xszheng2020/the_stack_dedup_python_hits_4 · Datasets at Hugging Face

1b9d875d3c8fbdfbff8a8f0a905ae500c016b540

202

py

Python

custom_components/hacs/models/frontend.py

sob/hass-conf

0d01004475b625c431245a1cee66a1c91b7bfb30

[ "MIT" ]

27

2018-10-13T10:00:53.000Z

2022-02-07T23:33:12.000Z

config/custom_components/hacs/models/frontend.py

TheGroundZero/home-assistant-config

a963e1cb3e2acf7beda2b466b334218ac27ee42f

[ "MIT" ]

33

2021-11-22T16:30:43.000Z

2022-03-29T18:00:13.000Z

config/custom_components/hacs/models/frontend.py

TheGroundZero/home-assistant-config

a963e1cb3e2acf7beda2b466b334218ac27ee42f

[ "MIT" ]

5

2019-06-01T10:27:37.000Z

2020-09-18T14:14:56.000Z

"""HacsFrontend.""" class HacsFrontend: """HacsFrontend.""" version_running: bool = None version_available: bool = None version_expected: bool = None update_pending: bool = False

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null

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1bc8c7f134659c12cac6d78670a0c91af4805587

130

py

Python

setup.py

Mario-Kart-Felix/oxygen

ed7548d264282f3c857f22a26cfe969af335b6d0

[ "MIT" ]

1

2018-12-18T16:06:17.000Z

setup.py

Mario-Kart-Felix/oxygen

ed7548d264282f3c857f22a26cfe969af335b6d0

[ "MIT" ]

5

2019-08-20T15:08:33.000Z

2019-10-24T15:17:39.000Z

setup.py

Mario-Kart-Felix/oxygen

ed7548d264282f3c857f22a26cfe969af335b6d0

[ "MIT" ]

1

2021-03-20T04:13:28.000Z

from distutils.core import setup from Cython.Build import cythonize setup( ext_modules = cythonize("*.pyx", annotate=True) )

18.571429

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5.705882

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null

0

1

0

1

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4

9403c5a5e889a912eca0732cc3e2ba320870daab

169

py

Python

py_tdlib/constructors/push_message_content_sticker.py

Mr-TelegramBot/python-tdlib

2e2d21a742ebcd439971a32357f2d0abd0ce61eb

[ "MIT" ]

24

2018-10-05T13:04:30.000Z

2020-05-12T08:45:34.000Z

py_tdlib/constructors/push_message_content_sticker.py

MrMahdi313/python-tdlib

2e2d21a742ebcd439971a32357f2d0abd0ce61eb

[ "MIT" ]

3

2019-06-26T07:20:20.000Z

2021-05-24T13:06:56.000Z

py_tdlib/constructors/push_message_content_sticker.py

MrMahdi313/python-tdlib

2e2d21a742ebcd439971a32357f2d0abd0ce61eb

[ "MIT" ]

5

2018-10-05T14:29:28.000Z

2020-08-11T15:04:10.000Z

from ..factory import Type class pushMessageContentSticker(Type): sticker = None # type: "sticker" emoji = None # type: "string" is_pinned = None # type: "Bool"

21.125

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null

0

1

0

4

94098b42b608e8d1fd06b53ff50da36e10318b49

112

py

Python

Patterns/horizontal table pattern.py

lazydinoz/HackFest21

84bfbfbb2c75a6511226a87d2e947984db878ba1

[ "MIT" ]

1

2021-11-12T10:51:19.000Z

Patterns/horizontal table pattern.py

lazydinoz/HackFest21

84bfbfbb2c75a6511226a87d2e947984db878ba1

[ "MIT" ]

null

Patterns/horizontal table pattern.py

lazydinoz/HackFest21

84bfbfbb2c75a6511226a87d2e947984db878ba1

[ "MIT" ]

null

rows = 11 for i in range(0, rows): for j in range(0, i + 1): print(i * j, end= ' ') print()

11.2

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null

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0

1

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null

0

4

941cfef536945d9e2a0eabaa26e92042d1fb5c28

4,074

py

Python

voltdb/tests/fixtures/generate_data.py

jejikenwogu/integrations-core

26d90d6e73c135e3bc0c590ec195d459165dc89f

[ "BSD-3-Clause" ]

null

voltdb/tests/fixtures/generate_data.py

jejikenwogu/integrations-core

26d90d6e73c135e3bc0c590ec195d459165dc89f

[ "BSD-3-Clause" ]

null

voltdb/tests/fixtures/generate_data.py

jejikenwogu/integrations-core

26d90d6e73c135e3bc0c590ec195d459165dc89f

[ "BSD-3-Clause" ]

null

# Not used by tests, but can be manually run to generate the mock_results.json file. import json import random import string import time from math import log2 MAPPING = { 'COMMANDLOG': ['timestamp', 'host_id', 'hostname', 'int', 'int', 'int', 'int', 'int'], 'CPU': ['timestamp', 'host_id', 'hostname', 'percent'], 'EXPORT': [ 'timestamp', 'host_id', 'hostname', 'site_id', 'partition_id', 'str', 'str', 'str', 'int', 'int', 'timestamp', 'timestamp', 'int', 'int', 'int', 'str', ], 'GC': ['timestamp', 'host_id', 'hostname', 'int', 'int', 'int', 'int'], 'IDLETIME': ['timestamp', 'host_id', 'hostname', 'site_id', 'int', 'percent', 'int', 'int', 'int', 'int'], 'IMPORT': ['timestamp', 'host_id', 'hostname', 'site_id', 'str', 'str', 'int', 'int', 'int', 'int'], 'INDEX': [ 'timestamp', 'host_id', 'hostname', 'site_id', 'partition_id', 'str', 'str', 'str', 'bool', 'bool', 'int', 'int', ], 'IOSTATS': ['timestamp', 'host_id', 'hostname', 'int', 'str', 'int', 'int', 'int', 'int'], 'LATENCY': [ 'timestamp', 'host_id', 'hostname', 'int', 'int', 'int', 'int', 'int', 'int', 'int', 'int', 'int', 'int', ], 'MEMORY': [ 'timestamp', 'host_id', 'hostname', 'int', 'int', 'int', 'int', 'int', 'int', 'int', 'int', 'int', 'int', 'int', ], 'PROCEDURE': [ 'timestamp', 'host_id', 'hostname', 'site_id', 'partition_id', 'str', 'int', 'int', 'int', 'int', 'int', 'int', 'int', 'int', 'int', 'int', 'int', 'int', 'int', 'bool', ], 'PROCEDUREOUTPUT': ['timestamp', 'str', 'int', 'int', 'int', 'int', 'int', 'int'], 'PROCEDUREPROFILE': ['timestamp', 'str', 'int', 'int', 'int', 'int', 'int', 'int', 'int'], 'QUEUE': ['timestamp', 'host_id', 'hostname', 'site_id', 'int', 'int', 'int', 'int'], 'SNAPSHOTSTATUS': [ 'timestamp', 'host_id', 'hostname', 'str', 'str', 'str', 'str', 'int', 'int', 'int', 'int', 'int', 'int', 'str', 'str', ], 'TABLE': [ 'timestamp', 'host_id', 'hostname', 'site_id', 'partition_id', 'str', 'str', 'int', 'int', 'int', 'int', 'int', 'int', ], } def generate_host_id(idx=0): return str(idx) def generate_hostname(idx=0): return "voltdb-host-%d" % idx def generate_site_id(idx=0): return idx def generate_partition_id(idx=0): return idx def generate_bool(idx=0): return idx % 2 == 0 def generate_int(idx=0): return int(-(idx * 10 + 1) * 100000 * log2(1 - random.random())) def generate_timestamp(idx=0): yesterday = time.time() - 24 * 60 * 60 yesterday -= random.random() * 3600 # yesterday is between 24h and 25h ago yesterday += idx * 3600 return int(yesterday * 1000) def generate_percent(idx=0): return random.random() def generate_str(idx=0): return ''.join(random.choice(string.ascii_lowercase) for i in range(15)) + "-" + str(idx) def generate_data(cnt=1): data = {} for component in MAPPING.keys(): data[component] = [] for idx in range(cnt): tmp_data = [] for elem_type in MAPPING[component]: tmp_data.append(globals()['generate_%s' % elem_type](idx)) data[component].append(tmp_data) return data with open('mock_results.json', 'w') as f: json.dump(generate_data(3), f, indent=4)

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1

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1

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null

0

4

943e23a1f02b30ea8b9234935869f5081ffec18e

284

py

Python

__init__.py

edwardyehuang/iSeg

256b0f7fdb6e854fe026fa8df41d9a4a55db34d5

[ "MIT" ]

4

2021-12-13T09:49:26.000Z

2022-02-19T11:16:50.000Z

__init__.py

edwardyehuang/iSeg

256b0f7fdb6e854fe026fa8df41d9a4a55db34d5

[ "MIT" ]

1

2021-07-28T10:40:56.000Z

2021-08-09T07:14:06.000Z

__init__.py

edwardyehuang/iSeg

256b0f7fdb6e854fe026fa8df41d9a4a55db34d5

[ "MIT" ]

null

# ================================================================ # MIT License # Copyright (c) 2021 edwardyehuang (https://github.com/edwardyehuang) # ================================================================ name = "iseg" from iseg.core_model import SegBase, SegFoundation

35.5

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6.052632

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0.015267

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284

7

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0

1

0

1

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null

0

1

0

4

9448ab98a76b40234a9adc8f35a5a28ad306e871

260

py

Python

tornado-book/external_auth/facebook/modules.py

avinassh/learning-tornado

98b17c40d76720abcf0ab5b33ee1b46cf91d25d0

[ "MIT" ]

2

2019-05-12T15:34:05.000Z

2019-05-13T14:45:51.000Z

tornado-book/external_auth/facebook/modules.py

avinassh/learning-tornado

98b17c40d76720abcf0ab5b33ee1b46cf91d25d0

[ "MIT" ]

null

tornado-book/external_auth/facebook/modules.py

avinassh/learning-tornado

98b17c40d76720abcf0ab5b33ee1b46cf91d25d0

[ "MIT" ]

3

2016-12-09T02:13:53.000Z

2019-11-02T07:57:58.000Z

import tornado.web from datetime import datetime class FeedListItem(tornado.web.UIModule): def render(self, statusItem): return self.render_string('entry.html', item=statusItem, format=lambda x: datetime.strptime(x,'%Y-%m-%dT%H:%M:%S+0000').strftime('%c'))

43.333333

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null

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null

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1

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4

944e8084dee179f7e645a620761b0014432fac2d

153

py

Python

solutions/week-1/variables_2.py

bekbolsky/stepik-python

91613178ef8401019fab01ad18f10ee84f2f4491

[ "MIT" ]

1

2022-02-23T09:05:47.000Z

solutions/week-1/variables_2.py

bekbolsky/stepik-python

91613178ef8401019fab01ad18f10ee84f2f4491

[ "MIT" ]

null

solutions/week-1/variables_2.py

bekbolsky/stepik-python

91613178ef8401019fab01ad18f10ee84f2f4491

[ "MIT" ]

null

x = int(input()) print(x // 60) # получение целой части от деления, чтобы определить часы print(x % 60) # остаток от деления, чтобы определить минуты

30.6

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1

0

null

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94538eb943d4bdcb800c4208785e2909bfa3eed9

83

py

Python

jrdb/apps.py

hankehly/JRDB

ad470e867d204ea975f7b98b57881d72fcfb41c7

[ "MIT" ]

1

2022-02-19T14:44:34.000Z

jrdb/apps.py

hankehly/JRDB

ad470e867d204ea975f7b98b57881d72fcfb41c7

[ "MIT" ]

null

jrdb/apps.py

hankehly/JRDB

ad470e867d204ea975f7b98b57881d72fcfb41c7

[ "MIT" ]

1

2022-02-19T14:46:40.000Z

from django.apps import AppConfig class JRDBConfig(AppConfig): name = "jrdb"

13.833333

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4

94564ee8227570a788a161c8f434b06c6868aba0

76

py

Python

gaia_beet/__init__.py

misode/gaia-beet

a2eeed5c42b70745144974a7b184d07061ab9aa3

[ "MIT" ]

null

gaia_beet/__init__.py

misode/gaia-beet

a2eeed5c42b70745144974a7b184d07061ab9aa3

[ "MIT" ]

3

2022-03-21T20:51:24.000Z

2022-03-21T20:51:52.000Z

gaia_beet/__init__.py

misode/gaia-beet

a2eeed5c42b70745144974a7b184d07061ab9aa3

[ "MIT" ]

null

__version__ = "0.5.1" from .api import * from .density_functions import *

12.666667

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1

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4

945df796d4f7bebb5940931ff0edc9d3236abf2f

135

py

Python

tests/test_tok.py

coast-team/sqlschm

b5d5a9c904bbaaece1cba9ea7251bda91d77b290

[ "Apache-2.0" ]

null

tests/test_tok.py

coast-team/sqlschm

b5d5a9c904bbaaece1cba9ea7251bda91d77b290

[ "Apache-2.0" ]

null

tests/test_tok.py

coast-team/sqlschm

b5d5a9c904bbaaece1cba9ea7251bda91d77b290

[ "Apache-2.0" ]

null

from sqlschm import tok def test_interned_consistent_val(): for val in tok.INTERNED: assert tok.INTERNED[val].val == val

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135

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null

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94706aaf057e7b8e13c3ee897611bc42900c45df

143

py

Python

after_deploy/models.py

dfop02/django-after-deploy

32406aea3d83dda7deff548c9f9c91183cb3dcf1

[ "MIT" ]

7

2020-02-22T13:13:53.000Z

2022-03-26T18:04:44.000Z

after_deploy/models.py

dfop02/django-after-deploy

32406aea3d83dda7deff548c9f9c91183cb3dcf1

[ "MIT" ]

null

after_deploy/models.py

dfop02/django-after-deploy

32406aea3d83dda7deff548c9f9c91183cb3dcf1

[ "MIT" ]

null

from django.db import models class tasks(models.Model): code = models.CharField(max_length=32, primary_key=True, null=False, blank=False)

28.6

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4.909091

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947a634a8b070ccbf5d5c560f9ed1194ee68117c

995

py

Python

openbook_tags/migrations/0003_auto_20190206_1843.py

TamaraAbells/okuna-api

f87d8e80d2f182c01dbce68155ded0078ee707e4

[ "MIT" ]

164

2019-07-29T17:59:06.000Z

2022-03-19T21:36:01.000Z

openbook_tags/migrations/0003_auto_20190206_1843.py

TamaraAbells/okuna-api

f87d8e80d2f182c01dbce68155ded0078ee707e4

[ "MIT" ]

188

2019-03-16T09:53:25.000Z

2019-07-25T14:57:24.000Z

openbook_tags/migrations/0003_auto_20190206_1843.py

TamaraAbells/okuna-api

f87d8e80d2f182c01dbce68155ded0078ee707e4

[ "MIT" ]

80

2019-08-03T17:49:08.000Z

2022-02-28T16:56:33.000Z

# Generated by Django 2.1.5 on 2019-02-06 17:43 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('openbook_tags', '0002_auto_20190206_1805'), ] operations = [ migrations.AddField( model_name='tag', name='description_en', field=models.CharField(max_length=64, null=True, verbose_name='description'), ), migrations.AddField( model_name='tag', name='description_es', field=models.CharField(max_length=64, null=True, verbose_name='description'), ), migrations.AddField( model_name='tag', name='title_en', field=models.CharField(max_length=64, null=True, verbose_name='title'), ), migrations.AddField( model_name='tag', name='title_es', field=models.CharField(max_length=64, null=True, verbose_name='title'), ), ]

29.264706

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90

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null

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8471127acba146ec9e6ec6fa7cf718398c5327e2

253

py

Python

py/epm/__init__.py

NikiRui/SNEPM

9b0735faa5d10c3c3e4fd26636475151f9448b00

[ "MIT" ]

1

2018-03-12T09:21:48.000Z

py/epm/__init__.py

NikiRui/SNEPM

9b0735faa5d10c3c3e4fd26636475151f9448b00

[ "MIT" ]

null

py/epm/__init__.py

NikiRui/SNEPM

9b0735faa5d10c3c3e4fd26636475151f9448b00

[ "MIT" ]

null

# # See top-level LICENSE.rst file for Copyright information # # -*- coding: utf-8 -*- """ epm === Tools for EPM analysis. .. _Python: http://python.org """ # help with 2to3 support. from __future__ import absolute_import, division, print_function

14.055556

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253

17

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true

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1

0

1

0

4

849a0d0da310dc0a1f60bf1f6cf15c727346766d

967

py

Python

test/Python/Compiler/structure.py

marbre/mlir-npcomp

30adf9e6b0c1e94db38050a9e143f20a5a461d17

[ "Apache-2.0" ]

11

2020-03-30T23:31:03.000Z

2021-06-16T01:17:58.000Z

test/Python/Compiler/structure.py

marbre/mlir-npcomp

30adf9e6b0c1e94db38050a9e143f20a5a461d17

[ "Apache-2.0" ]

1

2020-04-26T21:19:30.000Z

2020-04-29T18:22:59.000Z

test/Python/Compiler/structure.py

marbre/mlir-npcomp

30adf9e6b0c1e94db38050a9e143f20a5a461d17

[ "Apache-2.0" ]

null

# RUN: %PYTHON %s | npcomp-opt -split-input-file | FileCheck %s --dump-input=fail from npcomp.compiler import test_config import_global = test_config.create_import_dump_decorator() # CHECK-LABEL: func @positional_args # CHECK-SAME: (%arg0: !basicpy.UnknownType, %arg1: !basicpy.UnknownType) -> !basicpy.UnknownType @import_global def positional_args(a, b): # CHECK: basicpy.binary_expr %arg0 "Add" %arg1 return a + b # CHECK-LABEL: func @pass_no_return @import_global def pass_no_return(): # CHECK: %[[NONE:.*]] = basicpy.singleton : !basicpy.NoneType # CHECK: %[[NONE_CAST:.*]] = basicpy.unknown_cast %[[NONE]] : !basicpy.NoneType -> !basicpy.UnknownType # CHECK: return %[[NONE_CAST]] # CHECK-NOT: return pass # CHECK-LABEL: func @expr_statement @import_global def expr_statement(): # CHECK: basicpy.exec { # CHECK: %[[V:.*]] = basicpy.binary_expr # CHECK: basicpy.exec_discard %[[V]] # CHECK: } # CHECK: return a = 1 a + 2

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null

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1

0

1

0

4

84c8db01533702f0f12f75d86b51d99801e8ba92

146

py

Python

taxman/income/abstract_income.py

robinmitra/taxman

a7afc0b4a1449cd46e90cd3af05f4a5d65a8acbf

[ "MIT" ]

3

2019-01-07T13:08:59.000Z

2021-01-11T10:34:52.000Z

taxman/income/abstract_income.py

robinmitra/taxman

a7afc0b4a1449cd46e90cd3af05f4a5d65a8acbf

[ "MIT" ]

null

taxman/income/abstract_income.py

robinmitra/taxman

a7afc0b4a1449cd46e90cd3af05f4a5d65a8acbf

[ "MIT" ]

null

import abc class AbstractIncome(abc.ABC): @abc.abstractmethod def get_amount(self): """Get income amount for the income type"""

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0.219178

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null

0

1

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1

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4

84d44de7a484200ae72408fe3fe5f6ac98e16732

95

py

Python

quest/__init__.py

Fluorescence-Tools/quest

e17e5682f7686d1acc1fd8a22bdae33963bc16d6

[ "MIT" ]

null

quest/__init__.py

Fluorescence-Tools/quest

e17e5682f7686d1acc1fd8a22bdae33963bc16d6

[ "MIT" ]

1

2019-08-14T08:01:26.000Z

2019-08-15T22:59:05.000Z

quest/__init__.py

Fluorescence-Tools/quest

e17e5682f7686d1acc1fd8a22bdae33963bc16d6

[ "MIT" ]

null

import os import quest.utils as utils utils.set_search_paths( os.path.dirname(__file__) )

13.571429

29

0.768421

15

95

4.466667

0.733333

0

0.147368

95

6

30

15.833333

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0

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true

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null

0

1

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null

0

1

0

1

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4

84dd78e7d33cde171b3beb08515ab72d7ab44385

156

py

Python

scrapers/BPC-bournemouth-christchurch-and-poole/councillors.py

DemocracyClub/LGSF

21c2a049db08575e03db2fb63a8bccc8de0c636b

[ "MIT" ]

4

2018-10-17T13:30:08.000Z

2021-06-22T13:29:43.000Z

scrapers/BPC-bournemouth-christchurch-and-poole/councillors.py

DemocracyClub/LGSF

21c2a049db08575e03db2fb63a8bccc8de0c636b

[ "MIT" ]

46

2018-10-15T13:47:48.000Z

2022-03-23T10:26:18.000Z

scrapers/BPC-bournemouth-christchurch-and-poole/councillors.py

DemocracyClub/LGSF

21c2a049db08575e03db2fb63a8bccc8de0c636b

[ "MIT" ]

1

2018-10-15T13:36:03.000Z

from lgsf.councillors.scrapers import ModGovCouncillorScraper class Scraper(ModGovCouncillorScraper): base_url = "http://democracy.bcpcouncil.gov.uk"

26

61

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156

7.875

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156

5

62

31.2

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false

0

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0

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null

0

1

0

1

0

4

ca0a1988288f4f6bd45a49833d1d336de3137ae1

66

py

Python

python/testData/findUsages/ClassUsages.py

truthiswill/intellij-community

fff88cfb0dc168eea18ecb745d3e5b93f57b0b95

[ "Apache-2.0" ]

2

2019-04-28T07:48:50.000Z

2020-12-11T14:18:08.000Z

python/testData/findUsages/ClassUsages.py

truthiswill/intellij-community

fff88cfb0dc168eea18ecb745d3e5b93f57b0b95

[ "Apache-2.0" ]

173

2018-07-05T13:59:39.000Z

2018-08-09T01:12:03.000Z

python/testData/findUsages/ClassUsages.py

truthiswill/intellij-community

fff88cfb0dc168eea18ecb745d3e5b93f57b0b95

[ "Apache-2.0" ]

2

2020-03-15T08:57:37.000Z

2020-04-07T04:48:14.000Z

class C<caret>ow: def __init__(self): pass c = Cow()

11

23

0.545455

10

66

3.2

0.9

0

0.318182

66

5

24

13.2

0.711111

0

null

0.25

0

null

0

1

0

null

0

1

0

null

0

1

0

1

0

4

ca1ec0b8a4e7f5db178e340d5646c980f299550a

218

py

Python

music_from_my_tshirt/context_processors.py

craiga/music-from-my-tshirt

d6f051771c8d2c16fadde78df35a09fd9d8818aa

[ "MIT" ]

null

music_from_my_tshirt/context_processors.py

craiga/music-from-my-tshirt

d6f051771c8d2c16fadde78df35a09fd9d8818aa

[ "MIT" ]

9

2020-03-01T16:48:16.000Z

2020-04-29T16:20:26.000Z

music_from_my_tshirt/context_processors.py

craiga/music-from-my-tshirt

d6f051771c8d2c16fadde78df35a09fd9d8818aa

[ "MIT" ]

null

"""Context processors.""" from django.conf import settings def sentry_dsn(request): return {"sentry_dsn": settings.SENTRY_DSN} def canonical_host(request): return {"canonical_host": settings.ENFORCE_HOST}

18.166667

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218

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0

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0

null

0

null

0

1

0

1

0

4

ca28ed8ab2a0560487efe2001d6ce704923d282c

10,973

py

Python

pika/exceptions.py

hugovk/pika

03542ef616a2a849e8bfb0845427f50e741ea0c6

[ "BSD-3-Clause" ]

1

2019-08-28T10:10:56.000Z

pika/exceptions.py

goupper/pika

e2f26db4f41ac7ea6bdc50964a766472460dce4a

[ "BSD-3-Clause" ]

null

pika/exceptions.py

goupper/pika

e2f26db4f41ac7ea6bdc50964a766472460dce4a

[ "BSD-3-Clause" ]

null

"""Pika specific exceptions""" class AMQPError(Exception): def __repr__(self): return '%s: An unspecified AMQP error has occurred; %s' % ( self.__class__.__name__, self.args) class AMQPConnectionError(AMQPError): def __repr__(self): if len(self.args) == 2: return '{}: ({}) {}'.format(self.__class__.__name__, self.args[0], self.args[1]) else: return '{}: {}'.format(self.__class__.__name__, self.args) class ConnectionOpenAborted(AMQPConnectionError): """Client closed connection while opening.""" pass class StreamLostError(AMQPConnectionError): """Stream (TCP) connection lost.""" pass class IncompatibleProtocolError(AMQPConnectionError): def __repr__(self): return ('%s: The protocol returned by the server is not supported: %s' % (self.__class__.__name__, self.args,)) class AuthenticationError(AMQPConnectionError): def __repr__(self): return ('%s: Server and client could not negotiate use of the %s ' 'authentication mechanism' % (self.__class__.__name__, self.args[0])) class ProbableAuthenticationError(AMQPConnectionError): def __repr__(self): return ( '%s: Client was disconnected at a connection stage indicating a ' 'probable authentication error: %s' % (self.__class__.__name__, self.args,)) class ProbableAccessDeniedError(AMQPConnectionError): def __repr__(self): return ( '%s: Client was disconnected at a connection stage indicating a ' 'probable denial of access to the specified virtual host: %s' % (self.__class__.__name__, self.args,)) class NoFreeChannels(AMQPConnectionError): def __repr__(self): return '%s: The connection has run out of free channels' % ( self.__class__.__name__) class ConnectionWrongStateError(AMQPConnectionError): """Connection is in wrong state for the requested operation.""" def __repr__(self): if self.args: return super(ConnectionWrongStateError, self).__repr__() else: return ('%s: The connection is in wrong state for the requested ' 'operation.' % (self.__class__.__name__)) class ConnectionClosed(AMQPConnectionError): def __init__(self, reply_code, reply_text): """ :param int reply_code: reply-code that was used in user's or broker's `Connection.Close` method. NEW in v1.0.0 :param str reply_text: reply-text that was used in user's or broker's `Connection.Close` method. Human-readable string corresponding to `reply_code`. NEW in v1.0.0 """ super(ConnectionClosed, self).__init__(int(reply_code), str(reply_text)) def __repr__(self): return '{}: ({}) {!r}'.format(self.__class__.__name__, self.reply_code, self.reply_text) @property def reply_code(self): """ NEW in v1.0.0 :rtype: int """ return self.args[0] @property def reply_text(self): """ NEW in v1.0.0 :rtype: str """ return self.args[1] class ConnectionClosedByBroker(ConnectionClosed): """Connection.Close from broker.""" pass class ConnectionClosedByClient(ConnectionClosed): """Connection was closed at request of Pika client.""" pass class ConnectionBlockedTimeout(AMQPConnectionError): """RabbitMQ-specific: timed out waiting for connection.unblocked.""" pass class AMQPHeartbeatTimeout(AMQPConnectionError): """Connection was dropped as result of heartbeat timeout.""" pass class AMQPChannelError(AMQPError): def __repr__(self): return '{}: {!r}'.format(self.__class__.__name__, self.args) class ChannelWrongStateError(AMQPChannelError): """Channel is in wrong state for the requested operation.""" pass class ChannelClosed(AMQPChannelError): """The channel closed by client or by broker """ def __init__(self, reply_code, reply_text): """ :param int reply_code: reply-code that was used in user's or broker's `Channel.Close` method. One of the AMQP-defined Channel Errors. NEW in v1.0.0 :param str reply_text: reply-text that was used in user's or broker's `Channel.Close` method. Human-readable string corresponding to `reply_code`; NEW in v1.0.0 """ super(ChannelClosed, self).__init__(int(reply_code), str(reply_text)) def __repr__(self): return '{}: ({}) {!r}'.format(self.__class__.__name__, self.reply_code, self.reply_text) @property def reply_code(self): """ NEW in v1.0.0 :rtype: int """ return self.args[0] @property def reply_text(self): """ NEW in v1.0.0 :rtype: str """ return self.args[1] class ChannelClosedByBroker(ChannelClosed): """`Channel.Close` from broker; may be passed as reason to channel's on-closed callback of non-blocking connection adapters or raised by `BlockingConnection`. NEW in v1.0.0 """ pass class ChannelClosedByClient(ChannelClosed): """Channel closed by client upon receipt of `Channel.CloseOk`; may be passed as reason to channel's on-closed callback of non-blocking connection adapters, but not raised by `BlockingConnection`. NEW in v1.0.0 """ pass class DuplicateConsumerTag(AMQPChannelError): def __repr__(self): return ('%s: The consumer tag specified already exists for this ' 'channel: %s' % (self.__class__.__name__, self.args[0])) class ConsumerCancelled(AMQPChannelError): def __repr__(self): return '%s: Server cancelled consumer' % (self.__class__.__name__) class UnroutableError(AMQPChannelError): """Exception containing one or more unroutable messages returned by broker via Basic.Return. Used by BlockingChannel. In publisher-acknowledgements mode, this is raised upon receipt of Basic.Ack from broker; in the event of Basic.Nack from broker, `NackError` is raised instead """ def __init__(self, messages): """ :param messages: sequence of returned unroutable messages :type messages: sequence of `blocking_connection.ReturnedMessage` objects """ super(UnroutableError, self).__init__( "%s unroutable message(s) returned" % (len(messages))) self.messages = messages def __repr__(self): return '%s: %i unroutable messages returned by broker' % ( self.__class__.__name__, len(self.messages)) class NackError(AMQPChannelError): """This exception is raised when a message published in publisher-acknowledgements mode is Nack'ed by the broker. Used by BlockingChannel. """ def __init__(self, messages): """ :param messages: sequence of returned unroutable messages :type messages: sequence of `blocking_connection.ReturnedMessage` objects """ super(NackError, self).__init__( "%s message(s) NACKed" % (len(messages))) self.messages = messages def __repr__(self): return '%s: %i unroutable messages returned by broker' % ( self.__class__.__name__, len(self.messages)) class InvalidChannelNumber(AMQPError): def __repr__(self): return '%s: An invalid channel number has been specified: %s' % ( self.__class__.__name__, self.args[0]) class ProtocolSyntaxError(AMQPError): def __repr__(self): return '%s: An unspecified protocol syntax error occurred' % ( self.__class__.__name__) class UnexpectedFrameError(ProtocolSyntaxError): def __repr__(self): return '%s: Received a frame out of sequence: %r' % ( self.__class__.__name__, self.args[0]) class ProtocolVersionMismatch(ProtocolSyntaxError): def __repr__(self): return '%s: Protocol versions did not match: %r vs %r' % ( self.__class__.__name__, self.args[0], self.args[1]) class BodyTooLongError(ProtocolSyntaxError): def __repr__(self): return ('%s: Received too many bytes for a message delivery: ' 'Received %i, expected %i' % (self.__class__.__name__, self.args[0], self.args[1])) class InvalidFrameError(ProtocolSyntaxError): def __repr__(self): return '%s: Invalid frame received: %r' % (self.__class__.__name__, self.args[0]) class InvalidFieldTypeException(ProtocolSyntaxError): def __repr__(self): return '%s: Unsupported field kind %s' % (self.__class__.__name__, self.args[0]) class UnsupportedAMQPFieldException(ProtocolSyntaxError): def __repr__(self): return '%s: Unsupported field kind %s' % (self.__class__.__name__, type(self.args[1])) class MethodNotImplemented(AMQPError): pass class ChannelError(Exception): def __repr__(self): return '%s: An unspecified error occurred with the Channel' % ( self.__class__.__name__) class InvalidMinimumFrameSize(ProtocolSyntaxError): """ DEPRECATED; pika.connection.Parameters.frame_max property setter now raises the standard `ValueError` exception when the value is out of bounds. """ def __repr__(self): return '%s: AMQP Minimum Frame Size is 4096 Bytes' % ( self.__class__.__name__) class InvalidMaximumFrameSize(ProtocolSyntaxError): """ DEPRECATED; pika.connection.Parameters.frame_max property setter now raises the standard `ValueError` exception when the value is out of bounds. """ def __repr__(self): return '%s: AMQP Maximum Frame Size is 131072 Bytes' % ( self.__class__.__name__) class ReentrancyError(Exception): """The requested operation would result in unsupported recursion or reentrancy. Used by BlockingConnection/BlockingChannel """ class ShortStringTooLong(AMQPError): def __repr__(self): return ('%s: AMQP Short String can contain up to 255 bytes: ' '%.300s' % (self.__class__.__name__, self.args[0])) class DuplicateGetOkCallback(ChannelError): def __repr__(self): return ('%s: basic_get can only be called again after the callback for ' 'the previous basic_get is executed' % self.__class__.__name__)

28.952507

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0.068168

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10,973

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null

0

1

0

1

0

1

0

4

ca2a83885647c0e4b06a6b68eaaac73ccd7a6588

100

py

Python

SRC/Chapter_03-Functions-And-Looping/03_challenge.py

archeranimesh/tth-python-basics-3

accbc894324d084124ec001817edf4dc3afffa78

[ "MIT" ]

null

SRC/Chapter_03-Functions-And-Looping/03_challenge.py

archeranimesh/tth-python-basics-3

accbc894324d084124ec001817edf4dc3afffa78

[ "MIT" ]

null

SRC/Chapter_03-Functions-And-Looping/03_challenge.py

archeranimesh/tth-python-basics-3

accbc894324d084124ec001817edf4dc3afffa78

[ "MIT" ]

null

def square(number): """ return the square """ return number ** 2 print(square(5))

11.111111

22

0.55

12

100

4.583333

0.666667

0

0.028571

0.3

100

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0.17

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false

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0

1

0

null

0

1

0

1

0

4

ca4129045f4da6ed48004cc93fa82a6bb18566e9

2,432

gyp

Python

athena/main/athena_main.gyp

7kbird/chrome

f56688375530f1003e34c34f441321977c5af3c3

[ "BSD-3-Clause-No-Nuclear-License-2014", "BSD-3-Clause" ]

null

athena/main/athena_main.gyp

7kbird/chrome

f56688375530f1003e34c34f441321977c5af3c3

[ "BSD-3-Clause-No-Nuclear-License-2014", "BSD-3-Clause" ]

null

athena/main/athena_main.gyp

7kbird/chrome

f56688375530f1003e34c34f441321977c5af3c3

[ "BSD-3-Clause-No-Nuclear-License-2014", "BSD-3-Clause" ]

1

2020-11-04T07:23:37.000Z

# Copyright 2014 The Chromium Authors. All rights reserved. # Use of this source code is governed by a BSD-style license that can be # found in the LICENSE file. { 'variables': { 'chromium_code': 1, }, 'targets': [ { 'target_name': 'athena_main_lib', 'type': 'static_library', 'dependencies': [ '../athena.gyp:athena_lib', '../athena.gyp:athena_content_lib', '../athena.gyp:athena_content_support_lib', '../resources/athena_resources.gyp:athena_resources', # debug_widow.cc depends on this. Remove this once debug_window # is removed. '../../ash/ash_resources.gyp:ash_resources', '../../chromeos/chromeos.gyp:power_manager_proto', '../../components/components.gyp:component_metrics_proto', '../../components/components.gyp:history_core_browser', # infobars_test_support is required to declare some symbols used in the # search_engines and its dependencies. See crbug.com/386171 # TODO(mukai): declare those symbols for Athena. '../../components/components.gyp:infobars_test_support', '../../components/components.gyp:omnibox', '../../components/components.gyp:search_engines', '../../skia/skia.gyp:skia', '../../ui/app_list/app_list.gyp:app_list', '../../ui/chromeos/ui_chromeos.gyp:ui_chromeos', '../../ui/native_theme/native_theme.gyp:native_theme', '../../ui/views/views.gyp:views', '../../url/url.gyp:url_lib', ], 'include_dirs': [ '../..', ], 'sources': [ 'athena_launcher.cc', 'athena_launcher.h', 'debug/debug_window.cc', 'debug/debug_window.h', 'debug/network_selector.cc', 'debug/network_selector.h', 'url_search_provider.cc', 'url_search_provider.h', 'placeholder.cc', 'placeholder.h', ], }, { 'target_name': 'athena_main', 'type': 'executable', 'dependencies': [ '../../ui/accessibility/accessibility.gyp:ax_gen', '../resources/athena_resources.gyp:athena_pak', '../../extensions/shell/app_shell.gyp:app_shell_lib', 'athena_main_lib', ], 'include_dirs': [ '../..', ], 'sources': [ 'athena_app_window_controller.cc', 'athena_app_window_controller.h', 'athena_main.cc', ], } ], # targets }

33.315068

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0

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0

1

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ca4f85eab1aca1cd671819ea1e33aa6abfc91c48

237

gyp

Python

binding.gyp

Bruce17/nodejs-cpp-addon

11b329bc5705f102c76d17f465205aafe5291b1c

[ "MIT" ]

null

binding.gyp

Bruce17/nodejs-cpp-addon

11b329bc5705f102c76d17f465205aafe5291b1c

[ "MIT" ]

null

binding.gyp

Bruce17/nodejs-cpp-addon

11b329bc5705f102c76d17f465205aafe5291b1c

[ "MIT" ]

null

{ "targets": [ { "target_name": "binding-helloworld", "sources": [ "src/cpp/binding-helloworld.cc" ] }, { "target_name": "binding-fibonacci", "sources": [ "src/cpp/binding-fibonacci.cc" ] } ] }

19.75

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0

0.270042

237

12

53

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true

0

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0

null

0

1

0

null

0

1

0

4

ca8d9745c220c0d82c87b8a60b0e70a9f13fb54e

266

py

Python

pypartitions/__init__.py

klocey/partitions

0ce57b75007f9608f55b0a835410f0d0c1de5246

[ "Unlicense" ]

3

2015-12-27T07:06:23.000Z

2020-04-09T19:22:59.000Z

pypartitions/__init__.py

klocey/partitions

0ce57b75007f9608f55b0a835410f0d0c1de5246

[ "Unlicense" ]

