xszheng2020/the_stack_dedup_python_hits_5 · Datasets at Hugging Face

ff2a5bf6dbeb1144b91a070950e79c6e061e6015

42

py

Python

l10ndiff/__init__.py

zbraniecki/l10ndiff

93435ea9bab40cfcc15edf31b5928e2bc9c32954

[ "BSD-3-Clause" ]

1

2017-04-04T06:55:27.000Z

l10ndiff/__init__.py

zbraniecki/l10ndiff

93435ea9bab40cfcc15edf31b5928e2bc9c32954

[ "BSD-3-Clause" ]

null

l10ndiff/__init__.py

zbraniecki/l10ndiff

93435ea9bab40cfcc15edf31b5928e2bc9c32954

[ "BSD-3-Clause" ]

null

from .entity import * from .list import *

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5

ff462f2789e6a2a436b3c028283a2706cf3b0cb9

25

py

Python

trainer/__init__.py

JasonJYang/GraphSynergy

e2e69e9b55c09ec62af1e51f01988b7d7c46c616

[ "MIT" ]

1

2022-03-04T06:48:43.000Z

trainer/__init__.py

JasonJYang/GraphSynergy

e2e69e9b55c09ec62af1e51f01988b7d7c46c616

[ "MIT" ]

null

trainer/__init__.py

JasonJYang/GraphSynergy

e2e69e9b55c09ec62af1e51f01988b7d7c46c616

[ "MIT" ]

2

2021-05-21T01:23:50.000Z

2021-06-28T04:36:50.000Z

# from .trainer import *

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py

Python

src/metaDMG/fit/__init__.py

metaDMG-dev/metaDMG-core

8894a2069e4fe4261ca3d96c7dae7d0a580228fc

[ "MIT" ]

null

src/metaDMG/fit/__init__.py

metaDMG-dev/metaDMG-core

8894a2069e4fe4261ca3d96c7dae7d0a580228fc

[ "MIT" ]

null

src/metaDMG/fit/__init__.py

metaDMG-dev/metaDMG-core

8894a2069e4fe4261ca3d96c7dae7d0a580228fc

[ "MIT" ]

null

from metaDMG.fit.workflow import run_workflow from metaDMG.loggers.loggers import get_logger_port_and_path, setup_logger

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5

ff4fb3383856e7a3479f4553607ce259615f7d93

1,908

py

Python

test/unit/test_template_selector.py

izakp/hokusai

a54778755c4b0db43a1e5773677c3e40b2ac0578

[ "MIT" ]

85

2017-01-05T11:50:37.000Z

2022-02-05T13:22:50.000Z

test/unit/test_template_selector.py

izakp/hokusai

a54778755c4b0db43a1e5773677c3e40b2ac0578

[ "MIT" ]

192

2017-01-26T18:06:55.000Z

2022-03-29T16:28:36.000Z

test/unit/test_template_selector.py

izakp/hokusai

a54778755c4b0db43a1e5773677c3e40b2ac0578

[ "MIT" ]

23

2016-11-29T17:18:02.000Z

2021-08-23T16:51:31.000Z

import os import yaml from test import HokusaiUnitTestCase from hokusai import CWD from hokusai.lib.exceptions import HokusaiError from hokusai.lib.template_selector import TemplateSelector class TestTemplateSelector(HokusaiUnitTestCase): def setUp(self): self.template_path = os.path.join(CWD, 'test/fixtures/project/hokusai') def test_finds_yml_file(self): test_file = os.path.join(self.template_path, 'test.yml') open(test_file, 'a').close() self.assertEqual(TemplateSelector().get(os.path.join(self.template_path, 'test')), test_file) os.remove(test_file) def test_finds_yaml_file(self): test_file = os.path.join(self.template_path, 'test.yaml') open(test_file, 'a').close() self.assertEqual(TemplateSelector().get(os.path.join(self.template_path, 'test')), test_file) os.remove(test_file) def test_finds_yml_j2_file(self): test_file = os.path.join(self.template_path, 'test.yml.j2') open(test_file, 'a').close() self.assertEqual(TemplateSelector().get(os.path.join(self.template_path, 'test')), test_file) os.remove(test_file) def test_finds_yaml_j2_file(self): test_file = os.path.join(self.template_path, 'test.yaml.j2') open(test_file, 'a').close() self.assertEqual(TemplateSelector().get(os.path.join(self.template_path, 'test')), test_file) os.remove(test_file) def test_finds_explicit_file_or_errors(self): with self.assertRaises(HokusaiError): TemplateSelector().get(os.path.join(self.template_path, 'test.yml')) test_file = os.path.join(self.template_path, 'test.yml') open(test_file, 'a').close() self.assertEqual(TemplateSelector().get(os.path.join(self.template_path, 'test.yml')), test_file) os.remove(test_file) def test_errors_with_no_template_found(self): with self.assertRaises(HokusaiError): TemplateSelector().get(os.path.join(self.template_path, 'test'))

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5

ff59a83557658312e508613aa3caaa840d65c188

10,869

py

Python

tests/test_browser_user.py

pekrau/Pleko

63797835cea24e228710530111ae6c132f6fda94

[ "MIT" ]

null

tests/test_browser_user.py

pekrau/Pleko

63797835cea24e228710530111ae6c132f6fda94

[ "MIT" ]

235

2019-02-18T14:58:04.000Z

2019-04-23T15:19:09.000Z

tests/test_browser_user.py

pekrau/Pleko

63797835cea24e228710530111ae6c132f6fda94

[ "MIT" ]

null

"""Test operations for an ordinary logged-in user in a browser. Requires a user account specified in the file 'settings.json' given by - USER_USERNAME - USER_PASSWORD After installing from PyPi using the 'requirements.txt' file, one must do: $ playwright install To run while displaying browser window: $ pytest --headed Much of the code below was created using the playwright code generation feature: $ playwright codegen http://localhost:5001/ """ import http.client import urllib.parse import pytest import playwright.sync_api import utils @pytest.fixture(scope="module") def settings(): "Get the settings from file 'settings.json' in this directory." return utils.get_settings( BASE_URL="http://localhost:5001", USER_USERNAME=None, USER_PASSWORD=None ) def login_user(settings, page): "Login to the system as ordinary user." page.goto(settings["BASE_URL"]) page.click("text=Login") assert page.url == f"{settings['BASE_URL']}/user/login?" page.click('input[name="username"]') page.fill('input[name="username"]', settings["USER_USERNAME"]) page.press('input[name="username"]', "Tab") page.fill('input[name="password"]', settings["USER_PASSWORD"]) page.click("id=login") assert page.url == f"{settings['BASE_URL']}/" def test_table_data(settings, page): # 'page' fixture from 'pytest-playwright' "Test login, creating a table, inserting data 'by hand'." login_user(settings, page) # Create a database 'test'. page.goto(f"{settings['BASE_URL']}/dbs/owner/{settings['USER_USERNAME']}") page.click("text=Create") assert page.url == f"{settings['BASE_URL']}/db/" page.click('input[name="name"]') page.fill('input[name="name"]', "test") page.click('textarea[name="description"]') page.fill('textarea[name="description"]', "test database") page.click('button:has-text("Create")') assert page.url == f"{settings['BASE_URL']}/db/test" # Create a table 't1'. page.click("text=Create table") assert page.url == f"{settings['BASE_URL']}/table/test" page.click('input[name="name"]') page.fill('input[name="name"]', "t1") page.click('input[name="column0name"]') page.fill('input[name="column0name"]', "i") page.check("#column0primarykey") page.click('input[name="column1name"]') page.fill('input[name="column1name"]', "f") page.select_option('select[name="column1type"]', "REAL") page.click('input[name="column2name"]') page.fill('input[name="column2name"]', "s") page.select_option('select[name="column2type"]', "TEXT") page.click('input[name="column3name"]') page.fill('input[name="column3name"]', "r") page.select_option('select[name="column3type"]', "REAL") page.check('input[name="column3notnull"]') page.click('button:has-text("Create")') assert page.url == f"{settings['BASE_URL']}/table/test/t1" # Insert a row into the table. page.click("text=Insert row") assert page.url == f"{settings['BASE_URL']}/table/test/t1/row" page.click('input[name="i"]') page.fill('input[name="i"]', "1") page.click('input[name="f"]') page.fill('input[name="f"]', "3.0") page.click('input[name="s"]') page.fill('input[name="s"]', "apa") page.click('input[name="r"]') page.fill('input[name="r"]', "3.141") page.click('button:has-text("Insert")') assert page.url == f"{settings['BASE_URL']}/table/test/t1/row" # Insert another row into the table. page.click('input[name="i"]') page.fill('input[name="i"]', "2") page.click('input[name="f"]') page.click('input[name="s"]') page.fill('input[name="s"]', "blah") page.click('input[name="r"]') page.fill('input[name="r"]', "-1.0") page.click('button:has-text("Insert")') assert page.url == f"{settings['BASE_URL']}/table/test/t1/row" # Delete the table. page.click("text=2 rows") table_url = f"{settings['BASE_URL']}/table/test/t1" assert page.url == table_url page.once("dialog", lambda dialog: dialog.accept()) # Callback for next click. page.click("text=Delete") assert page.url == f"{settings['BASE_URL']}/db/test" page.goto(table_url) locator = page.locator("text=No such table") playwright.sync_api.expect(locator).to_have_count(1) # Delete the database. page.once("dialog", lambda dialog: dialog.accept()) # Callback for next click. page.click("text=Delete") assert page.url == f"{settings['BASE_URL']}/dbs/owner/{settings['USER_USERNAME']}" def test_table_csv(settings, page): # 'page' fixture from 'pytest-playwright' "Test login, creating a table, inserting data from a CSV file." login_user(settings, page) # Create a database 'test'. page.goto(f"{settings['BASE_URL']}/dbs/owner/{settings['USER_USERNAME']}") page.click("text=Create") assert page.url == f"{settings['BASE_URL']}/db/" page.click('input[name="name"]') page.fill('input[name="name"]', "test") page.click('button:has-text("Create")') assert page.url == f"{settings['BASE_URL']}/db/test" # Create a table 't1'. page.click("text=Create table") assert page.url == f"{settings['BASE_URL']}/table/test" page.click('input[name="name"]') page.fill('input[name="name"]', "t1") page.click('input[name="column0name"]') page.fill('input[name="column0name"]', "i") page.check("#column0primarykey") page.click('input[name="column1name"]') page.fill('input[name="column1name"]', "r") page.select_option('select[name="column1type"]', "REAL") page.click('input[name="column2name"]') page.fill('input[name="column2name"]', "j") page.click('input[name="column3name"]') page.fill('input[name="column3name"]', "t") page.select_option('select[name="column3type"]', "TEXT") page.check('input[name="column3notnull"]') page.click('button:has-text("Create")') assert page.url == f"{settings['BASE_URL']}/table/test/t1" # Insert data from file. page.click("text=Insert from file") assert page.url == "http://localhost:5001/table/test/t1/insert" with page.expect_file_chooser() as fc_info: page.click('input[name="csvfile"]') file_chooser = fc_info.value file_chooser.set_files("test.csv") page.click("text=Insert from CSV file") assert page.url == "http://localhost:5001/table/test/t1" page.click("text=Database test") assert page.url == f"{settings['BASE_URL']}/db/test" # Query the database. page.click("text=Query") assert page.url == "http://localhost:5001/query/test" page.click('textarea[name="select"]') page.fill('textarea[name="select"]', "i,r") page.click('textarea[name="from"]') page.fill('textarea[name="from"]', "t1") page.click('textarea[name="where"]') page.fill('textarea[name="where"]', 't = "blah"') page.click("text=Execute query") assert page.url == "http://localhost:5001/query/test/rows" assert page.locator("#nrows").text_content() == "1" locator = page.locator("#rows > tbody > tr") playwright.sync_api.expect(locator).to_have_count(1) # Modify the query. page.click("text=Edit query") assert page.url.startswith("http://localhost:5001/query/test") page.click('textarea[name="where"]') page.fill('textarea[name="where"]', "j = 3") page.click("text=Execute query") assert page.url == "http://localhost:5001/query/test/rows" assert page.locator("#nrows").text_content() == "2" locator = page.locator("#rows > tbody > tr") playwright.sync_api.expect(locator).to_have_count(2) # Delete the database. page.click("text=Database test") assert page.url == "http://localhost:5001/db/test" page.once("dialog", lambda dialog: dialog.accept()) # Callback for next click. page.click("text=Delete") assert page.url == f"{settings['BASE_URL']}/dbs/owner/{settings['USER_USERNAME']}" def test_db_upload(settings, page): "Test uploading a Sqlite3 database file." login_user(settings, page) # Upload database file. page.goto(f"{settings['BASE_URL']}/dbs/owner/{settings['USER_USERNAME']}") page.click("text=Upload") assert page.url == "http://localhost:5001/dbs/upload" page.once("filechooser", lambda fc: fc.set_files("test.sqlite3")) page.click('input[name="sqlite3file"]') page.click("text=Upload SQLite3 file") assert page.url == "http://localhost:5001/db/test" page.click("text=3 rows") assert page.url == "http://localhost:5001/table/test/t1" locator = page.locator("#rows > tbody > tr") playwright.sync_api.expect(locator).to_have_count(3) page.click('a[role="button"]:has-text("Schema")') assert page.url == "http://localhost:5001/table/test/t1/schema" locator = page.locator("#columns > tbody > tr") playwright.sync_api.expect(locator).to_have_count(7) # Delete the database. page.click("text=Database test") assert page.url == "http://localhost:5001/db/test" page.once("dialog", lambda dialog: dialog.accept()) # Callback for next click. page.click("text=Delete") assert page.url == f"{settings['BASE_URL']}/dbs/owner/{settings['USER_USERNAME']}" def test_view(settings, page): "Test view creation, based on a table in an uploaded Sqlite3 database file." login_user(settings, page) # Upload database file. page.goto(f"{settings['BASE_URL']}/dbs/owner/{settings['USER_USERNAME']}") page.click("text=Upload") assert page.url == "http://localhost:5001/dbs/upload" page.once("filechooser", lambda fc: fc.set_files("test.sqlite3")) page.click('input[name="sqlite3file"]') page.click("text=Upload SQLite3 file") assert page.url == "http://localhost:5001/db/test" # Create the view. page.click("text=Query") assert page.url == "http://localhost:5001/query/test" page.click('textarea[name="select"]') page.fill('textarea[name="select"]', "i, r1") page.click('textarea[name="from"]') page.fill('textarea[name="from"]', "t1") page.click('textarea[name="where"]') page.fill('textarea[name="where"]', "i2 < 0") page.click("text=Execute query") assert page.url == "http://localhost:5001/query/test/rows" page.click("text=Create view") page.click('input[name="name"]') page.fill('input[name="name"]', "v1") page.click('button:has-text("Create")') page.wait_for_timeout(3000) locator = page.locator("#rows > tbody > tr") playwright.sync_api.expect(locator).to_have_count(2) # Delete the database. page.click("text=Database test") assert page.url == "http://localhost:5001/db/test" page.once("dialog", lambda dialog: dialog.accept()) # Callback for next click. page.click("text=Delete") assert page.url == f"{settings['BASE_URL']}/dbs/owner/{settings['USER_USERNAME']}" # page.wait_for_timeout(3000)

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5

ffa60f1f8af18963c45ed7e712c185286768893f

217

wsgi

Python

CTF/HSCTF/apache/django.wsgi

IAryan/NULLify-HSCTF-2014

9e1c2aca9fc6b0cb98e73f8abd76299cb3cc0fb7

[ "MIT" ]

1

2016-03-20T19:35:33.000Z

CTF/HSCTF/apache/django.wsgi

IAryan/NULLify-HSCTF-2014

9e1c2aca9fc6b0cb98e73f8abd76299cb3cc0fb7

[ "MIT" ]

null

CTF/HSCTF/apache/django.wsgi

IAryan/NULLify-HSCTF-2014

9e1c2aca9fc6b0cb98e73f8abd76299cb3cc0fb7

[ "MIT" ]

null

import os import sys sys.path.append('/home/nullify/CTF/HSCTF') os.environ['DJANGO_SETTINGS_MODULE'] = 'HSCTF.settings' import django.core.handlers.wsgi application = django.core.handlers.wsgi.WSGIHandler()

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4402bc9c4a874e29d4c4f848093f9ecf53668762

658

py

Python

easyrec/models/__init__.py

xu-zhiwei/easyrec

4e42a356efe799bcd469a568d356852e4230bbc8

[ "MIT" ]

5

2021-08-12T22:54:07.000Z

2022-03-27T11:46:48.000Z

easyrec/models/__init__.py

xu-zhiwei/pyrec

4e42a356efe799bcd469a568d356852e4230bbc8

[ "MIT" ]

null

easyrec/models/__init__.py

xu-zhiwei/pyrec

4e42a356efe799bcd469a568d356852e4230bbc8

[ "MIT" ]

null

from easyrec.models.afm import AFM from easyrec.models.autoint import AutoInt from easyrec.models.dcn import DCN from easyrec.models.deep_crossing import DeepCrossing from easyrec.models.deepfm import DeepFM from easyrec.models.dssm import DSSM from easyrec.models.ffm import FFM from easyrec.models.fm import FM from easyrec.models.fnn import FNN from easyrec.models.lr import LR from easyrec.models.mlp import MLP from easyrec.models.mmoe import MMOE from easyrec.models.neumf import NeuMF from easyrec.models.nfm import NFM from easyrec.models.pnn import PNN from easyrec.models.wide_and_deep import WideAndDeep from easyrec.models.xdeepfm import xDeepFM

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440cc911d4a24c89013e1a56756fa37966a6a574

32

py

Python

CoinMktCap/__init__.py

sarthakkimtani/CoinMktCap

325ea27cb09440d325aa532e086054804f181366

[ "MIT" ]

1

2021-06-09T12:38:04.000Z

CoinMktCap/__init__.py

sarthakkimtani/CoinMktCap

325ea27cb09440d325aa532e086054804f181366

[ "MIT" ]

null

CoinMktCap/__init__.py

sarthakkimtani/CoinMktCap

325ea27cb09440d325aa532e086054804f181366

[ "MIT" ]

null

from .core import CoinMarketCap

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5

4437ad23b732a2d6e8ab0de7c1c961c5d80ddd4b

65

py

Python

snake_games/circlez/__init__.py

cclauss/snakeware

9b857d132dcabc2aedb407d694b8c1d9e13cad1e

[ "MIT" ]

null

snake_games/circlez/__init__.py

cclauss/snakeware

9b857d132dcabc2aedb407d694b8c1d9e13cad1e

[ "MIT" ]

null

snake_games/circlez/__init__.py

cclauss/snakeware

9b857d132dcabc2aedb407d694b8c1d9e13cad1e

[ "MIT" ]

null

from .circ import CirclezApp def load(): CirclezApp().run()

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4453276425c153e5e1c6052fecce86e9896f21f6

40

py

Python

boa3_test/test_sc/variable_test/MismatchedTypeAugAssign.py

hal0x2328/neo3-boa

6825a3533384cb01660773050719402a9703065b

[ "Apache-2.0" ]

25

2020-07-22T19:37:43.000Z

2022-03-08T03:23:55.000Z

boa3_test/test_sc/variable_test/MismatchedTypeAugAssign.py

hal0x2328/neo3-boa

6825a3533384cb01660773050719402a9703065b

[ "Apache-2.0" ]

419

2020-04-23T17:48:14.000Z

2022-03-31T13:17:45.000Z

boa3_test/test_sc/variable_test/MismatchedTypeAugAssign.py

hal0x2328/neo3-boa

6825a3533384cb01660773050719402a9703065b

[ "Apache-2.0" ]

15

2020-05-21T21:54:24.000Z

2021-11-18T06:17:24.000Z

def Main(): a: int = 1 a += '2'

10

14

0.35

7

40

2

0.857143

0

0.086957

0.425

40

3

15

13.333333

0.521739

0

0.025

0

1

0.333333

true

0

0.333333

0

1

0

null

0

1

0

null

0

1

0

5

445b3fd78610197862f170bee8136e37049c1dca

96

py

Python

venv/lib/python3.8/site-packages/parso/python/tree.py

Retraces/UkraineBot

3d5d7f8aaa58fa0cb8b98733b8808e5dfbdb8b71

[ "MIT" ]

2

2022-03-13T01:58:52.000Z

2022-03-31T06:07:54.000Z

venv/lib/python3.8/site-packages/parso/python/tree.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

19

2021-11-20T04:09:18.000Z

2022-03-23T15:05:55.000Z

venv/lib/python3.8/site-packages/parso/python/tree.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

null

/home/runner/.cache/pip/pool/c4/0e/2a/6f50bd15fe507d5bd9458501ecace9282a8d3bc0cc765fb7e1c22c283b

96

0.895833

9

96

9.555556

1

0

0.375

0

96

1

96

0.520833

0

1

0

null

0

null

0

1

0

null

0

1

0

1

0

null

1

0

1

0

5

4463bbb1b8349c07ee0a96280bf9dd61c02ce966

41

py

Python

catalyst/contrib/dl/__init__.py

denyhoof/catalyst

a340450076f7846007bc5695e5163e15b7ad9575

[ "Apache-2.0" ]

1

2020-09-24T00:34:06.000Z

catalyst/contrib/dl/__init__.py

denyhoof/catalyst

a340450076f7846007bc5695e5163e15b7ad9575

[ "Apache-2.0" ]

null

catalyst/contrib/dl/__init__.py

denyhoof/catalyst

a340450076f7846007bc5695e5163e15b7ad9575

[ "Apache-2.0" ]

1

2020-09-24T00:34:07.000Z

# flake8: noqa from .callbacks import *

10.25

24

0.707317

5

41

5.8

1

0

0.030303

0.195122

41

3

25

13.666667

0.848485

0.292683

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

44799b8f43072e0545ac854032ffc219475ec03a

188

py

Python

DNA_strand.py

lluxury/codewars

2cacc9d5411a248c199ad21949617c5acc9c7f24

[ "MIT" ]

null

DNA_strand.py

lluxury/codewars

2cacc9d5411a248c199ad21949617c5acc9c7f24

[ "MIT" ]

null

DNA_strand.py

lluxury/codewars

2cacc9d5411a248c199ad21949617c5acc9c7f24

[ "MIT" ]

null

pairs = {'A':'T','T':'A','C':'G','G':'C'} def DNA_strand(dna): ''' DNA_strand("AAAA") "TTTT" ''' return ''.join([pairs[x] for x in dna]) # 标准的解法,构造一个表,用值来替代键返回

20.888889

44

0.478723

28

188

3.142857

0.642857

0.204545

0

0.239362

188

9

45

20.888889

0.615385

0.25

0

0.070175

0

1

0.333333

false

0

0.666667

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

44934720143d8e56e5a86c09e91f767f70d0f5a7

91

py

Python

tabitha/__init__.py

robladbrook/tabitha

7beb87cc313eb841eb4a1da6c1116b8627590a92

[ "MIT" ]

1

2017-01-10T00:43:26.000Z

tabitha/__init__.py

robladbrook/tabitha

7beb87cc313eb841eb4a1da6c1116b8627590a92

[ "MIT" ]

1

2019-10-31T22:01:42.000Z

tabitha/__init__.py

robladbrook/tabitha

7beb87cc313eb841eb4a1da6c1116b8627590a92

[ "MIT" ]

null

""" init """ from __future__ import absolute_import from .voiceclient import VoiceClient

15.166667

38

0.78022

10

91

6.6

0.6

0

0.142857

91

5

39

18.2

0.846154

0.043956

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

9240633c551d1d88e59700e54c4969c97090ad93

114

py

Python

pbo1-project/Models/__init__.py

hifra01/PBO1-Project

82b9abbdf1cab4da18e0f9514e92d298d9661b26

[ "MIT" ]

null

pbo1-project/Models/__init__.py

hifra01/PBO1-Project

82b9abbdf1cab4da18e0f9514e92d298d9661b26

[ "MIT" ]

null

pbo1-project/Models/__init__.py

hifra01/PBO1-Project

82b9abbdf1cab4da18e0f9514e92d298d9661b26

[ "MIT" ]

null

from .CustomerModel import CustomerModel from .AdminModel import AdminModel from .OrderModel import OrderModel

28.5

41

0.842105

12

114

8

0.416667

0

0.131579

114

3

42

38

0.969697

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

9250dfbc33c90b4778b3efcc044337f4ae57fbc6

131

py

Python

Geometry/tests/test_rectangle.py

liuxiang0/Geometry

3500f815fa56c535b36d1b6fd0afe69ce5d055be

[ "MIT" ]

23

2015-10-28T15:21:41.000Z

2022-03-29T13:52:41.000Z

Geometry/tests/test_rectangle.py

liuxiang0/Geometry

3500f815fa56c535b36d1b6fd0afe69ce5d055be

[ "MIT" ]

7

2021-01-26T11:57:25.000Z

2022-02-07T11:00:06.000Z

Geometry/tests/test_rectangle.py

liuxiang0/Geometry

3500f815fa56c535b36d1b6fd0afe69ce5d055be

[ "MIT" ]

16

2016-07-17T12:47:05.000Z

2021-06-21T21:02:48.000Z

import unittest from .. import Point, Triangle from ..exceptions import * class RectangleTestCase(unittest.TestCase): pass

13.1

43

0.755725

14

131

7.071429

0.714286

0

0.167939

131

9

44

14.555556

0.908257

0

1

0

true

0.2

0.6

0

0.8

0

1

0

null

0

1

0

null

0

1

0

5

9255ce7fa25b1d5ce466954f3bd99a5838a635be

50

py

Python

lc_classifier/features/preprocess/__init__.py

alercebroker/late_classifier

72fb640ee37bff67f865945499b417f3ca36c3cb

[ "MIT" ]

6

2021-04-27T03:12:43.000Z

2022-01-23T06:36:48.000Z

lc_classifier/features/preprocess/__init__.py

alercebroker/late_classifier

72fb640ee37bff67f865945499b417f3ca36c3cb

[ "MIT" ]

8

2020-11-27T04:39:14.000Z

2022-01-13T17:47:45.000Z

lc_classifier/features/preprocess/__init__.py

alercebroker/lc_classifier

72fb640ee37bff67f865945499b417f3ca36c3cb

[ "MIT" ]

2

2021-08-10T08:06:23.000Z

2022-01-14T12:31:43.000Z

from .base import * from .preprocess_ztf import *

16.666667

29

0.76

7

50

5.285714

0.714286

0

0.16

50

2

30

25

0.880952

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

926d28c67fbc975ff39f397e1695245872e036bd

23

py

Python

models/__init__.py

Sacinandan/flask-api

f73a45a8ac2bd5aa2184866225dceaf78187f2f0

[ "MIT" ]

null

models/__init__.py

Sacinandan/flask-api

f73a45a8ac2bd5aa2184866225dceaf78187f2f0

[ "MIT" ]

null

models/__init__.py

Sacinandan/flask-api

f73a45a8ac2bd5aa2184866225dceaf78187f2f0

[ "MIT" ]

null

from .note import Note

11.5

22

0.782609

4

23

4.5

0.75

0

0.173913

23

1

23

0.947368

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

92756f38936311d66073dba333ec145ed7c6dbe7

174

py

Python

bnf/test/utils.py

Nikita-Boyarskikh/bnf

1293b0f2187593989e2484a7af9612477fa8bbe0

[ "MIT" ]

null

bnf/test/utils.py

Nikita-Boyarskikh/bnf

1293b0f2187593989e2484a7af9612477fa8bbe0

[ "MIT" ]

null

bnf/test/utils.py

Nikita-Boyarskikh/bnf

1293b0f2187593989e2484a7af9612477fa8bbe0

[ "MIT" ]

null

from typing import Iterator from bnf.rule import Rule def assert_all_tests_is_valid(rule: Rule, tests: Iterator[str]): assert all([rule(i).is_valid() for i in tests])

21.75

64

0.747126

30

174

4.166667

0.533333

0.144

0

0.149425

174

7

65

24.857143

0.844595

0

0.5

1

0.25

false

0

0.5

0

0.75

0

null

0

1

0

null

0

1

0

1

0

1

0

1

0

5

927968c937e65ca96fbdefdc5f63531d8f9151fb

160

py

Python

xlib/onnxruntime/__init__.py

kitiv/DeepFaceLive

ca3a005917ae067576b795d8b9fef5a8b3483010

[ "MIT" ]

4

2021-07-23T16:34:24.000Z

2022-03-01T18:31:59.000Z

xlib/onnxruntime/__init__.py

kitiv/DeepFaceLive

ca3a005917ae067576b795d8b9fef5a8b3483010

[ "MIT" ]

1

2022-02-08T01:29:03.000Z

xlib/onnxruntime/__init__.py

kitiv/DeepFaceLive

ca3a005917ae067576b795d8b9fef5a8b3483010

[ "MIT" ]

1

2021-12-14T09:18:15.000Z

from .device import (ORTDeviceInfo, get_available_devices_info, get_cpu_device) from .InferenceSession import InferenceSession_with_device

40

63

0.76875

17

160

6.823529

0.647059

0

0.19375

160

3

64

53.333333

0.899225

0

1

0

true

0

0.666667

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

92915c50d9cb8eb973748cd01dfd2bad96533da2

231

py

Python

simple_bson/__init__.py

DeltaLaboratory/simple-bson

fed15745e9cbc700b8573994041a0faca575d2d4

[ "Apache-2.0" ]

77

2021-07-01T16:29:27.000Z

2021-09-25T03:34:04.000Z

simple_bson/__init__.py

DeltaLaboratory/simple-bson

fed15745e9cbc700b8573994041a0faca575d2d4

[ "Apache-2.0" ]

1

2021-07-02T02:41:07.000Z

2021-07-02T05:02:55.000Z

simple_bson/__init__.py

DeltaLaboratory/simple-bson

fed15745e9cbc700b8573994041a0faca575d2d4

[ "Apache-2.0" ]

null

import typing from . import encoder, decoder def dumps(document: typing.Dict) -> bytes: return encoder.encode_document(document) def loads(document: bytes) -> typing.Dict: return decoder.decode_root_document(document)

19.25

49

0.757576

29

231

5.931034

0.517241

0.116279

0

0.147186

231

11

50

21

0.873096

0

1

0.333333

false

0

0.333333

1

0

1

0

null

0

null

0

1

0

1

0

5

92a0091a9fcc2e2776e26a98baed3d15155b44b6

89

py

Python

solutions/Leetcode_1689/leetcode_1689.py

YuhanShi53/Leetcode_solutions

cdcad34656d25d6af09b226e17250c6070305ab0

[ "MIT" ]

null

solutions/Leetcode_1689/leetcode_1689.py

YuhanShi53/Leetcode_solutions

cdcad34656d25d6af09b226e17250c6070305ab0

[ "MIT" ]

null

solutions/Leetcode_1689/leetcode_1689.py

YuhanShi53/Leetcode_solutions

cdcad34656d25d6af09b226e17250c6070305ab0

[ "MIT" ]

null

class Solution1: def min_partitions(self, n: str) -> int: return int(max(n))

22.25

44

0.617978

13

89

4.153846

0.846154

0

0.014925

0.247191

89

3

45

29.666667

0.791045

0

1

0.333333

false

0

0.333333

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

92a12011ba78b814ab1e55cf2facfb6c5a9947c0

559

py

Python

app.py

hmyhehe/hmy.git.io

15d6fd6204af8b1be05dd7738390aa61e72d3399

[ "MIT" ]

null

app.py

hmyhehe/hmy.git.io

15d6fd6204af8b1be05dd7738390aa61e72d3399

[ "MIT" ]

null

app.py

hmyhehe/hmy.git.io

15d6fd6204af8b1be05dd7738390aa61e72d3399

[ "MIT" ]

null

#!/usr/bin/python3 from flask import Flask, render_template, request app = Flask(__name__) @app.route("/") def index(): return render_template("login.html") @app.route("/login",methods=['POST','GET']) def login(): if request.method == 'GET': return render_template("login.html") else: name = request.form['id'] password = request.form['pwd'] if name == 'zhangsan' and password == '123' return render_template("welcome.html",name = name) else: return render_template("login.html")

26.619048

62

0.617174

67

559

5.014925

0.462687

0.208333

0.238095

0.223214

0.258929

0

0.009217

0.223614

559

20

63

27.95

0.764977

0.030411

0

0.3125

0

0.138632

0

null

0.125

0.0625

null

0

null

1

0

null

0

1

0

1

0

5

92b19077d649f9a75064da9c0b2b6f257669231b

334

py

Python

routing/exceptions.py

Atari4800/fast-backend

4864300351f5cb93638deb6c18017149eb7f17ca

[ "MIT" ]

1

2021-11-25T22:57:59.000Z

routing/exceptions.py

Capstone-Team-Fast/fast-backend

7854a2731ba0923e89581c1fbb6cc95ea210fed0

[ "MIT" ]

null

routing/exceptions.py

Capstone-Team-Fast/fast-backend

7854a2731ba0923e89581c1fbb6cc95ea210fed0

[ "MIT" ]

3

2022-01-29T21:32:54.000Z

2022-03-09T09:30:44.000Z

class GeocodeError(Exception): pass class MatrixServiceError(Exception): pass class RelationshipError(Exception): pass class EmptyRouteException(Exception): pass class RouteStateException(Exception): pass class LocationStateException(Exception): pass class LanguageOptionError(Exception): pass

12.37037

40

0.754491

28

334

9

0.357143

0.361111

0.428571

0

0.182635

334

26

41

12.846154

0.923077

0

0.5

0

1

0

true

0.5

0

0.5

0

1

0

null

1

0

null

0

1

0

5

92ba436788c4aef1d68009261d0b4834ceb1f22a

265

py

Python

firmware/gen_oldnewball.py

brainsmoke/hex2811-penta

449614ab6be273e92e8bffbd066acdeb4da7154b

[ "MIT" ]

4

2017-09-06T18:58:58.000Z

2021-08-05T00:04:21.000Z

firmware/gen_oldnewball.py

brainsmoke/hex2811-penta

449614ab6be273e92e8bffbd066acdeb4da7154b

[ "MIT" ]

null

firmware/gen_oldnewball.py

brainsmoke/hex2811-penta

449614ab6be273e92e8bffbd066acdeb4da7154b

[ "MIT" ]

1

2018-06-08T10:56:27.000Z

for i in [21,22,1,19,15,16,17,18,2,6,7,8,9,5,3,52,53,54,50,51,4,12,13,14,10,11,0,23,24,20,34,30,44,35,36,37,38,39,40,49,45,46,47,48,41,59,55,56,57,58,42,26,27,28,29,25,43,31,32,33]: print ("\t"+''.join(str(i*15+j*3+rgb)+"," for j in range(5) for rgb in (1,0,2)))

