xszheng2020/the_stack_dedup_python_hits_4 · Datasets at Hugging Face

cbdf0b893a5327d5e9558455c8c727b7c552b9b9

225

py

Python

custom_addons/books_log/models/quotations.py

MonwarAdeeb/Bista_Solutions

d261e31f21ff03b2cc82b0c26d680036dca6d799

[ "MIT" ]

null

custom_addons/books_log/models/quotations.py

MonwarAdeeb/Bista_Solutions

d261e31f21ff03b2cc82b0c26d680036dca6d799

[ "MIT" ]

null

custom_addons/books_log/models/quotations.py

MonwarAdeeb/Bista_Solutions

d261e31f21ff03b2cc82b0c26d680036dca6d799

[ "MIT" ]

null

from odoo import _, api, fields, models class Quotations(models.Model): _inherit = "sale.order" note_on_customer = fields.Text("Note on Customer", help="Add Notes on Customers!")

25

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225

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cbe01b859fdec5b75c6ca5d80bdb0090e7fffe18

129

py

Python

Curso de Cisco/Actividades/Usando una variable contador para salir de un ciclo.py

tomasfriz/Curso-de-Cisco

a50ee5fa96bd86d468403e29ccdc3565a181af60

[ "MIT" ]

null

Curso de Cisco/Actividades/Usando una variable contador para salir de un ciclo.py

tomasfriz/Curso-de-Cisco

a50ee5fa96bd86d468403e29ccdc3565a181af60

[ "MIT" ]

null

Curso de Cisco/Actividades/Usando una variable contador para salir de un ciclo.py

tomasfriz/Curso-de-Cisco

a50ee5fa96bd86d468403e29ccdc3565a181af60

[ "MIT" ]

null

contador = 5 while contador != 0: print("Dentro del ciclo: ", contador) contador -= 1 print("Fuera del ciclo", contador)

25.8

41

0.658915

18

129

4.777778

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0.372093

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0.029126

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129

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null

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1

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null

0

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4

1db845d595321edefc8d7c07f7a1547eb2a47cda

295

py

Python

pyimgaug3d/augmenters/src/base_augmenter.py

SiyuLiu0329/pyimgaug3d

cc99cd3ef12fab665df4f1d4ad08ed5e20c6da4a

[ "BSD-2-Clause" ]

1

2021-10-05T19:52:46.000Z

pyimgaug3d/augmenters/src/base_augmenter.py

SiyuLiu0329/pyimgaug3d

cc99cd3ef12fab665df4f1d4ad08ed5e20c6da4a

[ "BSD-2-Clause" ]

null

pyimgaug3d/augmenters/src/base_augmenter.py

SiyuLiu0329/pyimgaug3d

cc99cd3ef12fab665df4f1d4ad08ed5e20c6da4a

[ "BSD-2-Clause" ]

null

import random class BaseAugmenter: def __init__(self): self.augmentation = [] def add_augmentation(self, augmentation): self.augmentation.append(augmentation) def __call__(self, images): aug = random.choice(self.augmentation) return aug(images)

24.583333

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295

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1

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1

0

4

1de8a7f7a0dfcf9747c0d6f8afb235a1b4a3377a

184

py

Python

FNetEncDec.py

Dmitriuso/FNet-pytorch

bc744b4947b693604371b586e263ce72e90ff1df

[ "MIT" ]

null

FNetEncDec.py

Dmitriuso/FNet-pytorch

bc744b4947b693604371b586e263ce72e90ff1df

[ "MIT" ]

null

FNetEncDec.py

Dmitriuso/FNet-pytorch

bc744b4947b693604371b586e263ce72e90ff1df

[ "MIT" ]

null

import torch from torch import nn from fnet import FNet class FNetEncoderDecoder(nn.Module): def __init__(self): super().__init__() self.fnet_encoder = FNet(self)

20.444444

38

0.701087

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null

0

1

0

1

0

4

1df5f4fde009b7e0a5d7a811c122d66d8996be06

257

py

Python

src/seveno_pyutil/logging_utilities/__init__.py

tadams42/seveno_pyutil

9e3b4157408b0b54a4c609ff1a8c704be958543b

[ "MIT" ]

null

src/seveno_pyutil/logging_utilities/__init__.py

tadams42/seveno_pyutil

9e3b4157408b0b54a4c609ff1a8c704be958543b

[ "MIT" ]

null

src/seveno_pyutil/logging_utilities/__init__.py

tadams42/seveno_pyutil

9e3b4157408b0b54a4c609ff1a8c704be958543b

[ "MIT" ]

null

from .single_line_formatter import SingleLineColoredFormatter, SingleLineFormatter from .sql_filter import SQLFilter from .standard_metadata_filter import StandardMetadataFilter from .utilities import log_to_console_for, log_to_tmp_file_for, silence_logger

51.4

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257

6.8125

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0.07393

257

4

83

64.25

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null

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0

1

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4

69994ebdbc60714972b7b6734243517a9ccda0b2

104

py

Python

url`s_and_templates/django101_admin/apps.py

EmilianStoyanov/python-web

60ddb1f0cc4c5bb1615317967c4da33c4171b27b

[ "MIT" ]

3

2021-01-19T18:54:38.000Z

2022-01-05T17:28:41.000Z

url`s_and_templates/django101_admin/apps.py

EmilianStoyanov/python-web

60ddb1f0cc4c5bb1615317967c4da33c4171b27b

[ "MIT" ]

null

url`s_and_templates/django101_admin/apps.py

EmilianStoyanov/python-web

60ddb1f0cc4c5bb1615317967c4da33c4171b27b

[ "MIT" ]

null

from django.apps import AppConfig class Django101AdminConfig(AppConfig): name = 'django101_admin'

17.333333

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0.788462

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104

7.363636

0.909091

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0.067416

0.144231

104

5

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null

0

1

0

null

0

1

0

1

0

4

699ca3d21076959cbca46f1c771fb501bd6c1baa

310

py

Python

problem017.py

samidarko/euler

f5d2c0fe41c2cb5517d2dd7f7db075add0dbedb1

[ "MIT" ]

null

problem017.py

samidarko/euler

f5d2c0fe41c2cb5517d2dd7f7db075add0dbedb1

[ "MIT" ]

null

problem017.py

samidarko/euler

f5d2c0fe41c2cb5517d2dd7f7db075add0dbedb1

[ "MIT" ]

null

from num2words import num2words from functools import reduce def number_letters(n): return len(num2words(n).replace(' ', '').replace('-', '')) def main(): def fn(acc, n): return acc + number_letters(n) return reduce(fn, range(1, 1001), 0) if __name__ == "__main__": print(main())

18.235294

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310

4.536585

0.536585

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0.150538

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0.036585

0.206452

310

16

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null

0

1

0

null

0

1

0

1

0

4

69a5aa26a0f1e3229931aef186a19e148af8aad7

185

py

Python

location/apps.py

ohahlev/ahlev-django-location

7d6060ab7b21509f53790f5863b596f2b95c286a

[ "BSD-3-Clause" ]

null

location/apps.py

ohahlev/ahlev-django-location

7d6060ab7b21509f53790f5863b596f2b95c286a

[ "BSD-3-Clause" ]

null

location/apps.py

ohahlev/ahlev-django-location

7d6060ab7b21509f53790f5863b596f2b95c286a

[ "BSD-3-Clause" ]

null

from django.apps import AppConfig from . import __version__ as VERSION class LocationConfig(AppConfig): name = "location" verbose_name = "Location Management %s" % VERSION

26.428571

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0.740541

21

185

6.285714

0.666667

0.181818

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0.189189

185

7

54

26.428571

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null

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null

0

1

0

1

0

4

69a76c0a17eeaf86bff6be79b56692f86f13fcba

180

py

Python

src/core/managers/__init__.py

ablil/meistertask-cli

6c90802ac5dc7e5ac016e5c61c0e68db043e5784

[ "MIT" ]

3

2020-11-03T22:27:18.000Z

2021-12-11T23:13:55.000Z

src/core/managers/__init__.py

ablil/meistertask-cli

6c90802ac5dc7e5ac016e5c61c0e68db043e5784

[ "MIT" ]

1

2021-09-12T13:28:13.000Z

src/core/managers/__init__.py

ablil/meistertask-cli

6c90802ac5dc7e5ac016e5c61c0e68db043e5784

[ "MIT" ]

null

from .projectmanager import ProjectManager from .sectionmanager import SectionManager from .taskmanager import TaskManager __all__ = [ProjectManager, SectionManager, TaskManager]

30

55

0.855556

16

180

9.375

0.375

0

0.1

180

5

56

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null

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1

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null

0

1

0

1

0

4

69bb14fdb85056a9374cc9391656a6f438066089

159

py

Python

rbp_eclip/custom_keras_objects.py

Luma-1994/lama

60d802e2e4cce789f03eea11b038212ba5f7fd1b

[ "MIT" ]

137

2018-03-13T17:44:46.000Z

2022-02-18T06:07:45.000Z

rbp_eclip/custom_keras_objects.py

Luma-1994/lama

60d802e2e4cce789f03eea11b038212ba5f7fd1b

[ "MIT" ]

111

2018-03-14T08:16:35.000Z

2022-03-04T18:26:41.000Z

rbp_eclip/custom_keras_objects.py

Luma-1994/lama

60d802e2e4cce789f03eea11b038212ba5f7fd1b

[ "MIT" ]

57

2018-03-14T08:39:24.000Z

2022-02-01T15:56:04.000Z

import concise # all the custom objects are already loaded through importing concise OBJECTS = None # new concise version # OBJECTS = concise.custom_objects

19.875

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70

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null

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4

69dff319ca7dbf29be8e3384b7b90359f52f9b2b

84

py

Python

more practice/date_time.py

shinigami423/Election_Analysis

dee8c7b08ea3b5d3d8d7fa618fed25ecd56f0318

[ "MIT" ]

null

more practice/date_time.py

shinigami423/Election_Analysis

dee8c7b08ea3b5d3d8d7fa618fed25ecd56f0318

[ "MIT" ]

null

more practice/date_time.py

shinigami423/Election_Analysis

dee8c7b08ea3b5d3d8d7fa618fed25ecd56f0318

[ "MIT" ]

null

import datetime now = datetime.datetime.now() print(f"The time right now is {now}")

21

37

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14

84

4.428571

0.642857

0.354839

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1

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1

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null

0

1

0

4

69f328d7748034d4d4f469bc503c8527c26edd6a

115

py

Python

datacaptureapp/templatetags/tags.py

steftaz/PinPoint

c38c19e25a2f4ab6688c48d0c84f3b046be86059

[ "MIT" ]

1

2020-11-05T21:54:49.000Z

datacaptureapp/templatetags/tags.py

steftaz/PinPoint

c38c19e25a2f4ab6688c48d0c84f3b046be86059

[ "MIT" ]

null

datacaptureapp/templatetags/tags.py

steftaz/PinPoint

c38c19e25a2f4ab6688c48d0c84f3b046be86059

[ "MIT" ]

null

from django import template register = template.Library() @register.filter() def get(h, key): return h[key]

12.777778

29

0.704348

16

115

5.0625

0.75

0.098765

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0.173913

115

8

30

14.375

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1

0.2

false

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null

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null

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1

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4

69f8299e2688600dbe459e04364c8cbf6393451f

1,206

py

Python

app/migrations/0018_auto_20200918_0832.py

mapoetto/group2_CTFLab

5b492ce46875ea37a57701686897bd9613e2dd13

[ "MIT" ]

1

2021-10-15T14:37:33.000Z

app/migrations/0018_auto_20200918_0832.py

mapoetto/group2_CTFLab

5b492ce46875ea37a57701686897bd9613e2dd13

[ "MIT" ]

null

app/migrations/0018_auto_20200918_0832.py

mapoetto/group2_CTFLab

5b492ce46875ea37a57701686897bd9613e2dd13

[ "MIT" ]

null

# Generated by Django 2.1.15 on 2020-09-18 08:32 import app.models from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('app', '0017_auto_20200918_0831'), ] operations = [ migrations.AddField( model_name='sshtunnel_configs', name='DNS_NAME_SERVER', field=models.CharField(default='', max_length=220), ), migrations.AddField( model_name='sshtunnel_configs', name='FULL_PATH_SSH_KEY', field=models.CharField(default='', max_length=220), ), migrations.AddField( model_name='sshtunnel_configs', name='LOCAL_PORT', field=models.IntegerField(default=0, validators=[app.models.validate_flag]), ), migrations.AddField( model_name='sshtunnel_configs', name='REMOTE_PORT', field=models.IntegerField(default=0, validators=[app.models.validate_flag]), ), migrations.AddField( model_name='sshtunnel_configs', name='USER_SERVER', field=models.CharField(default='', max_length=64), ), ]

30.15

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0.12987

0.165945

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0.7114

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1,206

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89

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null

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4

3870844e7bd2480b2ad479af86334d7d26e0b9f7

2,987

py

Python

tests/codelets/test_frame_matcher.py

juliakzn/construction_finder

92e9f044163fbe8bde3a6c5f9ec125a7ecf96de8

[ "MIT" ]

null

tests/codelets/test_frame_matcher.py

juliakzn/construction_finder

92e9f044163fbe8bde3a6c5f9ec125a7ecf96de8

[ "MIT" ]

null

tests/codelets/test_frame_matcher.py

juliakzn/construction_finder

92e9f044163fbe8bde3a6c5f9ec125a7ecf96de8

[ "MIT" ]

null

import spacy from construction_finder import codelets, frame class TestFrameMatcher: def test_from_frame_and_sentence( self, dative_frame_matcher, codelet_info, dont_give_me_that_sentence_doc, dative_frame, ): assert dative_frame_matcher.urgency_level == 1 assert dative_frame_matcher.codelet_probability == 1 assert dative_frame_matcher.sentence_doc == dont_give_me_that_sentence_doc assert dative_frame_matcher.not_bonded_slot_ids is None assert str(dative_frame_matcher.frame) == str(dative_frame) def test_run(self, dative_frame_matcher): frame_matcher_result = dative_frame_matcher.run() for i, codelet in enumerate(frame_matcher_result.new_codelets): assert isinstance(codelet, codelets.SlotMatcher) assert codelet.urgency_level == 2 assert codelet.codelet_probability == 1 assert codelet.temp_modifier == 5.25 assert codelet.slot_id == i assert frame_matcher_result.temp_modifier == 21 def test_set_bond(self, dative_frame_matcher, codelet): new_codelets = dative_frame_matcher.set_bond(0, [2], codelet) assert dative_frame_matcher.frame.slots[0].bond == [2] assert dative_frame_matcher.frame.slots[0].form == "give" assert dative_frame_matcher.frame.all_required_slots_found == False assert dative_frame_matcher.frame.required_slots_to_find == 3 assert len(new_codelets) == 0 _ = dative_frame_matcher.set_bond(1, ["PRODROP"], codelet) assert dative_frame_matcher.frame.all_required_slots_found == False assert dative_frame_matcher.frame.required_slots_to_find == 2 _ = dative_frame_matcher.set_bond(2, [4], codelet) assert dative_frame_matcher.frame.all_required_slots_found == False assert dative_frame_matcher.frame.required_slots_to_find == 1 new_codelets = dative_frame_matcher.set_bond(3, [3], codelet) assert dative_frame_matcher.frame.all_required_slots_found == True assert dative_frame_matcher.frame.required_slots_to_find == 0 assert len(new_codelets) == 1 def test_get_form(self, dative_frame_matcher): output = dative_frame_matcher.get_form([2])[0] assert isinstance(output, spacy.tokens.token.Token) assert output.text == "give" def test_create_frame_finalizer(self, dative_frame_matcher): output = dative_frame_matcher.create_frame_finalizer( urgency_level=1, temp_modifier=42 ) assert isinstance(output, codelets.FrameFinalizer) assert output.frame_matcher == dative_frame_matcher assert output.urgency_level == 2 assert output.temp_modifier == 42 def test_assign_noun_phrases(self, dative_frame_matcher): dative_frame_matcher.assign_noun_phrases("TEST_NOUN_PHRASES") assert dative_frame_matcher.noun_phrases == "TEST_NOUN_PHRASES"

43.289855

82

0.719451

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2,987

5.198433

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2,987

68

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1

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null

0

1

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3873cccbe695a32cc61d6a7fa4bc58362dae9c36

810

py

Python

src/simpleAero.py

CB1204/LapSimulation

7d7f7c43a6bc3db3dbf02050d939da3f17647c2c

[ "MIT" ]

7

2018-02-22T16:58:26.000Z

2022-02-05T18:17:56.000Z

src/simpleAero.py

CB1204/LapSimulation

7d7f7c43a6bc3db3dbf02050d939da3f17647c2c

[ "MIT" ]

null

src/simpleAero.py

CB1204/LapSimulation

7d7f7c43a6bc3db3dbf02050d939da3f17647c2c

[ "MIT" ]

2

2019-04-15T21:07:03.000Z

2021-05-11T07:41:49.000Z

import numpy as np class Aero: def __init__(self,reference_down_force = np.array([ 1000, 1000 ]), reference_drag = 500, reference_speed = 20): self.reference_down_force = reference_down_force self.reference_drag = reference_drag self.reference_speed = reference_speed self.Cdft = np.sum(self.reference_down_force) / (self.reference_speed**2) self.Cdf = self.reference_down_force / (self.reference_speed**2) self.Cdr = self.reference_down_force / (self.reference_speed**2) self.Cd = self.reference_drag / (self.reference_speed**2) def down_force(self,state): return self.reference_down_force * (state.speed/self.reference_speed)**2 def drage(self,state): return self.reference_drag * (state.speed/self.reference_speed)**2

45

116

0.708642

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810

4.882883

0.252252

0.383764

0.232472

0.243542

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0.356089

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0.182716

810

18

117

45

0.78852

0

null

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0.071429

null

0

null

1

0

null

0

1

0

4

389c1346e26e21c539673f6e79c223d5a7e2315c

325

py

Python

test/torch/funcs/base.py

opendilab/DI-treetensor

fe5f681123c3d6e8d7507fba38586d2edf12e693

[ "Apache-2.0" ]

45

2021-09-04T15:57:44.000Z

2022-03-11T19:28:56.000Z

test/torch/funcs/base.py

opendilab/DI-treetensor

fe5f681123c3d6e8d7507fba38586d2edf12e693

[ "Apache-2.0" ]

7

2021-09-06T13:06:12.000Z

2022-03-03T13:38:05.000Z

test/torch/funcs/base.py

opendilab/DI-treetensor

fe5f681123c3d6e8d7507fba38586d2edf12e693

[ "Apache-2.0" ]

1

2021-09-30T15:18:06.000Z

import treetensor.torch as ttorch from treetensor.utils import replaceable_partial from ...tests import choose_mark_with_existence_check, get_mark_with_existence_check get_mark = replaceable_partial(get_mark_with_existence_check, base=ttorch) choose_mark = replaceable_partial(choose_mark_with_existence_check, base=ttorch)

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38ce571b51c703429bee58b9177005c964b1cc7b

5,125

py

Python

active_subspaces/utils/simrunners.py

carlosal1015/active_subspaces

caaf108fcb89548a374fea7704b0d92d38b4539a

[ "MIT" ]

1

2020-03-16T18:05:05.000Z

active_subspaces/utils/simrunners.py

carlosal1015/active_subspaces

caaf108fcb89548a374fea7704b0d92d38b4539a

[ "MIT" ]

null

active_subspaces/utils/simrunners.py

carlosal1015/active_subspaces

caaf108fcb89548a374fea7704b0d92d38b4539a

[ "MIT" ]

1

2020-03-16T18:05:09.000Z

"""Utilities for running several simulations at different inputs.""" import numpy as np import logging import time from misc import process_inputs class SimulationRunner(): """ A class for running several simulations at different input values. :cvar function fun: Runs the simulation for a fixed value of the input parameters, given as an ndarray. **See Also** utils.simrunners.SimulationGradientRunner **Notes** The function fun should take an ndarray of size 1-by-m and return a float. This float is the quantity of interest from the simulation. Often, the function is a wrapper to a larger simulation code. """ fun = None def __init__(self, fun): """ Initialize a SimulationRunner. :param function fun: A function that runs the simulation for a fixed value of the input parameters, given as an ndarray. This function returns the quantity of interest from the model. Often, this function is a wrapper to a larger simulation code. """ if not hasattr(fun, '__call__'): raise TypeError('fun should be a callable function.') self.fun = fun def run(self, X): """ Run the simulation at several input values. :param ndarray X: Contains all input points where one wishes to run the simulation. If the simulation takes m inputs, then `X` must have shape M-by-m, where M is the number of simulations to run. :return: F, Contains the simulation output at each given input point. The shape of `F` is M-by-1. :rtype: ndarray **Notes** In principle, the simulation calls can be executed independently and in parallel. Right now this function uses a sequential for-loop. Future development will take advantage of multicore architectures to parallelize this for-loop. """ # right now this just wraps a sequential for-loop. # should be parallelized X, M, m = process_inputs(X) F = np.zeros((M, 1)) logger = logging.getLogger(__name__) start = time.time() for i in range(M): F[i] = self.fun(X[i,:].reshape((1,m))) if ((i+1) % 10) == 0: logger.debug('\t{:d} of {:d}'.format(i+1, M)) end = time.time() - start logger.info('Completed {:d} function evaluations in {:4.2f} seconds.'.format(M, end)) return F class SimulationGradientRunner(): """ A class for running several simulations at different input values that return the gradients of the quantity of interest. :cvar function dfun: A function that runs the simulation for a fixed value of the input parameters, given as an ndarray. It returns the gradient of the quantity of interest at the given input. **See Also** utils.simrunners.SimulationRunner **Notes** The function dfun should take an ndarray of size 1-by-m and return an ndarray of shape 1-by-m. This ndarray is the gradient of the quantity of interest from the simulation. Often, the function is a wrapper to a larger simulation code. """ dfun = None def __init__(self, dfun): """ Initialize a SimulationGradientRunner. :param function dfun: A function that runs the simulation for a fixed value of the input parameters, given as an ndarray. It returns the gradient of the quantity of interest at the given input. """ if not hasattr(dfun, '__call__'): raise TypeError('fun should be a callable function.') self.dfun = dfun def run(self, X): """ Run the simulation at several input values and return the gradients of the quantity of interest. :param ndarray X: Contains all input points where one wishes to run the simulation. If the simulation takes m inputs, then `X` must have shape M-by-m, where M is the number of simulations to run. :return: dF, ontains the gradient of the quantity of interest at each given input point. The shape of `dF` is M-by-m. :rtype: ndarray **Notes** In principle, the simulation calls can be executed independently and in parallel. Right now this function uses a sequential for-loop. Future development will take advantage of multicore architectures to parallelize this for-loop. """ # right now this just wraps a sequential for-loop. # should be parallelized X, M, m = process_inputs(X) dF = np.zeros((M, m)) logger = logging.getLogger(__name__) start = time.time() for i in range(M): df = self.dfun(X[i,:].reshape((1,m))) dF[i,:] = df.reshape((1,m)) logger.debug('Completed {:d} of {:d} gradient evaluations.'.format(i+1, M)) end = time.time() - start logger.info('Completed {:d} gradient evaluations in {:4.2f} seconds.'.format(M, end)) return dF

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1

0

1

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null

0

4

2a19fb6964ed389691cceb1c6e1ce0aa7472b369

2,042

py

Python

keycloak/urls_patterns.py

c0mpiler/py-keycloak

e2fee4ab0d8b2fc0f3b98291d907ddf45290cbb4

[ "Apache-2.0" ]

1

2018-08-06T00:50:30.000Z

keycloak/urls_patterns.py

c0mpiler/py-keycloak

e2fee4ab0d8b2fc0f3b98291d907ddf45290cbb4

[ "Apache-2.0" ]

null

keycloak/urls_patterns.py

c0mpiler/py-keycloak

e2fee4ab0d8b2fc0f3b98291d907ddf45290cbb4

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # OPENID URLS URL_WELL_KNOWN = "realms/{realm-name}/.well-known/openid-configuration" URL_TOKEN = "realms/{realm-name}/protocol/openid-connect/token" URL_USERINFO = "realms/{realm-name}/protocol/openid-connect/userinfo" URL_LOGOUT = "realms/{realm-name}/protocol/openid-connect/logout" URL_CERTS = "realms/{realm-name}/protocol/openid-connect/certs" URL_INTROSPECT = "realms/{realm-name}/protocol/openid-connect/token/introspect" URL_ENTITLEMENT = "realms/{realm-name}/authz/entitlement/{resource-server-id}" # ADMIN URLS URL_ADMIN_USERS = "admin/realms/{realm-name}/users" URL_ADMIN_USERS_COUNT = "admin/realms/{realm-name}/users/count" URL_ADMIN_USER = "admin/realms/{realm-name}/users/{id}" URL_ADMIN_USER_CONSENTS = "admin/realms/{realm-name}/users/{id}/consents" URL_ADMIN_SEND_UPDATE_ACCOUNT = "admin/realms/{realm-name}/users/{id}/execute-actions-email" URL_ADMIN_SEND_VERIFY_EMAIL = "admin/realms/{realm-name}/users/{id}/send-verify-email" URL_ADMIN_RESET_PASSWORD = "admin/realms/{realm-name}/users/{id}/reset-password" URL_ADMIN_GET_SESSIONS = "admin/realms/{realm-name}/users/{id}/sessions" URL_ADMIN_USER_CLIENT_ROLES = "admin/realms/{realm-name}/users/{id}/role-mappings/clients/{client-id}" URL_ADMIN_USER_GROUP = "admin/realms/{realm-name}/users/{id}/groups/{group-id}" URL_ADMIN_SERVER_INFO = "admin/serverinfo" URL_ADMIN_GROUPS = "admin/realms/{realm-name}/groups" URL_ADMIN_GROUP = "admin/realms/{realm-name}/groups/{id}" URL_ADMIN_GROUP_CHILD = "admin/realms/{realm-name}/groups/{id}/children" URL_ADMIN_GROUP_PERMISSIONS = "admin/realms/{realm-name}/groups/{id}/management/permissions" URL_ADMIN_CLIENTS = "admin/realms/{realm-name}/clients" URL_ADMIN_CLIENT = "admin/realms/{realm-name}/clients/{id}" URL_ADMIN_CLIENT_ROLES = "admin/realms/{realm-name}/clients/{id}/roles" URL_ADMIN_CLIENT_ROLE = "admin/realms/{realm-name}/clients/{id}/roles/{role-name}" URL_ADMIN_REALM_ROLES = "admin/realms/{realm-name}/roles" URL_ADMIN_USER_STORAGE = "admin/realms/{realm-name}/user-storage/{id}/sync"

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0

4

2a23555ca32bcafd6bc0fec5a02872d41b8e45a4

1,771

py

Python

tests/changes/api/test_jenkins_master_blacklist.py

vault-the/changes

37e23c3141b75e4785cf398d015e3dbca41bdd56

[ "Apache-2.0" ]

443

2015-01-03T16:28:39.000Z

2021-04-26T16:39:46.000Z

tests/changes/api/test_jenkins_master_blacklist.py

vault-the/changes

37e23c3141b75e4785cf398d015e3dbca41bdd56

[ "Apache-2.0" ]

12

2015-07-30T19:07:16.000Z

2016-11-07T23:11:21.000Z

tests/changes/api/test_jenkins_master_blacklist.py

vault-the/changes

37e23c3141b75e4785cf398d015e3dbca41bdd56

[ "Apache-2.0" ]

47

2015-01-09T10:04:00.000Z

2020-11-18T17:58:19.000Z

from changes.testutils import APITestCase class JenkinsMasterBlacklist(APITestCase): def test_add_remove_blacklist(self): path = '/api/0/jenkins_master_blacklist/' # Add to blacklist data = dict(master_url='https://jenkins-master-a') resp = self.client.post(path, data=data) assert resp.status_code == 200 data = dict(master_url='https://jenkins-master-b') resp = self.client.post(path, data=data) assert resp.status_code == 200 resp = self.client.get(path) resp.status_code == 200 result = self.unserialize(resp) assert 'https://jenkins-master-a' in result['blacklist'] assert 'https://jenkins-master-b' in result['blacklist'] # Delete from blacklist data = dict(master_url='https://jenkins-master-a', remove=1) resp = self.client.post(path, data=data) resp.status_code == 200 assert ['https://jenkins-master-b'] == self.unserialize(resp)['blacklist'] def test_re_add(self): path = '/api/0/jenkins_master_blacklist/' data = dict(master_url='https://jenkins-master-a') resp = self.client.post(path, data=data) assert resp.status_code == 200 data = dict(master_url='https://jenkins-master-a') resp = self.client.post(path, data=data) assert resp.status_code == 200 result = self.unserialize(resp) assert 'warning' in result def test_remove_missing(self): path = '/api/0/jenkins_master_blacklist/' data = dict(master_url='https://jenkins-master-a', remove=1) resp = self.client.post(path, data=data) assert resp.status_code == 200 result = self.unserialize(resp) assert 'warning' in result

38.5

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null

0

1

0

1

0

1

0

null

0

4

2a3209bea5bb8001caa34c536b20c6629be15a1a

338

py

Python

bhabana/metrics/confusion_matrix.py

dashayushman/bhabana

7438505e20be53a4c524324abf9cf8985d0fc684

[ "Apache-2.0" ]

null

bhabana/metrics/confusion_matrix.py

dashayushman/bhabana

7438505e20be53a4c524324abf9cf8985d0fc684

[ "Apache-2.0" ]

null

bhabana/metrics/confusion_matrix.py

dashayushman/bhabana

7438505e20be53a4c524324abf9cf8985d0fc684

[ "Apache-2.0" ]

null

import numpy as np from bhabana.metrics import Metric from sklearn.metrics import classification_report class ClassificationReport(Metric): def __call__(self, pred, gt): return self.calculate(pred, gt) def calculate(self, pred, gt): return classification_report(np.argmax(gt, axis=1), np.argmax(pred, axis=1))

26

84

0.733728

46

338

5.26087

0.5

0.07438

0.082645

0.132231

0

0.007143

0.171598

338

13

84

26

0.857143

0

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false

0

0.375

0.25

1

0

null

0

null

0

1

0

1

0

4

2a42a748492a4ae4fd9c040967785cfc2ee17759

108

py

Python

tests/qrcode/svg.py

heuer/segno-mimos

0b1b220c63fcda9fcaa0e42725ea719651a1d53e

[ "BSD-3-Clause" ]

1

2017-02-08T21:24:37.000Z

tests/qrcode/svg.py

heuer/segno-mimos

0b1b220c63fcda9fcaa0e42725ea719651a1d53e

[ "BSD-3-Clause" ]

2

2016-09-01T18:36:06.000Z

2018-02-16T11:17:23.000Z

tests/qrcode/svg.py

heuer/segno-mimos

0b1b220c63fcda9fcaa0e42725ea719651a1d53e

[ "BSD-3-Clause" ]

null

from segno_mimos.qrcode.image.svg import SvgImage class SvgImageWhite(SvgImage): background = 'white'

18

49

0.777778

13

108

6.384615

0.923077

0

0.138889

108

5

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21.6

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false

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null

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1

0

null

0

1

0

1

0

4

2a478d767ca9f4fd848a7b06f22f49ab57c05f0a

59

py

Python

config.py

FAUSheppy/open-web-leaderboard

defcf5671d71bfa170c3c9267488b18a926b02d7

[ "MIT" ]

2

2020-12-27T01:55:32.000Z

2021-07-26T15:40:03.000Z

config.py

FAUSheppy/open-web-leaderboard

defcf5671d71bfa170c3c9267488b18a926b02d7

[ "MIT" ]

2

2020-12-22T15:39:06.000Z

2021-05-22T23:53:21.000Z

config.py

FAUSheppy/open-web-leaderboard

defcf5671d71bfa170c3c9267488b18a926b02d7

[ "MIT" ]

null

DB_PATH="/home/sheppy-gaming/insurgency-skillbird/python/"

29.5

58

0.813559

8

59

5.875

1

0

0.016949

59

1

59

0.810345

0

0.813559

0

1

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false

0

1

0

null

0

1

0

1

null

0

4

2a59054beae94715a7a2f7f2d4ab876c9dc1bc2c

110

py

Python

lap/conventions.py

kenkeiras/lxc-application-packages

29f11b3185e5078e41cb433c647bd87d1537490f

[ "MIT" ]

1

2020-02-16T15:02:18.000Z

lap/conventions.py

kenkeiras/lxc-application-packages

29f11b3185e5078e41cb433c647bd87d1537490f

[ "MIT" ]

6

2016-11-15T22:07:19.000Z

2016-11-20T22:54:08.000Z

lap/conventions.py

kenkeiras/lxc-application-packages

29f11b3185e5078e41cb433c647bd87d1537490f

[ "MIT" ]

null

import os from os.path import expanduser LOCAL_PATH = os.path.join(expanduser("~"), '.local', 'share', 'lap')

