed82d6b22fdd09d41f02f1ad50a73

[ "MIT" ]

null

PythonExercicios/ex025.py

gabjohann/python_3

380cb622669ed82d6b22fdd09d41f02f1ad50a73

[ "MIT" ]

null

# Crie um programa que leia o nome de uma pessoa e diga se ela tem 'Silva' no nome nome = str(input('Digite seu nome completo: ')).strip() print('Seu nome tem Silva? {}'.format('SILVA' in nome.upper())) # Resolução da aula # nome = str(input('Qual é seu nome completo? ')).strip() # print('Seu nome tem Silva? {}'.format('silva' in nome.lower()))

35

82

0.671429

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4.051724

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0.102128

0.170213

0.485106

0

0.162857

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9

83

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0

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false

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0.5

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null

0

1

0

1

0

null

0

1

0

3

ece51d84a80ce49f39d083d9811e55c06c50ed1e

3,242

py

Python

tests/test-xml.py

privazio/pyone

a20037e8442ee9b4d1bb71481531613bf50f978b

[ "Apache-2.0" ]

3

2018-01-07T16:56:24.000Z

2018-02-27T07:52:04.000Z

tests/test-xml.py

privazio/pyone

a20037e8442ee9b4d1bb71481531613bf50f978b

[ "Apache-2.0" ]

4

2018-01-06T18:27:29.000Z

2018-02-16T13:55:47.000Z

tests/test-xml.py

privazio/pyone

a20037e8442ee9b4d1bb71481531613bf50f978b

[ "Apache-2.0" ]

1

2020-04-26T14:22:09.000Z

# Copyright 2018 www.privaz.io Valletech AB # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest import pyone.bindings as bindings import xml.dom.minidom as dom domimp = dom.getDOMImplementation() nakedXmlSample = '''<MARKETPLACE_POOL><MARKETPLACE><ID>0</ID><UID>0</UID><GID>0</GID><UNAME>oneadmin</UNAME><GNAME>oneadmin</GNAME><NAME>OpenNebula Public</NAME><MARKET_MAD><![CDATA[one]]></MARKET_MAD><ZONE_ID><![CDATA[0]]></ZONE_ID><TOTAL_MB>0</TOTAL_MB><FREE_MB>0</FREE_MB><USED_MB>0</USED_MB><MARKETPLACEAPPS><ID>0</ID><ID>1</ID><ID>2</ID><ID>3</ID><ID>4</ID><ID>5</ID><ID>6</ID><ID>7</ID><ID>8</ID><ID>9</ID><ID>10</ID><ID>11</ID><ID>12</ID><ID>13</ID><ID>14</ID><ID>15</ID><ID>16</ID><ID>17</ID><ID>18</ID><ID>19</ID><ID>20</ID><ID>21</ID><ID>22</ID><ID>23</ID><ID>24</ID></MARKETPLACEAPPS><PERMISSIONS><OWNER_U>1</OWNER_U><OWNER_M>1</OWNER_M><OWNER_A>1</OWNER_A><GROUP_U>1</GROUP_U><GROUP_M>0</GROUP_M><GROUP_A>0</GROUP_A><OTHER_U>1</OTHER_U><OTHER_M>0</OTHER_M><OTHER_A>0</OTHER_A></PERMISSIONS><TEMPLATE><DESCRIPTION><![CDATA[OpenNebula Systems MarketPlace]]></DESCRIPTION><MARKET_MAD><![CDATA[one]]></MARKET_MAD></TEMPLATE></MARKETPLACE></MARKETPLACE_POOL>''' xmlSample = '''<MARKETPLACE_POOL xmlns='http://opennebula.org/XMLSchema'><MARKETPLACE><ID>0</ID><UID>0</UID><GID>0</GID><UNAME>oneadmin</UNAME><GNAME>oneadmin</GNAME><NAME>OpenNebula Public</NAME><MARKET_MAD><![CDATA[one]]></MARKET_MAD><ZONE_ID><![CDATA[0]]></ZONE_ID><TOTAL_MB>0</TOTAL_MB><FREE_MB>0</FREE_MB><USED_MB>0</USED_MB><MARKETPLACEAPPS><ID>0</ID><ID>1</ID><ID>2</ID><ID>3</ID><ID>4</ID><ID>5</ID><ID>6</ID><ID>7</ID><ID>8</ID><ID>9</ID><ID>10</ID><ID>11</ID><ID>12</ID><ID>13</ID><ID>14</ID><ID>15</ID><ID>16</ID><ID>17</ID><ID>18</ID><ID>19</ID><ID>20</ID><ID>21</ID><ID>22</ID><ID>23</ID><ID>24</ID></MARKETPLACEAPPS><PERMISSIONS><OWNER_U>1</OWNER_U><OWNER_M>1</OWNER_M><OWNER_A>1</OWNER_A><GROUP_U>1</GROUP_U><GROUP_M>0</GROUP_M><GROUP_A>0</GROUP_A><OTHER_U>1</OTHER_U><OTHER_M>0</OTHER_M><OTHER_A>0</OTHER_A></PERMISSIONS><TEMPLATE><DESCRIPTION><![CDATA[OpenNebula Systems MarketPlace]]></DESCRIPTION><MARKET_MAD><![CDATA[one]]></MARKET_MAD></TEMPLATE></MARKETPLACE></MARKETPLACE_POOL>''' ns = 'http://opennebula.org/XMLSchema' class XmlTests(unittest.TestCase): def test_raw_instanciation(self): marketpool = bindings.CreateFromDocument(xmlSample) m0 = marketpool.MARKETPLACE[0] self.assertEqual(m0.NAME, "OpenNebula Public") def test_adding_namespace(self): doc = dom.parseString(nakedXmlSample) doc.documentElement.setAttribute('xmlns', ns) marketpool = bindings.CreateFromDocument(doc.toxml()) m0 = marketpool.MARKETPLACE[0] self.assertEqual(m0.NAME, "OpenNebula Public")

81.05

1,003

0.710056

553

3,242

4.039783

0.271248

0.085944

0.008953

0.030439

0.59803

0

0.041888

0.072178

3,242

39

1,004

83.128205

0.700798

0.172424

0

0.222222

0

0.111111

0.749625

0.653298

0

0.111111

1

0.111111

false

0

0.166667

0

0.333333

0

null

0

1

0

1

null

0

3

eceaedfca5e8f20534c181a7f4d569fce86eec9a

405

py

Python

tests/line_markers/test_init.py

jamescooke/flake8-aaa

9df248e10538946531b67da4564bb229a91baece

[ "MIT" ]

44

2018-04-08T21:25:43.000Z

2022-01-20T14:28:16.000Z

tests/line_markers/test_init.py

jamescooke/flake8-aaa

9df248e10538946531b67da4564bb229a91baece

[ "MIT" ]

72

2018-03-30T14:30:48.000Z

2022-03-31T16:18:16.000Z

tests/line_markers/test_init.py

jamescooke/flake8-aaa

9df248e10538946531b67da4564bb229a91baece

[ "MIT" ]

1

2018-10-17T18:49:25.000Z

from flake8_aaa.line_markers import LineMarkers from flake8_aaa.types import LineType def test(): result = LineMarkers(5 * [''], 7) assert result.types == [ LineType.unprocessed, LineType.unprocessed, LineType.unprocessed, LineType.unprocessed, LineType.unprocessed, ] assert result.lines == ['', '', '', '', ''] assert result.fn_offset == 7

23.823529

47

0.624691

40

405

6.225

0.475

0.381526

0.433735

0.610442

0.381526

0

0.016447

0.249383

405

16

48

25.3125

0.802632

0

0.384615

0

0.230769

1

0.076923

false

0

0.153846

0

0.230769

0

null

1

0

null

0

3

019f4f96de4b2c86a926ff6c4501ee68c9ee1382

331

py

Python

src/NotYetSelfAware/layers/activations/tanh.py

ezalos/NotYetSelfAware

aa8374d24259be9c93b9b5fc00c07f03538a79df

[ "MIT" ]

1

2021-10-02T09:17:46.000Z

src/NotYetSelfAware/layers/activations/tanh.py

ezalos/NotYetSelfAware

aa8374d24259be9c93b9b5fc00c07f03538a79df

[ "MIT" ]

null

src/NotYetSelfAware/layers/activations/tanh.py

ezalos/NotYetSelfAware

aa8374d24259be9c93b9b5fc00c07f03538a79df

[ "MIT" ]

null

import numpy as np from .base import BaseActivation class Tanh(BaseActivation): def __init__(self) -> None: pass def forward(self, Z): # A = np.tanh(Z) up = np.exp(Z) - np.exp(-Z) dn = np.exp(Z) + np.exp(-Z) A = up / dn return A def backward(self, Z): A = self.forward(Z) dA = 1 - np.power(A, 2) return dA

16.55

32

0.607251

59

331

3.338983

0.440678

0.101523

0.121827

0.081218

0.121827

0

0.007905

0.23565

331

19

33

17.421053

0.770751

0.042296

0

1

0.214286

false

0.071429

0.142857

0

0.571429

0

null

0

null

0

1

0

1

0

1

0

3

01b2686f3033ccd51eff6f344cce645f1a3589f1

1,480

py

Python

site/trips/models.py

CKPBot/site

af8d4f7ebc370accefb61f1977782f127141ca68

[ "MIT" ]

null

site/trips/models.py

CKPBot/site

af8d4f7ebc370accefb61f1977782f127141ca68

[ "MIT" ]

null

site/trips/models.py

CKPBot/site

af8d4f7ebc370accefb61f1977782f127141ca68

[ "MIT" ]

null

from django.db import models class Post(models.Model): iden = models.CharField(max_length=100) content = models.CharField(max_length=100) domain = models.CharField(max_length=100, null=True, blank=True) created_at = models.DateTimeField(auto_now_add=True) class Article(models.Model): content = models.TextField(u'Content') frontId = models.CharField(u'frontId', max_length=50, null=True, blank=True) def __unicode__(self): return self.frontId class IDForm(models.Model): account = models.CharField(max_length=100) password = models.CharField(max_length=100) userRule = models.CharField(u'userRule', max_length=50, null=True, blank=True) login_stats = models. BooleanField() def __unicode__(self): return self.account class StateForm(models.Model): account = models.CharField(max_length=100) password = models.CharField(max_length=100) userRule = models.CharField(u'userRule', max_length=50, null=True, blank=True) def __unicode__(self): return self.account class QuestionForm(models.Model): account = models.CharField(max_length=100) data = models.TextField(u'data') def __unicode__(self): return self.account class LogForm(models.Model): user = models.CharField(max_length=100) logData = models.CharField(u'logData', max_length=50, null=True, blank=True) feature = models.TextField(u'feature') locate = models.TextField(u'locate') created_at = models.DateTimeField(auto_now_add=True) def __unicode__(self): return self.user

30.204082

79

0.766892

205

1,480

5.341463

0.234146

0.178082

0.147945

0.19726

0.665753

0.567123

0.545205

0.442922

0.325114

0

0.026616

0.111486

1,480

49

80

30.204082

0.806084

0

0.459459

0

0.036462

0

1

0.135135

false

0.054054

0.027027

0.135135

1

0

null

0

1

0

null

0

1

0

1

0

3

01b558ab4133478ccddc69bfe8b40cd7a7d6dd5f

33

py

Python

Algorithm-Selection/gripsPredictorPkg/__init__.py

GregorCH/algoselection

4919d6bede01298f269985fdce94d8176d2ad62b

[ "MIT" ]

2

2018-02-23T08:53:22.000Z

2021-05-12T11:17:46.000Z

Algorithm-Selection/gripsPredictorPkg/__init__.py

GregorCH/algoselection

4919d6bede01298f269985fdce94d8176d2ad62b

[ "MIT" ]

null

Algorithm-Selection/gripsPredictorPkg/__init__.py

GregorCH/algoselection

4919d6bede01298f269985fdce94d8176d2ad62b

[ "MIT" ]

1

2018-04-09T20:28:36.000Z

__all__ = ['predictor', 'tests']

16.5

32

0.636364

3

33

5.666667

1

0

0.121212

33

1

33

0.586207

0

0.424242

0

1

0

false

0

1

0

null

0

1

0

null

0

3

01bc408bbf11fba843d24a7f20cf34f3043f24bb

172

py

Python

profile_support.py

matikasiyanda/21cmNEST

949fb798efb5e419804cb692f876a9402e8e8dec

[ "Unlicense" ]

1

2019-08-27T10:11:37.000Z

profile_support.py

matikasiyanda/21cmNEST

949fb798efb5e419804cb692f876a9402e8e8dec

[ "Unlicense" ]

null

profile_support.py

matikasiyanda/21cmNEST

949fb798efb5e419804cb692f876a9402e8e8dec

[ "Unlicense" ]

1

2020-03-02T04:33:45.000Z

import __builtin__ try: profile = __builtin__.profile except AttributeError: # No line profiler, provide a pass-through version def profile(func): return func

21.5

54

0.75

21

172

5.761905

0.809524

0

0.19186

172

7

55

24.571429

0.870504

0.27907

0

1

0.2

false

0

0.2

0.4

0

1

0

null

0

1

0

null

0

1

0

3

01ce565d6cd9a41a3837274a9e1c33ec3d6a5025

102

py

Python

example/example_tr.py

tolgahanuzun/markovch

dea979e89715f0b882c4abecadfdfedffa04f24b

[ "MIT" ]

5

2018-01-12T09:14:15.000Z

2019-04-16T12:16:14.000Z

example/example_tr.py

tolgahanuzun/markovch

dea979e89715f0b882c4abecadfdfedffa04f24b

[ "MIT" ]

null

example/example_tr.py

tolgahanuzun/markovch

dea979e89715f0b882c4abecadfdfedffa04f24b

[ "MIT" ]

null

from markovch import markov diagram = markov.Markov('./data_tr.txt') print(diagram.result_list(50))

17

40

0.764706

15

102

5.066667

0.8

0

0.021739

0.098039

102

5

41

20.4

0.804348

0

0.127451

0

1

0

false

0

0.333333

0

0.333333

1

0

null

0

1

0

null

0

1

0

3

01d61b9775800e6fb6efa5373083c3233bc64162

471

py

Python

app/app/tests/utils/utils.py

cs-nerds/lishebora-shipping-service

ef98f13b4e560edc71a987b4ccf46c9144c6ad0f

[ "MIT" ]

null

app/app/tests/utils/utils.py

cs-nerds/lishebora-shipping-service

ef98f13b4e560edc71a987b4ccf46c9144c6ad0f

[ "MIT" ]

null

app/app/tests/utils/utils.py

cs-nerds/lishebora-shipping-service

ef98f13b4e560edc71a987b4ccf46c9144c6ad0f

[ "MIT" ]

null

import random import string def random_lower_string() -> str: return "".join(random.choices(string.ascii_lowercase, k=32)) def random_code() -> str: return str(random.randint(0, 1000)) def random_currency() -> str: return "".join(random.choices(string.ascii_uppercase, k=3)) def random_longitude() -> float: return random.random() * random.choice([180, 180]) def random_latitude() -> float: return random.random() * random.choice([90, -90])

20.478261

64

0.687898

63

471

5.015873

0.412698

0.142405

0.082278

0.120253

0.455696

0.234177

0

0.045226

0.154989

471

22

65

21.409091

0.748744

0

1

0.416667

true

0

0.166667

0.416667

1

0

null

0

null

0

1

0

1

0

3

01fcb851495352b86f6bfa6adea5c5f9d73b1d70

139

py

Python

clubadm/apps.py

clubadm/clubadm

1e460253cdd30271aa359b53bdb600d2c6ca91b0

[ "MIT" ]

25

2015-12-21T04:33:11.000Z

2021-12-13T17:55:00.000Z

clubadm/apps.py

clubadm/clubadm

1e460253cdd30271aa359b53bdb600d2c6ca91b0

[ "MIT" ]

16

2015-12-22T08:23:09.000Z

2020-12-23T20:00:10.000Z

clubadm/apps.py

clubadm/clubadm

1e460253cdd30271aa359b53bdb600d2c6ca91b0

[ "MIT" ]

6

2015-12-21T18:37:57.000Z

2016-02-22T23:45:46.000Z

from django.apps import AppConfig class ClubADMConfig(AppConfig): name = "clubadm" verbose_name = "Клуб анонимных Дедов Морозов"