66.25

181

0.603774

83

265

1.927711

0.843373

0

0.46988

0.060377

265

3

182

88.333333

0.172691

0

0.011364

0

1

0

false

0

0.5

0

1

null

0

1

0

1

0

1

0

null

0

1

0

5

2b7bf6ad248f61c6c95b7425e9770c842f5b4dd9

174,862

py

Python

CalculationSK.py

atranel/resqdb

76b8a5089732ae63c867b734c5053908687122bc

[ "MIT" ]

null

CalculationSK.py

atranel/resqdb

76b8a5089732ae63c867b734c5053908687122bc

[ "MIT" ]

null

CalculationSK.py

atranel/resqdb

76b8a5089732ae63c867b734c5053908687122bc

[ "MIT" ]

null

#### Filename: CalculationSK.py #### Version: v1.0 #### Author: Marie Jankujova #### Date: March 4, 2019 #### Description: Connect to database, export Slovakia data and calculate statistics. import psycopg2 import sys import os import pandas as pd import logging from configparser import ConfigParser from resqdb.CheckData import CheckData import numpy as np import time from multiprocessing import Process, Pool from threading import Thread from datetime import datetime, time, date import time import sqlite3 from numpy import inf import pytz import xlsxwriter from xlsxwriter.utility import xl_rowcol_to_cell, xl_col_to_name class Connection: """ The class connecting to the database and exporting the data for the Slovakia. :param nprocess: number of processes :type nprocess: int """ def __init__(self, nprocess=1): start = time.time() debug = 'debug_' + datetime.now().strftime('%d-%m-%Y') + '.log' log_file = os.path.join(os.getcwd(), debug) logging.basicConfig(filename=log_file, filemode='a', format='%(asctime)s,%(msecs)d %(name)s %(levelname)s %(message)s', datefmt='%H:%M:%S', level=logging.DEBUG) logging.info('CalculationSK: Connecting to datamix database!') # Get absolute path path = os.path.dirname(__file__) self.database_ini = os.path.join(path, 'database.ini') # Set section datamix = 'datamix-backup' # Create empty dictionary self.sqls = ['SELECT * from slovakia', 'SELECT * from slovakia_2018'] #self.sqls = ['SELECT * from slovakia'] # List of dataframe names self.names = ['slovakia', 'slovakia_2018'] #self.names = ['slovakia'] self.dictdb_df = {} # Dictioanry initialization - prepared dataframes self.dict_df = {} # Export data from the database for slovakia df_name = self.names[0] self.connect(self.sqls[0], datamix, nprocess, df_name=df_name) # Export data from the database for slovakia_2018 df_name = self.names[1] self.connect(self.sqls[1], datamix, nprocess, df_name=df_name) for k, v in self.dictdb_df.items(): self.prepare_df(df=v, name=k) self.df = pd.DataFrame() print(self.dict_df) for i in range(0, len(self.names)): self.df = self.df.append(self.dict_df[self.names[i]], sort=False) logging.info("Connection: {0} dataframe has been appended to the resulting dataframe!".format(self.names[i])) self.countries = self._get_countries(df=self.df) # Get preprocessed data # self.preprocessed_data = self.check_data(df=self.df) # Read temporary csv file with CZ report names and Angels Awards report names path = os.path.join(os.path.dirname(__file__), 'tmp', 'sk_mapping.csv') with open(path, 'r') as csv_file: sk_names_dict = pd.read_csv(csv_file) def change_name(name): if pd.isna(name): return '' else: changed_name = sk_names_dict.loc[ sk_names_dict['Hospital name'].str.contains(name), 'Angels Awards name'].iloc[0] return changed_name dateForm = '%Y-%m-%d' self.df['HOSPITAL_DATE'] = pd.to_datetime(self.df['HOSPITAL_DATE'], format=dateForm, errors="coerce") self.df['DISCHARGE_DATE'] = pd.to_datetime(self.df['DISCHARGE_DATE'], format=dateForm, errors="coerce") self.df['CT_DATE'] = pd.to_datetime(self.df['CT_DATE'], format=dateForm, errors="ignore") # raw_df = raw_df.loc[raw_df['ROK_SPRAC'] == 2019].copy() self.df['Protocol ID'] = self.df['HOSPITAL_NAME'] self.df['Protocol ID'] = self.df.apply( lambda x: change_name(x['HOSPITAL_NAME']), axis=1) self.df['Site Name'] = self.df['Protocol ID'] end = time.time() tdelta = (end-start)/60 logging.info('The conversion and merging run {0} minutes.'.format(tdelta)) def config(self, section): """ The function reading and parsing the config of database file. :param section: the name of the section in database.ini file :type section: str :returns: the dictionary with the parsed section values :raises: Exception """ parser = ConfigParser() parser.read(self.database_ini) db = {} if parser.has_section(section): params = parser.items(section) for param in params: db[param[0]] = param[1] else: logging.error('CalculationSK: Section {0} not found in the {1} file'.format(section, self.database_ini)) raise Exception('Section {0} not found in the {1} file'.format(section, self.database_ini)) return db def connect(self, sql, section, nprocess, df_name=None): """ The function connecting to te database. :param sql: the sql query :type sql: str :param section: the section from the database.ini :type section: str :param nprocess: the number of processes run simultaneously :type nprocess: int :param df_name: the name of the dataframe used as key in the dictionary :type df_name: str :raises: Exception """ conn = None try: params = self.config(section) # Get parameters from config file logging.info('Process{0}: Connecting to the PostgreSQL database... '.format(nprocess)) conn = psycopg2.connect(**params) # Connect to server if df_name is not None: # For each sql query create new dataframe in the dictionary using df_name as key self.dictdb_df[df_name] = pd.read_sql_query(sql, conn) logging.info('CalculationSK: Process{0}: Dataframe {1} has been created created.'.format(nprocess, df_name)) else: logging.info('CalculationSK: Process{0}: Name of dataframe is missing.'.format(nprocess)) except (Exception, psycopg2.DatabaseError) as error: logging.error(error) finally: if conn is not None: conn.close() # Close connection logging.info('Process{0}: Database connection has been closed.'.format(nprocess)) def _calculate_time(self, ct_date, hospital_date, rec_date, used_col=None): """ The function calculating difference between two times in minutes. The function checking if hospital date is after recanalization date, and if it's TRUE then CT date is used as hospitalization date. :param ct_date: the date when CT/MRI was performed :type ct_date: date :param hospital_date: the date of hospitalization :type hospital_date: date :param rec_date: the date when recanalization procedure was performed :type rec_date: date :param used_col: the column which was used for calculation of DTN :type used_col: str :returns: tdeltamin, used_col """ rec_time = rec_date - hospital_date tdeltamin = rec_time.total_seconds()/60.0 col = 'HOSPITAL_DATE' if used_col is None: if tdeltamin <= 1: try: if hospital_date.strftime('%Y-%m-%d') > rec_date.strftime('%Y-%m-%d'): rec_time = rec_date - ct_date tdeltamin = rec_time.total_seconds()/60.0 col = 'CT_TIME' else: if hospital_date.strftime('%Y-%m-%d') == rec_date.strftime('%Y-%m-%d'): rec_time = rec_date - hospital_date tdeltamin = rec_time.total_seconds()/60.0 col = 'HOSPITAL_DATE' if tdeltamin <= 1: rec_time = rec_date - ct_date tdeltamin = rec_time.total_seconds()/60.0 col = 'CT_TIME' except ValueError: return None elif tdeltamin > 1 and tdeltamin <= 10: hosp_time = rec_date - hospital_date hosp_time_mins = hosp_time.total_seconds()/60.0 rec_time = rec_date - ct_date tdeltamin = rec_time.total_seconds()/60.0 col = 'CT_TIME' if hosp_time_mins > tdeltamin: tdeltamin = hosp_time_mins col = 'HOSPITAL_DATE' else: if used_col == 'HOSPITAL_DATE': rec_time = rec_date - hospital_date tdeltamin = rec_time.total_seconds()/60.0 elif used_col == 'CT_TIME': rec_time = rec_date - ct_date tdeltamin = rec_time.total_seconds()/60.0 return tdeltamin, used_col def _calculate_ct_time(self, hospital_date, ct_date): """ The function calculating door to CT date time in minutes. :param hospital_date: the date of hospitalization :type hospital_date: timestamp :param ct_date: the date when the CT/MRI was performed :type ct_date: timestamp :returns: 1 if datetime > 0 and < 60, else returns 2 """ ct_diff = ct_date - hospital_date tdeltamin = ct_diff.total_seconds()/60.0 if tdeltamin < 0 or tdeltamin > 60: return 2 else: return 1 def prepare_df(self, df, name): """ The function preparing the raw data from the database to be used for statistic calculation. The prepared dataframe is entered into dict_df and the name is used as key. :param df: the raw dataframe exported from the database :type df: pandas dataframe :param name: the name of the database :type name: str """ if name == 'slovakia': res = df.copy() # Remove _en suffix from column names cols = res.columns new_cols = [] for c in cols: if c == 'anonym': new_cols.append("Protocol ID") elif c == 'subject_id': new_cols.append("Subject ID") else: new_cols.append(c.upper()) res.rename(columns=dict(zip(res.columns[0:], new_cols)), inplace=True) # Calculate the needle time in the minutes from hospital date and needle time. If hospital date is > needle time then as hospital time ct time is used res['NEEDLE_TIME_MIN'], res['USED_COL'] = zip(*res.apply(lambda x: self._calculate_time(x['CT_TIME'], x['HOSPITAL_DATE'], x['NEEDLE_TIME']) if x['NEEDLE_TIME'].date else (np.nan, None), axis=1)) # Calculate the groin time in the minutes from hospital date and groin time. If hospital date is > groin time then as hospital time ct time is used res['GROIN_TIME_MIN'], res['USED_COL'] = zip(*res.apply(lambda x: self._calculate_time(x['CT_TIME'], x['HOSPITAL_DATE'], x['GROIN_TIME'], x['USED_COL']) if x['GROIN_TIME'].date else (np.nan, None), axis=1)) # Get values if CT was performed within 1 hour after admission or after res['CT_TIME_WITHIN'] = res.apply(lambda x: self._calculate_ct_time(x['HOSPITAL_DATE'], x['CT_TIME']) if x['CT_MRI'] == 2 else np.nan, axis=1) res.drop(['USED_COL'], inplace=True, axis=1) res.rename(columns={'DOOR_TO_NEEDLE': 'DOOR_TO_NEEDLE_OLD', 'NEEDLE_TIME_MIN': 'DOOR_TO_NEEDLE', 'DOOR_TO_GROIN': 'DOOR_TO_GROIN_OLD', 'GROIN_TIME_MIN': 'DOOR_TO_GROIN', 'CT_TIME': 'CT_DATE', 'CT_TIME_WITHIN': 'CT_TIME'}, inplace=True) logging.info("CalculationSK: Connection: Column names in Slovakia were changed successfully.") self.dict_df[name] = res elif name == 'slovakia_2018': res = df.copy() # Remove _en suffix from column names cols = res.columns new_cols = [] for c in cols: if c == 'anonym': new_cols.append("Protocol ID") elif c == 'subject_id': new_cols.append("Subject ID") else: new_cols.append(c.upper()) res.rename(columns=dict(zip(res.columns[0:], new_cols)), inplace=True) # Calculate the needle time in the minutes from hospital date and needle time. If hospital date is > needle time then as hospital time ct time is used res['NEEDLE_TIME_MIN'], res['USED_COL'] = zip(*res.apply(lambda x: self._calculate_time(x['CT_TIME'], x['HOSPITAL_DATE'], x['NEEDLE_TIME']) if x['NEEDLE_TIME'].date else (np.nan, None), axis=1)) # Calculate the groin time in the minutes from hospital date and groin time. If hospital date is > groin time then as hospital time ct time is used res['GROIN_TIME_MIN'], res['USED_COL'] = zip(*res.apply(lambda x: self._calculate_time(x['CT_TIME'], x['HOSPITAL_DATE'], x['GROIN_TIME'], x['USED_COL']) if x['GROIN_TIME'].date else (np.nan, None), axis=1)) # Get values if CT was performed within 1 hour after admission or after res['CT_TIME_WITHIN'] = res.apply(lambda x: self._calculate_ct_time(x['HOSPITAL_DATE'], x['CT_TIME']) if x['CT_MRI'] == 2 else np.nan, axis=1) res.drop(['USED_COL'], inplace=True, axis=1) res.rename(columns={'DOOR_TO_NEEDLE': 'DOOR_TO_NEEDLE_OLD', 'NEEDLE_TIME_MIN': 'DOOR_TO_NEEDLE', 'DOOR_TO_GROIN': 'DOOR_TO_GROIN_OLD', 'GROIN_TIME_MIN': 'DOOR_TO_GROIN', 'CT_TIME': 'CT_DATE', 'CT_TIME_WITHIN': 'CT_TIME'}, inplace=True) logging.info("Connection: Column names in Slovakia_2018 were changed successfully.") self.dict_df[name] = res def _get_countries(self, df): """ The function obtaining all possible countries in the dataframe. :param df: the preprossed dataframe :type df: pandas dataframe :returns: the list of countries """ # site_ids = df['Protocol ID'].apply(lambda x: pd.Series(str(x).split("_"))) # countries_list = list(set(site_ids[0])) countries_list = ['SK'] logging.info("calculationSK: Data: Countries in the dataset: {0}.".format(countries_list)) return countries_list class FilterDataset: """ The class filtering preprocessed data by country or by date. :param df: the preprocessed dataframe :type df: pandas dataframe :param country: the country code of country included in the resulted dataframe :type country: str :param date1: the first date included in the filtered dataframe :type date1: date object :param date2: the last date included in the filtered dataframe :type date2: date object """ def __init__(self, df, country=None, date1=None, date2=None): debug = 'debug_' + datetime.now().strftime('%d-%m-%Y') + '.log' log_file = os.path.join(os.getcwd(), debug) logging.basicConfig(filename=log_file, filemode='a', format='%(asctime)s,%(msecs)d %(name)s %(levelname)s %(message)s', datefmt='%H:%M:%S', level=logging.DEBUG) self.fdf = df.copy() self.country = country self.date1 = date1 self.date2 = date2 if self.country is not None: # Append "_" to the country code, because e.g. ES_MD was included in dataset for MD as well. country = self.country + "_" self.fdf = self._filter_by_country() logging.info('CalculationSK: FilterDataset: Data have been filtered for country {0}!'.format(self.country)) if self.date1 is not None and self.date2 is not None: self.fdf = self._filter_by_date() logging.info('CalculationSK: FilterDataset: Data have been filtered for date {0} - {1}!'.format(self.date1, self.date2)) def _filter_by_country(self): """ The function filtering the dataframe by country. :returns: filtered dataframe including only rows belongs to the country """ df = self.fdf[self.fdf['Protocol ID'].str.startswith(self.country) == True] return df def _filter_by_date(self): """ The function filtering the dataframe by time period. :returns: filtered dataframe including only rows where discharge date is between date1 and date2 """ if isinstance(self.date1, datetime): self.date1 = self.date1.date() if isinstance(self.date2, datetime): self.date2 = self.date2.date() df = self.fdf[(self.fdf['DISCHARGE_DATE'] >= self.date1) & (self.fdf['DISCHARGE_DATE'] <= self.date2)].copy() return df class GeneratePreprocessedData: """ The class generating the preprocessed data and legend data in the excel file. :param df: the preprocessed data dataframe :type df: pandas dataframe :param split_sites: True if for each site should be generated individual reports including whole country :type split_sites: bool :param site: site ID :type site: str :param report: the type of the report (quater, year, half) :type report: str :param quarter: the type of the period (Q1_2019, H1_2019, ...) :type quarter: str """ def __init__(self, df, split_sites=False, site=None, report=None, quarter=None, country_code=None): debug = 'debug_' + datetime.now().strftime('%d-%m-%Y') + '.log' log_file = os.path.join(os.getcwd(), debug) logging.basicConfig(filename=log_file, filemode='a', format='%(asctime)s,%(msecs)d %(name)s %(levelname)s %(message)s', datefmt='%H:%M:%S', level=logging.DEBUG) self.df = df self.split_sites = split_sites self.report = report self.quarter = quarter self.country_code = country_code # # If Site is not None, filter dataset according to site code # if site is not None: # df = self.df[self.df['Protocol ID'].str.contains(site) == True] # self._generate_preprocessed_data(df=df, site_code=site) # logging.info('CalculationSK: Preprocessed data: The preprocessed data were generated for site {0}'.format(site)) # # Generate formatted statistics per site + country as site is included # if (split_sites) and site is None: # logging.info('CalculationSK: Preprocessed data: Generate preprocessed data per site.') # # Get set of all site ids # site_ids = set(self.df['Protocol ID'].tolist()) # for i in site_ids: # df = self.df[self.df['Protocol ID'].str.contains(i) == True] # self._generate_preprocessed_data(df=df, site_code=i) # logging.info('CalculationSK: Preprocessed data: The preprocessed data were generated for site {0}'.format(site)) self._generate_preprocessed_data(self.df, site_code=None) logging.info('CalculationSK: Preprocessed data: The preprocessed data were generate for all data.') def convert_to_string(datetime, format): """ The function converting the date, timestamp or time to the string. :param datetime: the date, timestamp or time value to be converted :type datetime: date/timestamp/time :param format: the format of the date, timestamp or time :type format: the string :returns: the datetime argument in the string """ if datetime is None or datetime is np.nan: return datetime else: return datetime.strftime(format) def _generate_preprocessed_data(self, df, site_code): """ The function creating the workbook and generating the preprocessed data in the excel file. :param df: the dataframe with preprocessed data :type df: pandas dataframe :param site_code: the site code if split sites is True :type site_code: str """ if site_code is not None: output_file = self.report + "_" + site_code + "_" + self.quarter + "_preprocessed_data.xlsx" else: output_file = self.report + "_" + self.country_code + "_" + self.quarter + "_preprocessed_data.xlsx" df = df.copy() # Set date/timestamp/time formats dateformat = "%Y-%m-%d" timestamp = "%Y-%m-%d %H:%M" timeformat = "%H:%M" # df['VISIT_DATE'] = df.apply(lambda x: convert_to_string(x['VISIT_DATE'], dateformat), axis=1) # df['VISIT_TIME'] = df.apply(lambda x: convert_to_string(x['VISIT_TIME'], timeformat), axis=1) df['HOSPITAL_DATE'] = df.apply(lambda x: convert_to_string(x['HOSPITAL_DATE'], timestamp), axis=1) # df['HOSPITAL_TIME'] = df.apply(lambda x: convert_to_string(x['HOSPITAL_TIME'], timeformat), axis=1) df['DISCHARGE_DATE'] = df.apply(lambda x: convert_to_string(x['DISCHARGE_DATE'], timestamp), axis=1) df.fillna(value="", inplace=True) workbook = xlsxwriter.Workbook(output_file) logging.info('Preprocessed data: The workbook was created.') preprocessed_data_sheet = workbook.add_worksheet('Preprocessed_raw_data') ### PREPROCESSED DATA ### preprocessed_data = df.values.tolist() # Set width of columns preprocessed_data_sheet.set_column(0, 150, 30) ncol = len(df.columns) - 1 nrow = len(df) # Create header col = [] for j in range(0, ncol + 1): tmp = {} tmp['header'] = df.columns.tolist()[j] col.append(tmp) options = {'data': preprocessed_data, 'header_row': True, 'columns': col, 'style': 'Table Style Light 1' } preprocessed_data_sheet.add_table(0, 0, nrow, ncol, options) logging.info('Preprocessed data: The sheet "Preprocessed data" was added.') workbook.close() class ComputeStats: """ The class calculating the statistics from Slovakia data. :param df: the dataframe with preprocessed data :type df: pandas dataframe :param country: `True` if country should be included as site into results :type country: bool :param country_code: the country code of country :type country_code: str :param comparison: `True` if comparison statistic is generated :type comparison: bool """ def __init__(self, df, country = False, country_code = "", comparison=False): self.df = df.copy() self.df.fillna(0, inplace=True) # def get_country_name(value): # """ The function obtaining the country name for the given country code. # :param value: the country code # :type value: str # :returns: country name # """ # if value == "UZB": # value = 'UZ' # country_name = pytz.country_names[value] # return country_name # if comparison == False: # self.df['Protocol ID'] = self.df.apply(lambda row: row['Protocol ID'].split()[2] if (len(row['Protocol ID'].split()) == 3) else row['Protocol ID'].split()[0], axis=1) # # uncomment if you want stats between countries and set comparison == True # #self.df['Protocol ID'] = self.df.apply(lambda x: x['Protocol ID'].split("_")[0], axis=1) # # If you want to compare, instead of Site Names will be Country names. # if comparison: # if self.df['Protocol ID'].dtype == np.object: # self.df['Site Name'] = self.df.apply(lambda x: get_country_name(x['Protocol ID']) if get_country_name(x['Protocol ID']) != "" else x['Protocol ID'], axis=1) # if (country): # country = self.df.copy() # self.country_name = pytz.country_names[country_code] # country['Protocol ID'] = self.country_name # country['Site Name'] = self.country_name # self.df = pd.concat([self.df, country]) # else: # self.country_name = "" # if comparison == False: # self.statsDf = self.df.groupby(['Protocol ID']).size().reset_index(name="Total Patients") # self.statsDf['Site Name'] = 'Slovakia' # self.statsDf = self.statsDf[['Protocol ID', 'Site Name', 'Total Patients']] # else: # self.statsDf = self.df.groupby(['Protocol ID', 'Site Name']).size().reset_index(name="Total Patients") self.statsDf = self.df.groupby(['Protocol ID']).size().reset_index(name="Total Patients") self.statsDf['Median patient age'] = self.df.groupby(['Protocol ID']).AGE.agg(['median']).rename(columns={'median': 'Median patient age'})['Median patient age'].tolist() self.df.drop(['ANTITHROMBOTICS'], inplace=True, axis=1) # get patietns with ischemic stroke (ISch) (1) isch = self.df[self.df['STROKE_TYPE'].isin([1])] self.statsDf['isch_patients'] = self._count_patients(dataframe=isch) # get patietns with ischemic stroke (IS), intracerebral hemorrhage (ICH), transient ischemic attack (TIA) or cerebral venous thrombosis (CVT) (1, 2, 3, 5) is_ich_tia_cvt = self.df[self.df['STROKE_TYPE'].isin([1, 2, 3, 5])] self.statsDf['is_ich_tia_cvt_patients'] = self._count_patients(dataframe=is_ich_tia_cvt) # get patietns with ischemic stroke (IS), intracerebral hemorrhage (ICH), or cerebral venous thrombosis (CVT) (1, 2, 5) is_ich_cvt = self.df[self.df['STROKE_TYPE'].isin([1, 2, 5])] self.statsDf['is_ich_cvt_patients'] = self._count_patients(dataframe=is_ich_cvt) # Get dataframe with patients who had ischemic stroke (IS) or intracerebral hemorrhage (ICH) is_ich = self.df[self.df['STROKE_TYPE'].isin([1,2])] self.statsDf['is_ich_patients'] = self._count_patients(dataframe=is_ich) # get patietns with ischemic stroke (IS) and transient ischemic attack (TIA) (1, 3) is_tia = self.df[self.df['STROKE_TYPE'].isin([1, 3])] self.statsDf['is_tia_patients'] = self._count_patients(dataframe=is_tia) # get patietns with ischemic stroke (IS), intracerebral hemorrhage (ICH), subarrachnoid hemorrhage (SAH) or cerebral venous thrombosis (CVT) (1, 2, 4, 5) is_ich_sah_cvt = self.df[self.df['STROKE_TYPE'].isin([1, 2, 4, 5])] self.statsDf['is_ich_sah_cvt_patients'] = self._count_patients(dataframe=is_ich_sah_cvt) # get patietns with ischemic stroke (IS), transient ischemic attack (TIA) or cerebral venous thrombosis (CVT) (1, 3, 5) is_tia_cvt = self.df[self.df['STROKE_TYPE'].isin([1, 3, 5])] self.statsDf['is_tia_cvt_patients'] = self._count_patients(dataframe=is_tia_cvt) # get patients with cerebral venous thrombosis (CVT) (5) cvt = self.df[self.df['STROKE_TYPE'].isin([5])] self.statsDf['cvt_patients'] = self._count_patients(dataframe=cvt) # get patietns with intracerebral hemorrhage (ICH) and subarrachnoid hemorrhage (SAH) (2, 4) ich_sah = self.df[self.df['STROKE_TYPE'].isin([2, 4])] self.statsDf['ich_sah_patients'] = self._count_patients(dataframe=ich_sah) # get patietns with intracerebral hemorrhage (ICH) (2) ich = self.df[self.df['STROKE_TYPE'].isin([2])] self.statsDf['ich_patients'] = self._count_patients(dataframe=ich) # get patietns with subarrachnoid hemorrhage (SAH) (4) sah = self.df[self.df['STROKE_TYPE'].isin([4])] self.statsDf['sah_patients'] = self._count_patients(dataframe=sah) # create subset with no referrals (RECANALIZATION_PROCEDURE != [5,6]) AND (HEMICRANIECTOMY != 3) discharge_subset = self.df[~self.df['RECANALIZATION_PROCEDURES'].isin([5, 6])] self.statsDf['discharge_subset_patients'] = self._count_patients(dataframe=discharge_subset) # Create discharge subset alive discharge_subset_alive = self.df[~self.df['DISCHARGE_DESTINATION'].isin([5])] self.statsDf['discharge_subset_alive_patients'] = self._count_patients(dataframe=discharge_subset_alive) ########## # GENDER # ########## # self.tmp = self.df.groupby(['Protocol ID', 'GENDER']).size().to_frame('count').reset_index() # self.statsDf = self._get_values_for_factors(column_name="GENDER", value=2, new_column_name='# patients female') # self.statsDf['% patients female'] = self.statsDf.apply(lambda x: round(((x['# patients female']/x['Total Patients']) * 100), 2) if x['Total Patients'] > 0 else 0, axis=1) # self.statsDf = self._get_values_for_factors(column_name="GENDER", value=1, new_column_name='# patients male') # self.statsDf['% patients male'] = self.statsDf.apply(lambda x: round(((x['# patients male']/x['Total Patients']) * 100), 2) if x['Total Patients'] > 0 else 0, axis=1) ################### # HOSPITALIZED IN # ################### self.tmp = self.df.groupby(['Protocol ID', 'HOSPITALIZED_IN']).size().to_frame('count').reset_index() self.statsDf = self._get_values_for_factors(column_name="HOSPITALIZED_IN", value=1, new_column_name='# patients hospitalized in stroke unit / ICU') self.statsDf['% patients hospitalized in stroke unit / ICU'] = self.statsDf.apply(lambda x: round(((x['# patients hospitalized in stroke unit / ICU']/x['Total Patients']) * 100), 2) if x['Total Patients'] > 0 else 0, axis=1) self.statsDf = self._get_values_for_factors(column_name="HOSPITALIZED_IN", value=2, new_column_name='# patients hospitalized in monitored bed with telemetry') self.statsDf['% patients hospitalized in monitored bed with telemetry'] = self.statsDf.apply(lambda x: round(((x['# patients hospitalized in monitored bed with telemetry']/x['Total Patients']) * 100), 2) if x['Total Patients'] > 0 else 0, axis=1) self.statsDf = self._get_values_for_factors(column_name="HOSPITALIZED_IN", value=3, new_column_name='# patients hospitalized in standard bed') self.statsDf['% patients hospitalized in standard bed'] = self.statsDf.apply(lambda x: round(((x['# patients hospitalized in standard bed']/x['Total Patients']) * 100), 2) if x['Total Patients'] > 0 else 0, axis=1) ############### # STROKE TYPE # ############### self.tmp = self.df.groupby(['Protocol ID', 'STROKE_TYPE']).size().to_frame('count').reset_index() self.statsDf = self._get_values_for_factors(column_name="STROKE_TYPE", value=1, new_column_name='# stroke type - ischemic stroke') self.statsDf['% stroke type - ischemic stroke'] = self.statsDf.apply(lambda x: round(((x['# stroke type - ischemic stroke']/x['Total Patients']) * 100), 2) if x['Total Patients'] > 0 else 0, axis=1) self.statsDf = self._get_values_for_factors(column_name="STROKE_TYPE", value=2, new_column_name='# stroke type - intracerebral hemorrhage') self.statsDf['% stroke type - intracerebral hemorrhage'] = self.statsDf.apply(lambda x: round(((x['# stroke type - intracerebral hemorrhage']/x['Total Patients']) * 100), 2) if x['Total Patients'] > 0 else 0, axis=1) self.statsDf = self._get_values_for_factors(column_name="STROKE_TYPE", value=3, new_column_name='# stroke type - transient ischemic attack') self.statsDf['% stroke type - transient ischemic attack'] = self.statsDf.apply(lambda x: round(((x['# stroke type - transient ischemic attack']/x['Total Patients']) * 100), 2) if x['Total Patients'] > 0 else 0, axis=1) self.statsDf = self._get_values_for_factors(column_name="STROKE_TYPE", value=4, new_column_name='# stroke type - subarrachnoid hemorrhage') self.statsDf['% stroke type - subarrachnoid hemorrhage'] = self.statsDf.apply(lambda x: round(((x['# stroke type - subarrachnoid hemorrhage']/x['Total Patients']) * 100), 2) if x['Total Patients'] > 0 else 0, axis=1) self.statsDf = self._get_values_for_factors(column_name="STROKE_TYPE", value=5, new_column_name='# stroke type - cerebral venous thrombosis') self.statsDf['% stroke type - cerebral venous thrombosis'] = self.statsDf.apply(lambda x: round(((x['# stroke type - cerebral venous thrombosis']/x['Total Patients']) * 100), 2) if x['Total Patients'] > 0 else 0, axis=1) self.statsDf = self._get_values_for_factors(column_name="STROKE_TYPE", value=6, new_column_name='# stroke type - undetermined stroke') self.statsDf['% stroke type - undetermined stroke'] = self.statsDf.apply(lambda x: round(((x['# stroke type - undetermined stroke']/x['Total Patients']) * 100), 2) if x['Total Patients'] > 0 else 0, axis=1) ######### # NIHSS # ######### # Get Median of NIHSS score # tmpDf = is_ich_cvt.groupby(['Protocol ID']).NIHSS_SCORE.agg(['median']).rename(columns={'median': 'NIHSS median score'}) # factorDf = self.statsDf.merge(tmpDf, how='outer', left_on='Protocol ID', right_on='Protocol ID') # factorDf.fillna(0, inplace=True) # self.statsDf['NIHSS median score'] = factorDf['NIHSS median score'] ########## # CT/MRI # ########## self.tmp = is_ich_tia_cvt.groupby(['Protocol ID', 'CT_MRI']).size().to_frame('count').reset_index() self.statsDf = self._get_values_for_factors(column_name="CT_MRI", value=3, new_column_name='# CT/MRI - In other hospital') self.statsDf['% CT/MRI - In other hospital'] = self.statsDf.apply(lambda x: round(((x['# CT/MRI - In other hospital']/x['is_ich_tia_cvt_patients']) * 100), 2) if x['is_ich_tia_cvt_patients'] > 0 else 0, axis=1) self.statsDf = self._get_values_for_factors(column_name="CT_MRI", value=2, new_column_name='# CT/MRI - performed') self.statsDf['% CT/MRI - performed'] = self.statsDf.apply(lambda x: round(((x['# CT/MRI - performed']/(x['is_ich_tia_cvt_patients'] - x['# CT/MRI - In other hospital'])) * 100), 2) if (x['is_ich_tia_cvt_patients'] - x['# CT/MRI - In other hospital']) > 0 else 0, axis=1) self.statsDf = self._get_values_for_factors(column_name="CT_MRI", value=1, new_column_name='# CT/MRI - Not performed') self.statsDf['% CT/MRI - Not performed'] = self.statsDf.apply(lambda x: round(((x['# CT/MRI - Not performed']/(x['is_ich_tia_cvt_patients'] - x['# CT/MRI - In other hospital'])) * 100), 2) if (x['is_ich_tia_cvt_patients'] - x['# CT/MRI - In other hospital']) > 0 else 0, axis=1) # Get CT/MRI performed subset ct_mri = is_ich_tia_cvt[is_ich_tia_cvt['CT_MRI'].isin([2])] self.tmp = ct_mri.groupby(['Protocol ID', 'CT_TIME']).size().to_frame('count').reset_index() self.statsDf = self._get_values_for_factors(column_name="CT_TIME", value=1, new_column_name='# CT/MRI - Performed within 1 hour after admission') self.statsDf['% CT/MRI - Performed within 1 hour after admission'] = self.statsDf.apply(lambda x: round(((x['# CT/MRI - Performed within 1 hour after admission']/x['# CT/MRI - performed']) * 100), 2) if x['# CT/MRI - performed'] > 0 else 0, axis=1) self.statsDf = self._get_values_for_factors(column_name="CT_TIME", value=2, new_column_name='# CT/MRI - Performed later than 1 hour after admission') self.statsDf['% CT/MRI - Performed later than 1 hour after admission'] = self.statsDf.apply(lambda x: round(((x['# CT/MRI - Performed later than 1 hour after admission']/x['# CT/MRI - performed']) * 100), 2) if x['# CT/MRI - performed'] > 0 else 0, axis=1) ############################# # RECANALIZATION PROCEDURES # ############################# # Filter negative or too high door to needle times needle = isch.loc[(isch['DOOR_TO_NEEDLE'] < 0) | (isch['DOOR_TO_NEEDLE'] > 400)].copy() # Filter negative and too high door to groin time groin = isch.loc[(isch['DOOR_TO_NEEDLE'] == 0) & ((isch['DOOR_TO_GROIN'] < 0) | (isch['DOOR_TO_GROIN'] > 700))].copy() number_of_patients = len(needle.index.values) + len(groin.index.values) recan_tmp = isch.drop(needle.index.values) recan_tmp.drop(groin.index.values, inplace=True) self.tmp = recan_tmp.groupby(['Protocol ID', 'RECANALIZATION_PROCEDURES']).size().to_frame('count').reset_index() self.statsDf = self._get_values_for_factors(column_name="RECANALIZATION_PROCEDURES", value=1, new_column_name='# recanalization procedures - Not done') self.statsDf['% recanalization procedures - Not done'] = self.statsDf.apply(lambda x: round(((x['# recanalization procedures - Not done']/x['isch_patients']) * 100), 2) if x['isch_patients'] > 0 else 0, axis=1) self.statsDf = self._get_values_for_factors(column_name="RECANALIZATION_PROCEDURES", value=2, new_column_name='# recanalization procedures - IV tPa') self.statsDf['% recanalization procedures - IV tPa'] = self.statsDf.apply(lambda x: round(((x['# recanalization procedures - IV tPa']/x['isch_patients']) * 100), 2) if x['isch_patients'] > 0 else 0, axis=1) self.statsDf = self._get_values_for_factors(column_name="RECANALIZATION_PROCEDURES", value=3, new_column_name='# recanalization procedures - IV tPa + endovascular treatment') self.statsDf['% recanalization procedures - IV tPa + endovascular treatment'] = self.statsDf.apply(lambda x: round(((x['# recanalization procedures - IV tPa + endovascular treatment']/x['isch_patients']) * 100), 2) if x['isch_patients'] > 0 else 0, axis=1) self.statsDf = self._get_values_for_factors(column_name="RECANALIZATION_PROCEDURES", value=4, new_column_name='# recanalization procedures - Endovascular treatment alone') self.statsDf['% recanalization procedures - Endovascular treatment alone'] = self.statsDf.apply(lambda x: round(((x['# recanalization procedures - Endovascular treatment alone']/x['isch_patients']) * 100), 2) if x['isch_patients'] > 0 else 0, axis=1) self.statsDf = self._get_values_for_factors(column_name="RECANALIZATION_PROCEDURES", value=5, new_column_name='# recanalization procedures - IV tPa + referred to another centre for endovascular treatment') self.statsDf['% recanalization procedures - IV tPa + referred to another centre for endovascular treatment'] = self.statsDf.apply(lambda x: round(((x['# recanalization procedures - IV tPa + referred to another centre for endovascular treatment']/x['isch_patients']) * 100), 2) if x['isch_patients'] > 0 else 0, axis=1) self.statsDf = self._get_values_for_factors(column_name="RECANALIZATION_PROCEDURES", value=6, new_column_name='# recanalization procedures - Referred to another centre for endovascular treatment') self.statsDf['% recanalization procedures - Referred to another centre for endovascular treatment'] = self.statsDf.apply(lambda x: round(((x['# recanalization procedures - Referred to another centre for endovascular treatment']/x['isch_patients']) * 100), 2) if x['isch_patients'] > 0 else 0, axis=1) self.statsDf = self._get_values_for_factors(column_name="RECANALIZATION_PROCEDURES", value=7, new_column_name='# recanalization procedures - Referred to another centre for endovascular treatment and hospitalization continues at the referred to centre') self.statsDf['% recanalization procedures - Referred to another centre for endovascular treatment and hospitalization continues at the referred to centre'] = self.statsDf.apply(lambda x: round(((x['# recanalization procedures - Referred to another centre for endovascular treatment and hospitalization continues at the referred to centre']/x['isch_patients']) * 100), 2) if x['isch_patients'] > 0 else 0, axis=1) self.statsDf = self._get_values_for_factors(column_name="RECANALIZATION_PROCEDURES", value=8, new_column_name='# recanalization procedures - Referred for endovascular treatment and patient is returned to the initial centre') self.statsDf['% recanalization procedures - Referred for endovascular treatment and patient is returned to the initial centre'] = self.statsDf.apply(lambda x: round(((x['# recanalization procedures - Referred for endovascular treatment and patient is returned to the initial centre']/x['isch_patients']) * 100), 2) if x['isch_patients'] > 0 else 0, axis=1) self.statsDf = self._get_values_for_factors(column_name="RECANALIZATION_PROCEDURES", value=9, new_column_name='# recanalization procedures - Returned to the initial centre after recanalization procedures were performed at another centre') self.statsDf['% recanalization procedures - Returned to the initial centre after recanalization procedures were performed at another centre'] = self.statsDf.apply(lambda x: round(((x['# recanalization procedures - Returned to the initial centre after recanalization procedures were performed at another centre']/x['isch_patients']) * 100), 2) if x['isch_patients'] > 0 else 0, axis=1) self.statsDf['# patients recanalized'] = self.statsDf.apply(lambda x: x['# recanalization procedures - IV tPa'] + x['# recanalization procedures - IV tPa + endovascular treatment'] + x['# recanalization procedures - IV tPa + referred to another centre for endovascular treatment'] + x['# recanalization