27.5

68

0.7

16

110

4.75

0.5625

0.157895

0

0.109091

110

3

69

36.666667

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0

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0.666667

0

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null

0

1

0

null

0

1

0

1

0

4

aab5d452c4d3b43731838736b61623cb29d14b52

89

py

Python

rcfg/reditor/apps.py

tony-mikhailov/Kalachakra

7a46be7e75bad0500914e5a7c44662c6740ebaa2

[ "MIT" ]

null

rcfg/reditor/apps.py

tony-mikhailov/Kalachakra

7a46be7e75bad0500914e5a7c44662c6740ebaa2

[ "MIT" ]

3

2021-03-19T01:19:04.000Z

2021-06-04T22:44:35.000Z

rcfg/reditor/apps.py

tony-mikhailov/Kalachakra

7a46be7e75bad0500914e5a7c44662c6740ebaa2

[ "MIT" ]

null

from django.apps import AppConfig class ReditorConfig(AppConfig): name = 'reditor'

14.833333

33

0.752809

10

89

6.7

0.9

0

0.168539

89

5

34

17.8

0.905405

0

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0

1

0

false

0

0.333333

0

1

0

1

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null

0

1

0

null

0

1

0

1

0

4

aab7eee288de33d4bfea121f908e81a3e0d27e03

264

py

Python

Winston/lesson-06/range.py

gfoo003/programming-together

225e0a2255dd8da1f1ef32d2a88deea27c050f10

[ "MIT" ]

2

2021-03-20T02:07:19.000Z

2021-03-20T02:07:26.000Z

Winston/lesson-06/range.py

gfoo003/programming-together

225e0a2255dd8da1f1ef32d2a88deea27c050f10

[ "MIT" ]

null

Winston/lesson-06/range.py

gfoo003/programming-together

225e0a2255dd8da1f1ef32d2a88deea27c050f10

[ "MIT" ]

8

2021-02-20T03:10:50.000Z

2021-03-20T02:42:45.000Z

indexes = range(5) same_indexes = range(0, 5) print("indexes are:") for i in indexes: print(i) print("same_indexes are:") for i in same_indexes: print(i) special_indexes = range(5, 9) print("special_indexes are:") for i in special_indexes: print(i)

16.5

29

0.689394

44

264

4

0.272727

0.204545

0.221591

0.238636

0.272727

0

0.023041

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264

16

30

16.5

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1

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false

0

0.5

0

null

1

0

null

0

1

0

4

aacd18d855883862825fbe188a40121058d8b604

406

py

Python

S9/model/utils/callbacks.py

abishek-raju/EVA4B2

189f4062c85d91f43c1381087a9c89ff794e5428

[ "Apache-2.0" ]

null

S9/model/utils/callbacks.py

abishek-raju/EVA4B2

189f4062c85d91f43c1381087a9c89ff794e5428

[ "Apache-2.0" ]

null

S9/model/utils/callbacks.py

abishek-raju/EVA4B2

189f4062c85d91f43c1381087a9c89ff794e5428

[ "Apache-2.0" ]

null

from torch.optim.lr_scheduler import StepLR def lr_scheduler(optimizer, step_size, gamma): """Create LR scheduler. Args: optimizer: Model optimizer. step_size: Frequency for changing learning rate. gamma: Factor for changing learning rate. Returns: StepLR: Learning rate scheduler. """ return StepLR(optimizer, step_size=step_size, gamma=gamma)

23.882353

62

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406

5.666667

0.479167

0.117647

0.1875

0.169118

0

0.241379

406

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0

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null

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1

0

null

0

1

0

1

0

4

aadb48b79c4dd455f0de4cbf65ebf3eae241c297

180

py

Python

venv/lib/python3.8/site-packages/restructuredtext_lint/__init__.py

trkohler/biopython

e7b7d010c71b19de439aa25de736748de4d6ca32

[ "BSD-3-Clause" ]

142

2015-01-27T13:37:45.000Z

2022-01-29T06:57:23.000Z

venv/lib/python3.8/site-packages/restructuredtext_lint/__init__.py

trkohler/biopython

e7b7d010c71b19de439aa25de736748de4d6ca32

[ "BSD-3-Clause" ]

50

2015-03-04T18:36:08.000Z

2022-02-26T20:34:08.000Z

venv/lib/python3.8/site-packages/restructuredtext_lint/__init__.py

trkohler/biopython

e7b7d010c71b19de439aa25de736748de4d6ca32

[ "BSD-3-Clause" ]

28

2015-04-09T16:52:08.000Z

2020-11-22T20:37:14.000Z

# Load in our dependencies from __future__ import absolute_import from restructuredtext_lint.lint import lint, lint_file # Export lint functions lint = lint lint_file = lint_file

22.5

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180

5.384615

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0

0.138889

180

7

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null

1

0

1

0

null

0

1

0

4

2aa412e48e8a6ff1301dae4f3f1796fd768131e1

227

py

Python

pytorch_learning/02.py

Howardhuang98/leet_code

b9985cfc163ca4de92dfeacd8fc3a167d0731d0b

[ "MIT" ]

1

2021-12-16T14:47:45.000Z

pytorch_learning/02.py

Howardhuang98/leet_code

b9985cfc163ca4de92dfeacd8fc3a167d0731d0b

[ "MIT" ]

null

pytorch_learning/02.py

Howardhuang98/leet_code

b9985cfc163ca4de92dfeacd8fc3a167d0731d0b

[ "MIT" ]

null

#!/usr/bin/env python # -*- encoding: utf-8 -*- """ @File : 02.py @Contact : huanghoward@foxmail.com @Modify Time : 2021/9/23 10:34 ------------ """ import torch.version print(torch.cuda.is_available())

17.461538

36

0.563877

29

227

4.37931

0.965517

0

0.078212

0.211454

227

12

37

18.916667

0.631285

0.700441

0

1

0

true

0

0.5

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0.5

1

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null

0

null

0

1

0

1

0

1

0

4

2dc2649ba578b740eb86d3b4d95eb6685fe0d885

501

py

Python

test/test.py

SarithT/xapitrader

0018bc37d9756a10c328def90d042ef39857cfb5

[ "MIT" ]

null

test/test.py

SarithT/xapitrader

0018bc37d9756a10c328def90d042ef39857cfb5

[ "MIT" ]

null

test/test.py

SarithT/xapitrader

0018bc37d9756a10c328def90d042ef39857cfb5

[ "MIT" ]

null

import unittest # def discover_and_run(start_dir: str = '.', pattern: str = 'test_*.py'): # """Discover and run tests cases, returning the result.""" # tests = unittest.defaultTestLoader(start_dir, pattern=pattern) # # We'll use the standard text runner which prints to stdout # runner = unittest.TextTestRunner() # result = runner.run(tests) # Returns a TestResult # print(result.errors, result.failures) # And more useful properties # return result # discover_and_run()

41.75

73

0.702595

63

501

5.47619

0.619048

0.095652

0.121739

0

0.183633

501

12

74

41.75

0.843521

0.922156

0

1

0

true

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

4

2dca63f9de7d6399cab3fe9b430dfbe457f199b3

171

py

Python

tests/web_platform/css_flexbox_1/test_flexbox_align_items_flexend.py

fletchgraham/colosseum

77be4896ee52b8f5956a3d77b5f2ccd2c8608e8f

[ "BSD-3-Clause" ]

null

tests/web_platform/css_flexbox_1/test_flexbox_align_items_flexend.py

fletchgraham/colosseum

77be4896ee52b8f5956a3d77b5f2ccd2c8608e8f

[ "BSD-3-Clause" ]

null

tests/web_platform/css_flexbox_1/test_flexbox_align_items_flexend.py

fletchgraham/colosseum

77be4896ee52b8f5956a3d77b5f2ccd2c8608e8f

[ "BSD-3-Clause" ]

1

2020-01-16T01:56:41.000Z

from tests.utils import W3CTestCase class TestFlexbox_AlignItemsFlexend(W3CTestCase): vars().update(W3CTestCase.find_tests(__file__, 'flexbox_align-items-flexend'))

28.5

82

0.818713

19

171

7

0.842105

0

0.019108

0.081871

171

5

83

34.2

0.828025

0

0.158824

0

1

0

true

0

0.333333

0

0.666667

0

1

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null

0

1

0

null

0

1

0

1

0

4

2df1023bf51a171ab5b57afaec5813a339bce0d3

154

py

Python

dfdata/source/collect/futures/futures.py

Eric2827/DFdata

4db142232fc7127da3faae7c608772c72005cd25

[ "MIT" ]

null

dfdata/source/collect/futures/futures.py

Eric2827/DFdata

4db142232fc7127da3faae7c608772c72005cd25

[ "MIT" ]

null

dfdata/source/collect/futures/futures.py

Eric2827/DFdata

4db142232fc7127da3faae7c608772c72005cd25

[ "MIT" ]

null

import pandas as pd def get_futures_contract(): df = pd.DataFrame([[1 for i in range(4)] for j in range(6)], columns=list('ABCD')) return df

25.666667

86

0.649351

27

154

3.62963

0.814815

0.142857

0

0.024793

0.214286

154

6

87

25.666667

0.785124

0

0.025806

0

1

0.25

false

0

0.25

0

0.75

0

1

0

null

0

1

0

null

0

1

0

1

0

4

2df37b0510f436db1261c060169d35b212d03ce2

127

py

Python

courses/python/cursoemvideo/exercicios/ex005.py

bdpcampos/public

dda57c265718f3e1cc0d6bce73f149051f5647ef

[ "MIT" ]

3

2020-04-28T01:42:09.000Z

2020-05-03T12:05:23.000Z

courses/python/cursoemvideo/exercicios/ex005.py

bdpcampos/public

dda57c265718f3e1cc0d6bce73f149051f5647ef

[ "MIT" ]

null

courses/python/cursoemvideo/exercicios/ex005.py

bdpcampos/public

dda57c265718f3e1cc0d6bce73f149051f5647ef

[ "MIT" ]

null

n = int(input('Digite um número: ')) print('Seu número é o {}, seu sucessor é o {} e seu antecessor o {}.'.format(n,n+1,n-1))

31.75

88

0.614173

25

127

3.12

0.6

0.051282

0

0.019048

0.173228

127

3

89

42.333333

0.72381

0

0.622047

0

1

0

false

0

0.5

1

0

null

0

1

0

1

0

null

0

1

0

4

9330076d7b9419e855d9d5ee3329b3ea3619b5f3

85

py

Python

rosalind/ini3.py

sowmyamanojna/BT3051-Data-Structures-and-Algorithms

09c17e42c2e173a6ab10339f08fbc1505db8ea56

[ "MIT" ]

1

2021-05-13T13:10:42.000Z

rosalind/ini3.py

sowmyamanojna/BT3051-Data-Structures-and-Algorithms

09c17e42c2e173a6ab10339f08fbc1505db8ea56

[ "MIT" ]

null

rosalind/ini3.py

sowmyamanojna/BT3051-Data-Structures-and-Algorithms

09c17e42c2e173a6ab10339f08fbc1505db8ea56

[ "MIT" ]

null

s = raw_input() [a, b, c, d] = map(int, raw_input().split()) print s[a:b+1], s[c:d+1]

28.333333

44

0.552941

21

85

2.142857

0.571429

0.355556

0

0.027397

0.141176

85

3

45

28.333333

0.589041

0

null

0

null

0.333333

1

0

null

1

0

1

0

null

0

1

0

4

933f7a5214934f481124da6f21936a740c310aaa

22

py

Python

project/__init__.py

Apriorhythm/PythonOpenSourceProjectTemplate

b7221ada02ec3a667e1c5b0e749b4c303fc29143

[ "MIT" ]

1

2018-01-15T10:32:23.000Z

project/__init__.py

Apriorhythm/PythonOpenSourceProjectTemplate

b7221ada02ec3a667e1c5b0e749b4c303fc29143

[ "MIT" ]

null

project/__init__.py

Apriorhythm/PythonOpenSourceProjectTemplate

b7221ada02ec3a667e1c5b0e749b4c303fc29143

[ "MIT" ]

null

""" I do not know """

5.5

13

0.454545

4

22

2.5

1

0

0.272727

22

3

14

7.333333

0.625

0.590909

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

null

0

1

0

4

fa8d2e179ded5d0ff287eb7219e7091c40b25555

195

py

Python

libraries/Arduino-LUFA/install.py

nullstalgia/Arduino-Lufa

44be19a68bea1e049f32e531bacc9c78e20d53b6

[ "Unlicense", "MIT" ]

78

2015-06-19T06:52:40.000Z

2022-03-26T18:54:14.000Z

libraries/Arduino-LUFA/install.py

nullstalgia/Arduino-Lufa

44be19a68bea1e049f32e531bacc9c78e20d53b6

[ "Unlicense", "MIT" ]

21

2016-12-05T14:28:17.000Z

2022-02-26T03:32:33.000Z

libraries/Arduino-LUFA/install.py

nullstalgia/Arduino-Lufa

44be19a68bea1e049f32e531bacc9c78e20d53b6

[ "Unlicense", "MIT" ]

22

2015-08-11T08:53:31.000Z

2021-12-10T11:30:29.000Z

#!/usr/bin/env python3 from activate import install """ Script to install LUFA boards for Arduino. More info can be found in the activate.py script. """ if __name__ == '__main__': install()

19.5

49

0.717949

29

195

4.551724

0.862069

0

0.006211

0.174359

195

9

50

21.666667

0.813665

0.107692

0

0.109589

0

1

0

true

0

0.333333

0

0.333333

0

1

0

null

0

1

0

null

0

1

0

1

0

4

fab023ee92eb288e52061c7cf9add0ffdb87f42b

219

py

Python

src/ensae_teaching_cs/pandas_helper.py

sdpython/ensae_teaching_cs

ac978c4031afe6a5b846402a28628791e547a841

[ "MIT" ]

73

2015-05-12T13:12:11.000Z

2021-12-21T11:44:29.000Z

src/ensae_teaching_cs/pandas_helper.py

Pandinosaurus/ensae_teaching_cs

3bc80f29d93c30de812e34c314bc96e6a4f0d025

[ "MIT" ]

90

2015-06-23T11:11:35.000Z

2021-03-31T22:09:15.000Z

src/ensae_teaching_cs/pandas_helper.py

Pandinosaurus/ensae_teaching_cs

3bc80f29d93c30de812e34c314bc96e6a4f0d025

[ "MIT" ]

65

2015-01-13T08:23:55.000Z

2022-02-11T22:42:07.000Z

# -*- coding: utf-8 -*- """ @file @brief Collection of function to help with pandas """ from .td_2a.serialization import dfs2excel, df2list from .faq.faq_pandas import read_csv, df_to_clipboard, groupby_topn, df_equal

24.333333

77

0.753425

33

219

4.787879

0.818182

0

0.021053

0.13242

219

8

78

27.375

0.810526

0.356164

0

1

0

true

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

4

fac15805ad411f491085c716d5d6dcd3b2469969

1,169

py

Python

cognito/identity/exceptions.py

Rome84/AWS

32f5b6a83e37e62b0e33658bdab03ea493c905cb

[ "MIT" ]

null

cognito/identity/exceptions.py

Rome84/AWS

32f5b6a83e37e62b0e33658bdab03ea493c905cb

[ "MIT" ]

null

cognito/identity/exceptions.py

Rome84/AWS

32f5b6a83e37e62b0e33658bdab03ea493c905cb

[ "MIT" ]

null

# 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 MERCHANTABIL- # ITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT # SHALL THE AUTHOR 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 boto.exception import BotoServerError class LimitExceededException(BotoServerError): pass class ResourceConflictException(BotoServerError): pass class DeveloperUserAlreadyRegisteredException(BotoServerError): pass class TooManyRequestsException(BotoServerError): pass class InvalidParameterException(BotoServerError): pass class ResourceNotFoundException(BotoServerError): pass class InternalErrorException(BotoServerError): pass class NotAuthorizedException(BotoServerError): pass

25.977778

75

0.763901

131

1,169

6.816794

0.580153

0.170213

0.18813

0

0.192472

1,169

44

76

26.568182

0.945975

0.48503

0

0.470588

0

1

0

true

0.470588

0.058824

0

0.529412

0

null

0

1

0

null

0

1

0

1

0

4

facc79a1a37a78d576d1f836f1065401065c6c7d

194

py

Python

project/payments/payment_methods/payments_stripe/urls.py

steetstyle/Django-Ecommerce-API

89c2c973e560346a5be74019709dc9a9f8ab7b2a

[ "MIT" ]

null

project/payments/payment_methods/payments_stripe/urls.py

steetstyle/Django-Ecommerce-API

89c2c973e560346a5be74019709dc9a9f8ab7b2a

[ "MIT" ]

null

project/payments/payment_methods/payments_stripe/urls.py

steetstyle/Django-Ecommerce-API

89c2c973e560346a5be74019709dc9a9f8ab7b2a

[ "MIT" ]

null

from django.urls import path, include from .views import custom_webhook urlpatterns = [ path("", include("djstripe.urls", namespace="djstripe")), path("custom_webhook", custom_webhook) ]

24.25

60

0.737113

23

194

6.086957

0.521739

0.278571

0

0.128866

194

8

61

24.25

0.828402

0

0.180412

0

1

0

false

0

0.333333

0

0.333333

0

1

0

null

1

0

1

0

null

0

1

0

4

fae8ed0591ca237e03f8e6e9cf8d2caa9320ff62

155

py

Python

source/models/interpolation_bilenear.py

1pkg/neura

b5ac79d2141a556f9b488b6ae07cc89f8b0cbccd

[ "MIT" ]

null

source/models/interpolation_bilenear.py

1pkg/neura

b5ac79d2141a556f9b488b6ae07cc89f8b0cbccd

[ "MIT" ]

null

source/models/interpolation_bilenear.py

1pkg/neura

b5ac79d2141a556f9b488b6ae07cc89f8b0cbccd

[ "MIT" ]

null

from PIL import Image from .base_interpolation import BaseInterpolation class InterpolationBilenear(BaseInterpolation): _scale_type = Image.BILINEAR

22.142857

49

0.83871

16

155

7.9375

0.75

0

0.122581

155

7

50

22.142857

0.933824

0

1

0

false

0

0.5

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

4

faf1f6b335867f37bfd1e359ef4799129ac27fa1

578

py

Python

pycritty/__init__.py

T1erno/pycritty

bf785c5aa464358f5b692832562fd983bd407b0f

[ "MIT" ]

null

pycritty/__init__.py

T1erno/pycritty

bf785c5aa464358f5b692832562fd983bd407b0f

[ "MIT" ]

null

pycritty/__init__.py

T1erno/pycritty

bf785c5aa464358f5b692832562fd983bd407b0f

[ "MIT" ]

null

"""Automated tools for managing alacritty configurations""" __version__ = "0.4.0" class PycrittyError(Exception): pass # Export public API from pycritty.api.config import Config, set_config # noqa: F401, E402 from pycritty.api.install import install # noqa: F401, E402 from pycritty.api.load import load_config # noqa: F401, E402 from pycritty.api.save import save_config # noqa: F401, E402 from pycritty.api.rm import remove # noqa: F401, E402 from pycritty.api.ls import ( # noqa: F401, E402 list_themes, list_fonts, list_configs, print_list, )

26.272727

70

0.735294

82

578

5.04878

0.426829

0.173913

0.217391

0.193237

0.369565

0.23913

0

0.081761

0.17474

578

21

71

27.52381

0.786164

0.301038

0

0.012755

0

1

0

false

0.071429

0.428571

0

0.5

0.071429

0

null

0

1

0

null

0

1

0

4

87ae1ab35400b530721e199d22e07a34b5c638e0

624

py

Python

main.py

duanyuluo/PyCookbook

c70fa22779997dad58ffc056f428c434a879ecca

[ "Apache-2.0" ]

null

main.py

duanyuluo/PyCookbook

c70fa22779997dad58ffc056f428c434a879ecca

[ "Apache-2.0" ]

null

main.py

duanyuluo/PyCookbook

c70fa22779997dad58ffc056f428c434a879ecca

[ "Apache-2.0" ]

null

#encoding=utf-8 # Python Language Cookbook # You can learn the Python programming language through same sample code. # Importing a module when you want to review the sample code's result. from tools import * # section 1: variables # variables name-rule, scope, casting and multi-assign import variable # section 2: datatype # python have 14 datetypes that belongs to text/number/sequence/map/set/boolean/binary catalog. # text, boolean and number are simple datatype. # sequence, map and set are collection datatype. # binary is a raw datatype of computer memery and storage. import datatype import string

31.2

97

0.767628

92

624

5.206522

0.684783

0.041754

0

0.009728

0.176282

624

20

98

31.2

0.922179

0.852564

0

1

0

true

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

4

87d0bb2afdf4893b27dfc53271b0b7d49e93e73f

334

py

Python

retropath2_wrapper/__init__.py

brsynth/retropath2-wrapper

1959e09fc97be0220ef28b87384c26a8ade818da

[ "MIT" ]

4

2021-10-13T22:12:16.000Z

2021-12-25T13:00:53.000Z

retropath2_wrapper/__init__.py

brsynth/retropath2-wrapper

1959e09fc97be0220ef28b87384c26a8ade818da

[ "MIT" ]

6

2020-08-14T15:02:35.000Z

2022-03-04T13:05:21.000Z

retropath2_wrapper/__init__.py

brsynth/retropath2-wrapper

1959e09fc97be0220ef28b87384c26a8ade818da

[ "MIT" ]

null

""" Created on June 16 2020 @author: Joan Hérisson """ from retropath2_wrapper.RetroPath2 import retropath2 from retropath2_wrapper.Args import build_args_parser from retropath2_wrapper._version import __version__ from retropath2_wrapper.__main__ import parse_and_check_args __all__ = ["retropath2", "build_args_parser"]

25.692308

62

0.811377

42

334

5.880952

0.5

0.226721

0.340081

0

0.044674

0.128743

334

12

63

27.833333

0.804124

0.140719

0

0.096774

0

1

0

false

0

0.8

0

0.8

0

null

1

0

null

0

1

0

1

0

4

87e93e57ac6241f54843651fe76c7909977f9d3b

164

py

Python

satchmo/projects/simple/urls.py

funwhilelost/satchmo

589a5d797533ea15dfde9af7f36e304092d22a94

[ "BSD-3-Clause" ]

16

2015-03-06T14:42:27.000Z

2019-12-23T21:37:01.000Z

satchmo/projects/simple/urls.py

funwhilelost/satchmo

589a5d797533ea15dfde9af7f36e304092d22a94

[ "BSD-3-Clause" ]

null

satchmo/projects/simple/urls.py

funwhilelost/satchmo

589a5d797533ea15dfde9af7f36e304092d22a94

[ "BSD-3-Clause" ]

8

2015-01-28T16:02:37.000Z

2022-03-03T21:29:40.000Z

from django.conf.urls.defaults import * from satchmo_store.urls import urlpatterns urlpatterns += patterns('', (r'test/', include('simple.localsite.urls')) )

20.5

48

0.731707

20

164

5.95

0.75

0

0.121951

164

7

49

23.428571

0.826389

0

0.158537

0.128049

0

1

0

true

0

0.4

0

0.4

0

1

0

null

0

1

0

null

0

1

0

1

0

4

e2133507a4953071bc10d698fb6e7ea212138716

137

py

Python

tests/interface_test.py

h0uter/sensor_director

5751aa564bccd44d0027476caef514833e013b49

[ "MIT" ]

null

tests/interface_test.py

h0uter/sensor_director

5751aa564bccd44d0027476caef514833e013b49

[ "MIT" ]

null

tests/interface_test.py

h0uter/sensor_director

5751aa564bccd44d0027476caef514833e013b49

[ "MIT" ]

null

import sensor_director def test_interface(): # frame_a = point_b = (0, 0,0) rot = sensor_director.determine_look_at_quat()

19.571429

50

0.693431

20

137

4.35

0.8

0.321839

0

0.027523

0.20438

137

7

50

19.571429

0.770642

0.065693

0

1

0.25

false

0

0.25

0

0.5

0

1

0

null

1

0

1

0

null

0

1

0

4

e21631424148ef6d8c8e9000f2f8fa24705d9e52

1,480

py

Python

cli/psym/graphql/input/survey_question_response.py

danielrh135568/symphony-1

54c92a0f8775d1a837ab7c7bd6a08ccd906d28a4

[ "BSD-3-Clause" ]

null

cli/psym/graphql/input/survey_question_response.py

danielrh135568/symphony-1

54c92a0f8775d1a837ab7c7bd6a08ccd906d28a4

[ "BSD-3-Clause" ]

12

2022-02-14T04:20:30.000Z

2022-03-28T04:20:17.000Z

cli/psym/graphql/input/survey_question_response.py

danielrh135568/symphony-1

54c92a0f8775d1a837ab7c7bd6a08ccd906d28a4

[ "BSD-3-Clause" ]

1

2022-02-24T21:47:51.000Z

#!/usr/bin/env python3 # @generated AUTOGENERATED file. Do not Change! from dataclasses import dataclass, field as _field from functools import partial from ...config import custom_scalars, datetime from numbers import Number from typing import Any, AsyncGenerator, Dict, List, Generator, Optional from dataclasses_json import DataClassJsonMixin, config from gql_client.runtime.enum_utils import enum_field_metadata from ..enum.survey_question_type import SurveyQuestionType from ..input.file_input import FileInput from ..input.survey_cell_scan_data import SurveyCellScanData from ..input.survey_wi_fi_scan_data import SurveyWiFiScanData @dataclass(frozen=True) class SurveyQuestionResponse(DataClassJsonMixin): formIndex: int questionText: str questionIndex: int wifiData: List[SurveyWiFiScanData] cellData: List[SurveyCellScanData] imagesData: List[FileInput] formName: Optional[str] = None formDescription: Optional[str] = None questionFormat: Optional[SurveyQuestionType] = None boolData: Optional[bool] = None emailData: Optional[str] = None latitude: Optional[Number] = None longitude: Optional[Number] = None locationAccuracy: Optional[Number] = None altitude: Optional[Number] = None phoneData: Optional[str] = None textData: Optional[str] = None floatData: Optional[Number] = None intData: Optional[int] = None dateData: Optional[int] = None photoData: Optional[FileInput] = None

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3563ad358c5ed56a82b69eab00453103199f9076

821

py

Python

Post/migrations/0022_auto_20210302_1402.py

singh-sushil/minorproject

02fe8c1dce41109447d5f394bb37e10cb34d9316

[ "MIT" ]

2

2020-12-27T11:28:02.000Z

2021-01-04T07:52:38.000Z

Post/migrations/0022_auto_20210302_1402.py

singh-sushil/minorproject

02fe8c1dce41109447d5f394bb37e10cb34d9316

[ "MIT" ]

1

2020-12-26T13:36:12.000Z

Post/migrations/0022_auto_20210302_1402.py

singh-sushil/minorproject

02fe8c1dce41109447d5f394bb37e10cb34d9316

[ "MIT" ]

null

# Generated by Django 3.1.1 on 2021-03-02 08:17 from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('Post', '0021_auto_20210227_2216'), ] operations = [ migrations.RenameField( model_name='post', old_name='image1', new_name='backsideview', ), migrations.RenameField( model_name='post', old_name='image2', new_name='frontview', ), migrations.RenameField( model_name='post', old_name='image3', new_name='leftsideview', ), migrations.RenameField( model_name='post', old_name='image4', new_name='rightsideview', ), ]

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py

Python

tests/archive/__init__.py

ZabeMath/pywikibot

856a197c53efcb80b16475a8d203a4ecd79eee2f

[ "MIT" ]

326

2017-11-21T07:04:19.000Z

2022-03-26T01:25:44.000Z

tests/archive/__init__.py

ZabeMath/pywikibot

856a197c53efcb80b16475a8d203a4ecd79eee2f

[ "MIT" ]

17

2017-12-20T13:41:32.000Z

2022-02-16T16:42:41.000Z

tests/archive/__init__.py

ZabeMath/pywikibot

856a197c53efcb80b16475a8d203a4ecd79eee2f

[ "MIT" ]

147

2017-11-22T19:13:40.000Z

2022-03-29T04:47:07.000Z

"""THIS DIRECTORY IS TO HOLD TESTS FOR ARCHIVED SCRIPTS."""

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359254f7fa77b039c0be62b4312f3b416701e544

13,099

py

Python

tests/test_api.py

pik-software/apiqa-storage

197235d0012737f9964cd5bcf60d20b17cbd1104

[ "MIT" ]

null

tests/test_api.py

pik-software/apiqa-storage

197235d0012737f9964cd5bcf60d20b17cbd1104

[ "MIT" ]

2

2019-06-13T07:17:56.000Z

2020-08-05T12:56:55.000Z

tests/test_api.py

pik-software/apiqa-storage

197235d0012737f9964cd5bcf60d20b17cbd1104

[ "MIT" ]