6

2016-04-28T05:36:37.000Z

2021-01-31T22:07:15.000Z

pypartitions/__init__.py

klocey/partitions

0ce57b75007f9608f55b0a835410f0d0c1de5246

[ "Unlicense" ]

null

""" pypartitions: Efficient Sampling Algorithims for Integer Partitioning Reference: Locey, K.J. and D.J. McGlinn. Efficient algorithms for sampling feasible sets of macroecological patterns. PeerJ. https://peerj.com/preprints/78/ """ from .pypartitions import *

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0

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047f2cd50ac08c58c8931db71bdbc881ffba80a6

83

py

Python

learnergy/models/__init__.py

anukaal/learnergy

704fc2b3fcb80df41ed28d750dc4e6475df23315

[ "Apache-2.0" ]

39

2020-02-27T00:47:45.000Z

2022-03-28T14:57:26.000Z

learnergy/models/__init__.py

anukaal/learnergy

704fc2b3fcb80df41ed28d750dc4e6475df23315

[ "Apache-2.0" ]

5

2021-05-11T08:23:37.000Z

2022-01-20T12:50:59.000Z

learnergy/models/__init__.py

anukaal/learnergy

704fc2b3fcb80df41ed28d750dc4e6475df23315

[ "Apache-2.0" ]

6

2020-04-15T00:23:13.000Z

2022-01-29T16:22:05.000Z

"""A package contaning subpackages of models for all common learnergy modules. """

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048c7f29024b46f7a834b0c18835c1a64a9b4cdf

1,988

py

Python

katas/romanNumeralsToInt/python/00001/romanNumeralsToIntTest.py

DerickMathew/kataLog

2fa999de9e41aa977dd74da89bc36c6d5703722b

[ "MIT" ]

null

katas/romanNumeralsToInt/python/00001/romanNumeralsToIntTest.py

DerickMathew/kataLog

2fa999de9e41aa977dd74da89bc36c6d5703722b

[ "MIT" ]

null

katas/romanNumeralsToInt/python/00001/romanNumeralsToIntTest.py

DerickMathew/kataLog

2fa999de9e41aa977dd74da89bc36c6d5703722b

[ "MIT" ]

null

import unittest from romanNumeralsToInt import RomanNumeralsToInt class TestRomanNumeralsToInt(unittest.TestCase): def setUp(self): self.romanNumeralsToInt = RomanNumeralsToInt() def test_IReturns1(self): self.assertEqual(1, self.romanNumeralsToInt.getNum('I')); def test_VReturns5(self): self.assertEqual(5, self.romanNumeralsToInt.getNum('V')); def test_XReturns10(self): self.assertEqual(10, self.romanNumeralsToInt.getNum('X')); def test_LReturns50(self): self.assertEqual(50, self.romanNumeralsToInt.getNum('L')); def test_CReturns100(self): self.assertEqual(100, self.romanNumeralsToInt.getNum('C')); def test_DReturns500(self): self.assertEqual(500, self.romanNumeralsToInt.getNum('D')); def test_MReturns1000(self): self.assertEqual(1000, self.romanNumeralsToInt.getNum('M')); def test_IIReturns2(self): self.assertEqual(2, self.romanNumeralsToInt.getNum('II')); def test_IVReturns4(self): self.assertEqual(4, self.romanNumeralsToInt.getNum('IV')); def test_IXReturns9(self): self.assertEqual(9, self.romanNumeralsToInt.getNum('IX')); def test_XIXReturns19(self): self.assertEqual(19, self.romanNumeralsToInt.getNum('XIX')); def test_XLReturns40(self): self.assertEqual(40, self.romanNumeralsToInt.getNum('XL')); def test_IXLReturns39(self): self.assertEqual(39, self.romanNumeralsToInt.getNum('IXL')); def test_IVXLReturns34(self): self.assertEqual(34, self.romanNumeralsToInt.getNum('IVXL')); def test_IVXLCDMReturns344(self): self.assertEqual(334, self.romanNumeralsToInt.getNum('IVXLCDM')); def test_MIVXLCDMReturns1344(self): self.assertEqual(1334, self.romanNumeralsToInt.getNum('MIVXLCDM')); def test_IMMMMReturns3999(self): self.assertEqual(3999, self.romanNumeralsToInt.getNum('IMMMM')); if __name__ == '__main__': unittest.main()

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049a4c985d1df8df9dcea1d38f16fdd68583d681

16,261

py

Python

boto3_type_annotations_with_docs/boto3_type_annotations/elb/paginator.py

cowboygneox/boto3_type_annotations

450dce1de4e066b939de7eac2ec560ed1a7ddaa2

[ "MIT" ]

119

2018-12-01T18:20:57.000Z

2022-02-02T10:31:29.000Z

boto3_type_annotations_with_docs/boto3_type_annotations/elb/paginator.py

cowboygneox/boto3_type_annotations

450dce1de4e066b939de7eac2ec560ed1a7ddaa2

[ "MIT" ]

15

2018-11-16T00:16:44.000Z

2021-11-13T03:44:18.000Z

boto3_type_annotations_with_docs/boto3_type_annotations/elb/paginator.py

cowboygneox/boto3_type_annotations

450dce1de4e066b939de7eac2ec560ed1a7ddaa2

[ "MIT" ]

11

2019-05-06T05:26:51.000Z

2021-09-28T15:27:59.000Z

from typing import List from typing import Dict from botocore.paginate import Paginator class DescribeAccountLimits(Paginator): def paginate(self, PaginationConfig: Dict = None) -> Dict: """ Creates an iterator that will paginate through responses from :py:meth:`ElasticLoadBalancing.Client.describe_account_limits`. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/elasticloadbalancing-2012-06-01/DescribeAccountLimits>`_ **Request Syntax** :: response_iterator = paginator.paginate( PaginationConfig={ 'MaxItems': 123, 'PageSize': 123, 'StartingToken': 'string' } ) **Response Syntax** :: { 'Limits': [ { 'Name': 'string', 'Max': 'string' }, ], 'NextToken': 'string' } **Response Structure** - *(dict) --* - **Limits** *(list) --* Information about the limits. - *(dict) --* Information about an Elastic Load Balancing resource limit for your AWS account. - **Name** *(string) --* The name of the limit. The possible values are: * classic-listeners * classic-load-balancers * classic-registered-instances - **Max** *(string) --* The maximum value of the limit. - **NextToken** *(string) --* A token to resume pagination. :type PaginationConfig: dict :param PaginationConfig: A dictionary that provides parameters to control pagination. - **MaxItems** *(integer) --* The total number of items to return. If the total number of items available is more than the value specified in max-items then a ``NextToken`` will be provided in the output that you can use to resume pagination. - **PageSize** *(integer) --* The size of each page. - **StartingToken** *(string) --* A token to specify where to start paginating. This is the ``NextToken`` from a previous response. :rtype: dict :returns: """ pass class DescribeLoadBalancers(Paginator): def paginate(self, LoadBalancerNames: List = None, PaginationConfig: Dict = None) -> Dict: """ Creates an iterator that will paginate through responses from :py:meth:`ElasticLoadBalancing.Client.describe_load_balancers`. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/elasticloadbalancing-2012-06-01/DescribeLoadBalancers>`_ **Request Syntax** :: response_iterator = paginator.paginate( LoadBalancerNames=[ 'string', ], PaginationConfig={ 'MaxItems': 123, 'PageSize': 123, 'StartingToken': 'string' } ) **Response Syntax** :: { 'LoadBalancerDescriptions': [ { 'LoadBalancerName': 'string', 'DNSName': 'string', 'CanonicalHostedZoneName': 'string', 'CanonicalHostedZoneNameID': 'string', 'ListenerDescriptions': [ { 'Listener': { 'Protocol': 'string', 'LoadBalancerPort': 123, 'InstanceProtocol': 'string', 'InstancePort': 123, 'SSLCertificateId': 'string' }, 'PolicyNames': [ 'string', ] }, ], 'Policies': { 'AppCookieStickinessPolicies': [ { 'PolicyName': 'string', 'CookieName': 'string' }, ], 'LBCookieStickinessPolicies': [ { 'PolicyName': 'string', 'CookieExpirationPeriod': 123 }, ], 'OtherPolicies': [ 'string', ] }, 'BackendServerDescriptions': [ { 'InstancePort': 123, 'PolicyNames': [ 'string', ] }, ], 'AvailabilityZones': [ 'string', ], 'Subnets': [ 'string', ], 'VPCId': 'string', 'Instances': [ { 'InstanceId': 'string' }, ], 'HealthCheck': { 'Target': 'string', 'Interval': 123, 'Timeout': 123, 'UnhealthyThreshold': 123, 'HealthyThreshold': 123 }, 'SourceSecurityGroup': { 'OwnerAlias': 'string', 'GroupName': 'string' }, 'SecurityGroups': [ 'string', ], 'CreatedTime': datetime(2015, 1, 1), 'Scheme': 'string' }, ], 'NextToken': 'string' } **Response Structure** - *(dict) --* Contains the parameters for DescribeLoadBalancers. - **LoadBalancerDescriptions** *(list) --* Information about the load balancers. - *(dict) --* Information about a load balancer. - **LoadBalancerName** *(string) --* The name of the load balancer. - **DNSName** *(string) --* The DNS name of the load balancer. - **CanonicalHostedZoneName** *(string) --* The DNS name of the load balancer. For more information, see `Configure a Custom Domain Name <http://docs.aws.amazon.com/elasticloadbalancing/latest/classic/using-domain-names-with-elb.html>`__ in the *Classic Load Balancers Guide* . - **CanonicalHostedZoneNameID** *(string) --* The ID of the Amazon Route 53 hosted zone for the load balancer. - **ListenerDescriptions** *(list) --* The listeners for the load balancer. - *(dict) --* The policies enabled for a listener. - **Listener** *(dict) --* The listener. - **Protocol** *(string) --* The load balancer transport protocol to use for routing: HTTP, HTTPS, TCP, or SSL. - **LoadBalancerPort** *(integer) --* The port on which the load balancer is listening. On EC2-VPC, you can specify any port from the range 1-65535. On EC2-Classic, you can specify any port from the following list: 25, 80, 443, 465, 587, 1024-65535. - **InstanceProtocol** *(string) --* The protocol to use for routing traffic to instances: HTTP, HTTPS, TCP, or SSL. If the front-end protocol is HTTP, HTTPS, TCP, or SSL, ``InstanceProtocol`` must be at the same protocol. If there is another listener with the same ``InstancePort`` whose ``InstanceProtocol`` is secure, (HTTPS or SSL), the listener's ``InstanceProtocol`` must also be secure. If there is another listener with the same ``InstancePort`` whose ``InstanceProtocol`` is HTTP or TCP, the listener's ``InstanceProtocol`` must be HTTP or TCP. - **InstancePort** *(integer) --* The port on which the instance is listening. - **SSLCertificateId** *(string) --* The Amazon Resource Name (ARN) of the server certificate. - **PolicyNames** *(list) --* The policies. If there are no policies enabled, the list is empty. - *(string) --* - **Policies** *(dict) --* The policies defined for the load balancer. - **AppCookieStickinessPolicies** *(list) --* The stickiness policies created using CreateAppCookieStickinessPolicy . - *(dict) --* Information about a policy for application-controlled session stickiness. - **PolicyName** *(string) --* The mnemonic name for the policy being created. The name must be unique within a set of policies for this load balancer. - **CookieName** *(string) --* The name of the application cookie used for stickiness. - **LBCookieStickinessPolicies** *(list) --* The stickiness policies created using CreateLBCookieStickinessPolicy . - *(dict) --* Information about a policy for duration-based session stickiness. - **PolicyName** *(string) --* The name of the policy. This name must be unique within the set of policies for this load balancer. - **CookieExpirationPeriod** *(integer) --* The time period, in seconds, after which the cookie should be considered stale. If this parameter is not specified, the stickiness session lasts for the duration of the browser session. - **OtherPolicies** *(list) --* The policies other than the stickiness policies. - *(string) --* - **BackendServerDescriptions** *(list) --* Information about your EC2 instances. - *(dict) --* Information about the configuration of an EC2 instance. - **InstancePort** *(integer) --* The port on which the EC2 instance is listening. - **PolicyNames** *(list) --* The names of the policies enabled for the EC2 instance. - *(string) --* - **AvailabilityZones** *(list) --* The Availability Zones for the load balancer. - *(string) --* - **Subnets** *(list) --* The IDs of the subnets for the load balancer. - *(string) --* - **VPCId** *(string) --* The ID of the VPC for the load balancer. - **Instances** *(list) --* The IDs of the instances for the load balancer. - *(dict) --* The ID of an EC2 instance. - **InstanceId** *(string) --* The instance ID. - **HealthCheck** *(dict) --* Information about the health checks conducted on the load balancer. - **Target** *(string) --* The instance being checked. The protocol is either TCP, HTTP, HTTPS, or SSL. The range of valid ports is one (1) through 65535. TCP is the default, specified as a TCP: port pair, for example "TCP:5000". In this case, a health check simply attempts to open a TCP connection to the instance on the specified port. Failure to connect within the configured timeout is considered unhealthy. SSL is also specified as SSL: port pair, for example, SSL:5000. For HTTP/HTTPS, you must include a ping path in the string. HTTP is specified as a HTTP:port;/;PathToPing; grouping, for example "HTTP:80/weather/us/wa/seattle". In this case, a HTTP GET request is issued to the instance on the given port and path. Any answer other than "200 OK" within the timeout period is considered unhealthy. The total length of the HTTP ping target must be 1024 16-bit Unicode characters or less. - **Interval** *(integer) --* The approximate interval, in seconds, between health checks of an individual instance. - **Timeout** *(integer) --* The amount of time, in seconds, during which no response means a failed health check. This value must be less than the ``Interval`` value. - **UnhealthyThreshold** *(integer) --* The number of consecutive health check failures required before moving the instance to the ``Unhealthy`` state. - **HealthyThreshold** *(integer) --* The number of consecutive health checks successes required before moving the instance to the ``Healthy`` state. - **SourceSecurityGroup** *(dict) --* The security group for the load balancer, which you can use as part of your inbound rules for your registered instances. To only allow traffic from load balancers, add a security group rule that specifies this source security group as the inbound source. - **OwnerAlias** *(string) --* The owner of the security group. - **GroupName** *(string) --* The name of the security group. - **SecurityGroups** *(list) --* The security groups for the load balancer. Valid only for load balancers in a VPC. - *(string) --* - **CreatedTime** *(datetime) --* The date and time the load balancer was created. - **Scheme** *(string) --* The type of load balancer. Valid only for load balancers in a VPC. If ``Scheme`` is ``internet-facing`` , the load balancer has a public DNS name that resolves to a public IP address. If ``Scheme`` is ``internal`` , the load balancer has a public DNS name that resolves to a private IP address. - **NextToken** *(string) --* A token to resume pagination. :type LoadBalancerNames: list :param LoadBalancerNames: The names of the load balancers. - *(string) --* :type PaginationConfig: dict :param PaginationConfig: A dictionary that provides parameters to control pagination. - **MaxItems** *(integer) --* The total number of items to return. If the total number of items available is more than the value specified in max-items then a ``NextToken`` will be provided in the output that you can use to resume pagination. - **PageSize** *(integer) --* The size of each page. - **StartingToken** *(string) --* A token to specify where to start paginating. This is the ``NextToken`` from a previous response. :rtype: dict :returns: """ pass

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04af9881bc940f1429a5955671bd3fe556f5e016

158

py

Python

app/api/model/model.py

nccr-itmo/FEDOT.Web

9b6f7b66de277ea34d6d5ed621b99a3f938db61b

[ "BSD-3-Clause" ]

23

2020-12-24T11:05:01.000Z

2022-03-31T20:29:12.000Z

app/api/model/model.py

nccr-itmo/FedotWeb

763fb1f39ad2b69104b6568e6f941c4c67762e34

[ "BSD-3-Clause" ]

42

2021-01-11T09:38:31.000Z

2022-03-25T17:19:05.000Z

app/api/model/model.py

nccr-itmo/FedotWeb

763fb1f39ad2b69104b6568e6f941c4c67762e34

[ "BSD-3-Clause" ]

5

2021-03-31T04:38:31.000Z

2022-03-31T20:29:26.000Z

from dataclasses import dataclass @dataclass class Model: model_id: str = '0' label: str = 'model_label' description: str = 'model description'

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04b89a8bcffbcc59a021546d22fd3c78d08cb876

69,904

py

Python

notebooks/analysis/visualizations_revised_manuscript.py

UGentBiomath/COVID19-Model

ef04f6ec78a3c3dc9217cea68ae5276d159ba0c1

[ "MIT" ]

22

2020-04-22T16:42:53.000Z

2021-05-06T08:44:02.000Z

notebooks/analysis/visualizations_revised_manuscript.py

UGentBiomath/COVID19-Model

ef04f6ec78a3c3dc9217cea68ae5276d159ba0c1

[ "MIT" ]

90

2020-04-17T19:25:52.000Z

2022-03-25T12:34:39.000Z

notebooks/analysis/visualizations_revised_manuscript.py

UGentBiomath/COVID19-Model

ef04f6ec78a3c3dc9217cea68ae5276d159ba0c1

[ "MIT" ]