procedures - Endovascular treatment alone'], axis=1) self.statsDf['% patients recanalized'] = self.statsDf.apply(lambda x: round(((x['# patients recanalized']/(x['isch_patients'] - x['# recanalization procedures - Referred to another centre for endovascular treatment'] - x['# recanalization procedures - Referred to another centre for endovascular treatment and hospitalization continues at the referred to centre'] - x['# recanalization procedures - Referred for endovascular treatment and patient is returned to the initial centre'] - x['# recanalization procedures - Returned to the initial centre after recanalization procedures were performed at another centre'])) * 100), 2) if (x['isch_patients'] - x['# recanalization procedures - Referred to another centre for endovascular treatment'] - x['# recanalization procedures - Referred to another centre for endovascular treatment and hospitalization continues at the referred to centre'] - x['# recanalization procedures - Referred for endovascular treatment and patient is returned to the initial centre'] - x['# recanalization procedures - Returned to the initial centre after recanalization procedures were performed at another centre']) > 0 else 0, axis=1) ############## # MEDIAN DTN # ############## # Get patients receiving IV tpa self.statsDf.loc[:, '# IV tPa'] = self.statsDf.apply(lambda x: x['# recanalization procedures - IV tPa'] + x['# recanalization procedures - IV tPa + endovascular treatment'] + x['# recanalization procedures - IV tPa + referred to another centre for endovascular treatment'], axis=1) self.statsDf['% IV tPa'] = self.statsDf.apply(lambda x: round(((x['# IV tPa']/x['isch_patients']) * 100), 2) if x['isch_patients'] > 0 else 0, axis=1) # Get only patients recanalized # recanalization_procedure_iv_tpa = isch.loc[(isch['RECANALIZATION_PROCEDURES'].isin([2, 3, 5])) & (isch['DOOR_TO_NEEDLE'] > 0) & (isch['DOOR_TO_NEEDLE'] <= 400)] recanalization_procedure_iv_tpa = isch[isch['RECANALIZATION_PROCEDURES'].isin([2, 3, 5])].copy() recanalization_procedure_iv_tpa.fillna(0, inplace=True) recanalization_procedure_iv_tpa['IVTPA'] = recanalization_procedure_iv_tpa['DOOR_TO_NEEDLE'] tmp = recanalization_procedure_iv_tpa.groupby(['Protocol ID']).IVTPA.agg(['median']).rename(columns={'median': 'Median DTN (minutes)'}).reset_index() self.statsDf = self.statsDf.merge(tmp, how='outer') self.statsDf.fillna(0, inplace=True) ############## # MEDIAN DTG # ############## # Get patients receiving TBY self.statsDf.loc[:, '# TBY'] = self.statsDf.apply(lambda x: x['# recanalization procedures - Endovascular treatment alone'] + x['# recanalization procedures - IV tPa + endovascular treatment'], axis=1) self.statsDf['% TBY'] = self.statsDf.apply(lambda x: round(((x['# TBY']/x['isch_patients']) * 100), 2) if x['isch_patients'] > 0 else 0, axis=1) # Get only patients recanalized TBY recanalization_procedure_tby_dtg = isch[isch['RECANALIZATION_PROCEDURES'].isin([4, 3])].copy() #recanalization_procedure_tby_dtg = isch.loc[(isch['RECANALIZATION_PROCEDURES'].isin([4, 3])) & (isch['DOOR_TO_GROIN'] > 0) & (isch['DOOR_TO_GROIN'] <= 700)] recanalization_procedure_tby_dtg.fillna(0, inplace=True) recanalization_procedure_tby_dtg['TBY'] = recanalization_procedure_tby_dtg['DOOR_TO_GROIN'] tmp = recanalization_procedure_tby_dtg.groupby(['Protocol ID']).TBY.agg(['median']).rename(columns={'median': 'Median DTG (minutes)'}).reset_index() self.statsDf = self.statsDf.merge(tmp, how='outer') self.statsDf.fillna(0, inplace=True) ####################### # DYPSHAGIA SCREENING # ####################### self.tmp = is_ich_cvt.groupby(['Protocol ID', 'DYSPHAGIA_SCREENING']).size().to_frame('count').reset_index() self.statsDf = self._get_values_for_factors(column_name="DYSPHAGIA_SCREENING", value=6, new_column_name='# dysphagia screening - not known') self.statsDf['% dysphagia screening - not known'] = self.statsDf.apply(lambda x: round(((x['# dysphagia screening - not known']/x['is_ich_cvt_patients']) * 100), 2) if x['is_ich_cvt_patients'] > 0 else 0, axis=1) self.statsDf = self._get_values_for_factors(column_name="DYSPHAGIA_SCREENING", value=1, new_column_name='# dysphagia screening - Guss test') self.statsDf['% dysphagia screening - Guss test'] = self.statsDf.apply(lambda x: round(((x['# dysphagia screening - Guss test']/(x['is_ich_cvt_patients'] - x['# dysphagia screening - not known'])) * 100), 2) if (x['is_ich_cvt_patients'] - x['# dysphagia screening - not known']) > 0 else 0, axis=1) self.statsDf = self._get_values_for_factors(column_name="DYSPHAGIA_SCREENING", value=2, new_column_name='# dysphagia screening - Other test') self.statsDf['% dysphagia screening - Other test'] = self.statsDf.apply(lambda x: round(((x['# dysphagia screening - Other test']/(x['is_ich_cvt_patients'] - x['# dysphagia screening - not known'])) * 100), 2) if (x['is_ich_cvt_patients'] - x['# dysphagia screening - not known']) > 0 else 0, axis=1) self.statsDf = self._get_values_for_factors(column_name="DYSPHAGIA_SCREENING", value=3, new_column_name='# dysphagia screening - Another centre') self.statsDf['% dysphagia screening - Another centre'] = self.statsDf.apply(lambda x: round(((x['# dysphagia screening - Another centre']/(x['is_ich_cvt_patients'] - x['# dysphagia screening - not known'])) * 100), 2) if (x['is_ich_cvt_patients'] - x['# dysphagia screening - not known']) > 0 else 0, axis=1) self.statsDf = self._get_values_for_factors(column_name="DYSPHAGIA_SCREENING", value=4, new_column_name='# dysphagia screening - Not done') self.statsDf['% dysphagia screening - Not done'] = self.statsDf.apply(lambda x: round(((x['# dysphagia screening - Not done']/(x['is_ich_cvt_patients'] - x['# dysphagia screening - not known'])) * 100), 2) if (x['is_ich_cvt_patients'] - x['# dysphagia screening - not known']) > 0 else 0, axis=1) self.statsDf = self._get_values_for_factors(column_name="DYSPHAGIA_SCREENING", value=5, new_column_name='# dysphagia screening - Unable to test') self.statsDf['% dysphagia screening - Unable to test'] = self.statsDf.apply(lambda x: round(((x['# dysphagia screening - Unable to test']/(x['is_ich_cvt_patients'] - x['# dysphagia screening - not known'])) * 100), 2) if (x['is_ich_cvt_patients'] - x['# dysphagia screening - not known']) > 0 else 0, axis=1) self.statsDf['# dysphagia screening done'] = self.statsDf['# dysphagia screening - Guss test'] + self.statsDf['# dysphagia screening - Other test'] + self.statsDf['# dysphagia screening - Another centre'] self.statsDf['% dysphagia screening done'] = self.statsDf.apply(lambda x: round(((x['# dysphagia screening done']/(x['is_ich_cvt_patients'] - x['# dysphagia screening - not known'])) * 100), 2) if (x['is_ich_cvt_patients'] - x['# dysphagia screening - not known']) > 0 else 0, axis=1) ############################ # DYPSHAGIA SCREENING TIME # ############################ self.tmp = self.df.groupby(['Protocol ID', 'DYSPHAGIA_SCREENING_TIME']).size().to_frame('count').reset_index() self.statsDf = self._get_values_for_factors(column_name="DYSPHAGIA_SCREENING_TIME", value=1, new_column_name='# dysphagia screening time - Within first 24 hours') self.statsDf = self._get_values_for_factors(column_name="DYSPHAGIA_SCREENING_TIME", value=2, new_column_name='# dysphagia screening time - After first 24 hours') self.statsDf['% dysphagia screening time - Within first 24 hours'] = self.statsDf.apply(lambda x: round(((x['# dysphagia screening time - Within first 24 hours']/(x['# dysphagia screening time - Within first 24 hours'] + x['# dysphagia screening time - After first 24 hours'])) * 100), 2) if (x['# dysphagia screening time - Within first 24 hours'] + x['# dysphagia screening time - After first 24 hours']) > 0 else 0, axis=1) self.statsDf['% dysphagia screening time - After first 24 hours'] = self.statsDf.apply(lambda x: round(((x['# dysphagia screening time - After first 24 hours']/(x['# dysphagia screening time - Within first 24 hours'] + x['# dysphagia screening time - After first 24 hours'])) * 100), 2) if (x['# dysphagia screening time - Within first 24 hours'] + x['# dysphagia screening time - After first 24 hours']) > 0 else 0, axis=1) ######## # AFIB # ######## # patients not reffered not_reffered = is_tia[~is_tia['RECANALIZATION_PROCEDURES'].isin([7])].copy() self.statsDf['not_reffered_patients'] = self._count_patients(dataframe=not_reffered) # patients referred to another hospital reffered = is_tia[is_tia['RECANALIZATION_PROCEDURES'].isin([7])].copy() self.statsDf['reffered_patients'] = self._count_patients(dataframe=reffered) self.tmp = not_reffered.groupby(['Protocol ID', 'AFIB_FLUTTER']).size().to_frame('count').reset_index() self.statsDf = self._get_values_for_factors(column_name="AFIB_FLUTTER", value=1, new_column_name='# afib/flutter - Known') self.statsDf['% afib/flutter - Known'] = self.statsDf.apply(lambda x: round(((x['# afib/flutter - Known']/(x['is_tia_patients'] - x['reffered_patients'])) * 100), 2) if (x['is_tia_patients'] - x['reffered_patients']) > 0 else 0, axis=1) self.statsDf = self._get_values_for_factors(column_name="AFIB_FLUTTER", value=2, new_column_name='# afib/flutter - Newly-detected at admission') self.statsDf['% afib/flutter - Newly-detected at admission'] = self.statsDf.apply(lambda x: round(((x['# afib/flutter - Newly-detected at admission']/(x['is_tia_patients'] - x['reffered_patients'])) * 100), 2) if (x['is_tia_patients'] - x['reffered_patients']) > 0 else 0, axis=1) self.statsDf = self._get_values_for_factors(column_name="AFIB_FLUTTER", value=3, new_column_name='# afib/flutter - Detected during hospitalization') self.statsDf['% afib/flutter - Detected during hospitalization'] = self.statsDf.apply(lambda x: round(((x['# afib/flutter - Detected during hospitalization']/(x['is_tia_patients'] - x['reffered_patients'])) * 100), 2) if (x['is_tia_patients'] - x['reffered_patients']) > 0 else 0, axis=1) self.statsDf = self._get_values_for_factors(column_name="AFIB_FLUTTER", value=4, new_column_name='# afib/flutter - Not detected') self.statsDf['% afib/flutter - Not detected'] = self.statsDf.apply(lambda x: round(((x['# afib/flutter - Not detected']/(x['is_tia_patients'] - x['reffered_patients'])) * 100), 2) if (x['is_tia_patients'] - x['reffered_patients']) > 0 else 0, axis=1) self.statsDf = self._get_values_for_factors(column_name="AFIB_FLUTTER", value=5, new_column_name='# afib/flutter - Not known') self.statsDf['% afib/flutter - Not known'] = self.statsDf.apply(lambda x: round(((x['# afib/flutter - Not known']/(x['is_tia_patients'] - x['reffered_patients'])) * 100), 2) if (x['is_tia_patients'] - x['reffered_patients']) > 0 else 0, axis=1) ############################ # CAROTID ARTERIES IMAGING # ############################ self.tmp = is_tia.groupby(['Protocol ID', 'CAROTID_ARTERIES_IMAGING']).size().to_frame('count').reset_index() self.statsDf = self._get_values_for_factors(column_name="CAROTID_ARTERIES_IMAGING", value=3, new_column_name='# carotid arteries imaging - Not known') self.statsDf['% carotid arteries imaging - Not known'] = self.statsDf.apply(lambda x: round(((x['# carotid arteries imaging - Not known']/x['is_tia_patients']) * 100), 2) if x['is_tia_patients'] > 0 else 0, axis=1) self.statsDf = self._get_values_for_factors(column_name="CAROTID_ARTERIES_IMAGING", value=1, new_column_name='# carotid arteries imaging - Yes') self.statsDf['% carotid arteries imaging - Yes'] = self.statsDf.apply(lambda x: round(((x['# carotid arteries imaging - Yes']/(x['is_tia_patients'] - x['# carotid arteries imaging - Not known'])) * 100), 2) if (x['is_tia_patients'] - x['# carotid arteries imaging - Not known']) > 0 else 0, axis=1) self.statsDf = self._get_values_for_factors(column_name="CAROTID_ARTERIES_IMAGING", value=2, new_column_name='# carotid arteries imaging - No') self.statsDf['% carotid arteries imaging - No'] = self.statsDf.apply(lambda x: round(((x['# carotid arteries imaging - No']/(x['is_tia_patients'] - x['# carotid arteries imaging - Not known'])) * 100), 2) if (x['is_tia_patients'] - x['# carotid arteries imaging - Not known']) > 0 else 0, axis=1) ############################### # ANTITHROMBOTICS WITHOUT CVT # ############################### def get_antithrombotics(vals): """ The function converting the values for antithrombotics in one value. :param vals: the list of values for antithrombotics :type vals: list """ set_vals = list(set(vals)) # remove duplicate values if len(set_vals) == 1: if set_vals[0] == 2: # no antithrombotics prescribed res = 2 elif set_vals[0] == 0: res = None else: res = 1 # antitrhbomtics prescribed else: res = 1 # if both values (1, 2) in set_vals then antithrombotics prescribed we don't care about which were prescribed return res is_tia.loc[:, 'ANTITHROMBOTICS'] = is_tia.apply(lambda x: get_antithrombotics([x['UKON_WARFARIN'], x['UKON_DABIGATRAN'], x['UKON_RIVAROXABAN'], x['UKON_APIXABAN'], x['UKON_EDOXABAN'], x['UKON_LMW'],x['UKON_ANTIKOAGULANCIA'], x['UKON_HEPARIN_VTE'], x['UKON_ASA'], x['UKON_CLOPIDOGREL']]), axis=1) # filter not dead patient with ischemic and transient CMP antithrombotics = is_tia[~is_tia['DISCHARGE_DESTINATION'].isin([5])].copy() # calculate antithrombotics df patients self.statsDf['antithrombotics_patients'] = self._count_patients(dataframe=antithrombotics) # Filter dead patients with ischemic and transient CMP ischemic_transient_dead = is_tia[is_tia['DISCHARGE_DESTINATION'].isin([5])].copy() # Count patients self.statsDf['ischemic_transient_dead_patients'] = self._count_patients(dataframe=ischemic_transient_dead) ischemic_transient_dead_prescribed = is_tia[is_tia['DISCHARGE_DESTINATION'].isin([5]) & is_tia['ANTITHROMBOTICS'].isin([1])].copy() self.statsDf['ischemic_transient_dead_patients_prescribed'] = self._count_patients(dataframe=ischemic_transient_dead_prescribed) # Calculate antiplatelets (ASA and clopidogrel) antithrombotics['ANTIPLATELETS'] = antithrombotics.apply(lambda x: 2 if x['UKON_ASA'] == 2 and x['UKON_CLOPIDOGREL'] == 2 else 1, axis=1) self.tmp = antithrombotics.groupby(['Protocol ID', 'ANTIPLATELETS']).size().to_frame('count').reset_index() self.statsDf = self._get_values_for_factors(column_name="ANTIPLATELETS", value=1, new_column_name='# patients receiving antiplatelets') self.statsDf['% patients receiving antiplatelets'] = self.statsDf.apply(lambda x: round(((x['# patients receiving antiplatelets']/(x['is_tia_patients'] - x['ischemic_transient_dead_patients'])) * 100), 2) if (x['is_tia_patients'] - x['ischemic_transient_dead_patients']) > 0 else 0, axis=1) self.tmp = antithrombotics.groupby(['Protocol ID', 'UKON_WARFARIN']).size().to_frame('count').reset_index() self.statsDf = self._get_values_for_factors(column_name="UKON_WARFARIN", value=1, new_column_name='# patients receiving Vit. K antagonist') # self.statsDf['% patients receiving Vit. K antagonist'] = self.statsDf.apply(lambda x: round(((x['# patients receiving Vit. K antagonist']/(x['is_tia_patients'] - x['ischemic_transient_dead_patients'])) * 100), 2) if (x['is_tia_patients'] - x['ischemic_transient_dead_patients']) > 0 else 0, axis=1) self.tmp = antithrombotics.groupby(['Protocol ID', 'UKON_DABIGATRAN']).size().to_frame('count').reset_index() self.statsDf = self._get_values_for_factors(column_name="UKON_DABIGATRAN", value=1, new_column_name='# patients receiving dabigatran') # self.statsDf['% patients receiving dabigatran'] = self.statsDf.apply(lambda x: round(((x['# patients receiving dabigatran']/(x['is_tia_patients'] - x['ischemic_transient_dead_patients'])) * 100), 2) if (x['is_tia_patients'] - x['ischemic_transient_dead_patients']) > 0 else 0, axis=1) self.tmp = antithrombotics.groupby(['Protocol ID', 'UKON_RIVAROXABAN']).size().to_frame('count').reset_index() self.statsDf = self._get_values_for_factors(column_name="UKON_RIVAROXABAN", value=1, new_column_name='# patients receiving rivaroxaban') # self.statsDf['% patients receiving rivaroxaban'] = self.statsDf.apply(lambda x: round(((x['# patients receiving rivaroxaban']/(x['is_tia_patients'] - x['ischemic_transient_dead_patients'])) * 100), 2) if (x['is_tia_patients'] - x['ischemic_transient_dead_patients']) > 0 else 0, axis=1) self.tmp = antithrombotics.groupby(['Protocol ID', 'UKON_APIXABAN']).size().to_frame('count').reset_index() self.statsDf = self._get_values_for_factors(column_name="UKON_APIXABAN", value=1, new_column_name='# patients receiving apixaban') # self.statsDf['% patients receiving apixaban'] = self.statsDf.apply(lambda x: round(((x['# patients receiving apixaban']/(x['is_tia_patients'] - x['ischemic_transient_dead_patients'])) * 100), 2) if (x['is_tia_patients'] - x['ischemic_transient_dead_patients']) > 0 else 0, axis=1) self.tmp = antithrombotics.groupby(['Protocol ID', 'UKON_EDOXABAN']).size().to_frame('count').reset_index() self.statsDf = self._get_values_for_factors(column_name="UKON_EDOXABAN", value=1, new_column_name='# patients receiving edoxaban') # self.statsDf['% patients receiving edoxaban'] = self.statsDf.apply(lambda x: round(((x['# patients receiving edoxaban']/(x['is_tia_patients'] - x['ischemic_transient_dead_patients'])) * 100), 2) if (x['is_tia_patients'] - x['ischemic_transient_dead_patients']) > 0 else 0, axis=1) self.tmp = antithrombotics.groupby(['Protocol ID', 'UKON_HEPARIN_VTE']).size().to_frame('count').reset_index() self.statsDf = self._get_values_for_factors(column_name="UKON_HEPARIN_VTE", value=1, new_column_name='# patients receiving LMWH or heparin in prophylactic dose') # self.statsDf['% patients receiving LMWH or heparin in prophylactic dose'] = self.statsDf.apply(lambda x: round(((x['# patients receiving LMWH or heparin in prophylactic dose']/(x['is_tia_patients'] - x['ischemic_transient_dead_patients'])) * 100), 2) if (x['is_tia_patients'] - x['ischemic_transient_dead_patients']) > 0 else 0, axis=1) antithrombotics['UKON_LMW_ANTICOAGULACNI'] = antithrombotics.apply(lambda x: 2 if x['UKON_LMW'] == 2 and x['UKON_ANTIKOAGULANCIA'] == 2 else 1, axis=1) self.tmp = antithrombotics.groupby(['Protocol ID', 'UKON_LMW_ANTICOAGULACNI']).size().to_frame('count').reset_index() self.statsDf = self._get_values_for_factors(column_name="UKON_LMW_ANTICOAGULACNI", value=1, new_column_name='# patients receiving LMWH or heparin in full anticoagulant dose') # self.statsDf['% patients receiving LMWH or heparin in full anticoagulant dose'] = self.statsDf.apply(lambda x: round(((x['# patients receiving LMWH or heparin in full anticoagulant dose']/(x['is_tia_patients'] - x['ischemic_transient_dead_patients'])) * 100), 2) if (x['is_tia_patients'] - x['ischemic_transient_dead_patients']) > 0 else 0, axis=1) self.statsDf['# patients not prescribed antithrombotics, but recommended'] = 0 # self.statsDf = self._get_values_for_factors(column_name="ANTITHROMBOTICS", value=9, new_column_name='# patients not prescribed antithrombotics, but recommended') self.statsDf['% patients not prescribed antithrombotics, but recommended'] = self.statsDf.apply(lambda x: round(((x['# patients not prescribed antithrombotics, but recommended']/(x['is_tia_patients'] - x['ischemic_transient_dead_patients'])) * 100), 2) if (x['is_tia_patients'] - x['ischemic_transient_dead_patients']) > 0 else 0, axis=1) self.statsDf['# patients neither receiving antithrombotics nor recommended'] = 0 # self.statsDf = self._get_values_for_factors(column_name="ANTITHROMBOTICS", value=10, new_column_name='# patients neither receiving antithrombotics nor recommended') self.statsDf['% patients neither receiving antithrombotics nor recommended'] = self.statsDf.apply(lambda x: round(((x['# patients neither receiving antithrombotics nor recommended']/(x['is_tia_patients'] - x['ischemic_transient_dead_patients'])) * 100), 2) if (x['is_tia_patients'] - x['ischemic_transient_dead_patients']) > 0 else 0, axis=1) ## ANTITHROMBOTICS - PATIENTS PRESCRIBED + RECOMMENDED # self.statsDf.loc[:, '# patients prescribed antithrombotics'] = self.statsDf.apply(lambda x: x['# patients receiving antiplatelets'] + x['# patients receiving Vit. K antagonist'] + x['# patients receiving dabigatran'] + x['# patients receiving rivaroxaban'] + x['# patients receiving apixaban'] + x['# patients receiving edoxaban'] + x['# patients receiving LMWH or heparin in prophylactic dose'] + x['# patients receiving LMWH or heparin in full anticoagulant dose'], axis=1) self.tmp = antithrombotics.groupby(['Protocol ID', 'ANTITHROMBOTICS']).size().to_frame('count').reset_index() self.statsDf = self._get_values_for_factors(column_name="ANTITHROMBOTICS", value=1, new_column_name='# patients prescribed antithrombotics') # self.statsDf['% patients prescribed antithrombotics'] = self.statsDf.apply(lambda x: round(((x['# patients prescribed antithrombotics']/(x['is_tia_cvt_patients'] - x['ischemic_transient_dead_patients'] - x['# patients not prescribed antithrombotics, but recommended'])) * 100), 2) if (x['is_tia_cvt_patients'] - x['ischemic_transient_dead_patients'] - x['# patients not prescribed antithrombotics, but recommended']) > 0 else 0, axis=1) self.statsDf['% patients prescribed antithrombotics'] = self.statsDf.apply(lambda x: round(((x['# patients prescribed antithrombotics']/(x['is_tia_patients'] - x['ischemic_transient_dead_patients'])) * 100), 2) if (x['is_tia_patients'] - x['ischemic_transient_dead_patients']) > 0 else 0, axis=1) self.statsDf = self._get_values_for_factors(column_name="ANTITHROMBOTICS", value=1, new_column_name='# patients prescribed or recommended antithrombotics') # From patients prescribed or recommended antithrombotics remove patient who had prescribed antithrombotics and were dead (nominator) # self.statsDf['% patients prescribed or recommended antithrombotics'] = self.statsDf.apply(lambda x: round(((x['# patients prescribed or recommended antithrombotics'] - x['ischemic_transient_dead_patients_prescribed'])/(x['is_tia_patients'] - x['ischemic_transient_dead_patients'] - x['# patients not prescribed antithrombotics, but recommended'])) * 100, 2) if ((x['is_tia_patients'] - x['ischemic_transient_dead_patients'] - x['# patients not prescribed antithrombotics, but recommended']) > 0) else 0, axis=1) self.statsDf['% patients prescribed or recommended antithrombotics'] = self.statsDf.apply(lambda x: round(((x['# patients prescribed or recommended antithrombotics'] - x['ischemic_transient_dead_patients_prescribed'])/(x['is_tia_patients'] - x['ischemic_transient_dead_patients'])) * 100, 2) if ((x['is_tia_patients'] - x['ischemic_transient_dead_patients']) > 0) else 0, axis=1) self.statsDf.drop(['# patients receiving Vit. K antagonist', '# patients receiving dabigatran', '# patients receiving rivaroxaban', '# patients receiving apixaban', '# patients receiving edoxaban', '# patients receiving LMWH or heparin in prophylactic dose','# patients receiving LMWH or heparin in full anticoagulant dose'], axis=1, inplace=True) self.statsDf.fillna(0, inplace=True) ########################################### # ANTIPLATELETS - PRESCRIBED WITHOUT AFIB # ########################################### is_tia['ANTIPLATELETS'] = is_tia.apply(lambda x: get_antithrombotics([x['UKON_ASA'], x['UKON_CLOPIDOGREL']]), axis=1) # patients not referred afib_flutter_not_detected_or_not_known = is_tia[is_tia['AFIB_FLUTTER'].isin([4, 5])].copy() self.statsDf['afib_flutter_not_detected_or_not_known_patients'] = self._count_patients(dataframe=afib_flutter_not_detected_or_not_known) afib_flutter_not_detected_or_not_known_dead = afib_flutter_not_detected_or_not_known[afib_flutter_not_detected_or_not_known['DISCHARGE_DESTINATION'].isin([5])].copy() self.statsDf['afib_flutter_not_detected_or_not_known_dead_patients'] = self._count_patients(dataframe=afib_flutter_not_detected_or_not_known_dead) prescribed_antiplatelets_no_afib = afib_flutter_not_detected_or_not_known[afib_flutter_not_detected_or_not_known['ANTIPLATELETS'].isin([1])].copy() self.statsDf['prescribed_antiplatelets_no_afib_patients'] = self._count_patients(dataframe=prescribed_antiplatelets_no_afib) prescribed_antiplatelets_no_afib_dead = prescribed_antiplatelets_no_afib[prescribed_antiplatelets_no_afib['DISCHARGE_DESTINATION'].isin([5])].copy() self.statsDf['prescribed_antiplatelets_no_afib_dead_patients'] = self._count_patients(dataframe=prescribed_antiplatelets_no_afib_dead) self.tmp = afib_flutter_not_detected_or_not_known.groupby(['Protocol ID', 'ANTIPLATELETS']).size().to_frame('count').reset_index() self.statsDf = self._get_values_for_factors(column_name="ANTIPLATELETS", value=1, new_column_name='# patients prescribed antiplatelets without aFib') self.statsDf['% patients prescribed antiplatelets without aFib'] = self.statsDf.apply(lambda x: round(((x['# patients prescribed antiplatelets without aFib'] - x['prescribed_antiplatelets_no_afib_dead_patients'])/(x['afib_flutter_not_detected_or_not_known_patients'] - x['afib_flutter_not_detected_or_not_known_dead_patients'])) * 100, 2) if ((x['afib_flutter_not_detected_or_not_known_patients'] - x['afib_flutter_not_detected_or_not_known_dead_patients']) > 0) else 0, axis=1) ######################################### # ANTICOAGULANTS - PRESCRIBED WITH AFIB # ######################################### # patients not referred afib_flutter_detected = is_tia[is_tia['AFIB_FLUTTER'].isin([1, 2, 3])].copy() self.statsDf['afib_flutter_detected_patients'] = self._count_patients(dataframe=afib_flutter_detected) # Get patients with prescribed anticoagulants afib_flutter_detected['ANTICOAGULANTS'] = afib_flutter_detected.apply(lambda x: get_antithrombotics([x['UKON_WARFARIN'], x['UKON_DABIGATRAN'], x['UKON_RIVAROXABAN'], x['UKON_APIXABAN'], x['UKON_EDOXABAN'], x['UKON_LMW'], x['UKON_ANTIKOAGULANCIA'], x['UKON_HEPARIN_VTE']]), axis=1) afib_flutter_detected_not_dead = afib_flutter_detected[~afib_flutter_detected['DISCHARGE_DESTINATION'].isin([5])].copy() self.statsDf['afib_flutter_detected_patients_not_dead'] = self._count_patients(dataframe=afib_flutter_detected_not_dead) anticoagulants_prescribed = afib_flutter_detected[afib_flutter_detected['ANTICOAGULANTS'].isin([1]) & ~afib_flutter_detected['DISCHARGE_DESTINATION'].isin([5])].copy() self.statsDf['# patients prescribed anticoagulants with aFib'] = self._count_patients(dataframe=anticoagulants_prescribed) self.tmp = anticoagulants_prescribed.groupby(['Protocol ID', 'UKON_WARFARIN']).size().to_frame('count').reset_index() self.statsDf = self._get_values_for_factors(column_name="UKON_WARFARIN", value=1, new_column_name='# patients receiving Vit. K antagonist') # self.statsDf['% patients receiving Vit. K antagonist'] = self.statsDf.apply(lambda x: round(((x['# patients receiving Vit. K antagonist']/x['# patients prescribed anticoagulants with aFib']) * 100), 2) if x['# patients prescribed anticoagulants with aFib'] > 0 else 0, axis=1) self.statsDf['% patients receiving Vit. K antagonist'] = self.statsDf.apply(lambda x: round(((x['# patients receiving Vit. K antagonist']/x['afib_flutter_detected_patients_not_dead']) * 100), 2) if x['afib_flutter_detected_patients_not_dead'] > 0 else 0, axis=1) self.tmp = anticoagulants_prescribed.groupby(['Protocol ID', 'UKON_DABIGATRAN']).size().to_frame('count').reset_index() self.statsDf = self._get_values_for_factors(column_name="UKON_DABIGATRAN", value=1, new_column_name='# patients receiving dabigatran') self.statsDf['% patients receiving dabigatran'] = self.statsDf.apply(lambda x: round(((x['# patients receiving dabigatran']/x['afib_flutter_detected_patients_not_dead']) * 100), 2) if x['afib_flutter_detected_patients_not_dead'] > 0 else 0, axis=1) self.tmp = anticoagulants_prescribed.groupby(['Protocol ID', 'UKON_RIVAROXABAN']).size().to_frame('count').reset_index() self.statsDf = self._get_values_for_factors(column_name="UKON_RIVAROXABAN", value=1, new_column_name='# patients receiving rivaroxaban') self.statsDf['% patients receiving rivaroxaban'] = self.statsDf.apply(lambda x: round(((x['# patients receiving rivaroxaban']/x['afib_flutter_detected_patients_not_dead']) * 100), 2) if x['afib_flutter_detected_patients_not_dead'] > 0 else 0, axis=1) self.tmp = anticoagulants_prescribed.groupby(['Protocol ID', 'UKON_APIXABAN']).size().to_frame('count').reset_index() self.statsDf = self._get_values_for_factors(column_name="UKON_APIXABAN", value=1, new_column_name='# patients receiving apixaban') self.statsDf['% patients receiving apixaban'] = self.statsDf.apply(lambda x: round(((x['# patients receiving apixaban']/x['afib_flutter_detected_patients_not_dead']) * 100), 2) if x['afib_flutter_detected_patients_not_dead'] > 0 else 0, axis=1) self.tmp = anticoagulants_prescribed.groupby(['Protocol ID', 'UKON_EDOXABAN']).size().to_frame('count').reset_index() self.statsDf = self._get_values_for_factors(column_name="UKON_EDOXABAN", value=1, new_column_name='# patients receiving edoxaban') self.statsDf['% patients receiving edoxaban'] = self.statsDf.apply(lambda x: round(((x['# patients receiving edoxaban']/x['afib_flutter_detected_patients_not_dead']) * 100), 2) if x['afib_flutter_detected_patients_not_dead'] > 0 else 0, axis=1) self.tmp = anticoagulants_prescribed.groupby(['Protocol ID', 'UKON_HEPARIN_VTE']).size().to_frame('count').reset_index() self.statsDf = self._get_values_for_factors(column_name="UKON_HEPARIN_VTE", value=1, new_column_name='# patients receiving LMWH or heparin in prophylactic dose') self.statsDf['% patients receiving LMWH or heparin in prophylactic dose'] = self.statsDf.apply(lambda x: round(((x['# patients receiving LMWH or heparin in prophylactic dose']/x['afib_flutter_detected_patients_not_dead']) * 100), 2) if x['afib_flutter_detected_patients_not_dead'] > 0 else 0, axis=1) anticoagulants_prescribed['UKON_LMW_ANTICOAGULACNI'] = anticoagulants_prescribed.apply(lambda x: 2 if x['UKON_LMW'] == 2 and x['UKON_ANTIKOAGULANCIA'] == 2 else 1, axis=1) self.tmp = anticoagulants_prescribed.groupby(['Protocol ID', 'UKON_LMW_ANTICOAGULACNI']).size().to_frame('count').reset_index() self.statsDf = self._get_values_for_factors(column_name="UKON_LMW_ANTICOAGULACNI", value=1, new_column_name='# patients receiving LMWH or heparin in full anticoagulant dose') self.statsDf['% patients receiving LMWH or heparin in full anticoagulant dose'] = self.statsDf.apply(lambda x: round(((x['# patients receiving LMWH or heparin in full anticoagulant dose']/x['afib_flutter_detected_patients_not_dead']) * 100), 2) if x['afib_flutter_detected_patients_not_dead'] > 0 else 0, axis=1) # anticoagulants_recommended = afib_flutter_detected[afib_flutter_detected['ANTITHROMBOTICS'].isin([9])].copy() # self.statsDf['anticoagulants_recommended_patients'] = self._count_patients(dataframe=anticoagulants_recommended) self.statsDf['anticoagulants_recommended_patients'] = 0 afib_flutter_detected_dead = afib_flutter_detected[afib_flutter_detected['DISCHARGE_DESTINATION'].isin([5])].copy() self.statsDf['afib_flutter_detected_dead_patients'] = self._count_patients(dataframe=afib_flutter_detected_dead) self.statsDf['% patients prescribed anticoagulants with aFib'] = self.statsDf.apply(lambda x: round(((x['# patients prescribed anticoagulants with aFib']/(x['afib_flutter_detected_patients'] - x['afib_flutter_detected_dead_patients'])) * 100), 2) if (x['afib_flutter_detected_patients'] - x['afib_flutter_detected_dead_patients']) > 0 else 0, axis=1) ########################################## # ANTITHROMBOTICS - PRESCRIBED WITH AFIB # ########################################## # patients not reffered antithrombotics_prescribed = afib_flutter_detected[afib_flutter_detected['ANTITHROMBOTICS'].isin([1]) & ~afib_flutter_detected['DISCHARGE_DESTINATION'].isin([5])].copy() self.statsDf['# patients prescribed antithrombotics with aFib'] = self._count_patients(dataframe=antithrombotics_prescribed) # recommended_antithrombotics_with_afib_alive = afib_flutter_detected[afib_flutter_detected['ANTITHROMBOTICS'].isin([9]) & ~afib_flutter_detected['DISCHARGE_DESTINATION'].isin([5])].copy() # self.statsDf['recommended_antithrombotics_with_afib_alive_patients'] = self._count_patients(dataframe=recommended_antithrombotics_with_afib_alive) self.statsDf['recommended_antithrombotics_with_afib_alive_patients'] = 0 self.statsDf['% patients prescribed antithrombotics with aFib'] = self.statsDf.apply(lambda x: round(((x['# patients prescribed antithrombotics with aFib']/(x['afib_flutter_detected_patients'] - x['afib_flutter_detected_dead_patients'] - x['recommended_antithrombotics_with_afib_alive_patients'])) * 100), 2) if (x['afib_flutter_detected_patients'] - x['afib_flutter_detected_dead_patients'] - x['recommended_antithrombotics_with_afib_alive_patients']) > 0 else 0, axis=1) ########### # STATINS # ########### self.tmp = is_tia.groupby(['Protocol ID', 'STATIN']).size().to_frame('count').reset_index() self.statsDf = self._get_values_for_factors(column_name="STATIN", value=1, new_column_name='# patients prescribed statins - Yes') self.statsDf['% patients prescribed statins - Yes'] = self.statsDf.apply(lambda x: round(((x['# patients prescribed statins - Yes']/x['is_tia_patients']) * 100), 2) if x['is_tia_patients'] > 0 else 0, axis=1) self.statsDf = self._get_values_for_factors(column_name="STATIN", value=2, new_column_name='# patients prescribed statins - No') self.statsDf['% patients prescribed statins - No'] = self.statsDf.apply(lambda x: round(((x['# patients prescribed statins - No']/x['is_tia_patients']) * 100), 2) if x['is_tia_patients'] > 0 else 0, axis=1) self.statsDf = self._get_values_for_factors(column_name="STATIN", value=3, new_column_name='# patients prescribed statins - Not known') self.statsDf['% patients prescribed statins - Not known'] = self.statsDf.apply(lambda x: round(((x['# patients prescribed statins - Not known']/x['is_tia_patients']) * 100), 2) if x['is_tia_patients'] > 0 else 0, axis=1) ######################### # DISCHARGE DESTINATION # ######################### self.tmp = discharge_subset.groupby(['Protocol ID', 'DISCHARGE_DESTINATION']).size().to_frame('count').reset_index() self.statsDf = self._get_values_for_factors(column_name="DISCHARGE_DESTINATION", value=1, new_column_name='# discharge destination - Home') self.statsDf['% discharge destination - Home'] = self.statsDf.apply(lambda x: round(((x['# discharge destination - Home']/x['discharge_subset_patients']) * 100), 2) if x['discharge_subset_patients'] > 0 else 0, axis=1) self.statsDf = self._get_values_for_factors(column_name="DISCHARGE_DESTINATION", value=2, new_column_name='# discharge destination - Transferred within the same centre') self.statsDf['% discharge destination - Transferred within the same centre'] = self.statsDf.apply(lambda x: round(((x['# discharge destination - Transferred within the same centre']/x['discharge_subset_patients']) * 100), 2) if x['discharge_subset_patients'] > 0 else 0, axis=1) self.statsDf = self._get_values_for_factors(column_name="DISCHARGE_DESTINATION", value=3, new_column_name='# discharge destination - Transferred to another centre') self.statsDf['% discharge destination - Transferred to another centre'] = self.statsDf.apply(lambda x: round(((x['# discharge destination - Transferred to another centre']/x['discharge_subset_patients']) * 100), 2) if x['discharge_subset_patients'] > 0 else 0, axis=1) self.statsDf = self._get_values_for_factors(column_name="DISCHARGE_DESTINATION", value=4, new_column_name='# discharge destination - Social care facility') self.statsDf['% discharge destination - Social care facility'] = self.statsDf.apply(lambda x: round(((x['# discharge destination - Social care facility']/x['discharge_subset_patients']) * 100), 2) if x['discharge_subset_patients'] > 0 else 0, axis=1) self.statsDf = self._get_values_for_factors(column_name="DISCHARGE_DESTINATION", value=5, new_column_name='# discharge destination - Dead') self.statsDf['% discharge destination - Dead'] = self.statsDf.apply(lambda x: round(((x['# discharge destination - Dead']/x['discharge_subset_patients']) * 100), 2) if x['discharge_subset_patients'] > 0 else 0, axis=1) ####################################### # DISCHARGE DESTINATION - SAME CENTRE # ####################################### # discharge_subset_same_centre = discharge_subset[discharge_subset['DISCHARGE_DESTINATION'].isin([2])].copy() # self.statsDf['discharge_subset_same_centre_patients'] = self._count_patients(dataframe=discharge_subset_same_centre) # self.tmp = discharge_subset_same_centre.groupby(['Protocol ID', 