6

2019-12-05T14:58:44.000Z

2021-03-07T08:51:14.000Z

import json import uuid from collections import OrderedDict from unittest.mock import patch import faker import pytest from django.contrib.contenttypes.models import ContentType from django.core.files.uploadedfile import SimpleUploadedFile from django.test.client import BOUNDARY, MULTIPART_CONTENT, encode_multipart from django.urls import reverse from django.utils.crypto import get_random_string from minio import S3Error from rest_framework import status from apiqa_storage import settings from apiqa_storage.files import file_info from apiqa_storage.models import Attachment from tests_storage.models import ModelWithAttachments from .factories import AttachmentFactory, UserFactory, create_attach_with_file @pytest.mark.django_db def test_post_file(storage, api_client): fake = faker.Faker('ru_RU') url = reverse('file_upload-list') file_size = fake.random_int(min=1, max=settings.MAX_FILE_SIZE) file_data = get_random_string(file_size).encode() attachment = SimpleUploadedFile( fake.file_name(category='image', extension='jpeg'), file_data, content_type='image/jpeg' ) post_data = { 'file': attachment, } with patch('apiqa_storage.serializers.storage', storage): res = api_client.post( url, data=encode_multipart(BOUNDARY, post_data), content_type=MULTIPART_CONTENT) assert res.status_code == status.HTTP_201_CREATED info = file_info(attachment) attachment = Attachment.objects.get(uid=res.data['uid']) assert attachment.user == api_client.user assert res.data == OrderedDict([ ('uid', str(attachment.uid)), ('created', attachment.created.isoformat()), ('name', info.name), ('size', info.size), ('content_type', info.content_type), ('tags', []), ('linked_from', attachment.linked_from), ]) @pytest.mark.django_db def test_post_file_with_custom_uid(storage, api_client): fake = faker.Faker('ru_RU') url = reverse('file_upload-list') file_data = get_random_string().encode() attachment = SimpleUploadedFile( fake.file_name(category='image', extension='jpeg'), file_data, content_type='image/jpeg' ) attachment_uid = uuid.uuid4() post_data = {'file': attachment} with patch('apiqa_storage.serializers.storage', storage): res = api_client.post( url + f'?uid={attachment_uid}', data=encode_multipart(BOUNDARY, post_data), content_type=MULTIPART_CONTENT) assert res.status_code == status.HTTP_201_CREATED info = file_info(attachment) attachment = Attachment.objects.get(uid=res.data['uid']) assert attachment.user == api_client.user assert res.data == OrderedDict([ ('uid', str(attachment_uid)), ('created', attachment.created.isoformat()), ('name', info.name), ('size', info.size), ('content_type', info.content_type), ('tags', []), ('linked_from', attachment.linked_from), ]) @pytest.mark.django_db def test_post_file_with_incorrect_uid(storage, api_client): fake = faker.Faker('ru_RU') url = reverse('file_upload-list') file_data = get_random_string().encode() attachment = SimpleUploadedFile( fake.file_name(category='image', extension='jpeg'), file_data, content_type='image/jpeg' ) attachment_uid = 'incorrect' post_data = {'file': attachment} with patch('apiqa_storage.serializers.storage', storage): res = api_client.post( url + f'?uid={attachment_uid}', data=encode_multipart(BOUNDARY, post_data), content_type=MULTIPART_CONTENT) assert res.status_code == status.HTTP_400_BAD_REQUEST @pytest.mark.django_db def test_post_file_with_duplicate_uid(storage, api_client): fake = faker.Faker('ru_RU') url = reverse('file_upload-list') file_data = get_random_string().encode() attachment = AttachmentFactory() attachment_file = SimpleUploadedFile( fake.file_name(category='image', extension='jpeg'), file_data, content_type='image/jpeg' ) post_data = {'file': attachment_file} with patch('apiqa_storage.serializers.storage', storage): res = api_client.post( url + f'?uid={attachment.uid}', data=encode_multipart(BOUNDARY, post_data), content_type=MULTIPART_CONTENT) assert res.status_code == status.HTTP_400_BAD_REQUEST assert res.data[0] == (f'Attachment with uid = {attachment.uid} ' f'already exists.') @pytest.mark.django_db def test_post_file_size_validation_error(storage, api_client): fake = faker.Faker('ru_RU') url = reverse('file_upload-list') file_data = get_random_string(settings.MAX_FILE_SIZE + 1).encode() attachment = SimpleUploadedFile( fake.file_name(category='image', extension='jpeg'), file_data, content_type='image/jpeg' ) post_data = {'file': attachment} with patch('apiqa_storage.serializers.storage', storage): res = api_client.post( url, data=encode_multipart(BOUNDARY, post_data), content_type=MULTIPART_CONTENT) assert res.status_code == status.HTTP_400_BAD_REQUEST assert res.data['file'][0] == (f'Max size of attach file:' f' {settings.MINIO_STORAGE_MAX_FILE_SIZE}') @pytest.mark.django_db def test_destroy_attachment(storage, api_client): attachment = create_attach_with_file(storage) url = reverse('file_upload-detail', args=(str(attachment.uid),)) with patch('apiqa_storage.serializers.storage', storage): res = api_client.delete(url) assert res.status_code == status.HTTP_204_NO_CONTENT with pytest.raises(S3Error): storage.file_get(attachment.path) @pytest.mark.django_db def test_destroy_related_attachment_validation_error(storage, api_client): user = UserFactory() attachment = AttachmentFactory( object_content_type=ContentType.objects.get_for_model(user), object_id=user.id ) url = reverse('file_upload-detail', args=(str(attachment.uid),)) res = api_client.delete(url) assert res.status_code == status.HTTP_400_BAD_REQUEST assert res.data[0] == 'Delete attachments with relations not allowed' @pytest.mark.django_db def test_post_model_with_attachment(storage, api_client): fake = faker.Faker('ru_RU') url = reverse('modelwithattachments-list') attachments = AttachmentFactory.create_batch( size=settings.MINIO_STORAGE_MAX_FILES_COUNT) post_data = { 'name': fake.name(), 'attachment_ids': [str(attachment.pk) for attachment in attachments] } res = api_client.post(url, data=json.dumps(post_data), content_type='application/json') assert res.status_code == status.HTTP_201_CREATED model_with_attachments = ModelWithAttachments.objects.get() assert res.data == OrderedDict([ ('uid', str(model_with_attachments.uid)), ('name', model_with_attachments.name), ('attachments', [OrderedDict([ ('uid', str(attachment.uid)), ('created', attachment.created.isoformat()), ('name', attachment.name), ('size', attachment.size), ('content_type', attachment.content_type), ('tags', []), ('linked_from', attachment.linked_from), ]) for attachment in model_with_attachments.attachments.all()]) ]) for attachment in attachments: attachment.refresh_from_db() assert attachment.object_id == model_with_attachments.pk assert (attachment.object_content_type == ContentType.objects .get_for_model(model_with_attachments)) @pytest.mark.django_db def test_post_model_with_exising_attachments(storage, api_client): fake = faker.Faker('ru_RU') file_count = settings.MINIO_STORAGE_MAX_FILES_COUNT url = reverse('modelwithattachments-list') attachments = AttachmentFactory.create_batch( size=file_count) post_data = { 'name': fake.name(), 'attachment_ids': [str(attachment.pk) for attachment in attachments] } res = api_client.post(url, data=json.dumps(post_data), content_type='application/json') assert res.status_code == status.HTTP_201_CREATED assert Attachment.objects.count() == file_count res = api_client.post(url, data=json.dumps(post_data), content_type='application/json') assert res.status_code == status.HTTP_201_CREATED for attach in res.data['attachments']: assert attach['name'] == Attachment.objects.filter( pk=attach['linked_from'], ).first().name assert Attachment.objects.count() == file_count * 2 attach = Attachment.objects.first() assert Attachment.objects.filter(path=attach.path).count() == 2 @pytest.mark.django_db def test_post_model_with_max_files_count_validation_error(storage, api_client): fake = faker.Faker('ru_RU') url = reverse('modelwithattachments-list') attachments = AttachmentFactory.create_batch( size=settings.MINIO_STORAGE_MAX_FILES_COUNT + 1) post_data = { 'name': fake.name(), 'attachment_ids': [str(attachment.pk) for attachment in attachments] } res = api_client.post(url, data=json.dumps(post_data), content_type='application/json') assert res.status_code == status.HTTP_400_BAD_REQUEST assert res.data['attachment_ids'][0] == ( f'Max files count: {settings.MINIO_STORAGE_MAX_FILES_COUNT}') @pytest.mark.django_db def test_post_file_with_tags(storage, api_client): fake = faker.Faker('ru_RU') url = reverse('file_upload-list') file_size = fake.random_int(min=1, max=settings.MAX_FILE_SIZE) file_data = get_random_string(file_size).encode() attachment = SimpleUploadedFile( fake.file_name(category='image', extension='jpeg'), file_data, content_type='image/jpeg' ) post_data = { 'file': attachment, 'tags': [fake.pystr( min_chars=1, max_chars=settings.TAGS_CHARACTER_LIMIT) for _ in range(fake.random_int( min=1, max=settings.TAGS_COUNT_MAX))] } with patch('apiqa_storage.serializers.storage', storage): res = api_client.post( url, data=encode_multipart(BOUNDARY, post_data), content_type=MULTIPART_CONTENT) assert res.status_code == status.HTTP_201_CREATED info = file_info(attachment) attachment = Attachment.objects.get(uid=res.data['uid']) assert attachment.user == api_client.user assert res.data == OrderedDict([ ('uid', str(attachment.uid)), ('created', attachment.created.isoformat()), ('name', info.name), ('size', info.size), ('content_type', info.content_type), ('tags', post_data['tags']), ('linked_from', attachment.linked_from), ]) @pytest.mark.django_db def test_post_file_with_tags_character_limit_validation_error( storage, api_client): fake = faker.Faker('ru_RU') url = reverse('file_upload-list') file_size = fake.random_int(min=1, max=settings.MAX_FILE_SIZE) file_data = get_random_string(file_size).encode() attachment = SimpleUploadedFile( fake.file_name(category='image', extension='jpeg'), file_data, content_type='image/jpeg' ) tags_with_character_limit_error = [ fake.pystr(min_chars=settings.TAGS_CHARACTER_LIMIT + 1, max_chars=settings.TAGS_CHARACTER_LIMIT + 20)] post_data = { 'file': attachment, 'tags': tags_with_character_limit_error } with patch('apiqa_storage.serializers.storage', storage): res = api_client.post( url, data=encode_multipart(BOUNDARY, post_data), content_type=MULTIPART_CONTENT) assert res.data['tags'][0][0] == ( f'Ensure this field has no more than ' f'{settings.TAGS_CHARACTER_LIMIT} characters.') @pytest.mark.django_db def test_post_file_with_tags_count_max_validation_error( storage, api_client): fake = faker.Faker('ru_RU') url = reverse('file_upload-list') file_size = fake.random_int(min=1, max=settings.MAX_FILE_SIZE) file_data = get_random_string(file_size).encode() attachment = SimpleUploadedFile( fake.file_name(category='image', extension='jpeg'), file_data, content_type='image/jpeg' ) tags_with_count_max_error = [fake.pystr() for _ in range(settings.TAGS_COUNT_MAX + 1)] post_data = { 'file': attachment, 'tags': tags_with_count_max_error } with patch('apiqa_storage.serializers.storage', storage): res = api_client.post( url, data=encode_multipart(BOUNDARY, post_data), content_type=MULTIPART_CONTENT) assert res.data['tags'][0] == ( f'Ensure this field has no more than {settings.TAGS_COUNT_MAX} ' f'elements.')

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359eb2cc0eff8f260579bfd0208e7c1b219c030e

28

py

Python

sample/__init__.py

sonalnikam/try

26ef8355d652ffd35f63564c3c7665ad0776a0c8

[ "CC0-1.0" ]

null

sample/__init__.py

sonalnikam/try

26ef8355d652ffd35f63564c3c7665ad0776a0c8

[ "CC0-1.0" ]

null

sample/__init__.py

sonalnikam/try

26ef8355d652ffd35f63564c3c7665ad0776a0c8

[ "CC0-1.0" ]

null

""" Package for sample. """

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1

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1

0

4

35b9eb3e088833b9ce44e1f62fa6545924433971

196

py

Python

transforms/__init__.py

Abhishek-Aditya-bs/Streaming-Spark-For-Machine-Learning

76f9c97e66d6171bc83d1183fadc30bd492422a7

[ "MIT" ]

1

2021-12-10T13:14:53.000Z

transforms/__init__.py

iVishalr/SSML-spark-streaming-for-machine-learning

ba95a7d2d6bb15bacfbbf5b3c95317310b36d54f

[ "MIT" ]

null

transforms/__init__.py

iVishalr/SSML-spark-streaming-for-machine-learning

ba95a7d2d6bb15bacfbbf5b3c95317310b36d54f

[ "MIT" ]

null

from .normalize import Normalize from .transforms import Transforms from .random_flips import RandomHorizontalFlip,RandomVerticalFlip from .resize import Resize from .color_shift import ColorShift

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35d4c318c39c8ae8035817cc2f9a7c72f926c120

139,019

py

Python

ckanext-hdx_theme/ckanext/hdx_theme/tests/mock_helper.py

OCHA-DAP/hdx-ckan

202e0c44adc4ea8d0b90141e69365b65cce68672

[ "Apache-2.0" ]

58

2015-01-11T09:05:15.000Z

2022-03-17T23:44:07.000Z

ckanext-hdx_theme/ckanext/hdx_theme/tests/mock_helper.py

OCHA-DAP/hdx-ckan

202e0c44adc4ea8d0b90141e69365b65cce68672

[ "Apache-2.0" ]

1,467

2015-01-01T16:47:44.000Z

2022-02-28T16:51:20.000Z

ckanext-hdx_theme/ckanext/hdx_theme/tests/mock_helper.py

OCHA-DAP/hdx-ckan

202e0c44adc4ea8d0b90141e69365b65cce68672

[ "Apache-2.0" ]