26

2020-04-06T06:09:04.000Z

2020-11-21T22:40:40.000Z

""" This script visualises the prevention parameters of the first and second COVID-19 waves. Arguments: ---------- -f: Filename of samples dictionary to be loaded. Default location is ~/data/interim/model_parameters/COVID19_SEIRD/calibrations/national/ Returns: -------- Example use: ------------ """ __author__ = "Tijs Alleman" __copyright__ = "Copyright (c) 2020 by T.W. Alleman, BIOMATH, Ghent University. All Rights Reserved." # ---------------------- # Load required packages # ---------------------- import json import argparse import datetime import random import numpy as np import pandas as pd import matplotlib.pyplot as plt from matplotlib.transforms import offset_copy from covid19model.models import models from covid19model.data import mobility, sciensano, model_parameters from covid19model.models.time_dependant_parameter_fncs import ramp_fun from covid19model.visualization.output import _apply_tick_locator from covid19model.visualization.utils import colorscale_okabe_ito, moving_avg # covid 19 specific parameters plt.rcParams.update({ "axes.prop_cycle": plt.cycler('color', list(colorscale_okabe_ito.values())), }) # ----------------------- # Handle script arguments # ----------------------- parser = argparse.ArgumentParser() parser.add_argument("-n", "--n_samples", help="Number of samples used to visualise model fit", default=100, type=int) parser.add_argument("-k", "--n_draws_per_sample", help="Number of binomial draws per sample drawn used to visualize model fit", default=1, type=int) args = parser.parse_args() ################################################# ## PART 1: Comparison of total number of cases ## ################################################# youth = moving_avg((df_sciensano['C_0_9']+df_sciensano['C_10_19']).to_frame()) cases_youth_nov21 = youth[youth.index == pd.to_datetime('2020-11-21')].values cases_youth_rel = moving_avg((df_sciensano['C_0_9']+df_sciensano['C_10_19']).to_frame())/cases_youth_nov21*100 work = moving_avg((df_sciensano['C_20_29']+df_sciensano['C_30_39']+df_sciensano['C_40_49']+df_sciensano['C_50_59']).to_frame()) cases_work_nov21 = work[work.index == pd.to_datetime('2020-11-21')].values cases_work_rel = work/cases_work_nov21*100 old = moving_avg((df_sciensano['C_60_69']+df_sciensano['C_70_79']+df_sciensano['C_80_89']+df_sciensano['C_90+']).to_frame()) cases_old_nov21 = old[old.index == pd.to_datetime('2020-11-21')].values cases_old_rel = old/cases_old_nov21*100 fig,ax=plt.subplots(figsize=(12,4.3)) ax.plot(df_sciensano.index, cases_youth_rel, linewidth=1.5, color='black') ax.plot(df_sciensano.index, cases_work_rel, linewidth=1.5, color='orange') ax.plot(df_sciensano.index, cases_old_rel, linewidth=1.5, color='blue') ax.axvspan(pd.to_datetime('2020-11-21'), pd.to_datetime('2020-12-18'), color='black', alpha=0.2) ax.axvspan(pd.to_datetime('2021-01-09'), pd.to_datetime('2021-02-15'), color='black', alpha=0.2) ax.set_xlim([pd.to_datetime('2020-11-05'), pd.to_datetime('2021-02-01')]) ax.set_ylim([0,320]) ax.set_ylabel('Relative number of cases as compared\n to November 16th, 2020 (%)') #ax.set_xticks([pd.to_datetime('2020-11-16'), pd.to_datetime('2020-12-18'), pd.to_datetime('2021-01-04')]) ax.legend(['$[0,20[$','$[20,60[$','$[60,\infty[$'], bbox_to_anchor=(1.05, 1), loc='upper left') ax = _apply_tick_locator(ax) ax.set_yticks([0,100,200,300]) ax.grid(False) plt.tight_layout() plt.show() def crosscorr(datax, datay, lag=0): """ Lag-N cross correlation. Parameters ---------- lag : int, default 0 datax, datay : pandas.Series objects of equal length Returns ---------- crosscorr : float """ return datax.corr(datay.shift(lag)) lag_series = range(-15,8) covariance_youth_work = [] covariance_youth_old = [] covariance_work_old = [] for lag in lag_series: covariance_youth_work.append(crosscorr(cases_youth_rel[pd.to_datetime('2020-11-02'):pd.to_datetime('2021-02-01')].squeeze(),cases_work_rel[pd.to_datetime('2020-11-02'):pd.to_datetime('2021-02-01')].squeeze(),lag=lag)) covariance_youth_old.append(crosscorr(cases_youth_rel[pd.to_datetime('2020-11-02'):pd.to_datetime('2021-02-01')].squeeze(),cases_old_rel[pd.to_datetime('2020-11-02'):pd.to_datetime('2021-02-01')].squeeze(),lag=lag)) covariance_work_old.append(crosscorr(cases_work_rel[pd.to_datetime('2020-11-02'):pd.to_datetime('2021-02-01')].squeeze(),cases_old_rel[pd.to_datetime('2020-11-02'):pd.to_datetime('2021-02-01')].squeeze(),lag=lag)) covariances = [covariance_youth_work, covariance_youth_old, covariance_work_old] for i in range(3): n = len(covariances[i]) k = max(covariances[i]) idx=np.argmax(covariances[i]) tau = lag_series[idx] sig = 2/np.sqrt(n-abs(k)) if k >= sig: print(tau, k, True) else: print(tau, k, False) fig,(ax1,ax2)=plt.subplots(nrows=2,ncols=1,figsize=(15,10)) # First part ax1.plot(df_sciensano.index, cases_youth_rel, linewidth=1.5, color='black') ax1.plot(df_sciensano.index, cases_work_rel, linewidth=1.5, color='orange') ax1.plot(df_sciensano.index, cases_old_rel, linewidth=1.5, color='blue') ax1.axvspan(pd.to_datetime('2020-11-21'), pd.to_datetime('2020-12-18'), color='black', alpha=0.2) ax1.axvspan(pd.to_datetime('2021-01-09'), pd.to_datetime('2021-02-15'), color='black', alpha=0.2) ax1.set_xlim([pd.to_datetime('2020-11-05'), pd.to_datetime('2021-02-01')]) ax1.set_ylim([0,300]) ax1.set_ylabel('Relative number of cases as compared\n to November 16th, 2020 (%)') #ax.set_xticks([pd.to_datetime('2020-11-16'), pd.to_datetime('2020-12-18'), pd.to_datetime('2021-01-04')]) ax1.legend(['$[0,20[$','$[20,60[$','$[60,\infty[$'], bbox_to_anchor=(1.05, 1), loc='upper left') ax1 = _apply_tick_locator(ax1) # Second part ax2.scatter(lag_series, covariance_youth_work, color='black',alpha=0.6,linestyle='None',facecolors='none', s=30, linewidth=1) ax2.scatter(lag_series, covariance_youth_old, color='black',alpha=0.6, linestyle='None',facecolors='none', s=30, linewidth=1, marker='s') ax2.scatter(lag_series, covariance_work_old, color='black',alpha=0.6, linestyle='None',facecolors='none', s=30, linewidth=1, marker='D') ax2.legend(['$[0,20[$ vs. $[20,60[$', '$[0,20[$ vs. $[60,\infty[$', '$[20,60[$ vs. $[60, \infty[$'], bbox_to_anchor=(1.05, 1), loc='upper left') ax2.plot(lag_series, covariance_youth_work, color='black', linestyle='--', linewidth=1) ax2.plot(lag_series, covariance_youth_old, color='black',linestyle='--', linewidth=1) ax2.plot(lag_series, covariance_work_old, color='black',linestyle='--', linewidth=1) ax2.axvline(0,linewidth=1, color='black') ax2.grid(False) ax2.set_ylabel('lag-$\\tau$ cross correlation (-)') ax2.set_xlabel('$\\tau$ (days)') plt.tight_layout() plt.show() fig,ax = plt.subplots(figsize=(15,5)) ax.scatter(lag_series, covariance_youth_work, color='black',alpha=0.6,linestyle='None',facecolors='none', s=30, linewidth=1) ax.scatter(lag_series, covariance_youth_old, color='black',alpha=0.6, linestyle='None',facecolors='none', s=30, linewidth=1, marker='s') ax.scatter(lag_series, covariance_work_old, color='black',alpha=0.6, linestyle='None',facecolors='none', s=30, linewidth=1, marker='D') ax.legend(['$[0,20[$ vs. $[20,60[$', '$[0,20[$ vs. $[60,\infty[$', '$[20,60[$ vs. $[60, \infty[$'], bbox_to_anchor=(1.05, 1), loc='upper left') ax.plot(lag_series, covariance_youth_work, color='black', linestyle='--', linewidth=1) ax.plot(lag_series, covariance_youth_old, color='black',linestyle='--', linewidth=1) ax.plot(lag_series, covariance_work_old, color='black',linestyle='--', linewidth=1) ax.axvline(0,linewidth=1, color='black') ax.grid(False) ax.set_ylabel('lag-$\\tau$ cross correlation (-)') ax.set_xlabel('$\\tau$ (days)') plt.tight_layout() plt.show() ##################################################### ## PART 1: Calibration robustness figure of WAVE 1 ## ##################################################### n_calibrations = 6 n_prevention = 3 conf_int = 0.05 # ------------------------- # Load samples dictionaries # ------------------------- samples_dicts = [ json.load(open('../../data/interim/model_parameters/COVID19_SEIRD/calibrations/national/BE_WAVE1_BETA_COMPLIANCE_2021-02-15.json')), # 2020-04-04 json.load(open('../../data/interim/model_parameters/COVID19_SEIRD/calibrations/national/BE_WAVE1_BETA_COMPLIANCE_2021-02-13.json')), # 2020-04-15 json.load(open('../../data/interim/model_parameters/COVID19_SEIRD/calibrations/national/BE_WAVE1_BETA_COMPLIANCE_2021-02-23.json')), # 2020-05-01 json.load(open('../../data/interim/model_parameters/COVID19_SEIRD/calibrations/national/BE_WAVE1_BETA_COMPLIANCE_2021-02-18.json')), # 2020-05-15 json.load(open('../../data/interim/model_parameters/COVID19_SEIRD/calibrations/national/BE_WAVE1_BETA_COMPLIANCE_2021-02-21.json')), # 2020-06-01 json.load(open('../../data/interim/model_parameters/COVID19_SEIRD/calibrations/national/BE_WAVE1_BETA_COMPLIANCE_2021-02-22.json')) # 2020-07-01 ] warmup = int(samples_dicts[0]['warmup']) # Start of data collection start_data = '2020-03-15' # First datapoint used in inference start_calibration = '2020-03-15' # Last datapoint used in inference end_calibrations = ['2020-04-04', '2020-04-15', '2020-05-01', '2020-05-15', '2020-06-01', '2020-07-01'] # Start- and enddate of plotfit start_sim = start_calibration end_sim = '2020-07-14' # --------- # Load data # --------- # Contact matrices initN, Nc_home, Nc_work, Nc_schools, Nc_transport, Nc_leisure, Nc_others, Nc_total = model_parameters.get_interaction_matrices(dataset='willem_2012') Nc_all = {'total': Nc_total, 'home':Nc_home, 'work': Nc_work, 'schools': Nc_schools, 'transport': Nc_transport, 'leisure': Nc_leisure, 'others': Nc_others} levels = initN.size # Google Mobility data df_google = mobility.get_google_mobility_data(update=False) # --------------------------------- # Time-dependant parameter function # --------------------------------- # Extract build contact matrix function from covid19model.models.time_dependant_parameter_fncs import make_contact_matrix_function, ramp_fun contact_matrix_4prev, all_contact, all_contact_no_schools = make_contact_matrix_function(df_google, Nc_all) # Define policy function def policies_wave1_4prev(t, states, param, l , tau, prev_schools, prev_work, prev_rest, prev_home): # Convert tau and l to dates tau_days = pd.Timedelta(tau, unit='D') l_days = pd.Timedelta(l, unit='D') # Define key dates of first wave t1 = pd.Timestamp('2020-03-15') # start of lockdown t2 = pd.Timestamp('2020-05-15') # gradual re-opening of schools (assume 50% of nominal scenario) t3 = pd.Timestamp('2020-07-01') # start of summer holidays t4 = pd.Timestamp('2020-09-01') # end of summer holidays # Define key dates of second wave t5 = pd.Timestamp('2020-10-19') # lockdown (1) t6 = pd.Timestamp('2020-11-02') # lockdown (2) t7 = pd.Timestamp('2020-11-16') # schools re-open t8 = pd.Timestamp('2020-12-18') # Christmas holiday starts t9 = pd.Timestamp('2021-01-04') # Christmas holiday ends t10 = pd.Timestamp('2021-02-15') # Spring break starts t11 = pd.Timestamp('2021-02-21') # Spring break ends t12 = pd.Timestamp('2021-04-05') # Easter holiday starts t13 = pd.Timestamp('2021-04-18') # Easter holiday ends # ------ # WAVE 1 # ------ if t <= t1: t = pd.Timestamp(t.date()) return all_contact(t) elif t1 < t < t1 + tau_days: t = pd.Timestamp(t.date()) return all_contact(t) elif t1 + tau_days < t <= t1 + tau_days + l_days: t = pd.Timestamp(t.date()) policy_old = all_contact(t) policy_new = contact_matrix_4prev(t, prev_home, prev_schools, prev_work, prev_rest, school=0) return ramp_fun(policy_old, policy_new, t, tau_days, l, t1) elif t1 + tau_days + l_days < t <= t2: t = pd.Timestamp(t.date()) return contact_matrix_4prev(t, prev_home, prev_schools, prev_work, prev_rest, school=0) elif t2 < t <= t3: t = pd.Timestamp(t.date()) return contact_matrix_4prev(t, prev_home, prev_schools, prev_work, prev_rest, school=0) elif t3 < t <= t4: t = pd.Timestamp(t.date()) return contact_matrix_4prev(t, prev_home, prev_schools, prev_work, prev_rest, school=0) # ------ # WAVE 2 # ------ elif t4 < t <= t5 + tau_days: return contact_matrix_4prev(t, school=1) elif t5 + tau_days < t <= t5 + tau_days + l_days: policy_old = contact_matrix_4prev(t, school=1) policy_new = contact_matrix_4prev(t, prev_schools, prev_work, prev_rest, school=1) return ramp_fun(policy_old, policy_new, t, tau_days, l, t5) elif t5 + tau_days + l_days < t <= t6: return contact_matrix_4prev(t, prev_home, prev_schools, prev_work, prev_rest, school=1) elif t6 < t <= t7: return contact_matrix_4prev(t, prev_home, prev_schools, prev_work, prev_rest, school=0) elif t7 < t <= t8: return contact_matrix_4prev(t, prev_home, prev_schools, prev_work, prev_rest, school=1) elif t8 < t <= t9: return contact_matrix_4prev(t, prev_home, prev_schools, prev_work, prev_rest, school=0) elif t9 < t <= t10: return contact_matrix_4prev(t, prev_home, prev_schools, prev_work, prev_rest, school=1) elif t10 < t <= t11: return contact_matrix_4prev(t, prev_home, prev_schools, prev_work, prev_rest, school=0) elif t11 < t <= t12: return contact_matrix_4prev(t, prev_home, prev_schools, prev_work, prev_rest, school=1) elif t12 < t <= t13: return contact_matrix_4prev(t, prev_home, prev_schools, prev_work, prev_rest, school=0) else: t = pd.Timestamp(t.date()) return contact_matrix_4prev(t, prev_home, prev_schools, prev_work, prev_rest, school=1) # -------------------- # Initialize the model # -------------------- # Load the model parameters dictionary params = model_parameters.get_COVID19_SEIRD_parameters() # Add the time-dependant parameter function arguments params.update({'l': 21, 'tau': 21, 'prev_schools': 0, 'prev_work': 0.5, 'prev_rest': 0.5, 'prev_home': 0.5}) # Define initial states initial_states = {"S": initN, "E": np.ones(9)} # Initialize model model = models.COVID19_SEIRD(initial_states, params, time_dependent_parameters={'Nc': policies_wave1_4prev}) # ------------------------ # Define sampling function # ------------------------ def draw_fcn(param_dict,samples_dict): # Sample first calibration idx, param_dict['beta'] = random.choice(list(enumerate(samples_dict['beta']))) param_dict['da'] = samples_dict['da'][idx] param_dict['omega'] = samples_dict['omega'][idx] param_dict['sigma'] = 5.2 - samples_dict['omega'][idx] # Sample second calibration param_dict['l'] = samples_dict['l'][idx] param_dict['tau'] = samples_dict['tau'][idx] param_dict['prev_home'] = samples_dict['prev_home'][idx] param_dict['prev_work'] = samples_dict['prev_work'][idx] param_dict['prev_rest'] = samples_dict['prev_rest'][idx] return param_dict # ------------------------------------- # Define necessary function to plot fit # ------------------------------------- LL = conf_int/2 UL = 1-conf_int/2 def add_poisson(state_name, output, n_samples, n_draws_per_sample, UL=1-0.05*0.5, LL=0.05*0.5): data = output[state_name].sum(dim="Nc").values # Initialize vectors vector = np.zeros((data.shape[1],n_draws_per_sample*n_samples)) # Loop over dimension draws for n in range(data.shape[0]): binomial_draw = np.random.poisson( np.expand_dims(data[n,:],axis=1),size = (data.shape[1],n_draws_per_sample)) vector[:,n*n_draws_per_sample:(n+1)*n_draws_per_sample] = binomial_draw # Compute mean and median mean = np.mean(vector,axis=1) median = np.median(vector,axis=1) # Compute quantiles LL = np.quantile(vector, q = LL, axis = 1) UL = np.quantile(vector, q = UL, axis = 1) return mean, median, LL, UL def plot_fit(ax, state_name, state_label, data_df, time, vector_mean, vector_LL, vector_UL, start_calibration='2020-03-15', end_calibration='2020-07-01' , end_sim='2020-09-01'): ax.fill_between(pd.to_datetime(time), vector_LL, vector_UL,alpha=0.30, color = 'blue') ax.plot(time, vector_mean,'--', color='blue', linewidth=1.5) ax.scatter(data_df[start_calibration:end_calibration].index,data_df[state_name][start_calibration:end_calibration], color='black', alpha=0.5, linestyle='None', facecolors='none', s=30, linewidth=1) ax.scatter(data_df[pd.to_datetime(end_calibration)+datetime.timedelta(days=1):end_sim].index,data_df[state_name][pd.to_datetime(end_calibration)+datetime.timedelta(days=1):end_sim], color='red', alpha=0.5, linestyle='None', facecolors='none', s=30, linewidth=1) ax = _apply_tick_locator(ax) ax.set_xlim(start_calibration,end_sim) ax.set_ylabel(state_label) return ax # ------------------------------- # Visualize prevention parameters # ------------------------------- # Method 1: all in on page fig,axes= plt.subplots(nrows=n_calibrations,ncols=n_prevention+1, figsize=(13,8.27), gridspec_kw={'width_ratios': [1, 1, 1, 3]}) prevention_labels = ['$\Omega_{home}$ (-)', '$\Omega_{work}$ (-)', '$\Omega_{rest}$ (-)'] prevention_names = ['prev_home', 'prev_work', 'prev_rest'] row_labels = ['(a)', '(b)', '(c)', '(d)', '(e)', '(f)'] pad = 5 # in points for i in range(n_calibrations): print('Simulation no. {} out of {}'.format(i+1,n_calibrations)) out = model.sim(end_sim,start_date=start_sim,warmup=warmup,N=args.n_samples,draw_fcn=draw_fcn,samples=samples_dicts[i]) vector_mean, vector_median, vector_LL, vector_UL = add_poisson('H_in', out, args.n_samples, args.n_draws_per_sample) for j in range(n_prevention+1): if j != n_prevention: n, bins, patches = axes[i,j].hist(samples_dicts[i][prevention_names[j]], color='blue', bins=15, density=True, alpha=0.6) axes[i,j].axvline(np.mean(samples_dicts[i][prevention_names[j]]), ymin=0, ymax=1, linestyle='--', color='black') max_n = 1.05*max(n) axes[i,j].annotate('$\hat{\mu} = $'+"{:.2f}".format(np.mean(samples_dicts[i][prevention_names[j]])), xy=(np.mean(samples_dicts[i][prevention_names[j]]),max_n), rotation=0,va='bottom', ha='center',annotation_clip=False,fontsize=10) if j == 0: axes[i,j].annotate(row_labels[i], xy=(0, 0.5), xytext=(-axes[i,j].yaxis.labelpad - pad, 0), xycoords=axes[i,j].yaxis.label, textcoords='offset points', ha='right', va='center') axes[i,j].set_xlim([0,1]) axes[i,j].set_xticks([0.0, 0.5, 1.0]) axes[i,j].set_yticks([]) axes[i,j].grid(False) if i == n_calibrations-1: axes[i,j].set_xlabel(prevention_labels[j]) axes[i,j].spines['left'].set_visible(False) else: axes[i,j] = plot_fit(axes[i,j], 'H_in','$H_{in}$ (-)', df_sciensano, out['time'].values, vector_median, vector_LL, vector_UL, end_calibration=end_calibrations[i], end_sim=end_sim) axes[i,j].xaxis.set_major_locator(plt.MaxNLocator(3)) axes[i,j].set_yticks([0,300, 600]) axes[i,j].set_ylim([0,700]) plt.tight_layout() plt.show() model_results_WAVE1 = {'time': out['time'].values, 'vector_mean': vector_mean, 'vector_median': vector_median, 'vector_LL': vector_LL, 'vector_UL': vector_UL} ##################################### ## PART 2: Hospitals vs. R0 figure ## ##################################### def compute_R0(initN, Nc, samples_dict, model_parameters): N = initN.size sample_size = len(samples_dict['beta']) R0 = np.zeros([N,sample_size]) R0_norm = np.zeros([N,sample_size]) for i in range(N): for j in range(sample_size): R0[i,j] = (model_parameters['a'][i] * samples_dict['da'][j] + samples_dict['omega'][j]) * samples_dict['beta'][j] * np.sum(Nc, axis=1)[i] R0_norm[i,:] = R0[i,:]*(initN[i]/sum(initN)) R0_age = np.mean(R0,axis=1) R0_overall = np.mean(np.sum(R0_norm,axis=0)) return R0, R0_overall R0, R0_overall = compute_R0(initN, Nc_all['total'], samples_dicts[-1], params) cumsum = out['H_in'].cumsum(dim='time').values cumsum_mean = np.mean(cumsum[:,:,-1], axis=0)/sum(np.mean(cumsum[:,:,-1],axis=0)) cumsum_LL = cumsum_mean - np.quantile(cumsum[:,:,-1], q = 0.05/2, axis=0)/sum(np.mean(cumsum[:,:,-1],axis=0)) cumsum_UL = np.quantile(cumsum[:,:,-1], q = 1-0.05/2, axis=0)/sum(np.mean(cumsum[:,:,-1],axis=0)) - cumsum_mean cumsum = (out['H_in'].mean(dim="draws")).cumsum(dim='time').values fraction = cumsum[:,-1]/sum(cumsum[:,-1]) fig,ax = plt.subplots(figsize=(12,4)) bars = ('$[0, 10[$', '$[10, 20[$', '$[20, 30[$', '$[30, 40[$', '$[40, 50[$', '$[50, 60[$', '$[60, 70[$', '$[70, 80[$', '$[80, \infty[$') x_pos = np.arange(len(bars)) #ax.bar(x_pos, np.mean(R0,axis=1), yerr = [np.mean(R0,axis=1) - np.quantile(R0,q=0.05/2,axis=1), np.quantile(R0,q=1-0.05/2,axis=1) - np.mean(R0,axis=1)], width=1, color='b', alpha=0.5, capsize=10) ax.bar(x_pos, np.mean(R0,axis=1), width=1, color='b', alpha=0.8) ax.set_ylabel('$R_0$ (-)') ax.grid(False) ax2 = ax.twinx() #ax2.bar(x_pos, cumsum_mean, yerr = [cumsum_LL, cumsum_UL], width=1,color='orange',alpha=0.9,hatch="/", capsize=10) ax2.bar(x_pos, cumsum_mean, width=1,color='orange',alpha=0.6,hatch="/") ax2.set_ylabel('Fraction of hospitalizations (-)') ax2.grid(False) plt.xticks(x_pos, bars) plt.tight_layout() plt.show() ######################################### ## Part 3: Robustness figure of WAVE 2 ## ######################################### n_prevention = 4 conf_int = 0.05 # ------------------------- # Load samples dictionaries # ------------------------- samples_dicts = [ json.load(open('../../data/interim/model_parameters/COVID19_SEIRD/calibrations/national/BE_WAVE2_BETA_COMPLIANCE_2021-03-06.json')), # 2020-11-04 json.load(open('../../data/interim/model_parameters/COVID19_SEIRD/calibrations/national/BE_WAVE2_BETA_COMPLIANCE_2021-03-05.json')), # 2020-11-16 json.load(open('../../data/interim/model_parameters/COVID19_SEIRD/calibrations/national/BE_WAVE2_BETA_COMPLIANCE_2021-03-04.json')), # 2020-12-24 json.load(open('../../data/interim/model_parameters/COVID19_SEIRD/calibrations/national/BE_WAVE2_BETA_COMPLIANCE_2021-03-02.json')), # 2021-02-01 ] n_calibrations = len(samples_dicts) warmup = int(samples_dicts[0]['warmup']) # Start of data collection start_data = '2020-03-15' # First datapoint used in inference start_calibration = '2020-09-01' # Last datapoint used in inference end_calibrations = ['2020-11-06','2020-11-16','2020-12-24','2021-02-01'] # Start- and enddate of plotfit start_sim = start_calibration end_sim = '2021-02-14' # -------------------- # Initialize the model # -------------------- # Load the model parameters dictionary params = model_parameters.get_COVID19_SEIRD_parameters() # Add the time-dependant parameter function arguments params.update({'l': 21, 'tau': 21, 'prev_schools': 0, 'prev_work': 0.5, 'prev_rest': 0.5, 'prev_home': 0.5}) # Model initial condition on September 1st warmup = 0 with open('../../data/interim/model_parameters/COVID19_SEIRD/calibrations/national/initial_states_2020-09-01.json', 'r') as fp: initial_states = json.load(fp) initial_states.update({ 'VE': np.zeros(9), 'V': np.zeros(9), 'V_new': np.zeros(9), 'alpha': np.zeros(9) }) #initial_states['ICU_tot'] = initial_states.pop('ICU') # Initialize model model = models.COVID19_SEIRD(initial_states, params, time_dependent_parameters={'Nc': policies_wave1_4prev}) # ------------------------ # Define sampling function # ------------------------ def draw_fcn(param_dict,samples_dict): # Sample first calibration idx, param_dict['beta'] = random.choice(list(enumerate(samples_dict['beta']))) param_dict['da'] = samples_dict['da'][idx] param_dict['omega'] = samples_dict['omega'][idx] param_dict['sigma'] = 5.2 - samples_dict['omega'][idx] # Sample second calibration param_dict['l'] = samples_dict['l'][idx] param_dict['tau'] = samples_dict['tau'][idx] param_dict['prev_schools'] = samples_dict['prev_schools'][idx] param_dict['prev_home'] = samples_dict['prev_home'][idx] param_dict['prev_work'] = samples_dict['prev_work'][idx] param_dict['prev_rest'] = samples_dict['prev_rest'][idx] return param_dict # ------------------------------- # Visualize prevention parameters # ------------------------------- # Method 1: all in on page fig,axes= plt.subplots(nrows=n_calibrations,ncols=n_prevention+1, figsize=(13,8.27), gridspec_kw={'width_ratios': [1, 1, 1, 1, 6]}) prevention_labels = ['$\Omega_{home}$ (-)', '$\Omega_{schools}$ (-)', '$\Omega_{work}$ (-)', '$\Omega_{rest}$ (-)'] prevention_names = ['prev_home', 'prev_schools', 'prev_work', 'prev_rest'] row_labels = ['(a)', '(b)', '(c)', '(d)', '(e)', '(f)'] pad = 5 # in points for i in range(n_calibrations): print('Simulation no. {} out of {}'.format(i+1,n_calibrations)) out = model.sim(end_sim,start_date=start_sim,warmup=warmup,N=args.n_samples,draw_fcn=draw_fcn,samples=samples_dicts[i]) vector_mean, vector_median, vector_LL, vector_UL = add_poisson('H_in', out, args.n_samples, args.n_draws_per_sample) for j in range(n_prevention+1): if j != n_prevention: n, bins, patches = axes[i,j].hist(samples_dicts[i][prevention_names[j]], color='blue', bins=15, density=True, alpha=0.6) axes[i,j].axvline(np.mean(samples_dicts[i][prevention_names[j]]), ymin=0, ymax=1, linestyle='--', color='black') max_n = 1.05*max(n) axes[i,j].annotate('$\hat{\mu} = $'+"{:.2f}".format(np.mean(samples_dicts[i][prevention_names[j]])), xy=(np.mean(samples_dicts[i][prevention_names[j]]),max_n), rotation=0,va='bottom', ha='center',annotation_clip=False,fontsize=10) if j == 0: axes[i,j].annotate(row_labels[i], xy=(0, 0.5), xytext=(-axes[i,j].yaxis.labelpad - pad, 0), xycoords=axes[i,j].yaxis.label, textcoords='offset points', ha='right', va='center') axes[i,j].set_xlim([0,1]) axes[i,j].set_xticks([0.0, 1.0]) axes[i,j].set_yticks([]) axes[i,j].grid(False) if i == n_calibrations-1: axes[i,j].set_xlabel(prevention_labels[j]) axes[i,j].spines['left'].set_visible(False) else: axes[i,j] = plot_fit(axes[i,j], 'H_in','$H_{in}$ (-)', df_sciensano, out['time'].values, vector_median, vector_LL, vector_UL, start_calibration = start_calibration, end_calibration=end_calibrations[i], end_sim=end_sim) axes[i,j].xaxis.set_major_locator(plt.MaxNLocator(3)) axes[i,j].set_yticks([0,250, 500, 750]) axes[i,j].set_ylim([0,850]) plt.tight_layout() plt.show() model_results_WAVE2 = {'time': out['time'].values, 'vector_mean': vector_mean, 'vector_median': vector_median, 'vector_LL': vector_LL, 'vector_UL': vector_UL} model_results = [model_results_WAVE1, model_results_WAVE2] ################################################################# ## Part 4: Comparing the maximal dataset prevention parameters ## ################################################################# samples_dict_WAVE1 = json.load(open('../../data/interim/model_parameters/COVID19_SEIRD/calibrations/national/BE_WAVE1_BETA_COMPLIANCE_2021-02-22.json')) samples_dict_WAVE2 = json.load(open('../../data/interim/model_parameters/COVID19_SEIRD/calibrations/national/BE_WAVE2_BETA_COMPLIANCE_2021-03-02.json')) labels = ['$\Omega_{schools}$','$\Omega_{work}$', '$\Omega_{rest}$', '$\Omega_{home}$'] keys = ['prev_schools','prev_work','prev_rest','prev_home'] fig,axes = plt.subplots(1,4,figsize=(12,4)) for idx,ax in enumerate(axes): if idx != 0: (n1, bins, patches) = ax.hist(samples_dict_WAVE1[keys[idx]],bins=15,color='blue',alpha=0.4, density=True) (n2, bins, patches) =ax.hist(samples_dict_WAVE2[keys[idx]],bins=15,color='black',alpha=0.4, density=True) max_n = max([max(n1),max(n2)])*1.10 ax.axvline(np.mean(samples_dict_WAVE1[keys[idx]]),ls=':',ymin=0,ymax=1,color='blue') ax.axvline(np.mean(samples_dict_WAVE2[keys[idx]]),ls=':',ymin=0,ymax=1,color='black') if idx ==1: ax.annotate('$\mu_1 = \mu_2 = $'+"{:.2f}".format(np.mean(samples_dict_WAVE1[keys[idx]])), xy=(np.mean(samples_dict_WAVE1[keys[idx]]),max_n), rotation=90,va='bottom', ha='center',annotation_clip=False,fontsize=12) else: ax.annotate('$\mu_1 = $'+"{:.2f}".format(np.mean(samples_dict_WAVE1[keys[idx]])), xy=(np.mean(samples_dict_WAVE1[keys[idx]]),max_n), rotation=90,va='bottom', ha='center',annotation_clip=False,fontsize=12) ax.annotate('$\mu_2 = $'+"{:.2f}".format(np.mean(samples_dict_WAVE2[keys[idx]])), xy=(np.mean(samples_dict_WAVE2[keys[idx]]),max_n), rotation=90,va='bottom', ha='center',annotation_clip=False,fontsize=12) ax.set_xlabel(labels[idx]) ax.set_yticks([]) ax.spines['left'].set_visible(False) else: ax.hist(samples_dict_WAVE2['prev_schools'],bins=15,color='black',alpha=0.6, density=True) ax.set_xlabel('$\Omega_{schools}$') ax.set_yticks([]) ax.spines['left'].set_visible(False) ax.set_xlim([0,1]) ax.xaxis.grid(False) ax.yaxis.grid(False) plt.tight_layout() plt.show() ################################################################ ## Part 5: Relative contributions of each contact: both waves ## ################################################################ # -------------------------------- # Re-define function to compute R0 # -------------------------------- def compute_R0(initN, Nc, samples_dict, model_parameters): N = initN.size sample_size = len(samples_dict['beta']) R0 = np.zeros([N,sample_size]) R0_norm = np.zeros([N,sample_size]) for i in range(N): for j in range(sample_size): R0[i,j] = (model_parameters['a'][i] * samples_dict['da'][j] + samples_dict['omega'][j]) * samples_dict['beta'][j] *Nc[i,j] R0_norm[i,:] = R0[i,:]*(initN[i]/sum(initN)) R0_age = np.mean(R0,axis=1) R0_mean = np.sum(R0_norm,axis=0) return R0, R0_mean # ----------------------- # Pre-allocate dataframes # ----------------------- index=df_google.index columns = [['1','1','1','1','1','1','1','1','1','1','1','1','1','1','1','2','2','2','2','2','2','2','2','2','2','2','2','2','2','2'],['work_mean','work_LL','work_UL','schools_mean','schools_LL','schools_UL','rest_mean','rest_LL','rest_UL', 'home_mean','home_LL','home_UL','total_mean','total_LL','total_UL','work_mean','work_LL','work_UL','schools_mean','schools_LL','schools_UL', 'rest_mean','rest_LL','rest_UL','home_mean','home_LL','home_UL','total_mean','total_LL','total_UL']] tuples = list(zip(*columns)) columns = pd.MultiIndex.from_tuples(tuples, names=["WAVE", "Type"]) data = np.zeros([len(df_google.index),30]) df_rel = pd.DataFrame(data=data, index=df_google.index, columns=columns) df_abs = pd.DataFrame(data=data, index=df_google.index, columns=columns) df_Re = pd.DataFrame(data=data, index=df_google.index, columns=columns) samples_dicts = [samples_dict_WAVE1, samples_dict_WAVE2] start_dates =[pd.to_datetime('2020-03-15'), pd.to_datetime('2020-10-19')] waves=["1", "2"] for j,samples_dict in enumerate(samples_dicts): print('\n WAVE: ' + str(j)+'\n') # --------------- # Rest prevention # --------------- print('Rest\n') data_rest = np.zeros([len(df_google.index.values), len(samples_dict['prev_rest'])]) Re_rest = np.zeros([len(df_google.index.values), len(samples_dict['prev_rest'])]) for idx, date in enumerate(df_google.index): tau = np.mean(samples_dict['tau']) l = np.mean(samples_dict['l']) tau_days = pd.Timedelta(tau, unit='D') l_days = pd.Timedelta(l, unit='D') date_start = start_dates[j] if date <= date_start + tau_days: data_rest[idx,:] = 0.01*(100+df_google['retail_recreation'][date])* (np.sum(np.mean(Nc_leisure,axis=0)))\ + 0.01*(100+df_google['transport'][date])* (np.sum(np.mean(Nc_transport,axis=0)))\ + 0.01*(100+df_google['grocery'][date])* (np.sum(np.mean(Nc_others,axis=0)))*np.ones(len(samples_dict['prev_rest'])) contacts = np.expand_dims(0.01*(100+df_google['retail_recreation'][date])* (np.sum(Nc_leisure,axis=1))\ + 0.01*(100+df_google['transport'][date])* (np.sum(Nc_transport,axis=1))\ + 0.01*(100+df_google['grocery'][date])* (np.sum(Nc_others,axis=1)),axis=1)*np.ones([1,len(samples_dict['prev_rest'])]) R0, Re_rest[idx,:] = compute_R0(initN, contacts, samples_dict, params) elif date_start + tau_days < date <= date_start + tau_days + l_days: old = 0.01*(100+df_google['retail_recreation'][date])* (np.sum(np.mean(Nc_leisure,axis=0)))\ + 0.01*(100+df_google['transport'][date])* (np.sum(np.mean(Nc_transport,axis=0)))\ + 0.01*(100+df_google['grocery'][date])* (np.sum(np.mean(Nc_others,axis=0)))*np.ones(len(samples_dict['prev_rest'])) new = (0.01*(100+df_google['retail_recreation'][date])* (np.sum(np.mean(Nc_leisure,axis=0)))\ + 0.01*(100+df_google['transport'][date])* (np.sum(np.mean(Nc_transport,axis=0)))\ + 0.01*(100+df_google['grocery'][date])* (np.sum(np.mean(Nc_others,axis=0)))\ )*np.array(samples_dict['prev_rest']) data_rest[idx,:]= old + (new-old)/l * (date-date_start-tau_days)/pd.Timedelta('1D') old_contacts = np.expand_dims(0.01*(100+df_google['retail_recreation'][date])* (np.sum(Nc_leisure,axis=1))\ + 0.01*(100+df_google['transport'][date])* (np.sum(Nc_transport,axis=1))\ + 0.01*(100+df_google['grocery'][date])* (np.sum(Nc_others,axis=1)),axis=1)*np.ones([1,len(samples_dict['prev_rest'])]) new_contacts = np.expand_dims(0.01*(100+df_google['retail_recreation'][date])* (np.sum(Nc_leisure,axis=1))\ + 0.01*(100+df_google['transport'][date])* (np.sum(Nc_transport,axis=1))\ + 0.01*(100+df_google['grocery'][date])* (np.sum(Nc_others,axis=1)),axis=1)*np.array(samples_dict['prev_rest']) contacts = old_contacts + (new_contacts-old_contacts)/l * (date-date_start-tau_days)/pd.Timedelta('1D') R0, Re_rest[idx,:] = compute_R0(initN, contacts, samples_dict, params) else: data_rest[idx,:] = (0.01*(100+df_google['retail_recreation'][date])* (np.sum(np.mean(Nc_leisure,axis=0)))\ + 0.01*(100+df_google['transport'][date])* (np.sum(np.mean(Nc_transport,axis=0)))\ + 0.01*(100+df_google['grocery'][date])* (np.sum(np.mean(Nc_others,axis=0)))\ )*np.array(samples_dict['prev_rest']) contacts = np.expand_dims(0.01*(100+df_google['retail_recreation'][date])* (np.sum(Nc_leisure,axis=1))\ + 0.01*(100+df_google['transport'][date])* (np.sum(Nc_transport,axis=1))\ + 0.01*(100+df_google['grocery'][date])* (np.sum(Nc_others,axis=1)),axis=1)*np.array(samples_dict['prev_rest']) R0, Re_rest[idx,:] = compute_R0(initN, contacts, samples_dict, params) Re_rest_mean = np.mean(Re_rest,axis=1) Re_rest_LL = np.quantile(Re_rest,q=0.05/2,axis=1) Re_rest_UL = np.quantile(Re_rest,q=1-0.05/2,axis=1) # --------------- # Work prevention # --------------- print('Work\n') data_work = np.zeros([len(df_google.index.values), len(samples_dict['prev_work'])]) Re_work = np.zeros([len(df_google.index.values), len(samples_dict['prev_work'])]) for idx, date in enumerate(df_google.index): tau = np.mean(samples_dict['tau']) l = np.mean(samples_dict['l']) tau_days = pd.Timedelta(tau, unit='D') l_days = pd.Timedelta(l, unit='D') date_start = start_dates[j] if date <= date_start + tau_days: data_work[idx,:] = 0.01*(100+df_google['work'][date])* (np.sum(np.mean(Nc_work,axis=0)))*np.ones(len(samples_dict['prev_work'])) contacts = np.expand_dims(0.01*(100+df_google['work'][date])* (np.sum(Nc_work,axis=1)),axis=1)*np.ones([1,len(samples_dict['prev_work'])]) R0, Re_work[idx,:] = compute_R0(initN, contacts, samples_dict, params) elif date_start + tau_days < date <= date_start + tau_days + l_days: old = 0.01*(100+df_google['work'][date])* (np.sum(np.mean(Nc_work,axis=0)))*np.ones(len(samples_dict['prev_work'])) new = 0.01*(100+df_google['work'][date])* (np.sum(np.mean(Nc_work,axis=0)))*np.array(samples_dict['prev_work']) data_work[idx,:] = old + (new-old)/l * (date-date_start-tau_days)/pd.Timedelta('1D') old_contacts = np.expand_dims(0.01*(100+df_google['work'][date])*(np.sum(Nc_work,axis=1)),axis=1)*np.ones([1,len(samples_dict['prev_work'])]) new_contacts = np.expand_dims(0.01*(100+df_google['work'][date])* (np.sum(Nc_work,axis=1)),axis=1)*np.array(samples_dict['prev_work']) contacts = old_contacts + (new_contacts-old_contacts)/l * (date-date_start-tau_days)/pd.Timedelta('1D') R0, Re_work[idx,:] = compute_R0(initN, contacts, samples_dict, params) else: data_work[idx,:] = (0.01*(100+df_google['work'][date])* (np.sum(np.mean(Nc_work,axis=0))))*np.array(samples_dict['prev_work']) contacts = np.expand_dims(0.01*(100+df_google['work'][date])* (np.sum(Nc_work,axis=1)),axis=1)*np.array(samples_dict['prev_work']) R0, Re_work[idx,:] = compute_R0(initN, contacts, samples_dict, params) Re_work_mean = np.mean(Re_work,axis=1) Re_work_LL = np.quantile(Re_work, q=0.05/2, axis=1) Re_work_UL = np.quantile(Re_work, q=1-0.05/2, axis=1) # ---------------- # Home prevention # ---------------- print('Home\n') data_home = np.zeros([len(df_google['work'].values),len(samples_dict['prev_home'])]) Re_home = np.zeros([len(df_google['work'].values),len(samples_dict['prev_home'])]) for idx, date in enumerate(df_google.index): tau = np.mean(samples_dict['tau']) l = np.mean(samples_dict['l']) tau_days = pd.Timedelta(tau, unit='D') l_days = pd.Timedelta(l, unit='D') date_start = start_dates[j] if date <= date_start + tau_days: data_home[idx,:] = np.sum(np.mean(Nc_home,axis=0))*np.ones(len(samples_dict['prev_home'])) contacts = np.expand_dims((np.sum(Nc_home,axis=1)),axis=1)*np.ones(len(samples_dict['prev_home'])) R0, Re_home[idx,:] = compute_R0(initN, contacts, samples_dict, params) elif date_start + tau_days < date <= date_start + tau_days + l_days: old = np.sum(np.mean(Nc_home,axis=0))*np.ones(len(samples_dict['prev_home'])) new = np.sum(np.mean(Nc_home,axis=0))*np.array(samples_dict['prev_home']) data_home[idx,:] = old + (new-old)/l * (date-date_start-tau_days)/pd.Timedelta('1D') old_contacts = np.expand_dims(np.sum(Nc_home,axis=1),axis=1)*np.ones([1,len(samples_dict['prev_home'])]) new_contacts = np.expand_dims((np.sum(Nc_home,axis=1)),axis=1)*np.array(samples_dict['prev_home']) contacts = old_contacts + (new_contacts-old_contacts)/l * (date-date_start-tau_days)/pd.Timedelta('1D') R0, Re_home[idx,:] = compute_R0(initN, contacts, samples_dict, params) else: data_home[idx,:] = np.sum(np.mean(Nc_home,axis=0))*np.array(samples_dict['prev_home']) contacts = np.expand_dims((np.sum(Nc_home,axis=1)),axis=1)*np.array(samples_dict['prev_home']) R0, Re_home[idx,:] = compute_R0(initN, contacts, samples_dict, params) Re_home_mean = np.mean(Re_home,axis=1) Re_home_LL = np.quantile(Re_home, q=0.05/2, axis=1) Re_home_UL = np.quantile(Re_home, q=1-0.05/2, axis=1) # ------------------ # School prevention # ------------------ if j == 0: print('School\n') data_schools = np.zeros([len(df_google.index.values), len(samples_dict['prev_work'])]) Re_schools = np.zeros([len(df_google.index.values), len(samples_dict['prev_work'])]) for idx, date in enumerate(df_google.index): tau = np.mean(samples_dict['tau']) l = np.mean(samples_dict['l']) tau_days = pd.Timedelta(tau, unit='D') l_days = pd.Timedelta(l, unit='D') date_start = start_dates[j] if date <= date_start + tau_days: data_schools[idx,:] = 1*(np.sum(np.mean(Nc_schools,axis=0)))*np.ones(len(samples_dict['prev_work'])) contacts = 1*np.expand_dims(np.sum(Nc_schools,axis=1),axis=1)*np.ones([1,len(samples_dict['prev_home'])]) R0, Re_schools[idx,:] = compute_R0(initN, contacts, samples_dict, params) elif date_start + tau_days < date <= date_start + tau_days + l_days: old = 1*(np.sum(np.mean(Nc_schools,axis=0)))*np.ones(len(samples_dict['prev_work'])) new = 0* (np.sum(np.mean(Nc_schools,axis=0)))*np.array(samples_dict['prev_work']) data_schools[idx,:] = old + (new-old)/l * (date-date_start-tau_days)/pd.Timedelta('1D') old_contacts = 1*np.expand_dims(np.sum(Nc_schools,axis=1),axis=1)*np.ones([1,len(samples_dict['prev_work'])]) new_contacts = 0*np.expand_dims(np.sum(Nc_schools,axis=1),axis=1)*np.ones([1,len(samples_dict['prev_work'])]) contacts = old_contacts + (new_contacts-old_contacts)/l * (date-date_start-tau_days)/pd.Timedelta('1D') R0, Re_schools[idx,:] = compute_R0(initN, contacts, samples_dict, params) elif date_start + tau_days + l_days < date <= pd.to_datetime('2020-09-01'): data_schools[idx,:] = 0* (np.sum(np.mean(Nc_schools,axis=0)))*np.array(samples_dict['prev_work']) contacts = 0*np.expand_dims(np.sum(Nc_schools,axis=1),axis=1)*np.ones([1,len(samples_dict['prev_home'])]) R0, Re_schools[idx,:] = compute_R0(initN, contacts, samples_dict, params) else: data_schools[idx,:] = 1 * (np.sum(np.mean(Nc_schools,axis=0)))*np.array(samples_dict['prev_work']) # This is wrong, but is never used contacts = 1*np.expand_dims(np.sum(Nc_schools,axis=1),axis=1)*np.ones([1,len(samples_dict['prev_home'])]) R0, Re_schools[idx,:] = compute_R0(initN, contacts, samples_dict, params) elif j == 1: print('School\n') data_schools = np.zeros([len(df_google.index.values), len(samples_dict['prev_schools'])]) Re_schools = np.zeros([len(df_google.index.values), len(samples_dict['prev_work'])]) for idx, date in enumerate(df_google.index): tau = np.mean(samples_dict['tau']) l = np.mean(samples_dict['l']) tau_days = pd.Timedelta(tau, unit='D') l_days = pd.Timedelta(l, unit='D') date_start = start_dates[j] if date <= date_start + tau_days: data_schools[idx,:] = 1*(np.sum(np.mean(Nc_schools,axis=0)))*np.ones(len(samples_dict['prev_schools'])) contacts = 1*np.expand_dims(np.sum(Nc_schools,axis=1),axis=1)*np.ones([1,len(samples_dict['prev_schools'])]) R0, Re_schools[idx,:] = compute_R0(initN, contacts, samples_dict, params) elif date_start + tau_days < date <= date_start + tau_days + l_days: old = 1*(np.sum(np.mean(Nc_schools,axis=0)))*np.ones(len(samples_dict['prev_schools'])) new = 0* (np.sum(np.mean(Nc_schools,axis=0)))*np.array(samples_dict['prev_schools']) data_schools[idx,:] = old + (new-old)/l * (date-date_start-tau_days)/pd.Timedelta('1D') old_contacts = 1*np.expand_dims(np.sum(Nc_schools,axis=1),axis=1)*np.ones([1,len(samples_dict['prev_schools'])]) new_contacts = 0*np.expand_dims(np.sum(Nc_schools,axis=1),axis=1)*np.ones([1,len(samples_dict['prev_schools'])]) contacts = old_contacts + (new_contacts-old_contacts)/l * (date-date_start-tau_days)/pd.Timedelta('1D') R0, Re_schools[idx,:] = compute_R0(initN, contacts, samples_dict, params) elif date_start + tau_days + l_days < date <= pd.to_datetime('2020-11-16'): data_schools[idx,:] = 0* (np.sum(np.mean(Nc_schools,axis=0)))*np.array(samples_dict['prev_schools']) contacts = 0*np.expand_dims(np.sum(Nc_schools,axis=1),axis=1)*np.ones([1,len(samples_dict['prev_schools'])]) R0, Re_schools[idx,:] = compute_R0(initN, contacts, samples_dict, params) elif pd.to_datetime('2020-11-16') < date <= pd.to_datetime('2020-12-18'): data_schools[idx,:] = 1* (np.sum(np.mean(Nc_schools,axis=0)))*np.array(samples_dict['prev_schools']) contacts = 1*np.expand_dims(np.sum(Nc_schools,axis=1),axis=1)*np.ones([1,len(samples_dict['prev_schools'])]) R0, Re_schools[idx,:] = compute_R0(initN, contacts, samples_dict, params) elif pd.to_datetime('2020-12-18') < date <= pd.to_datetime('2021-01-04'): data_schools[idx,:] = 0* (np.sum(np.mean(Nc_schools,axis=0)))*np.array(samples_dict['prev_schools']) contacts = tmp = 0*np.expand_dims(np.sum(Nc_schools,axis=1),axis=1)*np.ones([1,len(samples_dict['prev_schools'])]) R0, Re_schools[idx,:] = compute_R0(initN, contacts, samples_dict, params) elif pd.to_datetime('2021-01-04') < date <= pd.to_datetime('2021-02-15'): data_schools[idx,:] = 1* (np.sum(np.mean(Nc_schools,axis=0)))*np.array(samples_dict['prev_schools']) contacts = 1*np.expand_dims(np.sum(Nc_schools,axis=1),axis=1)*np.ones([1,len(samples_dict['prev_schools'])]) R0, Re_schools[idx,:] = compute_R0(initN, contacts, samples_dict, params) elif pd.to_datetime('2021-02-15') < date <= pd.to_datetime('2021-02-21'): data_schools[idx,:] = 0* (np.sum(np.mean(Nc_schools,axis=0)))*np.array(samples_dict['prev_schools']) contacts = 0*np.expand_dims(np.sum(Nc_schools,axis=1),axis=1)*np.ones([1,len(samples_dict['prev_schools'])]) R0, Re_schools[idx,:] = compute_R0(initN, contacts, samples_dict, params) else: data_schools[idx,:] = 1* (np.sum(np.mean(Nc_schools,axis=0)))*np.array(samples_dict['prev_schools']) contacts = 1*np.expand_dims(np.sum(Nc_schools,axis=1),axis=1)*np.ones([1,len(samples_dict['prev_schools'])]) R0, Re_schools[idx,:] = compute_R0(initN, contacts, samples_dict, params) Re_schools_mean = np.mean(Re_schools,axis=1) Re_schools_LL = np.quantile(Re_schools, q=0.05/2, axis=1) Re_schools_UL = np.quantile(Re_schools, q=1-0.05/2, axis=1) # ----- # Total # ----- data_total = data_rest + data_work + data_home + data_schools Re_total = Re_rest + Re_work + Re_home + Re_schools Re_total_mean = np.mean(Re_total,axis=1) Re_total_LL = np.quantile(Re_total, q=0.05/2, axis=1) Re_total_UL = np.quantile(Re_total, q=1-0.05/2, axis=1) # ----------------------- # Absolute contributions # ----------------------- abs_rest = np.zeros(data_rest.shape) abs_work = np.zeros(data_rest.shape) abs_home = np.zeros(data_rest.shape) abs_schools = np.zeros(data_schools.shape) abs_total = data_total for i in range(data_rest.shape[0]): abs_rest[i,:] = data_rest[i,:] abs_work[i,:] = data_work[i,:] abs_home[i,:] = data_home[i,:] abs_schools[i,:] = data_schools[i,:] abs_schools_mean = np.mean(abs_schools,axis=1) abs_schools_LL = np.quantile(abs_schools,LL,axis=1) abs_schools_UL = np.quantile(abs_schools,UL,axis=1) abs_rest_mean = np.mean(abs_rest,axis=1) abs_rest_LL = np.quantile(abs_rest,LL,axis=1) abs_rest_UL = np.quantile(abs_rest,UL,axis=1) abs_work_mean = np.mean(abs_work,axis=1) abs_work_LL = np.quantile(abs_work,LL,axis=1) abs_work_UL = np.quantile(abs_work,UL,axis=1) abs_home_mean = np.mean(abs_home,axis=1) abs_home_LL = np.quantile(abs_home,LL,axis=1) abs_home_UL = np.quantile(abs_home,UL,axis=1) abs_total_mean = np.mean(abs_total,axis=1) abs_total_LL = np.quantile(abs_total,LL,axis=1) abs_total_UL = np.quantile(abs_total,UL,axis=1) # ----------------------- # Relative contributions # ----------------------- rel_rest = np.zeros(data_rest.shape) rel_work = np.zeros(data_rest.shape) rel_home = np.zeros(data_rest.shape) rel_schools = np.zeros(data_schools.shape) rel_total = np.zeros(data_schools.shape) for i in range(data_rest.shape[0]): total = data_schools[i,:] + data_rest[i,:] + data_work[i,:] + data_home[i,:] rel_rest[i,:] = data_rest[i,:]/total rel_work[i,:] = data_work[i,:]/total rel_home[i,:] = data_home[i,:]/total rel_schools[i,:] = data_schools[i,:]/total rel_total[i,:] = total/total rel_schools_mean = np.mean(rel_schools,axis=1) rel_schools_LL = np.quantile(rel_schools,LL,axis=1) rel_schools_UL = np.quantile(rel_schools,UL,axis=1) rel_rest_mean = np.mean(rel_rest,axis=1) rel_rest_LL = np.quantile(rel_rest,LL,axis=1) rel_rest_UL = np.quantile(rel_rest,UL,axis=1) rel_work_mean = np.mean(rel_work,axis=1) rel_work_LL = np.quantile(rel_work,LL,axis=1) rel_work_UL = np.quantile(rel_work,UL,axis=1) rel_home_mean = np.mean(rel_home,axis=1) rel_home_LL = np.quantile(rel_home,LL,axis=1) rel_home_UL = np.quantile(rel_home,UL,axis=1) rel_total_mean = np.mean(rel_total,axis=1) rel_total_LL = np.quantile(rel_total,LL,axis=1) rel_total_UL = np.quantile(rel_total,UL,axis=1) # --------------------- # Append to dataframe # --------------------- df_rel[waves[j],"work_mean"] = rel_work_mean df_rel[waves[j],"work_LL"] = rel_work_LL df_rel[waves[j],"work_UL"] = rel_work_UL df_rel[waves[j], "rest_mean"] = rel_rest_mean df_rel[waves[j], "rest_LL"] = rel_rest_LL df_rel[waves[j], "rest_UL"] = rel_rest_UL df_rel[waves[j], "home_mean"] = rel_home_mean df_rel[waves[j], "home_LL"] = rel_home_LL df_rel[waves[j], "home_UL"] = rel_home_UL df_rel[waves[j],"schools_mean"] = rel_schools_mean df_rel[waves[j],"schools_LL"] = rel_schools_LL df_rel[waves[j],"schools_UL"] = rel_schools_UL df_rel[waves[j],"total_mean"] = rel_total_mean df_rel[waves[j],"total_LL"] = rel_total_LL df_rel[waves[j],"total_UL"] = rel_total_UL copy1 = df_rel.copy(deep=True) df_Re[waves[j],"work_mean"] = Re_work_mean df_Re[waves[j],"work_LL"] = Re_work_LL df_Re[waves[j],"work_UL"] = Re_work_UL df_Re[waves[j], "rest_mean"] = Re_rest_mean df_Re[waves[j],"rest_LL"] = Re_rest_LL df_Re[waves[j],"rest_UL"] = Re_rest_UL df_Re[waves[j], "home_mean"] = Re_home_mean df_Re[waves[j], "home_LL"] = Re_home_LL df_Re[waves[j], "home_UL"] = Re_home_UL df_Re[waves[j],"schools_mean"] = Re_schools_mean df_Re[waves[j],"schools_LL"] = Re_schools_LL df_Re[waves[j],"schools_UL"] = Re_schools_UL df_Re[waves[j],"total_mean"] = Re_total_mean df_Re[waves[j],"total_LL"] = Re_total_LL df_Re[waves[j],"total_UL"] = Re_total_UL copy2 = df_Re.copy(deep=True) df_abs[waves[j],"work_mean"] = abs_work_mean df_abs[waves[j],"work_LL"] = abs_work_LL df_abs[waves[j],"work_UL"] = abs_work_UL df_abs[waves[j], "rest_mean"] = abs_rest_mean df_abs[waves[j], "rest_LL"] = abs_rest_LL df_abs[waves[j], "rest_UL"] = abs_rest_UL df_abs[waves[j], "home_mean"] = abs_home_mean df_abs[waves[j], "home_LL"] = abs_home_LL df_abs[waves[j], "home_UL"] = abs_home_UL df_abs[waves[j],"schools_mean"] = abs_schools_mean df_abs[waves[j],"schools_LL"] = abs_schools_LL df_abs[waves[j],"schools_UL"] = abs_schools_UL df_abs[waves[j],"total_mean"] = abs_total_mean df_abs[waves[j],"total_LL"] = abs_total_LL df_abs[waves[j],"total_UL"] = abs_total_UL df_rel = copy1 df_Re = copy2 #df_abs.to_excel('test.xlsx', sheet_name='Absolute contacts') #df_rel.to_excel('test.xlsx', sheet_name='Relative contacts') #df_Re.to_excel('test.xlsx', sheet_name='Effective reproduction number') print(np.mean(df_abs["1","total_mean"][pd.to_datetime('2020-03-22'):pd.to_datetime('2020-05-04')])) print(np.mean(df_Re["1","total_LL"][pd.to_datetime('2020-03-22'):pd.to_datetime('2020-05-04')]), np.mean(df_Re["1","total_mean"][pd.to_datetime('2020-03-22'):pd.to_datetime('2020-05-04')]), np.mean(df_Re["1","total_UL"][pd.to_datetime('2020-03-22'):pd.to_datetime('2020-05-04')])) print(np.mean(df_abs["1","total_mean"][pd.to_datetime('2020-06-01'):pd.to_datetime('2020-07-01')])) print(np.mean(df_Re["1","total_LL"][pd.to_datetime('2020-06-01'):pd.to_datetime('2020-07-01')]), np.mean(df_Re["1","total_mean"][pd.to_datetime('2020-06-01'):pd.to_datetime('2020-07-01')]), np.mean(df_Re["1","total_UL"][pd.to_datetime('2020-06-01'):pd.to_datetime('2020-07-01')])) print(np.mean(df_abs["2","total_mean"][pd.to_datetime('2020-10-25'):pd.to_datetime('2020-11-16')])) print(np.mean(df_Re["2","total_LL"][pd.to_datetime('2020-10-25'):pd.to_datetime('2020-11-16')]), np.mean(df_Re["2","total_mean"][pd.to_datetime('2020-10-25'):pd.to_datetime('2020-11-16')]), np.mean(df_Re["2","total_UL"][pd.to_datetime('2020-10-25'):pd.to_datetime('2020-11-16')])) print(np.mean(df_abs["2","total_mean"][pd.to_datetime('2020-11-16'):pd.to_datetime('2020-12-18')])) print(np.mean(df_Re["2","total_LL"][pd.to_datetime('2020-11-16'):pd.to_datetime('2020-12-18')]), np.mean(df_Re["2","total_mean"][pd.to_datetime('2020-11-16'):pd.to_datetime('2020-12-18')]), np.mean(df_Re["2","total_UL"][pd.to_datetime('2020-11-16'):pd.to_datetime('2020-12-18')])) # ---------------------------- # Plot absolute contributions # ---------------------------- xlims = [[pd.to_datetime('2020-03-01'), pd.to_datetime('2020-07-14')],[pd.to_datetime('2020-09-01'), pd.to_datetime('2021-02-01')]] no_lockdown = [[pd.to_datetime('2020-03-01'), pd.to_datetime('2020-03-15')],[pd.to_datetime('2020-09-01'), pd.to_datetime('2020-10-19')]] fig,axes=plt.subplots(nrows=2,ncols=1,figsize=(12,7)) for idx,ax in enumerate(axes): ax.plot(df_abs.index, df_abs[waves[idx],"rest_mean"], color='blue', linewidth=2) ax.plot(df_abs.index, df_abs[waves[idx],"work_mean"], color='red', linewidth=2) ax.plot(df_abs.index, df_abs[waves[idx],"home_mean"], color='green', linewidth=2) ax.plot(df_abs.index, df_abs[waves[idx],"schools_mean"], color='orange', linewidth=2) ax.plot(df_abs.index, df_abs[waves[idx],"total_mean"], color='black', linewidth=1.5) ax.xaxis.grid(False) ax.yaxis.grid(False) ax.set_ylabel('Absolute contacts (-)') if idx == 0: ax.legend(['leisure','work','home','schools','total'], bbox_to_anchor=(1.20, 1), loc='upper left') ax.set_xlim(xlims[idx]) ax.axvspan(no_lockdown[idx][0], no_lockdown[idx][1], alpha=0.2, color='black') ax2 = ax.twinx() time = model_results[idx]['time'] vector_mean = model_results[idx]['vector_mean'] vector_LL = model_results[idx]['vector_LL'] vector_UL = model_results[idx]['vector_UL'] ax2.scatter(df_sciensano.index,df_sciensano['H_in'],color='black',alpha=0.6,linestyle='None',facecolors='none', s=30, linewidth=1) ax2.plot(time,vector_mean,'--', color='black', linewidth=1.5) ax2.fill_between(time,vector_LL, vector_UL,alpha=0.20, color = 'black') ax2.xaxis.grid(False) ax2.yaxis.grid(False) ax2.set_xlim(xlims[idx]) ax2.set_ylabel('New hospitalisations (-)') ax = _apply_tick_locator(ax) ax2 = _apply_tick_locator(ax2) plt.tight_layout() plt.show() plt.close() # ---------------------------- # Plot relative contributions # ---------------------------- fig,axes=plt.subplots(nrows=2,ncols=1,figsize=(12,7)) for idx,ax in enumerate(axes): ax.plot(df_rel.index, df_rel[waves[idx],"rest_mean"], color='blue', linewidth=1.5) ax.plot(df_rel.index, df_rel[waves[idx],"work_mean"], color='red', linewidth=1.5) ax.plot(df_rel.index, df_rel[waves[idx],"home_mean"], color='green', linewidth=1.5) ax.plot(df_rel.index, df_rel[waves[idx],"schools_mean"], color='orange', linewidth=1.5) ax.xaxis.grid(False) ax.yaxis.grid(False) ax.set_ylabel('Relative contacts (-)') if idx == 0: ax.legend(['leisure','work','home','schools'], bbox_to_anchor=(1.20, 1), loc='upper left') ax.set_xlim(xlims[idx]) ax.axvspan(no_lockdown[idx][0], no_lockdown[idx][1], alpha=0.2, color='black') ax.set_yticks([0,0.25,0.50,0.75]) ax.set_ylim([0,0.85]) ax2 = ax.twinx() time = model_results[idx]['time'] vector_mean = model_results[idx]['vector_mean'] vector_LL = model_results[idx]['vector_LL'] vector_UL = model_results[idx]['vector_UL'] ax2.scatter(df_sciensano.index,df_sciensano['H_in'],color='black',alpha=0.6,linestyle='None',facecolors='none', s=30, linewidth=1) ax2.plot(time,vector_mean,'--', color='black', linewidth=1.5) ax2.fill_between(time,vector_LL, vector_UL,alpha=0.20, color = 'black') ax2.xaxis.grid(False) ax2.yaxis.grid(False) ax2.set_xlim(xlims[idx]) ax2.set_ylabel('New hospitalisations (-)') ax = _apply_tick_locator(ax) ax2 = _apply_tick_locator(ax2) plt.tight_layout() plt.show() plt.close() # -------------------------------------------- # Plot relative contributions and cluster data # -------------------------------------------- # Perform calculation df_clusters = pd.read_csv('../../data/interim/sciensano/clusters.csv') population_total = 11539326 population_schools = 2344395 population_work = 4893800 #https://stat.nbb.be/Index.aspx?DataSetCode=POPULA&lang=nl home_rel = df_clusters['family']/population_total work_rel = df_clusters['work']/population_work schools_rel = df_clusters['schools']/population_schools others_rel = df_clusters['others']/population_total normalizer = df_clusters['family']/population_total + df_clusters['work']/population_work + df_clusters['schools']/population_schools + df_clusters['others']/population_total df_clusters['family_rel'] = df_clusters['family']/population_total/normalizer df_clusters['work_rel'] = df_clusters['work']/population_work/normalizer df_clusters['schools_rel'] = df_clusters['schools']/population_schools/normalizer df_clusters['others_rel'] = df_clusters['others']/population_total/normalizer df_clusters['midpoint_week'] = pd.to_datetime(df_clusters['startdate_week'])+(pd.to_datetime(df_clusters['enddate_week'])-pd.to_datetime(df_clusters['startdate_week']))/2 # Make plot fig,ax = plt.subplots(figsize=(12,5)) # Cluster data ax.plot(df_clusters['midpoint_week'], df_clusters['others_rel'], '--',color='blue',linewidth=1.5) ax.plot(df_clusters['midpoint_week'], df_clusters['work_rel'],'--', color='red',linewidth=1.5) ax.plot(df_clusters['midpoint_week'], df_clusters['family_rel'],'--',color='green',linewidth=1.5) ax.plot(df_clusters['midpoint_week'], df_clusters['schools_rel'],'--', color='orange',linewidth=1.5) # Model relative share #ax.plot(df_rel.index, df_rel['2',"rest_mean"], color='blue', linewidth=1.5) #ax.plot(df_rel.index, df_rel['2',"work_mean"], color='red', linewidth=1.5) #ax.plot(df_rel.index, df_rel['2',"home_mean"], color='green', linewidth=1.5) #ax.plot(df_rel.index, df_rel['2',"schools_mean"], color='orange', linewidth=1.5) ax.legend(['others','work','home','schools'], bbox_to_anchor=(1.10, 1), loc='upper left') ax.scatter(df_clusters['midpoint_week'], df_clusters['others_rel'], color='blue') ax.scatter(df_clusters['midpoint_week'], df_clusters['work_rel'], color='red') ax.scatter(df_clusters['midpoint_week'], df_clusters['family_rel'],color='green') ax.scatter(df_clusters['midpoint_week'], df_clusters['schools_rel'], color='orange') # Shading of no lockdown zone ax.axvspan('2020-09-01', '2020-10-19', alpha=0.2, color='black') # Other style options ax.set_ylabel('Normalized share of clusters (-)') ax.grid(False) ax = _apply_tick_locator(ax) ax.set_ylim([0,0.80]) ax.set_yticks([0,0.25,0.50,0.75]) ax2 = ax.twinx() time = model_results[1]['time'] vector_mean = model_results[1]['vector_mean'] vector_LL = model_results[1]['vector_LL'] vector_UL = model_results[1]['vector_UL'] ax2.scatter(df_sciensano.index,df_sciensano['H_in'],color='black',alpha=0.6,linestyle='None',facecolors='none', s=30, linewidth=1) ax2.plot(time,vector_mean,'--', color='black', linewidth=1.5) ax2.fill_between(time,vector_LL, vector_UL,alpha=0.20, color = 'black') ax2.scatter(df_sciensano.index,df_sciensano['H_in'],color='black',alpha=0.6,linestyle='None',facecolors='none', s=30, linewidth=1) ax2.set_xlim(['2020-09-01', '2021-02-20']) ax2.set_ylabel('New hospitalisations (-)') ax2.grid(False) ax2 = _apply_tick_locator(ax2) plt.tight_layout() plt.show() # ------------------------------ # Plot Reproduction numbers (1) # ------------------------------ xlims = [[pd.to_datetime('2020-03-01'), pd.to_datetime('2020-07-14')],[pd.to_datetime('2020-09-01'), pd.to_datetime('2021-02-01')]] no_lockdown = [[pd.to_datetime('2020-03-01'), pd.to_datetime('2020-03-15')],[pd.to_datetime('2020-09-01'), pd.to_datetime('2020-10-19')]] fig,axes=plt.subplots(nrows=2,ncols=1,figsize=(12,7)) for idx,ax in enumerate(axes): ax.plot(df_Re.index, df_Re[waves[idx],"rest_mean"], color='blue', linewidth=1.5) ax.fill_between(df_Re.index, df_Re[waves[idx], "rest_LL"], df_Re[waves[idx], "rest_UL"], color='blue', alpha=0.2) ax.plot(df_Re.index, df_Re[waves[idx],"work_mean"], color='red', linewidth=1.5) ax.fill_between(df_Re.index, df_Re[waves[idx], "work_LL"], df_Re[waves[idx], "work_UL"], color='red', alpha=0.2) ax.plot(df_Re.index, df_Re[waves[idx],"home_mean"], color='green', linewidth=1.5) ax.fill_between(df_Re.index, df_Re[waves[idx], "home_LL"], df_Re[waves[idx], "home_UL"], color='green', alpha=0.2) ax.plot(df_Re.index, df_Re[waves[idx],"schools_mean"], color='orange', linewidth=1.5) ax.fill_between(df_Re.index, df_Re[waves[idx], "schools_LL"], df_Re[waves[idx], "schools_UL"], color='orange', alpha=0.2) ax.plot(df_Re.index, df_Re[waves[idx],"total_mean"], color='black', linewidth=1.5) ax.fill_between(df_Re.index, df_Re[waves[idx], "total_LL"], df_Re[waves[idx], "total_UL"], color='black', alpha=0.2) ax.axhline(y=1.0, color='black', linestyle='--',linewidth=1.5) ax.xaxis.grid(False) ax.yaxis.grid(False) ax.set_ylabel('$R_{e}$ (-)') if idx == 0: ax.legend(['leisure','work','home','schools', 'total'], bbox_to_anchor=(1.20, 1), loc='upper left') ax.set_xlim(xlims[idx]) ax.axvspan(no_lockdown[idx][0], no_lockdown[idx][1], alpha=0.2, color='black') ax.set_yticks([0,1,2,3]) ax.set_ylim([0,4.5]) ax2 = ax.twinx() time = model_results[idx]['time'] vector_mean = model_results[idx]['vector_mean'] vector_LL = model_results[idx]['vector_LL'] vector_UL = model_results[idx]['vector_UL'] ax2.scatter(df_sciensano.index,df_sciensano['H_in'],color='black',alpha=0.6,linestyle='None',facecolors='none', s=30, linewidth=1) ax2.plot(time,vector_mean,'--', color='black', linewidth=1.5) ax2.fill_between(time,vector_LL, vector_UL,alpha=0.20, color = 'black') ax2.xaxis.grid(False) ax2.yaxis.grid(False) ax2.set_xlim(xlims[idx]) ax2.set_ylabel('New hospitalisations (-)') ax = _apply_tick_locator(ax) ax2 = _apply_tick_locator(ax2) plt.tight_layout() plt.show() plt.close() # ------------------------------ # Plot Reproduction numbers (2) # ------------------------------ xlims = [[pd.to_datetime('2020-03-01'), pd.to_datetime('2020-07-14')],[pd.to_datetime('2020-09-01'), pd.to_datetime('2021-02-01')]] no_lockdown = [[pd.to_datetime('2020-03-01'), pd.to_datetime('2020-03-15')],[pd.to_datetime('2020-09-01'), pd.to_datetime('2020-10-19')]] fig,axes=plt.subplots(nrows=2,ncols=1,figsize=(12,7)) for idx,ax in enumerate(axes): ax.plot(df_Re.index, df_Re[waves[idx],"rest_mean"], color='blue', linewidth=1.5) ax.plot(df_Re.index, df_Re[waves[idx],"work_mean"], color='red', linewidth=1.5) ax.plot(df_Re.index, df_Re[waves[idx],"home_mean"], color='green', linewidth=1.5) ax.plot(df_Re.index, df_Re[waves[idx],"schools_mean"], color='orange', linewidth=1.5) ax.plot(df_Re.index, df_Re[waves[idx],"total_mean"], color='black', linewidth=1.5) ax.axhline(y=1.0, color='black', linestyle='--',linewidth=1.5) ax.fill_between(df_Re.index, df_Re[waves[idx], "rest_LL"], df_Re[waves[idx], "rest_UL"], color='blue', alpha=0.2) ax.fill_between(df_Re.index, df_Re[waves[idx], "work_LL"], df_Re[waves[idx], "work_UL"], color='red', alpha=0.2) ax.fill_between(df_Re.index, df_Re[waves[idx], "home_LL"], df_Re[waves[idx], "home_UL"], color='green', alpha=0.2) ax.fill_between(df_Re.index, df_Re[waves[idx], "schools_LL"], df_Re[waves[idx], "schools_UL"], color='orange', alpha=0.2) ax.fill_between(df_Re.index, df_Re[waves[idx], "total_LL"], df_Re[waves[idx], "total_UL"], color='black', alpha=0.2) ax.xaxis.grid(False) ax.yaxis.grid(False) ax.set_ylabel('$R_{e}$ (-)') if idx == 0: ax.legend(['leisure','work','home','schools', 'total'], bbox_to_anchor=(1.20, 1), loc='upper left') ax.set_xlim(xlims[idx]) ax.axvspan(no_lockdown[idx][0], no_lockdown[idx][1], alpha=0.2, color='black') ax.set_yticks([0,1,2]) ax.set_ylim([0,2.5]) ax2 = ax.twinx() time = model_results[idx]['time'] vector_mean = model_results[idx]['vector_mean'] vector_LL = model_results[idx]['vector_LL'] vector_UL = model_results[idx]['vector_UL'] ax2.scatter(df_sciensano.index,df_sciensano['H_in'],color='black',alpha=0.6,linestyle='None',facecolors='none', s=30, linewidth=1) ax2.plot(time,vector_mean,'--', color='black', linewidth=1.5) ax2.fill_between(time,vector_LL, vector_UL,alpha=0.20, color = 'black') ax2.xaxis.grid(False) ax2.yaxis.grid(False) ax = _apply_tick_locator(ax) ax2 = _apply_tick_locator(ax2) plt.tight_layout() plt.show() plt.close() # ------------------------------------------------------- # Plot relative and reproduction number on one figure (1) # ------------------------------------------------------- dfs = [df_rel, df_Re] fig,axes=plt.subplots(nrows=2,ncols=2, figsize=(18,8.27)) for idx,ax_row in enumerate(axes): for jdx, ax in enumerate(ax_row): ax.plot(dfs[jdx].index, dfs[jdx][waves[idx],"rest_mean"], color='blue', linewidth=1.5) ax.plot(dfs[jdx].index, dfs[jdx][waves[idx],"work_mean"], color='red', linewidth=1.5) ax.plot(dfs[jdx].index, dfs[jdx][waves[idx],"home_mean"], color='green', linewidth=1.5) ax.plot(dfs[jdx].index, dfs[jdx][waves[idx],"schools_mean"], color='orange', linewidth=1.5) ax.plot(dfs[jdx].index, dfs[jdx][waves[idx],"total_mean"], color='black', linewidth=1.5) if jdx == 0: ax.set_ylabel('Relative contacts (-)') ax.set_xlim(xlims[idx]) ax.axvspan(no_lockdown[idx][0], no_lockdown[idx][1], alpha=0.2, color='black') ax.set_yticks([0,0.25,0.50,0.75]) ax.set_ylim([0,0.85]) if jdx == 1: ax.axhline(y=1.0, color='black', linestyle='--',linewidth=1.5) ax.set_ylabel('$R_{e}$ (-)') if idx == 0: ax.legend(['leisure','work','home','schools', 'total'], bbox_to_anchor=(1.20, 1), loc='upper left') ax.set_xlim(xlims[idx]) ax.axvspan(no_lockdown[idx][0], no_lockdown[idx][1], alpha=0.2, color='black') ax.set_yticks([0,1,2]) ax.set_ylim([0,2.5]) ax.xaxis.grid(False) ax.yaxis.grid(False) ax2 = ax.twinx() time = model_results[idx]['time'] vector_mean = model_results[idx]['vector_mean'] vector_LL = model_results[idx]['vector_LL'] vector_UL = model_results[idx]['vector_UL'] ax2.scatter(df_sciensano.index,df_sciensano['H_in'],color='black',alpha=0.6,linestyle='None',facecolors='none', s=30, linewidth=1) ax2.plot(time,vector_mean,'--', color='black', linewidth=1.5) ax2.fill_between(time,vector_LL, vector_UL,alpha=0.20, color = 'black') ax2.xaxis.grid(False) ax2.yaxis.grid(False) ax2.set_xlim(xlims[idx]) ax2.set_ylabel('New hospitalisations (-)') ax = _apply_tick_locator(ax) ax2 = _apply_tick_locator(ax2) plt.tight_layout() plt.show() plt.close()