19.857143

49

0.748201

16

139

6.4375

0.875

0

0.179856

139

6

50

23.166667

0.903509

0

0.251799

0

1

0

false

0

0.25

0

1

0

1

0

null

0

1

0

null

0

1

0

3

bf32021a158f8fe0334afdd817689a59b4174e4e

131

py

Python

src/ds-visuals/snippets/tree/create.py

Ray784/ds-visualization

47597e7cdb98f4f1692cbf9eaa88810c3d2b37c9

[ "MIT" ]

null

src/ds-visuals/snippets/tree/create.py

Ray784/ds-visualization

47597e7cdb98f4f1692cbf9eaa88810c3d2b37c9

[ "MIT" ]

1

2022-03-02T10:57:50.000Z

src/ds-visuals/snippets/tree/create.py

Ray784/ds-visualization

47597e7cdb98f4f1692cbf9eaa88810c3d2b37c9

[ "MIT" ]

null

def createTree(self, root, *elements): root = None for element in elements: root = self.insert(root, element) return root

14.555556

38

0.70229

18

131

5.111111

0.611111

0.26087

0

0.198473

131

9

39

14.555556

0.87619

0

1

0.2

false

0

0.4

0

1

0

null

1

0

1

0

null

0

3

1717458e20e9cc4db4d35021225344a7fe7edb8c

377

py

Python

Commands/Command.py

densikat/PyRobotSim

724418c6537fd5428e706fc3b7807c5b128ea677

[ "MIT" ]

null

Commands/Command.py

densikat/PyRobotSim

724418c6537fd5428e706fc3b7807c5b128ea677

[ "MIT" ]

null

Commands/Command.py

densikat/PyRobotSim

724418c6537fd5428e706fc3b7807c5b128ea677

[ "MIT" ]

null

from abc import ABC, abstractmethod class Command(ABC): def __init__(self,commandname): self.commandname = commandname @abstractmethod def initializecommand(self, command): pass @abstractmethod def validateinstruction(self, robot, table): pass @abstractmethod def executeinstruction(self, robot, table): pass

17.136364

48

0.671088

35

377

7.114286

0.457143

0.204819

0.168675

0.144578

0

0.257294

377

21

49

17.952381

0.889286

0

0.461538

0

1

0.307692

false

0.230769

0.076923

0

0.461538

0

null

1

0

null

0

1

0

1

0

3

17195d7a3c4f297277705fee26d57efe2d026230

87

py

Python

2020-11-02/meio.py

pufe/programa

7f79566597446e9e39222e6c15fa636c3dd472bb

[ "MIT" ]

2

2020-12-12T00:02:40.000Z

2021-04-21T19:49:59.000Z

2020-11-02/meio.py

pufe/programa

7f79566597446e9e39222e6c15fa636c3dd472bb

[ "MIT" ]

null

2020-11-02/meio.py

pufe/programa

7f79566597446e9e39222e6c15fa636c3dd472bb

[ "MIT" ]

null

n = int(input()) lado = 2 for i in range(n): lado = 2*lado-1 print(lado*lado)

14.5

20

0.563218

17

87

2.882353

0.647059

0.204082

0

0.046875

0.264368

87

5

21

17.4

0.71875

0

1

0

false

0

0.2

1

0

null

1

0

1

0

null

0

3

175bb838cee18336bd8a62ac16a8a963bd31ba34

7,122

py

Python

sampleScan.py

tink3rtanner/opc

b94b40bb176bfedacbaf4a2dafb7cdac619818a2

[ "MIT" ]

29

2019-09-22T07:02:40.000Z

2022-03-25T23:06:06.000Z

sampleScan.py

tink3rtanner/opc

b94b40bb176bfedacbaf4a2dafb7cdac619818a2

[ "MIT" ]

3

2019-10-07T16:11:38.000Z

2020-05-07T20:44:57.000Z

sampleScan.py

tink3rtanner/opc

b94b40bb176bfedacbaf4a2dafb7cdac619818a2

[ "MIT" ]

5

2019-10-07T17:03:56.000Z

2022-03-18T08:51:22.000Z

import os directory="/home/pi/Desktop/samplepacks/" sampleList=[["test","test"]] def main(): for file in os.listdir(directory): fullPath = directory + file if os.path.isdir(fullPath): #print #print "directory: ",file #print fullPath containsAif=0 #each folder in parent directory for subfile in os.listdir(fullPath): subfullPath=fullPath+"/"+subfile #a path within a path #print "SUBFILE: ",subfile if os.path.isdir(subfullPath): if subfile=="synth" or "drum": #print "nested directories, but it's okay cuz you named them" readAifDir(subfile,subfullPath) elif subfile.endswith(".aif") or subfile.endswith(".aiff"): containsAif=1 elif subfile.endswith(".DS_Store"): continue else: print "what's going on here. name your folders or hold it with the nesting" print "SUBFILE: ",subfile if containsAif==1: readAifDir(file,fullPath) # else: # sampleList.append([file,fullPath]) #adds file andfullpath to samplelist # #if file.endswith(".atm") or file.endswith(".py"): if ['test', 'test'] in sampleList: sampleList.remove(['test','test']) #print sampleList # for sample in sampleList: # print # print sample[1] #fullpath # atts=readAif(sample[1]) #reads aiff and gets attributes! # print atts['type'] # #print atts def readAifDir(name,path): #should return amount of .aif's found in dir aifsampleList=[["a","a"]] print print "readAif directory: ",name print path for file in os.listdir(path): fullPath=path+"/"+file if file.endswith('.aif')or file.endswith(".aiff"): #print "aif found at file: ",fullPath atts=readAif(fullPath) aifsampleList.append([file,fullPath]) #print atts['type'] elif file.endswith(".DS_Store"): #ignore .DS_Store mac files continue else: print fullPath, " is not a aif. what gives?" if ["a","a"] in aifsampleList: aifsampleList.remove(["a","a"]) for sample in aifsampleList: print sample[1] #fullpath atts=readAif(sample[1]) #reads aiff and gets attributes! print atts['type'] #print atts def readAif(path): #print "//READAIFF from file ", path #print # SAMPLE DRUM AIFF METADATA # /home/pi/Desktop/samplepacks/kits1/rz1.aif # drum_version : 1 # type : drum # name : user # octave : 0 # pitch : ['0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0'] # start : ['0', '24035422', '48070845', '86012969', '123955093', '144951088', '175722759', '206494430', '248851638', '268402991', '312444261', '428603973', '474613364', '601936581', '729259799', '860697810', '992135821', '1018188060', '1044240299', '1759004990', '1783040413', '1820982537', '1845017959', '1882960084'] # end : ['24031364', '48066787', '86008911', '123951035', '144947030', '175718701', '206490372', '248847580', '268398933', '312440203', '428599915', '474609306', '601932523', '729255741', '860693752', '992131763', '1018184002', '1044236241', '1759000932', '1783036355', '1820978479', '1845013902', '1882956026', '1906991448'] # playmode : ['8192', '8192', '8192', '8192', '8192', '8192', '8192', '8192', '8192', '8192', '8192', '8192', '8192', '8192', '8192', '8192', '8192', '8192', '8192', '8192', '8192', '8192', '8192', '8192'] # reverse : ['8192', '8192', '8192', '8192', '8192', '8192', '8192', '8192', '8192', '8192', '8192', '8192', '8192', '8192', '8192', '8192', '8192', '8192', '8192', '8192', '8192', '8192', '8192', '8192'] # volume : ['8192', '8192', '8192', '8192', '8192', '8192', '8192', '8192', '8192', '8192', '8192', '8192', '8192', '8192', '8192', '8192', '8192', '8192', '8192', '8192', '8192', '8192', '8192', '8192'] # dyna_env : ['0', '8192', '0', '8192', '0', '0', '0', '0'] # fx_active : false # fx_type : delay # fx_params : ['8000', '8000', '8000', '8000', '8000', '8000', '8000', '8000'] # lfo_active : false # lfo_type : tremolo # lfo_params : ['16000', '16000', '16000', '16000', '0', '0', '0', '0'] # SAMPLE SYNTH METADATA # /home/pi/Desktop/samplepacks/C-MIX/mtrap.aif # adsr : ['64', '10746', '32767', '14096', '4000', '64', '4000', '4000'] # base_freq : 440.0 # fx_active : true # fx_params : ['64', '0', '18063', '16000', '0', '0', '0', '0'] # fx_type : nitro # knobs : ['0', '2193', '2540', '4311', '12000', '12288', '28672', '8192'] # lfo_active : false # lfo_params : ['16000', '0', '0', '16000', '0', '0', '0', '0'] # lfo_type : tremolo # name : mtrap # octave : 0 # synth_version : 2 # type : sampler attdata={} with open(path,'rb') as fp: line=fp.readline() #print line if 'op-1' in line: #print #print 'op-1 appl chunk found!' #print subline=line.split("op-1") # subline=line.split("op-1")[0] # print subline[1] data=line.split('{', 1)[1].split('}')[0] #data is everything in brackets #print #print "data!" #print data data=switchBrack(data,",","|") attlist=data.split(",") #print #print "attlist" #print attlist #print #print "attname: attvalue" for i,line in enumerate(attlist): #print line linesplit=line.split(":") attname=linesplit[0] attname=attname[1:-1] attvalue=linesplit[1] valtype="" #print attvalue if isInt(attvalue): valtype='int' if isfloat(attvalue): valtype='float' if attvalue=="false" or attvalue=="true": valtype='bool' for j,char in enumerate(list(attvalue)): #print "j,char" #print j, char if valtype=="": if char=='"': #print "string: ",char valtype="string" elif char=="[": valtype="list" if valtype=="": valtype="no type detected" elif valtype=="string": attvalue=attvalue[1:-1] elif valtype=="list": attvalue=attvalue[1:-1] attvalue=attvalue.split("|") #print "list found" # for k,item in enumerate(attvalue): # print k,item #attvalue[k]= #print attvalue[1] #print attname,":",attvalue #print valtype #print attdata.update({attname:attvalue}) #print attdata['type'] if 'type' in attdata: #print "type exists" True else: #print "type doesn't exist" attdata.update({'type':'not specified'}) #except: # attdata.update({'type':'not specified'}) return attdata # attdata[attname]=value #print attdata def isInt(s): try: int(s) return True except ValueError: return False def isfloat(s): try: float(s) return True except ValueError: return False def switchBrack(data,fromdelim,todelim): datalist=list(data) inbrack=0 for i,char in enumerate(datalist): #print i, " ",char if char=="[": inbrack=1 #print "in brackets" if char=="]": inbrack=0 #print "out of brackets" if inbrack ==1: if char==fromdelim: #print "comma found!" if data[i-1].isdigit(): #print "num preceding comma found" datalist[i]=todelim newdata="".join(datalist) #print newdata return newdata main()

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Python

scripts/04_3d_concepts/modeling/polygon_reduction/polygonreduction_create_r19.py

PluginCafe/cinema4d_py_sdk_extended

aea195b47c15e1c94443292e489afe6779b68550

[ "Apache-2.0" ]

85

2019-09-06T22:53:15.000Z

2022-03-27T01:33:09.000Z

scripts/04_3d_concepts/modeling/polygon_reduction/polygonreduction_create_r19.py

PluginCafe/cinema4d_py_sdk_extended

aea195b47c15e1c94443292e489afe6779b68550

[ "Apache-2.0" ]

11

2019-09-03T22:59:19.000Z

2022-02-27T03:42:52.000Z

scripts/04_3d_concepts/modeling/polygon_reduction/polygonreduction_create_r19.py

PluginCafe/cinema4d_py_sdk_extended

aea195b47c15e1c94443292e489afe6779b68550

[ "Apache-2.0" ]

31

2019-09-09T09:35:35.000Z

2022-03-28T09:08:47.000Z

""" Copyright: MAXON Computer GmbH Author: Yannick Puech Description: - Creates a new PolygonReduction object. Class/method highlighted: - c4d.utils.PolygonReduction """ import c4d polyReduction = c4d.utils.PolygonReduction()

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17665acaef0965ab5ef31388109575fa38dc8d4f

780

py

Python

manticore/core/smtlib/__init__.py

ivanpustogarov/manticore

f17410b8427ddbd5d751d8824bdf10ce33c9f3ce

[ "Apache-2.0" ]

null

manticore/core/smtlib/__init__.py

ivanpustogarov/manticore

f17410b8427ddbd5d751d8824bdf10ce33c9f3ce

[ "Apache-2.0" ]

null

manticore/core/smtlib/__init__.py

ivanpustogarov/manticore

f17410b8427ddbd5d751d8824bdf10ce33c9f3ce

[ "Apache-2.0" ]

1

2018-08-12T17:29:11.000Z

from __future__ import absolute_import # noqa from .expression import Expression, Bool, BitVec, Array, BitVecConstant # noqa from .constraints import ConstraintSet # noqa from .solver import * # noqa from . import operators as Operators # noqa import logging logger = logging.getLogger(__name__) ''' class OperationNotPermited(SolverException): def __init__(self): super(OperationNotPermited, self).__init__("You cant build this expression") #no childrens class ConcretizeException(SolverException): def __init__(self, expression): super(ConcretizeException, self).__init__("Need to concretize the following and retry\n"+str(expression)) #no childrens self.expression = expression ''' class VisitorException(Exception): pass

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py

Python

sheets/serializers.py

LD31D/django_sheets

fa626012fd00d69fbbac4fe4542150902d2be8cb

[ "MIT" ]

null

sheets/serializers.py

LD31D/django_sheets

fa626012fd00d69fbbac4fe4542150902d2be8cb

[ "MIT" ]

null

sheets/serializers.py

LD31D/django_sheets

fa626012fd00d69fbbac4fe4542150902d2be8cb

[ "MIT" ]

null

from rest_framework import serializers from .models import Cell class CellSerializer(serializers.ModelSerializer): class Meta: model = Cell fields = ('coordinates', 'value')

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176c751a40ba19e4a05a51527d9a92d2dae692b0

257

py

Python

lwc/views.py

codingforentrepreneurs/launch-with-code

45b20b799819d7ab3ea528ae63a2cf2773fc6e13

[ "MIT" ]

64

2015-01-04T20:01:08.000Z

2021-09-08T16:40:48.000Z

lwc/views.py

codingforentrepreneurs/launch-with-code

45b20b799819d7ab3ea528ae63a2cf2773fc6e13

[ "MIT" ]

1

2016-01-05T16:52:10.000Z

2016-01-05T17:02:24.000Z

lwc/views.py

codingforentrepreneurs/launch-with-code

45b20b799819d7ab3ea528ae63a2cf2773fc6e13

[ "MIT" ]

85

2015-01-03T20:28:17.000Z

2022-03-02T20:25:44.000Z

from django.shortcuts import render def testhome(request): context = {} template = "donotuse.html" return render(request, template, context) # def home2(request): # context = {} # template = "home2.html" # return render(request, template, context)

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178d05b1d8bb6f8c30ffa020c7ddd40a9b93f7a2

265

py

Python

main.py

hiyoung123/ProxyPool

79f3d96e52873637ad6bff89908c7b8218d878d5

[ "MIT" ]

2

2021-05-10T07:59:22.000Z

2021-05-10T08:40:27.000Z

main.py

hiyoung123/ProxyPool

79f3d96e52873637ad6bff89908c7b8218d878d5

[ "MIT" ]

null

main.py

hiyoung123/ProxyPool

79f3d96e52873637ad6bff89908c7b8218d878d5

[ "MIT" ]

1

2021-05-31T06:28:46.000Z

#!/usr/bin/env python # -*- encoding: utf-8 -*- import os import sys from scrapy.cmdline import execute if __name__ == '__main__': sys.path.append(os.path.abspath(__file__)) # execute(['scrapy', 'crawl', 'XiLa']) execute(['scrapy', 'crawl', 'Kuai'])

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bd665cbb30872e6e356793046c9b4863e6327d40

67,153

py

Python

Caracteriz_nubes_Anio.py

cmcuervol/Estefania

13b564261dfc786b93c77fbc442a568018f87cc9

[ "MIT" ]