17

2015-05-06T14:04:21.000Z

2021-11-11T19:58:16.000Z

import ckan.model as model def populate_mock_as_c(mock_c, username): mock_c.user = username mock_c.userobj = model.User.by_name(username) def mock_faq_page_content(id): return {'topics': {'faq-Sensitive_Data': u'Sensitive Data', 'faq-Getting_Started': u'Getting Started', 'faq-Sharing_and_Using_Data': u'Sharing and Using Data', 'faq-Data_Licenses': u'Data Licenses', 'faq-Resources_for_Developers': u'Resources for Developers', 'faq-Geodata': u'Geodata', 'faq-Organisations': u'Organisations', 'faq-Contact': u'Contact', 'faq-HXL_and_HDX_Tools': u'HXL and HDX Tools', 'faq-Metadata_and_Data_Quality': u'Metadata and Data Quality', 'faq-Search': u'Search'}, 'faq_data': [{'id': 'faq-Getting_Started', 'questions': [{'q': u'How does HDX define humanitarian data?', 'a': u'<p>We define humanitarian data as:</p>\n<ol>\n<li>data about the context in which a humanitarian crisis is occurring (e.g., baseline/development data, damage assessments, geospatial data)</li>\n<li>data about the people affected by the crisis and their needs</li>\n<li>data about the response by organisations and people seeking to help those who need assistance.</li>\n</ol>\n', 'id': u'How_does_HDX_define_humanitarian_data_'}, {'q': u'Is HDX open source?', 'a': u'<p>Yes. HDX uses an open-source software called <a href="http://ckan.org/" target="_blank" rel="noopener noreferrer">CKAN</a> for our technical back-end. You can find all of our code on <a href="https://github.com/OCHA-DAP" target="_blank" rel="noopener noreferrer">GitHub</a>.</p>\n', 'id': u'Is_HDX_open_source_'}, {'q': u'What browsers are best to use for HDX?', 'a': u'<p>We build and test HDX using the latest versions of Chrome and Firefox. We also test on Microsoft Edge, but do not formally support it.</p>\n', 'id': u'What_browsers_are_best_to_use_for_HDX_'}, {'q': u'How do I register an account with HDX?', 'a': u'<p>You can register by clicking on <a href="https://data.humdata.org/user/register" target="_blank" rel="noopener noreferrer">‘Sign Up’</a>.</p>\n', 'id': u'How_do_I_register_an_account_with_HDX_'}, { 'q': u'What if I forget my username and password?', 'a': u'<p>Use our <a href="https://data.humdata.org/user/reset" target="_blank" rel="noopener noreferrer">password recovery form</a> to reset your account details. Enter your username or e-mail and we will send you an e-mail with a link to create a new password.</p>\n', 'id': u'What_if_I_forget_my_username_and_password_'}, { 'q': u'What are the benefits of being a registered user?', 'a': u'<p>Anyone can view and download the data from the site, but registered users can access more features. After signing up you can:</p>\n<ol>\n<li>Contact data contributors to ask for more information about their data.</li>\n<li>Request access to the underlying data for metadata only entries (our HDX Connect feature).</li>\n<li>Join organisations to share data or to access private data, depending on your role within the organisation, e.g. an admin, editor, or member of the organisation (see more below).</li>\n<li>Request to create a new organisation and if approved, share data publicly or privately.</li>\n<li>Add data visualizations as showcase items alongside your organisations datasets.</li>\n<li>Follow the latest changes to data.</li>\n</ol>\n', 'id': u'What_are_the_benefits_of_being_a_registered_user_'}, { 'q': u'What does it mean to ‘follow’ data?', 'a': u'<p>HDX allows registered users to follow the data they are interested in. Updates to the datasets that you follow will appear as a running list in your user dashboard(accessible from your user name in the top right of every page when you are logged in). You can follow data, organisations, locations, topics and crises.</p>\n', 'id': u'What_does_it_mean_to___8216_follow__8217__data_'}, { 'q': u'How do I request access to a dataset where I can only see metadata?', 'a': u'<p>You’ll find a ‘Request Access’ button for datasets where only metadata is provided. The HDX Connect feature makes it possible to discover what data is available or what data collection initiatives are underway. Only registered users have the ability to contact the organisation through the request access module. The administrator for the contributing organisation can decide whether to accept or deny the request. Once the connection is made, HDX is not involved in the decision to share the data. Learn more about HDX Connect <a class="link faq-google-embed-marker" id="faq-google-embed-link-hdx-connect">here</a>.</p>\n<div class="modal presentation-modal" id="faq-google-embed-hdx-connect" tabindex="-1" role="dialog" aria-hidden="true">\n<div class="modal-dialog" role="document"><button type="button" class="close" data-dismiss="modal" aria-label="Close"><span aria-hidden="true">\xd7</span></button></p>\n<div class="modal-content"><iframe load-src="https://docs.google.com/presentation/d/e/2PACX-1vQY05J7cbuRbbFyFGQ43dhPr6TfVjk0oXfdzqREIyFmkMAfZxjjiWofjhuYYieRvfHUBdRwQWqBpWov/embed?start=false&loop=false&delayms=3000" frameborder="0" width="900" height="560" allowfullscreen="true" mozallowfullscreen="true" webkitallowfullscreen="true" src="https://docs.google.com/presentation/d/e/2PACX-1vQY05J7cbuRbbFyFGQ43dhPr6TfVjk0oXfdzqREIyFmkMAfZxjjiWofjhuYYieRvfHUBdRwQWqBpWov/embed?start=false&loop=false&delayms=3000"></iframe></div>\n</div>\n</div>\n', 'id': u'How_do_I_request_access_to_a_dataset_where_I_can_only_see_metadata_'}, {'q': u'How do I contact a data contributor?', 'a': u'<p>You’ll find a ‘contact the contributor’ link below the title of the data on all the dataset pages. Please find more details <a href="https://centre.humdata.org/new-features-contact-the-contributor-and-group-message/" target="_blank" rel="noopener noreferrer">here</a>.</p>\n', 'id': u'How_do_I_contact_a_data_contributor_'}], 'title': u'Getting Started'}, {'id': 'faq-Organisations', 'questions': [ {'q': u'What is an organisation?', 'a': u'<p>Organisations in HDX can be legal entities, such as WFP, or informal groups, such as the Shelter Cluster or Information Management Working Group for a specific country. Data can only be shared on HDX through an organisation. The HDX team verifies all organisations to ensure they are trusted and have relevant data to share with the HDX user community.</p>\n', 'id': u'What_is_an_organisation_'}, {'q': u'Where can I see how popular an organisation’s datasets are?', 'a': u'<p>On an organisation’s page, click on the ‘Stats’ tab to see how many visitors an organisation has received and which datasets are most popular in terms of downloads. Here’s an <a href="https://data.humdata.org/organization/stats/un-operational-satellite-appplications-programme-unosat" target="_blank" rel="noopener noreferrer">example</a>. The number of unique visitors is approximate and is based on the browser someone uses when visiting HDX. A user visiting from different browsers or from different devices will be counted separately.</p>\n<p>You can also see a timeline of how often an individual dataset has been downloaded on each dataset page. The download timeline is located on the left side of a dataset page, just beside the dataset description. Downloads for a dataset are counted as the total number of downloads of any resource in a dataset, with repeated downloads of the same resource by the same user being counted a maximum of once per day.</p>\n<p>There is a delay, usually less than one day, between when a user views a page or downloads a resource on HDX and when the activity is visible in these graphs and figures.</p>\n', 'id': u'Where_can_I_see_how_popular_an_organisation__8217_s_datasets_are_'}, {'q': u'How do I create an organisation?', 'a': u'<p>You can request an organisation through the ‘Add Data’ button. We ask you to submit the following information: an organisation name, description and link to an organisation-related website (optional). We review this information and then either accept the request or ask for more information, such as a sample dataset. Approved organisations will remain inactive and not displayed under ‘Organisations’ page until at least one dataset has been shared through HDX.</p>\n', 'id': u'How_do_I_create_an_organisation_'}, {'q': u'How do I request organisation membership?', 'a': u'<p>Registered users have an option to join an organisation during signup. You can also request membership through the organisation’s page. Please keep in mind that you need to work for the organisation in order to click on the ‘Request Membership’ button and a request will be sent to the organisation’s administrator(s). The requestor can not specify the role (i.e., admin, editor or member). Instead, the person receiving the request assigns the role. If you do not see this option displayed on organisation page, the organisation is a closed group and is not accepting new members.</p>\n', 'id': u'How_do_I_request_organisation_membership_'}, {'q': u'How does organisation membership work?', 'a': u'<p>Organisation membership includes three roles:</p>\n<ul>\n<li>Administrators can add, edit and delete datasets belonging to the organisation and accept or refuse new member requests.</li>\n<li>Editors can add, edit and delete datasets belonging to the organisation but cannot manage membership.</li>\n<li>Members can view the organisation’s private datasets, but cannot add new datasets or manage membership.</li>\n</ul>\n<p>The user who requests the creation of an organisation is assigned an administrator role. That person can invite other HDX users into their organisation and assign them one of the three roles above, or registered users on HDX can request membership from the organisation’s administrator(s).</p>\n', 'id': u'How_does_organisation_membership_work_'}, {'q': u'I’m an organisation admin. How do I add/remove members?', 'a': u'<p>Organisation admins can invite new members, remove existing members or change their roles from the ‘Members’ tab on organisation page.</p>\n<p>Registered users can also initiate a request to join your organisation during the signup process or later on from your organisation page(if you want to disable this option, read the question below<a href="https://data.humdata.org/faq#auto-faq-Organisations-I_am_an_organisation_admin__I_don_t_want_anyone_to_request_membership_and_want_to_manually_add_remove_members_-q">‘I am an organisation admin. I don’t want anyone to request membership and want to manually add/remove members.’</a>).</p>\n<p>Membership requests are sent to your email and also added as a notification on HDX. If you can confirm that the user works for your organisation (ie. by using a company directory) or is in your trusted network, then you may approve the request. If you cannot verify who the user is, you should decline the request. Please do not approve membership requests for people outside your organisation or working group. For full details on managing members, please read <a href="https://humanitarian.atlassian.net/wiki/spaces/HDXKB/pages/1254490113/For+HDX+org+admins+How+to+Manage+Organizational+Membership" target="_blank" rel="noopener noreferrer">this document</a>. Please be aware that anyone added to your organisation on HDX can view the organisation’s private datasets.</p>\n', 'id': u'I__8217_m_an_organisation_admin__How_do_I_add_remove_members_'}, { 'q': u'I am an organisation admin. I don’t want anyone to request membership and want to manually add/remove members.', 'a': u'<p>Organisation admins have the option to make the organisation an open or closed group. By default, all organisations are an open group to allow new users to request membership. If you don’t want to allow any member to join your organisation, you can turn off the ‘Allow members’ checkbox under ‘Edit organisation page’. This will make your organization a closed group with existing members. No new member will be able to send a request to join your organization on HDX. The admin(s) of your organization can still manually invite new members, remove existing members or change their roles from the ‘Members’ tab.</p>\n', 'id': u'I_am_an_organisation_admin__I_don__8217_t_want_anyone_to_request_membership_and_want_to_manually_add_remove_members_'}, {'q': u'Can I be part of more than one organisation?', 'a': u'<p>Yes. Registered users can be part of several organisations.</p>\n', 'id': u'Can_I_be_part_of_more_than_one_organisation_'}, {'q': u'I don’t see my organisation. What should I do?', 'a': u'<p>If your organisation is not listed, you can request to create one or you may want to join an existing organisation via your <a href="https://data.humdata.org/dashboard/" target="_blank" rel="noopener noreferrer">dashboard</a>. For instance, there may be a WFP organisation that was created by its staff at headquarters in Rome. You may prefer to join that one rather than creating a separate organisation for a specific location, e.g., WFP Liberia. You can see the full list of organisations by clicking <a href="https://data.humdata.org/organization">Organisations</a> in the main navigation.</p>\n<p>If you have previously created an organisation and no longer see it on the site, this is because you have not yet shared a public dataset. Once you share a dataset, your organisation will become active and visible on the site. For details on how to upload a dataset, see <a href="https://data.humdata.org/faq#auto-faq-Sharing_and_Using_Data-How_do_I_add_a_dataset_-a" target="_blank" rel="noopener noreferrer">“How do I add a dataset?”</a>.</p>\n', 'id': u'I_don__8217_t_see_my_organisation__What_should_I_do_'}, {'q': u'Can an organisation have more than one administrator?', 'a': u'<p>Yes. Each administrator is able to manage datasets and membership. If a user requests membership, the request will be sent to all organisation administrators. The decision to accept or deny a membership request will be taken by whichever administrator acts first. The other administrators are not alerted to this action. We are planning to make this process more clear in future versions of the platform, so please bear with us!</p>\n', 'id': u'Can_an_organisation_have_more_than_one_administrator_'}, {'q': u'How do I create a branded organisation page on HDX?', 'a': u'<p>HDX offers custom organisation pages to all organisations on the site. The page includes the organisation’s logo and colour palette, topline figures, space for a data visualization and the list of datasets. If you would like a custom page, send a request to <a href="mailto:hdx@un.org">hdx@un.org</a>.</p>\n', 'id': u'How_do_I_create_a_branded_organisation_page_on_HDX_'}, {'q': u'How do I use the Group Message feature?', 'a': u'<p>‘Group message’ lets members of an organisation send messages to all other members of their organisation. Please find more details <a href="https://centre.humdata.org/new-features-contact-the-contributor-and-group-message/" target="_blank" rel="noopener noreferrer">here</a>.</p>\n', 'id': u'How_do_I_use_the_Group_Message_feature_'}, {'q': u'I changed my job – what happens to my account?', 'a': u'<p>You can keep your account. On the organisation page that you’re a part of, click the link to ‘Leave this organisation’. If you want to change the e-mail address associated with your account, click on your username on the upper-right corner of any HDX page and then select ‘User Settings’. From there, you can update your profile.</p>\n', 'id': u'I_changed_my_job___8211__what_happens_to_my_account_'}], 'title': u'Organisations'}, {'id': 'faq-Sharing_and_Using_Data', 'questions': [{'q': u'How do I share data on HDX?', 'a': u'<p>Data on HDX is shared through organisations. You need to be a member of an organisation (with appropriate privileges) before you can contribute data. If you have data to share, you can either request to create a new organisation or ask to join an existing one. (See the <a href="https://data.humdata.org/faq#body-faq-Organisations">Organisations section</a> above.)</p>\n<p>There are three ways to share data on HDX:</p>\n<p>Public – Data shared publicly is accessible to all users of the HDX platform, whether or not they are registered. All public data must be shared under an appropriate license. Select the ‘public’ setting in the metadata field when uploading data.</p>\n<p>Private – Organisations can share data privately with their members. The administrator of each organisation controls who can become a member. The default visibility is set to ‘private’ when uploading new data. Once shared, private datasets are only listed on your organisation page (make sure you are logged in to see them). They will not be included in search results or the <a href="https://data.humdata.org/dataset">data list</a> page.To make data accessible to HDX users, the contributing organisation needs to change the visibility to public.</p>\n<p>By Request – Organisations can share the metadata of a dataset and grant access to the underlying data when requested by a registered user. See how to share and request metadata only datasets through <a id="faq-google-embed-link-hdx-connect-2" class="link faq-google-embed-marker"></a>these walkthrough slides.</p>\n<p>Learn more about how HDX handles <a href="https://data.humdata.org/faq#body-faq-Sensitive_Data">sensitive data below</a>.</p>\n', 'id': u'How_do_I_share_data_on_HDX_'}, { 'q': u'What is the difference between a dataset and a resource?', 'a': u'<p>A dataset is a collection of related data resources. A resource is an individual file within a dataset. When sharing data, you first create a dataset and then you can add one or more resources to it. A resource can either be a file uploaded to HDX (such as a CSV or XLS file) or a link to another website with a downloadable file. A resource, such as a readme file, could also contain documentation that helps users to understand the dataset.</p>\n', 'id': u'What_is_the_difference_between_a_dataset_and_a_resource_'}, {'q': u'How do I add a dataset?', 'a': u'<p>Click on the ‘Add Data’ button from any page on HDX. You will be required to login and associate yourself with an organisation. <a id="faq-google-embed-link-1" class="link faq-google-embed-marker"></a>These slides provide a walkthrough of how to add a dataset. General information about all the metadata options in HDX is available in our <a href="https://centre.humdata.org/providing-metadata-for-your-datasets-on-hdx/" target="_blank" rel="noopener noreferrer">Guide to Metadata</a>.</p>\n', 'id': u'How_do_I_add_a_dataset_'}, {'q': u'Can I just share metadata?', 'a': u'<p>Let others know your data is available by publishing your metadata without uploading any file(s) via HDX Connect. Once users request access, you decide what to share.</p>\n<p>This is a good option if:</p>\n<ul>\n<li>You are in the process of collecting data but you are not finished.</li>\n<li>Your data contains personally identifiable information.</li>\n<li>You need to restrict access to your data.</li>\n</ul>\n<p>Learn more about HDX Connect through <a id="faq-google-embed-link-hdx-connect-3" class="link faq-google-embed-marker"></a>these walkthrough slides. <a href="https://centre.humdata.org/a-new-call-to-action-sharing-the-existence-of-data/" target="_blank" rel="noopener noreferrer">Read this blog</a> to understand the research and rationale behind HDX Connect.</p>\n', 'id': u'Can_I_just_share_metadata_'}, { 'q': u'How can I add links and formatting to my dataset page?', 'a': u'<p>There are 4 metadata fields that accept <a href="https://daringfireball.net/projects/markdown/syntax" target="_blank" rel="noopener noreferrer">markdown</a> which provides some simple formatting commands.</p>\n<p>The “description”, “methodology:other”, and “caveats/comments” fields, as well as the description field for each resource attached to the dataset, all accept markdown formatting. The most useful markdown commands are outlined here:</p>\n<p>Links can be entered like this:</p>\n<pre>[the linked text](https://data.humdata.org)</pre>\n<p>and will be rendered like this: <a href="https://data.humdata.org/">the linked text</a><br />\n<i>Italics</i> can be indicated by surrounding text with single asterisks, like this:</p>\n<pre>*A bit of italics text*</pre>\n<p><b>Bold</b> can be indicated by surrounding text with double asterisks, like this:</p>\n<pre>**A bit of bold text**</pre>\n<p>Bulleted lists must start with and be followed by a blank line. Each item in the list starts with an asterisk and a space:</p>\n<p>* item 1<br />\n* item 2<br />\n* etc.</p>\n<p>Numbered lists must also start with and be followed by a blank line. Each item starts with the number 1, a period, and a space:</p>\n<p>1. First item<br />\n1. Second item. Note that the lines always start with a one followed by a period and space.<br />\n1. 3rd item<br />\n1. etc.</p>\n', 'id': u'How_can_I_add_links_and_formatting_to_my_dataset_page_'}, {'q': u'How do I edit a dataset?', 'a': u'<p>You can only edit a dataset if you are an administrator or editor of your organisation. If you have the appropriate role, on the dataset page you will find an ‘Edit’ button just below the dataset title on the right. This will allow you to edit the dataset metadata and the resources. <a id="faq-google-embed-link-2" class="link faq-google-embed-marker"></a>These slides provide a walk-through of how to edit a dataset.</p>\n', 'id': u'How_do_I_edit_a_dataset_'}, { 'q': u'How can I add graphs and key figures to my dataset?', 'a': u'<p>If your data uses the <a href="http://hxlstandard.org/" target="_blank" rel="noopener noreferrer">HXL standard</a>, then HDX can automatically create customizable graphs and key figures to help you highlight the most important aspects of your dataset. We call these ‘Quick Charts’. For a Quick Chart to be generated, your dataset needs to be public and contain a CSV or XLSX resource with HXL tags. HXL is easy! Check out the <a href="http://hxlstandard.org/" target="_blank" rel="noopener noreferrer">30-second tutorial</a>.</p>\n<p>The resource can be stored on HDX or as a remote resource at another URL. Quick Charts will be generated from the first resource with HXL tags in the list of a dataset’s resources. The system will try to generate up to three charts based on the HXL tags, and these can be changed to best tell the story in your data. You can edit each Quick Chart’s title, axis labels, and description. Don’t forget to save the changes so they become the default view that users see when viewing your dataset. Here’s a good <a href="https://data.humdata.org/dataset/madagascar-cyclone-enawo-needs-assessment-data-5-april" target="_blank" rel="noopener noreferrer">example</a> to get you started.</p>\n<p>Learn more about HXL and HDX Tools in the section below.</p>\n', 'id': u'How_can_I_add_graphs_and_key_figures_to_my_dataset_'}, { 'q': u'How can I add data visualizations to my dataset?', 'a': u'<p>Organization admins and editors can add data visualizations to dataset pages to let users explore your data. The data visuals can be made using Tableau, Power BI or whatever software you prefer. The visuals will appear in the “Interactive Data” section at the top of the page.</p>\n<p>Learn how to do this by taking a quick look at <a class="link faq-google-embed-marker" id="faq-google-embed-link-3">these slides</a>.</p>\n<div class="modal presentation-modal" id="faq-google-embed-3" tabindex="-1" role="dialog">\n<div class="modal-dialog" role="document"><button type="button" class="close" data-dismiss="modal" aria-label="Close"><span aria-hidden="true">\xd7</span></button></p>\n<div class="modal-content"><iframe load-src="https://docs.google.com/presentation/d/e/2PACX-1vS1A1i-fg5PucF0hIRWDc_4_IUC_TXomWho8POCefSYuNHl9wN1SvS3_EM4jOsiWY4XvzZZDzquisgk/embed?start=false&loop=false&delayms=3000" frameborder="0" width="900" height="560" allowfullscreen="true" mozallowfullscreen="true" webkitallowfullscreen="true"></iframe></div>\n</div>\n</div>\n', 'id': u'How_can_I_add_data_visualizations_to_my_dataset_'}, { 'q': u'How can I check errors in my HXL-tagged spreadsheet?', 'a': u'<p>Data Check automatically detects and highlights common humanitarian data errors including validation against <a href="https://public.tableau.com/profile/ocha.field.information.services#!/vizhome/COD-Status_1/DetailedEvaluation" target="_blank" rel="noopener noreferrer">CODs</a> and other vocabularies from your <a href="https://tools.humdata.org/examples/hxl/" target="_blank" rel="noopener noreferrer">HXL-tagged spreadsheet</a>. You can access Data Check from:</p>\n<ol>\n<li>HDX via dataset pages (The “Validate with Data Check” option will appear under “More” button under HXL-tagged resources)</li>\n<li><a href="https://tools.humdata.org/wizard/#datacheck" target="_blank" rel="noopener noreferrer">HDX Tools</a>, for datasets that exist outside of HDX. For this option, you should not use Data Check to process personal or otherwise <a href="https://data.humdata.org/faq#body-faq-Sensitive_Data" target="_blank" rel="noopener noreferrer">sensitive data</a>.</li>\n</ol>\n<p>Data uploaded to HDX Tools is not retained within the HDX infrastructure, while data downloaded by HDX Tools from public URLs is cached only as long as necessary for processing.</p>\n<p>You can access both versions of Data Check without being a registered user of HDX. For instructions on how to use Data Check, review <a id="faq-google-embed-link-data-check" class="link faq-google-embed-marker"></a>these walkthrough slides.</p>\n<p>Data Check uses a generic schema that detects many kinds of common errors like possible spelling mistakes or atypical numeric values, but in some cases, an organisation will want to validate against its own more-specific rules. In that case, you can write your own, custom HXL schema and validate using the <a href="https://proxy.hxlstandard.org/" target="_blank" rel="noopener noreferrer">HXL Proxy</a> (Data Check’s backend engine) directly. Information is available on these pages in the HXL Proxy wiki: <a href="https://github.com/HXLStandard/hxl-proxy/wiki/HXL-schemas" target="_blank" rel="noopener noreferrer">HXL schemas</a>, <a href="https://github.com/HXLStandard/hxl-proxy/wiki/Validation-page" target="_blank" rel="noopener noreferrer">Validation page</a>, and <a href="https://github.com/HXLStandard/hxl-proxy/wiki/Validation-service" target="_blank" rel="noopener noreferrer">Validation service</a>.</p>\n', 'id': u'How_can_I_check_errors_in_my_HXL_tagged_spreadsheet_'}, { 'q': u'What are the recommended data formats?', 'a': u'<p>We define data as information that common software can read and analyse. We encourage contributions in any common data format. HDX has built-in preview support for tabular data in CSV and Microsoft Excel (xls only) formats, and for geographic data in zipped shapefile, kml and geojson formats. If multiple formats are available, each can be added as a resource to the dataset, or if you only wish to add one format, then for tabular data, csv is preferable and for geographic data, zipped shapefile is preferred.</p>\n<p>A PDF file is not data. If you have a data visualization in PDF format, you can add it as a showcase item on the dataset page. If you wish to share documents, graphics, or other types of humanitarian information that are not related to the data you are sharing, please visit our companion sites <a href="http://reliefweb.int/" target="_blank" rel="noopener noreferrer">ReliefWeb</a> and <a href="http://www.humanitarianresponse.info/" target="_blank" rel="noopener noreferrer">HumanitarianResponse</a>. A resource, such as a readme file, could also contain documentation that helps users to understand the dataset.</p>\n', 'id': u'What_are_the_recommended_data_formats_'}, { 'q': u'What are the best practices for managing resources in a dataset?', 'a': u'<p>Resources can be either different formats of the same data (such as XLSX and CSV) or different releases of the same data (such as March, April, and May needs assessments). Always put the resource with the most-recent or most-important information first, because the HDX system will by default use the first resource to create visualisations such as Quick Charts or geographic preview (this default can be overridden in the dataset edit page). </p>\n<p>If you have data that is substantially different, like a different type of assessment or data about a different province, we recommend creating a separate dataset.</p>\n', 'id': u'What_are_the_best_practices_for_managing_resources_in_a_dataset_'}, { 'q': u'What are the recommended best practices for naming datasets and resources?', 'a': u'<p>For datasets: the keywords in your dataset title are matched to the search terms users enter when looking for data in HDX. Avoid using abbreviations in the title that users may not be familiar with. Also avoid using words such as current, latest or previous when referring to the time period (e.g., latest 3W), as these terms become misleading as the dataset ages. The following is a good example of a dataset title: ‘Who is Doing What Where in Afghanistan in Dec 2016’.</p>\n<p>For resources: by default, the resource name is the name of the uploaded file. However, you can change this if needed to make it more clear to users. </p>\n<p>For zipped shapefiles: we recommend the filename be name_of_the_file.shp.zip. However, the system does not require this construction.</p>\n', 'id': u'What_are_the_recommended_best_practices_for_naming_datasets_and_resources_'}, { 'q': u'Is there a limit on file size for the data that I upload?', 'a': u'<p>If your resource is simply a link to a file hosted elsewhere, there is no size limit. If you are uploading a file onto HDX, the file size is limited to 300MB. If you have larger files that you want to share, e-mail us at <a href="mailto:hdx@un.org">hdx@un.org</a>.</p>\n', 'id': u'Is_there_a_limit_on_file_size_for_the_data_that_I_upload_'}, {'q': u'Can I share data hosted elsewhere?', 'a': u'<p>Yes. HDX can host the data for you, but it works equally well with a link to data hosted somewhere else on the web. For example, if your organisation already has a system or API that produces data for download, you can simply include a link to that data as a resource in your dataset, and the version on HDX will automatically stay up to date.</p>\n', 'id': u'Can_I_share_data_hosted_elsewhere_'}, { 'q': u'Can I drag&drop files from my computer?', 'a': u'<p>Yes. HDX allows you to drag and drop files from your computer. First, you need to click on the ‘Add Data’ link and then select files from your computer. Drop the files in the designated area. A new dataset form will appear with some fields already pre-filled.</p>\n', 'id': u'Can_I_drag__038_drop_files_from_my_computer_'}, { 'q': u'How can I share data from my Google Drive?', 'a': u'<p>First you need to be sure that the Google Drive file or files are publicly visible or accessible to anyone who has the link. For instructions on how to change, follow <a id="faq-google-embed-link-4" class="link faq-google-embed-marker"></a>this walkthrough.</p>\n<p>You can click on ‘Add Data’ and choose the option to import files from ‘Google Drive’. A ‘Google Drive’ pop-up will show and help you choose the file/files from your account. The files will not be copied into HDX. Instead, the HDX ‘Download’ button will always direct users to the live version of the Google document.</p>\n<p>The HDX Resource Picker for Google Drive will only have access to your list of Google Drive files when you are choosing Google Drive resources through the HDX interface. You can revoke this permission at any time in <a href="https://security.google.com/settings/security/permissions?pli=1" target="_blank" rel="noopener noreferrer">Google Drive’s App Manager</a>. However, this will not change the visibility of the Google Drive resources already created on HDX.</p>\n', 'id': u'How_can_I_share_data_from_my_Google_Drive_'}, { 'q': u'How do I share a live Google Sheet?', 'a': u'<p>To include a link to a Google Sheet, you must first set the sheet’s sharing permissions so that it is either publicly visible or at least accessible to anyone who has the link. We recommend creating at least two separate resources for each Google Sheet: 1) a link to the sheet itself in the regular Google Drive interface; and 2) a direct-download link to an Excel or CSV version of the sheet, so that users can preview it in HDX. The version in HDX will update automatically as you make changes to the original Google Sheet.</p>\n<p>To obtain the direct download link, select “Publish to the web…” from the “File” menu in Google Sheets, then in the dialog box that opens, under the ‘Link’ tab select your preferred file format (such as Excel or CSV), confirm, and Google Sheets will provide you the link. (Note that this process is not necessary simply for working with HXL-aware tools like Quick Charts, because they can open data directly from the regular Google Sheets link.)</p>\n', 'id': u'How_do_I_share_a_live_Google_Sheet_'}, { 'q': u'How do I share a live spreadsheet from Dropbox?', 'a': u'<p>HDX can live-link to and preview files stored in any Dropbox folder and even preview them if they are in CSV or XLS format. You must login to Dropbox via the web application and navigate to the folder containing the spreadsheet (or other file) that you want to share. Select the file and choose ‘Share link’, following the <a href="https://www.dropbox.com/en/help/167" target="_blank" rel="noopener noreferrer">instructions in the Dropbox help centre</a>. You will then receive a special link that allows anyone to download the file.</p>\n<p>Add that link as a resource to your HDX dataset. When you receive a Dropbox link, it normally looks something like this:<br />\nhttps://www.dropbox.com/etc/etc/your_file_name.csv?dl=0</p>\n<p>For HDX to be able to process and preview your file, you’ll need to change the last ‘0’ to a ‘1’ so that it looks like this:<br />\nhttps://www.dropbox.com/etc/etc/your_file_name.csv?dl=1</p>\n<p>The HDX resource will automatically track any changes you save to the Dropbox file on your own computer. Be careful not to move or rename the file after you share it.</p>\n', 'id': u'How_do_I_share_a_live_spreadsheet_from_Dropbox_'}, { 'q': u'If the dataset date on HDX did not change automatically after updating my remote resource, how do I change it to the correct date?', 'a': u'<p>The data that users download from HDX will always reflect updates made to the remote resource (such as a file on Dropbox or Google Drive). However, the metadata and activity stream will not automatically indicate the updated date of the data. This has to be done manually in HDX by the dataset owner. We are working to improve this functionality, so please bear with us!</p>\n', 'id': u'If_the_dataset_date_on_HDX_did_not_change_automatically_after_updating_my_remote_resource__how_do_I_change_it_to_the_correct_date_'}], 'title': u'Sharing and Using Data'}, {'id': 'faq-Geodata', 'questions': [ {'q': u'How can I generate a map with my geographic data?', 'a': u'<p>The HDX system will attempt to create a map, or geographic preview, from geodata formats that it recognizes. For a geographic preview to be generated, your data needs to be in either a zipped shapefile, kml or geojson format. Ensure that the ‘File type’ field for the resource also has one of the above formats. Pro tip: HDX will automatically add the correct format if the file extension is ‘.shp.zip’, ‘.kml’, or ‘.geojson’. Here are examples of geodata <a href="https://data.humdata.org/dataset/somalia-schools" target="_blank" rel="noopener noreferrer">points</a>, <a href="https://data.humdata.org/dataset/nigeria-water-courses-cod" target="_blank" rel="noopener noreferrer">lines</a>, and <a href="https://data.humdata.org/dataset/health-districts" target="_blank" rel="noopener noreferrer">polygons</a>showing the preview feature.</p>\n<p>The preview feature will continue to work when there are multiple geodata resources in a single dataset (i.e., one HDX dataset with many resources attached). The layers icon in the top-right corner of the map enables users to switch between geodata layers. Here is an <a href="https://data.humdata.org/dataset/nigeria-water-courses-cod" target="_blank" rel="noopener noreferrer">example</a>.</p>\n', 'id': u'How_can_I_generate_a_map_with_my_geographic_data_'}, {'q': u'Why is the geodata preview only working for one layer in my resource?', 'a': u'<p>To generate a map preview, a dataset can have multiple resources but each resource can only include one layer within it. Resources with multiple layers (e.g., multiple shapefiles in a single zip file) are not supported. In this case, the system will only create a preview of the first layer in the resource, however all the layers will still be available in the downloaded file. If you would like all of the layers to display, you need to create a separate resource for each layer.</p>\n', 'id': u'Why_is_the_geodata_preview_only_working_for_one_layer_in_my_resource_'}], 'title': u'Geodata'}, {'id': 'faq-Search', 'questions': [{ 'q': u'How does search work on HDX?', 'a': u'<p>Searching for datasets on HDX is done in two ways: by searching for terms that you type into the search bar found at the top of almost every page on HDX, and by filtering a list of search results.</p>\n<p>Entering a search term causes HDX to look for matching terms in the titles, descriptions, locations and tags of a dataset. The resulting list of items can be further refined using the filter options on the left side of the search result. You can filter by location, tag, organisation, license and format as well as filtering for some special classes of datasets (like <a href="https://data.humdata.org/search?ext_hxl=1" target="_blank" rel="noopener noreferrer">datasets with HXL tags</a> or <a href="https://data.humdata.org/search?ext_quickcharts=1" target="_blank" rel="noopener noreferrer">datasets with Quick Charts</a>) in the ‘featured’ filters.</p>\n', 'id': u'How_does_search_work_on_HDX_'}, { 'q': u'How do I find the Common Operational Datasets in HDX?', 'a': u'<p>In 2015, HDX migrated the Common Operational Datasets (CODs) from the COD Registry on HumanitarianResponse.info to HDX. Each of these datasets has a ‘cod’ tag. To limit search results to only CODs, use the ‘CODs’ filter in the filter panel on the left side of the dataset list.You can also find all CODs datasets <a href="https://data.humdata.org/cod" target="_blank" rel="noopener noreferrer">here</a>.</p>\n', 'id': u'How_do_I_find_the_Common_Operational_Datasets_in_HDX_'}, { 'q': u'How do I find a set of high quality datasets for a specific country? (How do I use the Data Grid?)', 'a': u'<p>The Data Grid is a prototype feature to help our users find the most critical and useful data. The Data Grid provides a quick way to find datasets that meet or partially meet the criteria for a set of core data categories, like internally displaced persons and refugee numbers, conflict events, transportation status, food prices, administrative divisions, health facilities, and baseline population. These categories of core data, determined from research with our users, may be customized to meet the needs of specific countries and the evolving data needs of humanitarian response. The small square to the left of the dataset name indicates if the dataset fully (solid blue) or partially (hashed blue and white) meets the criteria for the Data Grid category in which it appears. In the latter case, hovering on a dataset name displays some comments about the limitations of the dataset. Learn more in our <a href="https://centre.humdata.org/introducing-the-hdx-data-grid-a-way-to-find-and-fill-data-gaps/" target="_blank" rel="noopener noreferrer">blog post</a> about it.</p>\n<p>Data Grid is not available for all countries. Here is an <a href="https://data.humdata.org/group/som" target="_blank" rel="noopener noreferrer">overview</a>.</p>\n', 'id': u'How_do_I_find_a_set_of_high_quality_datasets_for_a_specific_country___How_do_I_use_the_Data_Grid__'}, { 'q': u'I shared a private dataset but cannot find it.', 'a': u'<p>Private datasets are only listed on your organisation page and will not be included in search results or the <a href="https://data.humdata.org/dataset" target="_blank" rel="noopener noreferrer">data list</a> page. Please make sure you are logged in to see them.</p>\n', 'id': u'I_shared_a_private_dataset_but_cannot_find_it_'}], 'title': u'Search'}, {'id': 'faq-Metadata_and_Data_Quality', 'questions': [ {'q': u'What metadata do I need to include when sharing data?', 'a': u'<p>All data on HDX must include a minimum set of metadata fields. You can read our <a href="https://centre.humdata.org/providing-metadata-for-your-datasets-on-hdx/" target="_blank" rel="noopener noreferrer">Guide to Metadata</a> to learn more. We encourage data contributors to include as much metadata as possible to make their data easier to understand and use for analysis.</p>\n', 'id': u'What_metadata_do_I_need_to_include_when_sharing_data_'}, {'q': u'How does HDX ensure data quality?', 'a': u'<p>Data quality is important to us, so we manually review every new dataset for relevance, timeliness, interpretability and comparability. We contact data contributors if we have any concerns or suggestions for improvement. You can learn more about our definition of the dimensions of data quality and our quality-assurance processes <a href="https://centre.humdata.org/wp-content/uploads/HDX_Quality_Assurance_Framework_Draft.pdf" target="_blank" rel="noopener noreferrer">here</a>.</p>\n', 'id': u'How_does_HDX_ensure_data_quality_'}, {'q': u'What should I put for expected update frequency?', 'a': u'<p>This metadata field indicates how often you expect the data in your dataset to be updated. It should reflect the frequency with which you believe your data will change. This can be different from how often you check your data. It includes values like “Every day” and “Every year” as well as the following:</p>\n<p> </p>\n<ul>\n<li>Live – for datasets where updates are continuous and ongoing</li>\n<li>As needed – for datasets with an unpredictable, widely varying update frequency</li>\n<li>Never – for datasets with data that will never be changed</li>\n</ul>\n<p>We recommend you choose the nearest less frequent regular value instead of “As needed” or “Never”. This helps with our monitoring of data freshness. For example, if your data will be updated every 1-6 days, pick “Every week”, or if every 2 to 9 weeks, choose “Every three months”.</p>\n', 'id': u'What_should_I_put_for_expected_update_frequency_'}, {'q': u'What does the green leaf symbol mean?', 'a': u'<p>The green leaf symbol indicates that a dataset is up to date – that there has been an update to the data in the dataset (not the dataset metadata) within the expected update frequency plus some leeway. For more information on the expected update frequency metadata field and the number of days a dataset qualifies as being fresh, see <a href="https://humanitarian.atlassian.net/wiki/spaces/HDX/pages/442826919/Expected+Update+Frequency+vs+Freshness+Status" target="_blank" rel="noopener noreferrer">here</a>.</p>\n', 'id': u'What_does_the_green_leaf_symbol_mean_'}, {'q': u'Does HDX make any changes to my dataset?', 'a': u'<p>No. HDX will never make changes to the data that has been shared. We do add tags, or make changes to dataset titles to help make your data more discoverable by HDX users. We may also add a data visualization for the data in the dataset showcase. A list of changes appears in the activity stream on the left-hand column of the dataset page.</p>\n', 'id': u'Does_HDX_make_any_changes_to_my_dataset_'}, {'q': u'What does it mean for a dataset to be ‘under review’?', 'a': u'<p>The HDX team manually reviews every dataset uploaded to the platform as part of a standard quality assurance (QA) process. This process exists to ensure compliance with the <a href="https://data.humdata.org/faqs/terms">HDX Terms of Service</a>, which prohibit the sharing of personal data. It also serves as a means to check different quality criteria, including the completeness of metadata, the relevance of the data to humanitarian action, and the integrity of the data file(s).</p>\n<p>If an issue is found, the resource(s) requiring additional review will be temporarily unavailable for download and marked as ‘under review’ in the dataset page on the public HDX interface.</p>\n', 'id': u'What_does_it_mean_for_a_dataset_to_be___8216_under_review__8217__'}], 'title': u'Metadata and Data Quality'}, {'id': 'faq-Resources_for_Developers', 'questions': [ {'q': u'How do I access the HDX API?', 'a': u'<p>Please see our <a href="https://data.humdata.org/documentation" target="_blank" rel="noopener noreferrer">Resources for Developers</a> page for more information.</p>\n', 'id': u'How_do_I_access_the_HDX_API_'}, {'q': u'Where can I read about coding with HDX?', 'a': u'<p>Please see our <a href="https://data.humdata.org/documentation" target="_blank" rel="noopener noreferrer">Resources for Developers</a> page for more information.</p>\n', 'id': u'Where_can_I_read_about_coding_with_HDX_'}], 'title': u'Resources for Developers'}, {'id': 'faq-HXL_and_HDX_Tools', 'questions': [ {'q': u'What is the Humanitarian Exchange Language?', 'a': u'<p>The Humanitarian Exchange Language (HXL) is a simple standard for messy data. It is based on spreadsheet formats such as CSV or Excel. The standard works by adding hashtags with semantic information in the row between the column header and data allow software to validate, clean, merge and analyse data more easily. To learn more about HXL and who’s currently using it, visit the <a href="http://hxlstandard.org/" target="_blank" rel="noopener noreferrer">HXL standard site</a>.</p>\n<p>HDX is currently adding features to visualise HXL-tagged data. To learn more about HXL and who’s currently using it, visit the <a href="http://hxlstandard.org/" target="_blank" rel="noopener noreferrer">HXL standard site</a>.</p>\n', 'id': u'What_is_the_Humanitarian_Exchange_Language_'}, {'q': u'What are HDX Tools?', 'a': u'<p>HDX Tools include a number of HXL-enabled support processes that help you do more with your data, more quickly. The tools include:</p>\n<ul>\n<li><a href="https://tools.humdata.org/wizard/#quickcharts" target="_blank" rel="noopener noreferrer">Quick Charts</a> – Automatically generate embeddable, live data charts, graphs and key figures from your spreadsheet.</li>\n<li><a href="https://tools.humdata.org/examples/hxl/" target="_blank" rel="noopener noreferrer">HXL Tag Assist</a> – See HXL hashtags in action and add them to your own spreadsheet.</li>\n<li><a href="https://tools.humdata.org/wizard/#datacheck" target="_blank" rel="noopener noreferrer">Data Check</a> – Data cleaning for humanitarian data, automatically detects and highlights common errors including validation against CODs and other vocabularies.</li>\n</ul>\n<p>You can find all HDX Tools through <a href="https://tools.humdata.org/" target="_blank" rel="noopener noreferrer">tools.humdata.org</a>. The tools will work with data that is stored on HDX, the cloud or local machines. The only requirement is that the data includes HXL hashtags.</p>\n', 'id': u'What_are_HDX_Tools_'}, {'q': u'How can I add Quick Charts to my dataset?', 'a': u'<p>If your data uses HXL hashtags, then the Quick Charts tool can automatically create customizable graphs and key figures to help you highlight the most important aspects of your dataset. Quick Charts require the following:</p>\n<ol>\n<li>The first resource in your dataset (stored on HDX or remotely) must have HXL hashtags.</li>\n<li>That dataset must have the HDX category tag ‘HXL’ (not to be confused with the actual HXL hashtags).</li>\n</ol>\n<p>For more details you can view <a class="link faq-google-embed-marker" id="faq-google-embed-link-5">these walkthrough slides</a>.</p>\n<div class="modal presentation-modal" id="faq-google-embed-5" tabindex="-1" role="dialog">\n<div class="modal-dialog" role="document"><button type="button" class="close" data-dismiss="modal" aria-label="Close"><span aria-hidden="true">\xd7</span></button></p>\n<div class="modal-content"><iframe load-src="https://docs.google.com/presentation/d/e/2PACX-1vR-gSY38muZE9SA27NjAcueKoobhKi_Dc3jN4BIDPTp7FJjOCiWIkhPU4ZkPyHvfR0pBdNpfswmKZ4p/embed?start=false&loop=false&delayms=3000" frameborder="0" width="900" height="560" allowfullscreen="true" mozallowfullscreen="true" webkitallowfullscreen="true"></iframe></div>\n</div>\n</div>\n', 'id': u'How_can_I_add_Quick_Charts_to_my_dataset_'}, {'q': u'How can I add Quick Charts to my own web sites or blogs?', 'a': u'<p>Every Quick Chart on HDX includes a small link icon at the bottom, that will give you HTML markup to copy into a web page or blog to add the chart. The chart will be live, and will update whenever the source data updates. If your data is not on HDX, you can also generate a Quick Chart using the standalone version of the service, available on <a href="https://tools.humdata.org/" target="_blank" rel="noopener noreferrer">https://tools.humdata.org</a>.</p>\n', 'id': u'How_can_I_add_Quick_Charts_to_my_own_web_sites_or_blogs_'}, {'q': u'Why isn’t Quick Charts recognizing the HXL hashtags in my dataset?', 'a': u'<p>At this stage, Quick Charts are working with a limited number of HXL hashtags, but we are constantly expanding the list. The current set of JSON-encoded Quick Charts recipes is available on <a href="https://github.com/OCHA-DAP/hxl-recipes/" target="_blank" rel="noopener noreferrer">GitHub</a>.</p>\n', 'id': u'Why_isn__8217_t_Quick_Charts_recognizing_the_HXL_hashtags_in_my_dataset_'}, {'q': u'How does HXL Tag Assist work?', 'a': u'<p>The <a href="https://tools.humdata.org/examples/hxl/" target="_blank" rel="noopener noreferrer">HXL Tag Assist tool</a> will show you different HXL hashtags in datasets that organisations have already uploaded to HDX. You can find a quick (and portable) list of the core HXL hashtags on the <a href="http://hxlstandard.org/standard/postcards/" target="_blank" rel="noopener noreferrer">HXL Postcard</a>. The detailed list of HXL hashtags and attributes is available in the <a href="http://hxlstandard.org/standard/1_1final/dictionary/" target="_blank" rel="noopener noreferrer">HXL hashtag dictionary</a>. Finally, an up-to-date machine-readable version of the hashtag dictionary is <a href="https://data.humdata.org/dataset/hxl-core-schemas/" target="_blank" rel="noopener noreferrer">available on HDX.</a></p>\n', 'id': u'How_does_HXL_Tag_Assist_work_'}, {'q': u'How does Data Check work?', 'a': u'<p>You can use <a href="https://centre.humdata.org/clean-your-data-with-data-check/" target="_blank" rel="noopener noreferrer">Data Check</a> to compare your HXL-tagged dataset against a collection of validation rules that you can configure. Data Check identifies the errors in your data such as spelling mistakes, incorrect geographical codes, extra whitespace, numerical outliers, and incorrect data types.</p>\n<p>For more details you can view <a class="link faq-google-embed-marker" id="faq-google-embed-link-6">these walkthrough slides</a>.</p>\n<div class="modal presentation-modal" id="faq-google-embed-6" tabindex="-1" role="dialog">\n<div class="modal-dialog" role="document"><button type="button" class="close" data-dismiss="modal" aria-label="Close"><span aria-hidden="true">\xd7</span></button></p>\n<div class="modal-content"><iframe load-src="https://docs.google.com/presentation/d/e/2PACX-1vQmqK3qgUchHmZ5YQ8M-ktJ0UccIDeBeuqAqjIAbZ2HIXfmZ5OdqFRb7AM1YJI6N1vmimBAbOVa7QMe/embed?start=false&loop=false&delayms=3000" frameborder="0" width="900" height="560" allowfullscreen="true" mozallowfullscreen="true" webkitallowfullscreen="true"></iframe></div>\n</div>\n</div>\n', 'id': u'How_does_Data_Check_work_'}], 'title': u'HXL and HDX Tools'}, {'id': 'faq-Sensitive_Data', 'questions': [ {'q': u'How does HDX define sensitive data?', 'a': u'<p>For the purpose of sharing data through HDX, we have <a href="https://centre.humdata.org/three-ways-to-share-data-on-hdx/" target="_blank" rel="noopener noreferrer">developed the following categories</a> to communicate data sensitivity:</p>\n<ol>\n<li>Non-Sensitive – This includes datasets containing country statistics, roadmaps, weather data and other data with no foreseeable risk associated with sharing.</li>\n<li>Uncertain Sensitivity – For this data, sensitivity depends on a number of factors, including other datasets collected in the same context, what technology is or could be used to extract insights, and the local context from which the data is collected or which will be impacted by use of the data.</li>\n<li>Sensitive – This includes any dataset containing personal data of affected populations or aid workers. Datasets containing demographically identifiable information (DII) or community identifiable information (CII) that can put affected populations or aid workers at risk, are also considered sensitive data. Depending on context, satellite imagery can also fall into this third category of sensitivity.</li>\n</ol>\n', 'id': u'How_does_HDX_define_sensitive_data_'}, {'q': u'Can I share personal data through HDX?', 'a': u'<p>HDX does not allow personal data or personally identifiable information (PII) to be shared in public or private datasets. All data shared through the platform must be sufficiently aggregated or anonymized so as to prevent identification of people or harm to affected people and the humanitarian community. We do allow private datasets to include contact information of aid workers if they have provided consentto the sharing of their data within the organisation. Read more in our <a href="https://data.humdata.org/faqs/terms" target="_blank" rel="noopener noreferrer">Terms of Service</a>.</p>\n', 'id': u'Can_I_share_personal_data_through_HDX_'}, {'q': u'How can I assess and manage the sensitivity of data before sharing on HDX?', 'a': u'<p>The <a href="https://centre.humdata.org/wp-content/uploads/2019/03/OCHA-DR-Guidelines-working-draft-032019.pdf" target="_blank" rel="noopener noreferrer">Working Draft of the OCHA Data Responsibility Guidelines</a> (‘the Guidelines’) helps staff better assess and manage the sensitivity of the data they handle in different crisis contexts. We recommend that HDX users familiarize themselves with the Guidelines.</p>\n<p>Different data can have different levels of sensitivity depending on the context. For example, locations of medical facilities in conflict settings can expose patients and staff to risk of attacks, whereas the same facility location data would likely not be considered sensitive in a natural disaster setting.</p>\n<p>Recognizing this complexity, the Guidelines include an <a href="https://centre.humdata.org/wp-content/uploads/2019/03/image1-768x596.png" target="_blank" rel="noopener noreferrer">Information and Data Sensitivity Classification model</a> to help colleagues assess and manage sensitivity in a standardized way.</p>\n<p>For microdata (survey and needs-assessment data), you can manage the sensitivity level by applying a Statistical Disclosure Control (SDC) process. There are several tools available online to do SDC – we use <a href="http://surveys.worldbank.org/sdcmicro" target="_blank" rel="noopener noreferrer">sdcMicro</a>.</p>\n<p>The Centre has developed a <a href="https://centre.humdata.org/guidance-note-statistical-disclosure-control/" target="_blank" rel="noopener noreferrer">Guidance Note on Statistical Disclosure Control</a> that outlines the steps involved in the SDC process, potential applications for its use, case studies and key actions for humanitarian data practitioners to take when managing sensitive microdata.</p>\n', 'id': u'How_can_I_assess_and_manage_the_sensitivity_of_data_before_sharing_on_HDX_'}, {'q': u'How does HDX assess the sensitivity of data?', 'a': u'<p>HDX endeavors not to allow publicly shared data that includes community identifiable information (CII) or demographically identifiable information (DII) that may put affected people at risk. However, this type of data is more challenging to identify within datasets during our quality assurance process without deeper analysis. In cases where we suspect that survey data may have a high risk of re-identification of affected people, we run an internal statistical disclosure control process using sdcMicro. Data is made private while we run this process. If the risk level is found to be too high for public sharing on HDX given the particular context to which the data relates, HDX will notify the data contributor to determine a course of action.</p>\n', 'id': u'How_does_HDX_assess_the_sensitivity_of_data_'}], 'title': u'Sensitive Data'}, {'id': 'faq-Data_Licenses', 'questions': [{'q': u'What data licences does HDX offer?', 'a': u'<p>HDX promotes the use of licenses developed by the <a href="http://creativecommons.org/" target="_blank" rel="noopener noreferrer">Creative Commons Foundation</a> and the <a href="http://opendatacommons.org/" target="_blank" rel="noopener noreferrer">Open Data Foundation</a>. The main difference between the two classes of licences is that the Creative Commons licences were developed for sharing creative works in general, while the Open Data Commons licences were developed more specifically for sharing databases. See the full list of licences <a href="https://data.humdata.org/faqs/licenses" target="_blank" rel="noopener noreferrer">here</a>.</p>\n', 'id': u'What_data_licences_does_HDX_offer_'}], 'title': u'Data Licenses'}, {'id': 'faq-Contact', 'questions': [ {'q': u'How do I contact the HDX team?', 'a': u'<p>For general enquiries or issues with the site, e-mail <a href="mailto:hdx@un.org">hdx@un.org</a>. You can also reach us on Twitter at <a href="https://twitter.com/humdata" target="_blank" rel="noopener noreferrer">@humdata</a>. Sign up to receive our newsletter <a href="http://humdata.us14.list-manage.com/subscribe?u=ea3f905d50ea939780139789d&id=99796325d1" target="_blank" rel="noopener noreferrer">here</a>.</p>\n', 'id': u'How_do_I_contact_the_HDX_team_'}], 'title': u'Contact'}]} def mock_documentation_page_content(id): return {'topics': {'faq-Contact_Us': u'Contact Us', 'faq-Accessing_HDX_by_API': u'Accessing HDX by API', 'faq-Other_HDX_Libraries': u'Other HDX Libraries', 'faq-Tools': u'Tools', 'faq-Coding_with_the_Humanitarian_Exchange_Language': u'Coding with the Humanitarian Exchange Language'}, 'faq_data': [ {'id': 'faq-Accessing_HDX_by_API', 'questions': [{'q': u'About the Humanitarian Data Exchange API', 'a': u'<p>This section contains information for developers who want to write code that interacts with the Humanitarian Data Exchange (HDX) and the datasets it contains. Anything that you can do by way of the HDX user interface, you can do programatically by making calls to the API and you can do a lot more. Typical uses of the API might be to script the creation and update of datasets in HDX or to read data for analysis and visualisation.