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04e34c2e2998f9009f105234d37b39d1092b9400

2,272

py

Python

krysztalki/workDir/tests/test matrices/test_matrices_like_2_0yy.py

woblob/Crystal_Symmetry

be2984b4487d6075986ef60822a347d0b0e6b885

[ "MIT" ]

null

krysztalki/workDir/tests/test matrices/test_matrices_like_2_0yy.py

woblob/Crystal_Symmetry

be2984b4487d6075986ef60822a347d0b0e6b885

[ "MIT" ]

null

krysztalki/workDir/tests/test matrices/test_matrices_like_2_0yy.py

woblob/Crystal_Symmetry

be2984b4487d6075986ef60822a347d0b0e6b885

[ "MIT" ]

null

import matrices_new_extended as mne import numpy as np import sympy as sp from equality_check import Point x, y, z = sp.symbols("x y z") Point.base_point = np.array([x, y, z, 1]) class Test_Axis_2_0yy: def test_matrix_2_0yy(self): expected = Point([ -x, z, y, 1]) calculated = Point.calculate(mne._matrix_2_0yy) assert calculated == expected def test_matrix_2_qyy(self): expected = Point([ 1-x, z, y, 1]) calculated = Point.calculate(mne._matrix_2_qyy) assert calculated == expected def test_matrix_2_1_0qyy_0qq(self): expected = Point([ -x, 1+z, y, 1]) calculated = Point.calculate(mne._matrix_2_1_0qyy_0qq) assert calculated == expected def test_matrix_2_0qyy_0qq(self): expected = Point([ -x, z, 1+y, 1]) calculated = Point.calculate(mne._matrix_2_0qyy_0qq) assert calculated == expected def test_matrix_2_1_qyy_0hh(self): expected = Point([ 1-x, 1+z, 1+y, 1]) calculated = Point.calculate(mne._matrix_2_1_qyy_0hh) assert calculated == expected def test_matrix_2_1_3omqyy_0hh(self): expected = Point([ 1.5-x, 0.5+z, 1.5+y, 1]) calculated = Point.calculate(mne._matrix_2_1_3omqyy_0hh) assert calculated == expected def test_matrix_2_1_oqyy_0hh(self): expected = Point([ 0.5-x, 1.5+z, 0.5+y, 1]) calculated = Point.calculate(mne._matrix_2_1_oqyy_0hh) assert calculated == expected def test_matrix_2_1_oyy_0qq(self): expected = Point([ 0.5-x, 0.5+z, 0.5+y, 1]) calculated = Point.calculate(mne._matrix_2_1_oyy_0qq) assert calculated == expected def test_matrix_2_1_oyy_03q3q(self): expected = Point([ 0.5-x, 1.5+z, 1.5+y, 1]) calculated = Point.calculate(mne._matrix_2_1_oyy_03q3q) assert calculated == expected def test_matrix_2_1_3ohmyy_0mqq(self): expected = Point([ 1.5-x, 0.5+z, 1.5+y, 1]) calculated = Point.calculate(mne._matrix_2_1_3ohmyy_0mqq) assert calculated == expected def test_matrix_2_1_3oqyy_0hh(self): expected = Point([ 1.5-x, 1.5+z, 0.5+y, 1]) calculated = Point.calculate(mne._matrix_2_1_3oqyy_0hh) assert calculated == expected

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4

04e78da809870931dead3f10430e92ccabd67973

129

py

Python

django/config/__init__.py

PiochU19/amazon-price-tracker

93a321d5799ee2b0b02487fea5577698a6da8aa3

[ "MIT" ]

null

django/config/__init__.py

PiochU19/amazon-price-tracker

93a321d5799ee2b0b02487fea5577698a6da8aa3

[ "MIT" ]

1

2021-06-10T22:05:12.000Z

django/config/__init__.py

PiochU19/amazon_price_tracker

93a321d5799ee2b0b02487fea5577698a6da8aa3

[ "MIT" ]

1

2022-01-09T03:23:19.000Z

from __future__ import absolute_import, unicode_literals from config.celery import app as celery_app __all__ = ("celery_app",)

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b6da848b86206d5255d9112ae53bae70f5a0bfe4

170

py

Python

password_validation.py

PaulinaKomorek/UAM

8995ca20d91e7c1ab4e59dbaa40cc02c4b7d9287

[ "MIT" ]

null

password_validation.py

PaulinaKomorek/UAM

8995ca20d91e7c1ab4e59dbaa40cc02c4b7d9287

[ "MIT" ]

null

password_validation.py

PaulinaKomorek/UAM

8995ca20d91e7c1ab4e59dbaa40cc02c4b7d9287

[ "MIT" ]

null

def validate_password(password: str): if len(password)<8: return False for i in password: if i.isdigit(): return True return False

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4

b6e00ecbddc0eeec136e5f6ba63d77ff7fbec6c7

8,552

py

Python

tests/test_operations.py

mbloem-Steelcase/traces

144c7bf4667e7c4554b84e78d7626200437b6c4a

[ "MIT" ]

3

2018-06-07T01:55:00.000Z

2018-11-12T14:38:34.000Z

tests/test_operations.py

nachereshata/traces

05f2f4d09a442ae6ce5439d2bbb59d7b63c687b7

[ "MIT" ]

null

tests/test_operations.py

nachereshata/traces

05f2f4d09a442ae6ce5439d2bbb59d7b63c687b7

[ "MIT" ]

null

import datetime import nose from traces import TimeSeries from traces.decorators import ignorant, strict def test_scalar_ops(): a = TimeSeries() a.set(datetime.datetime(2015, 3, 1), 1) a.set(datetime.datetime(2015, 3, 2), 0) a.set(datetime.datetime(2015, 3, 3), 3) a.set(datetime.datetime(2015, 3, 4), 2) ts_half = a.multiply(0.5) ts_bool = a.to_bool(invert=False) ts_threshold = a.threshold(value=1.1) # test before domain, should give default value assert ts_half[datetime.datetime(2015, 2, 24)] is None assert ts_bool[datetime.datetime(2015, 2, 24)] == None assert ts_threshold[datetime.datetime(2015, 2, 24)] == None # test values throughout series assert ts_half[datetime.datetime(2015, 3, 1, 6)] == 0.5 assert ts_bool[datetime.datetime(2015, 3, 1, 6)] == True assert ts_threshold[datetime.datetime(2015, 3, 1, 6)] == False assert ts_half[datetime.datetime(2015, 3, 2, 6)] == 0 assert ts_bool[datetime.datetime(2015, 3, 2, 6)] == False assert ts_threshold[datetime.datetime(2015, 3, 2, 6)] == False assert ts_half[datetime.datetime(2015, 3, 3, 6)] == 1.5 assert ts_bool[datetime.datetime(2015, 3, 3, 6)] == True assert ts_threshold[datetime.datetime(2015, 3, 3, 6)] == True # test after domain, should give last value assert ts_half[datetime.datetime(2015, 3, 4, 18)] == 1 assert ts_bool[datetime.datetime(2015, 3, 4, 18)] == True assert ts_threshold[datetime.datetime(2015, 3, 4, 18)] == True def test_sum(): a = TimeSeries() a.set(datetime.datetime(2015, 3, 1), 1) a.set(datetime.datetime(2015, 3, 2), 0) a.set(datetime.datetime(2015, 3, 3), 1) a.set(datetime.datetime(2015, 3, 4), 0) b = TimeSeries() b.set(datetime.datetime(2015, 3, 1), 0) b.set(datetime.datetime(2015, 3, 1, 12), 1) b.set(datetime.datetime(2015, 3, 2), 0) b.set(datetime.datetime(2015, 3, 2, 12), 1) b.set(datetime.datetime(2015, 3, 3), 0) c = TimeSeries() c.set(datetime.datetime(2015, 3, 1), 0) c.set(datetime.datetime(2015, 3, 1, 18), 1) c.set(datetime.datetime(2015, 3, 5), 0) ts_sum = TimeSeries.merge([a, b, c], operation=ignorant(sum)) # test before domain, should give default value assert ts_sum[datetime.datetime(2015, 2, 24)] == 0 # test values throughout sum assert ts_sum[datetime.datetime(2015, 3, 1)] == 1 assert ts_sum[datetime.datetime(2015, 3, 1, 6)] == 1 assert ts_sum[datetime.datetime(2015, 3, 1, 12)] == 2 assert ts_sum[datetime.datetime(2015, 3, 1, 13)] == 2 assert ts_sum[datetime.datetime(2015, 3, 1, 17)] == 2 assert ts_sum[datetime.datetime(2015, 3, 1, 18)] == 3 assert ts_sum[datetime.datetime(2015, 3, 1, 19)] == 3 assert ts_sum[datetime.datetime(2015, 3, 3)] == 2 assert ts_sum[datetime.datetime(2015, 3, 4)] == 1 assert ts_sum[datetime.datetime(2015, 3, 4, 18)] == 1 assert ts_sum[datetime.datetime(2015, 3, 5)] == 0 # test after domain, should give last value assert ts_sum[datetime.datetime(2015, 3, 6)] == 0 assert 0 + a + b == a + b def example_dictlike(): # test overwriting keys l = TimeSeries() l[datetime.datetime(2010, 1, 1)] = 5 l[datetime.datetime(2010, 1, 2)] = 4 l[datetime.datetime(2010, 1, 3)] = 3 l[datetime.datetime(2010, 1, 7)] = 2 l[datetime.datetime(2010, 1, 4)] = 1 l[datetime.datetime(2010, 1, 4)] = 10 l[datetime.datetime(2010, 1, 4)] = 5 l[datetime.datetime(2010, 1, 1)] = 1 l[datetime.datetime(2010, 1, 7)] = 1.2 l[datetime.datetime(2010, 1, 8)] = 1.3 l[datetime.datetime(2010, 1, 12)] = 1.3 # do some wackiness with a bunch of points dt = datetime.datetime(2010, 1, 12) for i in range(1000): dt += datetime.timedelta(hours=random.random()) l[dt] = math.sin(i / float(math.pi)) dt -= datetime.timedelta(hours=500) dt -= datetime.timedelta(minutes=30) for i in range(1000): dt += datetime.timedelta(hours=random.random()) l[dt] = math.cos(i / float(math.pi)) # what does this get? print >> sys.stderr, l[datetime.datetime(2010, 1, 3, 23, 59, 59)] # output the time series for i, j in l: print(i.isoformat(), j) def example_mean(): l = TimeSeries() l[datetime.datetime(2010, 1, 1)] = 0 l[datetime.datetime(2010, 1, 3, 10)] = 1 l[datetime.datetime(2010, 1, 5)] = 0 l[datetime.datetime(2010, 1, 8)] = 1 l[datetime.datetime(2010, 1, 17)] = 0 l[datetime.datetime(2010, 1, 19)] = 1 l[datetime.datetime(2010, 1, 23)] = 0 l[datetime.datetime(2010, 1, 26)] = 1 l[datetime.datetime(2010, 1, 28)] = 0 l[datetime.datetime(2010, 1, 31)] = 1 l[datetime.datetime(2010, 2, 5)] = 0 for time, value in l: print(time.isoformat(), 0.1 * value + 1.1) print('') timestep = {'hours': 25} start = datetime.datetime(2010, 1, 1) while start <= datetime.datetime(2010, 2, 5): end = start + datetime.timedelta(**timestep) print(start.isoformat(), l.mean(start, end)) start = end print('') start = datetime.datetime(2010, 1, 1) while start <= datetime.datetime(2010, 2, 5): end = start + datetime.timedelta(**timestep) print(start.isoformat(), -0.2) print(start.isoformat(), 1.2) start = end def example_arrow(): l = TimeSeries() l[arrow.Arrow(2010, 1, 1)] = 0 l[arrow.Arrow(2010, 1, 3, 10)] = 1 l[arrow.Arrow(2010, 1, 5)] = 0 l[arrow.Arrow(2010, 1, 8)] = 1 l[arrow.Arrow(2010, 1, 17)] = 0 l[arrow.Arrow(2010, 1, 19)] = 1 l[arrow.Arrow(2010, 1, 23)] = 0 l[arrow.Arrow(2010, 1, 26)] = 1 l[arrow.Arrow(2010, 1, 28)] = 0 l[arrow.Arrow(2010, 1, 31)] = 1 l[arrow.Arrow(2010, 2, 5)] = 0 for time, value in l: print(time.naive.isoformat(), 0.1 * value + 1.1) print('') start = arrow.Arrow(2010, 1, 1) end = arrow.Arrow(2010, 2, 5) unit = {'hours': 25} for start, end in span_range(start, end, unit): print(start.naive.isoformat(), l.mean(start, end)) print('') for start, end in span_range(start, end, unit): print(start.naive.isoformat(), -0.2) print(start.naive.isoformat(), 1.2) def example_sum(): a = TimeSeries() a.set(datetime.datetime(2015, 3, 1), 1) a.set(datetime.datetime(2015, 3, 2), 0) a.set(datetime.datetime(2015, 3, 3), 1) a.set(datetime.datetime(2015, 3, 5), 0) a.set(datetime.datetime(2015, 3, 6), 0) b = TimeSeries() b.set(datetime.datetime(2015, 3, 1), 0) b.set(datetime.datetime(2015, 3, 2, 12), 1) b.set(datetime.datetime(2015, 3, 3, 13, 13), 0) b.set(datetime.datetime(2015, 3, 4), 1) b.set(datetime.datetime(2015, 3, 5), 0) b.set(datetime.datetime(2015, 3, 5, 12), 1) b.set(datetime.datetime(2015, 3, 5, 19), 0) c = TimeSeries() c.set(datetime.datetime(2015, 3, 1, 17), 0) c.set(datetime.datetime(2015, 3, 1, 21), 1) c.set(datetime.datetime(2015, 3, 2, 13, 13), 0) c.set(datetime.datetime(2015, 3, 4, 18), 1) c.set(datetime.datetime(2015, 3, 5, 4), 0) # output the three time series for i, ts in enumerate([a, b, c]): for (t0, v0), (t1, v1) in ts.iterintervals(1): print(t0.isoformat(), i) print(t1.isoformat(), i) print('') for (t0, v0), (t1, v1) in ts.iterintervals(0): print(t0.isoformat(), i) print(t1.isoformat(), i) print('') # output the sum # for dt, i in sum([a, b, c]): # print dt.isoformat(), i # print '' for dt, i in TimeSeries.merge([a, b, c], operation=sum): print(dt.isoformat(), i) def test_interpolation(): ts = TimeSeries(data=[(0, 0), (1, 2)]) assert ts.get(0, interpolate='linear') == 0 assert ts.get(0.25, interpolate='linear') == 0.5 assert ts.get(0.5, interpolate='linear') == 1.0 assert ts.get(0.75, interpolate='linear') == 1.5 assert ts.get(1, interpolate='linear') == 2 assert ts.get(-1, interpolate='linear') == None assert ts.get(2, interpolate='linear') == 2 nose.tools.assert_raises(ValueError, ts.get, 0.5, 'spline') def test_default(): ts = TimeSeries(data=[(0, 0), (1, 2)]) ts_no_default = ts.operation(ts, lambda a, b: a + b) assert ts_no_default.default == None ts_default = ts.operation(ts, lambda a, b: a + b, default=1) assert ts_default.default == 1 ts_none = ts.operation(ts, lambda a, b: a + b, default=None) assert ts_none.default == None

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null

1

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1

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null

0

4

b6e737545d82e1ee3fcf7414405300b4fa918159

133

py

Python

hammers/__init__.py

ChameleonCloud/bag-o-hammers

0faaf9b21aceb155dc7da2ea92cf77af815c11e7

[ "Apache-2.0" ]

null

hammers/__init__.py

ChameleonCloud/bag-o-hammers

0faaf9b21aceb155dc7da2ea92cf77af815c11e7

[ "Apache-2.0" ]

8

2018-05-24T01:07:27.000Z

2021-09-01T18:02:29.000Z

hammers/__init__.py

ChameleonCloud/bag-o-hammers

0faaf9b21aceb155dc7da2ea92cf77af815c11e7

[ "Apache-2.0" ]

2

2016-12-07T01:12:41.000Z

2018-08-17T16:57:54.000Z

# coding: utf-8 from .mycnf import * from .mysqlshim import * from .mysqlargs import * from . import query __version__ = '0.3.1'

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1

0

4

b6ea53e8dc2b68ce566334442d0e5e69dd6893f3

1,308

py

Python

devilry/devilry_authenticate/urls.py

devilry/devilry-django

9ae28e462dfa4cfee966ebacbca04ade9627e715

[ "BSD-3-Clause" ]

29

2015-01-18T22:56:23.000Z

2020-11-10T21:28:27.000Z

devilry/devilry_authenticate/urls.py

devilry/devilry-django

9ae28e462dfa4cfee966ebacbca04ade9627e715

[ "BSD-3-Clause" ]

786

2015-01-06T16:10:18.000Z

2022-03-16T11:10:50.000Z

devilry/devilry_authenticate/urls.py

devilry/devilry-django

9ae28e462dfa4cfee966ebacbca04ade9627e715

[ "BSD-3-Clause" ]

15

2015-04-06T06:18:43.000Z

2021-02-24T12:28:30.000Z

from django.urls import path, include from cradmin_legacy.apps.cradmin_authenticate.views import logout from devilry.devilry_authenticate.views import CustomLoginView, allauth_views urlpatterns = [ path('login', CustomLoginView.as_view(), name='cradmin-authenticate-login'), path('logout', logout.cradmin_logoutview, name='cradmin-authenticate-logout'), path('allauth/login/', allauth_views.AllauthLoginView.as_view(), name='account_login'), path('allauth/logout/', allauth_views.AllauthLogoutView.as_view(), name='account_logout'), path('allauth/', include('allauth.urls')), ] # from django.conf.urls import url, include # from cradmin_legacy.apps.cradmin_authenticate.views import logout # from devilry.devilry_authenticate.views import CustomLoginView, allauth_views # urlpatterns = [ # url(r'^login$', CustomLoginView.as_view(), name='cradmin-authenticate-login'), # url(r'^logout$', logout.cradmin_logoutview, name='cradmin-authenticate-logout'), # url(r"^allauth/login/$", # allauth_views.AllauthLoginView.as_view(), # name="account_login"), # url(r"^allauth/logout/$", # allauth_views.AllauthLogoutView.as_view(), # name="account_logout"), # url(r'^allauth/', include('allauth.urls')), # ]