2

2020-09-13T07:55:25.000Z

2020-09-21T13:36:23.000Z

Caracteriz_nubes_Anio.py

cmcuervol/Estefania

13b564261dfc786b93c77fbc442a568018f87cc9

[ "MIT" ]

null

Caracteriz_nubes_Anio.py

cmcuervol/Estefania

13b564261dfc786b93c77fbc442a568018f87cc9

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- import pandas as pd from datetime import datetime, timedelta import numpy as np import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt import os import matplotlib.ticker as tck import matplotlib.font_manager as fm import math as m import matplotlib.dates as mdates import netCDF4 as nc from netCDF4 import Dataset id import itertools import datetime from scipy.stats import ks_2samp import matplotlib.colors as colors #------------------------------------------------------------------------------ # Motivación codigo ----------------------------------------------------------- "Codigo que permite la porderación de la nubosidad por la ponderación de sus horas. Se realiza para el " "horizonte de tiempo de mayor rango q se tenga de los datos de GOES CH2. Está sijeto a los umbrales de" "cada hora, por lo que se vrea un back up en la carpeta de Back uos de Drive" ################################################################################################# ## -----------------INCORPORANDO LOS DATOS DE RADIACIÓN Y DE LOS EXPERIMENTOS----------------- ## ################################################################################################# df_P975 = pd.read_table('/home/nacorreasa/Maestria/Datos_Tesis/Piranometro/60012018_2019.txt', parse_dates=[2]) df_P350 = pd.read_table('/home/nacorreasa/Maestria/Datos_Tesis/Piranometro/60022018_2019.txt', parse_dates=[2]) df_P348 = pd.read_table('/home/nacorreasa/Maestria/Datos_Tesis/Piranometro/60032018_2019.txt', parse_dates=[2]) df_P975 = df_P975.set_index(["fecha_hora"]) df_P975.index = df_P975.index.tz_localize('UTC').tz_convert('America/Bogota') df_P975.index = df_P975.index.tz_localize(None) df_P350 = df_P350.set_index(["fecha_hora"]) df_P350.index = df_P350.index.tz_localize('UTC').tz_convert('America/Bogota') df_P350.index = df_P350.index.tz_localize(None) df_P348 = df_P348.set_index(["fecha_hora"]) df_P348.index = df_P348.index.tz_localize('UTC').tz_convert('America/Bogota') df_P348.index = df_P348.index.tz_localize(None) df_P975.index = pd.to_datetime(df_P975.index, format="%Y-%m-%d %H:%M:%S", errors='coerce') df_P350.index = pd.to_datetime(df_P350.index, format="%Y-%m-%d %H:%M:%S", errors='coerce') df_P348.index = pd.to_datetime(df_P348.index, format="%Y-%m-%d %H:%M:%S", errors='coerce') ## ----------------ACOTANDO LOS DATOS A VALORES VÁLIDOS---------------- ## 'Como en este caso lo que interesa es la radiacion, para la filtración de los datos, se' 'considerarán los datos de radiacion mayores a 0.' df_P975 = df_P975[(df_P975['radiacion'] > 0) ] df_P350 = df_P350[(df_P350['radiacion'] > 0) ] df_P348 = df_P348[(df_P348['radiacion'] > 0) ] df_P975_h = df_P975.groupby(pd.Grouper(level='fecha_hora', freq='1H')).mean() df_P350_h = df_P350.groupby(pd.Grouper(level='fecha_hora', freq='1H')).mean() df_P348_h = df_P348.groupby(pd.Grouper(level='fecha_hora', freq='1H')).mean() df_P975_h = df_P975_h.between_time('06:00', '17:59') df_P350_h = df_P350_h.between_time('06:00', '17:59') df_P348_h = df_P348_h.between_time('06:00', '17:59') #----------------------------------------------------------------------------- # Rutas para las fuentes ----------------------------------------------------- prop = fm.FontProperties(fname='/home/nacorreasa/SIATA/Cod_Califi/AvenirLTStd-Heavy.otf' ) prop_1 = fm.FontProperties(fname='/home/nacorreasa/SIATA/Cod_Califi/AvenirLTStd-Book.otf') prop_2 = fm.FontProperties(fname='/home/nacorreasa/SIATA/Cod_Califi/AvenirLTStd-Black.otf') ################################################################################################ ## -------------------------------UMBRALES DE LAS REFLECTANCIAS------------------------------ ## ################################################################################################ Umbral_up_348 = pd.read_table('/home/nacorreasa/Maestria/Datos_Tesis/Umbrales_Horarios/Umbral_Hourly_348_Nuba.csv', sep=',', header = None) Umbral_down_348 = pd.read_table('/home/nacorreasa/Maestria/Datos_Tesis/Umbrales_Horarios/Umbral_Hourly_348_Desp.csv', sep=',', header = None) Umbral_up_348.columns=['Hora', 'Umbral'] Umbral_up_348.index = Umbral_up_348['Hora'] Umbral_up_348 = Umbral_up_348.drop(['Hora'], axis=1) Umbral_down_348.columns=['Hora', 'Umbral'] Umbral_down_348.index = Umbral_down_348['Hora'] Umbral_down_348 = Umbral_down_348.drop(['Hora'], axis=1) #Umbrales_348 = [Umbral_down_348, Umbral_up_348] Umbral_up_350 = pd.read_table('/home/nacorreasa/Maestria/Datos_Tesis/Umbrales_Horarios/Umbral_Hourly_350_Nuba.csv', sep=',', header = None) Umbral_down_350 = pd.read_table('/home/nacorreasa/Maestria/Datos_Tesis/Umbrales_Horarios/Umbral_Hourly_350_Desp.csv', sep=',', header = None) Umbral_up_350.columns=['Hora', 'Umbral'] Umbral_up_350.index = Umbral_up_350['Hora'] Umbral_up_350 = Umbral_up_350.drop(['Hora'], axis=1) Umbral_down_350.columns=['Hora', 'Umbral'] Umbral_down_350.index = Umbral_down_350['Hora'] Umbral_down_350 = Umbral_down_350.drop(['Hora'], axis=1) #Umbrales_350 = [Umbral_down_350, Umbral_up_350] Umbral_up_975 = pd.read_table('/home/nacorreasa/Maestria/Datos_Tesis/Umbrales_Horarios/Umbral_Hourly_975_Nuba.csv', sep=',', header = None) Umbral_down_975 = pd.read_table('/home/nacorreasa/Maestria/Datos_Tesis/Umbrales_Horarios/Umbral_Hourly_975_Desp.csv', sep=',', header = None) Umbral_up_975.columns=['Hora', 'Umbral'] Umbral_up_975.index = Umbral_up_975['Hora'] Umbral_up_975 = Umbral_up_975.drop(['Hora'], axis=1) Umbral_down_975.columns=['Hora', 'Umbral'] Umbral_down_975.index = Umbral_down_975['Hora'] Umbral_down_975 = Umbral_down_975.drop(['Hora'], axis=1) #Umbrales_975 = [Umbral_down_975, Umbral_up_975] #################################################################################### ## ----------------LECTURA DE LOS DATOS DE GOES CH2 MALLA GENERAL---------------- ## #################################################################################### Rad = np.load('/home/nacorreasa/Maestria/Datos_Tesis/Arrays/Array_Rad_2018_2019CH2.npy') ################################################################################################# ##-------------------LECTURA DE LOS DATOS DE CH2 GOES PARA CADA PIXEL--------------------------## ################################################################################################# Rad_pixel_975 = np.load('/home/nacorreasa/Maestria/Datos_Tesis/Arrays/Array_Rad_pix975_Anio.npy') Rad_pixel_350 = np.load('/home/nacorreasa/Maestria/Datos_Tesis/Arrays/Array_Rad_pix350_Anio.npy') Rad_pixel_348 = np.load('/home/nacorreasa/Maestria/Datos_Tesis/Arrays/Array_Rad_pix348_Anio.npy') fechas_horas = np.load('/home/nacorreasa/Maestria/Datos_Tesis/Arrays/Array_FechasHoras_Anio.npy') df_fh = pd.DataFrame() df_fh ['fecha_hora'] = fechas_horas df_fh['fecha_hora'] = pd.to_datetime(df_fh['fecha_hora'], format="%Y-%m-%d %H:%M", errors='coerce') df_fh.index = df_fh['fecha_hora'] w = pd.date_range(df_fh.index.min(), df_fh.index.max()).difference(df_fh.index) df_fh = df_fh[df_fh.index.hour != 5] fechas_horas = df_fh['fecha_hora'].values ## -- Selección del pixel de la TS Rad_df_975 = pd.DataFrame() Rad_df_975['Fecha_Hora'] = fechas_horas Rad_df_975['Radiacias'] = Rad_pixel_975 Rad_df_975['Fecha_Hora'] = pd.to_datetime(Rad_df_975['Fecha_Hora'], format="%Y-%m-%d %H:%M", errors='coerce') Rad_df_975.index = Rad_df_975['Fecha_Hora'] Rad_df_975 = Rad_df_975.drop(['Fecha_Hora'], axis=1) ## -- Selección del pixel de la CI Rad_df_350 = pd.DataFrame() Rad_df_350['Fecha_Hora'] = fechas_horas Rad_df_350['Radiacias'] = Rad_pixel_350 Rad_df_350['Fecha_Hora'] = pd.to_datetime(Rad_df_350['Fecha_Hora'], format="%Y-%m-%d %H:%M", errors='coerce') Rad_df_350.index = Rad_df_350['Fecha_Hora'] Rad_df_350 = Rad_df_350.drop(['Fecha_Hora'], axis=1) ## -- Selección del pixel de la JV Rad_df_348 = pd.DataFrame() Rad_df_348['Fecha_Hora'] = fechas_horas Rad_df_348['Radiacias'] = Rad_pixel_348 Rad_df_348['Fecha_Hora'] = pd.to_datetime(Rad_df_348['Fecha_Hora'], format="%Y-%m-%d %H:%M", errors='coerce') Rad_df_348.index = Rad_df_348['Fecha_Hora'] Rad_df_348 = Rad_df_348.drop(['Fecha_Hora'], axis=1) 'OJOOOO DESDE ACÁ-----------------------------------------------------------------------------------------' 'Se comenta porque se estaba perdiendo la utilidad de la información cada 10 minutos al suavizar la serie.' ## ------------------------CAMBIANDO LOS DATOS HORARIOS POR LOS ORIGINALES---------------------- ## Rad_df_348_h = Rad_df_348 Rad_df_350_h = Rad_df_350 Rad_df_975_h = Rad_df_975 ## ------------------------------------DATOS HORARIOS DE REFLECTANCIAS------------------------- ## # Rad_df_348_h = Rad_df_348.groupby(pd.Grouper(freq="H")).mean() # Rad_df_350_h = Rad_df_350.groupby(pd.Grouper(freq="H")).mean() # Rad_df_975_h = Rad_df_975.groupby(pd.Grouper(freq="H")).mean() 'OJOOOO HASTA ACÁ-----------------------------------------------------------------------------------------' Rad_df_348_h = Rad_df_348_h.between_time('06:00', '17:59') Rad_df_350_h = Rad_df_350_h.between_time('06:00', '17:59') Rad_df_975_h = Rad_df_975_h.between_time('06:00', '17:59') ## ---------------------------------FDP COMO GRÁFICA----------------------------------------- ## fig = plt.figure(figsize=[10, 6]) plt.rc('axes', edgecolor='gray') ax1 = fig.add_subplot(1, 3, 1) ax1.spines['top'].set_visible(False) ax1.spines['right'].set_visible(False) ax1.hist(Rad_df_348_h['Radiacias'].values[~np.isnan(Rad_df_348_h['Radiacias'].values)], bins='auto', alpha = 0.5) #Umbrales_line1 = [ax1.axvline(x=xc, color='k', linestyle='--') for xc in Umbrales_348] #ax1.text(Umbrales_348[0], 1000, str(Umbrales_348[0]) , fontsize=10, fontproperties=prop_1) ax1.set_title(u'Distribución del FR en JV', fontproperties=prop, fontsize = 15) ax1.set_ylabel(u'Frecuencia', fontproperties=prop_1, fontsize = 15) ax1.set_xlabel(u'Reflectancia', fontproperties=prop_1, fontsize = 15) ax2 = fig.add_subplot(1, 3, 2) ax2.spines['top'].set_visible(False) ax2.spines['right'].set_visible(False) ax2.hist(Rad_df_350_h['Radiacias'].values[~np.isnan(Rad_df_350_h['Radiacias'].values)], bins='auto', alpha = 0.5) #Umbrales_line2 = [ax2.axvline(x=xc, color='k', linestyle='--') for xc in Umbrales_350] ax2.set_title(u'Distribución del FR en CI', fontproperties=prop, fontsize = 15) ax2.set_ylabel(u'Frecuencia', fontproperties=prop_1, fontsize = 15) ax2.set_xlabel(u'Reflectancia', fontproperties=prop_1, fontsize = 15) ax3 = fig.add_subplot(1, 3, 3) ax3.spines['top'].set_visible(False) ax3.spines['right'].set_visible(False) ax3.hist(Rad_df_975_h['Radiacias'].values[~np.isnan(Rad_df_975_h['Radiacias'].values)], bins='auto', alpha = 0.5) #Umbrales_line3 = [ax3.axvline(x=xc, color='k', linestyle='--') for xc in Umbrales_975] ax3.set_title(u'Distribución del FR en TS', fontproperties=prop, fontsize = 15) ax3.set_ylabel(u'Frecuencia', fontproperties=prop_1, fontsize = 15) ax3.set_xlabel(u'Reflectancia', fontproperties=prop_1, fontsize = 15) plt.savefig('/home/nacorreasa/Escritorio/Figuras/HistogramaFrecuenciasCH2_2018.png') plt.close('all') os.system('scp /home/nacorreasa/Escritorio/Figuras/HistogramaFrecuenciasCH2_2018.png nacorreasa@192.168.1.74:/var/www/nacorreasa/Graficas_Resultados/Estudio') ################################################################################################ ## -------------------------OBTENER EL DF DEL ESCENARIO DESPEJADO---------------------------- ## ################################################################################################ Rad_desp_348 = [] FH_Desp_348 = [] for i in range(len(Rad_df_348_h)): for j in range(len(Umbral_down_348.index)): if (Rad_df_348_h.index[i].hour == Umbral_down_348.index[j]) & (Rad_df_348_h.Radiacias.values[i] <= Umbral_down_348.values[j]): Rad_desp_348.append(Rad_df_348_h.Radiacias.values[i]) FH_Desp_348.append(Rad_df_348_h.index[i]) df_348_desp = pd.DataFrame() df_348_desp['Radiacias'] = Rad_desp_348 df_348_desp['Fecha_Hora'] = FH_Desp_348 df_348_desp['Fecha_Hora'] = pd.to_datetime(df_348_desp['Fecha_Hora'], format="%Y-%m-%d %H:%M", errors='coerce') df_348_desp.index = df_348_desp['Fecha_Hora'] df_348_desp = df_348_desp.drop(['Fecha_Hora'], axis=1) Rad_desp_350 = [] FH_Desp_350 = [] for i in range(len(Rad_df_350_h)): for j in range(len(Umbral_down_350.index)): if (Rad_df_350_h.index[i].hour == Umbral_down_350.index[j]) & (Rad_df_350_h.Radiacias.values[i] <= Umbral_down_350.values[j]): Rad_desp_350.append(Rad_df_350_h.Radiacias.values[i]) FH_Desp_350.append(Rad_df_350_h.index[i]) df_350_desp = pd.DataFrame() df_350_desp['Radiacias'] = Rad_desp_350 df_350_desp['Fecha_Hora'] = FH_Desp_350 