</p>\n', 'id': u'About_the_Humanitarian_Data_Exchange_API'}, {'q': u'Programming Language Support', 'a': u'<p>HDX has a RESTful API largely unchanged from the underlying CKAN API which can be used from any programming language that supports HTTP GET and POST requests. However, the terminology that CKAN uses is a little different to the HDX user interface. Hence, we have developed wrappers for specific languages that harmonise the nomenclature and simplify the interaction with HDX.<br />\nThese APIs allow various operations such as searching, reading and writing dataset metadata, but not the direct querying of data within resources which can point to files or urls and of which there can be more than one per dataset.</p>\n', 'id': u'Programming_Language_Support'}, {'q': u'Python', 'a': u'<p>The recommended way of developing against HDX is to use the <a href="https://github.com/OCHA-DAP/hdx-python-api" target="_blank" rel="noopener noreferrer">HDX Python API</a>. This is a mature library that supports Python 2.7 and 3 with tests that have a high level of code coverage. The major goal of the library is to make pushing and pulling data from HDX as simple as possible for the end user. There are several ways this is achieved. It provides a simple interface that communicates with HDX using the CKAN Python API, a thin wrapper around the CKAN REST API. The HDX objects, such as datasets and resources, are represented by Python classes. This should make the learning curve gentle and enable users to quickly get started with using HDX programmatically. For example, to read a dataset and get its resources, you would simply do:</p>\n<pre><code class="python">from hdx.hdx_configuration import Configuration \r\nfrom hdx.data.dataset import Dataset\r\nConfiguration.create(hdx_site=\'prod\', user_agent=\'A_Quick_Example\', hdx_read_only=True)\'\r\ndataset = Dataset.read_from_hdx(\'reliefweb-crisis-app-data\')\r\nresources = dataset.get_resources()\r\n</code></pre>\n<p>There is <a href="http://ocha-dap.github.io/hdx-python-api/" target="_blank" rel="noopener noreferrer">library API-level documentation</a> available online.<br />\nIf you intend to push data to HDX, then it may be helpful to start with this <a href="https://github.com/OCHA-DAP/hdxscraper-template" target="_blank" rel="noopener noreferrer">scraper template</a> which shows what needs to be done to create datasets on HDX. It should be straightforward to adapt the template for your needs.</p>\n', 'id': u'Python'}, {'q': u'R', 'a': u'<p>If you wish to read data from HDX for analysis in R, then you can use the <a href="https://gitlab.com/dickoa/rhdx" target="_blank" rel="noopener noreferrer">rhdx</a> package. The goal of this package is to provide a simple interface to interact with HDX. Like the Python API, it is a wrapper around the CKAN REST API. rhdx is not yet fully mature and some breaking changes are expected.</p>\n', 'id': u'R'}, {'q': u'REST', 'a': u'<p>If you need to use another language or simply want to examine dataset metadata in detail in your web browser, then you can use <a href="http://docs.ckan.org/en/ckan-2.6.3/api/index.html" target="_blank" rel="noopener noreferrer">CKAN’s RESTful API</a>, a powerful, RPC-style interface that exposes all of CKAN’s core features to clients.</p>\n', 'id': u'REST'}], 'title': u'Accessing HDX by API'}, {'id': 'faq-Coding_with_the_Humanitarian_Exchange_Language', 'questions': [{'q': u'About the Humanitarian Exchange Language', 'a': u'<p>This section contains information for developers who want to write code to process datasets that use the Humanitarian Exchange Language (HXL). HXL is a different kind of data standard, adding hashtags to existing datasets to improve information sharing during a humanitarian crisis without adding extra reporting burdens. HXL has its <a href="http://hxlstandard.org/" target="_blank" rel="noopener noreferrer">own website</a> and of particular interest will be the <a href="http://hxlstandard.org/standard" target="_blank" rel="noopener noreferrer">documentation</a> section.</p>\n', 'id': u'About_the_Humanitarian_Exchange_Language'}, {'q': u'Python', 'a': u'<p>The most well developed HXL library, <a href="https://github.com/HXLStandard/libhxl-python" target="_blank" rel="noopener noreferrer">libhxl-python</a>, is written in Python. The most recent versions support Python 3 only, but there are earlier versions with Python 2.7 support. Features of the library include filtering, validation and the ingestion and generation of various formats. libhxl-python uses an idiom that is familiar from JQuery and other Javascript libraries; for example, to load a dataset, you would use simply</p>\n<pre><code>import hxl \r\nsource = hxl.data(\'http://example.org/dataset.xlsx\')</code></pre>\n<p>As in JQuery, you process the dataset by adding additional steps to the chain. The following example selects every row with the organisation “UNICEF” and removes the column with email addresses:</p>\n<pre><code>source.with_rows(\'#org=UNICEF\').without_columns(\'#contact+email\')</code></pre>\n<p>The library also includes a set of command-line tools for processing HXL data in shell scripts. For example, the following will perform the same operation shown above, without the need to write Python code:</p>\n<pre><code>$ cat dataset.xlsx | hxlselect -q "#org=UNICEF" | hxlcut -x \'#contact+email\'</code></pre>\n<p>There is library <a href="http://hxlstandard.github.io/libhxl-python/" target="_blank" rel="noopener noreferrer">API-level documentation</a> available online.</p>\n', 'id': u'Python'}, {'q': u'Javascript', 'a': u'<p><a href="https://github.com/HXLStandard/libhxl-js" target="_blank" rel="noopener noreferrer">libhxl-js</a> is a library for HXL written in Javascript. It supports high-level filtering and aggregation operations on HXL datasets. Its programming idiom is similar to libhxl-python, but it is smaller and contains fewer filters and no data-validation support.</p>\n', 'id': u'Javascript'}, {'q': u'R', 'a': u'<p>Third party support for R is available via the package <a href="https://github.com/dirkschumacher/rhxl" target="_blank" rel="noopener noreferrer">rhxl</a>. It has basic support for reading HXLated files to make them available for advanced data-processing and analytics inside R.</p>\n', 'id': u'R'}], 'title': u'Coding with the Humanitarian Exchange Language'}, {'id': 'faq-Tools', 'questions': [{'q': u'HDX Tools', 'a': u'<p>HDX provides a <a href="https://tools.humdata.org/" target="_blank" rel="noopener noreferrer">suite of tools</a> that leverage HXLated datasets:</p>\n<ol>\n<li>QuickCharts automatically generates embeddable, live data charts, graphs and key figures from your data. It uses the HXL hashtags to guess the best charts to display, but you can then go in and override with your own <a href="https://github.com/OCHA-DAP/hxl-recipes" target="_blank" rel="noopener noreferrer">preferences</a>.</li>\n<li>HXL Tag Assist allows you to find hashtag examples and definitions, and see how data managers are using the hashtags in their data.</li>\n<li>Data Check provides help with data cleaning for humanitarian data, automatically detecting and highlighting common errors. It includes validation against CODs and other vocabularies.</li>\n</ol>\n', 'id': u'HDX_Tools'}, {'q': u'HXL Proxy', 'a': u'<p>The <a href="https://proxy.hxlstandard.org/" target="_blank" rel="noopener noreferrer">HXL Proxy</a> is a tool for validating, cleaning, transforming, and visualising HXL-tagged data. You supply an input url pointing to a tabular or JSON dataset and then create a recipe that contains a series of steps for transforming the data. The result is a download link that you can share and use in HDX, and the output will update automatically whenever the source dataset changes. Full user documentation is available in the <a href="https://github.com/HXLStandard/hxl-proxy/wiki" target="_blank" rel="noopener noreferrer">HXL Proxy wiki</a>.<br />\nThe HXL Proxy is primarily a web wrapper around the libhxl-python library (see above), and makes the same functionality available via <a href="https://en.wikipedia.org/wiki/Representational_state_transfer" target="_blank" rel="noopener noreferrer">RESTful</a> web calls.</p>\n', 'id': u'HXL_Proxy'}], 'title': u'Tools'}, {'id': 'faq-Other_HDX_Libraries', 'questions': [{'q': u'HDX Python Country', 'a': u'<p>Humanitarian projects frequently require handling countries, locations and regions in particular dealing with inconsistent country naming between different data sources and different coding standards like ISO3 and M49. The <a href="https://github.com/OCHA-DAP/hdx-python-country" target="_blank" rel="noopener noreferrer">HDX Python Country</a> library was created to fulfill these requirements and is a dependency of the HDX Python API. It is also very useful as a standalone library and has <a href="https://ocha-dap.github.io/hdx-python-country/" target="_blank" rel="noopener noreferrer">library API-level documentation</a>available online.</p>\n', 'id': u'HDX_Python_Country'}, {'q': u'HDX Python Utilities', 'a': u'<p>All kinds of utility functions have been coded over time for use internally, so since we think these have value externally, it was decided that they should be packaged into the <a href="https://github.com/OCHA-DAP/hdx-python-utilities" target="_blank" rel="noopener noreferrer">HDX Python Utilities</a> library which has library API-level documentation available online.</p>\n', 'id': u'HDX_Python_Utilities'}], 'title': u'Other HDX Libraries'}, {'id': 'faq-Contact_Us', 'questions': [ {'q': u'How do I contact the HDX team?', 'a': u'<p>If you have any questions about these resources, we will do our best to answer them. We would also love to hear about how you are using them for your work.</p>\n<p>Please contact us at: <a href="mailto:hdx@un.org">hdx@un.org</a>. Sign up to receive our <a href="http://humdata.us14.list-manage1.com/subscribe?u=ea3f905d50ea939780139789d&id=99796325d1" target="_blank" rel="noopener noreferrer">newsletter here</a>.</p>\n', 'id': u'How_do_I_contact_the_HDX_team_'}], 'title': u'Contact Us'}]} def mock_data_responsability_page_content(id): return {'topics': {'faq-_Data_Responsibility_COVID_19_Content_': u'[Data Responsibility COVID-19 Content]'}, 'faq_data': [{'id': 'faq-_Data_Responsibility_COVID_19_Content_', 'questions': [{ 'q': u'What are some basic health data management precautions that all organizations should take in the COVID-19 response?', 'a': u'<p>The World Health Organization recommends the following measures to ensure the ethical and secure use of data:</p>\n<ol>\n<li>Use anonymization and other tools as appropriate.</li>\n<li>Comply with informed consent agreements where such consent is needed and respect assurances about ways in which the data (anonymized or otherwise) would be used, shared, stored or protected.</li>\n<li>Adopt appropriate security measures to foster public trust.</li>\n<li>Any platforms established to share data should have an explicit ethical framework governing data collection and use.</li>\n</ol>\n<p>In addition, consider the following:</p>\n<ol>\n<li>Ensure adequate de-identification of data within health data management activities. Consult relevant guidance to determine which tool is most appropriate for de-identification of the type of data you’re handling. When using digital (communication) technologies in healthcare, data protection is paramount. Determine which tools are used by healthcare professionals and only use tools that allow for the appropriate level of encryption.</li>\n<li>Clearly define the purpose of data management, measures for data minimisation and limitation of data retention, and the specific roles and responsibilities of different stakeholders throughout the data management process. This should include a clear overview of which parties are responsible for safeguarding data at different stages.</li>\n<li>When sharing data with specific recipients, be transparent regarding the appropriate use of the data, and make sure this is compatible with the original purpose for which the data was collected.</li>\n<li>Data can be vulnerable to interception at points of transfer between different organizations. Additionally, data may be misused intentionally or unintentionally after the transfer. Select the right method and tool for transfer, and to stipulate the licence or terms under which data may be used in a clear manner (see <a href="https://data.humdata.org/faq-data-responsibility-covid-19#auto-faq-Section_1-What_are_the_different_licenses_available_for_data_sharing_and_what_do_they_cover_-q">“What are the different licenses available for data sharing and what do they cover?”</a> for more information on this point).</li>\n</ol>\n<p>Following these best practices will help ensure responsible data management in the COVID-19 response.</p>\n', 'id': u'What_are_some_basic_health_data_management_precautions_that_all_organizations_should_take_in_the_COVID_19_response_'}, { 'q': u'What constitutes sensitive data generally and in the health sector specifically?', 'a': u'<p>Your organization may have standard definitions for data sensitivity included in a data policy or elsewhere. Data sensitivity definitions may also be found in applicable privacy or data protection legislation. In the absence of such guidance, any data that may put certain individuals, groups or organizations at risk of harm in a particular context should be considered sensitive. While personal data can categorically be considered sensitive, more nuanced issues arise for non-personal data. For example, locations of medical facilities in conflict settings can expose patients and staff to risk, while the same data would not necessarily be considered sensitive in a natural disaster response context.</p>\n<p>In the health sector specifically, all identifiable data concerning health, factors influencing health (for example, cultural and socio-economic details) and the history of individuals are sensitive and must be handled with care and professionalism. In addition, any data (identifiable or not) that can be voluntarily or involuntarily misused against the interests of patients, potential patients, their family, groups or communities and/or health service providers or other humanitarian organizations and their staff, or put any of them at risk for political reasons, financial gain or any other reasons shall be treated as “highly sensitive” data. Even some seemingly non-sensitive data can be highly sensitive in certain contexts (for example, details of cholera outbreaks). Finally, the metadata generated as a ‘byproduct’ of data management can create a distinct set of risks, which should not be overlooked. For more information on the risks associated with metadata, see <a href="https://www.icrc.org/en/document/digital-trails-could-endanger-people-receiving-humanitarian-aid-icrc-and-privacy" target="_blank" rel="noopener noreferrer">https://www.icrc.org/en/document/digital-trails-could-endanger-people-receiving-humanitarian-aid-icrc-and-privacy</a></p>\n', 'id': u'What_constitutes_sensitive_data_generally_and_in_the_health_sector_specifically_'}, { 'q': u'What are some common types of sensitive data in the COVID-19 response?', 'a': u'<p>In the COVID-19 response, the following common data types may be considered sensitive and should be treated with care:</p>\n<ol>\n<li>any directly identifiable data (such as datasets containing names or telephone numbers)</li>\n<li>any indirectly identifiable data (such as survey results or call detail records that have not been appropriately anonymized)</li>\n<li>non-identifiable data on sensitive topics, including but not limited to aggregated and/or anonymized data onviolence related injuries; rape; termination of pregnancy, and; patients in prisons or detention centers;</li>\n<li>information on the disease in a context where there is an obligation to abide by treatment or other related measures, such as quarantine;</li>\n<li>non-identifiable data which reveals or implies racial or ethnic origin, political opinions, religious or philosophical beliefs, offences or sex life or preferences.</li>\n</ol>\n<p>Assessing the sensitivity of data requires a clear understanding of the context and the different ways in which data may lead to harm. Data Sensitivity Classifications such as <a href="https://docs.google.com/document/d/1FYI9n2NcQAUTC-0XlQ5drPcYwfY_OXPRvaU0KMuMzHQ/edit" target="_blank" rel="noopener noreferrer">this example</a> (from the working draft <a href="https://centre.humdata.org/wp-content/uploads/2019/03/OCHA-DR-Guidelines-working-draft-032019.pdf" target="_blank" rel="noopener noreferrer">OCHA Data Responsibility Guidelines</a>) can help humanitarian organizations consistently assess and manage data sensitivity in different environments.</p>\n<p>These classifications can be developed at the country level and/or at the sector/cluster level where necessary (e.g. the health cluster may wish to establish a sensitivity classification specific to data required for COVID-19 response interventions in certain contexts). Humanitarians operating at the National or Sub-National level are encouraged to engage with the appropriate partners and coordinating bodies to ensure data management is conducted according to relevant standards for IM services in public health. This includes aligning with existing context-specific data sensitivity classifications.</p>\n', 'id': u'What_are_some_common_types_of_sensitive_data_in_the_COVID_19_response_'}, { 'q': u'What are the key measures I should take to ensure privacy and data protection in data management?', 'a': u'<p>Data management in the COVID-19 response should be principled and follow existing best practice in humanitarian data management. Some key measures for upholding privacy and data protection include:</p>\n<ol>\n<li><strong>Purpose limitation</strong>: clearly specify the purpose for which data is needed, explain this to the populations from whom data will be collected, and establish safeguards to ensure that data is used only for the intended purpose.</li>\n<li><strong>Privacy by design</strong>: anticipate and build-in technical and procedural measures to prevent privacy invasive events at the outset of a data management exercise.</li>\n<li><strong>Transparency</strong>: provide accurate and complete information to people about what data is being collected about them, for what purpose, how it will be used, how long it will be kept and who it will be shared with</li>\n<li><strong>Necessity and proportionality</strong>: only collect data that is relevant and necessary to achieve the purpose specified, thereby abiding by the principle of data minimisation.</li>\n<li><strong>Time limitations</strong>: ensure that any data processing is strictly limited in time and that data collected for COVID-19 response efforts is not retained beyond the time for which they are strictly needed to combat the pandemic.</li>\n</ol>\n<p>For additional resources and examples of best practice on data protection and privacy in the COVID-19 response, see this repository from UN Global Pulse: <a href="https://www.unglobalpulse.org/policy/covid-19-data-protection-and-privacy-resources/" target="_blank" rel="noopener noreferrer">https://www.unglobalpulse.org/policy/covid-19-data-protection-and-privacy-resources/</a></p>\n<p>For detailed recommendations on data privacy, data protection, and responsible data management in digital contact tracing, see this recent working paper from UNICEF: <a href="https://www.unicef-irc.org/publications/1096-digital-contact-tracing-surveillance-covid-19-response-child-specific-issues-iwp.html" target="_blank" rel="noopener noreferrer">https://www.unicef-irc.org/publications/1096-digital-contact-tracing-surveillance-covid-19-response-child-specific-issues-iwp.html</a></p>\n', 'id': u'What_are_the_key_measures_I_should_take_to_ensure_privacy_and_data_protection_in_data_management_'}, { 'q': u'What measures can I take to reduce the risk of re-identification of individuals and groups before publishing data?', 'a': u'<p>Data on the characteristics of units of a population (e.g. individuals, households or establishments) collected by a census, survey or experiment is referred to in statistics as ‘microdata’. In humanitarian response, this type of data is gathered through exercises such as household surveys, needs assessments, and other programme monitoring activities. Such data make up an increasingly significant volume of data in the humanitarian sector, and will play a key role in the COVID-19 response.</p>\n<p>In its raw form, microdata can contain both personal data and non-personal data on a range of topics. Most humanitarian organisations acknowledge the sensitivity of personal data such as names, biometric data, or ID numbers and anonymise data sets accordingly as a matter of standard practice. However, it is often still possible to re-identify individual respondents or groups by combining answers to different questions, even after such ‘anonymisation’ is applied.</p>\n<p>Depending on the type of data you’re managing, there are various tools available to determine and reduce the risk of re-identification in the data. For microdata, one such approach is Statistical Disclosure Control (SDC).</p>\n<p>SDC is a technique used to assess and lower the risk of a person or organization being re-identified from the analysis of microdata (data on the characteristics of a population). The purpose of applying disclosure control to microdata is to be able to share the data more widely in a responsible manner. An SDC process can lower the risk of re-identification to an acceptable level but the risk threshold may vary depending on the context to which the data relates. There are a variety of free and open source tools available for conducting SDC, including <a href="https://ihsn.org/software/disclosure-control-toolbox" target="_blank" rel="noopener noreferrer">sdcMicro</a>. Read this <a href="https://centre.humdata.org/wp-content/uploads/2019/07/guidance_note_sdc.pdf" target="_blank" rel="noopener noreferrer">guidance note</a> from the Centre for Humanitarian Data for more information on how to start using SDC.</p>\n', 'id': u'What_measures_can_I_take_to_reduce_the_risk_of_re_identification_of_individuals_and_groups_before_publishing_data_'}, { 'q': u'What are the existing standards for surveillance and case definition and reporting?', 'a': u'<p>The World Health Organization has published <a href="https://www.who.int/emergencies/diseases/novel-coronavirus-2019/technical-guidance/surveillance-and-case-definitions" target="_blank" rel="noopener noreferrer">technical guidance on surveillance and case definitions for COVID-19</a>. This guidance includes resources for use in case-based reporting — including a Case-based reporting form, a Data dictionary for case-based reporting form, and Template for Line list for case-based reporting — as well as aggregated reporting, including an Aggregated weekly reporting form.</p>\n', 'id': u'What_are_the_existing_standards_for_surveillance_and_case_definition_and_reporting_'}, { 'q': u'Where can I find the latest data about the ongoing COVID-19 emergency?', 'a': u'<p>The World Health Organization maintains a real-time dashboard providing an overview of the COVID-19 situation here <a href="https://who.sprinklr.com/" target="_blank" rel="noopener noreferrer">https://experience.arcgis.com/experience/685d0ace521648f8a5beeeee1b9125cd</a></p>\n<p>Their data is updated live and can be accessed here: <a href="https://data.humdata.org/dataset/coronavirus-covid-19-cases-and-deaths" target="_blank" rel="noopener noreferrer">https://data.humdata.org/dataset/coronavirus-covid-19-cases-and-deaths</a></p>\n<p>A number of humanitarian organizations are publishing data about different aspects of the global and country-level response to COVID-19. Many of these resources are available in a dedicated <a href="https://data.humdata.org/event/covid-19">COVID-19 crisis page on the Humanitarian Data Exchange</a>.</p>\n<p>Many national health authorities also provide updates on a daily basis. Visit your national health authority’s website for more information.</p>\n', 'id': u'Where_can_I_find_the_latest_data_about_the_ongoing_COVID_19_emergency_'}, { 'q': u'How can I determine the most appropriate method and/or tool for sharing or otherwise transferring data in a secure way?', 'a': u'<p>Consult the relevant guidance (such as a data policy or specific protocols for a given data management activity) or focal point within your organization to see which methods and tools are considered appropriate for the secure transfer of (sensitive) data. In general, a secure method or tool will enable encryption of the data in transit and at rest, offer secure authentication functionality and access restrictions, among other security features. For example, most email service providers allow you to turn on encryption of emails and their attachments.</p>\n', 'id': u'How_can_I_determine_the_most_appropriate_method_and_or_tool_for_sharing_or_otherwise_transferring_data_in_a_secure_way_'}, { 'q': u'What are the different licenses available for data sharing and what do they cover?', 'a': u'<p>Licenses stipulate the terms under which data is shared. This means that a license will describe how data may be used and shared further, as well as any attribution to the original source that should take place. A list of commonly used licenses is available here: <a href="https://data.humdata.org/faqs/licenses">https://data.humdata.org/faqs/licenses</a></p>\n', 'id': u'What_are_the_different_licenses_available_for_data_sharing_and_what_do_they_cover_'}, { 'q': u'How can my organization ensure responsible data practice when developing or using models in the COVID-19 response?', 'a': u'<p>Epidemic models are an essential tool in the hands of governments and policy makers for planning and responding to COVID-19. This crisis shows how predictive analytics can inform and maximise the impact of interventions, especially in resource-limited contexts. It also shows the importance of having models that are validated and ready to be deployed right before or at the beginning of a crisis.</p>\n<p>Unfortunately, translating the outputs of predictive models into timely and appropriate responses in the humanitarian sector remains a challenge for several reasons:</p>\n<ol>\n<li>First, there is no common standard for documenting predictive models and their intended use which highlights the critical aspects for the application of models in the humanitarian sector.</li>\n<li>Second, there is no common standard or mechanism for assessing the technical rigor and operational readiness of predictive models in the sector.</li>\n<li>Third, the development of predictive models is often led by technical specialists who may not consider important ethical concerns that the application of models in humanitarian contexts may entail.</li>\n</ol>\n<p>One approach for addressing these challenges is to submit models for peer review. The Centre for Humanitarian Data recently published an updated version of its <a href="https://centre.humdata.org/wp-content/uploads/2020/03/peer-review-framework-2020.pdf" target="_blank" rel="noopener noreferrer">Peer Review Framework for Predictive Analytics in Humanitarian Response</a>. The Framework aims to create standards and processes for the use of models in our sector. It is based on research with experts and stakeholders across a range of organizations that design and use predictive models. The Framework also draws on best practices from academia and the private sector.</p>\n', 'id': u'How_can_my_organization_ensure_responsible_data_practice_when_developing_or_using_models_in_the_COVID_19_response_'}, { 'q': u'What policies and guidelines currently exist to inform the management of data in public health emergencies?', 'a': u'<p>Many individual organizations have policies and guidelines specific to the safe, ethical, and effective management of different types of data. Institutional policies on personal data protection are particularly relevant to the responsible management of health data and should serve as a primary reference for staff in the COVID-19 response.</p>\n<p>In addition, many national and regional authorities have included provisions specific to health data management in national and regional data protection legislation and other relevant regulatory frameworks. National laws on medical practice may also include specific rules on health data management. Consult a local legal professional to ensure you are aware of and abide by all applicable data protection laws.</p>\n<p>The World Health Organization <a href="https://www.who.int/wer/2016/wer9118.pdf?ua=1" target="_blank" rel="noopener noreferrer">Policy statement on data sharing by WHO in the context of public health emergencies (as of 13 April 2016)</a> and <a href="https://www.who.int/medicines/publications/pharmprep/WHO_TRS_996_annex05.pdf?ua=1%22" target="_blank" rel="noopener noreferrer">Guidance on good data and record management practices</a> are the primary global frameworks of reference for the management of data in public health emergencies.</p>\n<p>The Global Health Cluster <a href="https://www.who.int/health-cluster/resources/publications/Final-PHIS-Standards.pdf" target="_blank" rel="noopener noreferrer">Standards for Public Health Information Services in Activated Health Clusters and other Humanitarian Health Coordination Mechanisms</a> should also serve as a key reference for humanitarian practitioners. Although this document refers to Public Health Information Services (PHIS) in activated health clusters (HCs), these PHIS Standards are by no means restricted to health clusters, and can be applied to support government led emergency coordination or other types of humanitarian sectoral coordination mechanisms.</p>\n<p>The WHO <a href="https://www.who.int/publishing/datapolicy/Policy_data_sharing_non_emergency_final.pdf" target="_blank" rel="noopener noreferrer">‘Policy on the use and sharing of data collected in Member States by the WHO, outside the context of public health emergencies’</a> contains <a href="https://www.who.int/about/who-we-are/publishing-policies/data-policy" target="_blank" rel="noopener noreferrer">extensive annexes on security, safeguards, ethics and guidance</a> on implementation and may also serve as a helpful reference. However, the policy excludes data shared in the context of public health emergencies, including Public Health Emergencies of International Concern (such as the COVID-19 pandemic) and data and reports from clinical trials and biological samples, and data collected by WHO prior to policy implementation.</p>\n<p>While there are a number of different sets of principles related to the responsible management of data in public health, international development and humanitarian action, the most directly relevant here are the <a href="https://www.go-fair.org/fair-principles/" target="_blank" rel="noopener noreferrer">FAIR data principles</a> and the <a href="https://www.unsystem.org/personal-data-protection-and-privacy-principles" target="_blank" rel="noopener noreferrer">United Nations Privacy Policy Group Personal Data Protection and Privacy Principles</a>.</p>\n<p>When data is used for purposes other than informing the response (e.g. research), additional frameworks and principles may apply. Researchers should refer to the <a href="https://www.who.int/about/ethics/code-of-conduct-for-responsible-research" target="_blank" rel="noopener noreferrer">WHO Code of Conduct for responsible Research</a>, which provides standards of good practice to guide individuals working on all research associated with WHO, including non-clinical research, in line with the principles of integrity, accountability, independence/impartiality, respect and professional commitment described in <a href="https://www.who.int/about/ethics/code_of_ethics_full_version.pdf?ua=1" target="_blank" rel="noopener noreferrer">WHO’s Code of Ethics and Professional Conduct</a>.</p>\n', 'id': u'What_policies_and_guidelines_currently_exist_to_inform_the_management_of_data_in_public_health_emergencies_'}, { 'q': u'Where can I learn more about data responsibility in humanitarian situations and in public health programming?', 'a': u'<p>Data responsibility entails a set of principles, processes and tools that support the safe, ethical and effective management of data in humanitarian response. This includes data privacy, protection, and security, as well as other practical measures to mitigate risk and prevent harm.</p>\n<p>There is a wealth of guidance available on how to responsibly manage data in public health emergencies and in humanitarian action more generally that should inform data management in the COVID-19 response. The following resources provide additional information and guidance on the safe, ethical, and effective management of data in humanitarian action:</p>\n<ol>\n<li><a href="https://www.accessnow.org/cms/assets/uploads/2020/03/Access-Now-recommendations-on-Covid-and-data-protection-and-privacy.pdf" target="_blank" rel="noopener noreferrer">Recommendations on privacy and data protection in the fight against COVID-19 (Access Now)</a></li>\n<li><a href="https://www.icrc.org/en/data-protection-humanitarian-action-handbook" target="_blank" rel="noopener noreferrer">Handbook on Data Protection in Humanitarian Action (ICRC and Brussels Privacy Hub)</a></li>\n<li><a href="https://centre.humdata.org/wp-content/uploads/2019/03/OCHA-DR-Guidelines-working-draft-032019.pdf" target="_blank" rel="noopener noreferrer">Working Draft Data Responsibility Guidelines (OCHA Centre for Humanitarian Data)</a></li>\n<li><a href="https://www.measureevaluation.org/resources/publications/ms-17-125a" target="_blank" rel="noopener noreferrer">mHealth Data Security, Privacy, and Confidentiality: Guidelines for Program Implementers and Policymakers</a></li>\n</ol>\n<p>For a broad range of resources related to data responsibility in development and humanitarian work, consult the <a href="https://docs.google.com/document/d/1Fa2QHusD5iJ8Woi8s7-SMFItAufKv4U5UR-PZ1szMNU/edit#heading=h.k5ayqcygtlml" target="_blank" rel="noopener noreferrer">Responsible Data Resource List</a> maintained by MERL Tech and the Engine Room.</p>\n', 'id': u'Where_can_I_learn_more_about_data_responsibility_in_humanitarian_situations_and_in_public_health_programming_'}, { 'q': u'How can my organization practice data responsibility when working and meeting remotely?', 'a': u'<p>Recent changes to working conditions have increased the use of online conferencing tools throughout the humanitarian sector. These conferencing technologies are invaluable when face-to-face meetings are not possible, but they also pose a significant information security and data protection risk when not used responsibly. Some steps for reducing these risks include:</p>\n<ol>\n<li>Familiarizing yourself with your organization’s approved online conferencing tools, their features and settings</li>\n<li>Using only online conferencing tools that are approved, configured and verified as secure by your organization</li>\n<li>Using a unique access code so that only those with the code for that meeting can access the room, particularly when a sensitive topic is being discussed</li>\n<li>Monitoring the dashboard of participants to ensure no uninvited parties are attending throughout the call</li>\n</ol>\n<p>For more information and additional recommendations, see <a href="https://centre.humdata.org/wp-content/uploads/2020/04/4459_002_Tip-Sheet-Responsible-Use-of-Online-Conferencing-Tools_WEB_1.pdf" target="_blank" rel="noopener noreferrer">this tip sheet</a> developed by the ICRC, IFRC and the Centre for Humanitarian Data on the responsible use of online conferencing tools</p>\n', 'id': u'How_can_my_organization_practice_data_responsibility_when_working_and_meeting_remotely_'}], 'title': u'[Data Responsibility COVID-19 Content]'}]} def mock_faqs_license_page_content(id): return { 'faq_data': [{'id': 'faq-_Data_Licenses_Content_', 'questions': [{'q': u'Creative Commons Attribution for Intergovernmental Organisations (CC BY-IGO)', 'a': u'<style></style><p>Under the CC BY-IGO license, you are free to share (copy and redistribute the material in any medium or format) and or adapt (remix, transform, and build upon the material) for any purpose, even commercially. The licensor cannot revoke these freedoms as long as you follow the license terms. The license terms are that you must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. Additionally, you may not apply legal terms or technological measures that legally restrict others from doing anything the license permits. When the Licensor is an intergovernmental organization, disputes will be resolved by mediation and arbitration unless otherwise agreed.</p>\n<p><span class="sspRegular12">[ more information: <a class="info-item-name" href="https://creativecommons.org/licenses/by/3.0/igo/" target="_blank" rel="noopener noreferrer">deed</a> | <a class="info-item-name" href="https://creativecommons.org/licenses/by/3.0/igo/legalcode" target="_blank" rel="noopener noreferrer">license</a>]</span></p>\n<div class=\'ewd-ufaq-faq-custom-fields\'>\n\n\t\n</div>', 'id': u'Creative_Commons_Attribution_for_Intergovernmental_Organisations__CC_BY_IGO_'}, {'q': u'Creative Commons Attribution International(CC BY)', 'a': u'<style></style><p>Under the CC BY license, you are free to share (copy and redistribute the material in any medium or format) and or adapt (remix, transform, and build upon the material) for any purpose, even commercially. The licensor cannot revoke these freedoms as long as you follow the license terms. The license terms are that you must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. Additionally, you may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.</p>\n<p><span class="sspRegular12">[ more information: <a class="info-item-name" href="https://creativecommons.org/licenses/by/4.0" target="_blank" rel="noopener noreferrer">deed</a> | <a class="info-item-name" href="https://creativecommons.org/licenses/by/4.0/legalcode" target="_blank" rel="noopener noreferrer">license</a>]</span></p>\n<div class=\'ewd-ufaq-faq-custom-fields\'>\n\n\t\n</div>', 'id': u'Creative_Commons_Attribution_International_CC_BY_'}, {'q': u'Creative Commons Attribution-ShareAlike (CC BY-SA)', 'a': u'<style></style><p>Under the CC BY-SA license, you are free to share (copy and redistribute the material in any medium or format) and or adapt (remix, transform, and build upon the material) for any purpose, even commercially. The licensor cannot revoke these freedoms as long as you follow the license terms. The license terms are that you must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original. Additionally, you may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.</p>\n<p><span class="sspRegular12">[ more information: <a class="info-item-name" href="https://creativecommons.org/licenses/by-sa/4.0" target="_blank" rel="noopener noreferrer">deed</a> | <a class="info-item-name" href="https://creativecommons.org/licenses/by-sa/4.0/legalcode" target="_blank" rel="noopener noreferrer">license</a>]</span></p>\n<div class=\'ewd-ufaq-faq-custom-fields\'>\n\n\t\n</div>', 'id': u'Creative_Commons_Attribution_ShareAlike__CC_BY_SA_'}, {'q': u'Open Database License (ODC-ODbL)', 'a': u'<style></style><p>Under the ODC-ODbL license, you are free:</p>\n<ul>\n<li>To Share: To copy, distribute and use the database.</li>\n<li>To Create: To produce works from the database.</li>\n<li>To Adapt: To modify, transform and build upon the database.</li>\n</ul>\n<p>As long as you:</p>\n<ul>\n<li>Attribute: You must attribute any public use of the database, or works produced from the database, in the manner specified in the ODbL. For any use or redistribution of the database, or works produced from it, you must make clear to others the license of the database and keep intact any notices on the original database.</li>\n<li>Share-Alike: If you publicly use any adapted version of this database, or works produced from an adapted database, you must also offer that adapted database under the ODbL.</li>\n<li>Keep open: If you redistribute the database, or an adapted version of it, then you may use technological measures that restrict the work (such as DRM) as long as you also redistribute a version without such measures.</li>\n</ul>\n<p><span class="sspRegular12">[ more information: <a class="info-item-name" href="https://opendatacommons.org/licenses/odbl/summary/" target="_blank" rel="noopener noreferrer">deed</a> | <a class="info-item-name" href="https://opendatacommons.org/licenses/odbl/1.0/" target="_blank" rel="noopener noreferrer">license</a>]</span></p>\n<div class=\'ewd-ufaq-faq-custom-fields\'>\n\n\t\n</div>', 'id': u'Open_Database_License__ODC_ODbL_'}, {'q': u'Open Data Commons Attribution License (ODC-BY)', 'a': u'<style></style><p>Under the ODC-BY license, you are free:</p>\n<ul>\n<li>To Share: To copy, distribute and use the database.</li>\n<li>To Create: To produce works from the database.</li>\n<li>To Adapt: To modify, transform and build upon the database.</li>\n</ul>\n<p>As long as you:</p>\n<ul>\n<li>Attribute: You must attribute any public use of the database, or works produced from the database, in the manner specified in the license. For any use or redistribution of the database, or works produced from it, you must make clear to others the license of the database and keep intact any notices on the original database.</li>\n</ul>\n<p><span class="sspRegular12">[ more information: <a class="info-item-name" href="https://opendatacommons.org/licenses/by/summary/" target="_blank" rel="noopener noreferrer">deed</a> | <a class="info-item-name" href="https://opendatacommons.org/licenses/by/1.0/" target="_blank" rel="noopener noreferrer">license</a>]</span></p>\n<div class=\'ewd-ufaq-faq-custom-fields\'>\n\n\t\n</div>', 'id': u'Open_Data_Commons_Attribution_License__ODC_BY_'}, {'q': u'Open Data Commons Public Domain Dedication and License (PDDL)', 'a': u'<style></style><p>Under the ODC-PDDL license, You are free:</p>\n<ul>\n<li>To Share: To copy, distribute and use the database.</li>\n<li>To Create: To produce works from the database.</li>\n<li>To Adapt: To modify, transform and build upon the database.</li>\n</ul>\n<p>As long as you:</p>\n<ul>\n<li>Blank: This section is intentionally left blank. The PDDL imposes no restrictions on your use of the PDDL licensed database.</li>\n</ul>\n<p><span class="sspRegular12">[ more information: <a class="info-item-name" href="https://opendatacommons.org/licenses/pddl/summary/" target="_blank" rel="noopener noreferrer">deed</a> | <a class="info-item-name" href="https://opendatacommons.org/licenses/pddl/1.0/" target="_blank" rel="noopener noreferrer">license</a>]</span></p>\n<div class=\'ewd-ufaq-faq-custom-fields\'>\n\n\t\n</div>', 'id': u'Open_Data_Commons_Public_Domain_Dedication_and_License__PDDL_'}, {'q': u'Public Domain/No restrictions (CC0)', 'a': u'<style></style><p>Under the terms of this license you are free to use the material for any purpose without any restrictions.</p>\n<p><span class="sspRegular12">[ more information: <a class="info-item-name" href="https://creativecommons.org/publicdomain/zero/1.0/" target="_blank" rel="noopener noreferrer">deed</a> | <a class="info-item-name" href="https://creativecommons.org/publicdomain/zero/1.0/legalcode" target="_blank" rel="noopener noreferrer">license</a>]</span></p>\n<div class=\'ewd-ufaq-faq-custom-fields\'>\n\n\t\n</div>', 'id': u'Public_Domain_No_restrictions__CC0_'}, {'q': u'Multiple Licenses', 'a': u'<style></style><div class="col-xs-12">\n<div class="row">\n<div class="col-xs-8 styleNo5">\n<p>The dataset contains data having different licenses or terms of use. The details of these licenses or terms of use should be listed in the file.</p>\n<p>\xa0</p>\n</div>\n</div>\n</div>\n<div class=\'ewd-ufaq-faq-custom-fields\'>\n\n\t\n</div>', 'id': u'Multiple_Licenses'}, {'q': u'Other', 'a': u"<style></style><p>Any other license or terms of use which are listed in the description of the dataset, or in the metadata fields of the dataset, or any other place in the dataset such as a specific license or terms of use file that is included as part of the dataset files.</p>\n<div class='ewd-ufaq-faq-custom-fields'>\n\n\t\n</div>", 'id': u'Other'}], 'title': u'[Data Licenses Content]'}], 'topics': {'faq-_Data_Licenses_Content_': u'[Data Licenses Content]'} } def mock_faqs_terms_page_content(id): return { 'faq_data': [{'id': 'faq-_HDX_Terms_of_Service_Content_', 'questions': [{'q': u'Account Management', 'a': u'<style></style><div class="col-xs-8 styleNo5">\n<ol>\n<li>User account. HDX is an open platform and anyone can use it without creating a user account. Signing up with HDX gives users access to additional features such as the ability to receive notifications about data; joining an organization as a member, editor or admin; and requesting access to datasets shared via HDX Connect, among other benefits.</li>\n<li>Organization account. Data can only be shared on HDX by approved organizations. Organizations can represent a formal legal entity such as a non-governmental organization, or an informal collective such as an Information Management Working Group. OCHA reviews requests to create an organization account to: (1) verify the identity of the requester and (2) determine whether the data that will be shared meets the requirements set out in the DATA SCOPE AND CRITERIA section below.</li>\n<li>You may delete your user or organization account at any time. When you delete your account, OCHA will delete any personal data we collected in order to create the account. When an organization account is deleted, the data shared by the organization is also deleted from HDX.</li>\n</ol>\n</div>\n<div class=\'ewd-ufaq-faq-custom-fields\'>\n\n\t\n</div>', 'id': u'Account_Management'}, {'q': u'Data Scope and Criteria', 'a': u'<style></style><div class="col-xs-8 styleNo5">\n<ol start="4">\n<li aria-level="1">There are three categories of humanitarian data which may be shared on HDX:<br />\na.\xa0Data about the context in which a humanitarian crisis is occurring (e.g. administrative boundaries, locations of schools, health facilities and other physical infrastructure, and baseline socio-economic indicators).<br />\nb. Data about the people affected by the crisis and their needs (e.g. needs assessment data, movement data and locations of affected people).<br />\nc. Data about the response by organizations seeking to help those who need assistance (e.g. who-is-doing-what-where, community perception surveys, and funding levels).</li>\n<li aria-level="1">All data shared on HDX must meet the following criteria:<br />\na. Public and private datasets may not contain any personal data. Aid worker contact details may be shared within a private dataset, if those aid workers have provided consent. Personal data is information, in any form, that relates to an identified or identifiable natural person.<br />\nb. Public and private datasets may not contain any sensitive non-personal data. This includes information which, while not relating to an identified or identifiable natural person, may, by reason of its sensitive context, put certain individuals or groups of individuals at risk of harm.<br />\nc. Data must have been collected in a fair and legitimate manner with a defined purpose and in line with principles of necessity and proportionality.<br />\nd. Data must be shared in a supported data format. HDX supports <a href="https://github.com/OCHA-DAP/hdx-ckan/blob/dev/ckanext-hdx_package/ckanext/hdx_package/config/resource_formats.json">all common data formats</a> and offers built-in preview support for CSV, TXT, XLS, and JSON formats. Map previews are possible from geographic data in zipped shapefile, KML and GeoJSON formats.</li>\n<li aria-level="1">Organizations should keep their data on HDX up-to-date in order to present the latest available information.</li>\n</ol>\n</div>\n<div class=\'ewd-ufaq-faq-custom-fields\'>\n\n\t\n</div>', 'id': u'Data_Scope_and_Criteria'}, {'q': u'Sharing Data', 'a': u'<style></style><div class="col-xs-8 styleNo5">\n<ol start="7">\n<li aria-level="1">There are three ways to share data on HDX:<br />\na. <i>Public</i>: Data is accessible to anyone who visits HDX, whether or not they are a registered user.<br />\n<i>b. Private</i>: Data is accessible only to registered users who are members of the organization that uploaded the data on HDX.<br />\n<i>c. HDX Connect:</i> The metadata of a dataset is available and the contributing organization can decide whether or not to grant access to the full dataset when requested by a registered user.</li>\n<li aria-level="1">Organizations must specify an appropriate license for all data they share publicly. Organizations are free to choose the license for their data. We have suggested some options <a href="https://data.humdata.org/about/license">here</a>.</li>\n<li aria-level="1">Organizations may use HDX to share data from other sources if the applicable license allows for onward sharing.</li>\n<li aria-level="1">After downloading a public dataset, users must follow the applicable license when using and sharing the data.</li>\n<li aria-level="1">Organizations may use the HDX Connect feature to direct users to data hosted outside of HDX. In such cases, organizations should link directly to the specific dataset described on HDX and not to a more general landing page of an external platform.</li>\n<li aria-level="1">When an organization grants access to data requested via HDX Connect, the data does not pass through the HDX infrastructure.</li>\n</ol>\n</div>\n<div class=\'ewd-ufaq-faq-custom-fields\'>\n\n\t\n</div>', 'id': u'Sharing_Data'}, {'q': u'Data Review', 'a': u'<style></style><div class="col-xs-8 styleNo5">\n<ol start="13">\n<li aria-level="1">In order to ensure data quality and to prevent any sensitive data from being exposed through HDX, OCHA reviews all datasets that are shared publicly or privately on the platform. This review consists of:<br />\na. An automated scan for sensitive data using Google\u2019s Data Loss Prevention (DLP) tool, to flag and prioritize data for manual review by OCHA.<br />\nb. A manual review based on a <a href="https://data.humdata.org/dataset/2048a947-5714-4220-905b-e662cbcd14c8/resource/658d5c4f-1680-4cb5-9fbf-10a0a64e2c39/download/hdx-qa-checklist.pdf">quality assurance checklist</a> that includes the completeness of metadata, the relevance of the data to humanitarian action, the integrity of the data resources, and the absence of any sensitive data, among other criteria.</li>\n<li aria-level="1">If the manual review under 13(b) shows that a dataset contains personal or sensitive data, the dataset is placed \u2018under review\u2019. While data is under review, users will only be able to consult the metadata.</li>\n<li aria-level="1">For microdata such as household survey results, OCHA runs a disclosure risk assessment to assess the risk of a person or group being re-identified. All datasets labeled as \u2018microdata\u2019 by the contributing organization at the point of upload are automatically placed under review. The dataset will remain under review until OCHA is able to determine that the risk of re-identification is below the risk threshold and that any sensitive data has been removed from the dataset by the organization. More information about this process is available <a href="https://humanitarian.atlassian.net/wiki/spaces/HDXKB/pages/1381498881/Statistical+Disclosure+Control+on+HDX">here</a>.</li>\n<li aria-level="1">If a user notices personal or sensitive data shared through the HDX platform they should contact <a href="mailto:hdx@un.org">hdx@un.org</a> immediately to request that the data be removed.</li>\n</ol>\n</div>\n<div class=\'ewd-ufaq-faq-custom-fields\'>\n\n\t\n</div>', 'id': u'Data_Review'}, {'q': u'Data Management', 'a': u'<style></style><div class="col-xs-8 styleNo5">\n<ol start="17">\n<li aria-level="1">HDX is built using <a href="https://ckan.org/">CKAN</a>, an open-source data management system.</li>\n<li aria-level="1">Data that is uploaded to HDX is stored by OCHA on servers provided by Amazon Web Services. Data is encrypted in transit and at rest. The servers are located in Virginia, the United States of America.</li>\n<li aria-level="1">All data uploaded to HDX is sent via Google\u2019s DLP API for automated scanning for sensitive data using the DLP algorithm. Data is encrypted in transit and scanned through DLP\u2019s <a href="https://cloud.google.com/dlp/docs/concepts-method-types">content method</a>. Data is not retained by Google in this process.</li>\n<li aria-level="1">OCHA will never alter the values within datasets shared through HDX without prior permission from the contributing organization.</li>\n<li aria-level="1">Data shared privately through the HDX platform will never be shared further by OCHA without prior permission from the contributing organization.</li>\n<li aria-level="1">OCHA will make a dataset private if it is found to violate these Terms and will contact the contributing organization to discuss next steps.</li>\n<li aria-level="1">Deleted datasets cannot be retrieved by users, but will continue to exist in backups of the HDX database which are maintained for 30 days.</li>\n</ol>\n</div>\n<div class=\'ewd-ufaq-faq-custom-fields\'>\n\n\t\n</div>', 'id': u'Data_Management'}, {'q': u'Generic Disclaimer of Liability', 'a': u'<style></style><ol start="24">\n<li aria-level="1">Organizations are responsible for the data they share on HDX. OCHA assumes no liability whatsoever for data shared on HDX. While OCHA upholds a high standard for the quality and timeliness of the data shared on HDX, we cannot verify data accuracy. Sharing data through HDX does not imply the transfer of any rights over this data to OCHA. OCHA disclaims all warranties, whether express or implied.</li>\n<li aria-level="1">Data and information on HDX do not imply the expression or endorsement of any opinion on the part of OCHA or the United Nations. This includes opinions concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.</li>\n</ol>\n<div class=\'ewd-ufaq-faq-custom-fields\'>\n\n\t\n</div>', 'id': u'Generic_Disclaimer_of_Liability'}, {'q': u'Privacy Notice', 'a': u'<style></style><ol start="26">\n<li aria-level="1">User contact details are only shared with the administrator of an HDX organization if the user requests access to an HDX Connect dataset.</li>\n<li aria-level="1">OCHA upholds the highest standard of data protection for the personal data of HDX users and organization administrators. In case such personal data is exposed, OCHA will notify all affected individuals and remedy the incident.</li>\n<li aria-level="1">OCHA continually seeks to understand the behavior of users on the HDX platform in order to make improvements. To do so, OCHA uses third-party analytics services, including Google Analytics and Mixpanel. Both of these services use cookies stored on users\u2019 devices to send encrypted information to Google Analytics and Mixpanel about how users arrived at HDX, what pages they visited on HDX, and their actions within those pages. Similar tracking is performed when users access HDX via our API or when directly downloading files from a shared link. OCHA does not send identifying information (including names, usernames, or email addresses) to either Google Analytics or Mixpanel. Google Analytics\u2019 and Mixpanel\u2019s use of the data collected from the HDX platform is governed by their respective Terms of Use.</li>\n<li aria-level="1">If you would like to disable the tracking described above under clause 28, you can install the <a href="https://tools.google.com/dlpage/gaoptout">Google Analytics Opt-out Browser Add-on</a> to disable Google Analytics tracking. Mixpanel respects <a href="https://allaboutdnt.com/">\u201cDo Not Track\u201d</a> settings in web browsers. Follow the instructions in <a href="https://allaboutdnt.com/#adjust-settings">this guide</a> to prevent your browser from sending data to Mixpanel. The data collected by these tracking systems will be retained indefinitely in order to understand how user behavior is changing over time.</li>\n<li aria-level="1">Emails sent by OCHA to registered HDX users may contain <a href="https://en.wikipedia.org/wiki/Web_beacon">web beacons</a>, which allow OCHA to track information about how many people have viewed its email campaigns. OCHA will never share personal data from this tracking with third parties other than with MailChimp, our mailing list provider, which has access by default. The data collected by this tracking system will be retained indefinitely in order to understand how readership of the emails is changing over time.</li>\n</ol>\n<div class=\'ewd-ufaq-faq-custom-fields\'>\n\n\t\n</div>', 'id': u'Privacy_Notice'}, {'q': u'Applicable Guidance and Policy', 'a': u'<style></style><div class="col-xs-8 styleNo5">\n<ol start="31">\n<li aria-level="1">OCHA is mandated by <a href="https://undocs.org/A/RES/46/182">United Nations General Assembly Resolution 46/182</a> and guided by the <a href="https://www.unocha.org/sites/dms/Documents/OOM-humanitarianprinciples_eng_June12.pdf">Humanitarian Principles</a>. OCHA is governed by the applicable guidance and policies established by the United Nations General Assembly and the United Nations Secretariat. Notably, personal data is processed according to the <a href="http://www.refworld.org/pdfid/3ddcafaac.pdf">1990 Guidelines for the Regulation of Computerized Data Files</a> and in line with the <a href="https://www.unsystem.org/privacy-principles">UN Principles on Personal Data Protection and Privacy</a>.</li>\n</ol>\n</div>\n<div class=\'ewd-ufaq-faq-custom-fields\'>\n\n\t\n</div>', 'id': u'Applicable_Guidance_and_Policy'}], 'title': u'[HDX Terms of Service Content]'}], 'topics': {'faq-_HDX_Terms_of_Service_Content_': u'[HDX Terms of Service Content]'} }