36.333333

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0.710245

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1,308

6.19863

0.184932

0.125967

0.066298

0.075138

0.844199

0.596685

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0.144495

1,308

35

87

37.371429

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null

0

1

0

null

0

4

8e17b2d7d92cf7a018f3f3b6d132d6b1d67d0393

22

py

Python

solutions/03-dask-dataframe-non-cancelled.py

xeroquark/dask-tutorial

0061ae7e0e18d0a75bdd67dc9c2a551ab174e057

[ "BSD-3-Clause" ]

331

2019-01-26T21:11:45.000Z

2022-03-02T11:35:16.000Z

solutions/03-dask-dataframe-non-cancelled.py

xeroquark/dask-tutorial

0061ae7e0e18d0a75bdd67dc9c2a551ab174e057

[ "BSD-3-Clause" ]

5

2019-11-15T02:00:26.000Z

2021-01-06T04:26:40.000Z

solutions/03-dask-dataframe-non-cancelled.py

xeroquark/dask-tutorial

0061ae7e0e18d0a75bdd67dc9c2a551ab174e057

[ "BSD-3-Clause" ]

88

2019-01-25T16:53:47.000Z

2022-03-03T00:05:08.000Z

len(df[~df.Cancelled])

22

0.727273

4

22

4

0.75

0

22

1

22

0.727273

0

1

0

true

0

1

0

null

0

1

0

null

0

1

0

4

8e31a0fcb2929be25b4708b6022423ac371a4bee

4,319

py

Python

pybossa_lists/FishingVesselsV2_HighConfidenceStudents_20160314/downloadjsons.py

GlobalFishingWatch/vessel-lists

4fb241f275d7327dc311db89bf86ef43d7ae8870

[ "Apache-2.0" ]

2

2017-09-16T10:29:34.000Z

2019-01-23T17:58:21.000Z

pybossa_lists/FishingVesselsV2_HighConfidenceStudents_20160314/downloadjsons.py

GlobalFishingWatch/vessel-lists

4fb241f275d7327dc311db89bf86ef43d7ae8870

[ "Apache-2.0" ]

16

2016-06-21T10:20:11.000Z

2016-07-07T16:03:02.000Z

pybossa_lists/FishingVesselsV2_HighConfidenceStudents_20160314/downloadjsons.py

GlobalFishingWatch/vessel-lists

4fb241f275d7327dc311db89bf86ef43d7ae8870

[ "Apache-2.0" ]

1

2021-05-25T08:33:32.000Z

#! /usr/bin/python import urllib2 import json task_ids = [4108 , 4109 , 4110 , 4111 , 4112 , 4113 , 4114 , 4115 , 4116 , 4117 , 4118 , 4119 , 4120 , 4121 , 4122 , 4123 , 4124 , 4125 , 4126 , 4127 , 4128 , 4129 , 4130 , 4131 , 4132 , 4133 , 4134 , 4135 , 4136 , 4137 , 4138 , 4139 , 4140 , 4141 , 4142 , 4143 , 4144 , 4145 , 4146 , 4147 , 4148 , 4149 , 4150 , 4151 , 4152 , 4153 , 4154 , 4155 , 4156 , 4157 , 4158 , 4159 , 4160 , 4161 , 4162 , 4163 , 4164 , 4165 , 4166 , 4167 , 4168 , 4169 , 4170 , 4171 , 4172 , 4173 , 4174 , 4175 , 4176 , 4177 , 4178 , 4179 , 4180 , 4181 , 4182 , 4183 , 4184 , 4185 , 4186 , 4187 , 4188 , 4189 , 4190 , 4191 , 4192 , 4193 , 4194 , 4195 , 4196 , 4197 , 4198 , 4199 , 4200 , 4201 , 4202 , 4203 , 4204 , 4205 , 4206 , 4207 , 4208 , 4209 , 4210 , 4211 , 4212 , 4213 , 4214 , 4215 , 4216 , 4217 , 4218 , 4219 , 4220 , 4221 , 4222 , 4223 , 4224 , 4225 , 4226 , 4227 , 4228 , 4229 , 4230 , 4231 , 4232 , 4233 , 4234 , 4235 , 4236 , 4237 , 4238 , 4239 , 4240 , 4241 , 4242 , 4243 , 4244 , 4245 , 4246 , 4247 , 4248 , 4249 , 4539 , 4540 , 4541 , 4542 , 4543 , 4544 , 4545 , 4546 , 4547 , 4548 , 4549 , 4550 , 4551 , 4552 , 4553 , 4554 , 4555 , 4556 , 4557 , 4558 , 4559 , 4560 , 4561 , 4562 , 4563 , 4564 , 4565 , 4566 , 4567 , 4568 , 4569 , 4570 , 4571 , 4572 , 4573 , 4574 , 4575 , 4576 , 4577 , 4578 , 4579 , 4580 , 4581 , 4582 , 4583 , 4584 , 4585 , 4586 , 4587 , 4588 , 4589 , 4590 , 4591 , 4592 , 4593 , 4594 , 4595]# , 4596 , 4597 , 4598 , 4599 , 4600 , 4601 , 4602 , 4603 , 4604 , 4605 , 4606 , 4607 , 4608 , 4609 , 4610 , 4611 , 4612 , 4613 , 4614 , 4615 , 4616 , 4617 , 4618 , 4619 , 4620 , 4621 , 4622 , 4623 , 4624 , 4625 , 4626 , 4627 , 4628 , 4629 , 4630 , 4631 , 4632 , 4633 , 4634 , 4635 , 4636 , 4637 , 4638 , 4639 , 4640 , 4641 , 4642 , 4643 , 4644 , 4645 , 4646 , 4647 , 4648 , 4649 , 4650 , 4651 , 4652 , 4653 , 4654 , 4655 , 4656 , 4657 , 4658 , 4659 , 4660 , 4661 , 4662 , 4663 , 4664 , 4665 , 4666 , 4667 , 4668 , 4669 , 4670 , 4671 , 4672 , 4673 , 4674 , 4675 , 4676 , 4677 , 4678 , 4679 , 4680 , 4681 , 4682 , 4683 , 4684 , 4685 , 4686 , 4687 , 4688 , 4689 , 4690 , 4691 , 4692 , 4693 , 4694 , 4695 , 4696 , 4697 , 4698 , 4699 , 4700 , 4701 , 4702 , 4703 , 4704 , 4705 , 4706 , 4707 , 4708 , 4709 , 4710 , 4711 , 4712 , 4713 , 4714 , 4715 , 4716 , 4717 , 4718 , 4719 , 4720 , 4721 , 4722 , 4723 , 4724 , 4725 , 4726 , 4727 , 4728 , 4729 , 4730 , 4731 , 4732 , 4733 , 4734 , 4735 , 4736 , 4737 , 4738 , 4739 , 4740 , 4741 , 4742 , 4743 , 4744 , 4745 , 4746 , 4747 , 4748 , 4749 , 4750 , 4751 , 4752 , 4753 , 4754 , 4755 , 4756 , 4757 , 4758 , 4759 , 4760 , 4761 , 4762 , 4763 , 4764 , 4765 , 4766 , 4767 , 4768 , 4769 , 4770 , 4771 , 4772 , 4773 , 4774 , 4775 , 4776 , 4777 , 4778 , 4779 , 4780 , 4781 , 4782 , 4783 , 4784 , 4785 , 4786 , 4787 , 4788 , 4789 , 4790 , 4791 , 4792 , 4793 , 4794 , 4795 , 4796 , 4797 , 4798 , 4799 , 4800 , 4801 , 4802 , 4803 , 4804 , 4805 , 4806 , 4807 , 4808 , 4809 , 4810 , 4811 , 4812 , 4813 , 4814 , 4815 , 4816 , 4817 , 4818 , 4819 , 4820 , 4821 , 4822 , 4823 , 4824 , 4825 , 4826 , 4827 , 4828 , 4829 , 4830 , 4831 , 4832 , 4833 , 4834 , 4835 , 4836 , 4837 , 4838 , 4839 , 4840 , 4841 , 4842 , 4843 , 4844 , 4845 , 4846 , 4847 , 4848 , 4849 , 4850 , 4851 , 4852 , 4853 , 4854 , 4855 , 4856 , 4857 , 4858 , 4859 , 4860 , 4861 , 4862 , 4863 , 4864 , 4865 , 4866 , 4867 , 4868 , 4869 , 4870 , 4871 , 4872 , 4873 , 4874 , 4875 , 4876 , 4877 , 4878 , 4879 , 4880 , 4881 , 4882 , 4883 , 4884 , 4885 , 4886 , 4887 , 4888 , 4889 , 4890 , 4891 , 4892 , 4893 , 4894 , 4895 , 4896 , 4897 , 4898 , 4899 , 4900 , 4901 , 4902 , 4903 , 4904 , 4905 , 4906 , 4907 , 4908 , 4909 , 4910 , 4911 , 4912 , 4913 , 4914 , 4915 , 4916 , 4917 , 4918 , 4919 , 4920 , 4921 , 4922 , 4923 , 4924 , 4925 , 4926 , 4927 , 4928 , 4929 , 4930 , 4931 , 4932 , 4933 , 4934 , 4935 , 4936 , 4937 , 4938 , 4939 , 4940 , 4941 , 4942 , 4943 , 4944 , 4945 , 4946 , 4947 , 4948 , 4949 , 4950 , 4951 , 4952 , 4953 , 4954 , 4955 , 4956 , 4957 , 4958 , 4959 , 4960 , 4961 , 4962 , 4963 , 4964 , 4965 , 4966 , 4967 , 4968 , 4969 , 4970 , 4971 , 4972] for t in task_ids: response = urllib2.urlopen('http://crowd.globalfishingwatch.org/api/taskrun?task_id='+str(t)) html = response.read() f = open("jsons/"+str(t)+".json", 'w') f.write(html) f.close()

269.9375

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1

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null

0

4

8e37450e97eaff17123f1141e0cf93222341403a

177

py

Python

python/random_list.py

twofist/stalin-sort

46d9434bde96daf6c5c957b60b928fc9eb6e4006

[ "MIT" ]

1,140

2018-10-30T13:03:09.000Z

2022-03-29T22:41:24.000Z

python/random_list.py

twofist/stalin-sort

46d9434bde96daf6c5c957b60b928fc9eb6e4006

[ "MIT" ]

77

2018-10-30T13:20:15.000Z

2021-11-06T03:44:55.000Z

python/random_list.py

twofist/stalin-sort

46d9434bde96daf6c5c957b60b928fc9eb6e4006

[ "MIT" ]

245

2018-10-30T13:10:53.000Z

2022-03-14T08:13:56.000Z

import random def random_list_maker(length): random_list = [] for i in range(0, length): random_list.append(random.randint(0, 100)) return random_list

19.666667

50

0.666667

25

177

4.52

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0.353982

0.283186

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0.037037

0.237288

177

9

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19.666667

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null

1

0

1

0

null

0

4

8e461b890feaaddd81ab6392702392e0f0ab82a5

221

py

Python

cmfg/__init__.py

mlares/CBR_CrossCorr

f3599aed997e003d2d838ba5ad345d2f783a5bda

[ "MIT" ]

null

cmfg/__init__.py

mlares/CBR_CrossCorr

f3599aed997e003d2d838ba5ad345d2f783a5bda

[ "MIT" ]

null

cmfg/__init__.py

mlares/CBR_CrossCorr

f3599aed997e003d2d838ba5ad345d2f783a5bda

[ "MIT" ]

null

# ========================================================= # DOCS # ========================================================= """Software for the study of Cosmic Microwave Foregrounds (CMFG) """ __version__ = "0.0.1"

22.1

64

0.312217

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221

4.642857

0.928571

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0.015075

0.099548

221

9

65

24.555556

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null

0

1

0

1

0

null

0

4

f3ef277f5055494ea5e815e534881b76e2f5adbe

332

py

Python

exercises/Desafio024.py

zThiago15/Curso-em-Video

ef25e0497edb79bdfbe71fde485f4dafc0d2a0e6

[ "MIT" ]

null

exercises/Desafio024.py

zThiago15/Curso-em-Video

ef25e0497edb79bdfbe71fde485f4dafc0d2a0e6

[ "MIT" ]

null

exercises/Desafio024.py

zThiago15/Curso-em-Video

ef25e0497edb79bdfbe71fde485f4dafc0d2a0e6

[ "MIT" ]

1

2021-07-24T21:39:26.000Z

print('\033[30mEx: Digite um nome de uma cidade e mostre se ela começa com SANTO.') print('\033[1;31m=-'*20) cid = str(input('\033[1;34mDigite o nome da cidade em que você nasceu: ')) div = cid.split() print('1° método: Começa com Santo?','Santo' in div[0].title()) print('2° método: Começa com SANTO?', div[0].upper() == 'SANTO')

47.428571

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0.665663

61

332

3.655738

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0.188341

0.143498

0.188341

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0.08042

0.138554

332

7

84

47.428571

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null

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1

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1

0

null

0

1

0

4

6d0e8dfaf95b112c12f0f9455ac922c1ec26e651

100

py

Python

src/megarest/__init__.py

aakash-sahai/megarest

fa3b1dbb6e36dc1c45689436e4bcc84a492185e1

[ "MIT" ]

null

src/megarest/__init__.py

aakash-sahai/megarest

fa3b1dbb6e36dc1c45689436e4bcc84a492185e1

[ "MIT" ]

null

src/megarest/__init__.py

aakash-sahai/megarest

fa3b1dbb6e36dc1c45689436e4bcc84a492185e1

[ "MIT" ]

null

from app import MegaRestApp from api import MegaRestAPI __all__ = [ 'MegaRestApp', 'MegaRestAPI' ]

20

42

0.77

11

100

6.636364

0.636364

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0.15

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4

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25

0.858824

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0.22

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0

null

0

1

0

1

0

4

6d4c6a0376f1af9497358078985f1a5d44005af9

115

py

Python

tryCal.py

diallog/GCPpy

dabd55ece1c12c1a390a228cd04cb7eb110e564b

[ "Unlicense" ]

null

tryCal.py

diallog/GCPpy

dabd55ece1c12c1a390a228cd04cb7eb110e564b

[ "Unlicense" ]

null

tryCal.py

diallog/GCPpy

dabd55ece1c12c1a390a228cd04cb7eb110e564b

[ "Unlicense" ]

null

#!/usr/bin/env python3 import calendar calendar.setfirstweekday((calendar.SUNDAY)) print(calendar.month(2020,1))

16.428571

43

0.782609

15

115

6

0.8

0

0.056075

0.069565

115

6

44

19.166667

0.785047

0.182609

0

1

0

true

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0.333333

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0.333333

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null

0

1

0

null

0

1

0

1

0

4

6d5b358806daeac1d711a019e32d0fd9b2e7663a

1,830

py

Python

Planeacion vuelo.py

ogarcia1704/Planeacion-del-vuelo-de-Dron

dc8a4ae818463c5450a5dfb8045c8361a8dfab71

[ "MIT" ]

null

Planeacion vuelo.py

ogarcia1704/Planeacion-del-vuelo-de-Dron

dc8a4ae818463c5450a5dfb8045c8361a8dfab71

[ "MIT" ]

null

Planeacion vuelo.py

ogarcia1704/Planeacion-del-vuelo-de-Dron

dc8a4ae818463c5450a5dfb8045c8361a8dfab71

[ "MIT" ]

null

Python 3.7.3 (v3.7.3:ef4ec6ed12, Mar 25 2019, 21:26:53) [MSC v.1916 32 bit (Intel)] on win32 Type "help", "copyright", "credits" or "license()" for more information. >>> from exif import Image >>> with open('C:\\Users\\oswal\\Pictures\\oswaldo.jpg', 'rb') as image_file: ... my_image = Image(image_file) ... >>> dir(my_image) ['_exif_ifd_pointer', '_gps_ifd_pointer', '_interoperability_ifd_Pointer', '_segments', 'cfa_pattern', 'color_space', 'components_configuration', 'compressed_bits_per_pixel', 'compression', 'contrast', 'custom_rendered', 'datetime', 'datetime_digitized', 'datetime_original', 'digital_zoom_ratio', 'exif_version', 'exposure_bias_value', 'exposure_mode', 'exposure_program', 'exposure_time', 'f_number', 'file_source', 'flash', 'flashpix_version', 'focal_length', 'focal_length_in_35mm_film', 'gain_control', 'get', 'get_file', 'gps_altitude', 'gps_altitude_ref', 'gps_datestamp', 'gps_latitude', 'gps_latitude_ref', 'gps_longitude', 'gps_longitude_ref', 'gps_map_datum', 'gps_satellites', 'gps_timestamp', 'gps_version_id', 'jpeg_interchange_format', 'jpeg_interchange_format_length', 'light_source', 'make', 'maker_note', 'max_aperture_value', 'metering_mode', 'model', 'orientation', 'photographic_sensitivity', 'pixel_x_dimension', 'pixel_y_dimension', 'resolution_unit', 'saturation', 'scene_capture_type', 'scene_type', 'sensing_method', 'sensitivity_type', 'sharpness', 'software', 'subject_distance_range', 'subsec_time', 'subsec_time_digitized', 'subsec_time_original', 'user_comment', 'white_balance', 'x_resolution', 'y_and_c_positioning', 'y_resolution'] >>> import pandas as pd >>> data = pd.DataFrame(dir(my_image)) >>> datatoexcel = pd.ExcelWriter("CaracteristicasImagen.xlsx",engine='xlsxwriter') >>> data.to_excel(datatoexcel, sheet_name='Sheet1') >>> datatoexcel.save()

130.714286

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5.440171

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0.015711

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0.019585

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1,830

13

1,267

140.769231

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0

0.605944

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0

null

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null

0

null

0

1

0

1

0

null

0

1

0

4

6d6009f3d376ae23ba85fcfcc4f39f5a5a547c02

298

py

Python

TUTORIAIS-FLASK/AULA_1/Chapter_1/app/routes.py

rodrigo-schmidt-lucchesi-85/FLASK

6607695ef0e298f84c32ba7aaf3f164a33cc3081

[ "MIT" ]

null

TUTORIAIS-FLASK/AULA_1/Chapter_1/app/routes.py

rodrigo-schmidt-lucchesi-85/FLASK

6607695ef0e298f84c32ba7aaf3f164a33cc3081

[ "MIT" ]

null

TUTORIAIS-FLASK/AULA_1/Chapter_1/app/routes.py

rodrigo-schmidt-lucchesi-85/FLASK

6607695ef0e298f84c32ba7aaf3f164a33cc3081

[ "MIT" ]

null

from app import app # Importing the Flask instance @app.route('/') # Defining two enpoints for the view function index @app.route('/index') def index(): # View function to handle the two endpoints return "Hello, World!" # Response to the get request

33.111111

80

0.624161

38

298

4.894737

0.657895

0.086022

0

0.291946

298

8

81

37.25

0.881517

0.5

0

0.138889

0

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true

0

0.2

0.6

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null

0

1

0

null

0

1

0

1

0

4

ed9e8890fc8f7ed6a4fa787b1c512b52f4aa635c

183

py

Python

etl/core/exceptions.py

cloud-cds/cds-stack

d68a1654d4f604369a071f784cdb5c42fc855d6e

[ "Apache-2.0" ]

6

2018-06-27T00:09:55.000Z

2019-03-07T14:06:53.000Z

etl/core/exceptions.py

cloud-cds/cds-stack

d68a1654d4f604369a071f784cdb5c42fc855d6e

[ "Apache-2.0" ]

3

2021-03-31T18:37:46.000Z

2021-06-01T21:49:41.000Z

etl/core/exceptions.py

cloud-cds/cds-stack

d68a1654d4f604369a071f784cdb5c42fc855d6e

[ "Apache-2.0" ]

3

2020-01-24T16:40:49.000Z

2021-09-30T02:28:55.000Z

class TransformError(Exception): def __init__(self, func_name, reason, context=''): self.func_name = func_name self.reason = reason self.context = context

30.5

54

0.661202

21

183

5.428571

0.47619

0.210526

0

0.240437

183

5

55

36.6

0.820144

0

1

0.2

false

0

0.4

0

1

0

null

1

0

1

0

null

0

4

6125884e22c320d881fb43189a225962fcb5d631

75

py

Python

contest/abc082/A.py

mola1129/atcoder

1d3b18cb92d0ba18c41172f49bfcd0dd8d29f9db

[ "MIT" ]

null

contest/abc082/A.py

mola1129/atcoder

1d3b18cb92d0ba18c41172f49bfcd0dd8d29f9db

[ "MIT" ]

null

contest/abc082/A.py

mola1129/atcoder

1d3b18cb92d0ba18c41172f49bfcd0dd8d29f9db

[ "MIT" ]

null

import math a, b = map(int, input().split()) print(math.ceil((a + b) / 2))

18.75

32

0.586667

14

75

3.142857

0.785714

0.090909

0

0.015873

0.16

75

3

33

25

0.68254

0

1

0

true

0

0.333333

0

0.333333

1

0

null

0

1

0

null

0

1

0

1

0

4

b62ad9c23d56e513d274d33fa06fd3cad0b2d19c

4,980

py

Python

figure_generation/Figure1/figure1.py

ramachandran-lab/multiancestry_enrichment

59f9eea4dbfbff6754224a9188730ebe393a3c18

[ "CC0-1.0" ]

1

2022-03-31T18:22:52.000Z

figure_generation/Figure1/figure1.py

ramachandran-lab/multiancestry_enrichment

59f9eea4dbfbff6754224a9188730ebe393a3c18

[ "CC0-1.0" ]

null

figure_generation/Figure1/figure1.py

ramachandran-lab/multiancestry_enrichment

59f9eea4dbfbff6754224a9188730ebe393a3c18

[ "CC0-1.0" ]

null

import pandas as pd import numpy as np import sys import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt from matplotlib.patches import Circle, Wedge, Polygon from matplotlib.collections import PatchCollection from collections import Counter color_dict = {'Anthropometric':'#1b9e77','Blood pressure':'#d95f02','Hematological':'#e7298a','Metabolic':'#7570b3','Kidney':'#66a61e','Other biochemical':'#e6ab02'} marker_dict = {'Anthropometric':'*','Blood pressure':'p','Hematological':'^','Metabolic':'s','Kidney':'d','Other biochemical':'8'} category_dict = pd.read_csv('trait_categories.txt',sep = '\t') category_dict = {r['Trait']:r['Category'] for i,r in category_dict.iterrows()} trait_order = ['Basophil','Eosinophil','Neutrophil','Monocyte','Lymphocyte','WBC','RBC','MCV','Hemoglobin','Hematocrit','MCH','MCHC','PLC','DBP','SBP','CRP','EGFR','Urate','HBA1C','LDL','HDL','Triglyceride','Cholesterol','BMI','Height'] ###########Once the scale files are gnereated you only have to run this to plot #1a proportions = pd.read_csv('scale_proportions.txt',sep = '\t',index_col = 'Traits') fig,ax = plt.subplots(figsize = (4,4), nrows = 1, ncols = 1) proportions = proportions.reset_index() proportions['color'] = proportions['Traits'].map(category_dict) proportions['color'] = proportions['color'].map(color_dict) proportions['shape'] = proportions['Traits'].map(category_dict) proportions['shape'] = proportions['shape'].map(marker_dict) ax.plot([0,1],[0,1], transform=ax.transAxes, color = 'black', linestyle = '--') proportions = proportions.set_index('Traits') for x,y in proportions.iterrows(): ax.scatter(y['Variants'],y['Genes'],color = y['color'],marker=y['shape']) plt.plot(np.mean(y['Variants']),0,marker = '*', color = 'black', markersize = 14) plt.plot(0,np.mean(y['Genes']),marker = '*', color = 'black', markersize = 14) ax.set_ylim([0,0.2]) ax.set_xlim([0,0.2]) ax.set_xticks([0,0.05,0.10,0.15,0.20]) ax.set_yticks([0,0.05,0.10,0.15,0.20]) plt.savefig('Figure1a.pdf') #1b proportions = pd.read_csv('scale_proportions_alt.txt',sep = '\t',index_col = 'Traits') fig,ax = plt.subplots(figsize = (4,4), nrows = 1, ncols = 1) proportions = proportions.reset_index() proportions['color'] = proportions['Traits'].map(category_dict) proportions['color'] = proportions['color'].map(color_dict) proportions['shape'] = proportions['Traits'].map(category_dict) proportions['shape'] = proportions['shape'].map(marker_dict) ax.plot([0,1],[0,1], transform=ax.transAxes, color = 'black', linestyle = '--') for x,y in proportions.iterrows(): ax.scatter(y['Variants'],y['Genes'],color = y['color'],marker=y['shape']) plt.plot(np.mean(y['Variants']),0,marker = '*', color = 'black', markersize = 14) plt.plot(0,np.mean(y['Genes']),marker = '*', color = 'black', markersize = 14) ax.set_ylim([0,0.2]) ax.set_xlim([0,0.2]) ax.set_xticks([0,0.05,0.10,0.15,0.20]) ax.set_yticks([0,0.05,0.10,0.15,0.20]) plt.savefig('Figure1b.pdf') #1c proportions = pd.read_csv('scale_proportions_alt_nothresh.txt',sep = '\t',index_col = 'Traits') fig,ax = plt.subplots(figsize = (4,4), nrows = 1, ncols = 1) proportions = proportions.reset_index() proportions['color'] = proportions['Traits'].map(category_dict) proportions['color'] = proportions['color'].map(color_dict) proportions['shape'] = proportions['Traits'].map(category_dict) proportions['shape'] = proportions['shape'].map(marker_dict) ax.plot([0,1],[0,1], transform=ax.transAxes, color = 'black', linestyle = '--') for x,y in proportions.iterrows(): ax.scatter(y['Variants'],y['Genes'],color = y['color'],marker=y['shape']) plt.plot(np.mean(y['Variants']),0,marker = '*', color = 'black', markersize = 14) plt.plot(0,np.mean(y['Genes']),marker = '*', color = 'black', markersize = 14) ax.set_ylim([0,0.2]) ax.set_xlim([0,0.2]) ax.set_xticks([0,0.05,0.10,0.15,0.20]) ax.set_yticks([0,0.05,0.10,0.15,0.20]) plt.savefig('Figure1c.pdf') #1d proportions = pd.read_csv('scale_proportions_nominal.txt',sep = '\t',index_col = 'Traits') fig,ax = plt.subplots(figsize = (4,4), nrows = 1, ncols = 1) proportions = proportions.reset_index() proportions['color'] = proportions['Traits'].map(category_dict) proportions['color'] = proportions['color'].map(color_dict) proportions['shape'] = proportions['Traits'].map(category_dict) proportions['shape'] = proportions['shape'].map(marker_dict) proportions[['Traits','Variants','Genes']].to_csv('scale_proportions_nominal.txt',sep = '\t',index = False) ax.plot([0,1],[0,1], transform=ax.transAxes, color = 'black', linestyle = '--') for x,y in proportions.iterrows(): ax.scatter(y['Variants'],y['Genes'],color = y['color'],marker=y['shape']) plt.plot(np.mean(y['Variants']),0,marker = '*', color = 'black', markersize = 14) plt.plot(0,np.mean(y['Genes']),marker = '*', color = 'black', markersize = 14) ax.set_ylim([0,0.1]) ax.set_xlim([0,1]) # ax.set_xticks([0,0.05,0.10,0.15,0.20]) # ax.set_yticks([0,0.05,0.10,0.15,0.20]) plt.savefig('Figure1d.pdf')

39.84

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0.688755

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4,980

4.476064

0.192819

0.023767

0.064171

0.066548

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0.70202

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0.047504

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4,980

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0

null

0

4

b62d46c9baee5c2a08f68c8a4c528b4329baf0e2

245

py

Python

src/get_the_dog.py

lipegomes/python-and-apis

0fd0b1daacfce5566e933651a3d4b0b5805c4e60

[ "MIT" ]

null

src/get_the_dog.py

lipegomes/python-and-apis

0fd0b1daacfce5566e933651a3d4b0b5805c4e60

[ "MIT" ]

null

src/get_the_dog.py

lipegomes/python-and-apis

0fd0b1daacfce5566e933651a3d4b0b5805c4e60

[ "MIT" ]

null

import requests response = requests.get("https://api.thedogapi.com/v1/breeds/1") print(f"{response.headers.get('Content-Type')}\n") print(f"{response.json()}\n") print(f"{response.json()['origin']}\n") print(f"{response.json()['name']}\n")

20.416667

64

0.673469

37

245

4.459459

0.540541

0.145455

0.339394

0.272727

0.345455

0

0.008658

0.057143

245

11

65

22.272727

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0.620408

0.391837

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0.666667

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null

0

1

0

1

0

null

0

1

0

4

b63666b409e624a77a0870bfa7b85e297dc79910

1,205

py

Python

vbpp/tf_utils.py

zcmail/vbpp_for_keystroke_dynamics

db03e7ab1a42479f35f8744c465c3825fca1ed04

[ "Apache-2.0" ]

1

2020-10-09T15:08:38.000Z

vbpp/tf_utils.py

zcmail/vbpp_for_keystroke_dynamics

db03e7ab1a42479f35f8744c465c3825fca1ed04

[ "Apache-2.0" ]

null

vbpp/tf_utils.py

zcmail/vbpp_for_keystroke_dynamics

db03e7ab1a42479f35f8744c465c3825fca1ed04

[ "Apache-2.0" ]

null

# Copyright (C) PROWLER.io 2017 # # Licensed under the Apache License, Version 2.0 """ Prototype Code! This code may not be fully tested, or in other ways fit-for-purpose. Use at your own risk! """ import tensorflow as tf def tf_squeeze_1d(A): return tf.reshape(A, (-1,)) # TODO should check that it's got the same length as before def tf_len(A): return tf.shape(A)[0] def tf_vec_dot(v1, v2): #两个张量的点积 """ Calculate the dot product between v1 and v2, regardless of shapes, as long as there is at most one dimension with a length > 1 in each vector. """ # turn into flat vectors: v1 = tf.squeeze(v1) #squeeze 去掉1维数组 v2 = tf.squeeze(v2) #XXX assert v1.ndims == 1 #XXX assert v2.ndims == 1 return tf.reduce_sum(tf.multiply(v1, v2)) def tf_vec_mat_vec_mul(v1, M, v2): #计算 v1^T * M * v2 """ Calculate the bilinear form v1^T M v2, where v1 and v2 are vectors of length N and M is a N x N matrix. """ #XXX assert tf.squeeze(v1).ndims == 1 #XXX assert tf.squeeze(v2).ndims == 1 v2 = tf.reshape(v2, [-1, 1]) # turn into column vector 转为列向量 M_dot_v2 = tf.matmul(M, v2) return tf_vec_dot(v1, M_dot_v2)

27.386364

92

0.643154

214

1,205

3.546729

0.481308

0.059289

0.023715

0.02635

0.044796

0

0.049614

0.247303

1,205

43

93

28.023256

0.787211

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0.307692

false

0

0.076923

0.153846

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0

null

0

null

0

1

0

1

0

1

0

4

b647a59928a09fd7113e5191d4a56199d01ba334

30

py

Python

tests/util/__init__.py

Vaskivskyi/asusrouter

de45d3e40fefc4851a15613b4789184a40ca0da4

[ "Apache-2.0" ]

null

tests/util/__init__.py

Vaskivskyi/asusrouter

de45d3e40fefc4851a15613b4789184a40ca0da4

[ "Apache-2.0" ]

null

tests/util/__init__.py

Vaskivskyi/asusrouter

de45d3e40fefc4851a15613b4789184a40ca0da4

[ "Apache-2.0" ]

null

"""Tests for the utilities"""

15

29

0.666667

4

30

5

1

0

0.133333

30

1

30

0.769231

0.766667

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

null

0

1

0

4

b64a2653a4c6ae96e971ec78fa3d8b5cef71254d

1,333

py

Python

study_roadmaps/python_sample_examples/pytorch/2_update_mode/evaluate.py

Shreyashwaghe/monk_v1

4ee4d9483e8ffac9b73a41f3c378e5abf5fc799b

[ "Apache-2.0" ]

7

2020-07-26T08:37:29.000Z

2020-10-30T10:23:11.000Z

study_roadmaps/python_sample_examples/pytorch/2_update_mode/evaluate.py

mursalfk/monk_v1

62f34a52f242772186ffff7e56764e958fbcd920

[ "Apache-2.0" ]

9

2020-01-28T21:40:39.000Z

2022-02-10T01:24:06.000Z

study_roadmaps/python_sample_examples/pytorch/2_update_mode/evaluate.py

mursalfk/monk_v1

62f34a52f242772186ffff7e56764e958fbcd920

[ "Apache-2.0" ]

1

2020-10-07T12:57:44.000Z

import os import sys sys.path.append("../../../monk/"); import psutil from pytorch_prototype import prototype ################################################### Foldered - Train Dataset ################################################################# ptf = prototype(verbose=1); ptf.Prototype("sample-project-1", "sample-experiment-1", eval_infer=True); ptf.Dataset_Params(dataset_path="../../../monk/system_check_tests/datasets/dataset_cats_dogs_eval"); ptf.Dataset(); accuracy, class_based_accuracy = ptf.Evaluate(); ############################################################################################################################################### ######################################################### CSV - Train Dataset ################################################################# ptf = prototype(verbose=1); ptf.Prototype("sample-project-1", "sample-experiment-1", eval_infer=True); ptf.Dataset_Params(dataset_path="../../../monk/system_check_tests/datasets/dataset_csv_id/train", path_to_csv="../../../monk/system_check_tests/datasets/dataset_csv_id/train.csv"); ptf.Dataset(); accuracy, class_based_accuracy = ptf.Evaluate(); ###############################################################################################################################################

27.770833

143

0.443361

108

1,333

5.231481

0.333333

0.084956

0.079646

0.106195

0.79469

0.628319

0.530973

0

0.004796

0.061515

1,333

47

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0

0.5

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0.252964

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0.25

0

null

0

1

0

1

0

null

0

4

b65c81531f1c4ac275db965ffe34b2739000b995

790

py

Python

backend/naki/naki/schemas/digital_item.py

iimcz/emod

432094c020247597a94e95f76cc524c20b68b685

[ "MIT" ]

null

backend/naki/naki/schemas/digital_item.py

iimcz/emod

432094c020247597a94e95f76cc524c20b68b685

[ "MIT" ]

6

2021-03-08T23:32:15.000Z

2022-02-26T08:11:38.000Z

backend/naki/naki/schemas/digital_item.py

iimcz/emod

432094c020247597a94e95f76cc524c20b68b685

[ "MIT" ]

null

import colander from naki.schemas.metadata import MetadataSequenceSchema from naki.schemas.link import LinkSequenceSchema class StringSequenceSchema(colander.SequenceSchema): value = colander.SchemaNode(colander.String()) class DigitalItemSchema(colander.MappingSchema): mime = colander.SchemaNode(colander.String()) created = colander.SchemaNode(colander.String(), missing='') description = colander.SchemaNode(colander.String(), missing='') id_user = colander.SchemaNode(colander.String(), missing='Unknown') rights = colander.SchemaNode(colander.String(), missing=0) src = colander.SchemaNode(colander.String(), missing='') metadata = MetadataSequenceSchema() links = LinkSequenceSchema(missing=[]) group_ids = StringSequenceSchema(missing=[])

41.578947

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0.764557

74

790

8.135135

0.405405

0.209302

0.302326

0.372093

0.32392

0

0.001433

0.116456

790

18

72

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0.861032

0

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1

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false

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0.2

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1

0

null

1

0

null

0

1

0

4

b68c8a9573133720d27b86e6b5ad4865424f3624

3,160

py

Python

Consuelo.py

digo-smithh/Consuelo-chatbot

9724fb421715da305fc3b9baa7ddb32c5fe0cc29

[ "MIT" ]

1

2020-10-13T01:04:46.000Z

Consuelo.py

digo-smithh/Consuelo-chatbot-Python

9724fb421715da305fc3b9baa7ddb32c5fe0cc29

[ "MIT" ]

null

Consuelo.py

digo-smithh/Consuelo-chatbot-Python

9724fb421715da305fc3b9baa7ddb32c5fe0cc29

[ "MIT" ]

null

from chatterbot import ChatBot from chatterbot.trainers import ChatterBotCorpusTrainer import PySimpleGUI as sg import time import os from random import randrange bot= ChatBot('Bot') trainer = ChatterBotCorpusTrainer(bot) trainer.train('chatterbot.corpus.portuguese') trainer.train('chatterbot.corpus.english') trainer.train('chatterbot.corpus.spanish') sg.theme('BlueMono') layout = [ [sg.Text(' Hola, eu sou Consuelo Jiménez! Habla que eu te escuto!')], [sg.Image(filename=f"{os.path.dirname(os.path.realpath(__file__))}\img.png", key='image'),sg.Input(key='msg',focus=True,size=(37,20)), sg.Button("Enviar",change_submits=True,bind_return_key=True)], [sg.Output(size=(50,20))]] window = sg.Window(' Consuelo Jiménez', layout,icon=f"{os.path.dirname(os.path.realpath(__file__))}\ico.ico") vazio = ['pode falar.','fala que eu te escuto','Digass','Estoy aqui','I will always wait for you.','Fale! Não tenha medo!','Não vai falar nada? Então vai embora e para de encher o saco','que','to te vendooooo'] despedida = ['tchau','hasta la vista baby','bjinhos','já vai?','ai que grosseria','bj','xoxo','bye','adiós'] while True: button, values = window.Read() message = values['msg'] print(f"Você: {message}") window.FindElement('msg').Update('') if message == '': print(f'Consuelo: {str(vazio[randrange(9)])}') elif message.strip().lower() == 'tchau' or message.strip().lower() == 'tchau!' or message.strip().lower() == 'tchau.' or message.strip().lower() == 'tchauu' or message.strip().lower() == 'bjs' or message.strip().lower() == 'bj' or message.strip().lower() == 'bjss' or message.strip().lower() == 'bjus' or message.strip().lower() == 'bjuus' or message.strip().lower() == 'bjsss' or message.strip().lower() == 'bjssss' or message.strip().lower() == 'bjo' or message.strip().lower() == 'bjoo' or message.strip().lower() == 'bjuus' or message.strip().lower() == 'ate' or message.strip().lower() == 'até mais!' or message.strip().lower() == 'ate mais' or message.strip().lower() == 'ate!' or message.strip().lower() == 'to indo' or message.strip().lower() == 'vou embora!' or message.strip().lower() == 'vou embora' or message.strip().lower() == 'bye' or message.strip().lower() == 'adiós.' or message.strip().lower() == 'adiós' or message.strip().lower() == 'adios' or message.strip().lower() == 'adios.' or message.strip().lower() == 'xoxo' or message.strip().lower() == 'hasta la vista baby!' or message.strip().lower() == 'hasta la vista baby' or message.strip().lower() == 'bye bye' or message.strip().lower() == 'bye-bye!' or message.strip().lower() == 'goodbye' or message.strip().lower() == 'goodbye!' or message.strip().lower() == 'goodbye.' or message.strip().lower() == 'tchauzinho' or message.strip().lower() == 'tchauzinho!' or message.strip().lower() == 'flw' or message.strip().lower() == 'flww' or message.strip().lower() == 'hasta la vista' or message.strip().lower() == 'hasta la vista!': print(f'Consuelo: {str(despedida[randrange(9)])}! E você que feche o programa.') else: reply = bot.get_response(message) print('Consuelo:', reply)