'DISCHARGE_SAME_FACILITY']).size().to_frame('count').reset_index() # self.statsDf = self._get_values_for_factors(column_name="DISCHARGE_SAME_FACILITY", value=1, new_column_name='# transferred within the same centre - Acute rehabilitation') # self.statsDf['% transferred within the same centre - Acute rehabilitation'] = self.statsDf.apply(lambda x: round(((x['# transferred within the same centre - Acute rehabilitation']/x['discharge_subset_same_centre_patients']) * 100), 2) if x['discharge_subset_same_centre_patients'] > 0 else 0, axis=1) # self.statsDf = self._get_values_for_factors(column_name="DISCHARGE_SAME_FACILITY", value=2, new_column_name='# transferred within the same centre - Post-care bed') # self.statsDf['% transferred within the same centre - Post-care bed'] = self.statsDf.apply(lambda x: round(((x['# transferred within the same centre - Post-care bed']/x['discharge_subset_same_centre_patients']) * 100), 2) if x['discharge_subset_same_centre_patients'] > 0 else 0, axis=1) # self.statsDf = self._get_values_for_factors(column_name="DISCHARGE_SAME_FACILITY", value=3, new_column_name='# transferred within the same centre - Another department') # self.statsDf['% transferred within the same centre - Another department'] = self.statsDf.apply(lambda x: round(((x['# transferred within the same centre - Another department']/x['discharge_subset_same_centre_patients']) * 100), 2) if x['discharge_subset_same_centre_patients'] > 0 else 0, axis=1) ################ # ANGEL AWARDS # ################ #### TOTAL PATIENTS ##### self.statsDf['# total patients >= 30'] = self.statsDf['Total Patients'] >= 30 #### DOOR TO THROMBOLYSIS THERAPY - MINUTES #### # self.statsDf.loc[:, 'patients_eligible_recanalization'] = self.statsDf.apply(lambda x: x['# recanalization procedures - Not done'] + x['# recanalization procedures - IV tPa'] + x['# recanalization procedures - IV tPa + endovascular treatment'] + x['# recanalization procedures - Endovascular treatment alone'] + x['# recanalization procedures - IV tPa + referred to another centre for endovascular treatment'], axis=1) self.statsDf.loc[:, 'patients_eligible_recanalization'] = self.statsDf.apply(lambda x: x['# recanalization procedures - IV tPa'] + x['# recanalization procedures - IV tPa + endovascular treatment'] + x['# recanalization procedures - Endovascular treatment alone'] + x['# recanalization procedures - IV tPa + referred to another centre for endovascular treatment'], axis=1) self.statsDf.loc[:, '# patients eligible thrombolysis'] = self.statsDf.apply(lambda x: x['# IV tPa'], axis=1) self.statsDf.loc[:, '# patients eligible thrombectomy'] = self.statsDf.apply(lambda x: (x['# TBY']), axis=1) # patients treated with door to recanalization therapy < 60 minutes # for tby, we are only looking at patients that have had ONLY tby, not tpa + tby, as we awould be counting those patients twice (penalizing twice) # recanalization_procedure_tby_only_dtg = recanalization_procedure_tby_dtg[recanalization_procedure_tby_dtg['RECANALIZATION_PROCEDURES'].isin([4])] ########### OLD recanalization_procedure_tby_only_dtg = recanalization_procedure_tby_dtg[recanalization_procedure_tby_dtg['RECANALIZATION_PROCEDURES'].isin([4])] recanalization_procedure_iv_tpa_under_60 = recanalization_procedure_iv_tpa.loc[(recanalization_procedure_iv_tpa['IVTPA'] > 0) & (recanalization_procedure_iv_tpa['IVTPA'] <= 60)] recanalization_procedure_tby_only_dtg_under_60 = recanalization_procedure_tby_only_dtg.loc[(recanalization_procedure_tby_only_dtg['TBY'] > 0) & (recanalization_procedure_tby_only_dtg['TBY'] <= 60)] self.statsDf['# patients treated with door to recanalization therapy < 60 minutes'] = self._count_patients(dataframe=recanalization_procedure_iv_tpa_under_60) + self._count_patients(dataframe=recanalization_procedure_tby_only_dtg_under_60) self.statsDf['% patients treated with door to recanalization therapy < 60 minutes'] = self.statsDf.apply(lambda x: round(((x['# patients treated with door to recanalization therapy < 60 minutes']/x['patients_eligible_recanalization']) * 100), 2) if x['patients_eligible_recanalization'] > 0 else 0, axis=1) recanalization_procedure_iv_tpa_under_45 = recanalization_procedure_iv_tpa.loc[(recanalization_procedure_iv_tpa['IVTPA'] > 0) & (recanalization_procedure_iv_tpa['IVTPA'] <= 45)] recanalization_procedure_tby_only_dtg_under_45 = recanalization_procedure_tby_only_dtg.loc[(recanalization_procedure_tby_only_dtg['TBY'] > 0) & (recanalization_procedure_tby_only_dtg['TBY'] <= 45)] self.statsDf['# patients treated with door to recanalization therapy < 45 minutes'] = self._count_patients(dataframe=recanalization_procedure_iv_tpa_under_45) + self._count_patients(dataframe=recanalization_procedure_tby_only_dtg_under_45) self.statsDf['% patients treated with door to recanalization therapy < 45 minutes'] = self.statsDf.apply(lambda x: round(((x['# patients treated with door to recanalization therapy < 45 minutes']/x['patients_eligible_recanalization']) * 100), 2) if x['patients_eligible_recanalization'] > 0 else 0, axis=1) ########### OLD recanalization_procedure_iv_tpa_under_60 = recanalization_procedure_iv_tpa.loc[(recanalization_procedure_iv_tpa['IVTPA'] > 0) & (recanalization_procedure_iv_tpa['IVTPA'] <= 60)] self.statsDf['# patients treated with door to thrombolysis < 60 minutes'] = self._count_patients(dataframe=recanalization_procedure_iv_tpa_under_60) self.statsDf['% patients treated with door to thrombolysis < 60 minutes'] = self.statsDf.apply(lambda x: round(((x['# patients treated with door to thrombolysis < 60 minutes']/x['# patients eligible thrombolysis']) * 100), 2) if x['# patients eligible thrombolysis'] > 0 else 0, axis=1) del recanalization_procedure_iv_tpa_under_60 recanalization_procedure_iv_tpa_under_45 = recanalization_procedure_iv_tpa.loc[(recanalization_procedure_iv_tpa['IVTPA'] > 0) & (recanalization_procedure_iv_tpa['IVTPA'] <= 45)] self.statsDf['# patients treated with door to thrombolysis < 45 minutes'] = self._count_patients(dataframe=recanalization_procedure_iv_tpa_under_45) self.statsDf['% patients treated with door to thrombolysis < 45 minutes'] = self.statsDf.apply(lambda x: round(((x['# patients treated with door to thrombolysis < 45 minutes']/x['# patients eligible thrombolysis']) * 100), 2) if x['# patients eligible thrombolysis'] > 0 else 0, axis=1) del recanalization_procedure_iv_tpa_under_45 recanalization_procedure_tby_only_dtg_under_120 = recanalization_procedure_tby_dtg.loc[(recanalization_procedure_tby_dtg['TBY'] > 0) & (recanalization_procedure_tby_dtg['TBY'] <= 120)] recanalization_procedure_tby_only_dtg_under_90 = recanalization_procedure_tby_dtg.loc[(recanalization_procedure_tby_dtg['TBY'] > 0) & (recanalization_procedure_tby_dtg['TBY'] <= 90)] self.statsDf['# patients treated with door to thrombectomy < 120 minutes'] = self._count_patients(dataframe=recanalization_procedure_tby_only_dtg_under_120) self.statsDf['% patients treated with door to thrombectomy < 120 minutes'] = self.statsDf.apply(lambda x: round(((x['# patients treated with door to thrombectomy < 120 minutes']/x['# patients eligible thrombectomy']) * 100), 2) if x['# patients eligible thrombectomy'] > 0 else 0, axis=1) del recanalization_procedure_tby_only_dtg_under_120 self.statsDf['# patients treated with door to thrombectomy < 90 minutes'] = self._count_patients(dataframe=recanalization_procedure_tby_only_dtg_under_90) self.statsDf['% patients treated with door to thrombectomy < 90 minutes'] = self.statsDf.apply(lambda x: round(((x['# patients treated with door to thrombectomy < 90 minutes']/x['# patients eligible thrombectomy']) * 100), 2) if x['# patients eligible thrombectomy'] > 0 else 0, axis=1) del recanalization_procedure_tby_only_dtg_under_90 # self.statsDf['# patients treated with door to recanalization therapy < 60 minutes'] = self._count_patients(dataframe=recanalization_procedure_iv_tpa_under_60) + self._count_patients(dataframe=recanalization_procedure_tby_only_dtg_under_60) # # self.statsDf['# patients treated with door to recanalization therapy < 60 minutes'] = self._count_patients(dataframe=recanalization_procedure_iv_tpa_under_60) # self.statsDf['% patients treated with door to recanalization therapy < 60 minutes'] = self.statsDf.apply(lambda x: round(((x['# patients treated with door to recanalization therapy < 60 minutes']/x['patients_eligible_recanalization']) * 100), 2) if x['patients_eligible_recanalization'] > 0 else 0, axis=1) # recanalization_procedure_iv_tpa_under_45 = recanalization_procedure_iv_tpa.loc[(recanalization_procedure_iv_tpa['IVTPA'] > 0) & (recanalization_procedure_iv_tpa['IVTPA'] <= 45)] # # recanalization_procedure_iv_tpa_under_45 = recanalization_procedure_iv_tpa[recanalization_procedure_iv_tpa['IVTPA'] <= 45] # recanalization_procedure_tby_only_dtg_under_45 = recanalization_procedure_tby_only_dtg.loc[(recanalization_procedure_tby_only_dtg['TBY'] > 0) & (recanalization_procedure_tby_only_dtg['TBY'] <= 45)] # # recanalization_procedure_tby_only_dtg_under_45 = recanalization_procedure_tby_only_dtg[recanalization_procedure_tby_only_dtg['TBY'] <= 45] # self.statsDf['# patients treated with door to recanalization therapy < 45 minutes'] = self._count_patients(dataframe=recanalization_procedure_iv_tpa_under_45) + self._count_patients(dataframe=recanalization_procedure_tby_only_dtg_under_45) # # self.statsDf['# patients treated with door to recanalization therapy < 45 minutes'] = self._count_patients(dataframe=recanalization_procedure_iv_tpa_under_45) # self.statsDf['% patients treated with door to recanalization therapy < 45 minutes'] = self.statsDf.apply(lambda x: round(((x['# patients treated with door to recanalization therapy < 45 minutes']/x['patients_eligible_recanalization']) * 100), 2) if x['patients_eligible_recanalization'] > 0 else 0, axis=1) # Get % patients recanalized # self.statsDf['patient_recan_%'] = self.statsDf.apply(lambda x: round(((x['patients_eligible_recanalization']/(x['isch_patients'] - x['# recanalization procedures - Referred to another centre for endovascular treatment'] - x['# recanalization procedures - Referred to another centre for endovascular treatment and hospitalization continues at the referred to centre'] - x['# recanalization procedures - Referred for endovascular treatment and patient is returned to the initial centre'] - x['# recanalization procedures - Returned to the initial centre after recanalization procedures were performed at another centre'] - x['# recanalization procedures - Endovascular treatment alone'])) * 100), 2) if (x['isch_patients'] - x['# recanalization procedures - Referred to another centre for endovascular treatment'] - x['# recanalization procedures - Referred to another centre for endovascular treatment and hospitalization continues at the referred to centre'] - x['# recanalization procedures - Referred for endovascular treatment and patient is returned to the initial centre'] - x['# recanalization procedures - Returned to the initial centre after recanalization procedures were performed at another centre'] - x['# recanalization procedures - Endovascular treatment alone']) > 0 else 0, axis=1) self.statsDf['patient_recan_%'] = self.statsDf.apply(lambda x: round(((x['patients_eligible_recanalization']/(x['isch_patients'] - x['# recanalization procedures - Referred to another centre for endovascular treatment'] - x['# recanalization procedures - Referred to another centre for endovascular treatment and hospitalization continues at the referred to centre'] - x['# recanalization procedures - Referred for endovascular treatment and patient is returned to the initial centre'] - x['# recanalization procedures - Returned to the initial centre after recanalization procedures were performed at another centre'])) * 100), 2) if (x['isch_patients'] - x['# recanalization procedures - Referred to another centre for endovascular treatment'] - x['# recanalization procedures - Referred to another centre for endovascular treatment and hospitalization continues at the referred to centre'] - x['# recanalization procedures - Referred for endovascular treatment and patient is returned to the initial centre'] - x['# recanalization procedures - Returned to the initial centre after recanalization procedures were performed at another centre']) > 0 else 0, axis=1) #### RECANALIZATION RATE #### self.statsDf['# recanalization rate out of total ischemic incidence'] = self.statsDf['patients_eligible_recanalization'] self.statsDf['% recanalization rate out of total ischemic incidence'] = self.statsDf['patient_recan_%'] self.statsDf.drop(['patient_recan_%'], inplace=True, axis=1) #### CT/MRI #### self.statsDf['# suspected stroke patients undergoing CT/MRI'] = self.statsDf['# CT/MRI - performed'] self.statsDf['% suspected stroke patients undergoing CT/MRI'] = self.statsDf['% CT/MRI - performed'] #### DYSPHAGIA SCREENING #### self.statsDf['# all stroke patients undergoing dysphagia screening'] = self.statsDf['# dysphagia screening - Guss test'] + self.statsDf['# dysphagia screening - Other test'] self.statsDf['% all stroke patients undergoing dysphagia screening'] = self.statsDf.apply(lambda x: round(((x['# all stroke patients undergoing dysphagia screening']/(x['# all stroke patients undergoing dysphagia screening'] + x['# dysphagia screening - Not done'])) * 100), 2) if (x['# all stroke patients undergoing dysphagia screening'] + x['# dysphagia screening - Not done']) > 0 else 0, axis=1) #### ISCHEMIC STROKE + NO AFIB + ANTIPLATELETS #### non_transferred_antiplatelets = antithrombotics[~antithrombotics['RECANALIZATION_PROCEDURES'].isin([5,6])] #antithrombotics_discharged_home = antithrombotics[antithrombotics['DISCHARGE_DESTINATION'].isin([1])] # Get temporary dataframe with patients who have prescribed antithrombotics and ischemic stroke antiplatelets = non_transferred_antiplatelets[non_transferred_antiplatelets['STROKE_TYPE'].isin([1])] #antiplatelets = antithrombotics[antithrombotics['STROKE_TYPE'].isin([1])] #antiplatelets = antithrombotics_discharged_home[antithrombotics_discharged_home['STROKE_TYPE'].isin([1])] # Filter temporary dataframe and get only patients who have not been detected or not known for aFib flutter. antiplatelets = antiplatelets[antiplatelets['AFIB_FLUTTER'].isin([4, 5])] # Get patients who have prescribed antithrombotics except_recommended = antiplatelets[antiplatelets['ANTITHROMBOTICS'].isin([1,2])] # Get number of patients who have prescribed antithrombotics and ischemic stroke, have not been detected or not known for aFib flutter. self.statsDf['except_recommended_patients'] = self._count_patients(dataframe=except_recommended) # Get temporary dataframe groupby protocol ID and antithrombotics column self.tmp = antiplatelets.groupby(['Protocol ID', 'ANTIPLATELETS']).size().to_frame('count').reset_index() self.statsDf = self._get_values_for_factors(column_name="ANTIPLATELETS", value=1, new_column_name='# ischemic stroke patients discharged with antiplatelets') self.statsDf['% ischemic stroke patients discharged with antiplatelets'] = self.statsDf.apply(lambda x: round(((x['# ischemic stroke patients discharged with antiplatelets']/x['except_recommended_patients']) * 100), 2) if x['except_recommended_patients'] > 0 else 0, axis=1) # discharged home antiplatelets_discharged_home = antiplatelets[antiplatelets['DISCHARGE_DESTINATION'].isin([1])] if (antiplatelets_discharged_home.empty): # Get temporary dataframe groupby protocol ID and antithrombotics column self.tmp = antiplatelets.groupby(['Protocol ID', 'ANTIPLATELETS']).size().to_frame('count').reset_index() self.statsDf = self._get_values_for_factors(column_name="ANTIPLATELETS", value=1, new_column_name='# ischemic stroke patients discharged home with antiplatelets') self.statsDf['% ischemic stroke patients discharged home with antiplatelets'] = self.statsDf.apply(lambda x: round(((x['# ischemic stroke patients discharged home with antiplatelets']/x['except_recommended_patients']) * 100), 2) if x['except_recommended_patients'] > 0 else 0, axis=1) self.statsDf['except_recommended_discharged_home_patients'] = self.statsDf['except_recommended_patients'] else: # Get temporary dataframe groupby protocol ID and antithrombotics column self.tmp = antiplatelets_discharged_home.groupby(['Protocol ID', 'ANTIPLATELETS']).size().to_frame('count').reset_index() # Get patients who have prescribed antithrombotics except_recommended_discharged_home = except_recommended[except_recommended['DISCHARGE_DESTINATION'].isin([1])] # Get number of patients who have prescribed antithrombotics and ischemic stroke, have not been detected or not known for aFib flutter. self.statsDf['except_recommended_discharged_home_patients'] = self._count_patients(dataframe=except_recommended_discharged_home) self.statsDf = self._get_values_for_factors(column_name="ANTIPLATELETS", value=1, new_column_name='# ischemic stroke patients discharged home with antiplatelets') self.statsDf['% ischemic stroke patients discharged home with antiplatelets'] = self.statsDf.apply(lambda x: round(((x['# ischemic stroke patients discharged home with antiplatelets']/x['except_recommended_discharged_home_patients']) * 100), 2) if x['except_recommended_discharged_home_patients'] > 0 else 0, axis=1) self.statsDf['# ischemic stroke patients discharged (home) with antiplatelets'] = self.statsDf.apply(lambda x: x['# ischemic stroke patients discharged with antiplatelets'] if x['# ischemic stroke patients discharged with antiplatelets'] > x['# ischemic stroke patients discharged home with antiplatelets'] else x['# ischemic stroke patients discharged home with antiplatelets'], axis=1) self.statsDf['% ischemic stroke patients discharged (home) with antiplatelets'] = self.statsDf.apply(lambda x: x['% ischemic stroke patients discharged with antiplatelets'] if x['% ischemic stroke patients discharged with antiplatelets'] > x['% ischemic stroke patients discharged home with antiplatelets'] else x['% ischemic stroke patients discharged home with antiplatelets'], axis=1) # afib patients discharged with anticoagulants self.statsDf['# afib patients discharged with anticoagulants'] = self._count_patients(dataframe=anticoagulants_prescribed) # Get temporary dataframe with patients who are not dead with detected aFib flutter and with prescribed antithrombotics #afib_detected_discharged_home = afib_flutter_detected[(~afib_flutter_detected['DISCHARGE_DESTINATION'].isin([5])) & (afib_flutter_detected['ANTICOAGULANTS'].isin([1]))] afib_detected_discharged_home = afib_flutter_detected[(~afib_flutter_detected['DISCHARGE_DESTINATION'].isin([5])) & (afib_flutter_detected['ANTICOAGULANTS'].isin([1, 2]))] # Get afib patients discharged and not dead self.statsDf['afib_detected_discharged_patients'] = self._count_patients(dataframe=afib_detected_discharged_home) # % afib patients discharged with anticoagulants #self.statsDf['% afib patients discharged with anticoagulants'] = self.statsDf.apply(lambda x: round(((x['# afib patients discharged with anticoagulants']/(x['afib_flutter_detected_patients'] - x['afib_flutter_detected_dead_patients'])) * 100), 2) if (x['afib_flutter_detected_patients'] - x['afib_flutter_detected_dead_patients']) > 0 else 0, axis=1) self.statsDf['% afib patients discharged with anticoagulants'] = self.statsDf.apply(lambda x: round(((x['# afib patients discharged with anticoagulants']/x['afib_detected_discharged_patients']) * 100), 2) if (x['afib_detected_discharged_patients']) > 0 else 0, axis=1) # Get temporary dataframe with patients who have prescribed anticoagulats and were discharged home non_trasferred_anticoagulants = anticoagulants_prescribed[~anticoagulants_prescribed['RECANALIZATION_PROCEDURES'].isin([5,6])] anticoagulants_prescribed_discharged_home = non_trasferred_anticoagulants[non_trasferred_anticoagulants['DISCHARGE_DESTINATION'].isin([1])] #anticoagulants_prescribed_discharged_home = anticoagulants_prescribed[anticoagulants_prescribed['DISCHARGE_DESTINATION'].isin([1])] # Get temporary dataframe with patients who have been discharge at home with detected aFib flutter and with prescribed antithrombotics #afib_detected_discharged_home = afib_flutter_detected[(afib_flutter_detected['DISCHARGE_DESTINATION'].isin([1])) & (~afib_flutter_detected['ANTITHROMBOTICS'].isin([9]))] afib_detected_discharged_home = afib_flutter_detected[(afib_flutter_detected['DISCHARGE_DESTINATION'].isin([1])) & (afib_flutter_detected['ANTICOAGULANTS'].isin([1, 2])) & (~afib_flutter_detected['RECANALIZATION_PROCEDURES'].isin([5,6]))] # Check if temporary dataframe is empty. If yes, the value is calculated not only for discharged home, but only dead patients are excluded if (anticoagulants_prescribed_discharged_home.empty): # afib patients discharged home with anticoagulants anticoagulants_prescribed_discharged_home = anticoagulants_prescribed[~anticoagulants_prescribed['DISCHARGE_DESTINATION'].isin([5])] # Get temporary dataframe with patients who are not dead with detected aFib flutter and with prescribed antithrombotics afib_detected_discharged_home = afib_flutter_detected[(~afib_flutter_detected['DISCHARGE_DESTINATION'].isin([5])) & (afib_flutter_detected['ANTICOAGULANTS'].isin([1, 2]))] # Get # afib patients discharged home with anticoagulants self.statsDf['# afib patients discharged home with anticoagulants'] = self._count_patients(dataframe=anticoagulants_prescribed_discharged_home) # Get afib patients discharged and not dead self.statsDf['afib_detected_discharged_home_patients'] = self._count_patients(dataframe=afib_detected_discharged_home) # Get % afib patients discharge with anticoagulants and not dead self.statsDf['% afib patients discharged home with anticoagulants'] = self.statsDf.apply(lambda x: round(((x['# afib patients discharged home with anticoagulants']/x['afib_detected_discharged_home_patients']) * 100), 2) if x['afib_detected_discharged_home_patients'] > 0 else 0, axis=1) else: # Get # afib patients discharged home with anticoagulants self.statsDf['# afib patients discharged home with anticoagulants'] = self._count_patients(dataframe=anticoagulants_prescribed_discharged_home) # Get afib patients discharged home self.statsDf['afib_detected_discharged_home_patients'] = self._count_patients(dataframe=afib_detected_discharged_home) # Get % afib patients discharged home with anticoagulants self.statsDf['% afib patients discharged home with anticoagulants'] = self.statsDf.apply(lambda x: round(((x['# afib patients discharged home with anticoagulants']/x['afib_detected_discharged_home_patients']) * 100), 2) if x['afib_detected_discharged_home_patients'] > 0 else 0, axis=1) self.statsDf['# afib patients discharged (home) with anticoagulants'] = self.statsDf.apply(lambda x: x['# afib patients discharged with anticoagulants'] if x['% afib patients discharged with anticoagulants'] > x['% afib patients discharged home with anticoagulants'] else x['# afib patients discharged home with anticoagulants'], axis=1) self.statsDf['% afib patients discharged (home) with anticoagulants'] = self.statsDf.apply(lambda x: x['% afib patients discharged with anticoagulants'] if x['% afib patients discharged with anticoagulants'] > x['% afib patients discharged home with anticoagulants'] else x['% afib patients discharged home with anticoagulants'], axis=1) #### STROKE UNIT #### # stroke patients treated in a dedicated stroke unit / ICU self.statsDf['# stroke patients treated in a dedicated stroke unit / ICU'] = self.statsDf['# patients hospitalized in stroke unit / ICU'] self.statsDf['% stroke patients treated in a dedicated stroke unit / ICU'] = self.statsDf['% patients hospitalized in stroke unit / ICU'] # SK doesn't collect the stroke unit, then we put here always 1 self.statsDf['% stroke patients treated in a dedicated stroke unit / ICU'] = self.statsDf.apply(lambda x: x['% patients hospitalized in stroke unit / ICU'] if x['# patients hospitalized in stroke unit / ICU'] > 0 else 1, axis=1) # Create temporary dataframe to calculate final award self.total_patient_column = '# total patients >= {0}'.format(30) # self.angels_awards_tmp = self.statsDf[[self.total_patient_column, '% patients treated with door to recanalization therapy < 60 minutes', '% patients treated with door to recanalization therapy < 45 minutes', '% recanalization rate out of total ischemic incidence', '% suspected stroke patients undergoing CT/MRI', '% all stroke patients undergoing dysphagia screening', '% ischemic stroke patients discharged (home) with antiplatelets', '% afib patients discharged (home) with anticoagulants', '% stroke patients treated in a dedicated stroke unit / ICU']] # self.statsDf.fillna(0, inplace=True) self.statsDf[self.total_patient_column] = self.statsDf['Total Patients'] >= 30 self.angels_awards_tmp = self.statsDf[[self.total_patient_column, '% patients treated with door to thrombolysis < 60 minutes', '% patients treated with door to thrombolysis < 45 minutes', '% patients treated with door to thrombectomy < 120 minutes', '% patients treated with door to thrombectomy < 90 minutes', '% recanalization rate out of total ischemic incidence', '% suspected stroke patients undergoing CT/MRI', '% all stroke patients undergoing dysphagia screening', '% ischemic stroke patients discharged (home) with antiplatelets', '% afib patients discharged (home) with anticoagulants', '% stroke patients treated in a dedicated stroke unit / ICU', '# patients eligible thrombectomy', '# patients eligible thrombolysis']] self.statsDf.fillna(0, inplace=True) self.angels_awards_tmp['Proposed Award'] = self.angels_awards_tmp.apply(lambda x: self._get_final_award(x), axis=1) self.statsDf['Proposed Award'] = self.angels_awards_tmp['Proposed Award'] self.statsDf.fillna(0, inplace=True) self.statsDf.rename(columns={"Protocol ID": "Site ID"}, inplace=True) self.statsDf['Site Name'] = self.statsDf['Site ID'] # self.sites = self._get_sites(self.statsDf) def _get_final_award(self, x): """ The function calculating the proposed award. :param x: the row from temporary dataframe :type x: pandas series :returns: award -- the proposed award """ # if x[self.total_patient_column] == False: # award = "NONE" # else: # award = "TRUE" # recan_therapy_lt_60min = x['% patients treated with door to recanalization therapy < 60 minutes'] # # Calculate award for thrombolysis, if no patients were eligible for thrombolysis and number of total patients was greater than minimum than the award is set to DIAMOND # if award == "TRUE": # if (float(recan_therapy_lt_60min) >= 50 and float(recan_therapy_lt_60min) <= 74.99): # award = "GOLD" # elif (float(recan_therapy_lt_60min) >= 75): # award = "DIAMOND" # else: # award = "NONE" # recan_therapy_lt_45min = x['% patients treated with door to recanalization therapy < 45 minutes'] # if award != "NONE": # if (float(recan_therapy_lt_45min) <= 49.99): # if (award != "GOLD" or award == "DIAMOND"): # award = "PLATINUM" # elif (float(recan_therapy_lt_45min) >= 50): # if (award != "GOLD"): # award = "DIAMOND" # else: # award = "NONE" # recan_rate = x['% recanalization rate out of total ischemic incidence'] # if award != "NONE": # if (float(recan_rate) >= 5 and float(recan_rate) <= 14.99): # if (award == "PLATINUM" or award == "DIAMOND"): # award = "GOLD" # elif (float(recan_rate) >= 15 and float(recan_rate) <= 24.99): # if (award == "DIAMOND"): # award = "PLATINUM" # elif (float(recan_rate) >= 25): # if (award == "DIAMOND"): # award = "DIAMOND" # else: # award = "NONE" # ct_mri = x['% suspected stroke patients undergoing CT/MRI'] # if award != "NONE": # if (float(ct_mri) >= 80 and float(ct_mri) <= 84.99): # if (award == "PLATINUM" or award == "DIAMOND"): # award = "GOLD" # elif (float(ct_mri) >= 85 and float(ct_mri) <= 89.99): # if (award == "DIAMOND"): # award = "PLATINUM" # elif (float(ct_mri) >= 90): # if (award == "DIAMOND"): # award = "DIAMOND" # else: # award = "NONE" # dysphagia_screening = x['% all stroke patients undergoing dysphagia screening'] # if award != "NONE": # if (float(dysphagia_screening) >= 80 and float(dysphagia_screening) <= 84.99): # if (award == "PLATINUM" or award == "DIAMOND"): # award = "GOLD" # elif (float(dysphagia_screening) >= 85 and float(dysphagia_screening) <= 89.99): # if (award == "DIAMOND"): # award = "PLATINUM" # elif (float(dysphagia_screening) >= 90): # if (award == "DIAMOND"): # award = "DIAMOND" # else: # award = "NONE" # discharged_with_antiplatelets_final = x['% ischemic stroke patients discharged (home) with antiplatelets'] # if award != "NONE": # if (float(discharged_with_antiplatelets_final) >= 80 and float(discharged_with_antiplatelets_final) <= 84.99): # if (award == "PLATINUM" or award == "DIAMOND"): # award = "GOLD" # elif (float(discharged_with_antiplatelets_final) >= 85 and float(discharged_with_antiplatelets_final) <= 89.99): # if (award == "DIAMOND"): # award = "PLATINUM" # elif (float(discharged_with_antiplatelets_final) >= 90): # if (award == "DIAMOND"): # award = "DIAMOND" # else: # award = "NONE" # discharged_with_anticoagulants_final = x['% afib patients discharged (home) with anticoagulants'] # if award != "NONE": # if (float(discharged_with_anticoagulants_final) >= 80 and float(discharged_with_anticoagulants_final) <= 84.99): # if (award == "PLATINUM" or award == "DIAMOND"): # award = "GOLD" # elif (float(discharged_with_anticoagulants_final) >= 85 and float(discharged_with_anticoagulants_final) <= 89.99): # if (award == "DIAMOND"): # award = "PLATINUM" # elif (float(discharged_with_anticoagulants_final) >= 90): # if (award == "DIAMOND"): # award = "DIAMOND" # else: # award = "NONE" # stroke_unit = x['% stroke patients treated in a dedicated stroke unit / ICU'] # if award != "NONE": # if (float(stroke_unit) <= 0.99): # if (award == "DIAMOND"): # award = "PLATINUM" # elif (float(stroke_unit) >= 1): # if (award == "DIAMOND"): # award = "DIAMOND" # else: # award = "NONE" # return award if x[self.total_patient_column] == False: award = "STROKEREADY" else: thrombolysis_therapy_lt_60min = x['% patients treated with door to thrombolysis < 60 minutes'] # Calculate award for thrombolysis, if no patients were eligible for thrombolysis and number of total patients was greater than minimum than the award is set to DIAMOND if (float(thrombolysis_therapy_lt_60min) >= 50 and float(thrombolysis_therapy_lt_60min) <= 74.99): award = "GOLD" elif (float(thrombolysis_therapy_lt_60min) >= 75): award = "DIAMOND" else: award = "STROKEREADY" thrombolysis_therapy_lt_45min = x['% patients treated with door to thrombolysis < 45 minutes'] if award != "STROKEREADY": if (float(thrombolysis_therapy_lt_45min) <= 49.99): if (award != "GOLD" or award == "DIAMOND"): award = "PLATINUM" elif (float(thrombolysis_therapy_lt_45min) >= 50): if (award != "GOLD"): award = "DIAMOND" else: award = "STROKEREADY" # Calculate award for thrombectomy, if no patients were eligible for thrombectomy and number of total patients was greater than minimum than the award is set to the possible proposed award (eg. if in thrombolysis step award was set to GOLD then the award will be GOLD) thrombectomy_pts = x['# patients eligible thrombectomy'] # if thrombectomy_pts != 0: if thrombectomy_pts > 3: thrombectomy_therapy_lt_120min = x['% patients treated with door to thrombectomy < 120 minutes'] if award != "STROKEREADY": if (float(thrombectomy_therapy_lt_120min) >= 50 and float(thrombectomy_therapy_lt_120min) <= 74.99): if (award == "PLATINUM" or award == "DIAMOND"): award = "GOLD" elif (float(thrombectomy_therapy_lt_120min) >= 75): if (award == "DIAMOND"): award = "DIAMOND" else: award = "STROKEREADY" thrombectomy_therapy_lt_90min = x['% patients treated with door to thrombectomy < 90 minutes'] if award != "STROKEREADY": if (float(thrombectomy_therapy_lt_90min) <= 49.99): if (award != "GOLD" or award == "DIAMOND"): award = "PLATINUM" elif (float(thrombectomy_therapy_lt_90min) >= 50): if (award == "DIAMOND"): award = "DIAMOND" else: award = "STROKEREADY" recan_rate = x['% recanalization rate out of total ischemic incidence'] if award != "STROKEREADY": if (float(recan_rate) >= 5 and float(recan_rate) <= 14.99): if (award == "PLATINUM" or award == "DIAMOND"): award = "GOLD" elif (float(recan_rate) >= 15 and float(recan_rate) <= 24.99): if (award == "DIAMOND"): award = "PLATINUM" elif (float(recan_rate) >= 25): if (award == "DIAMOND"): award = "DIAMOND" else: award = "STROKEREADY" ct_mri = x['% suspected stroke patients undergoing CT/MRI'] if award != "STROKEREADY": if (float(ct_mri) >= 80 and float(ct_mri) <= 84.99): if (award == "PLATINUM" or award == "DIAMOND"): award = "GOLD" elif (float(ct_mri) >= 85 and float(ct_mri) <= 89.99): if (award == "DIAMOND"): award = "PLATINUM" elif (float(ct_mri) >= 90): if (award == "DIAMOND"): award = "DIAMOND" else: award = "STROKEREADY" dysphagia_screening = x['% all stroke patients undergoing dysphagia screening'] if award != "STROKEREADY": if (float(dysphagia_screening) >= 80 and float(dysphagia_screening) <= 84.99): if (award == "PLATINUM" or award == "DIAMOND"): award = "GOLD" elif (float(dysphagia_screening) >= 85 and float(dysphagia_screening) <= 89.99): if (award == "DIAMOND"): award = "PLATINUM" elif (float(dysphagia_screening) >= 90): if (award == "DIAMOND"): award = "DIAMOND" else: award = "STROKEREADY" discharged_with_antiplatelets_final = x['% ischemic stroke patients discharged (home) with antiplatelets'] if award != "STROKEREADY": if (float(discharged_with_antiplatelets_final) >= 80 and float(discharged_with_antiplatelets_final) <= 84.99): if (award == "PLATINUM" or award == "DIAMOND"): award = "GOLD" elif (float(discharged_with_antiplatelets_final) >= 85 and float(discharged_with_antiplatelets_final) <= 89.99): if (award == "DIAMOND"): award = "PLATINUM" elif (float(discharged_with_antiplatelets_final) >= 90): if (award == "DIAMOND"): award = "DIAMOND" else: award = "STROKEREADY" discharged_with_anticoagulants_final = x['% afib patients discharged (home) with anticoagulants'] if award != "STROKEREADY": if (float(discharged_with_anticoagulants_final) >= 80 and float(discharged_with_anticoagulants_final) <= 84.99): if (award == "PLATINUM" or award == "DIAMOND"): award = "GOLD" elif (float(discharged_with_anticoagulants_final) >= 85 and float(discharged_with_anticoagulants_final) <= 89.99): if (award == "DIAMOND"): award = "PLATINUM" elif (float(discharged_with_anticoagulants_final) >= 90): if (award == "DIAMOND"): award = "DIAMOND" else: award = "STROKEREADY" stroke_unit = x['% stroke patients treated in a dedicated stroke unit / ICU'] if award != "STROKEREADY": if (float(stroke_unit) <= 0.99): if (award == "DIAMOND"): award = "PLATINUM" elif (float(stroke_unit) >= 1): if (award == "DIAMOND"): award = "DIAMOND" else: award = "STROKEREADY" return award def _count_patients(self, dataframe): """ The function calculating the number of patients per site. :param dataframe: the dataframe with preprocessed data :type dataframe: pandas dataframe :returns: the column with the number of patients """ tmpDf = dataframe.groupby(['Protocol ID']).size().reset_index(name='count_patients') factorDf = self.statsDf.merge(tmpDf, how='outer') factorDf.fillna(0, inplace=True) return factorDf['count_patients'] def _get_values_only_columns(self, column_name, value, dataframe): """ The function calculating the numbeer of patients per site for the given value from the temporary dataframe. :param column_name: the name of column name the number of patients should be calculated :type column_name: str :param value: the value for which we would like to get number of patients from the specific column :type value: int :param dataframe: the dataframe with the raw data :type dataframe: pandas dataframe :returns: the column with the number of patients """ tmpDf = dataframe[dataframe[column_name] == value].reset_index()[['Protocol ID', 'count']] factorDf = self.statsDf.merge(tmpDf, how='outer') factorDf.fillna(0, inplace=True) return factorDf['count'] def _get_values_for_factors(self, column_name, value, new_column_name, df=None): """ The function calculating the numbeer of patients per site for the given value from the temporary dataframe. :param column_name: the name of column name the number of patients should be calculated :type column_name: str :param value: the value for which we would like to get number of patients from