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35d92ece8021861f54e3487b58486d09fb120c2f

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py

Python

AtCoder Beginner Contest 208/B - Factorial Yen Coin.py

codedreamer-dg/AtCoder

6a4a9a2bc558bb0b21505877e00858d0c7981701

[ "MIT" ]

null

AtCoder Beginner Contest 208/B - Factorial Yen Coin.py

codedreamer-dg/AtCoder

6a4a9a2bc558bb0b21505877e00858d0c7981701

[ "MIT" ]

null

AtCoder Beginner Contest 208/B - Factorial Yen Coin.py

codedreamer-dg/AtCoder

6a4a9a2bc558bb0b21505877e00858d0c7981701

[ "MIT" ]

null

# _ _ _ # ___ ___ __| | ___ __| |_ __ ___ __ _ _ __ ___ ___ _ __ __| | __ _ # / __/ _ \ / _` |/ _ \/ _` | '__/ _ \/ _` | '_ ` _ \ / _ \ '__|/ _` |/ _` | # | (_| (_) | (_| | __/ (_| | | | __/ (_| | | | | | | __/ | | (_| | (_| | # \___\___/ \__,_|\___|\__,_|_| \___|\__,_|_| |_| |_|\___|_|___\__,_|\__, | # |_____| |___/ from sys import * '''sys.stdin = open('input.txt', 'r') sys.stdout = open('output.txt', 'w') ''' from collections import defaultdict as dd from math import * from bisect import * #sys.setrecursionlimit(10 ** 8) def sinp(): return input() def inp(): return int(sinp()) def minp(): return map(int, sinp().split()) def linp(): return list(minp()) def strl(): return list(sinp()) def pr(x): print(x) mod = int(1e9+7) res = 0 n = inp() while n: p = 1 i = 1 while p * i <= n and i <= 10: p *= i i += 1 res += 1 n -= p pr(res)

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ea1c0f80b7ecc37d6f8726ceeea5e84500b5ce4e

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py

Python

EstruturaDeDecisao/18 terminar.py

TheCarvalho/atividades-wikipython

9163d5de40dbed0d73917f6257e64a651a77e085

[ "Unlicense" ]

null

EstruturaDeDecisao/18 terminar.py

TheCarvalho/atividades-wikipython

9163d5de40dbed0d73917f6257e64a651a77e085

[ "Unlicense" ]

null

EstruturaDeDecisao/18 terminar.py

TheCarvalho/atividades-wikipython

9163d5de40dbed0d73917f6257e64a651a77e085

[ "Unlicense" ]

null

# ex18 - Faça um Programa que peça uma data no formato dd/mm/aaaa e determine se a mesma é uma data válida.

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doc/samples/uptodate_callable.py

m4ta1l/doit

d1a1b7b3abc7641d977d3b78b580d97aea4e27ea

[ "MIT" ]

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2022-03-31T11:35:44.000Z

doc/samples/uptodate_callable.py

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d1a1b7b3abc7641d977d3b78b580d97aea4e27ea

[ "MIT" ]

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2015-01-05T11:18:29.000Z

2022-03-20T11:46:46.000Z

doc/samples/uptodate_callable.py

m4ta1l/doit

d1a1b7b3abc7641d977d3b78b580d97aea4e27ea

[ "MIT" ]

176

2015-01-07T16:58:56.000Z

2022-03-28T12:12:11.000Z

def fake_get_value_from_db(): return 5 def check_outdated(): total = fake_get_value_from_db() return total > 10 def task_put_more_stuff_in_db(): def put_stuff(): pass return {'actions': [put_stuff], 'uptodate': [check_outdated], }

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Python

app/cmuxovik/templatetags/remove_newlines.py

artem343/cmuxovik

6f923f66ba47d0c513659c332fd8c89d21ea4abf

[ "MIT" ]

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2020-03-31T18:01:55.000Z

2020-03-31T18:45:02.000Z

app/cmuxovik/templatetags/remove_newlines.py

artem343/cmuxovik

6f923f66ba47d0c513659c332fd8c89d21ea4abf

[ "MIT" ]

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2020-03-31T17:47:09.000Z

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app/cmuxovik/templatetags/remove_newlines.py

artem343/cmuxovik

6f923f66ba47d0c513659c332fd8c89d21ea4abf

[ "MIT" ]

null

from django import template register = template.Library() @register.filter def remove_newlines(cmux: str) -> str: return cmux.replace('\n', ' ')

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ea63c7514a362bbf268f9a9ea9b6ae94c0c88422

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py

Python

sample/sample/views.py

Fhall21/django-datetimepicker

3f6b38d83bf52d3f48dca0ad843e4fdbf342a0f7

[ "Apache-2.0" ]

1

2020-11-13T06:48:23.000Z

sample/sample/views.py

Fhall21/django-datetimepicker

3f6b38d83bf52d3f48dca0ad843e4fdbf342a0f7

[ "Apache-2.0" ]

null

sample/sample/views.py

Fhall21/django-datetimepicker

3f6b38d83bf52d3f48dca0ad843e4fdbf342a0f7

[ "Apache-2.0" ]

6

2018-01-24T00:21:21.000Z

2022-03-09T06:06:51.000Z

from django.views.generic.edit import FormView from .forms import SampleForm class SampleView(FormView): form_class = SampleForm template_name = 'sample.html'

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ea75a121a7a7f2458731307018d9a50750c50d0b

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py

Python

tests/test_region.py

khurrumsaleem/dassh

8823e4b5256975a375391787558e5b6aba816251

[ "BSD-3-Clause" ]

11

2021-08-12T17:08:37.000Z

2021-12-09T22:35:48.000Z

tests/test_region.py

khurrumsaleem/dassh

8823e4b5256975a375391787558e5b6aba816251

[ "BSD-3-Clause" ]

3

2021-11-24T21:15:36.000Z

2022-03-25T14:00:52.000Z

tests/test_region.py

khurrumsaleem/dassh

8823e4b5256975a375391787558e5b6aba816251

[ "BSD-3-Clause" ]

2

2021-08-23T08:00:55.000Z

2021-09-16T02:26:59.000Z

######################################################################## # Copyright 2021, UChicago Argonne, LLC # # Licensed under the BSD-3 License (the "License"); you may not use # this file except in compliance with the License. You may obtain a # copy of the License at # # https://opensource.org/licenses/BSD-3-Clause # # 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. ######################################################################## """ date: 2022-01-05 author: matz Test the base Region class and its methods """ ######################################################################## import numpy as np import pytest from tests import conftest def test_activate_to_rr(c_fuel_params, c_lrefl_simple): """Test activation from simple model to pin bundle model (single adiabatic duct wall) - All subchannels in pin bundle equal simple model temperature. - Average coolant temperature should be maintained. - Duct wall temperatures are recalculated to maintain energy cons. - Since adiabatic, average duct temperature should be close. """ # Need to activate RR flowrate = 1.0 t_gap = np.ones(2) # Making adiabatic so it doesn't matter htc_gap = np.ones(2) # Making adiabatic so it doesn't matter rr = conftest.make_rodded_region_fixture('conceptual_fuel', c_fuel_params[0], c_fuel_params[1], flowrate) # SETUP THE PREVIOUS REGION: Update the flow rate on the simple region ur = c_lrefl_simple.clone(new_flowrate=flowrate) # Activate simple region manually (set temps equal to something) k = 'coolant_int' ur.temp[k] = np.random.random((ur.temp[k].shape)) * 10 + 623.15 ur._update_coolant_params(ur.temp['coolant_int'][0]) ur._calc_duct_temp(t_gap, htc_gap, True) # Now activate RR based on UR and see what happened rr.activate(ur, t_gap, htc_gap, True) # Subchannel temperatures msg = 'Subchannel coolant temperature error' diff = rr.temp['coolant_int'] - ur.temp['coolant_int'][0] assert np.all(np.abs(diff) < 1e-9), msg # Average coolant interior temperature msg = 'Average interior coolant temperature error' diff = rr.avg_coolant_int_temp - ur.avg_coolant_int_temp assert np.abs(diff) < 1e-9, msg # Overall avg coolant temp (since no bypass, same as above) msg = 'Average interior coolant temperature error' diff = rr.avg_coolant_temp - ur.avg_coolant_temp assert np.abs(diff) < 1e-9, msg # Average duct MW temp: for the outer duct, this is the only # thing that might be different. msg = 'Average duct midwall temperature' diff = rr.avg_duct_mw_temp - ur.avg_duct_mw_temp # Fails at tolerance 1e-9; 1e-8 K is still pretty close tho assert np.abs(diff) < 1e-8, msg # Corner duct MW and surface temperatures idx = np.arange(0, 6, 1, dtype=int) + 1 idx *= int(rr.subchannel.n_sc['duct']['total'] / 6) idx -= 1 msg = 'Corner duct midwall temperatures' rr_corner_temps = rr.temp['duct_mw'][-1, idx] diff = rr_corner_temps - ur.temp['duct_mw'][-1] assert np.all(np.isclose(diff, 0.0)), msg msg = 'Corner duct surface temperatures' rr_corner_temps = rr.temp['duct_surf'][:, :, idx] diff = rr_corner_temps - ur.temp['duct_surf'] assert np.all(np.isclose(diff, 0.0)), msg def test_activate_from_rr(c_fuel_params, c_lrefl_simple): """Test activation from pin bundle model to simple bundle model (single adiabatic duct wall) - Simple model coolant temperature equals average pin bundle model coolant temperature - Duct wall temperatures are recalculated to maintain energy cons. Therefore, duct wall temperatures may not be maintained (see note below). Note ---- Average duct temperature won't be preserved because the new duct temperatures are calculated based on the new coolant temperature (which for the simple model region is only 1 value). The old average duct temperature was based on the temperatures of only the edge and corner subchannels immediately adjacent to it. """ # Need to activate RR flowrate = 1.0 rr = conftest.make_rodded_region_fixture('conceptual_fuel', c_fuel_params[0], c_fuel_params[1], flowrate) rr = conftest.activate_rodded_region(rr, 650.0) # Muss up the temperatures so it's like it did something t_gap = np.ones(54) # Making adiabatic so it doesn't matter htc_gap = np.ones(54) # Making adiabatic so it doesn't matter p_duct = np.zeros(54) # Zero power in duct k = 'coolant_int' rr.temp[k] = np.random.random((rr.temp[k].shape)) * 10 + 650.15 rr._update_coolant_int_params(rr.avg_coolant_int_temp) rr._calc_duct_temp(p_duct, t_gap, htc_gap, True) # Update the flow rate on the simple region and activate ur = c_lrefl_simple.clone(new_flowrate=flowrate) ur.activate(rr, t_gap, htc_gap, True) # Average coolant interior temperature msg = 'Average interior coolant temperature error' diff = rr.avg_coolant_int_temp - ur.avg_coolant_int_temp assert np.abs(diff) < 1e-9, msg # Overall avg coolant temp (w/ no bypass, should be same as above) msg = 'Average interior coolant temperature error' diff = rr.avg_coolant_temp - ur.avg_coolant_temp assert np.abs(diff) < 1e-9, msg def test_activate_to_rr_dd(c_ctrl_params, c_lrefl_simple): """Test activation from simple model to pin bundle model (double duct with adiabatic boundary on outer duct wall surface) - All interior subchannels in pin bundle equal simple model temp. - All interior duct wall temperatures in pin bundle model equal simple model coolant temp. - All bypass gap temperatures in pin bundle model equal simple model coolant temp. - Outer duct wall is recalculated - may not match exactly. """ # Need to activate RR flowrate = 1.0 rr = conftest.make_rodded_region_fixture('conceptual_ctrl', c_ctrl_params[0], c_ctrl_params[1], flowrate) t_gap = np.ones(2) # Making adiabatic so it doesn't matter htc_gap = np.ones(2) # Making adiabatic so it doesn't matter # SETUP THE PREVIOUS REGION: Update the flow rate on the simple region ur = c_lrefl_simple.clone(new_flowrate=flowrate) # Activate simple region manually (set temps equal to something) k = 'coolant_int' ur.temp[k] = np.random.random((ur.temp[k].shape)) * 10 + 623.15 ur._update_coolant_params(ur.temp['coolant_int'][0]) ur._calc_duct_temp(t_gap, htc_gap, True) # Now activate RR based on UR and see what happened rr.activate(ur, t_gap, htc_gap, True) # Subchannel temperatures msg = 'Interior subchannel temperatures error' diff = rr.temp['coolant_int'] - ur.temp['coolant_int'][0] assert np.all(np.abs(diff) < 1e-9), msg # Interior duct wall temperatures msg = 'Interior duct wall temperatures error' diff = rr.temp['duct_mw'] - ur.temp['coolant_int'][0] # Note: this fails at 1e-9. assert np.all(np.abs(diff) < 2e-8), msg # Bypass gap temperatures msg = 'Subchannels between ducts temperature error' diff = rr.temp['coolant_byp'][0] - ur.temp['coolant_int'][0] assert np.all(np.abs(diff) < 1e-9), msg # Average coolant interior temperature; should be the same when # activated double-duct assembly because all coolant is the same # temp. msg = 'Average interior coolant temperature error' diff = rr.avg_coolant_int_temp - ur.avg_coolant_int_temp assert np.abs(diff) < 1e-9, msg # Overall avg coolant temp msg = 'Average interior coolant temperature error' diff = rr.avg_coolant_temp - ur.avg_coolant_temp assert np.abs(diff) < 1e-9, msg # Average duct MW temp msg = 'Average outer duct midwall temperature' diff = rr.avg_duct_mw_temp[-1] - ur.avg_duct_mw_temp # This fails at 1e-9, but 2e-8 degrees K is pretty close assert np.abs(diff) < 2e-8, msg msg = 'Average inner duct midwall temperature' diff = rr.avg_duct_mw_temp[0] - ur.avg_coolant_temp assert np.abs(diff) < 1e-9, msg # Corner duct MW and surface temperatures idx = np.arange(0, 6, 1, dtype=int) + 1 idx *= int(rr.subchannel.n_sc['duct']['total'] / 6) idx -= 1 msg = 'Corner duct midwall temperatures' rr_corner_temps = rr.temp['duct_mw'][-1, idx] diff = rr_corner_temps - ur.temp['duct_mw'][-1] # This fails at 1e-9, but 2e-8 degrees K is pretty close assert np.all(np.abs(diff) < 2e-8), msg msg = 'Corner duct surface temperatures' rr_corner_temps = rr.temp['duct_surf'][:, :, idx] diff = rr_corner_temps[-1] - ur.temp['duct_surf'][-1] # This fails at 1e-9, but 2e-8 degrees K is pretty close assert np.all(np.abs(diff) < 2e-8), msg def test_activate_from_rr_dd(c_ctrl_params, c_lrefl_simple): """Test activation from double-ducted pin bundle model to simple bundle model - Pin bundle overall average coolant temperature (interior and double-duct bypass) --> simple model coolant temperature - Simple model outer duct wall temperature recalculated to maintain energy conservation; temps may not be maintained. coolant temperature Note ---- Average duct temperature won't be preserved because the new duct temperatures are calculated based on the new coolant temperature (which for the simple model region is only 1 value). The old average duct temperature was based on the temperatures of only the edge and corner subchannels immediately adjacent to it. """ # Need to activate RR flowrate = 1.0 t_gap = np.ones(54) # Making adiabatic so it doesn't matter htc_gap = np.ones(54) # Making adiabatic so it doesn't matter rr = conftest.make_rodded_region_fixture('conceptual_ctrl', c_ctrl_params[0], c_ctrl_params[1], flowrate) rr = conftest.activate_rodded_region(rr, 650.0, base=False) # Muss up the temperatures so it's like it did something p_duct = np.zeros(54) # Zero power in duct k = 'coolant_int' rr.temp[k] = np.random.random((rr.temp[k].shape)) * 10 + 650.15 rr._update_coolant_int_params(rr.avg_coolant_int_temp) rr._calc_duct_temp(p_duct, t_gap, htc_gap, True) # Update the flow rate on the simple region and activate ur = c_lrefl_simple.clone(new_flowrate=flowrate) ur.activate(rr, t_gap, htc_gap, True) # Average coolant interior temperature not the same when activating # from double-duct assembly because it's assumed that all coolant # will mix. However, overall avg coolant temp should be same msg = 'Average coolant temperature error' diff = rr.avg_coolant_temp - ur.avg_coolant_temp assert np.abs(diff) < 1e-9, msg def test_material_update_errors(c_fuel_params, caplog): """Test that material update failures return error messages""" flowrate = 1.0 rr = conftest.make_rodded_region_fixture('conceptual_fuel', c_fuel_params[0], c_fuel_params[1], flowrate) rr = conftest.activate_rodded_region(rr, 650.0) with pytest.raises(SystemExit): rr._update_coolant(-50.0) msg = "Coolant material update failure; Name: conceptual_fuel" assert msg in caplog.text