79

1,606

0.656013

433

3,160

4.759815

0.332564

0.232897

0.329937

0.359534

0.418244

0.416788

0.402232

0.372149

0.341097

0.184862

0

0.003631

0.128481

3,160

39

1,607

81.025641

0.744735

0

0.298101

0.076266

0

1

0

false

0

0.2

0

0.2

0.133333

0

null

1

0

1

0

null

0

4

b68da884d0cdda8f224288bb67367622b8a00154

64,280

py

Python

dev/Tools/Python/2.7.13/mac/Python.framework/Versions/2.7/lib/python2.7/site-packages/pyxb/bundles/dc/raw/dcterms.py

jeikabu/lumberyard

07228c605ce16cbf5aaa209a94a3cb9d6c1a4115

[ "AML" ]

8

2019-10-07T16:33:47.000Z

2020-12-07T03:59:58.000Z

dev/Tools/Python/2.7.13/mac/Python.framework/Versions/2.7/lib/python2.7/site-packages/pyxb/bundles/dc/raw/dcterms.py

jeikabu/lumberyard

07228c605ce16cbf5aaa209a94a3cb9d6c1a4115

[ "AML" ]

null

dev/Tools/Python/2.7.13/mac/Python.framework/Versions/2.7/lib/python2.7/site-packages/pyxb/bundles/dc/raw/dcterms.py

jeikabu/lumberyard

07228c605ce16cbf5aaa209a94a3cb9d6c1a4115

[ "AML" ]

5

2020-08-27T20:44:18.000Z

2021-08-21T22:54:11.000Z

# ./pyxb/bundles/dc/raw/dcterms.py # -*- coding: utf-8 -*- # PyXB bindings for NM:62e52a6e1b0d23522982e9c2905e5cb67ad01951 # Generated 2014-10-19 06:25:02.925349 by PyXB version 1.2.4 using Python 2.7.3.final.0 # Namespace http://purl.org/dc/terms/ from __future__ import unicode_literals import pyxb import pyxb.binding import pyxb.binding.saxer import io import pyxb.utils.utility import pyxb.utils.domutils import sys import pyxb.utils.six as _six # Unique identifier for bindings created at the same time _GenerationUID = pyxb.utils.utility.UniqueIdentifier('urn:uuid:91a2ca00-5782-11e4-8790-c8600024e903') # Version of PyXB used to generate the bindings _PyXBVersion = '1.2.4' # Generated bindings are not compatible across PyXB versions if pyxb.__version__ != _PyXBVersion: raise pyxb.PyXBVersionError(_PyXBVersion) # Import bindings for namespaces imported into schema import pyxb.bundles.dc.dc import pyxb.binding.datatypes import pyxb.binding.xml_ import pyxb.bundles.dc.dcmitype # NOTE: All namespace declarations are reserved within the binding Namespace = pyxb.namespace.NamespaceForURI('http://purl.org/dc/terms/', create_if_missing=True) Namespace.configureCategories(['typeBinding', 'elementBinding']) _Namespace_dc = pyxb.bundles.dc.dc.Namespace _Namespace_dc.configureCategories(['typeBinding', 'elementBinding']) def CreateFromDocument (xml_text, default_namespace=None, location_base=None): """Parse the given XML and use the document element to create a Python instance. @param xml_text An XML document. This should be data (Python 2 str or Python 3 bytes), or a text (Python 2 unicode or Python 3 str) in the L{pyxb._InputEncoding} encoding. @keyword default_namespace The L{pyxb.Namespace} instance to use as the default namespace where there is no default namespace in scope. If unspecified or C{None}, the namespace of the module containing this function will be used. @keyword location_base: An object to be recorded as the base of all L{pyxb.utils.utility.Location} instances associated with events and objects handled by the parser. You might pass the URI from which the document was obtained. """ if pyxb.XMLStyle_saxer != pyxb._XMLStyle: dom = pyxb.utils.domutils.StringToDOM(xml_text) return CreateFromDOM(dom.documentElement, default_namespace=default_namespace) if default_namespace is None: default_namespace = Namespace.fallbackNamespace() saxer = pyxb.binding.saxer.make_parser(fallback_namespace=default_namespace, location_base=location_base) handler = saxer.getContentHandler() xmld = xml_text if isinstance(xmld, _six.text_type): xmld = xmld.encode(pyxb._InputEncoding) saxer.parse(io.BytesIO(xmld)) instance = handler.rootObject() return instance def CreateFromDOM (node, default_namespace=None): """Create a Python instance from the given DOM node. The node tag must correspond to an element declaration in this module. @deprecated: Forcing use of DOM interface is unnecessary; use L{CreateFromDocument}.""" if default_namespace is None: default_namespace = Namespace.fallbackNamespace() return pyxb.binding.basis.element.AnyCreateFromDOM(node, default_namespace) # Atomic simple type: [anonymous] class STD_ANON (pyxb.binding.datatypes.string): """An atomic simple type.""" _ExpandedName = None _XSDLocation = pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 144, 8) _Documentation = None STD_ANON._InitializeFacetMap() # Atomic simple type: [anonymous] class STD_ANON_ (pyxb.binding.datatypes.string): """An atomic simple type.""" _ExpandedName = None _XSDLocation = pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 155, 8) _Documentation = None STD_ANON_._InitializeFacetMap() # Atomic simple type: [anonymous] class STD_ANON_2 (pyxb.binding.datatypes.string): """An atomic simple type.""" _ExpandedName = None _XSDLocation = pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 166, 8) _Documentation = None STD_ANON_2._InitializeFacetMap() # Atomic simple type: [anonymous] class STD_ANON_3 (pyxb.binding.datatypes.string): """An atomic simple type.""" _ExpandedName = None _XSDLocation = pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 177, 8) _Documentation = None STD_ANON_3._InitializeFacetMap() # Atomic simple type: [anonymous] class STD_ANON_4 (pyxb.binding.datatypes.string): """An atomic simple type.""" _ExpandedName = None _XSDLocation = pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 188, 8) _Documentation = None STD_ANON_4._InitializeFacetMap() # Atomic simple type: [anonymous] class STD_ANON_5 (pyxb.binding.datatypes.string): """An atomic simple type.""" _ExpandedName = None _XSDLocation = pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 199, 8) _Documentation = None STD_ANON_5._InitializeFacetMap() # Union simple type: [anonymous] # superclasses pyxb.binding.datatypes.anySimpleType class STD_ANON_6 (pyxb.binding.basis.STD_union): """Simple type that is a union of pyxb.binding.datatypes.gYear, pyxb.binding.datatypes.gYearMonth, pyxb.binding.datatypes.date, pyxb.binding.datatypes.dateTime.""" _ExpandedName = None _XSDLocation = pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 210, 8) _Documentation = None _MemberTypes = ( pyxb.binding.datatypes.gYear, pyxb.binding.datatypes.gYearMonth, pyxb.binding.datatypes.date, pyxb.binding.datatypes.dateTime, ) STD_ANON_6._CF_pattern = pyxb.binding.facets.CF_pattern() STD_ANON_6._CF_enumeration = pyxb.binding.facets.CF_enumeration(value_datatype=STD_ANON_6) STD_ANON_6._InitializeFacetMap(STD_ANON_6._CF_pattern, STD_ANON_6._CF_enumeration) # Atomic simple type: [anonymous] class STD_ANON_7 (pyxb.binding.datatypes.string): """An atomic simple type.""" _ExpandedName = None _XSDLocation = pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 232, 8) _Documentation = None STD_ANON_7._InitializeFacetMap() # Atomic simple type: [anonymous] class STD_ANON_8 (pyxb.binding.datatypes.anyURI): """An atomic simple type.""" _ExpandedName = None _XSDLocation = pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 243, 8) _Documentation = None STD_ANON_8._InitializeFacetMap() # Atomic simple type: [anonymous] class STD_ANON_9 (pyxb.binding.datatypes.string): """An atomic simple type.""" _ExpandedName = None _XSDLocation = pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 254, 8) _Documentation = None STD_ANON_9._InitializeFacetMap() # Atomic simple type: [anonymous] class STD_ANON_10 (pyxb.binding.datatypes.string): """An atomic simple type.""" _ExpandedName = None _XSDLocation = pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 265, 8) _Documentation = None STD_ANON_10._InitializeFacetMap() # Atomic simple type: [anonymous] class STD_ANON_11 (pyxb.binding.datatypes.language): """An atomic simple type.""" _ExpandedName = None _XSDLocation = pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 276, 8) _Documentation = None STD_ANON_11._InitializeFacetMap() # Atomic simple type: [anonymous] class STD_ANON_12 (pyxb.binding.datatypes.language): """An atomic simple type.""" _ExpandedName = None _XSDLocation = pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 287, 8) _Documentation = None STD_ANON_12._InitializeFacetMap() # Atomic simple type: [anonymous] class STD_ANON_13 (pyxb.binding.datatypes.language): """An atomic simple type.""" _ExpandedName = None _XSDLocation = pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 298, 8) _Documentation = None STD_ANON_13._InitializeFacetMap() # Atomic simple type: [anonymous] class STD_ANON_14 (pyxb.binding.datatypes.string): """An atomic simple type.""" _ExpandedName = None _XSDLocation = pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 309, 8) _Documentation = None STD_ANON_14._InitializeFacetMap() # Atomic simple type: [anonymous] class STD_ANON_15 (pyxb.binding.datatypes.string): """An atomic simple type.""" _ExpandedName = None _XSDLocation = pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 320, 8) _Documentation = None STD_ANON_15._InitializeFacetMap() # Atomic simple type: [anonymous] class STD_ANON_16 (pyxb.binding.datatypes.string): """An atomic simple type.""" _ExpandedName = None _XSDLocation = pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 331, 8) _Documentation = None STD_ANON_16._InitializeFacetMap() # Atomic simple type: [anonymous] class STD_ANON_17 (pyxb.binding.datatypes.string): """An atomic simple type.""" _ExpandedName = None _XSDLocation = pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 342, 8) _Documentation = None STD_ANON_17._InitializeFacetMap() # Union simple type: [anonymous] # superclasses pyxb.bundles.dc.dcmitype.DCMIType class STD_ANON_18 (pyxb.binding.basis.STD_union): """Simple type that is a union of pyxb.bundles.dc.dcmitype.STD_ANON.""" _ExpandedName = None _XSDLocation = pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 221, 8) _Documentation = None _MemberTypes = ( pyxb.bundles.dc.dcmitype.STD_ANON, ) STD_ANON_18.Collection = 'Collection' # originally pyxb.bundles.dc.dcmitype.STD_ANON.Collection STD_ANON_18.Dataset = 'Dataset' # originally pyxb.bundles.dc.dcmitype.STD_ANON.Dataset STD_ANON_18.Event = 'Event' # originally pyxb.bundles.dc.dcmitype.STD_ANON.Event STD_ANON_18.Image = 'Image' # originally pyxb.bundles.dc.dcmitype.STD_ANON.Image STD_ANON_18.MovingImage = 'MovingImage' # originally pyxb.bundles.dc.dcmitype.STD_ANON.MovingImage STD_ANON_18.StillImage = 'StillImage' # originally pyxb.bundles.dc.dcmitype.STD_ANON.StillImage STD_ANON_18.InteractiveResource = 'InteractiveResource'# originally pyxb.bundles.dc.dcmitype.STD_ANON.InteractiveResource STD_ANON_18.Service = 'Service' # originally pyxb.bundles.dc.dcmitype.STD_ANON.Service STD_ANON_18.Software = 'Software' # originally pyxb.bundles.dc.dcmitype.STD_ANON.Software STD_ANON_18.Sound = 'Sound' # originally pyxb.bundles.dc.dcmitype.STD_ANON.Sound STD_ANON_18.Text = 'Text' # originally pyxb.bundles.dc.dcmitype.STD_ANON.Text STD_ANON_18.PhysicalObject = 'PhysicalObject' # originally pyxb.bundles.dc.dcmitype.STD_ANON.PhysicalObject STD_ANON_18._InitializeFacetMap() # Complex type {http://purl.org/dc/terms/}elementOrRefinementContainer with content type ELEMENT_ONLY class elementOrRefinementContainer (pyxb.binding.basis.complexTypeDefinition): """ This is included as a convenience for schema authors who need to define a root or container element for all of the DC elements and element refinements. """ _TypeDefinition = None _ContentTypeTag = pyxb.binding.basis.complexTypeDefinition._CT_ELEMENT_ONLY _Abstract = False _ExpandedName = pyxb.namespace.ExpandedName(Namespace, 'elementOrRefinementContainer') _XSDLocation = pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 368, 2) _ElementMap = {} _AttributeMap = {} # Base type is pyxb.binding.datatypes.anyType # Element {http://purl.org/dc/elements/1.1/}any uses Python identifier any __any = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(_Namespace_dc, 'any'), 'any', '__httppurl_orgdcterms_elementOrRefinementContainer_httppurl_orgdcelements1_1any', True, pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dc.xsd', 70, 2), ) any = property(__any.value, __any.set, None, None) _ElementMap.update({ __any.name() : __any }) _AttributeMap.update({ }) Namespace.addCategoryObject('typeBinding', 'elementOrRefinementContainer', elementOrRefinementContainer) # Complex type {http://purl.org/dc/terms/}LCSH with content type SIMPLE class LCSH (pyxb.bundles.dc.dc.SimpleLiteral): """Complex type {http://purl.org/dc/terms/}LCSH with content type SIMPLE""" _TypeDefinition = STD_ANON _ContentTypeTag = pyxb.binding.basis.complexTypeDefinition._CT_SIMPLE _Abstract = False _ExpandedName = pyxb.namespace.ExpandedName(Namespace, 'LCSH') _XSDLocation = pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 141, 2) _ElementMap = pyxb.bundles.dc.dc.SimpleLiteral._ElementMap.copy() _AttributeMap = pyxb.bundles.dc.dc.SimpleLiteral._AttributeMap.copy() # Base type is pyxb.bundles.dc.dc.SimpleLiteral # Attribute lang is restricted from parent # Attribute {http://www.w3.org/XML/1998/namespace}lang uses Python identifier lang __lang = pyxb.binding.content.AttributeUse(pyxb.namespace.ExpandedName(pyxb.namespace.XML, 'lang'), 'lang', '__httppurl_orgdcelements1_1_SimpleLiteral_httpwww_w3_orgXML1998namespacelang', pyxb.binding.xml_.STD_ANON_lang, prohibited=True) __lang._DeclarationLocation = None __lang._UseLocation = pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 147, 8) lang = property(__lang.value, __lang.set, None, None) _ElementMap.update({ }) _AttributeMap.update({ __lang.name() : __lang }) Namespace.addCategoryObject('typeBinding', 'LCSH', LCSH) # Complex type {http://purl.org/dc/terms/}MESH with content type SIMPLE class MESH (pyxb.bundles.dc.dc.SimpleLiteral): """Complex type {http://purl.org/dc/terms/}MESH with content type SIMPLE""" _TypeDefinition = STD_ANON_ _ContentTypeTag = pyxb.binding.basis.complexTypeDefinition._CT_SIMPLE _Abstract = False _ExpandedName = pyxb.namespace.ExpandedName(Namespace, 'MESH') _XSDLocation = pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 152, 2) _ElementMap = pyxb.bundles.dc.dc.SimpleLiteral._ElementMap.copy() _AttributeMap = pyxb.bundles.dc.dc.SimpleLiteral._AttributeMap.copy() # Base type is pyxb.bundles.dc.dc.SimpleLiteral # Attribute lang is restricted from parent # Attribute {http://www.w3.org/XML/1998/namespace}lang uses Python identifier lang __lang = pyxb.binding.content.AttributeUse(pyxb.namespace.ExpandedName(pyxb.namespace.XML, 'lang'), 'lang', '__httppurl_orgdcelements1_1_SimpleLiteral_httpwww_w3_orgXML1998namespacelang', pyxb.binding.xml_.STD_ANON_lang, prohibited=True) __lang._DeclarationLocation = None __lang._UseLocation = pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 158, 8) lang = property(__lang.value, __lang.set, None, None) _ElementMap.update({ }) _AttributeMap.update({ __lang.name() : __lang }) Namespace.addCategoryObject('typeBinding', 'MESH', MESH) # Complex type {http://purl.org/dc/terms/}DDC with content type SIMPLE class DDC (pyxb.bundles.dc.dc.SimpleLiteral): """Complex type {http://purl.org/dc/terms/}DDC with content type SIMPLE""" _TypeDefinition = STD_ANON_2 _ContentTypeTag = pyxb.binding.basis.complexTypeDefinition._CT_SIMPLE _Abstract = False _ExpandedName = pyxb.namespace.ExpandedName(Namespace, 'DDC') _XSDLocation = pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 163, 2) _ElementMap = pyxb.bundles.dc.dc.SimpleLiteral._ElementMap.copy() _AttributeMap = pyxb.bundles.dc.dc.SimpleLiteral._AttributeMap.copy() # Base type is pyxb.bundles.dc.dc.SimpleLiteral # Attribute lang is restricted from parent # Attribute {http://www.w3.org/XML/1998/namespace}lang uses Python identifier lang __lang = pyxb.binding.content.AttributeUse(pyxb.namespace.ExpandedName(pyxb.namespace.XML, 'lang'), 'lang', '__httppurl_orgdcelements1_1_SimpleLiteral_httpwww_w3_orgXML1998namespacelang', pyxb.binding.xml_.STD_ANON_lang, prohibited=True) __lang._DeclarationLocation = None __lang._UseLocation = pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 169, 8) lang = property(__lang.value, __lang.set, None, None) _ElementMap.update({ }) _AttributeMap.update({ __lang.name() : __lang }) Namespace.addCategoryObject('typeBinding', 'DDC', DDC) # Complex type {http://purl.org/dc/terms/}LCC with content type SIMPLE class LCC (pyxb.bundles.dc.dc.SimpleLiteral): """Complex type {http://purl.org/dc/terms/}LCC with content type SIMPLE""" _TypeDefinition = STD_ANON_3 _ContentTypeTag = pyxb.binding.basis.complexTypeDefinition._CT_SIMPLE _Abstract = False _ExpandedName = pyxb.namespace.ExpandedName(Namespace, 'LCC') _XSDLocation = pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 174, 2) _ElementMap = pyxb.bundles.dc.dc.SimpleLiteral._ElementMap.copy() _AttributeMap = pyxb.bundles.dc.dc.SimpleLiteral._AttributeMap.copy() # Base type is pyxb.bundles.dc.dc.SimpleLiteral # Attribute lang is restricted from parent # Attribute {http://www.w3.org/XML/1998/namespace}lang uses Python identifier lang __lang = pyxb.binding.content.AttributeUse(pyxb.namespace.ExpandedName(pyxb.namespace.XML, 'lang'), 'lang', '__httppurl_orgdcelements1_1_SimpleLiteral_httpwww_w3_orgXML1998namespacelang', pyxb.binding.xml_.STD_ANON_lang, prohibited=True) __lang._DeclarationLocation = None __lang._UseLocation = pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 180, 8) lang = property(__lang.value, __lang.set, None, None) _ElementMap.update({ }) _AttributeMap.update({ __lang.name() : __lang }) Namespace.addCategoryObject('typeBinding', 'LCC', LCC) # Complex type {http://purl.org/dc/terms/}UDC with content type SIMPLE class UDC (pyxb.bundles.dc.dc.SimpleLiteral): """Complex type {http://purl.org/dc/terms/}UDC with content type SIMPLE""" _TypeDefinition = STD_ANON_4 _ContentTypeTag = pyxb.binding.basis.complexTypeDefinition._CT_SIMPLE _Abstract = False _ExpandedName = pyxb.namespace.ExpandedName(Namespace, 'UDC') _XSDLocation = pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 185, 2) _ElementMap = pyxb.bundles.dc.dc.SimpleLiteral._ElementMap.copy() _AttributeMap = pyxb.bundles.dc.dc.SimpleLiteral._AttributeMap.copy() # Base type is pyxb.bundles.dc.dc.SimpleLiteral # Attribute lang is restricted from parent # Attribute {http://www.w3.org/XML/1998/namespace}lang uses Python identifier lang __lang = pyxb.binding.content.AttributeUse(pyxb.namespace.ExpandedName(pyxb.namespace.XML, 'lang'), 'lang', '__httppurl_orgdcelements1_1_SimpleLiteral_httpwww_w3_orgXML1998namespacelang', pyxb.binding.xml_.STD_ANON_lang, prohibited=True) __lang._DeclarationLocation = None __lang._UseLocation = pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 191, 8) lang = property(__lang.value, __lang.set, None, None) _ElementMap.update({ }) _AttributeMap.update({ __lang.name() : __lang }) Namespace.addCategoryObject('typeBinding', 'UDC', UDC) # Complex type {http://purl.org/dc/terms/}Period with content type SIMPLE class Period (pyxb.bundles.dc.dc.SimpleLiteral): """Complex type {http://purl.org/dc/terms/}Period with content type SIMPLE""" _TypeDefinition = STD_ANON_5 _ContentTypeTag = pyxb.binding.basis.complexTypeDefinition._CT_SIMPLE _Abstract = False _ExpandedName = pyxb.namespace.ExpandedName(Namespace, 'Period') _XSDLocation = pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 196, 2) _ElementMap = pyxb.bundles.dc.dc.SimpleLiteral._ElementMap.copy() _AttributeMap = pyxb.bundles.dc.dc.SimpleLiteral._AttributeMap.copy() # Base type is pyxb.bundles.dc.dc.SimpleLiteral # Attribute lang is restricted from parent # Attribute {http://www.w3.org/XML/1998/namespace}lang uses Python identifier lang __lang = pyxb.binding.content.AttributeUse(pyxb.namespace.ExpandedName(pyxb.namespace.XML, 'lang'), 'lang', '__httppurl_orgdcelements1_1_SimpleLiteral_httpwww_w3_orgXML1998namespacelang', pyxb.binding.xml_.STD_ANON_lang, prohibited=True) __lang._DeclarationLocation = None __lang._UseLocation = pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 202, 8) lang = property(__lang.value, __lang.set, None, None) _ElementMap.update({ }) _AttributeMap.update({ __lang.name() : __lang }) Namespace.addCategoryObject('typeBinding', 'Period', Period) # Complex type {http://purl.org/dc/terms/}W3CDTF with content type SIMPLE class W3CDTF (pyxb.bundles.dc.dc.SimpleLiteral): """Complex type {http://purl.org/dc/terms/}W3CDTF with content type SIMPLE""" _TypeDefinition = STD_ANON_6 _ContentTypeTag = pyxb.binding.basis.complexTypeDefinition._CT_SIMPLE _Abstract = False _ExpandedName = pyxb.namespace.ExpandedName(Namespace, 'W3CDTF') _XSDLocation = pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 207, 2) _ElementMap = pyxb.bundles.dc.dc.SimpleLiteral._ElementMap.copy() _AttributeMap = pyxb.bundles.dc.dc.SimpleLiteral._AttributeMap.copy() # Base type is pyxb.bundles.dc.dc.SimpleLiteral # Attribute lang is restricted from parent # Attribute {http://www.w3.org/XML/1998/namespace}lang uses Python identifier lang __lang = pyxb.binding.content.AttributeUse(pyxb.namespace.ExpandedName(pyxb.namespace.XML, 'lang'), 'lang', '__httppurl_orgdcelements1_1_SimpleLiteral_httpwww_w3_orgXML1998namespacelang', pyxb.binding.xml_.STD_ANON_lang, prohibited=True) __lang._DeclarationLocation = None __lang._UseLocation = pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 213, 8) lang = property(__lang.value, __lang.set, None, None) _ElementMap.update({ }) _AttributeMap.update({ __lang.name() : __lang }) Namespace.addCategoryObject('typeBinding', 'W3CDTF', W3CDTF) # Complex type {http://purl.org/dc/terms/}DCMIType with content type SIMPLE class DCMIType (pyxb.bundles.dc.dc.SimpleLiteral): """Complex type {http://purl.org/dc/terms/}DCMIType with content type SIMPLE""" _TypeDefinition = STD_ANON_18 _ContentTypeTag = pyxb.binding.basis.complexTypeDefinition._CT_SIMPLE _Abstract = False _ExpandedName = pyxb.namespace.ExpandedName(Namespace, 'DCMIType') _XSDLocation = pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 218, 2) _ElementMap = pyxb.bundles.dc.dc.SimpleLiteral._ElementMap.copy() _AttributeMap = pyxb.bundles.dc.dc.SimpleLiteral._AttributeMap.copy() # Base type is pyxb.bundles.dc.dc.SimpleLiteral # Attribute lang is restricted from parent # Attribute {http://www.w3.org/XML/1998/namespace}lang uses Python identifier lang __lang = pyxb.binding.content.AttributeUse(pyxb.namespace.ExpandedName(pyxb.namespace.XML, 'lang'), 'lang', '__httppurl_orgdcelements1_1_SimpleLiteral_httpwww_w3_orgXML1998namespacelang', pyxb.binding.xml_.STD_ANON_lang, prohibited=True) __lang._DeclarationLocation = None __lang._UseLocation = pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 224, 8) lang = property(__lang.value, __lang.set, None, None) _ElementMap.update({ }) _AttributeMap.update({ __lang.name() : __lang }) Namespace.addCategoryObject('typeBinding', 'DCMIType', DCMIType) # Complex type {http://purl.org/dc/terms/}IMT with content type SIMPLE class IMT (pyxb.bundles.dc.dc.SimpleLiteral): """Complex type {http://purl.org/dc/terms/}IMT with content type SIMPLE""" _TypeDefinition = STD_ANON_7 _ContentTypeTag = pyxb.binding.basis.complexTypeDefinition._CT_SIMPLE _Abstract = False _ExpandedName = pyxb.namespace.ExpandedName(Namespace, 'IMT') _XSDLocation = pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 229, 2) _ElementMap = pyxb.bundles.dc.dc.SimpleLiteral._ElementMap.copy() _AttributeMap = pyxb.bundles.dc.dc.SimpleLiteral._AttributeMap.copy() # Base type is pyxb.bundles.dc.dc.SimpleLiteral # Attribute lang is restricted from parent # Attribute {http://www.w3.org/XML/1998/namespace}lang uses Python identifier lang __lang = pyxb.binding.content.AttributeUse(pyxb.namespace.ExpandedName(pyxb.namespace.XML, 'lang'), 'lang', '__httppurl_orgdcelements1_1_SimpleLiteral_httpwww_w3_orgXML1998namespacelang', pyxb.binding.xml_.STD_ANON_lang, prohibited=True) __lang._DeclarationLocation = None __lang._UseLocation = pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 235, 8) lang = property(__lang.value, __lang.set, None, None) _ElementMap.update({ }) _AttributeMap.update({ __lang.name() : __lang }) Namespace.addCategoryObject('typeBinding', 'IMT', IMT) # Complex type {http://purl.org/dc/terms/}URI with content type SIMPLE class URI (pyxb.bundles.dc.dc.SimpleLiteral): """Complex type {http://purl.org/dc/terms/}URI with content type SIMPLE""" _TypeDefinition = STD_ANON_8 _ContentTypeTag = pyxb.binding.basis.complexTypeDefinition._CT_SIMPLE _Abstract = False _ExpandedName = pyxb.namespace.ExpandedName(Namespace, 'URI') _XSDLocation = pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 240, 2) _ElementMap = pyxb.bundles.dc.dc.SimpleLiteral._ElementMap.copy() _AttributeMap = pyxb.bundles.dc.dc.SimpleLiteral._AttributeMap.copy() # Base type is pyxb.bundles.dc.dc.SimpleLiteral # Attribute lang is restricted from parent # Attribute {http://www.w3.org/XML/1998/namespace}lang uses Python identifier lang __lang = pyxb.binding.content.AttributeUse(pyxb.namespace.ExpandedName(pyxb.namespace.XML, 'lang'), 'lang', '__httppurl_orgdcelements1_1_SimpleLiteral_httpwww_w3_orgXML1998namespacelang', pyxb.binding.xml_.STD_ANON_lang, prohibited=True) __lang._DeclarationLocation = None __lang._UseLocation = pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 246, 8) lang = property(__lang.value, __lang.set, None, None) _ElementMap.update({ }) _AttributeMap.update({ __lang.name() : __lang }) Namespace.addCategoryObject('typeBinding', 'URI', URI) # Complex type {http://purl.org/dc/terms/}ISO639-2 with content type SIMPLE class ISO639_2 (pyxb.bundles.dc.dc.SimpleLiteral): """Complex type {http://purl.org/dc/terms/}ISO639-2 with content type SIMPLE""" _TypeDefinition = STD_ANON_9 _ContentTypeTag = pyxb.binding.basis.complexTypeDefinition._CT_SIMPLE _Abstract = False _ExpandedName = pyxb.namespace.ExpandedName(Namespace, 'ISO639-2') _XSDLocation = pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 251, 2) _ElementMap = pyxb.bundles.dc.dc.SimpleLiteral._ElementMap.copy() _AttributeMap = pyxb.bundles.dc.dc.SimpleLiteral._AttributeMap.copy() # Base type is pyxb.bundles.dc.dc.SimpleLiteral # Attribute lang is restricted from parent # Attribute {http://www.w3.org/XML/1998/namespace}lang uses Python identifier lang __lang = pyxb.binding.content.AttributeUse(pyxb.namespace.ExpandedName(pyxb.namespace.XML, 'lang'), 'lang', '__httppurl_orgdcelements1_1_SimpleLiteral_httpwww_w3_orgXML1998namespacelang', pyxb.binding.xml_.STD_ANON_lang, prohibited=True) __lang._DeclarationLocation = None __lang._UseLocation = pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 257, 8) lang = property(__lang.value, __lang.set, None, None) _ElementMap.update({ }) _AttributeMap.update({ __lang.name() : __lang }) Namespace.addCategoryObject('typeBinding', 'ISO639-2', ISO639_2) # Complex type {http://purl.org/dc/terms/}ISO639-3 with content type SIMPLE class ISO639_3 (pyxb.bundles.dc.dc.SimpleLiteral): """Complex type {http://purl.org/dc/terms/}ISO639-3 with content type SIMPLE""" _TypeDefinition = STD_ANON_10 _ContentTypeTag = pyxb.binding.basis.complexTypeDefinition._CT_SIMPLE _Abstract = False _ExpandedName = pyxb.namespace.ExpandedName(Namespace, 'ISO639-3') _XSDLocation = pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 262, 2) _ElementMap = pyxb.bundles.dc.dc.SimpleLiteral._ElementMap.copy() _AttributeMap = pyxb.bundles.dc.dc.SimpleLiteral._AttributeMap.copy() # Base type is pyxb.bundles.dc.dc.SimpleLiteral # Attribute lang is restricted from parent # Attribute {http://www.w3.org/XML/1998/namespace}lang uses Python identifier lang __lang = pyxb.binding.content.AttributeUse(pyxb.namespace.ExpandedName(pyxb.namespace.XML, 'lang'), 'lang', '__httppurl_orgdcelements1_1_SimpleLiteral_httpwww_w3_orgXML1998namespacelang', pyxb.binding.xml_.STD_ANON_lang, prohibited=True) __lang._DeclarationLocation = None __lang._UseLocation = pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 268, 8) lang = property(__lang.value, __lang.set, None, None) _ElementMap.update({ }) _AttributeMap.update({ __lang.name() : __lang }) Namespace.addCategoryObject('typeBinding', 'ISO639-3', ISO639_3) # Complex type {http://purl.org/dc/terms/}RFC1766 with content type SIMPLE class RFC1766 (pyxb.bundles.dc.dc.SimpleLiteral): """Complex type {http://purl.org/dc/terms/}RFC1766 with content type SIMPLE""" _TypeDefinition = STD_ANON_11 _ContentTypeTag = pyxb.binding.basis.complexTypeDefinition._CT_SIMPLE _Abstract = False _ExpandedName = pyxb.namespace.ExpandedName(Namespace, 'RFC1766') _XSDLocation = pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 273, 2) _ElementMap = pyxb.bundles.dc.dc.SimpleLiteral._ElementMap.copy() _AttributeMap = pyxb.bundles.dc.dc.SimpleLiteral._AttributeMap.copy() # Base type is pyxb.bundles.dc.dc.SimpleLiteral # Attribute lang is restricted from parent # Attribute {http://www.w3.org/XML/1998/namespace}lang uses Python identifier lang __lang = pyxb.binding.content.AttributeUse(pyxb.namespace.ExpandedName(pyxb.namespace.XML, 'lang'), 'lang', '__httppurl_orgdcelements1_1_SimpleLiteral_httpwww_w3_orgXML1998namespacelang', pyxb.binding.xml_.STD_ANON_lang, prohibited=True) __lang._DeclarationLocation = None __lang._UseLocation = pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 279, 8) lang = property(__lang.value, __lang.set, None, None) _ElementMap.update({ }) _AttributeMap.update({ __lang.name() : __lang }) Namespace.addCategoryObject('typeBinding', 'RFC1766', RFC1766) # Complex type {http://purl.org/dc/terms/}RFC3066 with content type SIMPLE class RFC3066 (pyxb.bundles.dc.dc.SimpleLiteral): """Complex type {http://purl.org/dc/terms/}RFC3066 with content type SIMPLE""" _TypeDefinition = STD_ANON_12 _ContentTypeTag = pyxb.binding.basis.complexTypeDefinition._CT_SIMPLE _Abstract = False _ExpandedName = pyxb.namespace.ExpandedName(Namespace, 'RFC3066') _XSDLocation = pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 284, 2) _ElementMap = pyxb.bundles.dc.dc.SimpleLiteral._ElementMap.copy() _AttributeMap = pyxb.bundles.dc.dc.SimpleLiteral._AttributeMap.copy() # Base type is pyxb.bundles.dc.dc.SimpleLiteral # Attribute lang is restricted from parent # Attribute {http://www.w3.org/XML/1998/namespace}lang uses Python identifier lang __lang = pyxb.binding.content.AttributeUse(pyxb.namespace.ExpandedName(pyxb.namespace.XML, 'lang'), 'lang', '__httppurl_orgdcelements1_1_SimpleLiteral_httpwww_w3_orgXML1998namespacelang', pyxb.binding.xml_.STD_ANON_lang, prohibited=True) __lang._DeclarationLocation = None __lang._UseLocation = pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 290, 8) lang = property(__lang.value, __lang.set, None, None) _ElementMap.update({ }) _AttributeMap.update({ __lang.name() : __lang }) Namespace.addCategoryObject('typeBinding', 'RFC3066', RFC3066) # Complex type {http://purl.org/dc/terms/}RFC4646 with content type SIMPLE class RFC4646 (pyxb.bundles.dc.dc.SimpleLiteral): """Complex type {http://purl.org/dc/terms/}RFC4646 with content type SIMPLE""" _TypeDefinition = STD_ANON_13 _ContentTypeTag = pyxb.binding.basis.complexTypeDefinition._CT_SIMPLE _Abstract = False _ExpandedName = pyxb.namespace.ExpandedName(Namespace, 'RFC4646') _XSDLocation = pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 295, 2) _ElementMap = pyxb.bundles.dc.dc.SimpleLiteral._ElementMap.copy() _AttributeMap = pyxb.bundles.dc.dc.SimpleLiteral._AttributeMap.copy() # Base type is pyxb.bundles.dc.dc.SimpleLiteral # Attribute lang is restricted from parent # Attribute {http://www.w3.org/XML/1998/namespace}lang uses Python identifier lang __lang = pyxb.binding.content.AttributeUse(pyxb.namespace.ExpandedName(pyxb.namespace.XML, 'lang'), 'lang', '__httppurl_orgdcelements1_1_SimpleLiteral_httpwww_w3_orgXML1998namespacelang', pyxb.binding.xml_.STD_ANON_lang, prohibited=True) __lang._DeclarationLocation = None __lang._UseLocation = pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 301, 8) lang = property(__lang.value, __lang.set, None, None) _ElementMap.update({ }) _AttributeMap.update({ __lang.name() : __lang }) Namespace.addCategoryObject('typeBinding', 'RFC4646', RFC4646) # Complex type {http://purl.org/dc/terms/}Point with content type SIMPLE class Point (pyxb.bundles.dc.dc.SimpleLiteral): """Complex type {http://purl.org/dc/terms/}Point with content type SIMPLE""" _TypeDefinition = STD_ANON_14 _ContentTypeTag = pyxb.binding.basis.complexTypeDefinition._CT_SIMPLE _Abstract = False _ExpandedName = pyxb.namespace.ExpandedName(Namespace, 'Point') _XSDLocation = pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 306, 2) _ElementMap = pyxb.bundles.dc.dc.SimpleLiteral._ElementMap.copy() _AttributeMap = pyxb.bundles.dc.dc.SimpleLiteral._AttributeMap.copy() # Base type is pyxb.bundles.dc.dc.SimpleLiteral # Attribute lang is restricted from parent # Attribute {http://www.w3.org/XML/1998/namespace}lang uses Python identifier lang __lang = pyxb.binding.content.AttributeUse(pyxb.namespace.ExpandedName(pyxb.namespace.XML, 'lang'), 'lang', '__httppurl_orgdcelements1_1_SimpleLiteral_httpwww_w3_orgXML1998namespacelang', pyxb.binding.xml_.STD_ANON_lang, prohibited=True) __lang._DeclarationLocation = None __lang._UseLocation = pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 312, 8) lang = property(__lang.value, __lang.set, None, None) _ElementMap.update({ }) _AttributeMap.update({ __lang.name() : __lang }) Namespace.addCategoryObject('typeBinding', 'Point', Point) # Complex type {http://purl.org/dc/terms/}ISO3166 with content type SIMPLE class ISO3166 (pyxb.bundles.dc.dc.SimpleLiteral): """Complex type {http://purl.org/dc/terms/}ISO3166 with content type SIMPLE""" _TypeDefinition = STD_ANON_15 _ContentTypeTag = pyxb.binding.basis.complexTypeDefinition._CT_SIMPLE _Abstract = False _ExpandedName = pyxb.namespace.ExpandedName(Namespace, 'ISO3166') _XSDLocation = pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 317, 2) _ElementMap = pyxb.bundles.dc.dc.SimpleLiteral._ElementMap.copy() _AttributeMap = pyxb.bundles.dc.dc.SimpleLiteral._AttributeMap.copy() # Base type is pyxb.bundles.dc.dc.SimpleLiteral # Attribute lang is restricted from parent # Attribute {http://www.w3.org/XML/1998/namespace}lang uses Python identifier lang __lang = pyxb.binding.content.AttributeUse(pyxb.namespace.ExpandedName(pyxb.namespace.XML, 'lang'), 'lang', '__httppurl_orgdcelements1_1_SimpleLiteral_httpwww_w3_orgXML1998namespacelang', pyxb.binding.xml_.STD_ANON_lang, prohibited=True) __lang._DeclarationLocation = None __lang._UseLocation = pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 323, 8) lang = property(__lang.value, __lang.set, None, None) _ElementMap.update({ }) _AttributeMap.update({ __lang.name() : __lang }) Namespace.addCategoryObject('typeBinding', 'ISO3166', ISO3166) # Complex type {http://purl.org/dc/terms/}Box with content type SIMPLE class Box (pyxb.bundles.dc.dc.SimpleLiteral): """Complex type {http://purl.org/dc/terms/}Box with content type SIMPLE""" _TypeDefinition = STD_ANON_16 _ContentTypeTag = pyxb.binding.basis.complexTypeDefinition._CT_SIMPLE _Abstract = False _ExpandedName = pyxb.namespace.ExpandedName(Namespace, 'Box') _XSDLocation = pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 328, 2) _ElementMap = pyxb.bundles.dc.dc.SimpleLiteral._ElementMap.copy() _AttributeMap = pyxb.bundles.dc.dc.SimpleLiteral._AttributeMap.copy() # Base type is pyxb.bundles.dc.dc.SimpleLiteral # Attribute lang is restricted from parent # Attribute {http://www.w3.org/XML/1998/namespace}lang uses Python identifier lang __lang = pyxb.binding.content.AttributeUse(pyxb.namespace.ExpandedName(pyxb.namespace.XML, 'lang'), 'lang', '__httppurl_orgdcelements1_1_SimpleLiteral_httpwww_w3_orgXML1998namespacelang', pyxb.binding.xml_.STD_ANON_lang, prohibited=True) __lang._DeclarationLocation = None __lang._UseLocation = pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 334, 8) lang = property(__lang.value, __lang.set, None, None) _ElementMap.update({ }) _AttributeMap.update({ __lang.name() : __lang }) Namespace.addCategoryObject('typeBinding', 'Box', Box) # Complex type {http://purl.org/dc/terms/}TGN with content type SIMPLE class TGN (pyxb.bundles.dc.dc.SimpleLiteral): """Complex type {http://purl.org/dc/terms/}TGN with content type SIMPLE""" _TypeDefinition = STD_ANON_17 _ContentTypeTag = pyxb.binding.basis.complexTypeDefinition._CT_SIMPLE _Abstract = False _ExpandedName = pyxb.namespace.ExpandedName(Namespace, 'TGN') _XSDLocation = pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 339, 2) _ElementMap = pyxb.bundles.dc.dc.SimpleLiteral._ElementMap.copy() _AttributeMap = pyxb.bundles.dc.dc.SimpleLiteral._AttributeMap.copy() # Base type is pyxb.bundles.dc.dc.SimpleLiteral # Attribute lang is restricted from parent # Attribute {http://www.w3.org/XML/1998/namespace}lang uses Python identifier lang __lang = pyxb.binding.content.AttributeUse(pyxb.namespace.ExpandedName(pyxb.namespace.XML, 'lang'), 'lang', '__httppurl_orgdcelements1_1_SimpleLiteral_httpwww_w3_orgXML1998namespacelang', pyxb.binding.xml_.STD_ANON_lang, prohibited=True) __lang._DeclarationLocation = None __lang._UseLocation = pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 345, 8) lang = property(__lang.value, __lang.set, None, None) _ElementMap.update({ }) _AttributeMap.update({ __lang.name() : __lang }) Namespace.addCategoryObject('typeBinding', 'TGN', TGN) title = pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'title'), pyxb.binding.datatypes.anyType, location=pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 75, 3)) Namespace.addCategoryObject('elementBinding', title.name().localName(), title) creator = pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'creator'), pyxb.binding.datatypes.anyType, location=pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 76, 3)) Namespace.addCategoryObject('elementBinding', creator.name().localName(), creator) subject = pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'subject'), pyxb.binding.datatypes.anyType, location=pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 77, 3)) Namespace.addCategoryObject('elementBinding', subject.name().localName(), subject) description = pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'description'), pyxb.binding.datatypes.anyType, location=pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 78, 3)) Namespace.addCategoryObject('elementBinding', description.name().localName(), description) publisher = pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'publisher'), pyxb.binding.datatypes.anyType, location=pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 79, 3)) Namespace.addCategoryObject('elementBinding', publisher.name().localName(), publisher) contributor = pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'contributor'), pyxb.binding.datatypes.anyType, location=pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 80, 3)) Namespace.addCategoryObject('elementBinding', contributor.name().localName(), contributor) date = pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'date'), pyxb.binding.datatypes.anyType, location=pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 81, 3)) Namespace.addCategoryObject('elementBinding', date.name().localName(), date) type = pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'type'), pyxb.binding.datatypes.anyType, location=pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 82, 3)) Namespace.addCategoryObject('elementBinding', type.name().localName(), type) format = pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'format'), pyxb.binding.datatypes.anyType, location=pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 83, 3)) Namespace.addCategoryObject('elementBinding', format.name().localName(), format) identifier = pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'identifier'), pyxb.binding.datatypes.anyType, location=pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 84, 3)) Namespace.addCategoryObject('elementBinding', identifier.name().localName(), identifier) source = pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'source'), pyxb.binding.datatypes.anyType, location=pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 85, 3)) Namespace.addCategoryObject('elementBinding', source.name().localName(), source) language = pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'language'), pyxb.binding.datatypes.anyType, location=pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 86, 3)) Namespace.addCategoryObject('elementBinding', language.name().localName(), language) relation = pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'relation'), pyxb.binding.datatypes.anyType, location=pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 87, 3)) Namespace.addCategoryObject('elementBinding', relation.name().localName(), relation) coverage = pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'coverage'), pyxb.binding.datatypes.anyType, location=pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 88, 3)) Namespace.addCategoryObject('elementBinding', coverage.name().localName(), coverage) rights = pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'rights'), pyxb.binding.datatypes.anyType, location=pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 89, 3)) Namespace.addCategoryObject('elementBinding', rights.name().localName(), rights) alternative = pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'alternative'), pyxb.binding.datatypes.anyType, location=pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 91, 3)) Namespace.addCategoryObject('elementBinding', alternative.name().localName(), alternative) tableOfContents = pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'tableOfContents'), pyxb.binding.datatypes.anyType, location=pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 93, 3)) Namespace.addCategoryObject('elementBinding', tableOfContents.name().localName(), tableOfContents) abstract = pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'abstract'), pyxb.binding.datatypes.anyType, location=pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 94, 3)) Namespace.addCategoryObject('elementBinding', abstract.name().localName(), abstract) created = pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'created'), pyxb.binding.datatypes.anyType, location=pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 96, 3)) Namespace.addCategoryObject('elementBinding', created.name().localName(), created) valid = pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'valid'), pyxb.binding.datatypes.anyType, location=pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 97, 3)) Namespace.addCategoryObject('elementBinding', valid.name().localName(), valid) available = pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'available'), pyxb.binding.datatypes.anyType, location=pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 98, 3)) Namespace.addCategoryObject('elementBinding', available.name().localName(), available) issued = pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'issued'), pyxb.binding.datatypes.anyType, location=pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 99, 3)) Namespace.addCategoryObject('elementBinding', issued.name().localName(), issued) modified = pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'modified'), pyxb.binding.datatypes.anyType, location=pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 100, 3)) Namespace.addCategoryObject('elementBinding', modified.name().localName(), modified) dateAccepted = pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'dateAccepted'), pyxb.binding.datatypes.anyType, location=pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 101, 3)) Namespace.addCategoryObject('elementBinding', dateAccepted.name().localName(), dateAccepted) dateCopyrighted = pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'dateCopyrighted'), pyxb.binding.datatypes.anyType, location=pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 102, 3)) Namespace.addCategoryObject('elementBinding', dateCopyrighted.name().localName(), dateCopyrighted) dateSubmitted = pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'dateSubmitted'), pyxb.binding.datatypes.anyType, location=pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 103, 3)) Namespace.addCategoryObject('elementBinding', dateSubmitted.name().localName(), dateSubmitted) extent = pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'extent'), pyxb.binding.datatypes.anyType, location=pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 105, 3)) Namespace.addCategoryObject('elementBinding', extent.name().localName(), extent) medium = pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'medium'), pyxb.binding.datatypes.anyType, location=pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 106, 3)) Namespace.addCategoryObject('elementBinding', medium.name().localName(), medium) isVersionOf = pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'isVersionOf'), pyxb.binding.datatypes.anyType, location=pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 108, 3)) Namespace.addCategoryObject('elementBinding', isVersionOf.name().localName(), isVersionOf) hasVersion = pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'hasVersion'), pyxb.binding.datatypes.anyType, location=pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 109, 3)) Namespace.addCategoryObject('elementBinding', hasVersion.name().localName(), hasVersion) isReplacedBy = pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'isReplacedBy'), pyxb.binding.datatypes.anyType, location=pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 110, 3)) Namespace.addCategoryObject('elementBinding', isReplacedBy.name().localName(), isReplacedBy) replaces = pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'replaces'), pyxb.binding.datatypes.anyType, location=pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 111, 3)) Namespace.addCategoryObject('elementBinding', replaces.name().localName(), replaces) isRequiredBy = pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'isRequiredBy'), pyxb.binding.datatypes.anyType, location=pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 112, 3)) Namespace.addCategoryObject('elementBinding', isRequiredBy.name().localName(), isRequiredBy) requires = pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'requires'), pyxb.binding.datatypes.anyType, location=pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 113, 3)) Namespace.addCategoryObject('elementBinding', requires.name().localName(), requires) isPartOf = pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'isPartOf'), pyxb.binding.datatypes.anyType, location=pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 114, 3)) Namespace.addCategoryObject('elementBinding', isPartOf.name().localName(), isPartOf) hasPart = pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'hasPart'), pyxb.binding.datatypes.anyType, location=pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 115, 3)) Namespace.addCategoryObject('elementBinding', hasPart.name().localName(), hasPart) isReferencedBy = pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'isReferencedBy'), pyxb.binding.datatypes.anyType, location=pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 116, 3)) Namespace.addCategoryObject('elementBinding', isReferencedBy.name().localName(), isReferencedBy) references = pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'references'), pyxb.binding.datatypes.anyType, location=pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 117, 3)) Namespace.addCategoryObject('elementBinding', references.name().localName(), references) isFormatOf = pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'isFormatOf'), pyxb.binding.datatypes.anyType, location=pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 118, 3)) Namespace.addCategoryObject('elementBinding', isFormatOf.name().localName(), isFormatOf) hasFormat = pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'hasFormat'), pyxb.binding.datatypes.anyType, location=pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 119, 3)) Namespace.addCategoryObject('elementBinding', hasFormat.name().localName(), hasFormat) conformsTo = pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'conformsTo'), pyxb.binding.datatypes.anyType, location=pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 120, 3)) Namespace.addCategoryObject('elementBinding', conformsTo.name().localName(), conformsTo) spatial = pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'spatial'), pyxb.binding.datatypes.anyType, location=pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 122, 3)) Namespace.addCategoryObject('elementBinding', spatial.name().localName(), spatial) temporal = pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'temporal'), pyxb.binding.datatypes.anyType, location=pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 123, 3)) Namespace.addCategoryObject('elementBinding', temporal.name().localName(), temporal) audience = pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'audience'), pyxb.binding.datatypes.anyType, location=pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 125, 3)) Namespace.addCategoryObject('elementBinding', audience.name().localName(), audience) accrualMethod = pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'accrualMethod'), pyxb.binding.datatypes.anyType, location=pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 126, 3)) Namespace.addCategoryObject('elementBinding', accrualMethod.name().localName(), accrualMethod) accrualPeriodicity = pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'accrualPeriodicity'), pyxb.binding.datatypes.anyType, location=pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 127, 3)) Namespace.addCategoryObject('elementBinding', accrualPeriodicity.name().localName(), accrualPeriodicity) accrualPolicy = pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'accrualPolicy'), pyxb.binding.datatypes.anyType, location=pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 128, 3)) Namespace.addCategoryObject('elementBinding', accrualPolicy.name().localName(), accrualPolicy) instructionalMethod = pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'instructionalMethod'), pyxb.binding.datatypes.anyType, location=pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 129, 3)) Namespace.addCategoryObject('elementBinding', instructionalMethod.name().localName(), instructionalMethod) provenance = pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'provenance'), pyxb.binding.datatypes.anyType, location=pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 130, 3)) Namespace.addCategoryObject('elementBinding', provenance.name().localName(), provenance) rightsHolder = pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'rightsHolder'), pyxb.binding.datatypes.anyType, location=pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 131, 3)) Namespace.addCategoryObject('elementBinding', rightsHolder.name().localName(), rightsHolder) mediator = pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'mediator'), pyxb.binding.datatypes.anyType, location=pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 133, 3)) Namespace.addCategoryObject('elementBinding', mediator.name().localName(), mediator) educationLevel = pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'educationLevel'), pyxb.binding.datatypes.anyType, location=pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 134, 3)) Namespace.addCategoryObject('elementBinding', educationLevel.name().localName(), educationLevel) accessRights = pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'accessRights'), pyxb.binding.datatypes.anyType, location=pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 136, 3)) Namespace.addCategoryObject('elementBinding', accessRights.name().localName(), accessRights) license = pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'license'), pyxb.binding.datatypes.anyType, location=pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 137, 3)) Namespace.addCategoryObject('elementBinding', license.name().localName(), license) bibliographicCitation = pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'bibliographicCitation'), pyxb.binding.datatypes.anyType, location=pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 139, 3)) Namespace.addCategoryObject('elementBinding', bibliographicCitation.name().localName(), bibliographicCitation) elementOrRefinementContainer._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(_Namespace_dc, 'any'), pyxb.bundles.dc.dc.SimpleLiteral, abstract=pyxb.binding.datatypes.boolean(1), scope=elementOrRefinementContainer, location=pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dc.xsd', 70, 2))) def _BuildAutomaton (): # Remove this helper function from the namespace after it is invoked global _BuildAutomaton del _BuildAutomaton import pyxb.utils.fac as fac counters = set() cc_0 = fac.CounterCondition(min=0, max=None, metadata=pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 362, 4)) counters.add(cc_0) states = [] final_update = set() final_update.add(fac.UpdateInstruction(cc_0, False)) symbol = pyxb.binding.content.ElementUse(elementOrRefinementContainer._UseForTag(pyxb.namespace.ExpandedName(_Namespace_dc, 'any')), pyxb.utils.utility.Location('/tmp/pyxbdist.mqXn05k/PyXB-1.2.4/pyxb/bundles/dc/schemas/dcterms.xsd', 363, 1)) st_0 = fac.State(symbol, is_initial=True, final_update=final_update, is_unordered_catenation=False) states.append(st_0) transitions = [] transitions.append(fac.Transition(st_0, [ fac.UpdateInstruction(cc_0, True) ])) st_0._set_transitionSet(transitions) return fac.Automaton(states, counters, True, containing_state=None) elementOrRefinementContainer._Automaton = _BuildAutomaton() title._setSubstitutionGroup(pyxb.bundles.dc.dc.title) creator._setSubstitutionGroup(pyxb.bundles.dc.dc.creator) subject._setSubstitutionGroup(pyxb.bundles.dc.dc.subject) description._setSubstitutionGroup(pyxb.bundles.dc.dc.description) publisher._setSubstitutionGroup(pyxb.bundles.dc.dc.publisher) contributor._setSubstitutionGroup(pyxb.bundles.dc.dc.contributor) date._setSubstitutionGroup(pyxb.bundles.dc.dc.date) type._setSubstitutionGroup(pyxb.bundles.dc.dc.type) format._setSubstitutionGroup(pyxb.bundles.dc.dc.format) identifier._setSubstitutionGroup(pyxb.bundles.dc.dc.identifier) source._setSubstitutionGroup(pyxb.bundles.dc.dc.source) language._setSubstitutionGroup(pyxb.bundles.dc.dc.language) relation._setSubstitutionGroup(pyxb.bundles.dc.dc.relation) coverage._setSubstitutionGroup(pyxb.bundles.dc.dc.coverage) rights._setSubstitutionGroup(pyxb.bundles.dc.dc.rights) alternative._setSubstitutionGroup(title) tableOfContents._setSubstitutionGroup(description) abstract._setSubstitutionGroup(description) created._setSubstitutionGroup(date) valid._setSubstitutionGroup(date) available._setSubstitutionGroup(date) issued._setSubstitutionGroup(date) modified._setSubstitutionGroup(date) dateAccepted._setSubstitutionGroup(date) dateCopyrighted._setSubstitutionGroup(date) dateSubmitted._setSubstitutionGroup(date) extent._setSubstitutionGroup(format) medium._setSubstitutionGroup(format) isVersionOf._setSubstitutionGroup(relation) hasVersion._setSubstitutionGroup(relation) isReplacedBy._setSubstitutionGroup(relation) replaces._setSubstitutionGroup(relation) isRequiredBy._setSubstitutionGroup(relation) requires._setSubstitutionGroup(relation) isPartOf._setSubstitutionGroup(relation) hasPart._setSubstitutionGroup(relation) isReferencedBy._setSubstitutionGroup(relation) references._setSubstitutionGroup(relation) isFormatOf._setSubstitutionGroup(relation) hasFormat._setSubstitutionGroup(relation) conformsTo._setSubstitutionGroup(relation) spatial._setSubstitutionGroup(coverage) temporal._setSubstitutionGroup(coverage) audience._setSubstitutionGroup(pyxb.bundles.dc.dc.any) accrualMethod._setSubstitutionGroup(pyxb.bundles.dc.dc.any) accrualPeriodicity._setSubstitutionGroup(pyxb.bundles.dc.dc.any) accrualPolicy._setSubstitutionGroup(pyxb.bundles.dc.dc.any) instructionalMethod._setSubstitutionGroup(pyxb.bundles.dc.dc.any) provenance._setSubstitutionGroup(pyxb.bundles.dc.dc.any) rightsHolder._setSubstitutionGroup(pyxb.bundles.dc.dc.any) mediator._setSubstitutionGroup(audience) educationLevel._setSubstitutionGroup(audience) accessRights._setSubstitutionGroup(rights) license._setSubstitutionGroup(rights) bibliographicCitation._setSubstitutionGroup(identifier)