df_350_desp['Fecha_Hora'] = pd.to_datetime(df_350_desp['Fecha_Hora'], format="%Y-%m-%d %H:%M", errors='coerce') df_350_desp.index = df_350_desp['Fecha_Hora'] df_350_desp = df_350_desp.drop(['Fecha_Hora'], axis=1) Rad_desp_975 = [] FH_Desp_975 = [] for i in range(len(Rad_df_975_h)): for j in range(len(Umbral_down_975.index)): if (Rad_df_975_h.index[i].hour == Umbral_down_975.index[j]) & (Rad_df_975_h.Radiacias.values[i] <= Umbral_down_975.values[j]): Rad_desp_975.append(Rad_df_975_h.Radiacias.values[i]) FH_Desp_975.append(Rad_df_975_h.index[i]) df_975_desp = pd.DataFrame() df_975_desp['Radiacias'] = Rad_desp_975 df_975_desp['Fecha_Hora'] = FH_Desp_975 df_975_desp['Fecha_Hora'] = pd.to_datetime(df_975_desp['Fecha_Hora'], format="%Y-%m-%d %H:%M", errors='coerce') df_975_desp.index = df_975_desp['Fecha_Hora'] df_975_desp = df_975_desp.drop(['Fecha_Hora'], axis=1) ################################################################################################ ## --------------------------OBTENER EL DF DEL ESCENARIO NUBADO------------------------------ ## ################################################################################################ Rad_nuba_348 = [] FH_Nuba_348 = [] for i in range(len(Rad_df_348_h)): for j in range(len(Umbral_up_348.index)): if (Rad_df_348_h.index[i].hour == Umbral_up_348.index[j]) & (Rad_df_348_h.Radiacias.values[i] >= Umbral_up_348.values[j]): Rad_nuba_348.append(Rad_df_348_h.Radiacias.values[i]) FH_Nuba_348.append(Rad_df_348_h.index[i]) df_348_nuba = pd.DataFrame() df_348_nuba['Radiacias'] = Rad_nuba_348 df_348_nuba['Fecha_Hora'] = FH_Nuba_348 df_348_nuba['Fecha_Hora'] = pd.to_datetime(df_348_nuba['Fecha_Hora'], format="%Y-%m-%d %H:%M", errors='coerce') df_348_nuba.index = df_348_nuba['Fecha_Hora'] df_348_nuba = df_348_nuba.drop(['Fecha_Hora'], axis=1) Rad_nuba_350 = [] FH_Nuba_350 = [] for i in range(len(Rad_df_350_h)): for j in range(len(Umbral_up_350.index)): if (Rad_df_350_h.index[i].hour == Umbral_up_350.index[j]) & (Rad_df_350_h.Radiacias.values[i] >= Umbral_up_350.values[j]): Rad_nuba_350.append(Rad_df_350_h.Radiacias.values[i]) FH_Nuba_350.append(Rad_df_350_h.index[i]) df_350_nuba = pd.DataFrame() df_350_nuba['Radiacias'] = Rad_nuba_350 df_350_nuba['Fecha_Hora'] = FH_Nuba_350 df_350_nuba['Fecha_Hora'] = pd.to_datetime(df_350_nuba['Fecha_Hora'], format="%Y-%m-%d %H:%M", errors='coerce') df_350_nuba.index = df_350_nuba['Fecha_Hora'] df_350_nuba = df_350_nuba.drop(['Fecha_Hora'], axis=1) Rad_nuba_975 = [] FH_Nuba_975 = [] for i in range(len(Rad_df_975_h)): for j in range(len(Umbral_up_975.index)): if (Rad_df_975_h.index[i].hour == Umbral_up_975.index[j]) & (Rad_df_975_h.Radiacias.values[i] >= Umbral_up_975.values[j]): Rad_nuba_975.append(Rad_df_975_h.Radiacias.values[i]) FH_Nuba_975.append(Rad_df_975_h.index[i]) df_975_nuba = pd.DataFrame() df_975_nuba['Radiacias'] = Rad_nuba_975 df_975_nuba['Fecha_Hora'] = FH_Nuba_975 df_975_nuba['Fecha_Hora'] = pd.to_datetime(df_975_nuba['Fecha_Hora'], format="%Y-%m-%d %H:%M", errors='coerce') df_975_nuba.index = df_975_nuba['Fecha_Hora'] df_975_nuba = df_975_nuba.drop(['Fecha_Hora'], axis=1) ## -------------------------OBTENER LAS HORAS Y FECHAS DESPEJADAS---------------------------- ## Hora_desp_348 = df_348_desp.index.hour Fecha_desp_348 = df_348_desp.index.date Hora_desp_350 = df_350_desp.index.hour Fecha_desp_350 = df_350_desp.index.date Hora_desp_975 = df_975_desp.index.hour Fecha_desp_975 = df_975_desp.index.date ## ----------------------------OBTENER LAS HORAS Y FECHAS NUBADAS---------------------------- ## Hora_nuba_348 = df_348_nuba.index.hour Fecha_nuba_348 = df_348_nuba.index.date Hora_nuba_350 = df_350_nuba.index.hour Fecha_nuba_350 = df_350_nuba.index.date Hora_nuba_975 = df_975_nuba.index.hour Fecha_nuba_975 = df_975_nuba.index.date ## -----------------------------DIBUJAR LOS HISTOGRAMAS DE LAS HORAS ------ ----------------------- # fig = plt.figure(figsize=[10, 6]) plt.rc('axes', edgecolor='gray') ax1 = fig.add_subplot(1, 3, 1) ax1.spines['top'].set_visible(False) ax1.spines['right'].set_visible(False) ax1.hist(Hora_desp_348, bins='auto', alpha = 0.5, color = 'orange', label = 'Desp') ax1.hist(Hora_nuba_348, bins='auto', alpha = 0.5, label = 'Nub') ax1.set_title(u'Distribución de nubes por horas en JV', fontproperties=prop, fontsize = 8) ax1.set_ylabel(u'Frecuencia', fontproperties=prop_1) ax1.set_xlabel(u'Horas', fontproperties=prop_1) ax1.set_ylim(0, 1350) ax1.legend() ax2 = fig.add_subplot(1, 3, 2) ax2.spines['top'].set_visible(False) ax2.spines['right'].set_visible(False) ax2.hist(Hora_desp_350, bins='auto', alpha = 0.5, color = 'orange', label = 'Desp') ax2.hist(Hora_nuba_350, bins='auto', alpha = 0.5, label = 'Nub') ax2.set_title(u'Distribución de nubes por horas en CI', fontproperties=prop, fontsize = 8) ax2.set_ylabel(u'Frecuencia', fontproperties=prop_1) ax2.set_xlabel(u'Horas', fontproperties=prop_1) ax2.set_ylim(0, 1350) ax2.legend() ax3 = fig.add_subplot(1, 3, 3) ax3.spines['top'].set_visible(False) ax3.spines['right'].set_visible(False) ax3.hist(Hora_desp_975, bins='auto', alpha = 0.5, color = 'orange', label = 'Desp') ax3.hist(Hora_nuba_975, bins='auto', alpha = 0.5, label = 'Nub') ax3.set_title(u'Distribución de nubes por horas en TS', fontproperties=prop, fontsize = 8) ax3.set_ylabel(u'Frecuencia', fontproperties=prop_1) ax3.set_xlabel(u'Horas', fontproperties=prop_1) ax3.set_ylim(0, 1350) ax3.legend() plt.subplots_adjust(wspace=0.3, hspace=0.3) plt.savefig('/home/nacorreasa/Escritorio/Figuras/HistoNubaDespAnio2018.png') plt.close('all') os.system('scp /home/nacorreasa/Escritorio/Figuras/HistoNubaDespAnio2018.png nacorreasa@192.168.1.74:/var/www/nacorreasa/Graficas_Resultados/Estudio') ##----------ENCONTRANDO LAS RADIACIONES CORRESPONDIENTES A LAS HORAS NUBOSAS----------## df_FH_nuba_348 = pd.DataFrame() df_FH_nuba_348 ['Fechas'] = Fecha_nuba_348 df_FH_nuba_348 ['Horas'] = Hora_nuba_348 df_FH_nuba_350 = pd.DataFrame() df_FH_nuba_350 ['Fechas'] = Fecha_nuba_350 df_FH_nuba_350 ['Horas'] = Hora_nuba_350 df_FH_nuba_975 = pd.DataFrame() df_FH_nuba_975 ['Fechas'] = Fecha_nuba_975 df_FH_nuba_975 ['Horas'] = Hora_nuba_975 df_FH_nuba_348_groupH = df_FH_nuba_348.groupby('Horas')['Fechas'].unique() df_nuba_348_groupH = pd.DataFrame(df_FH_nuba_348_groupH[df_FH_nuba_348_groupH.apply(lambda x: len(x)>1)]) ##NO entiendo bien acá que se está haciendo df_FH_nuba_350_groupH = df_FH_nuba_350.groupby('Horas')['Fechas'].unique() df_nuba_350_groupH = pd.DataFrame(df_FH_nuba_350_groupH[df_FH_nuba_350_groupH.apply(lambda x: len(x)>1)]) df_FH_nuba_975_groupH = df_FH_nuba_975.groupby('Horas')['Fechas'].unique() df_nuba_975_groupH = pd.DataFrame(df_FH_nuba_975_groupH[df_FH_nuba_975_groupH.apply(lambda x: len(x)>1)]) c = np.arange(6, 18, 1) Sk_Nuba_stat_975 = {} Sk_Nuba_pvalue_975 = {} Composites_Nuba_975 = {} for i in df_FH_nuba_975_groupH.index: H = str(i) if len(df_FH_nuba_975_groupH.loc[i]) == 1 : list = df_P975_h[df_P975_h.index.date == df_FH_nuba_975_groupH.loc[i][0]]['radiacion'].values list_sk_stat = np.ones(12)*np.nan list_sk_pvalue = np.ones(12)*np.nan elif len(df_FH_nuba_975_groupH.loc[i]) > 1 : temporal = pd.DataFrame() for j in range(len(df_FH_nuba_975_groupH.loc[i])): temporal = temporal.append(pd.DataFrame(df_P975_h[df_P975_h.index.date == df_FH_nuba_975_groupH.loc[i][j]]['radiacion'])) stat_975 = [] pvalue_975 = [] for k in c: temporal_sk = temporal[temporal.index.hour == k].radiacion.values Rad_sk = df_P975_h['radiacion'][df_P975_h.index.hour == k].values try: SK = ks_2samp(temporal_sk,Rad_sk) stat_975.append(SK[0]) pvalue_975.append(SK[1]) except ValueError: stat_975.append(np.nan) pvalue_975.append(np.nan) temporal_CD = temporal.groupby(by=[temporal.index.hour]).mean() list = [temporal_CD['radiacion'].values[w] for w in range(len(temporal_CD['radiacion'].values))] list_sk_stat = stat_975 list_sk_pvalue = pvalue_975 Composites_Nuba_975[H] = list Sk_Nuba_stat_975 [H] = list_sk_stat Sk_Nuba_pvalue_975 [H] = list_sk_pvalue del H Comp_Nuba_975_df = pd.DataFrame(Composites_Nuba_975, index = c) #Comp_Nuba_975_df = pd.DataFrame.from_dict(Composites_Nuba_975,orient='index').transpose() Sk_Nuba_stat_975_df = pd.DataFrame(Sk_Nuba_stat_975, index = c) Sk_Nuba_pvalue_975_df = pd.DataFrame(Sk_Nuba_pvalue_975, index = c) Sk_Nuba_stat_350 = {} Sk_Nuba_pvalue_350 = {} Composites_Nuba_350 = {} for i in df_FH_nuba_350_groupH.index: H = str(i) if len(df_FH_nuba_350_groupH.loc[i]) == 1 : list = df_P350_h[df_P350_h.index.date == df_FH_nuba_350_groupH.loc[i][0]]['radiacion'].values list_sk_stat = np.ones(12)*np.nan list_sk_pvalue = np.ones(12)*np.nan elif len(df_FH_nuba_350_groupH.loc[i]) > 1 : temporal = pd.DataFrame() for j in range(len(df_FH_nuba_350_groupH.loc[i])): temporal = temporal.append(pd.DataFrame(df_P350_h[df_P350_h.index.date == df_FH_nuba_350_groupH.loc[i][j]]['radiacion'])) stat_350 = [] pvalue_350 = [] for k in c: temporal_sk = temporal[temporal.index.hour == k].radiacion.values Rad_sk = df_P350_h['radiacion'][df_P350_h.index.hour == k].values try: SK = ks_2samp(temporal_sk,Rad_sk) stat_350.append(SK[0]) pvalue_350.append(SK[1]) except ValueError: stat_350.append(np.nan) pvalue_350.append(np.nan) temporal_CD = temporal.groupby(by=[temporal.index.hour]).mean() list = temporal_CD['radiacion'].values list_sk_stat = stat_350 list_sk_pvalue = pvalue_350 Composites_Nuba_350[H] = list Sk_Nuba_stat_350 [H] = list_sk_stat Sk_Nuba_pvalue_350 [H] = list_sk_pvalue del H Comp_Nuba_350_df = pd.DataFrame(Composites_Nuba_350, index = c) Sk_Nuba_stat_350_df = pd.DataFrame(Sk_Nuba_stat_350, index = c) Sk_Nuba_pvalue_350_df = pd.DataFrame(Sk_Nuba_pvalue_350, index = c) Sk_Nuba_stat_348 = {} Sk_Nuba_pvalue_348 = {} Composites_Nuba_348 = {} for i in df_FH_nuba_348_groupH.index: H = str(i) if len(df_FH_nuba_348_groupH.loc[i]) == 1 : list = df_P348_h[df_P348_h.index.date == df_FH_nuba_348_groupH.loc[i][0]]['radiacion'].values list_sk_stat = np.ones(12)*np.nan list_sk_pvalue = np.ones(12)*np.nan elif len(df_FH_nuba_348_groupH.loc[i]) > 1 : temporal = pd.DataFrame() for j in range(len(df_FH_nuba_348_groupH.loc[i])): temporal = temporal.append(pd.DataFrame(df_P348_h[df_P348_h.index.date == df_FH_nuba_348_groupH.loc[i][j]]['radiacion'])) stat_348 = [] pvalue_348 = [] for k in c: temporal_sk = temporal[temporal.index.hour == k].radiacion.values Rad_sk = df_P348_h['radiacion'][df_P348_h.index.hour == k].values try: SK = ks_2samp(temporal_sk,Rad_sk) stat_348.append(SK[0]) pvalue_348.append(SK[1]) except ValueError: stat_348.append(np.nan) pvalue_348.append(np.nan) temporal_CD = temporal.groupby(by=[temporal.index.hour]).mean() list = temporal_CD['radiacion'].values list_sk_stat = stat_348 list_sk_pvalue = pvalue_348 Composites_Nuba_348[H] = list Sk_Nuba_stat_348 [H] = list_sk_stat Sk_Nuba_pvalue_348 [H] = list_sk_pvalue del H Comp_Nuba_348_df = pd.DataFrame(Composites_Nuba_348, index = c) Sk_Nuba_stat_348_df = pd.DataFrame(Sk_Nuba_stat_348, index = c) Sk_Nuba_pvalue_348_df = pd.DataFrame(Sk_Nuba_pvalue_348, index = c) ##----------ENCONTRANDO LAS RADIACIONES CORRESPONDIENTES A LAS HORAS DESPEJADAS----------## df_FH_desp_348 = pd.DataFrame() df_FH_desp_348 ['Fechas'] = Fecha_desp_348 df_FH_desp_348 ['Horas'] = Hora_desp_348 df_FH_desp_350 = pd.DataFrame() df_FH_desp_350 ['Fechas'] = Fecha_desp_350 df_FH_desp_350 ['Horas'] = Hora_desp_350 df_FH_desp_975 = pd.DataFrame() df_FH_desp_975 ['Fechas'] = Fecha_desp_975 df_FH_desp_975 ['Horas'] = Hora_desp_975 df_FH_desp_348_groupH = df_FH_desp_348.groupby('Horas')['Fechas'].unique() df_desp_348_groupH = pd.DataFrame(df_FH_desp_348_groupH[df_FH_desp_348_groupH.apply(lambda x: len(x)>1)]) ##NO entiendo bien acá que se está haciendo df_FH_desp_350_groupH = df_FH_desp_350.groupby('Horas')['Fechas'].unique() df_desp_350_groupH = pd.DataFrame(df_FH_desp_350_groupH[df_FH_desp_350_groupH.apply(lambda x: len(x)>1)]) df_FH_desp_975_groupH = df_FH_desp_975.groupby('Horas')['Fechas'].unique() df_desp_975_groupH = pd.DataFrame(df_FH_desp_975_groupH[df_FH_desp_975_groupH.apply(lambda x: len(x)>1)]) Sk_Desp_stat_975 = {} Sk_Desp_pvalue_975 = {} Composites_Desp_975 = {} for i in df_FH_desp_975_groupH.index: H = str(i) if len(df_FH_desp_975_groupH.loc[i]) == 1 : list = df_P975_h[df_P975_h.index.date == df_FH_desp_975_groupH.loc[i][0]]['radiacion'].values list_sk_stat = np.ones(12)*np.nan list_sk_pvalue = np.ones(12)*np.nan elif len(df_FH_desp_975_groupH.loc[i]) > 1 : temporal = pd.DataFrame() for j in range(len(df_FH_desp_975_groupH.loc[i])): temporal = temporal.append(pd.DataFrame(df_P975_h[df_P975_h.index.date == df_FH_desp_975_groupH.loc[i][j]]['radiacion'])) stat_975 = [] pvalue_975 = [] for k in c: temporal_sk = temporal[temporal.index.hour == k].radiacion.values Rad_sk = df_P975_h['radiacion'][df_P975_h.index.hour == k].values try: SK = ks_2samp(temporal_sk,Rad_sk) stat_975.append(SK[0]) pvalue_975.append(SK[1]) except