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ea99e88b70709ab303e6cbe4083c21e63503e33d

191

py

Python

dice.py

key999/oop-lab-term3

11c91f967029363b5c023253d7a629d097cca366

[ "Unlicense" ]

2

2021-03-02T12:14:13.000Z

2021-12-12T02:32:05.000Z

dice.py

key999/oop-lab-term3

11c91f967029363b5c023253d7a629d097cca366

[ "Unlicense" ]

null

dice.py

key999/oop-lab-term3

11c91f967029363b5c023253d7a629d097cca366

[ "Unlicense" ]

null

def toss(y): try: from secrets import randbelow return(randbelow(y) + 1) except ImportError: from random import randint return(randint(1, y))

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992

py

Python

simple_rl/abstraction/__init__.py

dwhit/simple_rl

32ba356680f2ea6c8913702fe2c7fee3ee511b3b

[ "Apache-2.0" ]

10

2021-11-22T12:29:30.000Z

2022-03-28T10:23:16.000Z

simple_rl/abstraction/__init__.py

samlobel/simple_rl_mbrl

ed868916d06dbf68f4af23bea83b0e852e88df6e

[ "Apache-2.0" ]

null

simple_rl/abstraction/__init__.py

samlobel/simple_rl_mbrl

ed868916d06dbf68f4af23bea83b0e852e88df6e

[ "Apache-2.0" ]

2

2022-03-19T07:42:56.000Z

2022-03-28T10:36:33.000Z

# Classes. from simple_rl.abstraction.AbstractionWrapperClass import AbstractionWrapper from simple_rl.abstraction.AbstractValueIterationClass import AbstractValueIteration from simple_rl.abstraction.state_abs.StateAbstractionClass import StateAbstraction from simple_rl.abstraction.state_abs.ProbStateAbstractionClass import ProbStateAbstraction from simple_rl.abstraction.action_abs.ActionAbstractionClass import ActionAbstraction from simple_rl.abstraction.action_abs.InListPredicateClass import InListPredicate from simple_rl.abstraction.action_abs.OptionClass import Option from simple_rl.abstraction.action_abs.PolicyClass import Policy from simple_rl.abstraction.action_abs.PolicyFromDictClass import PolicyFromDict from simple_rl.abstraction.action_abs.PredicateClass import Predicate # Scripts. from simple_rl.abstraction.state_abs import sa_helpers, indicator_funcs from simple_rl.abstraction.action_abs import aa_helpers from simple_rl.abstraction.abstr_mdp import abstr_mdp_funcs

62

90

0.903226

118

992

7.347458

0.313559

0.149942

0.179931

0.344867

0.365629

0

0.05746

992

16

91

62

0.927273

0.017137

0

1

0

true

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

4

57abc3e2ee45059b4a663df6a16aeb59ed8e234a

98

py

Python

PythonClub/PythonClub/PythonApp/apps.py

isadoracabral/PythonClub

75ca7d92d17e68d9e32523c473de13e7ef1d8628

[ "Apache-2.0" ]

null

PythonClub/PythonClub/PythonApp/apps.py

isadoracabral/PythonClub

75ca7d92d17e68d9e32523c473de13e7ef1d8628

[ "Apache-2.0" ]

null

PythonClub/PythonClub/PythonApp/apps.py

isadoracabral/PythonClub

75ca7d92d17e68d9e32523c473de13e7ef1d8628

[ "Apache-2.0" ]

null

from django.apps import AppConfig class PythonappConfig(AppConfig): name = 'PythonApp'

16.333333

34

0.72449

10

98

7.1

0.9

0

0.204082

98

5

35

19.6

0.910256

0

0.096774

0

1

0

false

0

0.333333

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

4

57ae5642a9cd5e851474795eab4fc5ad3d75d21e

387

py

Python

commands/slotsjackpot.py

Dabomstew/goldenrod

36c4f9e5321788779840371b09a78cc6b26b38b1

[ "MIT" ]

2

2015-05-20T00:42:03.000Z

2015-05-23T04:18:42.000Z

commands/slotsjackpot.py

Dabomstew/goldenrod

36c4f9e5321788779840371b09a78cc6b26b38b1

[ "MIT" ]

null

commands/slotsjackpot.py

Dabomstew/goldenrod

36c4f9e5321788779840371b09a78cc6b26b38b1

[ "MIT" ]

null

import config import random import datetime, time import math def execute(parser, bot, user, args): slotsPool = bot.execQuerySelectOne("SELECT * FROM slotspool") bot.addressUser(user, "The current slots jackpot is %d %s." % (slotsPool["slotspool"], config.currencyPlural)) def requiredPerm(): return "anyone" def canUseByWhisper(): return True

24.1875

115

0.684755

43

387

6.162791

0.697674

0.090566

0

0.211886

387

15

116

25.8

0.868852

0

0.196766

0

1

0.272727

false

0

0.363636

0.181818

0.818182

0

null

0

null

0

1

0

1

0

4

57c639a59e8e6f6fab6583cd007695587135a57d

144

py

Python

python/1619.A.py

arechesk/cf

8d2209398f0fc4a73c139f4101634a8ed8c62ff6

[ "BSD-3-Clause" ]

null

python/1619.A.py

arechesk/cf

8d2209398f0fc4a73c139f4101634a8ed8c62ff6

[ "BSD-3-Clause" ]

null

python/1619.A.py

arechesk/cf

8d2209398f0fc4a73c139f4101634a8ed8c62ff6

[ "BSD-3-Clause" ]

null

t=int(input()) for i in range(t): s=input() if s[:int(len(s)/2)]==s[int(len(s)/2):]: print("YES") else: print("NO")

18

44

0.458333

26

144

2.538462

0.576923

0.121212

0.212121

0.242424

0.272727

0

0.019048

0.270833

144

7

45

20.571429

0.609524

0

0.034722

0

1

0

false

0

0.285714

1

0

null

0

1

0

1

0

null

0

4

57cc30c51d33d6b2eaabc16495589e81fff6b361

76

py

Python

blue_st_sdk/features/audio/opus/__init__.py

cchangeur/BlueSTSDK_Python

e5c6e4bc5a58680bad0d867633dd9d92012b9baf

[ "BSD-3-Clause" ]

43

2019-03-08T08:03:19.000Z

2022-01-20T11:51:11.000Z

blue_st_sdk/features/audio/opus/__init__.py

cchangeur/BlueSTSDK_Python

e5c6e4bc5a58680bad0d867633dd9d92012b9baf

[ "BSD-3-Clause" ]

24

2019-04-01T20:50:40.000Z

2022-03-16T17:00:54.000Z

blue_st_sdk/features/audio/opus/__init__.py

cchangeur/BlueSTSDK_Python

e5c6e4bc5a58680bad0d867633dd9d92012b9baf

[ "BSD-3-Clause" ]

19

2019-02-20T08:41:20.000Z

2021-11-21T11:39:50.000Z

__all__ = [ 'feature_audio_opus', \ 'feature_audio_opus_conf' ]

15.2

30

0.631579

8

76

4.875

0.625

0.615385

0.820513

0

0.25

76

4

31

19

0.684211

0

0.569444

0.319444

0

1

0

false

0

1

0

null

1

0

1

0

null

0

4

57cd1515a5bf4b72f0c01bf2d150a95bc66f1b6b

2,618

py

Python

tests/fixtures/srt.py

rlaPHOENiX/pycaption

9824701ed74dfd3b0c39d0dc1fb2f00f4619c4a6

[ "Apache-2.0" ]

1

2021-08-28T07:03:27.000Z

tests/fixtures/srt.py

rlaphoenix/pycaption

9824701ed74dfd3b0c39d0dc1fb2f00f4619c4a6

[ "Apache-2.0" ]

null

tests/fixtures/srt.py

rlaphoenix/pycaption

9824701ed74dfd3b0c39d0dc1fb2f00f4619c4a6

[ "Apache-2.0" ]

null

import pytest @pytest.fixture(scope="session") def sample_srt(): return """1 00:00:09,209 --> 00:00:12,312 ( clock ticking ) 2 00:00:14,848 --> 00:00:17,000 MAN: When we think \u266a ...say bow, wow, \u266a 3 00:00:17,000 --> 00:00:18,752 we have this vision of Einstein 4 00:00:18,752 --> 00:00:20,887 as an old, wrinkly man with white hair. 5 00:00:20,887 --> 00:00:26,760 MAN 2: E equals m c-squared is not about an old Einstein. 6 00:00:26,760 --> 00:00:32,200 MAN 2: It's all about an eternal Einstein. 7 00:00:32,200 --> 00:00:36,200 <LAUGHING & WHOOPS!> """ @pytest.fixture(scope="session") def sample_srt_ascii(): return """1 00:00:09,209 --> 00:00:12,312 ( clock ticking ) 2 00:00:14,848 --> 00:00:17,000 MAN: When we think of "E equals m c-squared", 3 00:00:17,000 --> 00:00:18,752 we have this vision of Einstein 4 00:00:18,752 --> 00:00:20,887 as an old, wrinkly man with white hair. 5 00:00:20,887 --> 00:00:26,760 MAN 2: E equals m c-squared is not about an old Einstein. 6 00:00:26,760 --> 00:00:32,200 MAN 2: It's all about an eternal Einstein. 7 00:00:32,200 --> 00:00:34,400 <LAUGHING & WHOOPS!> 8 00:00:34,400 --> 00:00:38,400 some more text """ @pytest.fixture(scope="session") def sample_srt_numeric(): return """35 00:00:32,290 --> 00:00:32,890 TO FIND HIM. IF 36 00:00:32,990 --> 00:00:33,590 YOU HAVE ANY INFORMATION 37 00:00:33,690 --> 00:00:34,290 THAT CAN HELP, CALL THE 38 00:00:34,390 --> 00:00:35,020 STOPPERS LINE. THAT 39 00:00:35,120 --> 00:00:35,760 NUMBER IS 662-429-84-77. 40 00:00:35,860 --> 00:00:36,360 STD OUT 41 00:00:36,460 --> 00:02:11,500 3 """ @pytest.fixture(scope="session") def sample_srt_empty(): return """ """ @pytest.fixture(scope="session") def sample_srt_blank_lines(): return """35 00:00:32,290 --> 00:00:32,890 36 00:00:32,990 --> 00:00:33,590 YOU HAVE ANY INFORMATION """ @pytest.fixture(scope="session") def sample_srt_trailing_blanks(): return """35 00:00:32,290 --> 00:00:32,890 HELP I SAY 36 00:00:32,990 --> 00:00:33,590 YOU HAVE ANY INFORMATION """ @pytest.fixture(scope="session") def samples_srt_same_time(): return """1 00:00:05,213 --> 00:00:10,552 SO NO ONE TOLD YOU 2 00:00:05,213 --> 00:00:10,552 LIFE WAS GONNA BE THIS WAY 3 00:00:10,566 --> 00:00:10,580 YOUR JOB IS A JOKE, YOUR ARE BROKE 4 00:00:10,594 --> 00:00:10,600 IT IS LIKE YOU ARE ALWAYS STUCK 5 00:00:10,594 --> 00:00:10,600 IN A SECOND GEAR """ @pytest.fixture(scope="session") def sample_srt_empty_cue_output(): return """\ 1 00:00:01,209 --> 00:00:02,312 abc """

14.384615

35

0.646677

535

2,618

3.127103

0.302804

0.150628

0.046623

0.119546

0.698147

0.688583

0.59474

0.501494

0

0.296434

0.175325

2,618

181

36

14.464088

0.478462

0

0.552239

0

0.770053

0

1

0.059701

true

0

0.007463

0.059701

0.126866

0

null

0

1

0

1

0

1

0

null

0

1

0

4

57cd875f32afc94819661b070ae0dc9db05505f9

231

py

Python

scrubby/execution/execution_step.py

typerandom/scrubby

5cccfad6c735828e6eec1452162a4e58aea917a9

[ "MIT" ]

2

2019-05-27T22:28:21.000Z

2021-02-19T11:37:11.000Z

scrubby/execution/execution_step.py

typerandom/scrubby

5cccfad6c735828e6eec1452162a4e58aea917a9

[ "MIT" ]

1

2021-03-25T21:27:34.000Z

scrubby/execution/execution_step.py

typerandom/scrubby

5cccfad6c735828e6eec1452162a4e58aea917a9

[ "MIT" ]

null

class ExecutionStep(object): def run(self, db): raise NotImplementedError('Method run(self, db) is not implemented.') def explain(self): raise NotImplementedError('Method explain(self) is not implemented.')

38.5

77

0.701299

27

231

6

0.518519

0.08642

0.111111

0

0.190476

231

6

78

38.5

0.86631

0

0.344828

0

1

0.4

false

0

0.6

0

1

0

null

0

1

0

null

0

1

0

1

0

4

57d60d1c7cbc15aeef2cb33b932fce96ef62f017

104

py

Python

tests/test_stub.py

PlaidWeb/Subl

2fa3f30aa935df61c03ce614e6ea22eab519ec6c

[ "MIT" ]

null

tests/test_stub.py

PlaidWeb/Subl

2fa3f30aa935df61c03ce614e6ea22eab519ec6c

[ "MIT" ]

1

2020-07-20T08:28:47.000Z

tests/test_stub.py

PlaidWeb/Subl

2fa3f30aa935df61c03ce614e6ea22eab519ec6c

[ "MIT" ]

null

""" stub test, remove this when there's actual testing """ def test_nothing(): """ do nothing """

17.333333

58

0.625

14

104

4.571429

0.857143

0

0.211538

104

5

59

20.8

0.780488

0.596154

0

1

true

0

1

0

1

0

null

0

1

0

null

0

1

0

4

17acd44b2823516e5d98e96db26b6112f10205bc

965

py

Python

plugins/tff_backend/migrations/_007_referral_in_user_data.py

threefoldfoundation/app_backend

b3cea2a3ff9e10efcc90d3d6e5e8e46b9e84312a

[ "Apache-2.0" ]

null

plugins/tff_backend/migrations/_007_referral_in_user_data.py

threefoldfoundation/app_backend

b3cea2a3ff9e10efcc90d3d6e5e8e46b9e84312a

[ "Apache-2.0" ]

178

2017-08-02T12:58:06.000Z

2017-12-20T15:01:12.000Z

plugins/tff_backend/migrations/_007_referral_in_user_data.py

threefoldfoundation/app_backend

b3cea2a3ff9e10efcc90d3d6e5e8e46b9e84312a

[ "Apache-2.0" ]

2

2018-01-10T10:43:12.000Z

2018-03-18T10:42:23.000Z

# -*- coding: utf-8 -*- # Copyright 2017 GIG Technology NV # # 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. # # @@license_version:1.3@@ from framework.bizz.job import run_job from plugins.tff_backend.bizz.user import store_referral_in_user_data from plugins.tff_backend.models.user import TffProfile def migrate(dry_run=False): run_job(_profiles_with_referrer, [], store_referral_in_user_data, []) def _profiles_with_referrer(): return TffProfile.query()

33.275862

74

0.767876

147

965

4.904762

0.62585

0.083218

0.036061

0.044383

0.0638

0

0.013382

0.148187

965

28

75

34.464286

0.863747

0.621762

0

1

0.285714

false

0

0.428571

0.142857

0.857143

0

null

0

null

0

1

0

1

0

4

17b1c69a71056b42a57e141ebecede51d3855608

134

py

Python

project_e/dealers/forms.py

ElectricFleming/project-e

cf05d2a835a09555e3dba5813d635d329684a71c

[ "bzip2-1.0.6" ]

null

project_e/dealers/forms.py

ElectricFleming/project-e

cf05d2a835a09555e3dba5813d635d329684a71c

[ "bzip2-1.0.6" ]

1

2020-01-17T14:23:09.000Z

project_e/dealers/forms.py

ElectricFleming/project-e

cf05d2a835a09555e3dba5813d635d329684a71c

[ "bzip2-1.0.6" ]

1

2019-12-27T22:45:45.000Z

from django import forms class DealerCreationForm(forms.Form): name = forms.CharField() address = forms.CharField() #Address

22.333333

40

0.738806

15

134

6.6

0.666667

0.282828

0.424242

0

0.164179

134

5

41

26.8

0.883929

0.052239

0

1

0

false

0

0.25

0

1

0

1

0

null

1

0

1

0

null

0

4

17e8df55205e733065c94ba24fbd9e7e676ba5cf

89

py

Python

GreedDice_main.py

tjbruce19/codewars

ecc22421916d88589635d8781400acbd71c53c01

[ "Apache-2.0" ]

null

GreedDice_main.py

tjbruce19/codewars

ecc22421916d88589635d8781400acbd71c53c01

[ "Apache-2.0" ]

null

GreedDice_main.py

tjbruce19/codewars

ecc22421916d88589635d8781400acbd71c53c01

[ "Apache-2.0" ]

null

from GreedDice import score if __name__ == "__main__": print(score([2, 4, 6, 1, 1]))

22.25

33

0.640449

14

89

3.5

0.857143

0

0.069444

0.191011

89

4

33

22.25

0.611111

0

0.088889

0

1

0

true

0

0.333333

0

0.333333

1

0

null

0

1

0

null

0

1

0

1

0

4

17ef92a3a3fee906c46cb2b903ba87e92a25aa97

11,361

py

Python

sdk/python/pulumi_aws/sagemaker/_inputs.py

mdop-wh/pulumi-aws

05bb32e9d694dde1c3b76d440fd2cd0344d23376

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

null

sdk/python/pulumi_aws/sagemaker/_inputs.py

mdop-wh/pulumi-aws

05bb32e9d694dde1c3b76d440fd2cd0344d23376

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

null

sdk/python/pulumi_aws/sagemaker/_inputs.py

mdop-wh/pulumi-aws

05bb32e9d694dde1c3b76d440fd2cd0344d23376

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

null

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import warnings import pulumi import pulumi.runtime from typing import Any, Dict, List, Mapping, Optional, Tuple, Union from .. import _utilities, _tables __all__ = [ 'EndpointConfigurationProductionVariantArgs', 'ModelContainerArgs', 'ModelPrimaryContainerArgs', 'ModelVpcConfigArgs', ] @pulumi.input_type class EndpointConfigurationProductionVariantArgs: def __init__(__self__, *, initial_instance_count: pulumi.Input[float], instance_type: pulumi.Input[str], model_name: pulumi.Input[str], accelerator_type: Optional[pulumi.Input[str]] = None, initial_variant_weight: Optional[pulumi.Input[float]] = None, variant_name: Optional[pulumi.Input[str]] = None): """ :param pulumi.Input[float] initial_instance_count: Initial number of instances used for auto-scaling. :param pulumi.Input[str] instance_type: The type of instance to start. :param pulumi.Input[str] model_name: The name of the model to use. :param pulumi.Input[str] accelerator_type: The size of the Elastic Inference (EI) instance to use for the production variant. :param pulumi.Input[float] initial_variant_weight: Determines initial traffic distribution among all of the models that you specify in the endpoint configuration. If unspecified, it defaults to 1.0. :param pulumi.Input[str] variant_name: The name of the variant. If omitted, this provider will assign a random, unique name. """ pulumi.set(__self__, "initial_instance_count", initial_instance_count) pulumi.set(__self__, "instance_type", instance_type) pulumi.set(__self__, "model_name", model_name) if accelerator_type is not None: pulumi.set(__self__, "accelerator_type", accelerator_type) if initial_variant_weight is not None: pulumi.set(__self__, "initial_variant_weight", initial_variant_weight) if variant_name is not None: pulumi.set(__self__, "variant_name", variant_name) @property @pulumi.getter(name="initialInstanceCount") def initial_instance_count(self) -> pulumi.Input[float]: """ Initial number of instances used for auto-scaling. """ return pulumi.get(self, "initial_instance_count") @initial_instance_count.setter def initial_instance_count(self, value: pulumi.Input[float]): pulumi.set(self, "initial_instance_count", value) @property @pulumi.getter(name="instanceType") def instance_type(self) -> pulumi.Input[str]: """ The type of instance to start. """ return pulumi.get(self, "instance_type") @instance_type.setter def instance_type(self, value: pulumi.Input[str]): pulumi.set(self, "instance_type", value) @property @pulumi.getter(name="modelName") def model_name(self) -> pulumi.Input[str]: """ The name of the model to use. """ return pulumi.get(self, "model_name") @model_name.setter def model_name(self, value: pulumi.Input[str]): pulumi.set(self, "model_name", value) @property @pulumi.getter(name="acceleratorType") def accelerator_type(self) -> Optional[pulumi.Input[str]]: """ The size of the Elastic Inference (EI) instance to use for the production variant. """ return pulumi.get(self, "accelerator_type") @accelerator_type.setter def accelerator_type(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "accelerator_type", value) @property @pulumi.getter(name="initialVariantWeight") def initial_variant_weight(self) -> Optional[pulumi.Input[float]]: """ Determines initial traffic distribution among all of the models that you specify in the endpoint configuration. If unspecified, it defaults to 1.0. """ return pulumi.get(self, "initial_variant_weight") @initial_variant_weight.setter def initial_variant_weight(self, value: Optional[pulumi.Input[float]]): pulumi.set(self, "initial_variant_weight", value) @property @pulumi.getter(name="variantName") def variant_name(self) -> Optional[pulumi.Input[str]]: """ The name of the variant. If omitted, this provider will assign a random, unique name. """ return pulumi.get(self, "variant_name") @variant_name.setter def variant_name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "variant_name", value) @pulumi.input_type class ModelContainerArgs: def __init__(__self__, *, image: pulumi.Input[str], container_hostname: Optional[pulumi.Input[str]] = None, environment: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None, model_data_url: Optional[pulumi.Input[str]] = None): """ :param pulumi.Input[str] image: The registry path where the inference code image is stored in Amazon ECR. :param pulumi.Input[str] container_hostname: The DNS host name for the container. :param pulumi.Input[Mapping[str, pulumi.Input[str]]] environment: Environment variables for the Docker container. A list of key value pairs. :param pulumi.Input[str] model_data_url: The URL for the S3 location where model artifacts are stored. """ pulumi.set(__self__, "image", image) if container_hostname is not None: pulumi.set(__self__, "container_hostname", container_hostname) if environment is not None: pulumi.set(__self__, "environment", environment) if model_data_url is not None: pulumi.set(__self__, "model_data_url", model_data_url) @property @pulumi.getter def image(self) -> pulumi.Input[str]: """ The registry path where the inference code image is stored in Amazon ECR. """ return pulumi.get(self, "image") @image.setter def image(self, value: pulumi.Input[str]): pulumi.set(self, "image", value) @property @pulumi.getter(name="containerHostname") def container_hostname(self) -> Optional[pulumi.Input[str]]: """ The DNS host name for the container. """ return pulumi.get(self, "container_hostname") @container_hostname.setter def container_hostname(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "container_hostname", value) @property @pulumi.getter def environment(self) -> Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]: """ Environment variables for the Docker container. A list of key value pairs. """ return pulumi.get(self, "environment") @environment.setter def environment(self, value: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]): pulumi.set(self, "environment", value) @property @pulumi.getter(name="modelDataUrl") def model_data_url(self) -> Optional[pulumi.Input[str]]: """ The URL for the S3 location where model artifacts are stored. """ return pulumi.get(self, "model_data_url") @model_data_url.setter def model_data_url(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "model_data_url", value) @pulumi.input_type class ModelPrimaryContainerArgs: def __init__(__self__, *, image: pulumi.Input[str], container_hostname: Optional[pulumi.Input[str]] = None, environment: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None, model_data_url: Optional[pulumi.Input[str]] = None): """ :param pulumi.Input[str] image: The registry path where the inference code image is stored in Amazon ECR. :param pulumi.Input[str] container_hostname: The DNS host name for the container. :param pulumi.Input[Mapping[str, pulumi.Input[str]]] environment: Environment variables for the Docker container. A list of key value pairs. :param pulumi.Input[str] model_data_url: The URL for the S3 location where model artifacts are stored. """ pulumi.set(__self__, "image", image) if container_hostname is not None: pulumi.set(__self__, "container_hostname", container_hostname) if environment is not None: pulumi.set(__self__, "environment", environment) if model_data_url is not None: pulumi.set(__self__, "model_data_url", model_data_url) @property @pulumi.getter def image(self) -> pulumi.Input[str]: """ The registry path where the inference code image is stored in Amazon ECR. """ return pulumi.get(self, "image") @image.setter def image(self, value: pulumi.Input[str]): pulumi.set(self, "image", value) @property @pulumi.getter(name="containerHostname") def container_hostname(self) -> Optional[pulumi.Input[str]]: """ The DNS host name for the container. """ return pulumi.get(self, "container_hostname") @container_hostname.setter def container_hostname(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "container_hostname", value) @property @pulumi.getter def environment(self) -> Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]: """ Environment variables for the Docker container. A list of key value pairs. """ return pulumi.get(self, "environment") @environment.setter def environment(self, value: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]): pulumi.set(self, "environment", value) @property @pulumi.getter(name="modelDataUrl") def model_data_url(self) -> Optional[pulumi.Input[str]]: """ The URL for the S3 location where model artifacts are stored. """ return pulumi.get(self, "model_data_url") @model_data_url.setter def model_data_url(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "model_data_url", value) @pulumi.input_type class ModelVpcConfigArgs: def __init__(__self__, *, security_group_ids: pulumi.Input[List[pulumi.Input[str]]], subnets: pulumi.Input[List[pulumi.Input[str]]]): pulumi.set(__self__, "security_group_ids", security_group_ids) pulumi.set(__self__, "subnets", subnets) @property @pulumi.getter(name="securityGroupIds") def security_group_ids(self) -> pulumi.Input[List[pulumi.Input[str]]]: return pulumi.get(self, "security_group_ids") @security_group_ids.setter def security_group_ids(self, value: pulumi.Input[List[pulumi.Input[str]]]): pulumi.set(self, "security_group_ids", value) @property @pulumi.getter def subnets(self) -> pulumi.Input[List[pulumi.Input[str]]]: return pulumi.get(self, "subnets") @subnets.setter def subnets(self, value: pulumi.Input[List[pulumi.Input[str]]]): pulumi.set(self, "subnets", value)

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null

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17fa53f9a3919169c875057b943e2dffc7e0f871

128

py

Python

chess_py/core/algebraic/__init__.py

Aubhro/chess_py

14bebc2f8c49ae25c59375cc83d0b38d8ff7281d

[ "MIT" ]

14

2016-07-02T01:54:00.000Z

2020-12-16T19:26:48.000Z

chess_py/core/algebraic/__init__.py

Aubhro/chess_py

14bebc2f8c49ae25c59375cc83d0b38d8ff7281d

[ "MIT" ]

18

2016-09-01T04:27:49.000Z

2019-03-29T04:52:03.000Z

chess_py/core/algebraic/__init__.py

Aubhro/chess_py

14bebc2f8c49ae25c59375cc83d0b38d8ff7281d

[ "MIT" ]

7

2016-05-14T20:55:05.000Z

2020-10-30T05:42:02.000Z

from .location import Location, Direction from .move import Move __all__ = ['converter', 'Location', 'Move', 'notation_const']

25.6

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aa1c87f8e19ed40e3868ca3075fd0c0134127786

220

py

Python

katas/beta/builtin_product_function.py

the-zebulan/CodeWars

1eafd1247d60955a5dfb63e4882e8ce86019f43a

[ "MIT" ]

40

2016-03-09T12:26:20.000Z

2022-03-23T08:44:51.000Z

katas/beta/builtin_product_function.py

akalynych/CodeWars

1eafd1247d60955a5dfb63e4882e8ce86019f43a

[ "MIT" ]

null

katas/beta/builtin_product_function.py

akalynych/CodeWars

1eafd1247d60955a5dfb63e4882e8ce86019f43a

[ "MIT" ]

36

2016-11-07T19:59:58.000Z

2022-03-31T11:18:27.000Z

from functools import reduce from operator import mul def product(iterable=(), start=1): """ kata currently supports only Python 3.4.3 """ return reduce(mul, iterable, start) # __builtins__.product = product

20

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null

0

null

0

1

0

1

0

1

0

4

aa33cff724b4996ddbfd95978f87253e4f36e010

26

py

Python

dash_bootstrap_components/_version.py

sthagen/dash-bootstrap-components

d79ad7f8fdf4c26165038e6989e24f2ac17663b1

[ "Apache-2.0" ]

1

2021-09-05T10:01:30.000Z

dash_bootstrap_components/_version.py

sthagen/dash-bootstrap-components

d79ad7f8fdf4c26165038e6989e24f2ac17663b1

[ "Apache-2.0" ]

null

dash_bootstrap_components/_version.py

sthagen/dash-bootstrap-components

d79ad7f8fdf4c26165038e6989e24f2ac17663b1

[ "Apache-2.0" ]

null

__version__ = "1.0.3-dev"

13

25

0.653846

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26

2.6

1

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false

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1

0

null

0

1

0

1

0

null

0

4

aa3d4f11d90fe7be4ffb8b2fe71431c9922f7b7c

164

py

Python

bin/django-admin.py

zmac12/saleor

ef833c22a8260e31ba70c5b676061d78fcfe961a

[ "CC-BY-4.0" ]

null

bin/django-admin.py

zmac12/saleor

ef833c22a8260e31ba70c5b676061d78fcfe961a

[ "CC-BY-4.0" ]

null

bin/django-admin.py

zmac12/saleor

ef833c22a8260e31ba70c5b676061d78fcfe961a

[ "CC-BY-4.0" ]

null

#!/Users/zachmcquiston/ReactProjects/saleor/bin/python3.7 from django.core import management if __name__ == "__main__": management.execute_from_command_line()

27.333333

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0.79878

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164

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0.9

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0.013423

0.091463

164

5

58

32.8

0.791946

0.341463

0

0.074766

0

1

0

true

0

0.333333

0

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0

1

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null

0

1

0

null

0

1

0

1

0

4

a4ce9911c666642b70ee4c139bbc4ffad626a47c

91

py

Python

src/1672_maximum_wealth.py

soamsy/leetcode

091f3b33e44613fac130ff1018c8b63493798f09

[ "MIT" ]

null

src/1672_maximum_wealth.py

soamsy/leetcode

091f3b33e44613fac130ff1018c8b63493798f09

[ "MIT" ]

null

src/1672_maximum_wealth.py

soamsy/leetcode

091f3b33e44613fac130ff1018c8b63493798f09

[ "MIT" ]

null

def maximumWealth(accounts: list[list[int]]) -> int: return sum(max(accounts, key=sum))

45.5

52

0.703297

13

91

4.923077

0.692308

0

0.120879

91

2

53

45.5

0.8

0

1

0.5

false

0

0.5

1

0

1

0

null

0

1

0

null

0

1

0

1

0

4

a4d35c2f694f582dd39b062032d3b664a442bb08

331

py

Python

functions/arg_nomeados.py

Brunokrk/Learning-Python

36a3b1c4782dbb21af189760a451fd2e9c083bb6

[ "MIT" ]

null

functions/arg_nomeados.py

Brunokrk/Learning-Python

36a3b1c4782dbb21af189760a451fd2e9c083bb6

[ "MIT" ]

null

functions/arg_nomeados.py

Brunokrk/Learning-Python

36a3b1c4782dbb21af189760a451fd2e9c083bb6

[ "MIT" ]

null

def describe_pet (animal_type,pet_name): """Exibe informações sobre um animal de estimação""" print("\nI have a "+ animal_type) print("My "+animal_type+"'s name is "+pet_name.title()) #argumentos devem ser fornecidos na posição de seus respectivos parametros describe_pet(animal_type='hamster', pet_name='harry')

41.375

74

0.731118

48

331

4.854167

0.645833

0.171674

0.145923

0.180258

0

0.151057

331

7

75

47.285714

0.829181

0.362538

0

0.180488

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1

0.25

false

0

0.25

0.5

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null

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1

0

1

0

null

0

1

0

1

0

4

a4e1bcac6f0b35525f3426143180ef3dd92e3830

224

py

Python

test/test_project/backends.py

radopetrik/django-otp

ee8373fd9ceb02f8b53a21dd1806334c254d6200

[ "BSD-2-Clause" ]

null

test/test_project/backends.py

radopetrik/django-otp

ee8373fd9ceb02f8b53a21dd1806334c254d6200

[ "BSD-2-Clause" ]

null

test/test_project/backends.py

radopetrik/django-otp

ee8373fd9ceb02f8b53a21dd1806334c254d6200

[ "BSD-2-Clause" ]

null

from __future__ import absolute_import, division, print_function, unicode_literals class DummyBackend(object): def authenticate(self, request): return None def get_user(self, user_id): return None

22.4

82

0.732143

27

224

5.740741

0.777778

0.129032

0

0.205357

224

9

83

24.888889

0.870787

0

0.333333

0

1

0.333333

false

0

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0.333333

1

0.166667

1

0

null

0

1

0

null

0

1

0

1

0

4

3505e029551be6102fa9337ece600ebf58ef67a1

335

py

Python

Python/DocStruct/ASAPI/__init__.py

appcove/DocStruct

16c15bb59f9aab29abb78b0aa9f2ab63c10b8da4

[ "Apache-2.0" ]