53.880972

345

0.764795

7,926

64,280

6.053873

0.063714

0.053873

0.063668

0.059021

0.755372

0.731301

0.722297

0.698081

0.616656

0.613905

0

0.028053

0.103298

64,280

1,192

346

53.926175

0.804407

0.151587

0

0.396254

1

0.168588

0.214953

0.177191

0

1

0.004323

false

0

0.020173

0

0.430836

0

null

0

1

0

1

0

1

0

null

0

4

fce4091cc7e0cd3f3139c23809c116b185f16330

33

py

Python

custom_components/__init__.py

Velines/hass_datelist_countdown

892fda10d8d83b39b02699a840e3a30d018c0cf6

[ "MIT" ]

null

custom_components/__init__.py

Velines/hass_datelist_countdown

892fda10d8d83b39b02699a840e3a30d018c0cf6

[ "MIT" ]

null

custom_components/__init__.py

Velines/hass_datelist_countdown

892fda10d8d83b39b02699a840e3a30d018c0cf6

[ "MIT" ]

null

"""datelist_countdown module."""

16.5

32

0.727273

3

33

7.666667

1

0

0.060606

33

1

33

0.741935

0.787879

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

null

0

1

0

4

fceecd3497190e29715e529b227a0087f5159d2a

204

py

Python

pandas_utility/__init__.py

mmphego/pandas_utility

edb1679bef7d0517ea37b0163cec77e68d2599af

[ "MIT" ]

5

2019-08-13T22:07:17.000Z

2020-07-14T06:43:10.000Z

pandas_utility/__init__.py

mmphego/pandas_utility

edb1679bef7d0517ea37b0163cec77e68d2599af

[ "MIT" ]

null

pandas_utility/__init__.py

mmphego/pandas_utility

edb1679bef7d0517ea37b0163cec77e68d2599af

[ "MIT" ]

null

# -*- coding: utf-8 -*- """Top-level package for Pandas Utility.""" __author__ = """Mpho Mphego""" __email__ = "mpho112@gmail.com" from pandas_utility.pandas_utility import PandasUtilities # noqa:401

22.666667

69

0.710784

25

204

5.4

0.84

0.288889

0

0.039548

0.132353

204

8

70

25.5

0.723164

0.338235

0

0.21875

0

1

0

false

0

0.333333

0

0.333333

0

1

0

null

1

0

1

0

null

0

1

0

4

1e126ec2604ca327dc5cc99cbf66088d8862e4f1

90

py

Python

shell.py

truebit/ObjectPath

4f13d3fba79d46cfdac5974a6eed71712a4a7ac5

[ "MIT" ]

327

2015-01-02T13:20:39.000Z

2022-03-28T11:30:25.000Z

shell.py

truebit/ObjectPath

4f13d3fba79d46cfdac5974a6eed71712a4a7ac5

[ "MIT" ]

71

2015-02-03T08:22:58.000Z

2021-06-20T07:01:51.000Z

shell.py

truebit/ObjectPath

4f13d3fba79d46cfdac5974a6eed71712a4a7ac5

[ "MIT" ]

89

2015-02-10T01:02:42.000Z

2021-08-09T07:17:33.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- from objectpath import shell shell.main()

12.857143

28

0.655556

13

90

4.538462

0.923077

0

0.013158

0.155556

90

6

29

15

0.763158

0.466667

0

1

0

true

0

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

1

0

4

1e1d86d92373331aa1328e926df8c462cedba838

117

py

Python

code/abc160_c_03.py

KoyanagiHitoshi/AtCoder

731892543769b5df15254e1f32b756190378d292

[ "MIT" ]

3

2019-08-16T16:55:48.000Z

2021-04-11T10:21:40.000Z

code/abc160_c_03.py

KoyanagiHitoshi/AtCoder

731892543769b5df15254e1f32b756190378d292

[ "MIT" ]

null

code/abc160_c_03.py

KoyanagiHitoshi/AtCoder

731892543769b5df15254e1f32b756190378d292

[ "MIT" ]

null

K,N=map(int,input().split()) A=list(map(int,input().split())) A+=[A[0]+K] print(K-max(A[i+1]-A[i] for i in range(N)))

29.25

43

0.589744

29

117

2.37931

0.551724

0.173913

0.318841

0.463768

0.492754

0

0.018182

0.059829

117

4

43

29.25

0.609091

0

1

0

false

0

0.25

1

0

null

0

1

0

1

0

null

0

4

1e5909320bb1555eb5792e3107a5c94458cb233e

61

py

Python

atcoder/other/chokudai_s001_g.py

knuu/competitive-programming

16bc68fdaedd6f96ae24310d697585ca8836ab6e

[ "MIT" ]

1

2018-11-12T15:18:55.000Z

atcoder/other/chokudai_s001_g.py

knuu/competitive-programming

16bc68fdaedd6f96ae24310d697585ca8836ab6e

[ "MIT" ]

null

atcoder/other/chokudai_s001_g.py

knuu/competitive-programming

16bc68fdaedd6f96ae24310d697585ca8836ab6e

[ "MIT" ]

null

input() print(int(''.join(input().split())) % (10 ** 9 + 7))

20.333333

52

0.508197

9

61

3.444444

0.888889

0

0.075472

0.131148

61

2

53

30.5

0.509434

0

1

0

true

0

0.5

1

0

null

0

1

0

null

0

1

0

1

0

4

1e5e0a37fab256057dd1bc0ca2807de8311fbb4b

186

py

Python

staticforms/__init__.py

unocongafas/staticforms

d16d298fcb4b18f2a8658ba40c1ee14e17dd8305

[ "Apache-2.0" ]

null

staticforms/__init__.py

unocongafas/staticforms

d16d298fcb4b18f2a8658ba40c1ee14e17dd8305

[ "Apache-2.0" ]

null

staticforms/__init__.py

unocongafas/staticforms

d16d298fcb4b18f2a8658ba40c1ee14e17dd8305

[ "Apache-2.0" ]

null

"""Static forms.""" from starlette.applications import Starlette from .middlewares import middleware from .handlers import routes app = Starlette(routes=routes, middleware=middleware)

23.25

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null

0

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1eb9c525ff6619c20cdd3c9935ef546d390f475f

420

py

Python

src/vbr/errors.py

JoshuaUrrutia/python-vbr-1

a287031abd2a53db04631cce31e962f40705127b

[ "BSD-3-Clause" ]

null

src/vbr/errors.py

JoshuaUrrutia/python-vbr-1

a287031abd2a53db04631cce31e962f40705127b

[ "BSD-3-Clause" ]

null

src/vbr/errors.py

JoshuaUrrutia/python-vbr-1

a287031abd2a53db04631cce31e962f40705127b

[ "BSD-3-Clause" ]

null

class GenericRecordError(Exception): pass class ValidationError(GenericRecordError): pass class TableNotSupported(GenericRecordError): pass class DuplicateSignature(GenericRecordError): pass class GenericVBRError(Exception): pass class ConnectionFailedError(GenericVBRError): pass class RecordNotFoundError(GenericVBRError): pass class NotUniqueError(GenericVBRError): pass

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420

30

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1

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null

0

1

0

4

1eddfd1e6373f6c685269009ae6d057699e2c1fe

92

py

Python

PyOpdb/self_PyOpdb/tools/__init__.py

GodInLove/OPDB

d5d9c9ce5239037dcc57abba6377abbfccec32d1

[ "Apache-2.0" ]

1

2017-09-24T15:59:31.000Z

PyOpdb/self_PyOpdb/tools/__init__.py

GodInLove/OPDB

d5d9c9ce5239037dcc57abba6377abbfccec32d1

[ "Apache-2.0" ]

null

PyOpdb/self_PyOpdb/tools/__init__.py

GodInLove/OPDB

d5d9c9ce5239037dcc57abba6377abbfccec32d1

[ "Apache-2.0" ]

null

__author__ = "yd.liu" from .fastq import srr_n_to_fastq from .operon import operon_predict

18.4

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92

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92

4

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null

0

1

0

1

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4

949a37a92fc08d4b9a657f5e899a036e9e5a1f06

413

py

Python

PyTradier/utils.py

zlopez101/PyTradier

83397cf38bd636c471993b57fb71a12885affcb7

[ "MIT" ]

1

2021-04-30T23:59:20.000Z

PyTradier/utils.py

zlopez101/PyTradier

83397cf38bd636c471993b57fb71a12885affcb7

[ "MIT" ]

7

2021-05-08T00:47:59.000Z

2021-05-12T01:45:37.000Z

PyTradier/utils.py

zlopez101/PyTradier

83397cf38bd636c471993b57fb71a12885affcb7

[ "MIT" ]

null

import requests import pprint from functools import wraps from PyTradier.exceptions import * def printer(response: requests.Response): """Helper function for testing endpoints :param response: the response from API call :type response: Response """ pprint.pprint(response.status_code) pprint.pprint(response.headers) pprint.pprint(response.text) pprint.pprint(response.json())

22.944444

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49

413

6.244898

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0.261438

0

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null

0

1

0

null

0

1

0

1

0

4

949cfad7e19b1c359695164abec5f858885ddf25

214

py

Python

apps/kindeditor/models.py

dryprojects/MyBlog

ec04ba2bc658e96cddeb1d4766047ca8e89ff656

[ "BSD-3-Clause" ]

2

2021-08-17T13:29:21.000Z

2021-09-04T05:00:01.000Z

apps/kindeditor/models.py

dryprojects/MyBlog

ec04ba2bc658e96cddeb1d4766047ca8e89ff656

[ "BSD-3-Clause" ]

1

2020-07-16T11:22:32.000Z

apps/kindeditor/models.py

dryprojects/MyBlog

ec04ba2bc658e96cddeb1d4766047ca8e89ff656

[ "BSD-3-Clause" ]

1

2020-09-18T10:41:59.000Z

from django.db import models # Create your models here. class ImageUpload(models.Model): imgFile = models.ImageField(verbose_name='kindeditor上传的图片', upload_to='kindeditor/images/%Y/%m', max_length=200)

30.571429

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214

5.642857

0.892857

0

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0.130841

214

6

118

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0.208791

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0

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0

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null

0

1

0

null

0

1

0

1

0

4

94bdb9ae075cdf2432a6ba928ec85ebee311d8b9

163

py

Python

portfolio/myportfolio/admin.py

annaadhiambo/Portfolio

02befd352c64ed9198f44d87e550912401f8bca9

[ "MIT" ]

null

portfolio/myportfolio/admin.py

annaadhiambo/Portfolio

02befd352c64ed9198f44d87e550912401f8bca9

[ "MIT" ]

5

2020-06-05T20:44:18.000Z

2021-09-22T18:31:13.000Z

portfolio/myportfolio/admin.py

annaadhiambo/Portfolio

02befd352c64ed9198f44d87e550912401f8bca9

[ "MIT" ]

2

2020-06-16T03:51:45.000Z

2020-07-06T14:07:42.000Z

from django.contrib import admin from .models import * # Register your models here. admin.site.register(Project) admin.site.site_header = 'My Portfolio Dashboard'

27.166667

49

0.797546

23

163

5.608696

0.652174

0.139535

0

0.116564

163

6

49

27.166667

0.895833

0.159509

0

0.161765

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true

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0

1

0

null

0

1

0

1

0

4

94c27edb62d2ba453bd2d3a92a92bcaab2aeddfb

101

py

Python

holobot/discord/sdk/models/message.py

rexor12/holobot

89b7b416403d13ccfeee117ef942426b08d3651d

[ "MIT" ]

1

2021-05-24T00:17:46.000Z

holobot/discord/sdk/models/message.py

rexor12/holobot

89b7b416403d13ccfeee117ef942426b08d3651d

[ "MIT" ]

41

2021-03-24T22:50:09.000Z

2021-12-17T12:15:13.000Z

holobot/discord/sdk/models/message.py

rexor12/holobot

89b7b416403d13ccfeee117ef942426b08d3651d

[ "MIT" ]

null

from dataclasses import dataclass @dataclass class Message: channel_id: str message_id: str

14.428571

33

0.762376

13

101

5.769231

0.692308

0.133333

0

0.19802

101

6

34

16.833333

0.925926

0

1

0

true

0

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null

0

1

0

null

0

1

0

1

0

4

94eb02a33e45ee6db4fc431874a190b615b235a7

3,843

py

Python

vkontakte_api/api.py

mcfoton/django-vkontakte-api

14f1be79452e448e1b2b53fd89ae577131e35898

[ "BSD-3-Clause" ]

null

vkontakte_api/api.py

mcfoton/django-vkontakte-api

14f1be79452e448e1b2b53fd89ae577131e35898

[ "BSD-3-Clause" ]

null

vkontakte_api/api.py

mcfoton/django-vkontakte-api

14f1be79452e448e1b2b53fd89ae577131e35898

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- from django.conf import settings from oauth_tokens.api import ApiAbstractBase, Singleton from oauth_tokens.models import AccessToken from vkontakte import VKError as VkontakteError, API __all__ = ['api_call', 'VkontakteError'] class VkontakteApi(ApiAbstractBase): __metaclass__ = Singleton provider = 'vkontakte' error_class = VkontakteError request_timeout = getattr(settings, 'VKONTAKTE_API_REQUEST_TIMEOUT', 1) def get_consistent_token(self): return getattr(settings, 'VKONTAKTE_API_ACCESS_TOKEN', None) def get_tokens(self, **kwargs): return AccessToken.objects.filter_active_tokens_of_provider(self.provider, **kwargs) def get_api(self, **kwargs): return API(token=self.get_token(**kwargs)) def get_api_response(self, *args, **kwargs): return self.api.get(self.method, timeout=self.request_timeout, *args, **kwargs) def handle_error_code_5(self, e, *args, **kwargs): self.logger.info("Updating vkontakte access token, recursion count: %d" % self.recursion_count) self.update_tokens() return self.repeat_call(*args, **kwargs) def handle_error_code_6(self, e, *args, **kwargs): # try access_token by another user self.logger.info( "Vkontakte error 'Too many requests per second' on method: %s, recursion count: %d" % (self.method, self.recursion_count)) self.used_access_tokens += [self.api.token] return self.repeat_call(*args, **kwargs) def handle_error_code_9(self, e, *args, **kwargs): self.logger.warning("Vkontakte flood control registered while executing method %s with params %s, \ recursion count: %d" % (self.method, kwargs, self.recursion_count)) self.used_access_tokens += [self.api.token] return self.sleep_repeat_call(*args, **kwargs) def handle_error_code_10(self, e, *args, **kwargs): self.logger.warning("Internal server error: Database problems, try later. Error registered while executing \ method %s with params %s, recursion count: %d" % (self.method, kwargs, self.recursion_count)) return self.sleep_repeat_call(*args, **kwargs) def handle_error_code_17(self, e, *args, **kwargs): # Validation required: please open redirect_uri in browser # TODO: cover with tests self.logger.warning("Request error: %s. Error registered while executing \ method %s with params %s, recursion count: %d" % (e, self.method, kwargs, self.recursion_count)) user = AccessToken.objects.get(access_token=self.api.token).user_credentials auth_request = AccessToken.objects.get_token_for_user('vkontakte', user).auth_request auth_request.form_action_domain = 'https://m.vk.com' response = auth_request.session.get(e.redirect_uri) try: method, action, data = auth_request.get_form_data_from_content(response.content) except: raise Exception("There is no any form in response: %s" % response.content) data = {'code': auth_request.additional} response = getattr(auth_request.session, method)(url=action, headers=auth_request.headers, data=data) if 'success' not in response.url: raise Exception("Wrong response. Can not handle VK error 17. response: %s" % response.content) return self.sleep_repeat_call(*args, **kwargs) def handle_error_code_500(self, e, *args, **kwargs): # strange HTTP error appears sometimes return self.sleep_repeat_call(*args, **kwargs) def handle_error_code_501(self, e, *args, **kwargs): # strange HTTP error appears sometimes return self.sleep_repeat_call(*args, **kwargs) def api_call(*args, **kwargs): api = VkontakteApi() return api.call(*args, **kwargs)

43.670455

134

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3,843

5.190669

0.269777

0.07034

0.049238

0.051973

0.370457

0.326299

0.30129

0

0.005516

0.198022

3,843

87

135

44.172414

0.824789

0.054124

0

0.152542

0

0.095645

0.01516

0

0.011494

0

1

0.20339

false

0

0.067797

0.101695

0.559322

0

null

0

null

0

1

0

1

0

1

0

4

a203b565deba6165f07cc6bcf4f7fb4721ab0c7a

85

py

Python

outlier_project/envs/__init__.py

bmmlab/Distributional-RL-Tail-Risk

cb53bf473810e7a63a5e0602af67ff2aa67637e5

[ "MIT" ]

null

outlier_project/envs/__init__.py

bmmlab/Distributional-RL-Tail-Risk

cb53bf473810e7a63a5e0602af67ff2aa67637e5

[ "MIT" ]

null

outlier_project/envs/__init__.py

bmmlab/Distributional-RL-Tail-Risk

cb53bf473810e7a63a5e0602af67ff2aa67637e5

[ "MIT" ]

1

2021-11-06T09:37:59.000Z

# -*- coding:utf-8 -*- from outlier_project.envs.leptokurtosis import LeptokurticEnv

28.333333

61

0.776471

10

85

6.5

1

0

0.012987

0.094118

85

2

62

42.5

0.831169

0.235294

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

4

bfab2be98882cd049ab7e24d77adfad40074f10f

62

py

Python

code/abc130_a_03.py

KoyanagiHitoshi/AtCoder

731892543769b5df15254e1f32b756190378d292

[ "MIT" ]