the specific column :type value: int :param new_column_name: to this value will be renamed the created column containing the number of patients :type new_column_name: str :param df: the dataframe with the raw data :type df: pandas dataframe :returns: the dataframe with calculated statistics """ # Check if type of column name is type of number, if not convert value into string if (np.issubdtype(self.tmp[column_name].dtype, np.number)): value = value else: value = str(value) tmpDf = self.tmp[self.tmp[column_name] == value].reset_index()[['Protocol ID', 'count']] factorDf = self.statsDf.merge(tmpDf, how='outer') factorDf.rename(columns={'count': new_column_name}, inplace=True) factorDf.fillna(0, inplace=True) return factorDf def _get_values_for_factors_more_values(self, column_name, value, new_column_name, df=None): """ The function calculating the number of patients per site for the given value from the temporary dataframe. :param column_name: the name of column name the number of patients should be calculated :type column_name: str :param value: the list of values for which we would like to get number of patients from the specific column :type value: list :param new_column_name: to this value will be renamed the created column containing the number of patients :type new_column_name: str :param df: the dataframe with the raw data :type df: pandas dataframe :returns: the dataframe with calculated statistics """ if df is None: tmpDf = self.tmp[self.tmp[column_name].isin(value)].reset_index()[['Protocol ID', 'count']] tmpDf = tmpDf.groupby('Protocol ID').sum().reset_index() factorDf = self.statsDf.merge(tmpDf, how='outer') factorDf.rename(columns={'count': new_column_name}, inplace=True) factorDf.fillna(0, inplace=True) else: tmpDf = df[df[column_name].isin(value)].reset_index()[['Protocol ID', 'count']] tmpDf = tmpDf.groupby('Protocol ID').sum().reset_index() factorDf = self.statsDf.merge(tmpDf, how='outer') factorDf.rename(columns={'count': new_column_name}, inplace=True) factorDf.fillna(0, inplace=True) return factorDf def _get_values_for_factors_containing(self, column_name, value, new_column_name, df=None): """ The function calculating the number of patients per site for the given value from the temporary dataframe. :param column_name: the name of column name the number of patients should be calculated :type column_name: str :param value: the value of string type for which we would like to get number of patients from the specific column :type value: str :param new_column_name: to this value will be renamed the created column containing the number of patients :type new_column_name: str :param df: the dataframe with the raw data :type df: pandas dataframe :returns: the dataframe with calculated statistics """ if df is None: tmpDf = self.tmp[self.tmp[column_name].str.contains(value)].reset_index()[['Protocol ID', 'count']] tmpDf = tmpDf.groupby('Protocol ID').sum().reset_index() factorDf = self.statsDf.merge(tmpDf, how='outer') factorDf.rename(columns={'count': new_column_name}, inplace=True) factorDf.fillna(0, inplace=True) else: tmpDf = df[df[column_name].str.contains(value)].reset_index()[['Protocol ID', 'count']] tmpDf = tmpDf.groupby('Protocol ID').sum().reset_index() factorDf = self.statsDf.merge(tmpDf, how='outer') factorDf.rename(columns={'count': new_column_name}, inplace=True) factorDf.fillna(0, inplace=True) return factorDf def _get_ctmri_delta(self, hosp_time, ct_time): """ The function calculating the difference between two times in minutes. :param hosp_time: the time of hospitalization :type hosp_time: time :param ct_time: the time when CT/MRI was performed :type ct_time: time :returns: tdelta between two times in minutes """ timeformat = '%H:%M:%S' # Check if both time are not None if yes, return 0 else return tdelta if hosp_time is None or ct_time is None or pd.isnull(hosp_time) or pd.isnull(ct_time): tdeltaMin = 0 elif hosp_time == 0 or ct_time == 0: tdeltaMin = 0 else: if isinstance(ct_time, time) and isinstance(hosp_time, time): tdelta = datetime.combine(date.today(), ct_time) - datetime.combine(date.today(), hosp_time) elif isinstance(ct_time, time): tdelta = datetime.combine(date.today(), ct_time) - datetime.strptime(hosp_time, timeformat) elif isinstance(hosp_time, time): tdelta = datetime.strptime(ct_time, timeformat) - datetime.strptime(hosp_time, timeformat) else: tdelta = datetime.strptime(ct_time, timeformat) - datetime.strptime(hosp_time, timeformat) tdeltaMin = tdelta.total_seconds()/60.0 if tdeltaMin > 60: res = 2 elif tdeltaMin <= 60 and tdeltaMin > 0: res = 1 else: res = -2 return res def _return_dataset(self): """ The function returning dataframe. """ return self.df def _return_stats(self): """ The function returning the dataframe with the calculated statistics! :returns: the dataframe with the statistics """ return self.statsDf def _get_sites(self, df): """ The function returning the list of sites in the preprocessed data. :returns: the list of sites """ site_ids = df['Site ID'].tolist() site_list = list(set(site_ids)) return site_list def _return_sites(self): return self.sites class GenerateFormattedStats: """ The class generating the formatted statistics in Excel format. Angels Awards columns are colored based on the meeting of the condition. :param df: the dataframe with calculated statistics :type df: pandas dataframe :param country: True if country should be included as site :type country: bool :param country_code: the code of country used in filenames :type country_code: str :param split_sites: `True` if the reports should be generated per sites :type split_sites: bool :param site: the site code :type site: str :param report: type of the report, eg. quarter :type report: str :param quarter: type of the period, eq. Q1_2019 :type quarter: str :param comp: `True` if the comparison reports are calculated :type comp: bool """ def __init__(self, df, country=False, country_code=None, split_sites=False, site=None, report=None, quarter=None, comp=False): self.df_unformatted = df.copy() self.df = df.copy() self.country_code = country_code self.report = report self.quarter = quarter self.comp = comp def delete_columns(columns): """ The function deleting the columns from the dataframe which should not be displayed in the excel statistics (temporary columns used to generate graphs). :param columns: the list of column names to be deleted :type columns: list """ for i in columns: if i in self.df.columns: self.df.drop([i], inplace=True, axis=1) delete_columns(['isch_patients', 'is_ich_patients', 'is_ich_tia_cvt_patients', 'is_ich_cvt_patients', 'is_tia_patients', 'is_ich_sah_cvt_patients', 'is_tia_cvt_patients', 'cvt_patients', 'ich_sah_patients', 'ich_patients', 'sah_patients', 'discharge_subset_patients','discharge_subset_alive_patients', 'neurosurgery_patients', 'not_reffered_patients', 'reffered_patients', 'afib_detected_during_hospitalization_patients', 'afib_not_detected_or_not_known_patients', 'antithrombotics_patients', 'ischemic_transient_dead_patients', 'afib_flutter_not_detected_or_not_known_patients', 'afib_flutter_not_detected_or_not_known_dead_patients', 'prescribed_antiplatelets_no_afib_patients', 'prescribed_antiplatelets_no_afib_dead_patients', 'afib_flutter_detected_patients', 'anticoagulants_recommended_patients', 'afib_flutter_detected_dead_patients', 'recommended_antithrombotics_with_afib_alive_patients', 'discharge_subset_same_centre_patients', 'discharge_subset_another_centre_patients', 'patients_eligible_recanalization', '# patients having stroke in the hospital - No', '% patients having stroke in the hospital - No', '# recurrent stroke - No', '% recurrent stroke - No', '# patients assessed for rehabilitation - Not known', '% patients assessed for rehabilitation - Not known', '# level of consciousness - not known', '% level of consciousness - not known', '# CT/MRI - Performed later than 1 hour after admission', '% CT/MRI - Performed later than 1 hour after admission', '# patients put on ventilator - Not known', '% patients put on ventilator - Not known', '# patients put on ventilator - No', '% patients put on ventilator - No', '# IV tPa', '% IV tPa', '# TBY', '% TBY', '# DIDO TBY', '# dysphagia screening - not known', '% dysphagia screening - not known', '# dysphagia screening time - After first 24 hours', '% dysphagia screening time - After first 24 hours', '# other afib detection method - Not detected or not known', '% other afib detection method - Not detected or not known', '# carotid arteries imaging - Not known', '% carotid arteries imaging - Not known', '# carotid arteries imaging - No', '% carotid arteries imaging - No', 'vascular_imaging_cta_norm', 'vascular_imaging_mra_norm', 'vascular_imaging_dsa_norm', 'vascular_imaging_none_norm', 'bleeding_arterial_hypertension_perc_norm', 'bleeding_aneurysm_perc_norm', 'bleeding_arterio_venous_malformation_perc_norm', 'bleeding_anticoagulation_therapy_perc_norm', 'bleeding_amyloid_angiopathy_perc_norm', 'bleeding_other_perc_norm', 'intervention_endovascular_perc_norm', 'intervention_neurosurgical_perc_norm', 'intervention_other_perc_norm', 'intervention_referred_perc_norm', 'intervention_none_perc_norm', 'vt_treatment_anticoagulation_perc_norm', 'vt_treatment_thrombectomy_perc_norm', 'vt_treatment_local_thrombolysis_perc_norm', 'vt_treatment_local_neurological_treatment_perc_norm', 'except_recommended_patients', 'afib_detected_discharged_home_patients', '% dysphagia screening done', '# dysphagia screening done', 'alert_all', 'alert_all_perc', 'drowsy_all', 'drowsy_all_perc', 'comatose_all', 'comatose_all_perc', 'antithrombotics_patients_with_cvt', 'ischemic_transient_cerebral_dead_patients', '# patients receiving antiplatelets with CVT', '% patients receiving antiplatelets with CVT', '# patients receiving Vit. K antagonist with CVT', '% patients receiving Vit. K antagonist with CVT', '# patients receiving dabigatran with CVT', '% patients receiving dabigatran with CVT', '# patients receiving rivaroxaban with CVT', '% patients receiving rivaroxaban with CVT', '# patients receiving apixaban with CVT', '% patients receiving apixaban with CVT', '# patients receiving edoxaban with CVT', '% patients receiving edoxaban with CVT', '# patients receiving LMWH or heparin in prophylactic dose with CVT', '% patients receiving LMWH or heparin in prophylactic dose with CVT', '# patients receiving LMWH or heparin in full anticoagulant dose with CVT', '% patients receiving LMWH or heparin in full anticoagulant dose with CVT', '# patients not prescribed antithrombotics, but recommended with CVT', '% patients not prescribed antithrombotics, but recommended with CVT', '# patients neither receiving antithrombotics nor recommended with CVT', '% patients neither receiving antithrombotics nor recommended with CVT', '# patients prescribed antithrombotics with CVT', '% patients prescribed antithrombotics with CVT', '# patients prescribed or recommended antithrombotics with CVT', '% patients prescribed or recommended antithrombotics with CVT', 'afib_flutter_not_detected_or_not_known_patients_with_cvt', 'afib_flutter_not_detected_or_not_known_dead_patients_with_cvt', 'prescribed_antiplatelets_no_afib_patients_with_cvt', 'prescribed_antiplatelets_no_afib_dead_patients_with_cvt', '# patients prescribed antiplatelets without aFib with CVT', '% patients prescribed antiplatelets without aFib with CVT', 'afib_flutter_detected_patients_with_cvt', '# patients prescribed anticoagulants with aFib with CVT', 'anticoagulants_recommended_patients_with_cvt', 'afib_flutter_detected_dead_patients_with_cvt', '% patients prescribed anticoagulants with aFib with CVT', '# patients prescribed antithrombotics with aFib with CVT', 'recommended_antithrombotics_with_afib_alive_patients_with_cvt', '% patients prescribed antithrombotics with aFib with CVT', 'afib_flutter_detected_patients_not_dead', 'except_recommended_discharged_home_patients', 'afib_detected_discharged_patients', 'ischemic_transient_dead_patients_prescribed', 'is_tia_discharged_home_patients']) def select_country(value): """ The function getting the country name from the database using country code. :param value: the country code :type value: str """ country_name = pytz.country_names[value] return country_name # If country is used as site, the country name is selected from countries dictionary by country code. :) if (country): if self.country_code == 'UZB': self.country_code = 'UZ' self.country_name = select_country(self.country_code) else: self.country_name = None # If split_sites is True, then go through dataframe and generate graphs for each site (the country will be included as site in each file). site_ids = self.df['Site ID'].tolist() # Delete country name from side ids list. try: site_ids.remove(self.country_name) except: pass if site is not None: df = self.df[self.df['Site ID'].isin([site, self.country_name])].copy() df_unformatted = self.df_unformatted[self.df_unformatted['Site ID'].isin([site, self.country_name])].copy() self._generate_formatted_statistics(df=df, df_tmp=df_unformatted, site_code=site) # Generate formatted statistics for all sites individualy + country as site is included if (split_sites) and site is None: for i in site_ids: df = self.df[self.df['Site ID'].isin([i, self.country_name])].copy() df_unformatted = self.df_unformatted[self.df_unformatted['Site ID'].isin([i, self.country_name])].copy() self._generate_formatted_statistics(df=df, df_tmp=df_unformatted, site_code=i) # Produce formatted statistics for all sites + country as site if site is None: self._generate_formatted_statistics(df=self.df, df_tmp=self.df_unformatted) def _generate_formatted_statistics(self, df, df_tmp, site_code=None): """ The function creating the new excel document with the statistic data. :param df: the dataframe with statistics with already deleted temporary columns :type df: pandas dataframe :param df_tmp: the dataframe with statistics containing temporary columns :type df_tmp: pandas dataframe :param site_code: the site code :type site_code: str """ if self.country_code is None and site_code is None: # General report containing all sites in one document name_of_unformatted_stats = self.report + "_" + self.quarter + ".csv" name_of_output_file = self.report + "_" + self.quarter + ".xlsx" elif site_code is None: # General report for whole country name_of_unformatted_stats = self.report + "_" + self.country_code + "_" + self.quarter + ".csv" name_of_output_file = self.report + "_" + self.country_code + "_" + self.quarter + ".xlsx" else: # General report for site name_of_unformatted_stats = self.report + "_" + site_code + "_" + self.quarter + ".csv" name_of_output_file = self.report + "_" + site_code + "_" + self.quarter + ".xlsx" df_tmp.to_csv(name_of_unformatted_stats, sep=",", encoding='utf-8', index=False) workbook1 = xlsxwriter.Workbook(name_of_output_file, {'strings_to_numbers': True}) worksheet = workbook1.add_worksheet() # set width of columns worksheet.set_column(0, 4, 15) worksheet.set_column(4, 350, 60) thrombectomy_patients = df['# patients eligible thrombectomy'].values df.drop(['# patients eligible thrombectomy'], inplace=True, axis=1) ncol = len(df.columns) - 1 nrow = len(df) + 2 col = [] column_names = df.columns.tolist() # Set headers for i in range(0, ncol + 1): tmp = {} tmp['header'] = column_names[i] col.append(tmp) statistics = df.values.tolist() ######################## # DICTIONARY OF COLORS # ######################## colors = { "gender": "#477187", "stroke_hosp": "#535993", "recurrent_stroke": "#D4B86A", "department_type": "#D4A46A", "hospitalization": "#D4916A", "rehab": "#D4BA6A", "stroke": "#565595", "consciousness": "#468B78", "gcs": "#B9D6C1", "nihss": "#C5D068", "ct_mri": "#AA8739", "vasc_img": "#277650", "ventilator": "#AA5039", "recanalization_procedure": "#7F4C91", "median_times": "#BEBCBC", "dysphagia": "#F49B5B", "hemicraniectomy": "#A3E4D7", "neurosurgery": "#F8C471", "neurosurgery_type": "#CACFD2", "bleeding_reason": "#CB4335", "bleeding_source": "#9B59B6", "intervention": "#5DADE2", "vt_treatment": "#F5CBA7", "afib": "#A2C3F3", "carot": "#F1C40F", "antithrombotics": "#B5E59F", "statin": "#28B463", "carotid_stenosis": "#B9D6C1", "carot_foll": "#BFC9CA", "antihypertensive": "#7C7768", "smoking": "#F9C991", "cerebrovascular": "#91C09E", "discharge_destination": "#C0EFF5", "discharge_destination_same_centre": "#56A3A6", "discharge_destination_another_centre": "#E8DF9C", "discharge_destination_within_another_centre": "#538083", "angel_awards": "#B87333", "angel_resq_awards": "#341885", "columns": "#3378B8", "green": "#A1CCA1", "orange": "#DF7401", "gold": "#FFDF00", "platinum": "#c0c0c0", "black": "#ffffff", "red": "#F45D5D" } ################ # angel awards # ################ awards = workbook1.add_format({ 'bold': 2, 'border': 0, 'align': 'center', 'valign': 'vcenter', 'fg_color': colors.get("angel_awards")}) awards_color = workbook1.add_format({ 'fg_color': colors.get("angel_awards")}) self.total_patients_column = '# total patients >= {0}'.format(30) first_index = column_names.index(self.total_patients_column) last_index = column_names.index('% stroke patients treated in a dedicated stroke unit / ICU') first_cell = xl_rowcol_to_cell(0, first_index) last_cell = xl_rowcol_to_cell(0, last_index) worksheet.merge_range(first_cell + ":" + last_cell, 'ESO ANGELS AWARDS', awards) for i in range(first_index, last_index+1): if column_names[i].startswith('%'): worksheet.write(xl_rowcol_to_cell(1, i), '', awards_color) else: worksheet.write(xl_rowcol_to_cell(1, i), '', awards_color) hidden_columns = ['# patients treated with door to recanalization therapy < 60 minutes', '% patients treated with door to recanalization therapy < 60 minutes', '# patients treated with door to recanalization therapy < 45 minutes', '% patients treated with door to recanalization therapy < 45 minutes', '# patients treated with door to thrombolysis < 60 minutes', '# patients treated with door to thrombolysis < 60 minutes', '# patients treated with door to thrombolysis < 45 minutes', '# patients treated with door to thrombectomy < 120 minutes', '# patients treated with door to thrombectomy < 90 minutes', '# recanalization rate out of total ischemic incidence', '# suspected stroke patients undergoing CT/MRI', '# all stroke patients undergoing dysphagia screening', '# ischemic stroke patients discharged with antiplatelets', '% ischemic stroke patients discharged with antiplatelets', '# ischemic stroke patients discharged home with antiplatelets', '% ischemic stroke patients discharged home with antiplatelets', '# ischemic stroke patients discharged (home) with antiplatelets', '# afib patients discharged with anticoagulants', '% afib patients discharged with anticoagulants', '# afib patients discharged home with anticoagulants', '% afib patients discharged home with anticoagulants', '# afib patients discharged (home) with anticoagulants', '# stroke patients treated in a dedicated stroke unit / ICU'] for i in hidden_columns: index = column_names.index(i) column = xl_col_to_name(index) worksheet.set_column(column + ":" + column, None, None, {'hidden': True}) # format for green color green = workbook1.add_format({ 'bold': 2, 'align': 'center', 'valign': 'vcenter', 'bg_color': colors.get("green")}) # format for gold color gold = workbook1.add_format({ 'bold': 1, 'align': 'center', 'valign': 'vcenter', 'bg_color': colors.get("gold")}) # format for platinum color plat = workbook1.add_format({ 'bold': 1, 'align': 'center', 'valign': 'vcenter', 'bg_color': colors.get("platinum")}) # format for gold black black = workbook1.add_format({ 'bold': 1, 'align': 'center', 'valign': 'vcenter', 'bg_color': '#000000', 'color': colors.get("black")}) # format for red color red = workbook1.add_format({ 'bold': 1, 'align': 'center', 'valign': 'vcenter', 'bg_color': colors.get("red")}) # add table into worksheet options = {'data': statistics, 'header_row': True, 'columns': col, 'style': 'Table Style Light 8' } #worksheet.set_column('E:V', 100) worksheet.add_table(2, 0, nrow, ncol, options) # total number of rows number_of_rows = len(statistics) + 2 if not self.comp: row = 4 index = column_names.index(self.total_patients_column) while row < nrow + 2: cell_n = xl_col_to_name(index) + str(row) worksheet.conditional_format(cell_n, {'type': 'text', 'criteria': 'containing', 'value': 'TRUE', 'format': green}) row += 1 def angels_awards_ivt_60(column_name, tmp_column=None): """Add conditional formatting to angels awards for ivt < 60.""" row = 4 while row < number_of_rows + 2: cell_n = column_name + str(row) worksheet.conditional_format(cell_n, {'type': 'cell', 'criteria': 'between', 'minimum': 50, 'maximum': 74.99, 'format': gold}) worksheet.conditional_format(cell_n, {'type': 'cell', 'criteria': '>=', 'value': 75, 'format': black}) row += 1 row = 4 if tmp_column is not None: while row < number_of_rows + 2: cell_n = column_name + str(row) tmp_value = thrombectomy_patients[row-4] if (float(tmp_value) == 0.0): worksheet.conditional_format(cell_n, {'type': 'cell', 'criteria': '==', 'value': 0.0, 'format': black}) row += 1 index = column_names.index('% patients treated with door to thrombolysis < 60 minutes') column = xl_col_to_name(index) angels_awards_ivt_60(column) index = column_names.index('% patients treated with door to thrombectomy < 120 minutes') column = xl_col_to_name(index) angels_awards_ivt_60(column, tmp_column='# patients eligible thrombectomy') def angels_awards_ivt_45(column_name, tmp_column=None): """Add conditional formatting to angels awards for ivt < 45.""" row = 4 while row < number_of_rows + 2: cell_n = column_name + str(row) if tmp_column is not None: worksheet.conditional_format(cell_n, {'type': 'cell', 'criteria': 'between', 'minimum': 0.99, 'maximum': 49.99, 'format': plat}) else: worksheet.conditional_format(cell_n, {'type': 'cell', 'criteria': '<=', 'value': 49.99, 'format': plat}) worksheet.conditional_format(cell_n, {'type': 'cell', 'criteria': '>=', 'value': 50, 'format': black}) row += 1 if tmp_column is not None: row = 4 while row < number_of_rows + 2: cell_n = column_name + str(row) tmp_value = thrombectomy_patients[row-4] if (float(tmp_value) == 0.0): worksheet.conditional_format(cell_n, {'type': 'cell', 'criteria': '<=', 'value': 0.99, 'format': black}) row += 1 index = column_names.index('% patients treated with door to thrombolysis < 45 minutes') column = xl_col_to_name(index) angels_awards_ivt_45(column) index = column_names.index('% patients treated with door to thrombectomy < 90 minutes') column = xl_col_to_name(index) angels_awards_ivt_45(column, tmp_column='# patients eligible thrombectomy') # setting colors of cells according to their values def angels_awards_recan(column_name): """Add conditional formatting to angels awards for recaalization procedures.""" row = 4 while row < number_of_rows + 2: cell_n = column_name + str(row) worksheet.conditional_format(cell_n, {'type': 'cell', 'criteria': 'between', 'minimum': 5, 'maximum': 14.99, 'format': gold}) row += 1 row = 4 while row < number_of_rows + 2: cell_n = column_name + str(row) worksheet.conditional_format(cell_n, {'type': 'cell', 'criteria': 'between', 'minimum': 15, 'maximum': 24.99, 'format': plat}) row += 1 row = 4 while row < number_of_rows + 2: cell_n = column_name + str(row) worksheet.conditional_format(cell_n, {'type': 'cell', 'criteria': '>=', 'value': 25, 'format': black}) row += 1 index = column_names.index('% recanalization rate out of total ischemic incidence') column = xl_col_to_name(index) angels_awards_recan(column) def angels_awards_processes(column_name, count=True): """Add conditional formatting to angels awards for processes.""" count = count row = 4 while row < number_of_rows + 2: cell_n = column_name + str(row) worksheet.conditional_format(cell_n, {'type': 'cell', 'criteria': 'between', 'minimum': 80, 'maximum': 84.99, 'format': gold}) row += 1 row = 4 while row < number_of_rows + 2: cell_n = column_name + str(row) worksheet.conditional_format(cell_n, {'type': 'cell', 'criteria': 'between', 'minimum': 85, 'maximum': 89.99, 'format': plat}) row += 1 row = 4 while row < number_of_rows + 2: cell_n = column_name + str(row) worksheet.conditional_format(cell_n, {'type': 'cell', 'criteria': '>=', 'value': 90, 'format': black}) row += 1 index = column_names.index('% suspected stroke patients undergoing CT/MRI') column = xl_col_to_name(index) angels_awards_processes(column) index = column_names.index('% all stroke patients undergoing dysphagia screening') column = xl_col_to_name(index) angels_awards_processes(column) index = column_names.index('% ischemic stroke patients discharged (home) with antiplatelets') column = xl_col_to_name(index) angels_awards_processes(column) index = column_names.index('% afib patients discharged (home) with anticoagulants') column = xl_col_to_name(index) angels_awards_processes(column) # setting colors of cells according to their values def angels_awards_hosp(column_name): """Add conditional formatting to angels awards for hospitalization.""" row = 4 while row < number_of_rows + 2: cell_n = column_name + str(row) worksheet.conditional_format(cell_n, {'type': 'cell', 'criteria': '<=', 'value': 0, 'format': plat}) row += 1 row = 4 while row < number_of_rows + 2: cell_n = column_name + str(row) worksheet.conditional_format(cell_n, {'type': 'cell', 'criteria': '>=', 'value': 0.99, 'format': black}) row += 1 index = column_names.index('% stroke patients treated in a dedicated stroke unit / ICU') column = xl_col_to_name(index) angels_awards_hosp(column) # set color for proposed angel award def proposed_award(column_name): row = 4 while row < nrow + 2: cell_n = column + str(row) worksheet.conditional_format(cell_n, {'type': 'text', 'criteria': 'containing', 'value': 'STROKEREADY', 'format': green}) row += 1 row = 4 while row < nrow + 2: cell_n = column + str(row) worksheet.conditional_format(cell_n, {'type': 'text', 'criteria': 'containing', 'value': 'GOLD', 'format': gold}) row += 1 row = 4 while row < nrow + 2: cell_n = column + str(row) worksheet.conditional_format(cell_n, {'type': 'text', 'criteria': 'containing', 'value': 'PLATINUM', 'format': plat}) row += 1 row = 4 while row < nrow + 2: cell_n = column + str(row) worksheet.conditional_format(cell_n, {'type': 'text', 'criteria': 'containing', 'value': 'DIAMOND', 'format': black}) row += 1 index = column_names.index('Proposed Award') column = xl_col_to_name(index) proposed_award(column) else: pass workbook1.close() class GenerateFormattedAngelsAwards: """ This class generate formatted statistics only for angels awards. :param df: the dataframe with angels awards statistics :type df: pandas dataframe :param report: the type of report, eg. quarter :type report: str :param quarter: the type of the period, eg. Q1_2019 :type quarter: str """ def __init__(self, df, report=None, quarter=None, minimum_patients=30): self.df = df self.report = report self.quarter = quarter self.minimum_patients = minimum_patients self.formate(self.df) def formate(self, df): if self.report is None and self.quarter is None: output_file = "angels_awards.xslx" else: output_file = self.report + "_" + self.quarter + "_angels_awards.xlsx" workbook1 = xlsxwriter.Workbook(output_file, {'strings_to_numbers': True}) worksheet = workbook1.add_worksheet() # set width of columns worksheet.set_column(0, 2, 15) worksheet.set_column(2, 20, 40) thrombectomy_patients = df['# patients eligible thrombectomy'].values df.drop(['# patients eligible thrombectomy'], inplace=True, axis=1) ncol = len(df.columns) - 1 nrow = len(df) + 2 col = [] column_names = df.columns.tolist() for i in range(0, ncol + 1): tmp = {} tmp['header'] = column_names[i] col.append(tmp) statistics = df.values.tolist() colors = { "angel_awards": "#B87333", "angel_resq_awards": "#341885", "columns": "#3378B8", "green": "#A1CCA1", "orange": "#DF7401", "gold": "#FFDF00", "platinum": "#c0c0c0", "black": "#ffffff", "red": "#F45D5D" } ################ # angel awards # ################ awards = workbook1.add_format({ 'bold': 2, 'border': 0, 'align': 'center', 'valign': 'vcenter', 'fg_color': colors.get("angel_awards")}) awards_color = workbook1.add_format({ 'fg_color': colors.get("angel_awards")}) first_cell = xl_rowcol_to_cell(0, 2) last_cell = xl_rowcol_to_cell(0, ncol) worksheet.merge_range(first_cell + ":" + last_cell, 'ESO ANGELS AWARDS', awards) for i in range(2, ncol + 1): cell = xl_rowcol_to_cell(1, i) worksheet.write(cell, '', awards_color) # format for green color green = workbook1.add_format({ 'bold': 2, 'align': 'center', 'valign': 'vcenter', 'bg_color': colors.get("green")}) # format for gold color gold = workbook1.add_format({ 'bold': 1, 'align': 'center', 'valign': 'vcenter', 'bg_color': colors.get("gold")}) # format for platinum color plat = workbook1.add_format({ 'bold': 1, 'align': 'center', 'valign': 'vcenter', 'bg_color': colors.get("platinum")}) # format for gold black black = workbook1.add_format({ 'bold': 1, 'align': 'center', 'valign': 'vcenter', 'bg_color': '#000000', 'color': colors.get("black")}) # format for red color red = workbook1.add_format({ 'bold': 1, 'align': 'center', 'valign': 'vcenter', 'bg_color': colors.get("red")}) # add table into worksheet options = {'data': statistics, 'header_row': True, 'columns': col, 'style': 'Table Style Light 8' } first_col = xl_col_to_name(0) last_col = xl_col_to_name(ncol + 1) worksheet.set_column(first_col + ":" + last_col, 30) worksheet.add_table(2, 0, nrow, ncol, options) # total number of rows number_of_rows = len(statistics) + 2 self.total_patients_column = '# total patients >= {0}'.format(self.minimum_patients) # if cell contain TRUE in column > 30 patients (DR) it will be colored to green awards = [] row = 4 while row < nrow + 2: index = column_names.index(self.total_patients_column) cell_n = xl_col_to_name(index) + str(row) worksheet.conditional_format(cell_n, {'type': 'text', 'criteria': 'containing', 'value': 'TRUE', 'format': green}) row += 1 def angels_awards_ivt_60(column_name, tmp_column=None): """Add conditional formatting to angels awards for ivt < 60.""" row = 4 while row < number_of_rows + 2: cell_n = column_name + str(row) worksheet.conditional_format(cell_n, {'type': 'cell', 'criteria': 'between', 'minimum': 50, 'maximum': 74.99, 'format': gold}) worksheet.conditional_format(cell_n, {'type': 'cell', 'criteria': '>=', 'value': 75, 'format': black}) row += 1 row = 4 if tmp_column is not None: while row < number_of_rows + 2: cell_n = column_name + str(row) tmp_value = thrombectomy_patients[row-4] if (float(tmp_value) == 0.0): worksheet.conditional_format(cell_n, {'type': 'cell', 'criteria': '==', 'value': 0.0, 'format': black}) row += 1 index = column_names.index('% patients treated with door to thrombolysis < 60 minutes') column = xl_col_to_name(index) angels_awards_ivt_60(column) index = column_names.index('% patients treated with door to thrombectomy < 120 minutes') column = xl_col_to_name(index) angels_awards_ivt_60(column, tmp_column='# patients eligible thrombectomy') def angels_awards_ivt_45(column_name, tmp_column=None): """Add conditional formatting to angels awards for ivt < 45.""" row = 4 while row < number_of_rows + 2: cell_n = column_name + str(row) if tmp_column is not None: worksheet.conditional_format(cell_n, {'type': 'cell', 'criteria': 'between', 'minimum': 0.99, 'maximum': 49.99, 'format': plat}) else: worksheet.conditional_format(cell_n, {'type': 'cell', 'criteria': '<=', 'value': 49.99, 'format': plat}) worksheet.conditional_format(cell_n, {'type': 'cell', 'criteria': '>=', 'value': 50, 'format': black}) row += 1 if tmp_column is not None: row = 4 while row < number_of_rows + 2: cell_n = column_name + str(row) tmp_value = thrombectomy_patients[row-4] if (float(tmp_value) == 0.0): worksheet.conditional_format(cell_n, {'type': 'cell', 'criteria': '<=', 'value': 0.99, 'format': black}) row += 1 index = column_names.index('% patients treated with door to thrombolysis < 45 minutes') column = xl_col_to_name(index) angels_awards_ivt_45(column) index = column_names.index('% patients treated with door to thrombectomy < 90 minutes') column = xl_col_to_name(index) angels_awards_ivt_45(column, tmp_column='# patients eligible thrombectomy') # setting colors of cells according to their values def angels_awards_recan(column_name, coln): """Add conditional formatting to angels awards for recaalization procedures.""" row = 4 while row < number_of_rows + 2: cell_n = column_name + str(row) worksheet.conditional_format(cell_n, {'type': 'cell', 'criteria': 'between', 'minimum': 5, 'maximum': 14.99, 'format': gold}) row += 1 row = 4 while row < number_of_rows + 2: cell_n = column_name + str(row) worksheet.conditional_format(cell_n, {'type': 'cell', 'criteria': 'between', 'minimum': 15, 'maximum': 24.99, 'format': plat}) row += 1 row = 4 while row < number_of_rows + 2: cell_n = column_name + str(row) worksheet.conditional_format(cell_n, {'type': 'cell', 'criteria': '>=', 'value': 25, 'format': black}) row += 1 index = column_names.index('% recanalization rate out of total ischemic incidence') angels_awards_recan(column_name=xl_col_to_name(index), coln=index) #angels_awards_recan('F') def angels_awards_processes(column_name, coln, count=True): """Add conditional formatting to angels awards for processes.""" count = count row = 4 while row < number_of_rows + 2: cell_n = column_name + str(row) worksheet.conditional_format(cell_n, {'type': 'cell', 'criteria': 'between', 'minimum': 80, 'maximum': 84.99, 'format': gold}) row += 1 row = 4 while row < number_of_rows + 2: cell_n = column_name + str(row) worksheet.conditional_format(cell_n, {'type': 'cell', 'criteria': 'between', 'minimum': 85, 'maximum': 89.99, 'format': plat}) row += 1 row = 4 while row < number_of_rows + 2: cell_n = column_name + str(row) worksheet.conditional_format(cell_n, {'type': 'cell', 'criteria': '>=', 'value': 90, 'format': black}) row += 1 index = column_names.index('% suspected stroke patients undergoing CT/MRI') angels_awards_processes(column_name=xl_col_to_name(index), coln=index) index = column_names.index('% all stroke patients undergoing dysphagia screening') angels_awards_processes(column_name=xl_col_to_name(index), coln=index) index = column_names.index('% ischemic stroke patients discharged (home) with antiplatelets') angels_awards_processes(column_name=xl_col_to_name(index), coln=index) index = column_names.index('% afib patients discharged (home) with anticoagulants') angels_awards_processes(column_name=xl_col_to_name(index), coln=index) #angels_awards_processes('G', 4) #angels_awards_processes('H', 5) #angels_awards_processes('I', 6) #angels_awards_processes('J', 7) # setting colors of cells according to their values def angels_awards_hosp(column_name, coln): """Add conditional formatting to angels awards for hospitalization.""" row = 4 while row < number_of_rows + 2: cell_n = column_name + str(row) worksheet.conditional_format(cell_n, {'type': 'cell', 'criteria': '<=', 'value': 0, 'format': plat}) row += 1 row = 4 while row < number_of_rows + 2: cell_n = column_name + str(row) worksheet.conditional_format(cell_n, {'type': 'cell', 'criteria': '>=', 'value': 0.99, 'format': black}) row += 1 index = column_names.index('% stroke patients treated in a dedicated stroke unit / ICU') angels_awards_hosp(column_name=xl_col_to_name(index), coln=index) # set color for proposed angel award def proposed_award(column_name, coln): row = 4 while row < nrow + 2: cell_n = column + str(row) worksheet.conditional_format(cell_n, {'type': 'text', 'criteria': 'containing', 'value': 'STROKEREADY', 'format': green}) row += 1 row = 4 while row < nrow + 2: cell_n = column + str(row) worksheet.conditional_format(cell_n, {'type': 'text', 'criteria': 'containing', 'value': 'GOLD', 'format': gold}) row += 1 row = 4 while row < nrow + 2: cell_n = column + str(row) worksheet.conditional_format(cell_n, {'type': 'text', 'criteria': 'containing', 'value': 'PLATINUM', 'format': plat}) row += 1 row = 4 while row < nrow + 2: cell_n = column + str(row) worksheet.conditional_format(cell_n, {'type': 'text', 'criteria': 'containing', 'value': 'DIAMOND', 'format': black}) row += 1 index = column_names.index('Proposed Award') column = xl_col_to_name(index) proposed_award(column, coln=index) workbook1.close()