ValueError: stat_975.append(np.nan) pvalue_975.append(np.nan) temporal_CD = temporal.groupby(by=[temporal.index.hour]).mean() list = temporal_CD['radiacion'].values list_sk_stat = stat_975 list_sk_pvalue = pvalue_975 Composites_Desp_975[H] = list Sk_Desp_stat_975 [H] = list_sk_stat Sk_Desp_pvalue_975 [H] = list_sk_pvalue del H Comp_Desp_975_df = pd.DataFrame(Composites_Desp_975, index = c) Sk_Desp_stat_975_df = pd.DataFrame(Sk_Desp_stat_975, index = c) Sk_Desp_pvalue_975_df = pd.DataFrame(Sk_Desp_pvalue_975, index = c) Sk_Desp_stat_350 = {} Sk_Desp_pvalue_350 = {} Composites_Desp_350 = {} for i in df_FH_desp_350_groupH.index: H = str(i) if len(df_FH_desp_350_groupH.loc[i]) == 1 : list = df_P350_h[df_P350_h.index.date == df_FH_desp_350_groupH.loc[i][0]]['radiacion'].values list_sk_stat = np.ones(12)*np.nan list_sk_pvalue = np.ones(12)*np.nan elif len(df_FH_desp_350_groupH.loc[i]) > 1 : temporal = pd.DataFrame() for j in range(len(df_FH_desp_350_groupH.loc[i])): temporal = temporal.append(pd.DataFrame(df_P350_h[df_P350_h.index.date == df_FH_desp_350_groupH.loc[i][j]]['radiacion'])) stat_350 = [] pvalue_350 = [] for k in c: temporal_sk = temporal[temporal.index.hour == k].radiacion.values Rad_sk = df_P350_h['radiacion'][df_P350_h.index.hour == k].values try: SK = ks_2samp(temporal_sk,Rad_sk) stat_350.append(SK[0]) pvalue_350.append(SK[1]) except ValueError: stat_350.append(np.nan) pvalue_350.append(np.nan) temporal_CD = temporal.groupby(by=[temporal.index.hour]).mean() list = temporal_CD['radiacion'].values list_sk_stat = stat_350 list_sk_pvalue = pvalue_350 Composites_Desp_350[H] = list Sk_Desp_stat_350 [H] = list_sk_stat Sk_Desp_pvalue_350 [H] = list_sk_pvalue del H Comp_Desp_350_df = pd.DataFrame(Composites_Desp_350, index = c) Sk_Desp_stat_350_df = pd.DataFrame(Sk_Desp_stat_350, index = c) Sk_Desp_pvalue_350_df = pd.DataFrame(Sk_Desp_pvalue_350, index = c) Sk_Desp_stat_348 = {} Sk_Desp_pvalue_348 = {} Composites_Desp_348 = {} for i in df_FH_desp_348_groupH.index: H = str(i) if len(df_FH_desp_348_groupH.loc[i]) == 1 : list = df_P348_h[df_P348_h.index.date == df_FH_desp_348_groupH.loc[i][0]]['radiacion'].values list_sk_stat = np.ones(12)*np.nan list_sk_pvalue = np.ones(12)*np.nan elif len(df_FH_desp_348_groupH.loc[i]) > 1 : temporal = pd.DataFrame() for j in range(len(df_FH_desp_348_groupH.loc[i])): temporal = temporal.append(pd.DataFrame(df_P348_h[df_P348_h.index.date == df_FH_desp_348_groupH.loc[i][j]]['radiacion'])) stat_348 = [] pvalue_348 = [] for k in c: temporal_sk = temporal[temporal.index.hour == k].radiacion.values Rad_sk = df_P348_h['radiacion'][df_P348_h.index.hour == k].values try: SK = ks_2samp(temporal_sk,Rad_sk) stat_348.append(SK[0]) pvalue_348.append(SK[1]) except ValueError: stat_348.append(np.nan) pvalue_348.append(np.nan) temporal_CD = temporal.groupby(by=[temporal.index.hour]).mean() list = temporal_CD['radiacion'].values list_sk_stat = stat_348 list_sk_pvalue = pvalue_348 Composites_Desp_348[H] = list Sk_Desp_stat_348 [H] = list_sk_stat Sk_Desp_pvalue_348 [H] = list_sk_pvalue del H Comp_Desp_348_df = pd.DataFrame(Composites_Desp_348, index = c) Sk_Desp_stat_348_df = pd.DataFrame(Sk_Desp_stat_348, index = c) Sk_Desp_pvalue_348_df = pd.DataFrame(Sk_Desp_pvalue_348, index = c) ##-------------------ESTANDARIZANDO LAS FORMAS DE LOS DATAFRAMES A LAS HORAS CASO DESPEJADO----------------## Comp_Desp_348_df = Comp_Desp_348_df[(Comp_Desp_348_df.index >= 6)&(Comp_Desp_348_df.index <18)] Comp_Desp_350_df = Comp_Desp_350_df[(Comp_Desp_350_df.index >= 6)&(Comp_Desp_350_df.index <18)] Comp_Desp_975_df = Comp_Desp_975_df[(Comp_Desp_975_df.index >= 6)&(Comp_Desp_975_df.index <18)] s = [str(i) for i in Comp_Nuba_348_df.index.values] ListNan = np.empty((1,len(Comp_Desp_348_df))) ListNan [:] = np.nan def convert(set): return [*set, ] a_Desp_348 = convert(set(s).difference(Comp_Desp_348_df.columns.values)) a_Desp_348.sort(key=int) if len(a_Desp_348) > 0: idx = [i for i,x in enumerate(s) if x in a_Desp_348] for i in range(len(a_Desp_348)): Comp_Desp_348_df.insert(loc = idx[i], column = a_Desp_348[i], value=ListNan[0]) del idx a_Desp_350 = convert(set(s).difference(Comp_Desp_350_df.columns.values)) a_Desp_350.sort(key=int) if len(a_Desp_350) > 0: idx = [i for i,x in enumerate(s) if x in a_Desp_350] for i in range(len(a_Desp_350)): Comp_Desp_350_df.insert(loc = idx[i], column = a_Desp_350[i], value=ListNan[0]) del idx a_Desp_975 = convert(set(s).difference(Comp_Desp_975_df.columns.values)) a_Desp_975.sort(key=int) if len(a_Desp_975) > 0: idx = [i for i,x in enumerate(s) if x in a_Desp_975] for i in range(len(a_Desp_975)): Comp_Desp_975_df.insert(loc = idx[i], column = a_Desp_975[i], value=ListNan[0]) del idx s = [str(i) for i in Comp_Desp_348_df.index.values] Comp_Desp_348_df = Comp_Desp_348_df[s] Comp_Desp_350_df = Comp_Desp_350_df[s] Comp_Desp_975_df = Comp_Desp_975_df[s] ##-------------------ESTANDARIZANDO LAS FORMAS DE LOS DATAFRAMES A LAS HORAS CASO NUBADO----------------## Comp_Nuba_348_df = Comp_Nuba_348_df[(Comp_Nuba_348_df.index >= 6)&(Comp_Nuba_348_df.index <18)] Comp_Nuba_350_df = Comp_Nuba_350_df[(Comp_Nuba_350_df.index >= 6)&(Comp_Nuba_350_df.index <18)] Comp_Nuba_975_df = Comp_Nuba_975_df[(Comp_Nuba_975_df.index >= 6)&(Comp_Nuba_975_df.index <18)] s = [str(i) for i in Comp_Nuba_348_df.index.values] ListNan = np.empty((1,len(Comp_Nuba_348_df))) ListNan [:] = np.nan def convert(set): return [*set, ] a_Nuba_348 = convert(set(s).difference(Comp_Nuba_348_df.columns.values)) a_Nuba_348.sort(key=int) if len(a_Nuba_348) > 0: idx = [i for i,x in enumerate(s) if x in a_Nuba_348] for i in range(len(a_Nuba_348)): Comp_Nuba_348_df.insert(loc = idx[i], column = a_Nuba_348[i], value=ListNan[0]) del idx a_Nuba_350 = convert(set(s).difference(Comp_Nuba_350_df.columns.values)) a_Nuba_350.sort(key=int) if len(a_Nuba_350) > 0: idx = [i for i,x in enumerate(s) if x in a_Nuba_350] for i in range(len(a_Nuba_350)): Comp_Nuba_350_df.insert(loc = idx[i], column = a_Nuba_350[i], value=ListNan[0]) del idx a_Nuba_975 = convert(set(s).difference(Comp_Nuba_975_df.columns.values)) a_Nuba_975.sort(key=int) if len(a_Nuba_975) > 0: idx = [i for i,x in enumerate(s) if x in a_Nuba_975] for i in range(len(a_Nuba_975)): Comp_Nuba_975_df.insert(loc = idx[i], column = a_Nuba_975[i], value=ListNan[0]) del idx Comp_Nuba_348_df = Comp_Nuba_348_df[s] Comp_Nuba_350_df = Comp_Nuba_350_df[s] Comp_Nuba_975_df = Comp_Nuba_975_df[s] ##-------------------CONTEO DE LA CANTIDAD DE DÍAS CONSIDERADOS NUBADOS Y DESPEJADOS----------------## Cant_Days_Nuba_348 = [] for i in range(len(s)): try: Cant_Days_Nuba_348.append(len(df_FH_nuba_348_groupH[df_FH_nuba_348_groupH .index == int(s[i])].values[0])) except IndexError: Cant_Days_Nuba_348.append(0) Cant_Days_Nuba_350 = [] for i in range(len(s)): try: Cant_Days_Nuba_350.append(len(df_FH_nuba_350_groupH[df_FH_nuba_350_groupH .index == int(s[i])].values[0])) except IndexError: Cant_Days_Nuba_350.append(0) Cant_Days_Nuba_975 = [] for i in range(len(s)): try: Cant_Days_Nuba_975.append(len(df_FH_nuba_975_groupH[df_FH_nuba_975_groupH .index == int(s[i])].values[0])) except IndexError: Cant_Days_Nuba_975.append(0) Cant_Days_Desp_348 = [] for i in range(len(s)): try: Cant_Days_Desp_348.append(len(df_FH_desp_348_groupH[df_FH_desp_348_groupH .index == int(s[i])].values[0])) except IndexError: Cant_Days_Desp_348.append(0) Cant_Days_Desp_350 = [] for i in range(len(s)): try: Cant_Days_Desp_350.append(len(df_FH_desp_350_groupH[df_FH_desp_350_groupH .index == int(s[i])].values[0])) except IndexError: Cant_Days_Desp_350.append(0) Cant_Days_Desp_975 = [] for i in range(len(s)): try: Cant_Days_Desp_975.append(len(df_FH_desp_975_groupH[df_FH_desp_975_groupH .index == int(s[i])].values[0])) except IndexError: Cant_Days_Desp_975.append(0) ##-------------------AJUSTADO LOS DATAFRAMES DE LOS ESTADÍSTICOS Y DEL VALOR P----------------## for i in range(len(c)): if str(c[i]) not in Sk_Desp_pvalue_975_df.columns: Sk_Desp_pvalue_975_df.insert(int(c[i]-6), str(c[i]), np.ones(12)*np.nan) if str(c[i]) not in Sk_Desp_pvalue_350_df.columns: Sk_Desp_pvalue_350_df.insert(int(c[i]-6), str(c[i]), np.ones(12)*np.nan) if str(c[i]) not in Sk_Desp_pvalue_348_df.columns: Sk_Desp_pvalue_348_df.insert(int(c[i]-6), str(c[i]), np.ones(12)*np.nan) if str(c[i]) not in Sk_Nuba_pvalue_350_df.columns: Sk_Nuba_pvalue_350_df.insert(int(c[i]-6), str(c[i]), np.ones(12)*np.nan) if str(c[i]) not in Sk_Nuba_pvalue_348_df.columns: Sk_Nuba_pvalue_348_df.insert(int(c[i]-6), str(c[i]), np.ones(12)*np.nan) if str(c[i]) not in Sk_Nuba_pvalue_975_df.columns: Sk_Nuba_pvalue_975_df.insert(int(c[i]-6), str(c[i]), np.ones(12)*np.nan) Significancia = 0.05 for i in c: Sk_Desp_pvalue_348_df.loc[Sk_Desp_pvalue_348_df[str(i)]< Significancia, str(i)] = 100 Sk_Desp_pvalue_350_df.loc[Sk_Desp_pvalue_350_df[str(i)]< Significancia, str(i)] = 100 Sk_Desp_pvalue_975_df.loc[Sk_Desp_pvalue_975_df[str(i)]< Significancia, str(i)] = 100 Sk_Nuba_pvalue_348_df.loc[Sk_Nuba_pvalue_348_df[str(i)]< Significancia, str(i)] = 100 Sk_Nuba_pvalue_350_df.loc[Sk_Nuba_pvalue_350_df[str(i)]< Significancia, str(i)] = 100 Sk_Nuba_pvalue_975_df.loc[Sk_Nuba_pvalue_975_df[str(i)]< Significancia, str(i)] = 100 row_Desp_348 = [] col_Desp_348 = [] for row in range(Sk_Desp_pvalue_348_df.shape[0]): for col in range(Sk_Desp_pvalue_348_df.shape[1]): if Sk_Desp_pvalue_348_df.get_value((row+6),str(col+6)) == 100: row_Desp_348.append(row) col_Desp_348.append(col) #print(row+6, col+6) row_Desp_350 = [] col_Desp_350 = [] for row in range(Sk_Desp_pvalue_350_df.shape[0]): for col in range(Sk_Desp_pvalue_350_df.shape[1]): if Sk_Desp_pvalue_350_df.get_value((row+6),str(col+6)) == 100: row_Desp_350.append(row) col_Desp_350.append(col) row_Desp_975 = [] col_Desp_975 = [] for row in range(Sk_Desp_pvalue_975_df.shape[0]): for col in range(Sk_Desp_pvalue_975_df.shape[1]): if Sk_Desp_pvalue_975_df.get_value((row+6),str(col+6)) == 100: row_Desp_975.append(row) col_Desp_975.append(col) row_Nuba_348 = [] col_Nuba_348 = [] for row in range(Sk_Nuba_pvalue_348_df.shape[0]): for col in range(Sk_Nuba_pvalue_348_df.shape[1]): if Sk_Nuba_pvalue_348_df.get_value((row+6),str(col+6)) == 100: row_Nuba_348.append(row) col_Nuba_348.append(col) #print(row+6, col+6) row_Nuba_350 = [] col_Nuba_350 = [] for row in range(Sk_Nuba_pvalue_350_df.shape[0]): for col in range(Sk_Nuba_pvalue_350_df.shape[1]): if Sk_Nuba_pvalue_350_df.get_value((row+6),str(col+6)) == 100: row_Nuba_350.append(row) col_Nuba_350.append(col) row_Nuba_975 = [] col_Nuba_975 = [] for row in range(Sk_Nuba_pvalue_975_df.shape[0]): for col in range(Sk_Nuba_pvalue_975_df.shape[1]): if Sk_Nuba_pvalue_975_df.get_value((row+6),str(col+6)) == 100: row_Nuba_975.append(row) col_Nuba_975.append(col) ##-------------------GRÁFICO DEL COMPOSITE NUBADO DE LA RADIACIÓN PARA CADA PUNTO Y LA CANT DE DÍAS----------------## s_f = [int(s[i]) for i in range(len(s))] plt.close("all") fig = plt.figure(figsize=(10., 8.),facecolor='w',edgecolor='w') ax1=fig.add_subplot(2,3,1) mapa = ax1.imshow(Comp_Nuba_348_df, interpolation = 'none', cmap = 'Spectral_r') ax1.set_yticks(range(0,12), minor=False) ax1.set_yticklabels(s, minor=False) ax1.set_xticks(range(0,12), minor=False) ax1.set_xticklabels(s, minor=False, rotation = 20) ax1.set_xlabel('Hora del caso', fontsize=10, fontproperties = prop_1) ax1.set_ylabel('Hora en el CD de radiación', fontsize=10, fontproperties = prop_1) ax1.scatter(range(0,12),range(0,12), marker='x', facecolor = 'k', edgecolor = 'k', linewidth='1.', s=30) ax1.set_title(' x = Horas nubadas en JV', loc = 'center', fontsize=9) ax2=fig.add_subplot(2,3,2) mapa = ax2.imshow(Comp_Nuba_350_df, interpolation = 'none', cmap = 'Spectral_r') ax2.set_yticks(range(0,12), minor=False) ax2.set_yticklabels(s, minor=False) ax2.set_xticks(range(0,12), minor=False) ax2.set_xticklabels(s, minor=False, rotation = 20) ax2.set_xlabel('Hora del caso', fontsize=10, fontproperties = prop_1) ax2.set_ylabel('Hora en el CD de radiación', fontsize=10, fontproperties = prop_1) ax2.scatter(range(0,12),range(0,12), marker='x', facecolor = 'k', edgecolor = 'k', linewidth='1.', s=30) ax2.set_title(' x = Horas nubadas en CI', loc = 'center', fontsize=9) ax3 = fig.add_subplot(2,3,3) mapa = ax3.imshow(Comp_Nuba_975_df, interpolation = 'none', cmap = 'Spectral_r') ax3.set_yticks(range(0,12), minor=False) ax3.set_yticklabels(s, minor=False) ax3.set_xticks(range(0,12), minor=False) ax3.set_xticklabels(s, minor=False, rotation = 20) ax3.set_xlabel('Hora del caso', fontsize=10, fontproperties = prop_1) ax3.set_ylabel('Hora en el CD de radiación', fontsize=10, fontproperties = prop_1) ax3.scatter(range(0,12),range(0,12), marker='x', facecolor = 'k', edgecolor = 'k', linewidth='1.', s=30) ax3.set_title(' x = Horas nubadas en TS', loc = 'center', fontsize=9) cbar_ax = fig.add_axes([0.11, 0.93, 0.78, 0.008]) cbar = fig.colorbar(mapa, cax=cbar_ax, orientation='horizontal', format="%.2f") cbar.set_label(u"Intensidad de la radiación $[W/m^{2}]$", fontsize=8, fontproperties=prop) ax4 = fig.add_subplot(2,3,4) ax4.spines['top'].set_visible(False) ax4.spines['right'].set_visible(False) ax4.bar(np.arange(len(s)), Cant_Days_Nuba_348, color='orange', align='center', alpha=0.5) ax4.set_xlabel(u'Hora', fontproperties = prop_1) ax4.set_ylabel(r"Cantidad de días", fontproperties = prop_1) ax4.set_xticks(range(0,12), minor=False) ax4.set_xticklabels(s, minor=False, rotation = 20) ax4.set_title(u' Cantidad de días en JV', loc = 'center', fontsize=9) ax5 = fig.add_subplot(2,3,5) ax5.spines['top'].set_visible(False) ax5.spines['right'].set_visible(False) ax5.bar(np.arange(len(s)), Cant_Days_Nuba_350, color='orange', align='center', alpha=0.5) ax5.set_xlabel(u'Hora', fontproperties = prop_1) ax5.set_ylabel(r"Cantidad de días", fontproperties = prop_1) ax5.set_xticks(range(0,12), minor=False) ax5.set_xticklabels(s, minor=False, rotation = 20) ax5.set_title(u' Cantidad de días en CI', loc = 'center', fontsize=9) ax6 = fig.add_subplot(2,3,6) ax6.spines['top'].set_visible(False) ax6.spines['right'].set_visible(False) ax6.bar(np.arange(len(s)), Cant_Days_Nuba_975, color='orange', align='center', alpha=0.5) ax6.set_xlabel(u'Hora', fontproperties = prop_1) ax6.set_ylabel(r"Cantidad de días", fontproperties = prop_1) ax6.set_xticks(range(0,12), minor=False) ax6.set_xticklabels(s, minor=False, rotation = 20) ax6.set_title(u' Cantidad de días en TS', loc = 'center', fontsize=9) plt.subplots_adjust(wspace=0.3, hspace=0.3) plt.savefig('/home/nacorreasa/Escritorio/Figuras/Composites_Nuba_Cant_Dias2018.png') plt.close('all') os.system('scp /home/nacorreasa/Escritorio/Figuras/Composites_Nuba_Cant_Dias2018.png nacorreasa@192.168.1.74:/var/www/nacorreasa/Graficas_Resultados/Estudio') ##-------------------GRÁFICO DEL COMPOSITE DESPEJADO DE LA RADIACIÓN PARA CADA PUNTO Y LA CANT DE DÍAS----------------## plt.close("all") fig = plt.figure(figsize=(10., 8.),facecolor='w',edgecolor='w') ax1=fig.add_subplot(2,3,1) mapa = ax1.imshow(Comp_Desp_348_df, interpolation = 'none', cmap = 'Spectral_r') ax1.set_yticks(range(0,12), minor=False) ax1.set_yticklabels(s, minor=False) ax1.set_xticks(range(0,12), minor=False) ax1.set_xticklabels(s, minor=False, rotation = 20) ax1.set_xlabel('Hora del caso', fontsize=10, fontproperties = prop_1) ax1.set_ylabel('Hora en el CD de radiación', fontsize=10, fontproperties = prop_1) ax1.scatter(range(0,12),range(0,12), marker='x', facecolor = 'k', edgecolor = 'k', linewidth='1.', s=30) ax1.set_title(' x = Horas despejadas en JV', loc = 'center', fontsize=9) ax2=fig.add_subplot(2,3,2) mapa = ax2.imshow(Comp_Desp_350_df, interpolation = 'none', cmap = 'Spectral_r') ax2.set_yticks(range(0,12), minor=False) ax2.set_yticklabels(s, minor=False) ax2.set_xticks(range(0,12), minor=False) ax2.set_xticklabels(s, minor=False, rotation = 20) ax2.set_xlabel('Hora del caso', fontsize=10, fontproperties = prop_1) ax2.set_ylabel('Hora en el CD de radiación', fontsize=10, fontproperties = prop_1) ax2.scatter(range(0,12),range(0,12), marker='x', facecolor = 'k', edgecolor = 'k', linewidth='1.', s=30) ax2.set_title(' x = Horas despejadas en CI', loc = 'center', fontsize=9) ax3 = fig.add_subplot(2,3,3) mapa = ax3.imshow(Comp_Desp_975_df, interpolation = 'none', cmap = 'Spectral_r') ax3.set_yticks(range(0,12), minor=False) ax3.set_yticklabels(s, minor=False) ax3.set_xticks(range(0,12), minor=False) ax3.set_xticklabels(s, minor=False, rotation = 20) ax3.set_xlabel('Hora del caso', fontsize=10, fontproperties = prop_1) ax3.set_ylabel('Hora en el CD de radiación', fontsize=10, fontproperties = prop_1) ax3.scatter(range(0,12),range(0,12), marker='x', facecolor = 'k', edgecolor = 'k', linewidth='1.', s=30) ax3.set_title(' x = Horas despejadas en TS', loc = 'center', fontsize=9) cbar_ax = fig.add_axes([0.11, 0.93, 0.78, 0.008]) cbar = fig.colorbar(mapa, cax=cbar_ax, orientation='horizontal', format="%.2f") cbar.set_label(u"Intensidad de la radiación $[W/m^{2}]$", fontsize=8, fontproperties=prop) ax4 = fig.add_subplot(2,3,4) ax4.spines['top'].set_visible(False) ax4.spines['right'].set_visible(False) ax4.bar(np.arange(len(s)), Cant_Days_Desp_348, color='orange', align='center', alpha=0.5) ax4.set_xlabel(u'Hora', fontproperties = prop_1) ax4.set_ylabel(r"Cantidad de días", fontproperties = prop_1) ax4.set_xticks(range(0,12), minor=False) ax4.set_xticklabels(s, minor=False, rotation = 20) ax4.set_title(u' Cantidad de días en JV', loc = 'center', fontsize=9) ax5 = fig.add_subplot(2,3,5) ax5.spines['top'].set_visible(False) ax5.spines['right'].set_visible(False) ax5.bar(np.arange(len(s)), Cant_Days_Desp_350, color='orange', align='center', alpha=0.5) ax5.set_xlabel(u'Hora', fontproperties = prop_1) ax5.set_ylabel(r"Cantidad de días", fontproperties = prop_1) ax5.set_xticks(range(0,12), minor=False) ax5.set_xticklabels(s, minor=False, rotation = 20) ax5.set_title(u' Cantidad de días en CI', loc = 'center', fontsize=9) ax6 = fig.add_subplot(2,3,6) ax6.spines['top'].set_visible(False) ax6.spines['right'].set_visible(False) ax6.bar(np.arange(len(s)), Cant_Days_Desp_975, color='orange', align='center', alpha=0.5) ax6.set_xlabel(u'Hora', fontproperties = prop_1) ax6.set_ylabel(r"Cantidad de días", fontproperties = prop_1) ax6.set_xticks(range(0,12), minor=False) ax6.set_xticklabels(s, minor=False, rotation = 20) ax6.set_title(u' Cantidad de días en TS', loc = 'center', fontsize=9) #plt.title(u'Composites caso nubado', fontproperties=prop, fontsize = 8) plt.subplots_adjust(wspace=0.3, hspace=0.3) plt.savefig('/home/nacorreasa/Escritorio/Figuras/Composites_Desp_Cant_Dias2018.png') plt.close('all') os.system('scp /home/nacorreasa/Escritorio/Figuras/Composites_Desp_Cant_Dias2018.png nacorreasa@192.168.1.74:/var/www/nacorreasa/Graficas_Resultados/Estudio') ##--------------------------TOTAL DE DÍAS DE REGISTRO Y FRECUENCIA DE LA CONDICIÓN---------------------## Total_dias_348 = len(Rad_df_348.groupby(pd.Grouper(freq="D")).mean()) Total_dias_350 = len(Rad_df_350.groupby(pd.Grouper(freq="D")).mean()) Total_dias_975 = len(Rad_df_975.groupby(pd.Grouper(freq="D")).mean()) Porc_Days_Desp_348 = (np.array(Cant_Days_Desp_348)/Total_dias_348)*100 Porc_Days_Desp_350 = (np.array(Cant_Days_Desp_350)/Total_dias_350)*100 Porc_Days_Desp_975 = (np.array(Cant_Days_Desp_975)/Total_dias_975)*100 Porc_Days_Nuba_348 = (np.array(Cant_Days_Nuba_348)/Total_dias_348)*100 Porc_Days_Nuba_350 = (np.array(Cant_Days_Nuba_350)/Total_dias_350)*100 Porc_Days_Nuba_975 = (np.array(Cant_Days_Nuba_975)/Total_dias_975)*100 print('Total de dias JV: ' + str(Total_dias_348)) print('Total de dias CI: ' + str(Total_dias_350)) print('Total de dias TV: ' + str(Total_dias_975)) ##-------------------CD EN BOX PLOT HORARIO PARA CADA PUNTO----------------## DF_348_horas = {} for i in range(1, 13): A = Rad_df_348_h[Rad_df_348_h.index.hour == (i + 5)]['Radiacias'] H = A.index.hour[0] print(H) DF_348_horas[H] = A.values del H, A DF_348_horas = pd.DataFrame.from_dict(DF_348_horas,orient='index').transpose() #DF_348_horas = pd.DataFrame(DF_348_horas) DF_350_horas = {} for i in range(1, 13): A = Rad_df_350_h[Rad_df_350_h.index.hour == (i + 5)]['Radiacias'] H = A.index.hour[0] print(H) DF_350_horas[H] = A del H, A #DF_350_horas = pd.DataFrame(DF_350_horas) DF_350_horas = pd.DataFrame.from_dict(DF_350_horas,orient='index').transpose() DF_975_horas = {} for i in range(1, 13): A = Rad_df_975_h[Rad_df_975_h.index.hour == (i + 5)]['Radiacias'] H = A.index.hour[0] print(H) DF_975_horas[H] = A del H, A #DF_975_horas = pd.DataFrame(DF_975_horas) DF_975_horas = pd.DataFrame.from_dict(DF_975_horas,orient='index').transpose() fig = plt.figure(figsize=(12,5)) plt.rc('axes', edgecolor='gray') ax1 = fig.add_subplot(1, 3, 1) ax1.spines['top'].set_visible(False) ax1.spines['right'].set_visible(False) DF_348_horas.boxplot(grid=False) #Umbrales_line1y = [ax1.axhline(y=xc, color='k', linestyle='--') for xc in Umbrales_348] ax1.set_title(u'Distribución de FR por horas en JV', fontproperties=prop, fontsize = 8) ax1.set_ylabel(u'Factor Reflectancia[%]', fontproperties=prop_1) ax1.set_xlabel(u'Horas', fontproperties=prop_1) ax2 = fig.add_subplot(1, 3, 2) ax2.spines['top'].set_visible(False) ax2.spines['right'].set_visible(False) DF_350_horas.boxplot(grid=False) #Umbrales_line2y = [ax2.axhline(y=xc, color='k', linestyle='--') for xc in Umbrales_350] ax2.set_title(u'Distribución de FR por horas en CI', fontproperties=prop, fontsize = 8) ax2.set_ylabel(u'Factor Reflectancia[%]', fontproperties=prop_1) ax2.set_xlabel(u'Horas', fontproperties=prop_1) ax3 = fig.add_subplot(1, 3, 3) ax3.spines['top'].set_visible(False) ax3.spines['right'].set_visible(False) DF_975_horas.boxplot(grid=False) #Umbrales_line3y = [ax3.axhline(y=xc, color='k', linestyle='--') for xc in Umbrales_975] ax3.set_title(u'Distribución de FR por horas en TS', fontproperties=prop, fontsize = 8) ax3.set_ylabel(u'Factor Reflectancia[%]', fontproperties=prop_1) ax3.set_xlabel(u'Horas', fontproperties=prop_1) plt.savefig('/home/nacorreasa/Escritorio/Figuras/FRBoxPlotHora.png') plt.close('all') os.system('scp /home/nacorreasa/Escritorio/Figuras/FRBoxPlotHora.png nacorreasa@192.168.1.74:/var/www/nacorreasa/Graficas_Resultados/Estudio') ##-------------------ANOMALÍAS DE LOS COMPOSITES DE LA RADIACIÓN EN LOS DÍAS NUBADOS Y DESPEJADOS----------------## new_idx = np.arange(6, 18, 1) df_CDRad_348 = df_P348_h.radiacion.groupby(by=[df_P348_h.index.hour]).mean() df_CDRad_348 = df_CDRad_348.reindex(new_idx) df_STDRad_348 = df_P348_h.radiacion.groupby(by=[df_P348_h.index.hour]).std() df_STDRad_348 = df_STDRad_348.reindex(new_idx) df_CDRad_350 = df_P350_h.radiacion.groupby(by=[df_P350_h.index.hour]).mean() df_CDRad_350 = df_CDRad_350.reindex(new_idx) df_STDRad_350 = df_P350_h.radiacion.groupby(by=[df_P350_h.index.hour]).std() df_STDRad_350 = df_STDRad_350.reindex(new_idx) df_CDRad_975 = df_P975_h.radiacion.groupby(by=[df_P975_h.index.hour]).mean() df_CDRad_975 = df_CDRad_975.reindex(new_idx) df_STDRad_975 = df_P975_h.radiacion.groupby(by=[df_P975_h.index.hour]).std() df_STDRad_975 = df_STDRad_975.reindex(new_idx) Comp_Desp_348_anomal = (Comp_Desp_348_df.sub(df_CDRad_348, axis='index')) Comp_Desp_350_anomal = (Comp_Desp_350_df.sub(df_CDRad_350, axis='index')) Comp_Desp_975_anomal = (Comp_Desp_975_df.sub(df_CDRad_975, axis='index')) Comp_Nuba_348_anomal = (Comp_Nuba_348_df.sub(df_CDRad_348, axis='index')) Comp_Nuba_350_anomal = (Comp_Nuba_350_df.sub(df_CDRad_350, axis='index')) Comp_Nuba_975_anomal = (Comp_Nuba_975_df.sub(df_CDRad_975, axis='index')) ##-------------------CONFIGURACIÓN DE LA COLORBAR DE LAS ANOMALÍAS----------------## class MidpointNormalize(colors.Normalize): """ Normalise the colorbar so that diverging bars work there way either side from a prescribed midpoint value) e.g. im=ax1.imshow(array, norm=MidpointNormalize(midpoint=0.,vmin=-100, vmax=100)) """ def __init__(self, vmin=None, vmax=None, midpoint=None, clip=False): self.midpoint = midpoint colors.Normalize.