3

2019-08-16T16:55:48.000Z

2021-04-11T10:21:40.000Z

code/abc130_a_03.py

KoyanagiHitoshi/AtCoder

731892543769b5df15254e1f32b756190378d292

[ "MIT" ]

null

code/abc130_a_03.py

KoyanagiHitoshi/AtCoder

731892543769b5df15254e1f32b756190378d292

[ "MIT" ]

null

X, A = map(int, input().split()) print("0" if X < A else "10")

31

32

0.548387

13

62

2.615385

0.846154

0.117647

0

0.058824

0.177419

62

2

33

31

0.607843

0

0.047619

0

1

0

true

0

0.5

1

0

null

0

1

0

null

0

1

0

1

0

4

bfc7559d64d1a2e8f79300840ba156f64d583011

81

py

Python

modules/2.79/bpy/types/CompositorNodeFlip.py

cmbasnett/fake-bpy-module

acb8b0f102751a9563e5b5e5c7cd69a4e8aa2a55

[ "MIT" ]

null

modules/2.79/bpy/types/CompositorNodeFlip.py

cmbasnett/fake-bpy-module

acb8b0f102751a9563e5b5e5c7cd69a4e8aa2a55

[ "MIT" ]

null

modules/2.79/bpy/types/CompositorNodeFlip.py

cmbasnett/fake-bpy-module

acb8b0f102751a9563e5b5e5c7cd69a4e8aa2a55

[ "MIT" ]

null

class CompositorNodeFlip: axis = None def update(self): pass

9

25

0.592593

8

81

6

1

0

0.345679

81

8

26

10.125

0.90566

0

1

0.25

false

0.25

0

0.75

0

1

0

null

0

1

0

null

0

1

0

1

0

4

44d2bac4226a1cac28e625cd3a7c8d9e154ce538

1,154

py

Python

testing_school/math_app/forms.py

vv31415926/training_school

4839a6bd05b1fc3616a92bd879c80dae58e02aa4

[ "MIT" ]

null

testing_school/math_app/forms.py

vv31415926/training_school

4839a6bd05b1fc3616a92bd879c80dae58e02aa4

[ "MIT" ]

null

testing_school/math_app/forms.py

vv31415926/training_school

4839a6bd05b1fc3616a92bd879c80dae58e02aa4

[ "MIT" ]

null

from django import forms from .models import * class TaskForm( forms.ModelForm ): #test = forms.BooleanField() #question = forms.CharField( widget=forms.Textarea(attrs={'cols': 60, 'rows': 3})) class Meta: model=Task fields=['numtask', 'variant', 'question', 'comment', 'numclass','level','theme','img'] widgets = { 'question': forms.Textarea(attrs={'cols': 35, 'rows':5}) } class NewTaskForm( forms.ModelForm ): class Meta: model=Task fields=['numtask', 'variant', 'question', 'comment', 'numclass','level','theme','img'] widgets = { 'question': forms.Textarea(attrs={'cols': 35, 'rows':5}) } class NewVersionForm( forms.ModelForm ): class Meta: model=Version fields=['answer', 'correct'] #fields = ['npp', 'answer', 'correct', 'task'] #widgets = {'task': forms.Textarea(attrs={'cols': 35, 'rows': 5})} class VersionForm( forms.ModelForm ): class Meta: model=Version fields=['answer', 'correct' ] class TaskUsersForm( forms.Form ): question = forms.CharField( widget=forms.Textarea(attrs={'cols': 60, 'rows': 3}) )

34.969697

94

0.607452

124

1,154

5.653226

0.33871

0.092725

0.128388

0.156919

0.736091

0.71612

0.667618

0.513552

0

0.016411

0.207972

1,154

32

95

36.0625

0.750547

0.188908

0

0.636364

0

0.178112

0

1

0

false

0

0.090909

0

0.545455

0

null

0

1

0

null

0

1

0

4

44dc3aa81669f6ee82ce7c263f2faede5348f903

92

py

Python

2015/01/sp/graphic_config.py

nprapps/graphics-archive

97b0ef326b46a959df930f5522d325e537f7a655

[ "FSFAP" ]

14

2015-05-08T13:41:51.000Z

2021-02-24T12:34:55.000Z

graphic_templates/table/graphic_config.py

tophtucker/dailygraphics

abc8fa7fb0e4d15800bb3edcf2c864fe98f40197

[ "MIT" ]

null

graphic_templates/table/graphic_config.py

tophtucker/dailygraphics

abc8fa7fb0e4d15800bb3edcf2c864fe98f40197

[ "MIT" ]

7

2015-04-04T04:45:54.000Z

2021-02-18T11:12:48.000Z

#!/usr/bin/env python COPY_GOOGLE_DOC_KEY = '1ciRc--h8HuBpQzMebVygC4x_y9dvKxp6OA45ccRrIX4'

23

68

0.826087

11

92

6.545455

1

0

0.093023

0.065217

92

3

69

30.666667

0.744186

0.217391

0

0.619718

0

1

0

false

0

1

0

null

0

1

0

1

null

0

4

44dc50b2d392fc02424a77fb43250d2cb16dd5f3

320

py

Python

bridge/library_v2.py

rlelito/DesignPatterns

4e59442a10c1407ed4d9cdceea790263c30223b3

[ "MIT" ]

null

bridge/library_v2.py

rlelito/DesignPatterns

4e59442a10c1407ed4d9cdceea790263c30223b3

[ "MIT" ]

null

bridge/library_v2.py

rlelito/DesignPatterns

4e59442a10c1407ed4d9cdceea790263c30223b3

[ "MIT" ]

null

from library import Library from gl2 import GL2 class LibraryV2(Library): @staticmethod def draw_line(x1: float, x2: float, y1: float, y2: float) -> None: GL2.drawing_line(x1, x2, y1, y2) @staticmethod def draw_circle(x: float, y: float, r: float) -> None: GL2.drawing_circle(x, y, r)

24.615385

70

0.65

48

320

4.25

0.4375

0.147059

0.186275

0

0.052846

0.23125

320

12

71

26.666667

0.776423

0

0.222222

0

1

0.222222

false

0

0.222222

0

0.555556

0

null

0

1

0

null

0

1

0

1

0

4

44ed9b2e1614232c3586ba073cb70ee7b341d76d

474

py

Python

2059.py

heltonricardo/URI

160cca22d94aa667177c9ebf2a1c9864c5e55b41

[ "MIT" ]

6

2021-04-13T00:33:43.000Z

2022-02-10T10:23:59.000Z

2059.py

heltonricardo/URI

160cca22d94aa667177c9ebf2a1c9864c5e55b41

[ "MIT" ]

null

2059.py

heltonricardo/URI

160cca22d94aa667177c9ebf2a1c9864c5e55b41

[ "MIT" ]

3

2021-03-23T18:42:24.000Z

2022-02-10T10:24:07.000Z

entrada = str(input()).split(' ') p = int(entrada[0]) j1 = int(entrada[1]) j2 = int(entrada[2]) r = int(entrada[3]) a = int(entrada[4]) if r == 0 and a == 0: if (j1 + j2) % 2 == 0 and p == 1: print('Jogador 1 ganha!') elif (j1 + j2) % 2 != 0 and p == 0: print('Jogador 1 ganha!') else: print('Jogador 2 ganha!') elif r == 1 and a == 0: print('Jogador 1 ganha!') elif r == 0 and a == 1: print('Jogador 1 ganha!') elif r == 1 and a == 1: print('Jogador 2 ganha!')

33.857143

65

0.556962

89

474

2.966292

0.247191

0.272727

0.19697

0.272727

0.556818

0.416667

0

0.084239

0.223629

474

13

66

36.461538

0.633152

0

0.204641

0

1

0

false

0

0.461538

0

null

1

0

null

0

1

0

4

785a7d92375ec8281caa7c8a056a6f6d5872fcac

92

py

Python

ptest/exception.py

KarlGong/ptest

a17e82ff64465daeadcc8b73a44650a4044901d4

[ "Apache-2.0" ]

65

2015-05-22T12:35:35.000Z

2022-01-12T10:14:32.000Z

ptest/exception.py

KarlGong/ptest

a17e82ff64465daeadcc8b73a44650a4044901d4

[ "Apache-2.0" ]

3

2019-07-24T23:21:08.000Z

2020-10-28T12:14:50.000Z

ptest/exception.py

KarlGong/ptest

a17e82ff64465daeadcc8b73a44650a4044901d4

[ "Apache-2.0" ]

10

2017-06-26T06:05:03.000Z

2021-12-08T07:50:10.000Z

class PTestException(Exception): pass class ScreenshotError(PTestException): pass

13.142857

38

0.76087

8

92

8.75

0.625

0

0.173913

92

6

39

15.333333

0.921053

0

0.5

0

1

0

true

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

4

786460ac1f59a78a91fdf6a8c5a09493ffeaa066

844

py

Python

scripts/get-python-version.py

YuanyuanNi/azure-cli

63844964374858bfacd209bfe1b69eb456bd64ca

[ "MIT" ]

3,287

2016-07-26T17:34:33.000Z

2022-03-31T09:52:13.000Z

scripts/get-python-version.py

YuanyuanNi/azure-cli

63844964374858bfacd209bfe1b69eb456bd64ca

[ "MIT" ]

19,206

2016-07-26T07:04:42.000Z

2022-03-31T23:57:09.000Z

scripts/get-python-version.py

YuanyuanNi/azure-cli

63844964374858bfacd209bfe1b69eb456bd64ca

[ "MIT" ]

2,575

2016-07-26T06:44:40.000Z

2022-03-31T22:56:06.000Z

# -------------------------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for license information. # -------------------------------------------------------------------------------------------- ################################################################################################### # This script returns a short string describing the major version of python being used. # # While it should be versatile, it was written with the intention that it will help resolve which # requirements.txt to pull concrete-dependencies from. ################################################################################################### import sys print('py{}'.format(sys.version_info[0]))

52.75

99

0.420616

69

844

5.130435

0.855072

0

0.0013

0.088863

844

15

100

56.266667

0.459038

0.67654

0

0.060606

0

1

0

true

0

0.5

0

0.5

0

null

0

1

0

null

0

1

0

1

0

1

0

4

7872bc600d7a111056017033e8f1ce65b62f17dd

149

py

Python

inference/test/test_load.py

knowledgevis/wsi_infer_web

f5201e767a502fde6f17dd17a70aabcd388d5cc2

[ "Apache-2.0" ]

null

inference/test/test_load.py

knowledgevis/wsi_infer_web

f5201e767a502fde6f17dd17a70aabcd388d5cc2

[ "Apache-2.0" ]

null

inference/test/test_load.py

knowledgevis/wsi_infer_web

f5201e767a502fde6f17dd17a70aabcd388d5cc2

[ "Apache-2.0" ]

null

import pytest from girder.plugin import loadedPlugins @pytest.mark.plugin('vxapi') def test_import(server): assert 'vxapi' in loadedPlugins()

16.555556

39

0.765101

19

149

5.947368

0.684211

0

0.134228

149

8

40

18.625

0.875969

0

0.067114

0

0.2

1

0.2

false

0

0.6

0

0.8

0

1

0

null

0

1

0

null

0

1

0

1

0

4

15545f6881d16dff764312e0311b84cb85fb5dda

272

py

Python

app/classes/filters.py

robjporter/PYTHON-APIServer-1

57df8e8189834504b3f473993ae12586ec32d5c9

[ "MIT" ]

null

app/classes/filters.py

robjporter/PYTHON-APIServer-1

57df8e8189834504b3f473993ae12586ec32d5c9

[ "MIT" ]

null

app/classes/filters.py

robjporter/PYTHON-APIServer-1

57df8e8189834504b3f473993ae12586ec32d5c9

[ "MIT" ]

null

def reverse_filter( s ): return s[ ::-1 ] def string_trim_upper( value ): return value.strip().upper() def string_trim_lower( value ): return value.strip().lower() def datetimeformat( value, format='%H:%M / %d-%m-%Y' ): return value.strftime( format )

20.923077

55

0.647059

38

272

4.5

0.5

0.192982

0.152047

0.245614

0

0.004525

0.1875

272

12

56

22.666667

0.769231

0

0.059041

0

1

0.5

false

0

0.5

1

0

null

0

1

0

null

0

1

0

1

0

4

155dc4dff676e63bf0854d45520b57b5a8ed59e1

117

py

Python

run.py

avagut/mheshimiwa-api

b0a22be36646352e255516954655823319c7cd42

[ "MIT" ]

null

run.py

avagut/mheshimiwa-api

b0a22be36646352e255516954655823319c7cd42

[ "MIT" ]

null

run.py

avagut/mheshimiwa-api

b0a22be36646352e255516954655823319c7cd42

[ "MIT" ]

null

"""Create a master runner for the system.""" from api_files.app import app if __name__ == "__main__": app.run()

19.5

44

0.683761

18

117

3.944444

0.888889

0

0.179487

117

6

45

19.5

0.739583

0.324786

0

0.108108

0

1

0

true

0

0.333333

0

0.333333

0

1

0

null

0

1

0

null

0

1

0

1

0

4

156aec56355b0ba336c7468f9709b543c2bf80d6

233

py

Python

problems/extract_the_domain_name_from_a_url.py

stereoabuse/codewars

d6437afaef38c3601903891b8b9cb0f84c108c54

[ "MIT" ]

null

problems/extract_the_domain_name_from_a_url.py

stereoabuse/codewars

d6437afaef38c3601903891b8b9cb0f84c108c54

[ "MIT" ]

null

problems/extract_the_domain_name_from_a_url.py

stereoabuse/codewars

d6437afaef38c3601903891b8b9cb0f84c108c54

[ "MIT" ]

null

## Extract the domain name from a URL ## 5 kyu ## https://www.codewars.com//kata/514a024011ea4fb54200004b import re def domain_name(url): return re.match(r'(www\.|http[s]?://)*([a-z0-9-]*)\..*$', url, re.I).group(2)

25.888889

81

0.609442

36

233

3.916667

0.777778

0.141844

0

0.112821

0.16309

233

9

81

25.888889

0.610256

0.420601

0

0.308333

0

1

0.333333

false

0

0.333333

1

0

null

0

1

0

null

0

1

0

1

0

4

1571985a11cfb1549bf107a161dd9cf89e45b31e

433

py

Python

botc/commands/abilities/tb/__init__.py

Xinverse/BOTC-Bot

1932c649c81a5a1eab735d7abdee0761c2853940

[ "MIT" ]

1

2020-06-21T17:20:17.000Z

botc/commands/abilities/tb/__init__.py

BlueLenz/Blood-on-the-Clocktower-Storyteller-Discord-Bot

1932c649c81a5a1eab735d7abdee0761c2853940

[ "MIT" ]

1

2020-07-07T03:47:44.000Z

botc/commands/abilities/tb/__init__.py

BlueLenz/Blood-on-the-Clocktower-Storyteller-Discord-Bot

1932c649c81a5a1eab735d7abdee0761c2853940

[ "MIT" ]

1

2022-02-18T00:42:19.000Z

from .kill import Kill from .learn import Learn from .poison import Poison from .protect import Protect from .read import Read from .serve import Serve from .slay import Slay def setup(client): client.add_cog(Kill(client)) client.add_cog(Learn(client)) client.add_cog(Poison(client)) client.add_cog(Protect(client)) client.add_cog(Read(client)) client.add_cog(Serve(client)) client.add_cog(Slay(client))

25.470588

35

0.743649

66

433

4.772727

0.212121

0.266667

0.333333

0.4

0

0.152425

433

16

36

27.0625

0.858311

0

1

0.066667

false

0

0.466667

0

0.533333

0

null

1

0

null

0

1

0

4

1598de54b414b8cfe48948e709b732f0bd8e1fb7

141

py

Python

front-end/python/dictionary.py

chuducthang77/Summer_code

48e0b507c8b4eebcb1eb641b11db8c0eced46c7d

[ "MIT" ]

1

2021-05-17T11:50:20.000Z

front-end/python/dictionary.py

chuducthang77/Summer_code

48e0b507c8b4eebcb1eb641b11db8c0eced46c7d

[ "MIT" ]

null

front-end/python/dictionary.py

chuducthang77/Summer_code

48e0b507c8b4eebcb1eb641b11db8c0eced46c7d

[ "MIT" ]

null

dictionary = { "CSS": "101", "Python": ["101", "201", "301"] } print(dictionary.get("CSS", None)) print(dictionary.get("HTML", None))

23.5

35

0.58156

17

141

4.823529

0.588235

0.365854

0.439024

0

0.1

0.148936

141

6

36

23.5

0.583333

0

0.197183

0

1

0

false

0

0.333333

1

0

null

1

0

1

0

null

0

4

15a6a7d8bcabeff9f632073ce95646d59e1471da

130

py

Python

sort/abstract_sort.py

vasili-byl/algorithms

4e37609ab9b724e140cfec4b01495a0952d28724

[ "MIT" ]

1

2020-05-02T13:40:10.000Z

sort/abstract_sort.py

vasili-byl/algorithms

4e37609ab9b724e140cfec4b01495a0952d28724

[ "MIT" ]

null

sort/abstract_sort.py

vasili-byl/algorithms

4e37609ab9b724e140cfec4b01495a0952d28724

[ "MIT" ]

null

class Sort(object): def __call__(self, array, left_bound, right_bound): raise RuntimeError("Do not implemented yet!")

32.5

55

0.707692

17

130

5.058824

0.941176

0

0.184615

130

3

56

43.333333

0.811321

0

0.176923

0

1

0.333333

false

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

4

ec594e54cffa4a807267a88cf9a2a82205f8294d

160

py

Python

tests_here/test_open_browser.py

workflowsconnectortest/test_add_user_2_repo

f72ab43ad309f34347afdc4d93b3f93afe5f1737

[ "Apache-2.0" ]

null

tests_here/test_open_browser.py

workflowsconnectortest/test_add_user_2_repo

f72ab43ad309f34347afdc4d93b3f93afe5f1737

[ "Apache-2.0" ]

33

2020-11-21T14:04:27.000Z

2021-04-12T16:46:23.000Z

tests_here/test_open_browser.py

workflowsconnectortest/test_add_user_2_repo

f72ab43ad309f34347afdc4d93b3f93afe5f1737

[ "Apache-2.0" ]

null

""" TEST """ import pytest @pytest.mark.test def test_open_browser(driver_session): """ TEST :param driver_session: :return: """ pass

12.307692

38

0.60625

18

160

5.166667

0.666667

0.27957

0

0.25625

160

12

39

13.333333

0.781513

0.25625

0

1

0.25

false

0.25

0

0.5

0

1

0

null

1

0

null

0

1

0

1

0

4

ec5b114aa276d4fe5d81757d74aa88f55e87c536

35

py

Python

python-packages/order_utils/test/__init__.py

bryan-liu-nova/ZRXFork

f29578ea7e37bf3b6cea9180df7452dbdb6fd788

[ "Apache-2.0" ]

1,075

2018-03-04T13:18:52.000Z

2022-03-29T06:33:59.000Z

python-packages/order_utils/test/__init__.py

bryan-liu-nova/ZRXFork

f29578ea7e37bf3b6cea9180df7452dbdb6fd788

[ "Apache-2.0" ]

1,873

2018-03-03T14:37:53.000Z

2021-06-26T03:02:12.000Z

python-packages/order_utils/test/__init__.py

bryan-liu-nova/ZRXFork

f29578ea7e37bf3b6cea9180df7452dbdb6fd788

[ "Apache-2.0" ]

500

2018-03-03T20:39:43.000Z

2022-03-21T21:01:55.000Z

"""Tests of zero_x.order_utils."""

17.5

34

0.685714

6

35

3.666667

1

0

0.085714

35

1

35

0.6875

0.8

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

null

0

1

0

4

01a0085fa56bd63c42d92735e96ec1cd1e61e998

113

py

Python

sksurgerybard/__init__.py

SciKit-Surgery/scikit-surgerybard

4ac2ea28acb150437361c9abd53db3e3bba6d803

[ "BSD-3-Clause" ]

1

2021-06-30T15:55:21.000Z

sksurgerybard/__init__.py

UCL/scikit-surgerybard

7ebe4d15d3d3fa67218424c9f737a9e8d93bfbf3

[ "BSD-3-Clause" ]

68

2020-04-30T07:29:33.000Z

2022-01-20T09:47:54.000Z

sksurgerybard/__init__.py

SciKit-Surgery/scikit-surgerybard

4ac2ea28acb150437361c9abd53db3e3bba6d803

[ "BSD-3-Clause" ]

1

2021-06-30T15:55:48.000Z

# coding=utf-8 """scikit-surgerybard""" from . import _version __version__ = _version.get_versions()['version']

18.833333

48

0.734513

13

113

5.846154

0.769231

0.368421

0

0.009804

0.097345

113

5

49

22.6

0.735294

0.283186

0

0.093333

0

1

0

false

0

0.5

0

0.5

0

1

0

null

1

0

1

0

null

0

1

0

4

01a3f9b3ca481b3b5647bf3654dc83a547551fa3

205

py

Python

Company_Based_Questions/Goldman Sachs/Check If two arrays with same length are equal.py

Satyam-Bhalla/Competitive-Coding

5814f5f60572f1e76495efe751b94bf4d2845198

[ "MIT" ]

1

2021-12-09T10:36:48.000Z

Company_Based_Questions/Goldman Sachs/Check If two arrays with same length are equal.py

Satyam-Bhalla/Competitive-Coding

5814f5f60572f1e76495efe751b94bf4d2845198

[ "MIT" ]

null

Company_Based_Questions/Goldman Sachs/Check If two arrays with same length are equal.py

Satyam-Bhalla/Competitive-Coding

5814f5f60572f1e76495efe751b94bf4d2845198

[ "MIT" ]

null

t = int(input()) for _ in range(t): n = int(input()) l1 = set(list(map(int,input().split()))) l2 = set(list(map(int,input().split()))) if l1==l2: print(1) else: print(0)

22.777778

44

0.497561

32

205

3.15625

0.5625

0.316832

0.19802

0.257426

0.455446

0

0.040268

0.273171

205

9

45

22.777778

0.637584

0

1

0

false

0

0.222222

0

null

1

0

1

0

null

0

4

01a805d2061ecb1ff063d3a34d0252b34d06a9e6

114

py

Python

src/qdmdealer/dataset.py

CaoRX/sdd

4aa6612a195f579d6bef26a3e5cd9f1ce2c66e7c

[ "MIT" ]

null

src/qdmdealer/dataset.py

CaoRX/sdd

4aa6612a195f579d6bef26a3e5cd9f1ce2c66e7c

[ "MIT" ]

null

src/qdmdealer/dataset.py

CaoRX/sdd

4aa6612a195f579d6bef26a3e5cd9f1ce2c66e7c

[ "MIT" ]

null

import qdmdealer.settingLoader as settingLoader if __name__ == '__main__': print(settingLoader.loadSetting())

28.5

47

0.789474

11

114

7.454545

0.818182

0

0.114035

114

4

48

28.5

0.811881

0

0.069565

0

1

0

true

0

0.333333

0

0.333333

1

0

null

0

1

0

null

0

1

0

1

0

4

01e205f1f3ae63b7f86c90a692e2672bd4818afd

168

py

Python

python/pr3.py

Surya-06/My-Solutions

58f7c7f1a0a3a28f25eff22d03a09cb2fbcf7806

[ "MIT" ]

null

python/pr3.py

Surya-06/My-Solutions

58f7c7f1a0a3a28f25eff22d03a09cb2fbcf7806

[ "MIT" ]

null

python/pr3.py

Surya-06/My-Solutions

58f7c7f1a0a3a28f25eff22d03a09cb2fbcf7806

[ "MIT" ]

null

import mechanize from bs4 import BeautifulSoup br = mechanize.Browser() response = br.open("https://en.wikipedia.org/wiki/Simha_(film)") print response.text

18.666667

65

0.732143

22

168

5.545455

0.818182

0

0.006993

0.14881

168

8

66

21

0.846154

0

0.2625

0

null

0

0.4

null

0.2

1

0

null

0

1

0

null

0

1

0

1

0

4

01f1bcd751ad568d22d189a0f876460dc8800ab4

339

py

Python

sionna/fec/__init__.py

NVlabs/sionna

488e6c3ff6ff2b3313d0ca0f94e4247b8dd6ff35

[ "Apache-2.0" ]

163

2022-03-22T19:47:47.000Z

2022-03-31T23:56:45.000Z

sionna/fec/__init__.py

Maryammhsnv/sionna

527d0f7866b379afffad34a6bef7ed3bf6f33ad2

[ "Apache-2.0" ]

2

2022-03-24T12:43:07.000Z

2022-03-29T07:17:16.000Z

sionna/fec/__init__.py

Maryammhsnv/sionna

527d0f7866b379afffad34a6bef7ed3bf6f33ad2

[ "Apache-2.0" ]

19

2022-03-23T02:31:22.000Z

2022-03-30T06:35:12.000Z

# # SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # """FEC sub-package of the Sionna library""" from . import ldpc from . import polar from . import conv from . import crc from . import scrambling from . import interleaving from . import utils

24.214286

103

0.755162

46

339

5.565217

0.717391

0.273438

0

0.034843

0.153392

339

13

104

26.076923

0.857143

0.519174

0

1

0

true

0

1

0

1

0

null

1

0

null

0

1

0

1

0

1

0

4

bf096468668eb0ed6554f281ce1ce49d63e79f21

2,803

py

Python

magni/imaging/dictionaries/_visualisations.py

SIP-AAU/Magni

6328dc98a273506f433af52e6bd394754a844550

[ "BSD-2-Clause" ]

42

2015-02-09T10:17:26.000Z

2021-12-21T09:38:04.000Z

magni/imaging/dictionaries/_visualisations.py

SIP-AAU/Magni

6328dc98a273506f433af52e6bd394754a844550

[ "BSD-2-Clause" ]

3

2015-03-20T12:00:40.000Z

2015-03-20T12:01:16.000Z

magni/imaging/dictionaries/_visualisations.py

SIP-AAU/Magni

6328dc98a273506f433af52e6bd394754a844550

[ "BSD-2-Clause" ]

14

2015-04-28T03:08:32.000Z

2021-07-24T13:29:24.000Z

""" .. Copyright (c) 2014-2017, Magni developers. All rights reserved. See LICENSE.rst for further information. Module providing functionality for visualising dictionary coefficients. Routine listings ---------------- visualise_DCT(shape) Function for visualising DCT coefficients. visualise_DFT(shape) Function for visualising DFT coefficients. """ from __future__ import division import numpy as np from magni.imaging import vec2mat as _vec2mat def visualise_DCT(shape): """ Return utilities for visualising DCT coefficients. A handle to a function to transform the coefficients into a 'displayable' format is returned along with a tuple of ranges of the axes in the 2D coefficient plane. Parameters ---------- shape : tuple The shape of the 2D DCT being visualised. Returns ------- display_coefficients : Function The function used to transform coefficients into a 'displayable' format. axes_extent : tuple The ranges of the axes in the 2D coefficient plane. Notes ----- The display_coefficients function takes log10 to the absolute value of the transform cofficient vector given to it as an argument. The returned displayable coefficients is a matrix. The axes_extent consists of (abcissa_min, abcissa_max, ordinate_min, ordinate_max). """ h, w = shape def display_coefficients(x): return np.log10(np.abs(_vec2mat(x, (h, w)))) axes_extent = (0, w - 1, h - 1, 0) return display_coefficients, axes_extent def visualise_DFT(shape): """ Return utilities for visualising DFT coefficients. A handle to a function to transform the coefficients into a 'displayable' format is returned along with a tuple of ranges of the axes in the 2D coefficient plane. Parameters ---------- shape : tuple The shape of the 2D DFT being visualised. Returns ------- display_coefficients : Function The function used to transform coefficients into a 'displayable' format. axes_extent : tuple The ranges of the axes in the 2D coefficient plane. Notes ----- The display_coefficients function takes log10 to the absolute value of the transform cofficient vector given to it as an argument. The returned displayable coefficients is a matrix that is flipped up/down and fftshifted. The axes_extent consists of (abcissa_min, abcissa_max, ordinate_min, ordinate_max). """ h, w = shape def display_coefficients(x): return np.flipud( np.fft.fftshift(np.log10(np.abs(_vec2mat(x, (h, w)))))) axes_extent = (-(w // 2), (w - 1) // 2, -(h // 2), (h - 1) // 2) return display_coefficients, axes_extent

25.715596

78

0.677132

367

2,803

5.076294

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0.021471

0.0365

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2,803

108

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25.953704

0.862606

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null

0

1

0

null

0

1

0

1

0

4

1726192b846dc0fe37fbbd26f7947d5c82bed2e8

85

py

Python

eight/three_fred.py

frrad/eopi

ff5d1c40c721edd16480a98e07fb36f47f2416bf

[ "MIT" ]

null

eight/three_fred.py

frrad/eopi

ff5d1c40c721edd16480a98e07fb36f47f2416bf

[ "MIT" ]

7

2018-06-04T16:28:49.000Z

2018-07-09T01:35:24.000Z

eight/three_fred.py

frrad/eopi

ff5d1c40c721edd16480a98e07fb36f47f2416bf

[ "MIT" ]

null

def ans(x): if len(x) == 2 or len(x) == 10: return False return True

17

35

0.505882

15

85

2.866667

0.733333

0.186047

0

0.054545

0.352941

85

4

36

21.25

0.727273

0

1

0.25

false

0

0.75

0

1

0

null

0

1

0

null

0

1

0

1

0

4

173d07d75692e2a3fd44ed162047266be76ba02f

44

py

Python

Solutions/problem 10718/python.py

Egnima/baekjoon_solution

d1da3c70bcfa34a93e049cc2a40fbfe394e7d4c3

[ "MIT" ]

null

Solutions/problem 10718/python.py

Egnima/baekjoon_solution

d1da3c70bcfa34a93e049cc2a40fbfe394e7d4c3

[ "MIT" ]

null

Solutions/problem 10718/python.py

Egnima/baekjoon_solution

d1da3c70bcfa34a93e049cc2a40fbfe394e7d4c3

[ "MIT" ]

null

for i in range(0, 2): print('강한친구 대한육군')

22

0.590909

9

44

2.888889

1

0

0.058824

0.227273

44

2

22

0.705882

0

0.2

0

1

0

false

0

0.5

1

0

null

0

1

0

null

0

1

0

4

1763ea5d77a020976c710a23f2b98ceaa4147cd5

1,101

py

Python

thinkific/bundles.py

OmniPro-Group/thinkific-python

7aeb4b86ad5357f6a078c270416d68b311d107b6

[ "MIT" ]

2

2019-12-30T13:47:02.000Z

2021-07-03T07:21:37.000Z

thinkific/bundles.py

OmniPro-Group/thinkific-python

7aeb4b86ad5357f6a078c270416d68b311d107b6

[ "MIT" ]

2

2021-09-08T10:22:52.000Z

2021-09-14T13:39:17.000Z

thinkific/bundles.py

OmniPro-Group/thinkific-python

7aeb4b86ad5357f6a078c270416d68b311d107b6

[ "MIT" ]

3

2021-05-28T10:46:34.000Z

2022-01-26T03:42:27.000Z

from .client import Client class Bundles: def __init__(self, client): self.__client = client def retrieve_bundle(self, id: int): return self.__client.request('get', '/bundles/%s' % id) def retrieve_courses_in_bundle(self, id: int, page: int = None, limit: int = None): return self.__client.request('get', 'bundles/%s/courses' % id, params={ "page": page, "limit": limit }) def create_enrollment_in_bundle(self, bundle_id: int, values: dict): return self.__client.request('post', '/bundles/%s/courses' % bundle_id, data=values) def get_enrollments_in_bundle(self, bundle_id: int, page: int = None, limit: int = None): return self.__client.request('get', 'bundles/%s/enrollments' % bundle_id, params={ "page": page, "limit": limit }) def update_enrollments_in_bundle(self, bundle_id: int, values: dict): return self.__client.request('put', 'bundles/%s/enrollments' % bundle_id, data=values)

36.7

94

0.59673

131

1,101

4.748092

0.236641

0.11254

0.128617

0.184887

0.662379

0.59164

0.379421

0

0.275204

1,101

29

95

37.965517

0.779449

0

0.363636

0

0.114441

0.039964

0

1

0.272727

false

0

0.045455

0.227273

0.590909

0

null

0

1

0

null

0

1

0

1

0

4

176b2af669424bcd81acd498dc6ee517c52f48cc

127

py

Python

Viewer/cleanupJSON.py

WorldViews/Spirals

7efe2d145298a53c9d79db73b79428efff1e0908

[ "MIT" ]

null

Viewer/cleanupJSON.py

WorldViews/Spirals

7efe2d145298a53c9d79db73b79428efff1e0908

[ "MIT" ]

2

2021-02-08T20:23:17.000Z

2021-04-30T20:42:00.000Z

Viewer/cleanupJSON.py

WorldViews/Spirals

7efe2d145298a53c9d79db73b79428efff1e0908

[ "MIT" ]

2

2016-05-26T20:24:00.000Z

2019-08-01T03:11:49.000Z

import json obj = json.load(file("PAL_porter_0.json")) json.dump(obj, file("PAL_porter.json","w"), indent=4, sort_keys=True)

21.166667

69

0.716535

23

127

3.782609

0.652174

0.16092

0.298851

0

0.017241

0.086614

127

5

70

25.4

0.732759

0

0.261905

0

1

0

false

0

0.333333

0

0.333333

0

1

0

null

0

1

0

1

0

null

0

1

0

4

1772795acde0374d5d479129fde7ae536c3601ec

327

py

Python

app/test.py

jerry0chu/Experiment

18c70cf00328f8b80c9be1a324ed51c83bd16028

[ "MIT" ]

null

app/test.py

jerry0chu/Experiment

18c70cf00328f8b80c9be1a324ed51c83bd16028

[ "MIT" ]

null

app/test.py

jerry0chu/Experiment

18c70cf00328f8b80c9be1a324ed51c83bd16028

[ "MIT" ]

null

from app.lab.handle.handleExpData import handleUploadExpData path0 = 'D:\\CodeSpace\\PySpace\\Experiment\\app\\static\\upload\\data.xlsx' path1='D:\\CodeSpace\\PySpace\\Experiment\\app\\static\\upload\\my.xls' #print(path1) handleUploadExpData(path0,1) print('-----------------------------------') handleUploadExpData(path1,1)

40.875

76

0.691131

36

327

6.277778

0.583333

0.212389

0.150442

0.238938

0.371681

0

0.022222

0.036697

327

8

77

40.875

0.695238

0.036697

0

0.520635

0

1

0

false

0

0.166667

0

0.166667

0

null

1

0

1

0

1

0

1

null

0

4

bded2696f6ac6099d15730ef64ca9489b378507a

71

py

Python

receparser/__init__.py

stagira13/receparser

99524468b5aa41e4e6a5ea900c9873472a06297a

[ "MIT" ]

7

2019-11-29T04:55:18.000Z

2021-06-05T15:10:29.000Z

receparser/__init__.py

stagira13/receparser

99524468b5aa41e4e6a5ea900c9873472a06297a

[ "MIT" ]

1

2019-11-22T05:00:34.000Z

2019-11-25T01:18:50.000Z

receparser/__init__.py

stagira13/receparser

99524468b5aa41e4e6a5ea900c9873472a06297a

[ "MIT" ]

null

""" ReceParser""" from receparser.core import ( MonthlyRece, Rece )

11.833333

29

0.690141

7

71

7

0.857143

0

0.169014

71

5

30

14.2

0.830508

0.140845

0

1

0

true

0

0.333333

0

0.333333

0

1

0

null

0

1

0

null

0

1

0

1

0

4

bdef69edbb8d8fc73748e174288ac8eda71b56b8

187

py

Python

brick_wall_build/tests/build_scripts/annotation_misuse_2.py

IndeedTokyo-BrickWall/brick-wall-build

b3c38f671e1913e6607285cd3e8f2b5240fd4aa7

[ "MIT" ]

null

brick_wall_build/tests/build_scripts/annotation_misuse_2.py

IndeedTokyo-BrickWall/brick-wall-build

b3c38f671e1913e6607285cd3e8f2b5240fd4aa7

[ "MIT" ]

null

brick_wall_build/tests/build_scripts/annotation_misuse_2.py

IndeedTokyo-BrickWall/brick-wall-build

b3c38f671e1913e6607285cd3e8f2b5240fd4aa7

[ "MIT" ]

null

from brick_wall_build import task @task() def clean(): pass # Should be marked as task. def html(): pass # References a non task. @task(clean,html) def android(): pass

12.466667

33

0.652406

28

187

4.285714

0.642857

0.133333

0

0.240642

187

14

34

13.357143

0.84507

0.256684

0

0.333333

0

1

0.333333

true

0.333333

0.111111

0

0.444444

0

1

0

null

0

null

0

1

0

4

bdfddcb4da4e24ee1539b0b2ab902bf1539d81cb

149

py

Python

{{cookiecutter.repo_name}}/tasks.py

pylabs/cookiecutter-pyramid-starter

d5dbf20ee2d29080eefad277f4488c6607e1a846

[ "MIT" ]

null

{{cookiecutter.repo_name}}/tasks.py

pylabs/cookiecutter-pyramid-starter

d5dbf20ee2d29080eefad277f4488c6607e1a846

[ "MIT" ]

null

{{cookiecutter.repo_name}}/tasks.py

pylabs/cookiecutter-pyramid-starter

d5dbf20ee2d29080eefad277f4488c6607e1a846

[ "MIT" ]

null

from invoke import task @task def test(c): c.run('pytest') @task def test_coverage(c): c.run('pytest --cov={{ cookiecutter.repo_name }}')

13.545455

54

0.651007

23

149

4.130435

0.608696

0.147368

0.231579

0

0.174497

149

10

55

14.9

0.772358

0

0.285714

0

0.315436

0.147651

0

1

0.285714

false

0

0.142857

0

0.428571

0

1

0

null

0

1

0

null

0

1

0

4