1

2015-06-18T07:30:02.000Z

Python/DocStruct/ASAPI/__init__.py

appcove/DocStruct

16c15bb59f9aab29abb78b0aa9f2ab63c10b8da4

[ "Apache-2.0" ]

null

Python/DocStruct/ASAPI/__init__.py

appcove/DocStruct

16c15bb59f9aab29abb78b0aa9f2ab63c10b8da4

[ "Apache-2.0" ]

null

# vim:fileencoding=utf-8:ts=2:sw=2:expandtab ''' A DocStruct_Release table in the schema Every time we do an upgrade, we'll replace the column in this table with the correct version number. ALTER TABLE DocStruct_Release RENAME 1.0.1 TO 1.0.2 ... this will fail if you are out of sync. ''' from .Client import Client

12.407407

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335

3.966667

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0

0.033708

0.202985

335

26

101

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0.857678

0.838806

0

1

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true

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null

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1

0

null

0

1

0

1

0

1

0

4

35404fe562dde5ec897f9e61b58efa79fa47b9d5

87

py

Python

fedota/apps.py

fedota/fl-webserver

8015f59445529edf13589d7c9339a6e48e58640f

[ "MIT" ]

null

fedota/apps.py

fedota/fl-webserver

8015f59445529edf13589d7c9339a6e48e58640f

[ "MIT" ]

1

2022-02-10T15:02:06.000Z

fedota/apps.py

fedota/fl-webserver

8015f59445529edf13589d7c9339a6e48e58640f

[ "MIT" ]

null

from django.apps import AppConfig class FedotaConfig(AppConfig): name = 'fedota'

14.5

33

0.747126

10

87

6.5

0.9

0

0.172414

87

5

34

17.4

0.902778

0

0.068966

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1

0

false

0

0.333333

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

4

354923cf5d29963d203829eb86638d49e168a079

80

py

Python

src/tcgmanager/helpers/__init__.py

BenjaminLSmith/TCGManager

f367dc33bcdd59ab89ac0066ab5f7cf330ccaa38

[ "Apache-2.0" ]

null

src/tcgmanager/helpers/__init__.py

BenjaminLSmith/TCGManager

f367dc33bcdd59ab89ac0066ab5f7cf330ccaa38

[ "Apache-2.0" ]

1

2021-06-01T23:54:41.000Z

src/tcgmanager/helpers/__init__.py

BenjaminLSmith/TCGManager

f367dc33bcdd59ab89ac0066ab5f7cf330ccaa38

[ "Apache-2.0" ]

null

from .tcgplayer import TCGPlayerBase from .esconnection import ESConnectionBase

26.666667

42

0.875

8

80

8.75

0.75

0

0.1

80

2

43

40

0.972222

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

4

101bdafd290a2e452f78b159e4e987041c4404ab

1,232

py

Python

data/train/python/101bdafd290a2e452f78b159e4e987041c4404aburls.py

harshp8l/deep-learning-lang-detection

2a54293181c1c2b1a2b840ddee4d4d80177efb33

[ "MIT" ]

84

2017-10-25T15:49:21.000Z

2021-11-28T21:25:54.000Z

data/train/python/101bdafd290a2e452f78b159e4e987041c4404aburls.py

vassalos/deep-learning-lang-detection

cbb00b3e81bed3a64553f9c6aa6138b2511e544e

[ "MIT" ]

5

2018-03-29T11:50:46.000Z

2021-04-26T13:33:18.000Z

data/train/python/101bdafd290a2e452f78b159e4e987041c4404aburls.py

vassalos/deep-learning-lang-detection

cbb00b3e81bed3a64553f9c6aa6138b2511e544e

[ "MIT" ]

24

2017-11-22T08:31:00.000Z

2022-03-27T01:22:31.000Z

#encoding:utf-8 urls = ( '/admin/?', 'controller.admin.index', '/admin/login', 'controller.admin.login', '/admin/logout', 'controller.admin.logout', #--------------user ----------- #----用户信息表---- "/admin/user_list", "controller.admin.user.user_list", "/admin/user_read/(\d+)", "controller.admin.user.user_read", "/admin/user_edit/(\d+)", "controller.admin.user.user_edit", "/admin/user_delete/(\d+)", "controller.admin.user.user_delete", #--------------end user ------- #--------------area ----------- #----区域表---- "/admin/area_list", "controller.admin.area.area_list", "/admin/area_read/(\d+)", "controller.admin.area.area_read", "/admin/area_edit/(\d+)", "controller.admin.area.area_edit", "/admin/area_delete/(\d+)", "controller.admin.area.area_delete", #--------------end area ------- #--------------policy ----------- #----政策传递---- "/admin/policy_list", "controller.admin.policy.policy_list", "/admin/policy_read/(\d+)", "controller.admin.policy.policy_read", "/admin/policy_edit/(\d+)", "controller.admin.policy.policy_edit", "/admin/policy_delete/(\d+)", "controller.admin.policy.policy_delete", #--------------end policy ------- )

36.235294

73

0.568994

135

1,232

5.014815

0.155556

0.332349

0.212703

0.135894

0.33678

0

0.000921

0.118506

1,232

33

74

37.333333

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0

0.763158

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0

1

0

false

0

null

1

0

1

null

0

4

10410e756a285fea48e1a27560f7895332b5d0d9

368

py

Python

helloflask/guestbook/forms.py

walterfan/helloworld

8d2e6465f36500ba8e28308b17b6c1a2c2059be1

[ "Apache-2.0" ]

null

helloflask/guestbook/forms.py

walterfan/helloworld

8d2e6465f36500ba8e28308b17b6c1a2c2059be1

[ "Apache-2.0" ]

9

2020-03-04T23:40:56.000Z

2022-03-02T02:34:58.000Z

helloflask/guestbook/forms.py

walterfan/helloworld

8d2e6465f36500ba8e28308b17b6c1a2c2059be1

[ "Apache-2.0" ]

5

2018-11-10T16:13:40.000Z

2021-09-18T06:09:15.000Z

from flask_wtf import FlaskForm from wtforms import StringField, SubmitField, TextAreaField from wtforms.validators import DataRequired, Length class MessageForm(FlaskForm): subject = StringField('Subject', validators=[DataRequired(), Length(1, 32)]) content = TextAreaField('Content', validators=[DataRequired(), Length(1 ,4096)]) submit = SubmitField()

40.888889

84

0.769022

38

368

7.421053

0.526316

0.191489

0.198582

0.205674

0

0.024768

0.122283

368

9

85

40.888889

0.848297

0

0.03794

0

1

0

false

0

0.428571

0

1

0

null

0

1

0

1

0

null

0

1

0

4

52ca20dc89e31cbfbc1f79a4df4c90d1a1930775

167

py

Python

weather/forms.py

BianSuma/weather_prediction

ab4b9744fef5ceb8cfdf9d06439ae849c132736e

[ "Unlicense" ]

1

2021-06-28T14:47:25.000Z

weather/forms.py

BianSuma/weather_prediction

ab4b9744fef5ceb8cfdf9d06439ae849c132736e

[ "Unlicense" ]

null

weather/forms.py

BianSuma/weather_prediction

ab4b9744fef5ceb8cfdf9d06439ae849c132736e

[ "Unlicense" ]

null

from django import forms from weather.models import Weathers class WeatherForm(forms.ModelForm): class Meta: model = Weathers fields = "__all__"

18.555556

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0.700599

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167

5.947368

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0.239521

167

8

36

20.875

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null

0

1

0

null

0

1

0

1

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4

52d7fda17aa30a801b7d21bd536e64574c9058af

83

py

Python

Lunchtime/Lproj/Lapp/apps.py

WildernessBear/447-COVID-lunch-proj

1ab440bb9025d2c2eaf6daca90bb35503265be60

[ "Apache-2.0" ]

null

Lunchtime/Lproj/Lapp/apps.py

WildernessBear/447-COVID-lunch-proj

1ab440bb9025d2c2eaf6daca90bb35503265be60

[ "Apache-2.0" ]

null

Lunchtime/Lproj/Lapp/apps.py

WildernessBear/447-COVID-lunch-proj

1ab440bb9025d2c2eaf6daca90bb35503265be60

[ "Apache-2.0" ]

null

from django.apps import AppConfig class LappConfig(AppConfig): name = 'Lapp'

13.833333

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0.73494

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83

6.1

0.9

0

0.180723

83

5

34

16.6

0.897059

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1

0

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0

null

0

1

0

1

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52d8f0426494784af131233604040efd8dd75298

32

py

Python

__init__.py

loop333/ha_dunehd_media_player

f6b50434b25213b573390c390466b642d5ce3dc7

[ "MIT" ]

1

2019-10-24T16:07:59.000Z

__init__.py

loop333/ha_dunehd_media_player

f6b50434b25213b573390c390466b642d5ce3dc7

[ "MIT" ]

null

__init__.py

loop333/ha_dunehd_media_player

f6b50434b25213b573390c390466b642d5ce3dc7

[ "MIT" ]

null

""" Custom DuneHD component """

16

31

0.65625

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32

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1

0

0.15625

32

1

32

0.777778

0.71875

0

null

0

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null

0

null

1

null

true

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null

1

0

null

0

1

0

null

0

1

0

4

5e0c2967dd9151bf5117b4cdc60758a73c084a77

186

py

Python

test.py

saibotshamtul/rimp

1d35b85aa84ab4430ba56afdd6ef4737c214adcd

[ "MIT" ]

3

2018-12-26T15:19:48.000Z

2020-10-02T00:22:09.000Z

test.py

saibotshamtul/rimp

1d35b85aa84ab4430ba56afdd6ef4737c214adcd

[ "MIT" ]

1

2020-10-02T12:34:33.000Z

test.py

saibotshamtul/rimp

1d35b85aa84ab4430ba56afdd6ef4737c214adcd

[ "MIT" ]

4

2020-08-31T13:50:03.000Z

2021-09-26T14:50:16.000Z

from rimp import load_repl load_repl("21natzil", "Permissions", verbose=False) load_repl("21natzil", "discordy", force_reinstall=True) import perms print(dir(perms)) import discordy

16.909091

55

0.77957

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186

5.64

0.64

0.170213

0.22695

0

0.023952

0.102151

186

10

56

18.6

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0

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0

1

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null

0

1

0

1

0

4

5e1c75266ed37d4bea14363b9a2ca6a019aae48e

317

py

Python

homura/optim.py

Fragile-azalea/homura

900d1d63affb9c8af3accd9b196b5276cb2e14b6

[ "Apache-2.0" ]

1

2020-06-30T01:55:41.000Z

homura/optim.py

Fragile-azalea/homura

900d1d63affb9c8af3accd9b196b5276cb2e14b6

[ "Apache-2.0" ]

null

homura/optim.py

Fragile-azalea/homura

900d1d63affb9c8af3accd9b196b5276cb2e14b6

[ "Apache-2.0" ]

null

from functools import partial import torch def Adam(lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0, amsgrad=False): return partial(torch.optim.Adam, **locals()) def SGD(lr=1e-1, momentum=0, dampening=0, weight_decay=0, nesterov=False): return partial(torch.optim.SGD, **locals())

22.642857

48

0.66877

50

317

4.2

0.56

0.038095

0.114286

0.219048

0.266667

0

0.061069

0.173502

317

13

49

24.384615

0.740458

0

1

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0

0.25

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0

null

0

1

0

null

0

1

0

1

0

4

5e308dda1a8fb32d560a360848b9f6a8f72925bf

346

py

Python

pic/data/__init__.py

hankyul2/pytorch-image-classification

2da942aaf806de961941d57e9daa0b9a37798530

[ "Apache-2.0" ]

null

pic/data/__init__.py

hankyul2/pytorch-image-classification

2da942aaf806de961941d57e9daa0b9a37798530

[ "Apache-2.0" ]

null

pic/data/__init__.py

hankyul2/pytorch-image-classification

2da942aaf806de961941d57e9daa0b9a37798530

[ "Apache-2.0" ]

null

from .custom_dataset import MyImageFolder, MiTIndoor, CUB200, TinyImageNet, MyCaltech101 from .mix import MixUP, CutMix from .sampler import RepeatAugSampler from .cifar import MyCIFAR100 from .transforms import TrainTransform, ValTransform from .dataloader import get_dataloader from .dataset import get_dataset, _dataset_dict, register_dataset

43.25

88

0.852601

41

346

7.04878

0.560976

0.089965

0

0.029032

0.104046

346

7

89

49.428571

0.903226

0

1

0

true

0

1

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1

0

null

0

1

0

null

0

1

0

1

0

1

0

4

5e39618dca4ad6c3f0d4c8cb20af59ab85fb0eba

98

py

Python

Funções Analíticas/Virtualenv/Lib/site-packages/setuptools/tests/textwrap.py

Leonardo-Maciel/PSO_Maciel

3939448da45716260f3ac7811afdd13be670f346

[ "MIT" ]

1,744

2016-03-29T15:46:26.000Z

2022-03-31T23:51:04.000Z

Funções Analíticas/Virtualenv/Lib/site-packages/setuptools/tests/textwrap.py

Leonardo-Maciel/PSO_Maciel

3939448da45716260f3ac7811afdd13be670f346

[ "MIT" ]

2,404

2016-03-29T16:24:00.000Z

2022-03-31T22:25:20.000Z

Funções Analíticas/Virtualenv/Lib/site-packages/setuptools/tests/textwrap.py

Leonardo-Maciel/PSO_Maciel

3939448da45716260f3ac7811afdd13be670f346

[ "MIT" ]

1,042

2016-03-29T15:28:34.000Z

2022-03-31T16:27:27.000Z

import textwrap def DALS(s): "dedent and left-strip" return textwrap.dedent(s).lstrip()

14

38

0.683673

14

98

4.785714

0.785714

0

0.193878

98

6

39

16.333333

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0.214286

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0

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0

null

0

1

0

1

0

4

eaa6206d06f3187fc7d345293454afcdc53d26c6

211

py

Python

jewels_and_stones_771.py

cthi/LeetCode

fbeb077e382ab4c4e8d8cc4707b9f1a9f33c5a89

[ "MIT" ]

null

jewels_and_stones_771.py

cthi/LeetCode

fbeb077e382ab4c4e8d8cc4707b9f1a9f33c5a89

[ "MIT" ]

null

jewels_and_stones_771.py

cthi/LeetCode

fbeb077e382ab4c4e8d8cc4707b9f1a9f33c5a89

[ "MIT" ]

null

class Solution: def numJewelsInStones(self, J, S): """ :type J: str :type S: str :rtype: int """ J = set(J) return sum(1 for stone in S if stone in J)

21.1

50

0.469194

29

211

3.413793

0.655172

0.141414

0

0.008197

0.421801

211

9

51

23.444444

0.803279

0.175355

0

1

0.25

false

0

0.75

0

1

0

null

0

1

0

null

0

1

0

1

0

4

eab41880287b15414e0b5e2002c88c67a15b1310

416

py

Python

books_scrapy/utils/misc.py

hdtls/books-scrapy

d8e72463df05de16fafc4207e3c292284a7c126d

[ "Apache-2.0" ]

null

books_scrapy/utils/misc.py

hdtls/books-scrapy

d8e72463df05de16fafc4207e3c292284a7c126d

[ "Apache-2.0" ]

null

books_scrapy/utils/misc.py

hdtls/books-scrapy

d8e72463df05de16fafc4207e3c292284a7c126d

[ "Apache-2.0" ]

null

import json import re def eval_js_variable(label, text): match = re.findall(r"var %s ?= ?(.*?);" % (label), text) if not match: return None return json.loads(match[0]) def list_extend(lhs, rhs): lhs = lhs or [] rhs = rhs or [] return list(set(lhs + rhs)) or None def formatted_meta(arg): return {"__meta__": arg} def revert_formatted_meta(arg): return arg["__meta__"]

17.333333

60

0.617788

61

416

3.983607

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0.08642

0.131687

0.18107

0

0.003155

0.237981

416

23

61

18.086957

0.763407

0

0.079327

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1

0.266667

false

0

0.133333

0.733333

0

null

0

1

0

null

0

1

0

1

0

4

eaef353a679d961958704468cd3dcdd8028176ca

247

py

Python

lnked/colleges/admin.py

NewsNerdsAtCoJMC/ProjectTicoTeam4

26d2430a8ab63b585c00ac8530bc476c15597685

[ "MIT" ]

null

lnked/colleges/admin.py

NewsNerdsAtCoJMC/ProjectTicoTeam4

26d2430a8ab63b585c00ac8530bc476c15597685

[ "MIT" ]

null

lnked/colleges/admin.py

NewsNerdsAtCoJMC/ProjectTicoTeam4

26d2430a8ab63b585c00ac8530bc476c15597685

[ "MIT" ]

null

from django.contrib import admin # Register your models here. from django.contrib import admin from .models import SignificantMajors, College, Blog admin.site.register(SignificantMajors) admin.site.register(College) admin.site.register(Blog)

19

52

0.813765

32

247

6.28125

0.40625

0.134328

0.253731

0.228856

0.278607

0

0.109312

247

12

53

20.583333

0.913636

0.105263

0

0.333333

0

1

0

true

0

0.5

0

0.5

0

null

0

1

0

null

0

1

0

1

0

4

dc215ffa1b85cdeb8869900176cc31d71e7ee65f

116

py

Python

userena/compat.py

WisChrendel/django-userena-ce

38aeb22900aba3945ec37369bb627c84f1a507fe

[ "BSD-3-Clause" ]

null

userena/compat.py

WisChrendel/django-userena-ce

38aeb22900aba3945ec37369bb627c84f1a507fe

[ "BSD-3-Clause" ]

1

2022-03-10T16:20:49.000Z

userena/compat.py

WisChrendel/django-userena-ce

38aeb22900aba3945ec37369bb627c84f1a507fe

[ "BSD-3-Clause" ]

2

2016-01-13T02:52:24.000Z

2019-03-15T18:37:02.000Z

# -*- coding: utf-8 -*- # SiteProfileNotAvailable compatibility class SiteProfileNotAvailable(Exception): pass

19.333333

41

0.75

9

116

9.666667

0.888889

0

0.01

0.137931

116

5

42

23.2

0.86

0.508621

0

1

0

true

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

4

dc8bee9b56f7f5bd4aaf68375b1a1a0b202348e8

724

py

Python

src/sfcparse/__xml/xmlbuildmanual.py

aaronater10/sfconfig

f1ebd0a4dc5e6ec235d30b0ef1540fb65422729a

[ "MIT" ]

null

src/sfcparse/__xml/xmlbuildmanual.py

aaronater10/sfconfig

f1ebd0a4dc5e6ec235d30b0ef1540fb65422729a

[ "MIT" ]

null

src/sfcparse/__xml/xmlbuildmanual.py

aaronater10/sfconfig

f1ebd0a4dc5e6ec235d30b0ef1540fb65422729a

[ "MIT" ]

null

# xmlbuildmanual ######################################################################################################### # Imports import xml.etree.ElementTree as __xml_etree ######################################################################################################### # Build manual xml data def xmlbuildmanual() -> __xml_etree: """ Returns a empty xml ElementTree obj to build/work with xml data Assign the output to var This is using the native xml library via etree shipped with the python standard library. For more information on the xml.etree api, visit: https://docs.python.org/3/library/xml.etree.elementtree.html#module-xml.etree.ElementTree """ return __xml_etree

40.222222

143

0.524862

75

724

4.946667

0.586667

0.150943

0.153639

0

0.001597

0.135359

724

17

144

42.588235

0.591054

0.504144

0

1

0.333333

true

0

0.333333

0

1

0

null

0

null

0

1

0

1

0

1

0

4

f4f4a2f56f7c778d95aef45a681962974fd48420

182

py

Python

crazy-filters/main.py

isabella232/je-code-crazy-filters

f9b654cab445bd2ad42e75fcb69a18c17241dd83

[ "Apache-2.0" ]

6

2018-06-28T08:52:42.000Z

2019-04-05T20:46:25.000Z

crazy-filters/main.py

criteo/je-code-crazy-filters

f9b654cab445bd2ad42e75fcb69a18c17241dd83

[ "Apache-2.0" ]

4

2018-11-20T13:40:04.000Z

2022-03-11T23:24:26.000Z

crazy-filters/main.py

isabella232/je-code-crazy-filters

f9b654cab445bd2ad42e75fcb69a18c17241dd83

[ "Apache-2.0" ]

2

2019-01-17T14:41:33.000Z

2022-02-21T11:14:25.000Z

""" Rien de très intéressant à modifier ici. Va plutôt voir transforms.py """ from ui.crazyfiltersapp import CrazyFiltersApp if __name__ == '__main__': CrazyFiltersApp().run()

20.222222

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0.741758

22

182

5.772727

0.909091

0

0.153846

182

8

70

22.75

0.824675

0.379121

0

0.07619

0

1

0

true

0

0.333333

0

0.333333

0

1

0

null

0

1

0

null

0

1

0

1

0

4

521d97e4653663d34f0babeb18fedc140dec34be

82

py

Python

lessnless/__init__.py

codyd51/lessnless

7279158aabd9136b49a485ed61bfc46a836e9232

[ "MIT" ]

null

lessnless/__init__.py

codyd51/lessnless

7279158aabd9136b49a485ed61bfc46a836e9232

[ "MIT" ]

null

lessnless/__init__.py

codyd51/lessnless

7279158aabd9136b49a485ed61bfc46a836e9232

[ "MIT" ]

null

from mingus.midi import fluidsynth fluidsynth.init('Nice-Keys-Ultimate-V2.3.sf2')

27.333333

46

0.804878

13

82

5.076923

0.923077

0

0.038961

0.060976

82

2

47

41

0.818182

0

0.329268

0

1

0

true

0

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

1

0

4

52264d1188d9165f456acfe10d098370f48a1446

271

py

Python

code/28 - subst.py

alaudo/coderdojo-python

c0a4c284810d8da5217398ae8964d12f27c8ecc2

[ "CC0-1.0" ]

null

code/28 - subst.py

alaudo/coderdojo-python

c0a4c284810d8da5217398ae8964d12f27c8ecc2

[ "CC0-1.0" ]

null

code/28 - subst.py

alaudo/coderdojo-python

c0a4c284810d8da5217398ae8964d12f27c8ecc2

[ "CC0-1.0" ]

null

def subst(text): s = { 'т' : 't', '$' : 's', '@' : 'a', '!' : 'i', 'Я' : 'r', '1' : 'l', 'ш' : 'w', '0' : 'o', 'п' : 'n'} return "".join([t if not(t in s) else s[t] for t in text ]) print(subst("тhe$e @Яe que$т!0п$ f0Я @cт!0п, п0т $pecu1@т!0п, шh!ch !$ !d1e."))

45.166667

108

0.409594

52

271

2.134615

0.730769

0.054054

0

0.043478

0.236162

271

5

109

54.2

0.492754

0

0.25

0.298893

0

1

0.25

false

0

0.5

0.25

0

null

0

1

0

1

0

1

0

null

0

1

0

4

5261e5982339728e92a80d035718256946539c9b

882

py

Python

check_tools_version.py

mikedlr/arnparse

626662f8a6ffb31d6f514e2f3c3e2785d2a170ef

[ "MIT" ]

30

2018-05-22T23:03:58.000Z

2022-03-19T18:43:56.000Z

check_tools_version.py

mikedlr/arnparse

626662f8a6ffb31d6f514e2f3c3e2785d2a170ef

[ "MIT" ]

7

2018-05-25T18:18:12.000Z

2020-11-12T22:49:52.000Z

check_tools_version.py

mikedlr/arnparse

626662f8a6ffb31d6f514e2f3c3e2785d2a170ef

[ "MIT" ]

7

2018-05-23T00:48:27.000Z

2021-02-18T11:49:45.000Z

from distutils.version import StrictVersion import setuptools import twine import wheel if __name__ == '__main__': """ Ensure that all tools are correctly installed. See https://stackoverflow.com/a/26737258 """ assert StrictVersion(setuptools.__version__) >= StrictVersion('38.6.0'), 'Please upgrade setuptools. ' \ 'See https://stackoverflow.com/a/26737258' assert StrictVersion(twine.__version__) >= StrictVersion('1.11.0'), 'Please upgrade twine. ' \ 'See https://stackoverflow.com/a/26737258' assert StrictVersion(wheel.__version__) >= StrictVersion('0.31.0'), 'Please upgrade wheel. ' \ 'See https://stackoverflow.com/a/26737258'

51.882353

119

0.55102

78

882

5.974359

0.423077

0.06867

0.180258

0.206009

0.405579

0.334764

0

0.076257

0.345805

882

16

120

55.125

0.731369

0

0.272727

0

0.278562

0

0.272727

1

0

true

0

0.363636

0

0.363636

0

null

0

1

0

null

0

1

0

1

0

4

52878a824818296676a13c8bdcf3758188815fde

42

py

Python

backend/const.py

sshaman1101/what-about-blank

66df54fcc3d715aafb50ed9be347698b7a4b14d3

[ "BSD-3-Clause" ]

null

backend/const.py

sshaman1101/what-about-blank

66df54fcc3d715aafb50ed9be347698b7a4b14d3

[ "BSD-3-Clause" ]

1

2018-06-16T23:19:26.000Z

2018-06-17T10:48:38.000Z

backend/const.py

sshaman1101/what-about-blank

66df54fcc3d715aafb50ed9be347698b7a4b14d3

[ "BSD-3-Clause" ]

null

GITHUB_PULLS_PROVIDER_ID = 'github_pulls'

21

41

0.857143

6

42

5.333333

0.666667

0.6875

0

0.071429

42

1

42

0.820513

0

0.285714

0

1

0

false

0

1

0

null

1

0

1

0

null

0

4

872040306dccb8a6a6460b1affa4df7a6cf79306

6,603

py

Python

migrations/versions/c2f65a03dbdb_.py

Ashaba/API-Monitor

533eb6698fcb5decb48f746784af6894844b3c69

[ "MIT" ]

null

migrations/versions/c2f65a03dbdb_.py

Ashaba/API-Monitor

533eb6698fcb5decb48f746784af6894844b3c69

[ "MIT" ]

22

2018-02-06T19:53:11.000Z

2021-04-30T20:35:01.000Z

migrations/versions/c2f65a03dbdb_.py

Ashaba/API-Monitor

533eb6698fcb5decb48f746784af6894844b3c69

[ "MIT" ]

null

"""empty message Revision ID: c2f65a03dbdb Revises: Create Date: 2018-05-03 18:18:27.470606 """ from alembic import op import sqlalchemy as sa # revision identifiers, used by Alembic. revision = 'c2f65a03dbdb' down_revision = None branch_labels = None depends_on = None def upgrade(): # ### commands auto generated by Alembic - please adjust! ### op.create_table('User', sa.Column('id', sa.Integer(), nullable=False), sa.Column('date_created', sa.DateTime(), server_default=sa.text('now()'), nullable=True), sa.Column('date_modified', sa.DateTime(), nullable=True), sa.Column('name', sa.String(length=250), nullable=False), sa.Column('email', sa.String(length=250), nullable=True), sa.Column('image_url', sa.String(), nullable=False), sa.PrimaryKeyConstraint('id'), sa.UniqueConstraint('email') ) op.create_table('Team', sa.Column('id', sa.Integer(), nullable=False), sa.Column('date_created', sa.DateTime(), server_default=sa.text('now()'), nullable=True), sa.Column('date_modified', sa.DateTime(), nullable=True), sa.Column('name', sa.String(length=128), nullable=False), sa.Column('user_id', sa.Integer(), nullable=True), sa.ForeignKeyConstraint(['user_id'], ['User.id'], ), sa.PrimaryKeyConstraint('id') ) op.create_table('Collection', sa.Column('id', sa.Integer(), nullable=False), sa.Column('date_created', sa.DateTime(), server_default=sa.text('now()'), nullable=True), sa.Column('date_modified', sa.DateTime(), nullable=True), sa.Column('name', sa.String(length=128), nullable=False), sa.Column('user_id', sa.Integer(), nullable=False), sa.Column('team_id', sa.Integer(), nullable=True), sa.Column('interval', sa.Integer(), nullable=True), sa.ForeignKeyConstraint(['team_id'], ['Team.id'], ), sa.ForeignKeyConstraint(['user_id'], ['User.id'], ), sa.PrimaryKeyConstraint('id') ) op.create_table('team_members', sa.Column('user_id', sa.Integer(), nullable=False), sa.Column('team_id', sa.Integer(), nullable=False), sa.ForeignKeyConstraint(['team_id'], ['Team.id'], ), sa.ForeignKeyConstraint(['user_id'], ['User.id'], ) ) op.create_table('Request', sa.Column('id', sa.Integer(), nullable=False), sa.Column('date_created', sa.DateTime(), server_default=sa.text('now()'), nullable=True), sa.Column('date_modified', sa.DateTime(), nullable=True), sa.Column('collection_id', sa.Integer(), nullable=True), sa.Column('method', sa.String(length=128), nullable=False), sa.Column('body', sa.String(length=255), nullable=True), sa.Column('url', sa.String(length=255), nullable=False), sa.ForeignKeyConstraint(['collection_id'], ['Collection.id'], ), sa.PrimaryKeyConstraint('id') ) op.create_table('ResponseSummary', sa.Column('id', sa.Integer(), nullable=False), sa.Column('date_created', sa.DateTime(), server_default=sa.text('now()'), nullable=True), sa.Column('date_modified', sa.DateTime(), nullable=True), sa.Column('status', sa.String(), nullable=False), sa.Column('failures', sa.Integer(), nullable=False), sa.Column('run_from', sa.String(), nullable=True), sa.Column('collection_id', sa.Integer(), nullable=False), sa.ForeignKeyConstraint(['collection_id'], ['Collection.id'], ), sa.PrimaryKeyConstraint('id') ) op.create_table('Header', sa.Column('id', sa.Integer(), nullable=False), sa.Column('date_created', sa.DateTime(), server_default=sa.text('now()'), nullable=True), sa.Column('date_modified', sa.DateTime(), nullable=True), sa.Column('key', sa.String(), nullable=False), sa.Column('value', sa.String(), nullable=False), sa.Column('request_id', sa.Integer(), nullable=False), sa.ForeignKeyConstraint(['request_id'], ['Request.id'], ), sa.PrimaryKeyConstraint('id') ) op.create_table('RequestAssertion', sa.Column('id', sa.Integer(), nullable=False), sa.Column('date_created', sa.DateTime(), server_default=sa.text('now()'), nullable=True), sa.Column('date_modified', sa.DateTime(), nullable=True), sa.Column('assertion_type', sa.String(), nullable=False), sa.Column('comparison', sa.String(), nullable=False), sa.Column('value', sa.Integer(), nullable=False), sa.Column('request_id', sa.Integer(), nullable=False), sa.ForeignKeyConstraint(['request_id'], ['Request.id'], ), sa.PrimaryKeyConstraint('id') ) op.create_table('Response', sa.Column('id', sa.Integer(), nullable=False), sa.Column('date_created', sa.DateTime(), server_default=sa.text('now()'), nullable=True), sa.Column('date_modified', sa.DateTime(), nullable=True), sa.Column('status_code', sa.Integer(), nullable=False), sa.Column('status', sa.String(), nullable=True), sa.Column('failures', sa.Integer(), nullable=False), sa.Column('response_time', sa.Integer(), nullable=True), sa.Column('data', sa.String(), nullable=False), sa.Column('headers', sa.String(), nullable=False), sa.Column('request_id', sa.Integer(), nullable=False), sa.Column('response_summary_id', sa.Integer(), nullable=False), sa.ForeignKeyConstraint(['request_id'], ['Request.id'], ), sa.ForeignKeyConstraint(['response_summary_id'], ['ResponseSummary.id'], ), sa.PrimaryKeyConstraint('id') ) op.create_table('ResponseAssertion', sa.Column('id', sa.Integer(), nullable=False), sa.Column('date_created', sa.DateTime(), server_default=sa.text('now()'), nullable=True), sa.Column('date_modified', sa.DateTime(), nullable=True), sa.Column('assertion_type', sa.String(), nullable=False), sa.Column('comparison', sa.String(), nullable=False), sa.Column('value', sa.Integer(), nullable=False), sa.Column('status', sa.String(), nullable=True), sa.Column('request_assertion_id', sa.Integer(), nullable=False), sa.Column('response_id', sa.Integer(), nullable=False), sa.ForeignKeyConstraint(['request_assertion_id'], ['RequestAssertion.id'], ), sa.ForeignKeyConstraint(['response_id'], ['Response.id'], ), sa.PrimaryKeyConstraint('id') ) # ### end Alembic commands ### def downgrade(): # ### commands auto generated by Alembic - please adjust! ### op.drop_table('ResponseAssertion') op.drop_table('Response') op.drop_table('RequestAssertion') op.drop_table('Header') op.drop_table('ResponseSummary') op.drop_table('Request') op.drop_table('team_members') op.drop_table('Collection') op.drop_table('Team') op.drop_table('User') # ### end Alembic commands ###

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