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1

0

1

0

null

0

5

2ba45c52a1df277d8243a7c389f1e7071d05b66d

104

py

Python

IHOPython.py

MrAnonymous5635/CSCircles

010ac82942c88da357e214ea5462ec378f3667b8

[ "MIT" ]

17

2018-09-19T09:44:33.000Z

2022-01-17T15:17:11.000Z

IHOPython.py

MrAnonymous5635/CSCircles

010ac82942c88da357e214ea5462ec378f3667b8

[ "MIT" ]

2

2020-02-24T15:28:33.000Z

2021-11-16T00:04:52.000Z

IHOPython.py

MrAnonymous5635/CSCircles

010ac82942c88da357e214ea5462ec378f3667b8

[ "MIT" ]

8

2020-02-20T00:02:06.000Z

2022-01-06T17:25:51.000Z

pancakes = int(input()) if pancakes > 3: print('Yum!') if pancakes <= 3: print('Still hungry!')

17.333333

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104

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null

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1

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null

0

5

2bb0e646301d382fa19ef4ac3fe22bff82b972da

125

py

Python

PythonServer/server.py

MarkFranciscus/DevMIDbot

c50212488babce79f362954b689b92deda6ef30f

[ "MIT" ]

null

PythonServer/server.py

MarkFranciscus/DevMIDbot

c50212488babce79f362954b689b92deda6ef30f

[ "MIT" ]

null

PythonServer/server.py

MarkFranciscus/DevMIDbot

c50212488babce79f362954b689b92deda6ef30f

[ "MIT" ]

null

import time import daemon import MIDBot import BotInfo with daemon.DaemonContext(): MIDBot.midbot.run(BotInfo.BOT_TOKEN)

17.857143

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125

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null

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0

5

921122a50ba8eb6b64f5809d777068d920c2699f

1,460

py

Python

tests/synapse/test_detect_tbars.py

jingpengw/reneu

f69a9ab53ea1f4852493f3d92ec142e60ad0812b

[ "Apache-2.0" ]

null

tests/synapse/test_detect_tbars.py

jingpengw/reneu

f69a9ab53ea1f4852493f3d92ec142e60ad0812b

[ "Apache-2.0" ]

13

2019-09-04T03:56:05.000Z

2020-04-28T00:37:42.000Z

tests/synapse/test_detect_tbars.py

jingpengw/reneu

f69a9ab53ea1f4852493f3d92ec142e60ad0812b

[ "Apache-2.0" ]

1

2019-11-07T11:24:21.000Z

import numpy as np import fill_voids from edt import edt from reneu.lib.synapse import detect_points, get_object_average_intensity def test_detect_tbars(): seg = np.zeros((7, 7, 7), dtype=bool) seg[2:5, 2:5, 2:5] = True df = edt(seg) seg = seg.astype(np.uint64) points = detect_points(seg, df) # print('points: ', points) np.testing.assert_array_equal(points, np.asarray([[3,3,3]], dtype=np.uint64)) average_intensity = get_object_average_intensity(seg, df) # print('average intensity: ', average_intensity) avg = np.sum(df[seg>0]) / np.count_nonzero(seg) np.testing.assert_array_almost_equal(average_intensity, np.asarray([avg], dtype=np.float32)) # def test_fill_voids(): # seg[2, 3, 4] = False # fill_voids.fill(seg, in_place=True) # assert seg[2,3,4] == True def test_detect_tbars_non_symmetric(): seg = np.zeros((7, 7, 7), dtype=bool) seg[1:4, 2:5, 3:6] = True # seg[2, 3, 4] = False # fill_voids.fill(seg, in_place=True) # assert seg[2,3,4] == True df = edt(seg) seg = seg.astype(np.uint64) points = detect_points(seg, df) # print('points: ', points) np.testing.assert_array_equal(points, np.asarray([[2,3,4]], dtype=np.uint64)) average_intensity = get_object_average_intensity(seg, df) avg = np.sum(df[seg>0]) / np.count_nonzero(seg) np.testing.assert_array_almost_equal(average_intensity, np.asarray([avg], dtype=np.float32))

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null

0

1

0

null

0

5

9211ce7b19db9b4c6caab69e7ea3d94d0f74dee1

202

py

Python

Trakttv.bundle/Contents/Libraries/Shared/pyllist/compat.py

disrupted/Trakttv.bundle

24712216c71f3b22fd58cb5dd89dad5bb798ed60

[ "RSA-MD" ]

1,346

2015-01-01T14:52:24.000Z

2022-03-28T12:50:48.000Z

Trakttv.bundle/Contents/Libraries/Shared/pyllist/compat.py

alcroito/Plex-Trakt-Scrobbler

4f83fb0860dcb91f860d7c11bc7df568913c82a6

[ "RSA-MD" ]

474

2015-01-01T10:27:46.000Z

2022-03-21T12:26:16.000Z

Trakttv.bundle/Contents/Libraries/Shared/pyllist/compat.py

alcroito/Plex-Trakt-Scrobbler

4f83fb0860dcb91f860d7c11bc7df568913c82a6

[ "RSA-MD" ]

191

2015-01-02T18:27:22.000Z

2022-03-29T10:49:48.000Z

import sys PY2 = sys.version_info[0] == 2 PY3 = sys.version_info[0] == 3 if PY3: def u(s): return s else: def u(s): return unicode(s.replace(r'\\', r'\\\\'), "unicode_escape")

16.833333

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0.256881

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0

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null

0

1

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1

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5

921aaf96e0a9ba0fa54fbed38422036b910199f7

10,566

py

Python

ckanext-showcase/ckanext/showcase/tests/action/test_create.py

smallmedia/iod-ckan

dfd85b41286fe86924ec16b0a88efc7292848ceb

[ "Apache-2.0" ]

4

2017-06-12T15:18:30.000Z

2019-10-11T15:12:43.000Z

ckanext-showcase/ckanext/showcase/tests/action/test_create.py

smallmedia/iod-ckan

dfd85b41286fe86924ec16b0a88efc7292848ceb

[ "Apache-2.0" ]

64

2017-05-14T22:15:53.000Z

2020-03-08T15:26:49.000Z

ckanext-showcase/ckanext/showcase/tests/action/test_create.py

smallmedia/iod-ckan

dfd85b41286fe86924ec16b0a88efc7292848ceb

[ "Apache-2.0" ]

2

2018-09-08T08:02:25.000Z

2020-04-24T13:02:06.000Z

from nose import tools as nosetools from ckan.model.package import Package import ckan.model as model import ckan.plugins.toolkit as toolkit try: import ckan.tests.factories as factories except ImportError: # for ckan <= 2.3 import ckan.new_tests.factories as factories try: import ckan.tests.helpers as helpers except ImportError: # for ckan <= 2.3 import ckan.new_tests.helpers as helpers from ckanext.showcase.model import ShowcasePackageAssociation, ShowcaseAdmin from ckanext.showcase.tests import ShowcaseFunctionalTestBase class TestCreateShowcase(ShowcaseFunctionalTestBase): def test_showcase_create_no_args(self): ''' Calling showcase create without args raises ValidationError. ''' sysadmin = factories.Sysadmin() context = {'user': sysadmin['name']} # no showcases exist. nosetools.assert_equal(model.Session.query(Package) .filter(Package.type == 'showcase').count(), 0) nosetools.assert_raises(toolkit.ValidationError, helpers.call_action, 'ckanext_showcase_create', context=context) # no showcases (dataset of type 'showcase') created. nosetools.assert_equal(model.Session.query(Package) .filter(Package.type == 'showcase').count(), 0) def test_showcase_create_with_name_arg(self): ''' Calling showcase create with a name arg creates a showcase package. ''' sysadmin = factories.Sysadmin() context = {'user': sysadmin['name']} # no showcases exist. nosetools.assert_equal(model.Session.query(Package) .filter(Package.type == 'showcase').count(), 0) helpers.call_action('ckanext_showcase_create', context=context, name='my-showcase') # a showcases (dataset of type 'showcase') created. nosetools.assert_equal(model.Session.query(Package) .filter(Package.type == 'showcase').count(), 1) def test_showcase_create_with_existing_name(self): ''' Calling showcase create with an existing name raises ValidationError. ''' sysadmin = factories.Sysadmin() context = {'user': sysadmin['name']} factories.Dataset(type='showcase', name='my-showcase') # a single showcases exist. nosetools.assert_equal(model.Session.query(Package) .filter(Package.type == 'showcase').count(), 1) nosetools.assert_raises(toolkit.ValidationError, helpers.call_action, 'ckanext_showcase_create', context=context, name='my-showcase') # still only one showcase exists. nosetools.assert_equal(model.Session.query(Package) .filter(Package.type == 'showcase').count(), 1) class TestCreateShowcasePackageAssociation(ShowcaseFunctionalTestBase): def test_association_create_no_args(self): ''' Calling sc/pkg association create with no args raises ValidationError. ''' sysadmin = factories.User(sysadmin=True) context = {'user': sysadmin['name']} nosetools.assert_raises(toolkit.ValidationError, helpers.call_action, 'ckanext_showcase_package_association_create', context=context) nosetools.assert_equal(model.Session.query(ShowcasePackageAssociation).count(), 0) def test_association_create_missing_arg(self): ''' Calling sc/pkg association create with a missing arg raises ValidationError. ''' sysadmin = factories.User(sysadmin=True) package_id = factories.Dataset()['id'] context = {'user': sysadmin['name']} nosetools.assert_raises(toolkit.ValidationError, helpers.call_action, 'ckanext_showcase_package_association_create', context=context, package_id=package_id) nosetools.assert_equal(model.Session.query(ShowcasePackageAssociation).count(), 0) def test_association_create_by_id(self): ''' Calling sc/pkg association create with correct args (package ids) creates an association. ''' sysadmin = factories.User(sysadmin=True) package_id = factories.Dataset()['id'] showcase_id = factories.Dataset(type='showcase')['id'] context = {'user': sysadmin['name']} association_dict = helpers.call_action('ckanext_showcase_package_association_create', context=context, package_id=package_id, showcase_id=showcase_id) # One association object created nosetools.assert_equal(model.Session.query(ShowcasePackageAssociation).count(), 1) # Association properties are correct nosetools.assert_equal(association_dict.get('showcase_id'), showcase_id) nosetools.assert_equal(association_dict.get('package_id'), package_id) def test_association_create_by_name(self): ''' Calling sc/pkg association create with correct args (package names) creates an association. ''' sysadmin = factories.User(sysadmin=True) package = factories.Dataset() package_name = package['name'] showcase = factories.Dataset(type='showcase') showcase_name = showcase['name'] context = {'user': sysadmin['name']} association_dict = helpers.call_action('ckanext_showcase_package_association_create', context=context, package_id=package_name, showcase_id=showcase_name) nosetools.assert_equal(model.Session.query(ShowcasePackageAssociation).count(), 1) nosetools.assert_equal(association_dict.get('showcase_id'), showcase['id']) nosetools.assert_equal(association_dict.get('package_id'), package['id']) def test_association_create_existing(self): ''' Attempt to create association with existing details returns Validation Error. ''' sysadmin = factories.User(sysadmin=True) package_id = factories.Dataset()['id'] showcase_id = factories.Dataset(type='showcase')['id'] context = {'user': sysadmin['name']} # Create association helpers.call_action('ckanext_showcase_package_association_create', context=context, package_id=package_id, showcase_id=showcase_id) # Attempted duplicate creation results in ValidationError nosetools.assert_raises(toolkit.ValidationError, helpers.call_action, 'ckanext_showcase_package_association_create', context=context, package_id=package_id, showcase_id=showcase_id) class TestCreateShowcaseAdmin(ShowcaseFunctionalTestBase): def test_showcase_admin_add_creates_showcase_admin_user(self): ''' Calling ckanext_showcase_admin_add adds user to showcase admin list. ''' user_to_add = factories.User() nosetools.assert_equal(model.Session.query(ShowcaseAdmin).count(), 0) helpers.call_action('ckanext_showcase_admin_add', context={}, username=user_to_add['name']) nosetools.assert_equal(model.Session.query(ShowcaseAdmin).count(), 1) nosetools.assert_true(user_to_add['id'] in ShowcaseAdmin.get_showcase_admin_ids()) def test_showcase_admin_add_multiple_users(self): ''' Calling ckanext_showcase_admin_add for multiple users correctly adds them to showcase admin list. ''' user_to_add = factories.User() second_user_to_add = factories.User() nosetools.assert_equal(model.Session.query(ShowcaseAdmin).count(), 0) helpers.call_action('ckanext_showcase_admin_add', context={}, username=user_to_add['name']) helpers.call_action('ckanext_showcase_admin_add', context={}, username=second_user_to_add['name']) nosetools.assert_equal(model.Session.query(ShowcaseAdmin).count(), 2) nosetools.assert_true(user_to_add['id'] in ShowcaseAdmin.get_showcase_admin_ids()) nosetools.assert_true(second_user_to_add['id'] in ShowcaseAdmin.get_showcase_admin_ids()) def test_showcase_admin_add_existing_user(self): ''' Calling ckanext_showcase_admin_add twice for same user raises a ValidationError. ''' user_to_add = factories.User() # Add once helpers.call_action('ckanext_showcase_admin_add', context={}, username=user_to_add['name']) nosetools.assert_equal(model.Session.query(ShowcaseAdmin).count(), 1) # Attempt second add nosetools.assert_raises(toolkit.ValidationError, helpers.call_action, 'ckanext_showcase_admin_add', context={}, username=user_to_add['name']) # Still only one ShowcaseAdmin object. nosetools.assert_equal(model.Session.query(ShowcaseAdmin).count(), 1) def test_showcase_admin_add_username_doesnot_exist(self): ''' Calling ckanext_showcase_admin_add with non-existent username raises ValidationError and no ShowcaseAdmin object is created. ''' nosetools.assert_raises(toolkit.ObjectNotFound, helpers.call_action, 'ckanext_showcase_admin_add', context={}, username='missing') nosetools.assert_equal(model.Session.query(ShowcaseAdmin).count(), 0) nosetools.assert_equal(ShowcaseAdmin.get_showcase_admin_ids(), []) def test_showcase_admin_add_no_args(self): ''' Calling ckanext_showcase_admin_add with no args raises ValidationError and no ShowcaseAdmin object is created. ''' nosetools.assert_raises(toolkit.ValidationError, helpers.call_action, 'ckanext_showcase_admin_add', context={}) nosetools.assert_equal(model.Session.query(ShowcaseAdmin).count(), 0) nosetools.assert_equal(ShowcaseAdmin.get_showcase_admin_ids(), [])

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0.638842

1,070

10,566

6.066355

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0.792482

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0.205882

0

null

0

1

0

null

0

5

a6263d7f1359691c24925f4520c1820b4adb4410

80

py

Python

torchOnVideo/super_resolution/SuperResolution.py

torchOnVideo/torchOnVideo

aa07d5661f772eca027ecc6b79e14bd68a515aa1

[ "MIT" ]

2

2021-03-19T08:05:06.000Z

2021-05-22T21:54:10.000Z

torchOnVideo/super_resolution/SuperResolution.py

torchOnVideo/torchOnVideo

aa07d5661f772eca027ecc6b79e14bd68a515aa1

[ "MIT" ]

null

torchOnVideo/super_resolution/SuperResolution.py

torchOnVideo/torchOnVideo

aa07d5661f772eca027ecc6b79e14bd68a515aa1

[ "MIT" ]

null

class SuperResolution(): def __init__(self, scale): self.scale = 4

16

30

0.625

9

80

5.111111

0.777778

0.391304

0

0.016949

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4

31

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1

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1

0

1

0

null

0

1

0

1

0

5

a68e82144de35283ffec3359e93c3f724d52832f

47

py

Python

Game/gameRunner.py

AhmedAlzubairi1/COMS4995

c56c2549bf13538d89b001357f658ae04e5f3f8d

[ "MIT" ]

2

2021-09-23T01:58:35.000Z

2022-01-30T00:34:52.000Z

Game/gameRunner.py

AhmedAlzubairi1/Chess

c56c2549bf13538d89b001357f658ae04e5f3f8d

[ "MIT" ]

10

2020-10-02T00:37:52.000Z

2020-12-02T07:12:28.000Z

Game/gameRunner.py

AhmedAlzubairi1/COMS4995

c56c2549bf13538d89b001357f658ae04e5f3f8d

[ "MIT" ]

2

2020-11-06T21:10:31.000Z

2020-12-08T19:27:57.000Z

from Game import Game x = Game() x.startGame()

11.75

21

0.702128

8

47

4.125

0.625

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0.170213

47

3

22

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1

0

1

0

null

0

1

0

5

a68fa46eed4ed1e6e783afef10e305992fe31d5a

53

py

Python

planet/control/__init__.py

alec-tschantz/planet

bf68722993c93129263bb9606a582d24cb4f2a58

[ "MIT" ]

7

2020-03-08T08:28:12.000Z

2022-01-23T17:19:56.000Z

planet/control/__init__.py

alec-tschantz/planet

bf68722993c93129263bb9606a582d24cb4f2a58

[ "MIT" ]

null

planet/control/__init__.py

alec-tschantz/planet

bf68722993c93129263bb9606a582d24cb4f2a58

[ "MIT" ]

null

from .agent import Agent from .planner import Planner

26.5

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0.830189

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5.5

0.5

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26.5

0.956522

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1

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null

0

1

0

1

0

1

0

5

a6aa63e521c191fe8439353ac9b539ce7d3e904f

141

py

Python

care/care/doctype/item_expiry/test_item_expiry.py

mohsinalimat/care

5b2f85839c5fa9882eb0d0097825e149402a6a8c

[ "MIT" ]

1

2021-08-07T12:49:13.000Z

care/care/doctype/item_expiry/test_item_expiry.py

mohsinalimat/care

5b2f85839c5fa9882eb0d0097825e149402a6a8c

[ "MIT" ]

null

care/care/doctype/item_expiry/test_item_expiry.py

mohsinalimat/care

5b2f85839c5fa9882eb0d0097825e149402a6a8c

[ "MIT" ]

1

2021-08-07T12:49:13.000Z

15.666667

41

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18

141

6.055556

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0

0.033333

0.148936

141

8

42

17.625

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0

1

0

true

0.333333

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0

null

0

1

0

null

0

1

0

5

a6fde74de1b3253ae0e88418fb7c3fd572921b2c

3,612

py

Python

solicitudes/admin.py

shiminasai/cantera

90f162351e1ad6ffaaf79cf90c361e302ab6e09f

[ "MIT" ]

null

solicitudes/admin.py

shiminasai/cantera

90f162351e1ad6ffaaf79cf90c361e302ab6e09f

[ "MIT" ]

null

solicitudes/admin.py

shiminasai/cantera

90f162351e1ad6ffaaf79cf90c361e302ab6e09f

[ "MIT" ]

2

2019-04-10T19:45:42.000Z

2019-04-24T17:16:40.000Z

from django.contrib import admin from .models import * from users.models import * from django.core.mail import send_mail, EmailMultiAlternatives from django.template.loader import render_to_string # Register your models here. class SolicitudesOrgAdmin(admin.ModelAdmin): list_display = ('usuario','organizacion','aprobado') def add_view(self, request, form_url='', extra_context=None): self.readonly_fields = ('usuario','organizacion','aprobado') return super(SolicitudesOrgAdmin, self).add_view(request) def change_view(self,request, object_id, form_url='', extra_context=None): obj = SolicitudesOrg.objects.get(id = object_id) if obj.aprobado == False: self.readonly_fields = ('usuario','organizacion') else: self.readonly_fields = ('usuario','organizacion','aprobado') return super(SolicitudesOrgAdmin, self).change_view(request,object_id) def save_model(self, request, obj, form, change): if obj.aprobado == True: user = User.objects.get(id = obj.usuario.id) user.organizacion = obj.organizacion user.save() try: subject, from_email = 'Plataforma Género y Metodologías', 'generoymetodologias@gmail.com' text_content = render_to_string('email/solicitud_aprobada.txt', {'obj': user,}) html_content = render_to_string('email/solicitud_aprobada.txt', {'obj': user,}) list_mail = User.objects.filter(id = user.id).values_list('email',flat=True) msg = EmailMultiAlternatives(subject, text_content, from_email, list_mail) msg.attach_alternative(html_content, "text/html") msg.send() except: pass super(SolicitudesOrgAdmin, self).save_model(request, obj, form, change) def has_delete_permission(self, request, obj=None): return False class SolicitudesNuevasOrgAdmin(admin.ModelAdmin): list_display = ('usuario','nombre_org','aprobado') def add_view(self, request, form_url='', extra_context=None): self.readonly_fields = ('usuario','nombre_org','siglas_org','pais_org','aprobado') return super(SolicitudesNuevasOrgAdmin, self).add_view(request) def change_view(self, request, object_id, form_url='', extra_context=None): obj = SolicitudesNuevasOrg.objects.get(id = object_id) if obj.aprobado == False: self.readonly_fields = ('usuario','nombre_org','siglas_org','pais_org') else: self.readonly_fields = ('usuario','nombre_org','siglas_org','pais_org','aprobado') return super(SolicitudesNuevasOrgAdmin, self).change_view(request,object_id) def save_model(self, request, obj, form, change): if obj.aprobado == True: org = Contraparte(nombre = obj.nombre_org,siglas = obj.siglas_org,pais = obj.pais_org) org.save() user = User.objects.get(id = obj.usuario.id) user.organizacion = org user.save() try: subject, from_email = 'Plataforma Género y Metodologías', 'generoymetodologias@gmail.com' text_content = render_to_string('email/solicitud_aprobada.txt', {'obj': user,}) html_content = render_to_string('email/solicitud_aprobada.txt', {'obj': user,}) list_mail = User.objects.filter(id = user.id).values_list('email',flat=True) msg = EmailMultiAlternatives(subject, text_content, from_email, list_mail) msg.attach_alternative(html_content, "text/html") msg.send() except: pass super(SolicitudesNuevasOrgAdmin, self).save_model(request, obj, form, change) def has_delete_permission(self, request, obj=None): return False admin.site.register(SolicitudesOrg,SolicitudesOrgAdmin) admin.site.register(SolicitudesNuevasOrg,SolicitudesNuevasOrgAdmin)

38.83871

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3,612

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39.26087

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0.333333

0

null

0

1

0

null

0

5

47176d5a9b25d878b434e125117311dab8121dcd

290

py

Python

jupyter_releaser/actions/draft_changelog.py

jupyter1029/jupyter_releaser

f7f896886d0cef2b83a6a61f3433dd2c10bb09ea

[ "BSD-3-Clause" ]

null

jupyter_releaser/actions/draft_changelog.py

jupyter1029/jupyter_releaser

f7f896886d0cef2b83a6a61f3433dd2c10bb09ea

[ "BSD-3-Clause" ]

59

2021-03-09T10:11:27.000Z

2021-04-13T09:06:46.000Z

jupyter_releaser/actions/draft_changelog.py

jupyter1029/jupyter_releaser

f7f896886d0cef2b83a6a61f3433dd2c10bb09ea

[ "BSD-3-Clause" ]

1

2021-05-02T16:04:02.000Z

# Copyright (c) Jupyter Development Team. # Distributed under the terms of the Modified BSD License. from jupyter_releaser.util import run run("jupyter-releaser prep-git") run("jupyter-releaser bump-version") run("jupyter-releaser build-changelog") run("jupyter-releaser draft-changelog")

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null

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0

1

0

1

0

null

0

1

0

5

5b2f469d02d7221f07c83c36f4371dabd4b8e600

41

py

Python

python/myfunc.py

chaojie-fu/robotics_tutorial

12affebfe6cb3810cc1e8fde4c674ed077b926a5

[ "MIT" ]

1

2021-12-23T13:05:26.000Z

python/myfunc.py

cyoahs/robotics_tutorial

3aed846c5e95eb32dbcdeebac0b22e54cd74ea02

[ "MIT" ]

null

python/myfunc.py

cyoahs/robotics_tutorial

3aed846c5e95eb32dbcdeebac0b22e54cd74ea02

[ "MIT" ]

1

2020-04-06T11:25:51.000Z

def my_func(a): print(f'This is {a}')

20.5

25

0.585366

9

41

2.555556

0.888889

0

0.195122

41

2

25

20.5

0.69697

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0.261905

0

1

0.5

false

0

0.5

1

0

null

0

1

0

null

0

1

0

1

0

5

5b6a6275ef0c5566227c9bf1e2faebb0985e8444

157

py

Python

models/game/bots/__init__.py

zachdj/ultimate-tic-tac-toe

b8e6128d9d19628f6f889a3958d30854527a8645

[ "MIT" ]

null

models/game/bots/__init__.py

zachdj/ultimate-tic-tac-toe

b8e6128d9d19628f6f889a3958d30854527a8645

[ "MIT" ]

null

models/game/bots/__init__.py

zachdj/ultimate-tic-tac-toe

b8e6128d9d19628f6f889a3958d30854527a8645

[ "MIT" ]

null

from .Bot import Bot from .BogoBot import BogoBot from .RandoMaxBot import RandoMaxBot from .MCTSBot import MCTSBot from .Heuristic1Bot import Heuristic1Bot

26.166667

40

0.840764

20

157

6.6

0.35

0

0.014599

0.127389

157

5

41

31.4

0.948905

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

5b73c47354ba360c1ed639c535a91bb55b97b735

126

py

Python

katas/kyu_8/barking_mad.py

the-zebulan/CodeWars

1eafd1247d60955a5dfb63e4882e8ce86019f43a

[ "MIT" ]

40

2016-03-09T12:26:20.000Z

2022-03-23T08:44:51.000Z

katas/kyu_8/barking_mad.py

akalynych/CodeWars

1eafd1247d60955a5dfb63e4882e8ce86019f43a

[ "MIT" ]

null

katas/kyu_8/barking_mad.py

akalynych/CodeWars

1eafd1247d60955a5dfb63e4882e8ce86019f43a

[ "MIT" ]

36

2016-11-07T19:59:58.000Z

2022-03-31T11:18:27.000Z

class Dog: def __init__(self, breed): self.breed = breed @staticmethod def bark(): return 'Woof'

15.75

30

0.563492

14

126

4.785714

0.714286

0.268657

0

0.333333

126

7

31

18

0.797619

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0.031746

0

1

0.333333

false

0

0.166667

0.666667

0

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1

0

1

0

null

0

1

0

1

0

5

5b7959ae72b93ac7365403ad2d24b31a120af54a

203

py

Python

FWCore/Framework/python/test/cmsExceptionsFatal_cff.py

ckamtsikis/cmssw

ea19fe642bb7537cbf58451dcf73aa5fd1b66250

[ "Apache-2.0" ]

852

2015-01-11T21:03:51.000Z

2022-03-25T21:14:00.000Z

FWCore/Framework/python/test/cmsExceptionsFatal_cff.py

ckamtsikis/cmssw

ea19fe642bb7537cbf58451dcf73aa5fd1b66250

[ "Apache-2.0" ]

30,371

2015-01-02T00:14:40.000Z

2022-03-31T23:26:05.000Z

FWCore/Framework/python/test/cmsExceptionsFatal_cff.py

ckamtsikis/cmssw

ea19fe642bb7537cbf58451dcf73aa5fd1b66250

[ "Apache-2.0" ]

3,240

2015-01-02T05:53:18.000Z

2022-03-31T17:24:21.000Z

import FWCore.ParameterSet.Config as cms import FWCore.Framework.test.cmsExceptionsFatalOption_cff options = cms.untracked.PSet( Rethrow = FWCore.Framework.test.cmsExceptionsFatalOption_cff.Rethrow )

29

70

0.847291

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7.391304

0.608696

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0.223529

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0

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6

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0

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false

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0

null

0

1

0

1

0

null

0

1

0

5

5bb29eaea93a1ff514403563a4b1757dca026252

50

py

Python

Asymmetric/RSA/common-modulus/secret.py

killua4564/Symmetric

183ea2ec1d1342e9124e710a2de0fcad8b399f3d

[ "MIT" ]

1

2021-05-05T14:03:10.000Z

Asymmetric/RSA/common-modulus/secret.py

killua4564/Symmetric

183ea2ec1d1342e9124e710a2de0fcad8b399f3d

[ "MIT" ]

null

Asymmetric/RSA/common-modulus/secret.py

killua4564/Symmetric

183ea2ec1d1342e9124e710a2de0fcad8b399f3d

[ "MIT" ]

null

FLAG = 'flag{xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx}'

50

0.86

3

50

14.333333

0.666667

0

0.04

50

1

50

0.895833

0

0.803922

0

1

0

false

0

1

0

1

null

0

1

0

1

null

0

5

5bbbebe6068539670bf06280e2dc47c0484d0409

40

py

Python

fairing/strategies/pbt/__init__.py

wbuchwalter/fairing-1

e6d459dc8413ffd3e8c4b0771a2ec79c74c383ab

[ "Apache-2.0" ]

21

2018-08-09T19:13:47.000Z

2020-07-22T05:21:11.000Z

fairing/strategies/pbt/__init__.py

wbuchwalter/fairing-1

e6d459dc8413ffd3e8c4b0771a2ec79c74c383ab

[ "Apache-2.0" ]

14

2018-08-02T18:44:09.000Z

2018-11-08T15:32:55.000Z

fairing/strategies/pbt/__init__.py

wbuchwalter/fairing-1

e6d459dc8413ffd3e8c4b0771a2ec79c74c383ab

[ "Apache-2.0" ]

4

2018-08-09T19:13:59.000Z

2018-10-08T05:44:31.000Z

from .pbt import PopulationBasedTraining

40

0.9

4

40

9

1

0

0.075

40

1

40

0.972973

0

1

0

true

0

1

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1

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null

0

1

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null

0

1

0

1

0

5

5bc28c8e5fe0d68a4178a18ff74bc1c5ee07698d

34

py

Python

bokeh-app/scripts/crossplot.py

rmsare/ec-dashboard

2be3315620ce50000357ed9b18099e0c41068107

[ "MIT" ]

null

bokeh-app/scripts/crossplot.py

rmsare/ec-dashboard

2be3315620ce50000357ed9b18099e0c41068107

[ "MIT" ]

null

bokeh-app/scripts/crossplot.py

rmsare/ec-dashboard

2be3315620ce50000357ed9b18099e0c41068107

[ "MIT" ]

null

def crossplot_tab(data): pass

11.333333

24

0.705882

5

34

4.6

1

0

0.205882

34

2

25

17

0.851852

0

1

0.5

false

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

1

0

5

5be7b2ad4dce5f5889d787dfe5475bc8f5c1c0d6

86

py

Python

tests/event_log/test_event_log_aggregate.py

nadirhamid/protean

d31bc634e05c9221e82136bf18c2ceaa0982c1c8

[ "BSD-3-Clause" ]

null

tests/event_log/test_event_log_aggregate.py

nadirhamid/protean

d31bc634e05c9221e82136bf18c2ceaa0982c1c8

[ "BSD-3-Clause" ]

null

tests/event_log/test_event_log_aggregate.py

nadirhamid/protean

d31bc634e05c9221e82136bf18c2ceaa0982c1c8

[ "BSD-3-Clause" ]

null

import pytest @pytest.mark.skip def test_timestamps_are_logged_properly(): pass

12.285714

42

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12

86

5.333333

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0

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86

6

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14.333333

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true

0.25

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1

0

null

0

1

0

null

0

1

0

5

7513992e21effe068998d9ebcc9323881e5ecab6

491

py

Python

tests/conftest.py

mwegrzynek/pysupla

b28ba23a551e70dcf9151ee70f252519e71b1f13

[ "Apache-2.0" ]

2

2019-10-03T16:01:05.000Z

2020-05-09T19:34:16.000Z

tests/conftest.py

mwegrzynek/pysupla

b28ba23a551e70dcf9151ee70f252519e71b1f13

[ "Apache-2.0" ]

1

2020-09-04T11:32:46.000Z

tests/conftest.py

mwegrzynek/pysupla

b28ba23a551e70dcf9151ee70f252519e71b1f13

[ "Apache-2.0" ]

2

2019-02-12T18:39:35.000Z

2020-09-04T06:26:12.000Z

# -*- coding: UTF-8 -*- import pytest import os @pytest.fixture def SERVER(): return os.environ['SUPLA_SERVER'] @pytest.fixture def PERSONAL_ACCESS_TOKEN(): return os.environ['SUPLA_PERSONAL_ACCESS_TOKEN'] @pytest.fixture def SHUTTER_ID(): return int(os.environ['SUPLA_SHUTTER_ID']) @pytest.fixture def api(SERVER, PERSONAL_ACCESS_TOKEN): from pysupla import SuplaAPI return SuplaAPI( server=SERVER, personal_access_token=PERSONAL_ACCESS_TOKEN )

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75217400c8b3f96824962a12628baf8021aa718c

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py

Python

backend/medtagger/ground_truth/algorithms/__init__.py

kolszewska/MedTagger

c691c822dd23a9fb402d1314e7fe2e6bde898e9c

[ "Apache-2.0" ]

71

2019-01-31T19:50:31.000Z

2022-02-20T07:36:49.000Z

backend/medtagger/ground_truth/algorithms/__init__.py

kolszewska/MedTagger

c691c822dd23a9fb402d1314e7fe2e6bde898e9c

[ "Apache-2.0" ]

379

2019-02-16T19:12:01.000Z

2022-03-11T23:12:24.000Z

backend/medtagger/ground_truth/algorithms/__init__.py

kolszewska/MedTagger

c691c822dd23a9fb402d1314e7fe2e6bde898e9c

[ "Apache-2.0" ]

16

2019-01-31T16:44:39.000Z

2022-02-14T15:23:29.000Z

"""Module responsible for definition of all algorithms that may used during Ground Truth data set generation."""