__init__(self, vmin, vmax, clip) def __call__(self, value, clip=None): # I'm ignoring masked values and all kinds of edge cases to make a # simple eaxmple... x, y = [self.vmin, self.midpoint, self.vmax], [0, 0.5, 1] return np.ma.masked_array(np.interp(value, x, y), np.isnan(value)) cmap = matplotlib.cm.RdBu_r ##-------------------GRÁFICO DE LAS ANOMALÍAS DEL COMPOSITE DESPEJADO DE LA RADIACIÓN PARA CADA PUNTO ----------------## """ Se deben verificar los valores de elev_min y elev_max de acuerdo al set de datos de las anomalías """ elev_min = min(np.nanmin(Comp_Desp_348_anomal.values) , np.nanmin(Comp_Desp_350_anomal.values), np.nanmin(Comp_Desp_975_anomal.values))-1 elev_max = max(np.nanmax(Comp_Desp_348_anomal.values) , np.nanmax(Comp_Desp_350_anomal.values), np.nanmax(Comp_Desp_975_anomal.values))+1 mid_val = 0 plt.close("all") fig = plt.figure(figsize=(10., 8.),facecolor='w',edgecolor='w') ax1=fig.add_subplot(2,3,1) mapa = ax1.imshow(Comp_Desp_350_anomal, interpolation = 'none', cmap=cmap, clim=(elev_min, elev_max), norm=MidpointNormalize(midpoint=mid_val,vmin=elev_min, vmax=elev_max)) #cont = ax1.contour(Sk_Desp_pvalue_350_df, levels=[99, 101], linewidths=0.5) #ax1.clabel(cont, fmt = '%.2f', colors = 'k', fontsize=8) ax1.set_yticks(range(0,12), minor=False) ax1.set_yticklabels(s, minor=False) ax1.set_xticks(range(0,12), minor=False) ax1.set_xticklabels(s, minor=False, rotation = 20) ax1.set_xlabel('Horas despejadas', fontsize=10, fontproperties = prop_1) ax1.set_ylabel(u'Horas de radiación solar', fontsize=10, fontproperties = prop_1) ax1.scatter(col_Desp_350,row_Desp_350, marker='o', facecolor = 'k', edgecolor = 'k', linewidth='1.', s=30) ax1.set_title(' x = Horas despejadas en el Oeste', loc = 'center', fontsize=9) ax2=fig.add_subplot(2,3,2) mapa = ax2.imshow(Comp_Desp_975_anomal, interpolation = 'none', cmap=cmap, clim=(elev_min, elev_max), norm=MidpointNormalize(midpoint=mid_val,vmin=elev_min, vmax=elev_max)) #cont = ax2.contour(Sk_Desp_pvalue_975_df, levels=[99, 101], linewidths=0.5) #ax2.clabel(cont, fmt = '%.2f', colors = 'k', fontsize=8) ax2.set_yticks(range(0,12), minor=False) ax2.set_yticklabels(s, minor=False) ax2.set_xticks(range(0,12), minor=False) ax2.set_xticklabels(s, minor=False, rotation = 20) ax2.set_ylabel(u'Horas de radiación solar', fontsize=10, fontproperties = prop_1) ax2.set_xlabel('Horas despejadas', fontsize=10, fontproperties = prop_1) ax2.scatter(col_Desp_975,row_Desp_975, marker='o', facecolor = 'k', edgecolor = 'k', linewidth='1.', s=30) ax2.set_title(' x = Horas despejadas en el Centro-Oeste', loc = 'center', fontsize=9) ax3 = fig.add_subplot(2,3,3) mapa = ax3.imshow(Comp_Desp_348_anomal, interpolation = 'none', cmap=cmap, clim=(elev_min, elev_max), norm=MidpointNormalize(midpoint=mid_val,vmin=elev_min, vmax=elev_max)) #cont = ax3.contour(Sk_Desp_pvalue_348_df, levels=[99, 101], linewidths=0.5) #ax3.clabel(cont, fmt = '%.2f', colors = 'k', fontsize=8) ax3.set_yticks(range(0,12), minor=False) ax3.set_yticklabels(s, minor=False) ax3.set_xticks(range(0,12), minor=False) ax3.set_xticklabels(s, minor=False, rotation = 20) ax3.set_ylabel(u'Horas de radiación solar', fontsize=10, fontproperties = prop_1) ax3.set_xlabel('Horas despejadas', fontsize=10, fontproperties = prop_1) ax3.scatter(col_Desp_348,row_Desp_348, marker='o', facecolor = 'k', edgecolor = 'k', linewidth='1.', s=30) ax3.set_title(' x = Horas despejadas en el Este', loc = 'center', fontsize=9) #cbar_ax = fig.add_axes([0.11, 0.28, 0.78, 0.008]) cbar_ax = fig.add_axes([0.11, 0.93, 0.78, 0.008]) cbar = fig.colorbar(mapa, cax=cbar_ax, orientation='horizontal', format="%.2f") cbar.set_label(u"Anomalías de la radiación en el caso despejado $[W/m^{2}]$", fontsize=8, fontproperties=prop) ax4 = fig.add_subplot(2,3,4) ax4.spines['top'].set_visible(False) ax4.spines['right'].set_visible(False) #ax4.bar(np.arange(len(s)), Cant_Days_Desp_348, color='orange', align='center', alpha=0.5) ax4.plot(s, Porc_Days_Desp_350, color = '#8ABB73', lw=1.5) ax4.scatter(s, Porc_Days_Desp_350, marker='.', color = '#8ABB73', s=30) ax4.set_xlabel(u'Horas despejadas', fontproperties = prop_1) ax4.set_ylabel(u"Frecuencia de días [%]", fontproperties = prop_1) ax4.set_xticks(range(0, 12), minor=False) ax4.set_xticklabels(s, minor=False, rotation = 20) ax4.set_ylim(0, 100) ax4.set_title(u'Frecuencia de días en el Oeste', loc = 'center', fontsize=9) ax5 = fig.add_subplot(2,3,5) ax5.spines['top'].set_visible(False) ax5.spines['right'].set_visible(False) #ax5.bar(np.arange(len(s)), Cant_Days_Desp_350, color='orange', align='center', alpha=0.5) ax5.set_xlabel(u'Horas despejadas', fontproperties = prop_1) ax5.set_ylabel(u"Frecuencia de días [%]", fontproperties = prop_1) ax5.plot(s, Porc_Days_Desp_975, color = '#8ABB73', lw=1.5) ax5.scatter(s, Porc_Days_Desp_975, marker='.', color = '#8ABB73', s=30) ax5.set_xticks(range(0, 12), minor=False) ax5.set_xticklabels(s, minor=False, rotation = 20) ax5.set_ylim(0, 100) ax5.set_title(u'Frecuencia de días en el Centro-Oeste', loc = 'center', fontsize=9) ax6 = fig.add_subplot(2,3,6) ax6.spines['top'].set_visible(False) ax6.spines['right'].set_visible(False) #ax6.bar(np.arange(len(s)), Cant_Days_Desp_975, color='orange', align='center', alpha=0.5) ax6.plot(s, Porc_Days_Desp_348, color = '#8ABB73', lw=1.5) ax6.scatter(s, Porc_Days_Desp_348, marker='.', color = '#8ABB73', s=30) ax6.set_xlabel(u'Horas despejadas', fontproperties = prop_1) ax6.set_ylabel(u"Frecuencia de días [%]", fontproperties = prop_1) ax6.set_xticks(range(0, 12), minor=False) ax6.set_xticklabels(s, minor=False, rotation = 20) ax6.set_ylim(0, 100) ax6.set_title(u'Frecuencia de días en el Este', loc = 'center', fontsize=9) plt.subplots_adjust(wspace=0.3, hspace = 0.3) plt.savefig('/home/nacorreasa/Escritorio/Figuras/AnomalComposites_Desp_Cant_Dias.pdf', format='pdf', transparent=True) plt.close('all') os.system('scp /home/nacorreasa/Escritorio/Figuras/AnomalComposites_Desp_Cant_Dias.pdf nacorreasa@192.168.1.74:/var/www/nacorreasa/Graficas_Resultados/Estudio') ##-------------------GRÁFICO DE LAS ANOMALÍAS DEL COMPOSITE NUBADO DE LA RADIACIÓN PARA CADA PUNTO ----------------## """ Se deben verificar los valores de elev_min y elev_max de acuerdo al set de datos de las anomalías """ elev_min = min(np.nanmin(Comp_Nuba_348_anomal.values) , np.nanmin(Comp_Nuba_350_anomal.values), np.nanmin(Comp_Nuba_975_anomal.values))-1 elev_max = max(np.nanmax(Comp_Nuba_348_anomal.values) , np.nanmax(Comp_Nuba_350_anomal.values), np.nanmax(Comp_Nuba_975_anomal.values))+1 mid_val = 0 plt.close("all") fig = plt.figure(figsize=(10., 8.),facecolor='w',edgecolor='w') ax1=fig.add_subplot(2,3,1) mapa = ax1.imshow(Comp_Nuba_350_anomal, interpolation = 'none', cmap=cmap, clim=(elev_min, elev_max), norm=MidpointNormalize(midpoint=mid_val,vmin=elev_min, vmax=elev_max)) #cont = ax1.contour(Sk_Nuba_pvalue_350_df, levels=[99, 101], linewidths=0.5) ax1.set_yticks(range(0,12), minor=False) ax1.set_yticklabels(s, minor=False) ax1.set_xticks(range(0,12), minor=False) ax1.set_xticklabels(s, minor=False, rotation = 20) ax1.set_xlabel('Horas nubladas', fontsize=10, fontproperties = prop_1) ax1.set_ylabel(u'Horas de radiación solar', fontsize=10, fontproperties = prop_1) ax1.scatter(col_Nuba_350,row_Nuba_350, marker='o', facecolor = 'k', edgecolor = 'k', linewidth='1.', s=30) ax1.set_title('x = Horas nubladas en el Oeste', loc = 'center', fontsize=9) ax2=fig.add_subplot(2,3,2) mapa = ax2.imshow(Comp_Nuba_975_anomal, interpolation = 'none', cmap=cmap, clim=(elev_min, elev_max), norm=MidpointNormalize(midpoint=mid_val,vmin=elev_min, vmax=elev_max)) #cont = ax2.contour(Sk_Nuba_pvalue_975_df, levels=[99, 101], linewidths=0.5) #ax2.clabel(cont, fmt = '%.2f', colors = 'k', fontsize=8) ax2.set_yticks(range(0,12), minor=False) ax2.set_yticklabels(s, minor=False) ax2.set_xticks(range(0,12), minor=False) ax2.set_xticklabels(s, minor=False, rotation = 20) ax2.set_xlabel('Horas nubladas', fontsize=10, fontproperties = prop_1) ax2.set_ylabel(u'Horas de radiación solar', fontsize=10, fontproperties = prop_1) ax2.scatter(col_Nuba_975,row_Nuba_975, marker='o', facecolor = 'k', edgecolor = 'k', linewidth='1.', s=30) ax2.set_title('x = Horas nubladas en el Centro-Oeste', loc = 'center', fontsize=9) ax3 = fig.add_subplot(2,3,3) mapa = ax3.imshow(Comp_Nuba_348_anomal, interpolation = 'none', cmap=cmap, clim=(elev_min, elev_max), norm=MidpointNormalize(midpoint=mid_val,vmin=elev_min, vmax=elev_max)) #cont = ax3.contour(Sk_Nuba_pvalue_348_df, levels=[99, 101], linewidths=0.5) #ax3.clabel(cont, fmt = '%.2f', colors = 'k', fontsize=8) ax3.set_yticks(range(0,12), minor=False) ax3.set_yticklabels(s, minor=False) ax3.set_xticks(range(0,12), minor=False) ax3.set_xticklabels(s, minor=False, rotation = 20) ax3.set_xlabel('Horas nubladas', fontsize=10, fontproperties = prop_1) ax3.set_ylabel(u'Horas de radiación solar', fontsize=10, fontproperties = prop_1) ax3.scatter(col_Nuba_348,row_Nuba_348, marker='o', facecolor = 'k', edgecolor = 'k', linewidth='1.', s=30) ax3.set_title('x = Horas nubladas en el Este', loc = 'center', fontsize=9) #cbar_ax = fig.add_axes([0.11, 0.28, 0.78, 0.008]) cbar_ax = fig.add_axes([0.11, 0.93, 0.78, 0.008]) cbar = fig.colorbar(mapa, cax=cbar_ax, orientation='horizontal', format="%.2f") cbar.set_label(u"Anomalías de radiación en el caso nublado $[W/m^{2}]$", fontsize=8, fontproperties=prop) ax4 = fig.add_subplot(2,3,4) ax4.spines['top'].set_visible(False) ax4.spines['right'].set_visible(False) #ax4.bar(np.arange(len(s)), Cant_Days_Nuba_348, color='orange', align='center', alpha=0.5) ax4.plot(s, Porc_Days_Nuba_350, color = '#8ABB73', lw=1.5) ax4.scatter(s, Porc_Days_Nuba_350, marker='.', color = '#8ABB73', s=30) ax4.set_xlabel('Horas nubladas', fontproperties = prop_1) ax4.set_ylabel(u"Frecuencia de días [%]", fontproperties = prop_1) ax4.set_xticks(range(0, 12), minor=False) ax4.set_xticklabels(s, minor=False, rotation = 20) ax4.set_ylim(0, 100) ax4.set_title(u'Frecuencia de días en el Oeste', loc = 'center', fontsize=9) ax5 = fig.add_subplot(2,3,5) ax5.spines['top'].set_visible(False) ax5.spines['right'].set_visible(False) #ax5.bar(np.arange(len(s)), Cant_Days_Nuba_350, color='orange', align='center', alpha=0.5) ax5.plot(s, Porc_Days_Nuba_975, color = '#8ABB73', lw=1.5) ax5.scatter(s, Porc_Days_Nuba_975, marker='.', color = '#8ABB73', s=30) ax5.set_xlabel('Horas nubladas', fontproperties = prop_1) ax5.set_ylabel(u"Frecuencia de días [%]", fontproperties = prop_1) ax5.set_xticks(range(0, 12), minor=False) ax5.set_xticklabels(s, minor=False, rotation = 20) ax5.set_ylim(0, 100) ax5.set_title(u'Frecuencia de días en el Centro-Oeste', loc = 'center', fontsize=9) ax6 = fig.add_subplot(2,3,6) ax6.spines['top'].set_visible(False) ax6.spines['right'].set_visible(False) #ax6.bar(np.arange(len(s)), Cant_Days_Nuba_975, color='orange', align='center', alpha=0.5) ax6.plot(s, Porc_Days_Nuba_348, color = '#8ABB73', lw=1.5) ax6.scatter(s, Porc_Days_Nuba_348, marker='.', color = '#8ABB73', s=30) ax6.set_xlabel('Horas nubladas', fontproperties = prop_1) ax6.set_ylabel(u"Frecuencia de días [%]", fontproperties = prop_1) ax6.set_xticks(range(0, 12), minor=False) ax6.set_xticklabels(s, minor=False, rotation = 20) ax6.set_ylim(0, 100) ax6.set_title(u'Frecuencia de días en el Este', loc = 'center', fontsize=9) plt.subplots_adjust(wspace=0.3, hspace=0.3) plt.savefig('/home/nacorreasa/Escritorio/Figuras/AnomalComposites_Nuba_Cant_Dias.pdf', format='pdf', transparent=True) plt.close('all') os.system('scp /home/nacorreasa/Escritorio/Figuras/AnomalComposites_Nuba_Cant_Dias.pdf nacorreasa@192.168.1.74:/var/www/nacorreasa/Graficas_Resultados/Estudio') ## -------------------------RANGO DE LAS ANOMALIAS RADIACIÓN PARA VER EL EFECTO DE LA NUBE EN LA RADIACIÓN---------------------------- ## Rango_348 = Comp_Desp_348_anomal-Comp_Nuba_348_anomal Rango_350 = Comp_Desp_350_anomal-Comp_Nuba_350_anomal Rango_975 = Comp_Desp_975_anomal-Comp_Nuba_975_anomal range_min = min(np.nanmin(Rango_348.values) , np.nanmin(Rango_350.values), np.nanmin(Rango_975.values))-1 range_max = max(np.nanmax(Rango_348.values) , np.nanmax(Rango_350.values), np.nanmax(Rango_975.values))+1 mid_val = 0 plt.close("all") fig = plt.figure(figsize=(10., 8.),facecolor='w',edgecolor='w') ax1=fig.add_subplot(1,3,1) mapa = ax1.imshow(Rango_350, interpolation = 'none', cmap=cmap, clim=(range_min, range_max), norm=MidpointNormalize(midpoint=mid_val,vmin=range_min, vmax=range_max)) ax1.set_yticks(range(0,12), minor=False) ax1.set_yticklabels(s, minor=False) ax1.set_xticks(range(0,12), minor=False) ax1.set_xticklabels(s, minor=False, rotation = 20) ax1.set_xlabel('Horas condicionadas', fontsize=10, fontproperties = prop_1) ax1.set_ylabel(u'Horas de radiación solar', fontsize=10, fontproperties = prop_1) ax1.set_title(u'Rango de anomalías en el Oeste', loc = 'center', fontsize=9) ax2=fig.add_subplot(1,3,2) mapa = ax2.imshow(Rango_975, interpolation = 'none', cmap=cmap, clim=(range_min, range_max), norm=MidpointNormalize(midpoint=mid_val,vmin=range_min, vmax=range_max)) ax2.set_yticks(range(0,12), minor=False) ax2.set_yticklabels(s, minor=False) ax2.set_xticks(range(0,12), minor=False) ax2.set_xticklabels(s, minor=False, rotation = 20) ax2.set_xlabel('Horas condicionadas', fontsize=10, fontproperties = prop_1) ax2.set_ylabel(u'Horas de radiación solar', fontsize=10, fontproperties = prop_1) ax2.set_title(u'Rango de anomalías en el Centro-Oeste', loc = 'center', fontsize=9) ax3 = fig.add_subplot(1,3,3) mapa = ax3.imshow(Rango_348, interpolation = 'none', cmap=cmap, clim=(range_min, range_max), norm=MidpointNormalize(midpoint=mid_val,vmin=range_min, vmax=range_max)) ax3.set_yticks(range(0,12), minor=False) ax3.set_yticklabels(s, minor=False) ax3.set_xticks(range(0,12), minor=False) ax3.set_xticklabels(s, minor=False, rotation = 20) ax3.set_xlabel('Horas condicionadas', fontsize=10, fontproperties = prop_1) ax3.set_ylabel(u'Horas de radiación solar', fontsize=10, fontproperties = prop_1) ax3.set_title(u'Rango de anomalías en el Este', loc = 'center', fontsize=9) cbar_ax = fig.add_axes([0.11, 0.28, 0.78, 0.008]) cbar = fig.colorbar(mapa, cax=cbar_ax, orientation='horizontal', format="%.2f") cbar.set_label(u"Anomaly range $[W/m^{2}]$", fontsize=8, fontproperties=prop) plt.subplots_adjust(wspace=0.3) plt.savefig('/home/nacorreasa/Escritorio/Figuras/Rango_Anomal_Composite.pdf', format='pdf', transparent=True) plt.close('all') os.system('scp /home/nacorreasa/Escritorio/Figuras/Rango_Anomal_Composite.pdf nacorreasa@192.168.1.74:/var/www/nacorreasa/Graficas_Resultados/Estudio')