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7523c59c5baecb2bc1170b61e00177aad839ab9f

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py

Python

src/strigiform/secrets/__init__.py

X-McKay/strigiform

5db74c99c6778303ec98f30f77097b9cb0cd7a36

[ "MIT" ]

null

src/strigiform/secrets/__init__.py

X-McKay/strigiform

5db74c99c6778303ec98f30f77097b9cb0cd7a36

[ "MIT" ]

76

2021-10-31T21:14:46.000Z

2022-03-30T18:32:49.000Z

src/strigiform/secrets/__init__.py

X-McKay/kingfisher

5db74c99c6778303ec98f30f77097b9cb0cd7a36

[ "MIT" ]

null

"""Placeholder for secrets management."""

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752dcb29f28f3f3cfa2d89a1d96860ce6a93b13e

93

py

Python

main.py

2022AC12SDD/Aurora_SDD_Template

aa0653ee917e68d8e9fdac8811403424b784835c

[ "CC0-1.0" ]

null

main.py

2022AC12SDD/Aurora_SDD_Template

aa0653ee917e68d8e9fdac8811403424b784835c

[ "CC0-1.0" ]

1

2021-10-13T00:58:39.000Z

main.py

2022AC12SDD/Aurora_SDD_Template

aa0653ee917e68d8e9fdac8811403424b784835c

[ "CC0-1.0" ]

null

""" Don't forget your docstring.""" import helpers as h print('main finished successfully')

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1

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5

754525ccdb1cd01198e678ab30edcaffb65c5991

78

py

Python

scripts/quest/autogen_q57961s.py

doriyan13/doristory

438caf3b123922da3f5f3b16fcc98a26a8ab85ce

[ "MIT" ]

null

scripts/quest/autogen_q57961s.py

doriyan13/doristory

438caf3b123922da3f5f3b16fcc98a26a8ab85ce

[ "MIT" ]

null

scripts/quest/autogen_q57961s.py

doriyan13/doristory

438caf3b123922da3f5f3b16fcc98a26a8ab85ce

[ "MIT" ]

null

# Character field ID when accessed: 100010000 # ObjectID: 0 # ParentID: 57961

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1

0

null

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1

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5

f37be23d9f777d081baa3638605b096961f52fe7

233

py

Python

broker/__init__.py

davidkaggulire/kafkacli_interfaces

08dd42217b1ca0c3afa4a55f3e007b75a512c297

[ "MIT" ]

null

broker/__init__.py

davidkaggulire/kafkacli_interfaces

08dd42217b1ca0c3afa4a55f3e007b75a512c297

[ "MIT" ]

null

broker/__init__.py

davidkaggulire/kafkacli_interfaces

08dd42217b1ca0c3afa4a55f3e007b75a512c297

[ "MIT" ]

null

# init.py from .broker_interface import IMessageBroker from .kafka_broker import KafkaBroker from .kafka_broker_listener import KafkaBrokerListener from .inmemory_broker import InMemoryBroker from .kafka_mock import KafkaBrokerMock

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5

f38b22df905451c5cedd68786a309f544c358d2c

115

py

Python

owmeta_core/commands/__init__.py

mwatts15/owmeta-core

b072178f8e7b83cc8665a29f4d038554d18adc35

[ "MIT" ]

2

2021-03-06T16:25:35.000Z

2022-03-24T15:00:03.000Z

owmeta_core/commands/__init__.py

mwatts15/owmeta-core

b072178f8e7b83cc8665a29f4d038554d18adc35

[ "MIT" ]

39

2020-02-08T21:58:33.000Z

2022-01-03T15:28:18.000Z

owmeta_core/commands/__init__.py

openworm/owmeta-core

b072178f8e7b83cc8665a29f4d038554d18adc35

[ "MIT" ]

null

''' Various commands of the same kind as `~owmeta_core.command.OWM`, mostly intended as sub-commands of `OWM`. '''

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5

340e527984ffb3e4c0b55d79b0a8ca42cdbfa58f

97

py

Python

tests/integration/lambdas/python3/lambda1/handler1.py

cknave/localstack

67941331c74dded97284698aba64984ab69cdf43

[ "Apache-2.0" ]

31,928

2017-07-04T03:06:28.000Z

2022-03-31T22:33:27.000Z

tests/integration/lambdas/python3/lambda1/handler1.py

cknave/localstack

67941331c74dded97284698aba64984ab69cdf43

[ "Apache-2.0" ]

5,216

2017-07-04T11:45:41.000Z

2022-03-31T22:02:14.000Z

tests/integration/lambdas/python3/lambda1/handler1.py

cknave/localstack

67941331c74dded97284698aba64984ab69cdf43

[ "Apache-2.0" ]

3,056

2017-06-05T13:29:11.000Z

2022-03-31T20:54:43.000Z

import settings constant = settings.SETTING1 def handler(event, context): return constant

12.125

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97

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97

7

29

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1

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5

341250a0673882d87fb5d7a841e9efe66cbb2bf0

274

py

Python

niworkflows/anat/__init__.py

effigies/niworkflows

2b3d9aa8fa81d312bdf148a9af590ecacaea8c84

[ "BSD-3-Clause" ]

null

niworkflows/anat/__init__.py

effigies/niworkflows

2b3d9aa8fa81d312bdf148a9af590ecacaea8c84

[ "BSD-3-Clause" ]

null

niworkflows/anat/__init__.py

effigies/niworkflows

2b3d9aa8fa81d312bdf148a9af590ecacaea8c84

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- # @Author: oesteban # @Date: 2016-07-21 10:47:37 # @Last Modified by: oesteban # @Last Modified time: 2016-09-23 15:17:32 from niworkflows.anat.mni import RobustMNINormalization from niworkflows.anat.skullstrip import afni_wf as skullstrip_afni

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0

5

3415cc121424e86c412fa8b7ced570431fa0a311

236

py

Python

pytorch_tabular/models/category_embedding/__init__.py

Actis92/pytorch_tabular

78dabf5e7b97d8ff24db4bc83d9d0a2273941bbe

[ "MIT" ]

1

2021-12-11T03:18:36.000Z

pytorch_tabular/models/category_embedding/__init__.py

Actis92/pytorch_tabular

78dabf5e7b97d8ff24db4bc83d9d0a2273941bbe

[ "MIT" ]

null

pytorch_tabular/models/category_embedding/__init__.py

Actis92/pytorch_tabular

78dabf5e7b97d8ff24db4bc83d9d0a2273941bbe

[ "MIT" ]

null

from .category_embedding_model import CategoryEmbeddingModel, CategoryEmbeddingBackbone from .config import CategoryEmbeddingModelConfig __all__ = ["CategoryEmbeddingModel", "CategoryEmbeddingModelConfig", "CategoryEmbeddingBackbone"]

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343a0534e4ce6a86e3c440ac771d04db0a978161

62

py

Python

geopayment/providers/credo/__init__.py

Lh4cKg/tbcpay

481ef6148defc9897643919f7c47ce78d149acbf

[ "MIT" ]

7

2020-07-18T16:11:45.000Z

2022-01-30T20:47:57.000Z

geopayment/providers/credo/__init__.py

Lh4cKg/tbcpay

481ef6148defc9897643919f7c47ce78d149acbf

[ "MIT" ]

3

2017-12-01T05:55:39.000Z

2020-07-17T17:37:28.000Z

geopayment/providers/credo/__init__.py

Lh4cKg/tbcpay

481ef6148defc9897643919f7c47ce78d149acbf

[ "MIT" ]

1

2021-12-18T02:42:09.000Z

from geopayment.providers.credo.provider import CredoProvider

31

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1

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1

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5

caa3736f73450099dce590b19ac27adaf0b42016

1,232

py

Python

pysnowball/finance.py

xing2387/pysnowball

64e680b7d339cf8cdafdce6e5540725408bbb130

[ "Apache-2.0" ]

null

pysnowball/finance.py

xing2387/pysnowball

64e680b7d339cf8cdafdce6e5540725408bbb130

[ "Apache-2.0" ]

null

pysnowball/finance.py

xing2387/pysnowball

64e680b7d339cf8cdafdce6e5540725408bbb130

[ "Apache-2.0" ]

null

from __future__ import absolute_import import json import os import sys from . import cons from . import api_ref from . import utls def cash_flow(symbol, is_annals=0, count=10): url = api_ref.finance_cash_flow_url+symbol if is_annals == 1: url = url + '&type=Q4' url = url + '&count='+str(count) return utls.fetch(url) def indicator(symbol, is_annals=0, count=10): url = api_ref.finance_indicator_url+symbol if is_annals == 1: url = url + '&type=Q4' url = url + '&count='+str(count) return utls.fetch(url) def balance(symbol, is_annals=0, count=10): url = api_ref.finance_balance_url+symbol if is_annals == 1: url = url + '&type=Q4' url = url + '&count='+str(count) return utls.fetch(url) def income(symbol, is_annals=0, count=10): url = api_ref.finance_income_url+symbol if is_annals == 1: url = url + '&type=Q4' url = url + '&count='+str(count) return utls.fetch(url) def business(symbol, is_annals=0, count=10): url = api_ref.finance_business_url+symbol if is_annals == 1: url = url + '&type=Q4' url = url + '&count='+str(count) return utls.fetch(url)

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null

0

1

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null

0

5

cabbfab934c0bf057e98af356a7c83006140b262

4,517

py

Python

ProjectEuler/8.py

RobVor/Python

5cfcd9a72c3899a453c0ec8f4fadea71fe453c49

[ "FSFAP" ]

null

ProjectEuler/8.py

RobVor/Python

5cfcd9a72c3899a453c0ec8f4fadea71fe453c49

[ "FSFAP" ]

4

2021-06-02T03:44:24.000Z

2022-03-12T00:52:58.000Z

ProjectEuler/8.py

RobVor/Python

5cfcd9a72c3899a453c0ec8f4fadea71fe453c49

[ "FSFAP" ]

null

"""The four adjacent digits in the 1000-digit number that have the greatest product are 9 × 9 × 8 × 9 = 5832. 73167176531330624919225119674426574742355349194934 96983520312774506326239578318016984801869478851843 85861560789112949495459501737958331952853208805511 12540698747158523863050715693290963295227443043557 66896648950445244523161731856403098711121722383113 62229893423380308135336276614282806444486645238749 30358907296290491560440772390713810515859307960866 70172427121883998797908792274921901699720888093776 65727333001053367881220235421809751254540594752243 52584907711670556013604839586446706324415722155397 53697817977846174064955149290862569321978468622482 83972241375657056057490261407972968652414535100474 82166370484403199890008895243450658541227588666881 16427171479924442928230863465674813919123162824586 17866458359124566529476545682848912883142607690042 24219022671055626321111109370544217506941658960408 07198403850962455444362981230987879927244284909188 84580156166097919133875499200524063689912560717606 05886116467109405077541002256983155200055935729725 71636269561882670428252483600823257530420752963450 Find the thirteen adjacent digits in the 1000-digit number that have the greatest product. What is the value of this product?""" Series =[7, 3, 1, 6, 7, 1, 7, 6, 5, 3, 1, 3, 3, 0, 6, 2, 4, 9, 1, 9, 2, 2, 5, 1, 1, 9, 6, 7, 4, 4, 2, 6, 5, 7, 4, 7, 4, 2, 3, 5, 5, 3, 4, 9, 1, 9, 4, 9, 3, 4, 9, 6, 9, 8, 3, 5, 2, 0, 3, 1, 2, 7, 7, 4, 5, 0, 6, 3, 2, 6, 2, 3, 9, 5, 7, 8, 3, 1, 8, 0, 1, 6, 9, 8, 4, 8, 0, 1, 8, 6, 9, 4, 7, 8, 8, 5, 1, 8, 4, 3, 8, 5, 8, 6, 1, 5, 6, 0, 7, 8, 9, 1, 1, 2, 9, 4, 9, 4, 9, 5, 4, 5, 9, 5, 0, 1, 7, 3, 7, 9, 5, 8, 3, 3, 1, 9, 5, 2, 8, 5, 3, 2, 0, 8, 8, 0, 5, 5, 1, 1, 1, 2, 5, 4, 0, 6, 9, 8, 7, 4, 7, 1, 5, 8, 5, 2, 3, 8, 6, 3, 0, 5, 0, 7, 1, 5, 6, 9, 3, 2, 9, 0, 9, 6, 3, 2, 9, 5, 2, 2, 7, 4, 4, 3, 0, 4, 3, 5, 5, 7, 6, 6, 8, 9, 6, 6, 4, 8, 9, 5, 0, 4, 4, 5, 2, 4, 4, 5, 2, 3, 1, 6, 1, 7, 3, 1, 8, 5, 6, 4, 0, 3, 0, 9, 8, 7, 1, 1, 1, 2, 1, 7, 2, 2, 3, 8, 3, 1, 1, 3, 6, 2, 2, 2, 9, 8, 9, 3, 4, 2, 3, 3, 8, 0, 3, 0, 8, 1, 3, 5, 3, 3, 6, 2, 7, 6, 6, 1, 4, 2, 8, 2, 8, 0, 6, 4, 4, 4, 4, 8, 6, 6, 4, 5, 2, 3, 8, 7, 4, 9, 3, 0, 3, 5, 8, 9, 0, 7, 2, 9, 6, 2, 9, 0, 4, 9, 1, 5, 6, 0, 4, 4, 0, 7, 7, 2, 3, 9, 0, 7, 1, 3, 8, 1, 0, 5, 1, 5, 8, 5, 9, 3, 0, 7, 9, 6, 0, 8, 6, 6, 7, 0, 1, 7, 2, 4, 2, 7, 1, 2, 1, 8, 8, 3, 9, 9, 8, 7, 9, 7, 9, 0, 8, 7, 9, 2, 2, 7, 4, 9, 2, 1, 9, 0, 1, 6, 9, 9, 7, 2, 0, 8, 8, 8, 0, 9, 3, 7, 7, 6, 6, 5, 7, 2, 7, 3, 3, 3, 0, 0, 1, 0, 5, 3, 3, 6, 7, 8, 8, 1, 2, 2, 0, 2, 3, 5, 4, 2, 1, 8, 0, 9, 7, 5, 1, 2, 5, 4, 5, 4, 0, 5, 9, 4, 7, 5, 2, 2, 4, 3, 5, 2, 5, 8, 4, 9, 0, 7, 7, 1, 1, 6, 7, 0, 5, 5, 6, 0, 1, 3, 6, 0, 4, 8, 3, 9, 5, 8, 6, 4, 4, 6, 7, 0, 6, 3, 2, 4, 4, 1, 5, 7, 2, 2, 1, 5, 5, 3, 9, 7, 5, 3, 6, 9, 7, 8, 1, 7, 9, 7, 7, 8, 4, 6, 1, 7, 4, 0, 6, 4, 9, 5, 5, 1, 4, 9, 2, 9, 0, 8, 6, 2, 5, 6, 9, 3, 2, 1, 9, 7, 8, 4, 6, 8, 6, 2, 2, 4, 8, 2, 8, 3, 9, 7, 2, 2, 4, 1, 3, 7, 5, 6, 5, 7, 0, 5, 6, 0, 5, 7, 4, 9, 0, 2, 6, 1, 4, 0, 7, 9, 7, 2, 9, 6, 8, 6, 5, 2, 4, 1, 4, 5, 3, 5, 1, 0, 0, 4, 7, 4, 8, 2, 1, 6, 6, 3, 7, 0, 4, 8, 4, 4, 0, 3, 1, 9, 9, 8, 9, 0, 0, 0, 8, 8, 9, 5, 2, 4, 3, 4, 5, 0, 6, 5, 8, 5, 4, 1, 2, 2, 7, 5, 8, 8, 6, 6, 6, 8, 8, 1, 1, 6, 4, 2, 7, 1, 7, 1, 4, 7, 9, 9, 2, 4, 4, 4, 2, 9, 2, 8, 2, 3, 0, 8, 6, 3, 4, 6, 5, 6, 7, 4, 8, 1, 3, 9, 1, 9, 1, 2, 3, 1, 6, 2, 8, 2, 4, 5, 8, 6, 1, 7, 8, 6, 6, 4, 5, 8, 3, 5, 9, 1, 2, 4, 5, 6, 6, 5, 2, 9, 4, 7, 6, 5, 4, 5, 6, 8, 2, 8, 4, 8, 9, 1, 2, 8, 8, 3, 1, 4, 2, 6, 0, 7, 6, 9, 0, 0, 4, 2, 2, 4, 2, 1, 9, 0, 2, 2, 6, 7, 1, 0, 5, 5, 6, 2, 6, 3, 2, 1, 1, 1, 1, 1, 0, 9, 3, 7, 0, 5, 4, 4, 2, 1, 7, 5, 0, 6, 9, 4, 1, 6, 5, 8, 9, 6, 0, 4, 0, 8, 0, 7, 1, 9, 8, 4, 0, 3, 8, 5, 0, 9, 6, 2, 4, 5, 5, 4, 4, 4, 3, 6, 2, 9, 8, 1, 2, 3, 0, 9, 8, 7, 8, 7, 9, 9, 2, 7, 2, 4, 4, 2, 8, 4, 9, 0, 9, 1, 8, 8, 8, 4, 5, 8, 0, 1, 5, 6, 1, 6, 6, 0, 9, 7, 9, 1, 9, 1, 3, 3, 8, 7, 5, 4, 9, 9, 2, 0, 0, 5, 2, 4, 0, 6, 3, 6, 8, 9, 9, 1, 2, 5, 6, 0, 7, 1, 7, 6, 0, 6, 0, 5, 8, 8, 6, 1, 1, 6, 4, 6, 7, 1, 0, 9, 4, 0, 5, 0, 7, 7, 5, 4, 1, 0, 0, 2, 2, 5, 6, 9, 8, 3, 1, 5, 5, 2, 0, 0, 0, 5, 5, 9, 3, 5, 7, 2, 9, 7, 2, 5, 7, 1, 6, 3, 6, 2, 6, 9, 5, 6, 1, 8, 8, 2, 6, 7, 0, 4, 2, 8, 2, 5, 2, 4, 8, 3, 6, 0, 0, 8, 2, 3, 2, 5, 7, 5, 3, 0, 4, 2, 0, 7, 5, 2, 9, 6, 3, 4, 5, 0] def GetProd_13(Num): Track = 0 for i in Num: Prod = 1 for j in range(13): Prod *= Num[j] if Prod > Track: Track = Prod del Num[0] return Track print(GetProd_13(Series))

115.820513

3,008

0.50963

1,100

4,517

2.093636

0.060909

0.014763

0.006513

0.0165

0.065132

0.052106

0

0.606106

0.26035

4,517

39

3,009

115.820513

0.082311

0.277618

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1

0

1

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0

0.166667

0.083333

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0

1

0

1

0

null

1

0

5

cafc93f6a096e49c56bfe5b87cac989f899ad6a3

2,621

py

Python

test/test_phones.py

kesbo/python_training

5d7908c13bd00b06738d6f9eff93cff0041acab8

[ "Apache-2.0" ]

null

test/test_phones.py

kesbo/python_training

5d7908c13bd00b06738d6f9eff93cff0041acab8

[ "Apache-2.0" ]

null

test/test_phones.py

kesbo/python_training

5d7908c13bd00b06738d6f9eff93cff0041acab8

[ "Apache-2.0" ]

null

import re from random import randrange from model.contact import Contact def test_random_contact_on_home_page_from_edit_page(app): if app.contact.count() == 0: app.contact.create(Contact(firstname="Bruce", lastname="Wayne", address="Gotam", home="a", mobile="b", work="c", email="batman@32.32", email2="123", email3="2")) contacts_list = app.contact.get_contact_list() index = randrange(len(contacts_list)) contact_from_home_page = contacts_list[index] contact_from_edit_page = app.contact.get_contact_info_from_edit_page(index) assert contact_from_home_page.firstname == contact_from_edit_page.firstname assert contact_from_home_page.lastname == contact_from_edit_page.lastname assert contact_from_home_page.address == contact_from_edit_page.address assert contact_from_home_page.all_phones_home_page == merge_phones_like_one_home_page(contact_from_edit_page) assert contact_from_home_page.all_emails_home_page == merge_emails_like_one_home_page(contact_from_edit_page) def test_phones_on_home_page(app): contact_from_home_page = app.contact.get_contact_list()[0] contact_from_edit_page = app.contact.get_contact_info_from_edit_page(0) assert contact_from_home_page.all_phones_home_page == merge_phones_like_one_home_page(contact_from_edit_page) def test_phones_contact_view_page(app): if app.contact.count() == 0: app.contact.create(Contact(firstname="Bruce", lastname="Wayne", address="Gotam", home="a", mobile="b", work="c", email="batman@32.32", email2="123", email3="2")) contacts_list = app.contact.get_contact_list() index = randrange(len(contacts_list)) contact_from_home_page = contacts_list[index] contact_from_edit_page = app.contact.get_contact_info_from_edit_page(0) assert contact_from_home_page.all_emails_home_page == merge_emails_like_one_home_page(contact_from_edit_page) def clear(s): return re.sub("[() -]", "", s) def merge_phones_like_one_home_page(contact): return "\n".join(filter(lambda x: x != "", map(lambda x: clear(x), filter(lambda x: x is not None, [contact.home, contact.mobile, contact.work])))) def merge_emails_like_one_home_page(contact): return "\n".join(filter(lambda x: x != "", filter(lambda x: x is not None, [contact.email, contact.email2, contact.email3])))

46.803571

113

0.682564

358

2,621

4.592179

0.175978

0.107056

0.10219

0.115572

0.788321

0.725669

0.712287

0.675182

0

0.01312

0.214804

2,621

55

114

47.654545

0.785714

0

0.571429

0

0.029771

0

0.166667

1

0.142857

false

0

0.071429

0.285714

0

null

0

1

0

null

0

5

1b67f3317af7635fd307c92bb386b9a8704e933e

87

py

Python

ex001.py

amandagsilveira/Exercicios_cursoemvideo_Python

9055d24d44d8e195df1691767a635b0b54357672

[ "MIT" ]

null

ex001.py

amandagsilveira/Exercicios_cursoemvideo_Python

9055d24d44d8e195df1691767a635b0b54357672

[ "MIT" ]

null

ex001.py

amandagsilveira/Exercicios_cursoemvideo_Python

9055d24d44d8e195df1691767a635b0b54357672

[ "MIT" ]

null

#Crie um programa que mostre "Olá, Mundo!" na tela. print('\033[33mOlá, Mundo!')

17.4

52

0.643678

13

87

4.307692

0.923077

0

0.071429

0.195402

87

4

53

21.75

0.728571

0.574713

0

0.633333

0

1

0

true

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

5

1b6fc99b21c5d4ec2c8f8abc9afaa17f88ee2a0b

518

py

Python

pybutton/resources/__init__.py

button/button-client-python

82f9be86885ed87ec20dc20e87f3722cdba67fef

[ "MIT" ]

8

2016-08-12T00:21:55.000Z

2019-04-21T12:22:05.000Z

pybutton/resources/__init__.py

button/button-client-python

82f9be86885ed87ec20dc20e87f3722cdba67fef

[ "MIT" ]

16

2016-10-03T20:13:09.000Z

2019-09-23T17:34:43.000Z

pybutton/resources/__init__.py

button/button-client-python

82f9be86885ed87ec20dc20e87f3722cdba67fef

[ "MIT" ]

2

2017-01-09T10:18:45.000Z

2017-02-03T01:29:30.000Z

from __future__ import absolute_import from __future__ import division from __future__ import print_function from __future__ import unicode_literals from pybutton.resources.accounts import Accounts # noqa: 401 from pybutton.resources.customers import Customers # noqa: 401 from pybutton.resources.links import Links # noqa: 401 from pybutton.resources.merchants import Merchants # noqa: 401 from pybutton.resources.orders import Orders # noqa: 401 from pybutton.resources.transactions import Transactions # noqa: 401

43.166667

68

0.839768

67

518

6.208955

0.283582

0.173077

0.302885

0.228365

0.336538

0

0.039216

0.1139

518

11

69

47.090909

0.867102

0.1139

0

1

0

true

0

1

0

1

0.1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

1b7c1f0b62880000b1beaa6e93accde1060c3664

117

py

Python

Bots/Bot.py

dan1510123/stock-history-simulator

a970531f650513a4c76c250796aeecc4d7e4c39b

[ "MIT" ]

1

2021-12-25T21:06:50.000Z

Bots/Bot.py

dan1510123/stock-history-simulator

a970531f650513a4c76c250796aeecc4d7e4c39b

[ "MIT" ]

null

Bots/Bot.py

dan1510123/stock-history-simulator

a970531f650513a4c76c250796aeecc4d7e4c39b

[ "MIT" ]

null

class Bot: # Returns orders to make with list of ticker pairs def get_orders(self, date, limit): pass

29.25

54

0.666667

18

117

4.277778

0.944444

0

0.273504

117

4

55

29.25

0.905882

0.410256

0

1

0.333333

false

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

1b7f4f5ce1d2ea657d3d2683c070a4bcb5c7f227

148

py

Python

Python Fundamentals/Data types and Variables/Exercise/Task06.py

IvanTodorovBG/SoftUni

7b667f6905d9f695ab1484efbb02b6715f6d569e

[ "MIT" ]

1

2022-03-16T10:23:04.000Z

Python Fundamentals/Data types and Variables/Exercise/Task06.py

IvanTodorovBG/SoftUni

7b667f6905d9f695ab1484efbb02b6715f6d569e

[ "MIT" ]

null

Python Fundamentals/Data types and Variables/Exercise/Task06.py

IvanTodorovBG/SoftUni

7b667f6905d9f695ab1484efbb02b6715f6d569e

[ "MIT" ]

null

n = int(input()) for a in range(n): for b in range(n): for c in range(n): print(f"{chr(a + 97)}{chr(b + 97)}{chr(c + 97)}")

24.666667

61

0.466216

29

148

2.37931

0.448276

0.304348

0.347826

0.318841

0

0.058824

0.310811

148

6

61

24.666667

0.617647

0

0.261745

0

1

0

false

0

0.2

1

0

null

1

0

1

0

null

0

5

1baacb4c5b1444f576f4c13e9436b73330758b08

145

py

Python

src/magnesium/path_processor/base_path_processor.py

kokaib/magnesium

0765ab89c30bfb8060c67826dd912ea26e4a4155

[ "MIT" ]

null

src/magnesium/path_processor/base_path_processor.py

kokaib/magnesium

0765ab89c30bfb8060c67826dd912ea26e4a4155

[ "MIT" ]

null

src/magnesium/path_processor/base_path_processor.py

kokaib/magnesium

0765ab89c30bfb8060c67826dd912ea26e4a4155

[ "MIT" ]

null

from abc import ABC, abstractmethod class BasePathProcessor(ABC): """""" @abstractmethod def process(self, x): """"""

14.5

35

0.57931

13

145

6.461538

0.769231

0.404762

0

0.275862

145

9

36

16.111111

0.8

0

1

0.25

false

0

0.25

0

0.75

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

59ffe9f8964e6b973ab9db6f18186e91ccb061b8

53

py

Python

words_counter/test.py

dimaveshkin/py-practive

851f2c359645f1dd50ca9952625810b4188c88dc

[ "MIT" ]

null

words_counter/test.py

dimaveshkin/py-practive

851f2c359645f1dd50ca9952625810b4188c88dc

[ "MIT" ]

null

words_counter/test.py

dimaveshkin/py-practive

851f2c359645f1dd50ca9952625810b4188c88dc

[ "MIT" ]

null

#!/usr/bin/env python import sys print(sys.argv[1])

10.6

21

0.698113

10

53

3.7

0.9

0

0.021277

0.113208

53

5

22

10.6

0.765957

0.377358

0

1

0

true

0

0.5

0

0.5

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

941bf0ee9b69c9898c1230e9c904c1d7b4f451c6

23

py

Python

template/example/main.py

cheetosysst/FDep

b7bae64db00121196544dc91d7c4b67d93d11af6

[ "MIT" ]

null

template/example/main.py

cheetosysst/FDep

b7bae64db00121196544dc91d7c4b67d93d11af6

[ "MIT" ]

null

template/example/main.py

cheetosysst/FDep

b7bae64db00121196544dc91d7c4b67d93d11af6

[ "MIT" ]

null

print("Hello, wolrd!")