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null

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bd66aa54eb82c259a3905c51ba14621d46ff0be5

677

py

Python

frappe-bench/apps/erpnext/erpnext/accounts/doctype/purchase_taxes_and_charges_template/purchase_taxes_and_charges_template.py

Semicheche/foa_frappe_docker

a186b65d5e807dd4caf049e8aeb3620a799c1225

[ "MIT" ]

null

frappe-bench/apps/erpnext/erpnext/accounts/doctype/purchase_taxes_and_charges_template/purchase_taxes_and_charges_template.py

Semicheche/foa_frappe_docker

a186b65d5e807dd4caf049e8aeb3620a799c1225

[ "MIT" ]

null

frappe-bench/apps/erpnext/erpnext/accounts/doctype/purchase_taxes_and_charges_template/purchase_taxes_and_charges_template.py

Semicheche/foa_frappe_docker

a186b65d5e807dd4caf049e8aeb3620a799c1225

[ "MIT" ]

null

# -*- coding: utf-8 -*- # Copyright (c) 2015, Frappe Technologies Pvt. Ltd. and Contributors # License: GNU General Public License v3. See license.txt from __future__ import unicode_literals import frappe from frappe.model.document import Document from erpnext.accounts.doctype.sales_taxes_and_charges_template.sales_taxes_and_charges_template \ import valdiate_taxes_and_charges_template class PurchaseTaxesandChargesTemplate(Document): def validate(self): valdiate_taxes_and_charges_template(self) def autoname(self): if self.company and self.title: abbr = frappe.db.get_value('Company', self.company, 'abbr') self.name = '{0} - {1}'.format(self.title, abbr)

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null

0

1

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1

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bd767db9b6f302cda84fdccb673d677adaa3588b

163

py

Python

task/urls.py

bubaley/air-drf-relation

6f40c481fcabe9162aa1ad1a7635f3d79b855582

[ "MIT" ]

4

2021-07-06T11:09:39.000Z

2021-09-01T12:58:40.000Z

task/urls.py

bubaley/air-drf-relation

6f40c481fcabe9162aa1ad1a7635f3d79b855582

[ "MIT" ]

null

task/urls.py

bubaley/air-drf-relation

6f40c481fcabe9162aa1ad1a7635f3d79b855582

[ "MIT" ]

null

from rest_framework import routers from .views import TaskViewSet router = routers.SimpleRouter() router.register('tasks', TaskViewSet) urlpatterns = router.urls

23.285714

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null

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1

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3

bd826f06fa05db4e8283cfd07172cf2a2e3a0ec9

956

py

Python

docker_interface/plugins/__init__.py

tillahoffmann/docker_interface

ed9ac691ca22dc2937e717af7f6b5fc7de19c660

[ "Apache-2.0" ]

32

2018-03-11T01:09:47.000Z

2022-03-20T19:21:17.000Z

docker_interface/plugins/__init__.py

tillahoffmann/docker_interface

ed9ac691ca22dc2937e717af7f6b5fc7de19c660

[ "Apache-2.0" ]

7

2018-05-01T15:28:55.000Z

2018-09-24T13:16:33.000Z

docker_interface/plugins/__init__.py

tillahoffmann/docker_interface

ed9ac691ca22dc2937e717af7f6b5fc7de19c660

[ "Apache-2.0" ]

14

2018-03-09T12:40:46.000Z

2022-03-19T09:59:08.000Z

# Copyright 2018 Spotify AB # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # http://www.apache.org/licenses/LICENSE-2.0 # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from .base import Plugin, BasePlugin, HomeDirPlugin, SubstitutionPlugin, WorkspaceMountPlugin, \ ValidationPlugin, ExecutePlugin from .user import UserPlugin from .run import RunPlugin, RunConfigurationPlugin from .build import BuildPlugin, BuildConfigurationPlugin from .python import JupyterPlugin from .google import GoogleCloudCredentialsPlugin, GoogleContainerRegistryPlugin

43.454545

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956

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null

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1

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1

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bd83fc524801bf8d46b580dc48d08980682ce6eb

683

py

Python

rl/agents/policy/random_policy_agent.py

ManuelMeraz/ReinforcementLearning

5d42a88776428308d35c8031c01bf5afdf080079

[ "MIT" ]

1

2020-04-19T15:29:47.000Z

rl/agents/policy/random_policy_agent.py

ManuelMeraz/ReinforcementLearning

5d42a88776428308d35c8031c01bf5afdf080079

[ "MIT" ]

null

rl/agents/policy/random_policy_agent.py

ManuelMeraz/ReinforcementLearning

5d42a88776428308d35c8031c01bf5afdf080079

[ "MIT" ]

null

#!/usr/bin/env python3 import numpy from rl.agents.policy.policy_agent import PolicyAgent class Random(PolicyAgent): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) def act(self, state: numpy.ndarray, available_actions: numpy.ndarray): """ Uses a uniform random distribution to determine it's action given a state TODO: Act according to different distributions :param state: The state of the environment :param available_actions: A list of available possible actions (positions on the board to mark) :return: a random action """ return numpy.random.choice(available_actions)

32.52381

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683

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bd9eee4ee9738708798aa7488a942d713aab919a

259

py

Python

apimux/__init__.py

zeeguu-ecosystem/apimux

4a0f28921615d713d976af1d2078efca5c51a4bd

[ "MIT" ]

null

apimux/__init__.py

zeeguu-ecosystem/apimux

4a0f28921615d713d976af1d2078efca5c51a4bd

[ "MIT" ]

1

2019-04-12T11:35:46.000Z

apimux/__init__.py

zeeguu-ecosystem/apimux

4a0f28921615d713d976af1d2078efca5c51a4bd

[ "MIT" ]

1

2019-04-07T21:10:05.000Z

import sys from apimux.log import logger logger.debug("==== API Multiplexer imported ====") major = sys.version_info.major minor = sys.version_info.minor micro = sys.version_info.micro logger.debug("Running Python version %s.%s.%s" % (major, minor, micro))

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1

0

1

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null

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1

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3

bdb83e73a16dd064163bd65b6c3a02bd6b06ceb5

187

py

Python

1. Python/1. Getting Started with Python/5. print_variables.py

theparitoshkumar/Data-Structures-Algorithms-using-python

445b9dee56bca637f21267114cc1686d333ea4c4

[ "Apache-2.0" ]

1

2021-12-05T18:02:15.000Z

1. Python/1. Getting Started with Python/5. print_variables.py

theparitoshkumar/Data-Structures-Algorithms-using-python

445b9dee56bca637f21267114cc1686d333ea4c4

[ "Apache-2.0" ]

null

1. Python/1. Getting Started with Python/5. print_variables.py

theparitoshkumar/Data-Structures-Algorithms-using-python

445b9dee56bca637f21267114cc1686d333ea4c4

[ "Apache-2.0" ]

null

""" Write a program to display values of variables in Python. """ message = "Keep Smiling!" print(message) userNo = 101 print("User No is ", userNo) gender = 'M' print("Gender: ",gender)

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4.777778

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0.160428

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7

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1

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null

0

1

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null

0

1

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3

bdc5844645346783e54e248ff78672cbab8ddee8

531

py

Python

faker_tax/de_DE/__init__.py

mastacheata/faker_tax

178cbb106cac1fc215857438da667560f9d0d8d3

[ "MIT" ]

null

faker_tax/de_DE/__init__.py

mastacheata/faker_tax

178cbb106cac1fc215857438da667560f9d0d8d3

[ "MIT" ]

null

faker_tax/de_DE/__init__.py

mastacheata/faker_tax

178cbb106cac1fc215857438da667560f9d0d8d3

[ "MIT" ]

null

# -*- coding: utf-8 -*- from .. import Provider as TaxProvider class Provider(TaxProvider): """ A Faker provider for the Empto project """ def tax_id(self): """ Returns a random generated Tax ID """ return "DE" + str(self.bothify('#########')) def tax_number(self): """ Generates a random tax number """ return str(self.random_number(3, True)) + '/' + str(self.random_number(3, True)) + '/' + \ str(self.random_number(3, True))

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531

24

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0

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null

0

1

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1

0

3

bdfebf77834253f5884d71147594f2ecb2b724e7

381

py

Python

ringity/readwrite/diagram.py

ClusterDuck123/ringity

44505d192b72a1d47f6b7b0a90f0db83d98b6156

[ "MIT" ]

null

ringity/readwrite/diagram.py

ClusterDuck123/ringity

44505d192b72a1d47f6b7b0a90f0db83d98b6156

[ "MIT" ]

null

ringity/readwrite/diagram.py

ClusterDuck123/ringity

44505d192b72a1d47f6b7b0a90f0db83d98b6156

[ "MIT" ]

null

import numpy as np from ringity.classes.diagram import PersistenceDiagram def read_pdiagram(fname, **kwargs): """ Wrapper for numpy.genfromtxt. """ return PersistenceDiagram(np.genfromtxt(fname, **kwargs)) def write_pdiagram(dgm, fname, **kwargs): """ Wrapper for numpy.savetxt. """ array = np.array(dgm) np.savetxt(fname, array, **kwargs)

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0.204724

0

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null

0

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0

null

0

1

0

1

0

3

da00a04e105af98a1f2533401de370dbc9e4e477

191

py

Python

vsr/common/helpers/validators.py

queirozfcom/vector_space_retrieval

3d31963bb2a9851d9801b7317677bb47d5dc3e4f

[ "MIT" ]

null

vsr/common/helpers/validators.py

queirozfcom/vector_space_retrieval

3d31963bb2a9851d9801b7317677bb47d5dc3e4f

[ "MIT" ]

6

2015-04-06T00:57:06.000Z

2015-04-27T05:48:22.000Z

vsr/common/helpers/validators.py

queirozfcom/vector_space_retrieval

3d31963bb2a9851d9801b7317677bb47d5dc3e4f

[ "MIT" ]

null

def validate_positive_integer(param): if isinstance(param,int) and (param > 0): return(None) else: raise ValueError("Invalid value, expected positive integer, got {0}".format(param))

31.833333

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0

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da0619e0b3f90a7657f61daf019b596715684431

733

py

Python

setup.py

mollinaca/slack_utils

5e1a4bc0ce6026b2816bf14dc1f328c15415c1d7

[ "MIT" ]

null

setup.py

mollinaca/slack_utils

5e1a4bc0ce6026b2816bf14dc1f328c15415c1d7

[ "MIT" ]

null

setup.py

mollinaca/slack_utils

5e1a4bc0ce6026b2816bf14dc1f328c15415c1d7

[ "MIT" ]

null

from setuptools import setup, find_packages with open('requirements.txt') as requirements_file: install_requirements = requirements_file.read().splitlines() setup( name="slack_utils", version="0.0.1", description="my slack utils", author="mollinaca", packages=find_packages(), install_requires=install_requirements, entry_points={ "console_scripts": [ "slack_utils=slack_utils.__main__:main", ] }, classifiers=[ 'Programming Language :: Python :: 3.4', 'Programming Language :: Python :: 3.5', 'Programming Language :: Python :: 3.6', 'Programming Language :: Python :: 3.7', 'Programming Language :: Python :: 3.8', ] )

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733

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null

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null

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da1ad99b4e4020c41c2f1959c24a089b0e9d4360

2,097

py

Python

setup.py

venkatachalamlab/lambda

838f0acef7da9bd9fb3ab933db5b732e8221e86a

[ "MIT" ]

null

setup.py

venkatachalamlab/lambda

838f0acef7da9bd9fb3ab933db5b732e8221e86a

[ "MIT" ]

26

2021-09-22T01:07:32.000Z

2022-01-07T17:06:32.000Z

setup.py

venkatachalamlab/lambda

838f0acef7da9bd9fb3ab933db5b732e8221e86a

[ "MIT" ]

null

import setuptools requirements = [ 'docopt', 'numpy', 'pyzmq' ] console_scripts = [ 'lambda_client=lambda_scope.zmq.client:main', 'lambda_forwarder=lambda_scope.zmq.forwarder:main', 'lambda_hub=lambda_scope.devices.hub_relay:main', 'lambda_publisher=lambda_scope.zmq.publisher:main', 'lambda_server=lambda_scope.zmq.server:main', 'lambda_subscriber=lambda_scope.zmq.subscriber:main', 'lambda_logger=lambda_scope.devices.logger:main', 'lambda_dragonfly=lambda_scope.devices.dragonfly:main', 'lambda_acquisition_board=lambda_scope.devices.acquisition_board:main', 'lambda_displayer=lambda_scope.devices.displayer:main', 'lambda_data_hub=lambda_scope.devices.data_hub:main', 'lambda_stage_data_hub=lambda_scope.devices.stage_data_hub:main', 'lambda_writer=lambda_scope.devices.writer:main', 'lambda_processor=lambda_scope.devices.processor:main', 'lambda_commands=lambda_scope.devices.commands:main', 'lambda_zaber=lambda_scope.devices.zaber:main', 'lambda_stage_tracker=lambda_scope.devices.stage_tracker:main', 'lambda_valve_control=lambda_scope.devices.valve_control:main', 'lambda_microfluidic=lambda_scope.devices.microfluidic:main', 'lambda_app=lambda_scope.devices.app:main', 'lambda_stage=lambda_scope.system.stage:main', 'lambda=lambda_scope.system.lambda:main' ] setuptools.setup( name="lambda_scope", version="0.0.1", author="Mahdi Torkashvand", author_email="mmt.mahdi@gmail.com", description="Software to operate the customized imaging system, lambda.", url="https://github.com/venkatachalamlab/lambda", project_urls={ "Bug Tracker": "https://github.com/venkatachalamlab/lambda/issues", }, classifiers=[ "Programming Language :: Python :: 3", "License :: OSI Approved :: MIT License", "Operating System :: Microsoft :: Windows :: Windows 10", ], entry_points={ 'console_scripts': console_scripts }, install_requires=requirements, packages=['lambda_scope'], python_requires=">=3.6", )

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2,097

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null

0

1

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1

null

0

3

da1c62f51825a651ce542eb88803a19261cddbfe

423

py

Python

handlers/users/__init__.py

Asadbek07/e-commerce-bot

df6c1bb625becf95bf53f4cece12752dca9f7f67

[ "Unlicense", "MIT" ]

null

handlers/users/__init__.py

Asadbek07/e-commerce-bot

df6c1bb625becf95bf53f4cece12752dca9f7f67

[ "Unlicense", "MIT" ]

null

handlers/users/__init__.py

Asadbek07/e-commerce-bot

df6c1bb625becf95bf53f4cece12752dca9f7f67

[ "Unlicense", "MIT" ]

null

from . import start from . import edits from . import payment from . import pickup from . import get_location from . import information_handler from . import fikr_bildirish_handler from . import savat_ortga from . import biz_bilan_aloqa from . import product_menu_handler from . import menus_handlers from . import product_add_for_admins from . import product_delete_for_admins from . import ordering from . import r_phone

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