11.5

22

0.652174

3

23

5

1

0

0.086957

23

1

23

0.714286

0

0.565217

0

1

0

true

0

1

0

null

0

1

0

null

0

1

0

1

0

5

846ee166fb5411fc42a10f2a3df6268352badb21

92

py

Python

pulsus/services/apns/__init__.py

pennersr/pulsus

ace014ca40e3928b235e1bcfebe22301c7f3cafe

[ "MIT" ]

14

2015-01-16T07:48:43.000Z

2019-04-19T23:13:50.000Z

pulsus/services/apns/__init__.py

pennersr/pulsus

ace014ca40e3928b235e1bcfebe22301c7f3cafe

[ "MIT" ]

null

pulsus/services/apns/__init__.py

pennersr/pulsus

ace014ca40e3928b235e1bcfebe22301c7f3cafe

[ "MIT" ]

2

2015-08-06T12:52:56.000Z

2019-02-07T18:09:23.000Z

from .notification import APNSNotification # noqa from .service import APNSService # noqa

30.666667

50

0.804348

10

92

7.4

0.7

0

0.152174

92

2

51

46

0.948718

0.097826

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

84a8f7bfa0dcd2366cb93a9f601ef514f65c39d3

131

py

Python

drdown/events/admin.py

fga-gpp-mds/2018.1-Cris-Down

3423374360105b06ac2c57a320bf2ee8deaa08a3

[ "MIT" ]

11

2018-03-11T01:21:43.000Z

2018-06-19T21:51:33.000Z

drdown/events/admin.py

fga-gpp-mds/2018.1-Grupo12

3423374360105b06ac2c57a320bf2ee8deaa08a3

[ "MIT" ]

245

2018-03-13T19:07:14.000Z

2018-07-07T22:46:00.000Z

drdown/events/admin.py

fga-gpp-mds/2018.1-Grupo12

3423374360105b06ac2c57a320bf2ee8deaa08a3

[ "MIT" ]

12

2018-08-24T13:26:04.000Z

2021-03-27T16:28:22.000Z

from django.contrib import admin from .models.model_events import Events # Register your models here. admin.site.register(Events)

21.833333

39

0.816794

19

131

5.578947

0.631579

0

0.114504

131

5

40

26.2

0.913793

0.198473

0

1

0

true

0

0.666667

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

84c8ee311c39ac0043e2715541bfe5b9861e8899

3,354

py

Python

setup.py

WRY-learning/k3http

095a49118d052c43eb0e1dd82b6764eee9fcb158

[ "MIT" ]

null

setup.py

WRY-learning/k3http

095a49118d052c43eb0e1dd82b6764eee9fcb158

[ "MIT" ]

2

2021-11-10T22:16:25.000Z

2022-03-23T06:59:52.000Z

setup.py

WRY-learning/k3http

095a49118d052c43eb0e1dd82b6764eee9fcb158

[ "MIT" ]

1

2021-08-18T05:16:59.000Z

# DO NOT EDIT!!! built with `python _building/build_setup.py` import setuptools setuptools.setup( name="k3http", packages=["k3http"], version="0.1.0", license='MIT', description="We find that 'httplib' must work in blocking mode and it can not have a timeout when recving response.", long_description="# k3http\n\n[![Action-CI](https://github.com/pykit3/k3http/actions/workflows/python-package.yml/badge.svg)](https://github.com/pykit3/k3http/actions/workflows/python-package.yml)\n[![Build Status](https://travis-ci.com/pykit3/k3http.svg?branch=master)](https://travis-ci.com/pykit3/k3http)\n[![Documentation Status](https://readthedocs.org/projects/k3http/badge/?version=stable)](https://k3http.readthedocs.io/en/stable/?badge=stable)\n[![Package](https://img.shields.io/pypi/pyversions/k3http)](https://pypi.org/project/k3http)\n\nWe find that 'httplib' must work in blocking mode and it can not have a timeout when recving response.\n\nk3http is a component of [pykit3] project: a python3 toolkit set.\n\n\nHTTP/1.1 client\n\nUse this module, we can set timeout, if timeout raise a 'socket.timeout'.\n\n\n\n# Install\n\n```\npip install k3http\n```\n\n# Synopsis\n\n```python\n\nimport k3http\nimport urllib\nimport socket\n\nheaders = {\n 'Host': '127.0.0.1',\n 'Accept-Language': 'en, mi',\n}\n\ntry:\n h = k3http.Client('127.0.0.1', 80)\n\n # send http reqeust without body\n # read response status line\n # read response headers\n h.request('/test.txt', method='GET', headers=headers)\n\n status = h.status\n # response code return from http server, type is int\n # 200\n # 302\n # 404\n # ...\n\n res_headers = h.headers\n # response headers except status line\n # res_headers = {\n # 'Content-Type': 'text/html;charset=utf-8',\n # 'Content-Length': 1024,\n # ...\n # }\n\n # get response body\n print(h.read_body(None))\nexcept (socket.error, k3http.HttpError) as e:\n print(repr(e))\n\n\n\ncontent = urllib.urlencode({'f': 'foo', 'b': 'bar'})\nheaders = {\n 'Host': 'www.example.com',\n 'Content-Type': 'application/x-www-form-urlencoded;charset=utf-8',\n 'Content-Length': len(content),\n}\n\ntry:\n h = k3http.Client('127.0.0.1', 80)\n\n # send http reqeust\n h.send_request('http://www.example.com', method='POST', headers=headers)\n\n # send http request body\n h.send_body(content)\n\n # read response status line and headers\n status, headers = h.read_response()\n\n # read response body\n print(h.read_body(None))\nexcept (socket.error, k3http.HttpError) as e:\n print(repr(e))\n\n```\n\n# Author\n\nZhang Yanpo (张炎泼) <drdr.xp@gmail.com>\n\n# Copyright and License\n\nThe MIT License (MIT)\n\nCopyright (c) 2015 Zhang Yanpo (张炎泼) <drdr.xp@gmail.com>\n\n\n[pykit3]: https://github.com/pykit3", long_description_content_type="text/markdown", author='Zhang Yanpo', author_email='drdr.xp@gmail.com', url='https://github.com/pykit3/k3http', keywords=['python', 'http'], python_requires='>=3.0', install_requires=['k3ut>=0.1.15,<0.2', 'k3stopwatch>=0.1.1,<0.2'], classifiers=[ 'Development Status :: 4 - Beta', 'Intended Audience :: Developers', 'Topic :: Software Development :: Libraries', ] + ['Programming Language :: Python :: 3'], )

139.75

2,548

0.673822

531

3,354

4.225989

0.352166

0.026738

0.013369

0.035651

0.335116

0.30303

0.255793

0.234403

0

0.031608

0.141622

3,354

23

2,549

145.826087

0.747829

0.017591

0

1

0.095238

0.8843

0.153052

0

1

0

true

0

0.095238

0

0.095238

0.047619

0

null

0

1

0

1

0

1

0

null

0

1

0

5

84f6ba19da782d7caf00b5db6ebe6cc541c95c13

19,939

py

Python

ncellapp/ncellapp.py

sanam1357/ncellsms

e3be1c4a87b4f55b2ef68ceec33124c4c5d836a6

[ "MIT" ]

null

ncellapp/ncellapp.py

sanam1357/ncellsms

e3be1c4a87b4f55b2ef68ceec33124c4c5d836a6

[ "MIT" ]

null

ncellapp/ncellapp.py

sanam1357/ncellsms

e3be1c4a87b4f55b2ef68ceec33124c4c5d836a6

[ "MIT" ]

null

import requests from base64 import (b64encode, b64decode) from ast import literal_eval from datetime import datetime from Crypto.Cipher import AES class AESCipher(object): def __init__(self): self.key = b'zSXdd0rx59ThQlul' self.bs = AES.block_size def encrypt(self, raw): raw = self._pad(raw) # zero based byte[16] iv = b'\0'*16 cipher = AES.new(self.key, AES.MODE_CBC, iv) return b64encode(cipher.encrypt(raw.encode())).decode('UTF-8') def decrypt(self, enc): enc = b64decode(enc) # zero based byte[16] iv = b'\0'*16 cipher = AES.new(self.key, AES.MODE_CBC, iv) return self._unpad(cipher.decrypt(enc)).decode('utf-8') def _pad(self, s): return s + (self.bs - len(s) % self.bs) * chr(self.bs - len(s) % self.bs) @staticmethod def _unpad(s): return s[:-ord(s[len(s)-1:])] class register(AESCipher): def __init__(self, msidn): AESCipher.__init__(self) self.msidn = msidn self.baseUrl = 'http://ssa.ncell.com.np:8080/mc/selfcare/v2/proxy' self.headers = { 'X-MobileCare-AppClientVersion': 'SHn7MOIW3T/R/OL8LsAvxw==', 'Cache-Control': 'no-cache', 'X-MobileCare-PreferredLocale': 'cAsAM2g0t7oB6OSJKH1ptQ==', 'Content-Type': 'application/xml', 'X-MobileCare-APIKey': 'ABC_KEY', 'X-MobileCare-AppResolution': 'iRRhXh87ipDTZpyEWGWteg==', 'X-MobileCare-AppPlatformVersion': 'QJ2ZR3DKpuBfBr7GuTQh7w==', 'ACCEPT': 'application/json', 'X-MobileCare-AppPlatformName': 'yEHXRN3mrQMvwG4bfE2ApQ==', 'Host': 'ssa.ncell.com.np:8080', 'Connection': 'Keep-Alive', } def sendOtp(self): '''[Send OTP to the number for registration] Returns: [dict]: [response from the Ncell server] ''' url = self.baseUrl + '/register' data = f"<?xml version='1.0' encoding='UTF-8' standalone='yes' ?><mAppData><userOperationData><serviceInstance>{self.msidn}</serviceInstance></userOperationData></mAppData>" data = AESCipher.encrypt(self, data) self.request = requests.post(url, headers=self.headers, data=data) response = literal_eval(AESCipher.decrypt(self, self.request.text))['businessOutput'] try: self.deviceClientId = AESCipher.encrypt(self, response['deviceClientId']) except KeyError: self.deviceClientId = None return response def getToken(self, otp): '''[Send the OTP to the Ncell server and return the token if successful] Args: otp ([string]): [OTP sent in the phone number] Returns: [dict]: [response from the Ncell server with token] ''' self.headers.update({ 'X-MobileCare-DeviceClientID': self.deviceClientId, 'X-MobileCare-MSISDN': self.msidn, }) url = self.baseUrl + '/register' data = f"<?xml version='1.0' encoding='UTF-8' standalone='yes' ?><mAppData><userOperationData><otp>{otp}</otp></userOperationData></mAppData>" data = AESCipher.encrypt(self, data) self.request = requests.post(url, headers=self.headers, data=data) response = literal_eval(AESCipher.decrypt(self, self.request.text))['businessOutput'] if response['opStatus'] == '0': token = b64encode(str({'msidn':self.msidn, 'deviceClientId':self.deviceClientId}).encode()).decode() response.update({'token':token}) return response class ncell(AESCipher): def __init__(self, token): AESCipher.__init__(self) self.token = token self.baseUrl = 'http://ssa.ncell.com.np:8080/mc/selfcare/v2/proxy' def login(self): '''[Extract the msidn and client ID from the token and login] Returns: [dict]: [returns opStatus=0 if successful] ''' try: self.msidn = literal_eval(b64decode(self.token).decode())['msidn'] self.deviceClientId = literal_eval(b64decode(self.token).decode())['deviceClientId'] except Exception: self.msidn = self.deviceClientId = None return {'opStatus': 'invalid', 'errorMessage': 'The token you provided is not valid.'} self.headers = { 'X-MobileCare-AppClientVersion': 'SHn7MOIW3T/R/OL8LsAvxw==', 'Cache-Control': 'no-cache', 'X-MobileCare-PreferredLocale': 'cAsAM2g0t7oB6OSJKH1ptQ==', 'Content-Type': 'application/xml', 'X-MobileCare-APIKey': 'ABC_KEY', 'X-MobileCare-AppResolution': 'iRRhXh87ipDTZpyEWGWteg==', 'X-MobileCare-DeviceClientID': self.deviceClientId, 'X-MobileCare-MSISDN': self.msidn, 'X-MobileCare-AppPlatformVersion': 'QJ2ZR3DKpuBfBr7GuTQh7w==', 'ACCEPT': 'application/json', 'X-MobileCare-AppPlatformName': 'yEHXRN3mrQMvwG4bfE2ApQ==', 'Host': 'ssa.ncell.com.np:8080', 'Connection': 'Keep-Alive', } profile = self.viewProfile() try: self.name = profile['myProfile']['name'] self.status = profile['myProfile']['status'] self.partyID = profile['myProfile']['partyID'] self.accountId = profile['myProfile']['accountID'] self.serviceFlag = profile['myProfile']['serviceFlag'] self.currentPlan = profile['myProfile']['currentPlan'] self.secureToken = profile['myProfile']['secureToken'] self.hubID = profile['myProfile']['hubID'] return {'opStatus': '0', 'errorMessage': 'SUCCESS'} except KeyError: self.name = self.status = self.partyID = self.accountId = self.serviceFlag = self.currentPlan = self.secureToken = self.hubID = None return {'opStatus': 'expired', 'errorMessage': 'The token you provided has expired.'} def viewProfile(self): '''[View the profile of the account] Returns: [dict]: [response from the Ncell server] ''' url = self.baseUrl + '/viewMyProfile' data = "<?xml version='1.0' encoding='UTF-8' standalone='yes' ?><mAppData><userOperationData /></mAppData>" data = AESCipher.encrypt(self, data) self.request = requests.post(url, headers=self.headers, data=data) return literal_eval(AESCipher.decrypt(self, self.request.text))['businessOutput'] def sendSms(self, destination, message, schedule=None): '''[Send SMS with the currentPlan] Args: destination ([int]): [MSIDN of the destination] message ([String]): [Message to send] schedule ([int], optional): [Schedule date in order of YYYYMMDDHHMMSS format, eg.20201105124500]. Defaults to None. Returns: [dict]: [response from the Ncell server] ''' url = self.baseUrl + '/updateServiceRequest' schedule = schedule or datetime.now().strftime("%Y%m%d%H%M%S") data = f"<?xml version='1.0' encoding='UTF-8' standalone='yes' ?><mAppData><userOperationData><lob>{self.serviceFlag}</lob><userId>{schedule}</userId><problemDesc>{message}</problemDesc><serviceId>SENDSMS</serviceId><accountId>{self.accountId}</accountId><code>{destination}</code><offerId>yes</offerId></userOperationData></mAppData>" data = AESCipher.encrypt(self, data) self.request = requests.post(url, headers=self.headers, data=data) return literal_eval(AESCipher.decrypt(self, self.request.text))['businessOutput'] def sendFreeSms(self, destination, message, schedule=None): '''[Send free 10 SMS] Args: destination ([int]): [MSIDN of the destination] message ([String]): [Message to send] schedule ([int], optional): [Schedule date in order of YYYYMMDDHHMMSS format, eg.20201105124500]. Defaults to None. Returns: [dict]: [response from the Ncell server] ''' url = self.baseUrl + '/updateServiceRequest' schedule = schedule or datetime.now().strftime("%Y%m%d%H%M%S") data = f"<?xml version='1.0' encoding='UTF-8' standalone='yes' ?><mAppData><userOperationData><lob>{self.serviceFlag}</lob><userId>{schedule}</userId><problemDesc>{message}</problemDesc><serviceId>SENDSMS</serviceId><accountId>{self.accountId}</accountId><code>{destination}</code><offerId>no</offerId></userOperationData></mAppData>" data = AESCipher.encrypt(self, data) self.request = requests.post(url, headers=self.headers, data=data) return literal_eval(AESCipher.decrypt(self, self.request.text))['businessOutput'] def viewBalance(self): '''[View the current balance] Returns: [dict]: [response from the Ncell server] ''' url = self.baseUrl + '/myBalance' data = f"<?xml version='1.0' encoding='UTF-8' standalone='yes' ?><mAppData><userOperationData><lob>{self.serviceFlag}</lob><contractId></contractId><customerId></customerId><code>{self.accountId}</code><accountId>{self.accountId}</accountId><offerId>{self.hubID}</offerId></userOperationData></mAppData>" data = AESCipher.encrypt(self, data) self.request = requests.post(url, headers=self.headers, data=data) return literal_eval(AESCipher.decrypt(self, self.request.text))['businessOutput'] def selfRecharge(self, rpin): '''[Recharging the current account] Args: rpin ([int]): [16 digit PIN of the recharge card] Returns: [dict]: [response from the Ncell server] ''' url = self.baseUrl + '/updateServiceRequest' data = f"<?xml version='1.0' encoding='UTF-8' standalone='yes' ?><mAppData><userOperationData><lob>{self.serviceFlag}</lob><alternateContactNumber></alternateContactNumber><contractId></contractId><customerId></customerId><serviceId>RECHARGENOW</serviceId><code>{rpin}</code></userOperationData></mAppData>" data = AESCipher.encrypt(self, data) self.request = requests.post(url, headers=self.headers, data=data) return literal_eval(AESCipher.decrypt(self, self.request.text))['businessOutput'] def recharge(self, destination, rpin): '''[Recharging other's account] Args: destination ([int]): [MSIDN of the destination] rpin ([int]): [16 digit PIN of the recharge card] Returns: [dict]: [response from the Ncell server] ''' url = self.baseUrl + '/updateServiceRequest' data = f"<?xml version='1.0' encoding='UTF-8' standalone='yes' ?><mAppData><userOperationData><lob>{self.serviceFlag}</lob><alternateContactNumber>{destination}</alternateContactNumber><contractId></contractId><customerId></customerId><serviceId>RECHARGENOW</serviceId><code>{rpin}</code></userOperationData></mAppData>" data = AESCipher.encrypt(self, data) self.request = requests.post(url, headers=self.headers, data=data) return literal_eval(AESCipher.decrypt(self, self.request.text))['businessOutput'] def rechargeHistory(self): '''[latest balance transfer history] Returns: [dict]: [response from the Ncell server] ''' url = self.baseUrl + '/rechargeHistory' data = f"<?xml version='1.0' encoding='UTF-8' standalone='yes' ?><mAppData><userOperationData><lob>{self.serviceFlag}</lob><contractId></contractId><customerId></customerId><userId>TransferHistory</userId><accountId>{self.accountId}</accountId></userOperationData></mAppData>" data = AESCipher.encrypt(self, data) self.request = requests.post(url, headers=self.headers, data=data) return literal_eval(AESCipher.decrypt(self, self.request.text))['businessOutput'] def balanceTransfer(self, destination, amount): '''[Initiate the balance transformation to the destination number] Args: destination ([int]): [MSIDN of the destination] amount ([int]): [Amount of balance to transfer] Returns: [dict]: [response from the Ncell server] ''' url = self.baseUrl + '/updateServiceRequest' data = f"<?xml version='1.0' encoding='UTF-8' standalone='yes' ?><mAppData><userOperationData><lob>{self.serviceFlag}</lob><alternateContactNumber>{destination}</alternateContactNumber><contractId></contractId><customerId></customerId><action>NEW</action><serviceId>BALANCETRANSFER</serviceId><code>{amount}</code></userOperationData></mAppData>" data = AESCipher.encrypt(self, data) self.request = requests.post(url, headers=self.headers, data=data) return literal_eval(AESCipher.decrypt(self, self.request.text))['businessOutput'] def confirmBalanceTransfer(self, otp): '''[Confirm the balance transfer] Args: otp ([int]): [OTP sent in phone number] Returns: [type]: [response from the Ncell server] ''' url = self.baseUrl + '/updateServiceRequest' data = f"<?xml version='1.0' encoding='UTF-8' standalone='yes' ?><mAppData><userOperationData><lob>{self.serviceFlag}</lob><password>{otp}</password><contractId></contractId><customerId></customerId><action>NEW</action><serviceId>BALANCETRANSFER</serviceId><offerId>validate</offerId></userOperationData></mAppData>" data = AESCipher.encrypt(self, data) self.request = requests.post(url, headers=self.headers, data=data) return literal_eval(AESCipher.decrypt(self, self.request.text))['businessOutput'] def viewTransaction(self, transactionsFrom, transactionsTo): '''[Initiate to view call history] Args: transactionsFrom ([int]): [From date in YYYYMMDDHHMMSS order] transactionsTo ([int]): [To date in YYYYMMDDHHMMSS order] Returns: [dict]: [response from the Ncell server] ''' url = self.baseUrl + '/viewTransactions' self.transactionsFrom = transactionsFrom self.transactionsTo = transactionsTo data = f"<?xml version='1.0' encoding='UTF-8' standalone='yes' ?><mAppData><userOperationData><lob>prepaid</lob><userId>{self.transactionsFrom}</userId><code>GET</code><accountId>{self.accountId}</accountId><offerId>{self.transactionsTo}</offerId></userOperationData></mAppData>" data = AESCipher.encrypt(self, data) self.request = requests.post(url, headers=self.headers, data=data) return literal_eval(AESCipher.decrypt(self, self.request.text))['businessOutput'] def confirmViewTransaction(self, otp): '''[Confirm to view call history] Args: otp ([int]): [OTP sent in phone number] Returns: [dict]: [response from the Ncell server] ''' url = self.baseUrl + '/viewTransactions' data = f"<?xml version='1.0' encoding='UTF-8' standalone='yes' ?><mAppData><userOperationData><lob>prepaid</lob><action>{otp}</action><userId>{self.transactionsFrom}</userId><code>VALIDATE</code><accountId>{self.accountId}</accountId><offerId>{self.transactionsTo}</offerId></userOperationData></mAppData>" data = AESCipher.encrypt(self, data) self.request = requests.post(url, headers=self.headers, data=data) return literal_eval(AESCipher.decrypt(self, self.request.text))['businessOutput'] def viewService(self, serviceCategory=''): '''[View the list of available services to activate] Args: serviceCategory ([str], optional): [Category of the service]. Defaults to None. Returns: [dict]: [response from the Ncell server] ''' url = self.baseUrl + '/viewMyService' data = f"<?xml version='1.0' encoding='UTF-8' standalone='yes' ?><mAppData><userOperationData><lob>{self.serviceFlag}</lob><contractId></contractId><customerId></customerId><code>R3027</code><serviceCategory>{serviceCategory}</serviceCategory><accountId>{self.accountId}</accountId><offerId>{self.hubID}</offerId></userOperationData></mAppData>" data = AESCipher.encrypt(self, data) self.request = requests.post(url, headers=self.headers, data=data) return literal_eval(AESCipher.decrypt(self, self.request.text))['businessOutput'] def activateService(self, serviceId): '''[Activate the certain service] Args: serviceId ([int]): [Service ID found in isMandatory field of viewService()] Returns: [dict]: [response from the Ncell server] ''' url = self.baseUrl + '/updateServiceRequest' data = f"<?xml version='1.0' encoding='UTF-8' standalone='yes' ?><mAppData><userOperationData><lob>{self.serviceFlag}</lob><contractId></contractId><customerId></customerId><serviceId>SUBSCRIBEAPRODUCT</serviceId><code>{serviceId}</code></userOperationData></mAppData>" data = AESCipher.encrypt(self, data) self.request = requests.post(url, headers=self.headers, data=data) return literal_eval(AESCipher.decrypt(self, self.request.text))['businessOutput'] def viewOffer(self): '''[View the available offer for the account] Returns: [dict]: [response from the Ncell server] ''' url = self.baseUrl + '/viewOffers' data = f"<?xml version='1.0' encoding='UTF-8' standalone='yes' ?><mAppData><userOperationData><customerId></customerId><lob>{self.serviceFlag}</lob><accountId>{self.accountId}</accountId><contractId></contractId></userOperationData></mAppData>" data = AESCipher.encrypt(self, data) self.request = requests.post(url, headers=self.headers, data=data) return literal_eval(AESCipher.decrypt(self, self.request.text))['businessOutput'] def activateOffer(self, offerId): '''[Activate the certain offer] Args: offerId ([int]): [offer ID found in offerID field of viewOffer()] Returns: [type]: [description] ''' url = self.baseUrl + '/updateServiceRequest' data = f"<?xml version='1.0' encoding='UTF-8' standalone='yes' ?><mAppData><userOperationData><lob>{self.serviceFlag}</lob><contractId></contractId><customerId></customerId><serviceId>SUBSCRIBEAPRODUCT</serviceId><code>{offerId}</code></userOperationData></mAppData>" data = AESCipher.encrypt(self, data) self.request = requests.post(url, headers=self.headers, data=data) return literal_eval(AESCipher.decrypt(self, self.request.text))['businessOutput'] def view3gPlans(self): '''[View available plans for 3G] Returns: [dict]: [response from the Ncell server] ''' url = self.baseUrl + '/view3gPlans' data = f"<?xml version='1.0' encoding='UTF-8' standalone='yes' ?><mAppData><userOperationData><lob>{self.serviceFlag}</lob><contractId></contractId><customerId></customerId><code>{self.accountId}</code><accountId>{self.accountId}</accountId><offerId>{self.hubID}</offerId></userOperationData></mAppData>" data = AESCipher.encrypt(self, data) self.request = requests.post(url, headers=self.headers, data=data) return literal_eval(AESCipher.decrypt(self, self.request.text))['businessOutput']

45.11086

354

0.62611

2,045

19,939

6.080196

0.128606

0.031848

0.030561

0.017372

0.742159

0.728326

0.71401

0.707737

0.698488

0

0.011844

0.22935

19,939

442

355

45.11086

0.797345

0.154271

0

0.543269

0

0.086538

0.420656

0.286525

0

1

0.125

false

0.004808

0.024038

0.009615

0.283654

0

null

0

1

0

null

0

5

ca2e54cebeee275ffeb4db21314349e043a0bb32

58

py

Python

stockviewer/stockviewer/view/__init__.py

vyacheslav-bezborodov/skt

58551eed497687adec5b56336037613a78cc5b2d

[ "MIT" ]

null

stockviewer/stockviewer/view/__init__.py

vyacheslav-bezborodov/skt

58551eed497687adec5b56336037613a78cc5b2d

[ "MIT" ]

null

stockviewer/stockviewer/view/__init__.py

vyacheslav-bezborodov/skt

58551eed497687adec5b56336037613a78cc5b2d

[ "MIT" ]

null

from viewmanager import viewmanager from main import main

19.333333

35

0.862069

8

58

6.25

0.5

0

0.137931

58

2

36

29

1

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

ca4a01bee340fe08ea41c9d3772628e46d9874c7

185

py

Python

ExerciciosPython/ex013.py

LucasBalbinoSS/Exercicios-Python

2e9d3a8ec4ab24a2732c461a84f51bde54902a24

[ "MIT" ]

null

ExerciciosPython/ex013.py

LucasBalbinoSS/Exercicios-Python

2e9d3a8ec4ab24a2732c461a84f51bde54902a24

[ "MIT" ]

null

ExerciciosPython/ex013.py

LucasBalbinoSS/Exercicios-Python

2e9d3a8ec4ab24a2732c461a84f51bde54902a24

[ "MIT" ]

null

s = float(input('\033[35mQual é o salário do funcionário?: R$ ')) print('Um funcionário que ganhava {:.2f}, com 15% de aumento, passa a receber: {:.2f}R$'.format(s, s + (s * 15/100)))

46.25

117

0.637838

32

185

3.6875

0.78125

0.033898

0

0.089744

0.156757

185

3

118

61.666667

0.666667

0

0.5

0.675676

0

1

0

false

0.5

0

0.5

0

null

0

1

0

1

0

null

0

1

0

1

0

5

ca4bb540e1da17445d44f02dcbecb4603a9904a6

2,074

py

Python

EvaMap/Metrics/disjointWith.py

benjimor/EvaMap

42e616abe9f15925b885797d30496e30615989a0

[ "MIT" ]

1

2021-01-29T18:53:26.000Z

EvaMap/Metrics/disjointWith.py

benjimor/EvaMap

42e616abe9f15925b885797d30496e30615989a0

[ "MIT" ]

1

2021-06-06T17:56:00.000Z

EvaMap/Metrics/disjointWith.py

benjimor/EvaMap

42e616abe9f15925b885797d30496e30615989a0

[ "MIT" ]

null

import rdflib from EvaMap.Metrics.metric import metric def disjointWith(g_onto, liste_map, g_map, raw_data, g_link) : result = metric() result['name'] = "Misuse of disjointWith" points = 0 nbPossible = 0 for s, _, o in g_map.triples((None, None, None)) : nbPossible = nbPossible + 1 for _, _, o1 in g_onto.triples((s, rdflib.term.URIRef('https://www.w3.org/2002/07/owl#disjointWith'), None)) : if g_onto.triples((o, (rdflib.term.URIRef('a')|rdflib.term.URIRef('http://www.w3.org/1999/02/22-rdf-syntax-ns#type')), o1)) is not None : points = points + 1 result['feedbacks'].append(str(o) + "is disjoint with" + s) else : for s1, _, _ in g_onto.triples((None, rdflib.term.URIRef('http://www.w3.org/2000/01/rdf-schema#subClassOf') ,o)): if g_onto.triples((s1, (rdflib.term.URIRef('a')|rdflib.term.URIRef('http://www.w3.org/1999/02/22-rdf-syntax-ns#type')), o1)) is not None : points = points + 1 result['feedbacks'].append(str(o) + "is disjoint with" + s) for _, _, o1 in g_onto.triples((o, rdflib.term.URIRef('https://www.w3.org/2002/07/owl#disjointWith'), None)) : if g_onto.triples((s, (rdflib.term.URIRef('a')|rdflib.term.URIRef('http://www.w3.org/1999/02/22-rdf-syntax-ns#type')), o1)) is not None : points = points + 1 result['feedbacks'].append(str(o) + "is disjoint with" + s) else : for s1, _, _ in g_onto.triples((None, rdflib.term.URIRef('http://www.w3.org/2000/01/rdf-schema#subClassOf') ,s)): if g_onto.triples((s1, (rdflib.term.URIRef('a')|rdflib.term.URIRef('http://www.w3.org/1999/02/22-rdf-syntax-ns#type')), o1)) is not None : points = points + 1 result['feedbacks'].append(str(o) + "is disjoint with" + s) if nbPossible == 0: result['score'] = 1 else: result['score'] = 1-points/nbPossible return result

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1

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null

0

5

04639d2f91fe6d8fc1a5eb8e5fe6a3de216d7361

153

py

Python

service/__init__.py

moshebeeri/datap

9ff99bb435728cd69f2589e3ee858a06768ea85e

[ "Apache-2.0" ]

null

service/__init__.py

moshebeeri/datap

9ff99bb435728cd69f2589e3ee858a06768ea85e

[ "Apache-2.0" ]

null

service/__init__.py

moshebeeri/datap

9ff99bb435728cd69f2589e3ee858a06768ea85e

[ "Apache-2.0" ]

null

from .service import Service from .mongodb import MongoDB from .elasticsearch import Elasticsearch from .druid import Druid from .sqlite import SQLiteDB

25.5

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153

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null

0

1

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1

0

1

0

5

0476cd74f2f47547bea6e40cd61ce2f275ccb4a0

6,415

py

Python

tests/test_userviews.py

kumaraditya303/Mess-Management-System

9930e2eae485c29100133e2e030bb979ef920fe1

[ "MIT" ]

2

2021-02-26T03:04:37.000Z

2021-03-28T04:02:02.000Z

tests/test_userviews.py

kumaraditya303/Mess-Management-System

9930e2eae485c29100133e2e030bb979ef920fe1

[ "MIT" ]

18

2020-09-11T15:49:52.000Z

2022-03-28T21:20:34.000Z

tests/test_userviews.py

kumaraditya303/Mess-Management-System

9930e2eae485c29100133e2e030bb979ef920fe1

[ "MIT" ]

null

from tests import Test class TestGet(Test): def test_main_page(self): response = self.client.get('/') self.assertIn(b'Welcome to Mess Management System', response.data) self.assert200(response) self.assertTemplateUsed('index.html') self.assertMessageFlashed( "No dishes are available in Mess!", category='warning') def test_login_page(self): response = self.client.get('/login') self.assertIn(b'Login', response.data) self.assert200(response) self.assertTemplateUsed('login.html') def test_register_page(self): response = self.client.get('/register') self.assertIn(b'Register', response.data) self.assert200(response) self.assertTemplateUsed('register.html') def test_balance_page(self): response = self.client.get('/balance') self.assertRedirects(response, '/') self.assertMessageFlashed("You are unauthorized to access the page!", category='warning') def test_order_page(self): response = self.client.get('/order') self.assertRedirects(response, '/') self.assertMessageFlashed("You are unauthorized to access the page!", category='warning') def test_forgot_page(self): response = self.client.get('/forgot') self.assertIn(b'Reset Password', response.data) self.assert200(response) self.assertTemplateUsed('reset.html') def test_password_reset_page(self): response = self.client.get('/forgot/token', follow_redirects=False) self.assertIn(b'Reset Password', response.data) self.assert200(response) self.assertTemplateUsed('reset_password.html') def test_logout_page(self): response = self.client.get('/logout') self.assertRedirects(response, '/') self.assertMessageFlashed("You are unauthorized to access the page!", category='warning') class TestPost(Test): def test_register(self): response = self.client.post( '/register', data=dict(name='Test', email="test@test.com", password="testingpassword" ), follow_redirects=True ) self.assertIn(b'Welcome Test', response.data) self.assert200(response) self.assertTemplateUsed('dashboard.html') def test_dashboard(self): response = self.client.post( '/register', data=dict(name='Test', email="test@test.com", password="testingpassword" ), follow_redirects=True ) self.assertIn(b'Welcome Test', response.data) self.assert200(response) self.assertTemplateUsed('dashboard.html') def test_login(self): response = self.client.post( '/register', data=dict(name='Test', email="test@test.com", password="testingpassword" ), follow_redirects=True ) self.assertIn(b'Welcome Test', response.data) self.assert200(response) self.assertTemplateUsed('dashboard.html') response = self.client.post( '/login', data=dict( email="test@test.com", password="testingpassword" ), follow_redirects=True ) self.assertIn(b'Welcome Test', response.data) self.assert200(response) self.assertTemplateUsed('dashboard.html') def test_balance(self): response = self.client.post( '/register', data=dict(name='Test', email="test@test.com", password="testingpassword" ), follow_redirects=True ) self.assertIn(b'Welcome Test', response.data) self.assert200(response) self.assertTemplateUsed('dashboard.html') response = self.client.post( '/balance', data=dict( balance=1000 ), follow_redirects=True ) self.assert200(response) self.assertMessageFlashed("₹ 1000 was added successfully to your Mess account!", category='success') self.assertIn(b'Welcome Test', response.data) def test_order(self): response = self.client.post( '/register', data=dict(name='Test', email="test@test.com", password="testingpassword" ), follow_redirects=True ) self.assertIn(b'Welcome Test', response.data) self.assert200(response) self.assertTemplateUsed('dashboard.html') response = self.client.get( '/order', follow_redirects=True ) self.assertIn(b'Order Food', response.data) self.assert200(response) self.assertTemplateUsed('order.html') def test_logout(self, ): response = self.client.post( '/register', data=dict(name='Test', email="test@test.com", password="testingpassword" ), follow_redirects=True ) self.assertIn(b'Welcome Test', response.data) self.assert200(response) self.assertTemplateUsed('dashboard.html') response = self.client.get('/logout',) self.assertRedirects(response, '/') def test_password_reset_email(self): response = self.client.post( '/register', data=dict(name='Test', email="test@test.com", password="testingpassword" ), follow_redirects=True ) self.assertIn(b'Welcome Test', response.data) self.assert200(response) self.assertTemplateUsed('dashboard.html') response = self.client.get('/logout') self.assertRedirects(response, '/') response = self.client.post( '/forgot', data=dict( email='test@test.com' ) ) self.assertRedirects(response, '/') self.assertMessageFlashed( 'Email sent successfully!', category='success')

35.054645

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6,415

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0.10609

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182

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35.247253

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null

0

1

0

1

0

null

0

1

0

5

048d021364d03b9388d0f6314fd49dec08369504

80

py

Python

template_client/libs/sample.py

Braiiin/blog-client

4e0fe8768b1504c808ff56e1705904a01cb70907

[ "Apache-2.0" ]

null

template_client/libs/sample.py

Braiiin/blog-client

4e0fe8768b1504c808ff56e1705904a01cb70907

[ "Apache-2.0" ]

null

template_client/libs/sample.py

Braiiin/blog-client

4e0fe8768b1504c808ff56e1705904a01cb70907

[ "Apache-2.0" ]

null

from client.libs.base import Entity class Sample(Entity): """Sample object"""

16

35

0.7375

11

80

5.363636

0.818182

0

0.125

80

5

36

16

0.842857

0.1625

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true

0

0.5

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1

0

null

0

1

0

1

0

1

0

5

04beb3966b903f9b8bfa0034a861cded74dfeccf

59

py

Python

pypgapack/__init__.py

robertsj/pypgapack

c24b4a58f347ec02c20929aaaec25010fa603eb8

[ "MIT" ]

4

2015-12-16T09:44:32.000Z

2021-05-23T23:52:33.000Z

pypgapack/__init__.py

robertsj/pypgapack

c24b4a58f347ec02c20929aaaec25010fa603eb8

[ "MIT" ]

null

pypgapack/__init__.py

robertsj/pypgapack

c24b4a58f347ec02c20929aaaec25010fa603eb8

[ "MIT" ]

1

2022-01-01T17:44:21.000Z

# pypgapack/pypgapack/__init__.py from pypgapack import *

14.75

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0

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59

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34

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null

0

1

0

1

0

1

0

5

b6d23ca10281dd46a28d9d4358ab420e34b1e56b

1,769

py

Python

test/test_homogeneity_checks.py

kilgore92/PyStatCheck

427744a4c98676630633362ed2a8e31f51189768

[ "MIT" ]

null

test/test_homogeneity_checks.py

kilgore92/PyStatCheck

427744a4c98676630633362ed2a8e31f51189768

[ "MIT" ]

null

test/test_homogeneity_checks.py

kilgore92/PyStatCheck

427744a4c98676630633362ed2a8e31f51189768

[ "MIT" ]

null

# -*- coding: utf-8 -*- from unittest import TestCase from pystatcheck.tests import CheckHomogeneity import numpy as np __author__ = "Ishaan Bhat" __copyright__ = "Ishaan Bhat" __license__ = "mit" class TestHomogeneityChecks(TestCase): """ Test suite to check if the module can detect homogeneity in known cases """ def test_same_distribution(self): arr1 = np.random.normal(loc=0, scale=3.0, size=(1000,)) arr2 = np.random.normal(loc=0, scale=3.0, size=(1000,)) assert(CheckHomogeneity(arr1=arr1, arr2=arr2, verbose=False).perform_homogeneity_tests() is True) def test_different_distribution_equal_variance(self): arr1 = np.random.normal(loc=0, scale=3.0, size=(1000,)) arr2 = np.random.normal(loc=1.0, scale=3.0, size=(1000,)) assert(CheckHomogeneity(arr1=arr1, arr2=arr2, verbose=False).perform_homogeneity_tests() is False) def test_different_distribution_unequal_variance(self): arr1 = np.random.normal(loc=0, scale=3.0, size=(1000,)) arr2 = np.random.normal(loc=1.0, scale=5.0, size=(1000,)) assert(CheckHomogeneity(arr1=arr1, arr2=arr2, verbose=False).perform_homogeneity_tests() is False) def test_same_distribution_non_normal(self): arr1 = np.random.binomial(n=10, p=0.5, size=(1000,)) arr2 = np.random.binomial(n=10, p=0.5, size=(1000,)) assert(CheckHomogeneity(arr1=arr1, arr2=arr2, verbose=False).perform_homogeneity_tests() is True) def test_different_distribution_non_normal(self): arr1 = np.random.binomial(n=10, p=0.5, size=(1000,)) arr2 = np.random.binomial(n=10, p=0.8, size=(1000,)) assert(CheckHomogeneity(arr1=arr1, arr2=arr2, verbose=False).perform_homogeneity_tests() is False)

36.102041

106

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4.74

0.26

0.067511

0.070886

0.086076

0.752743

0

0.072789

0.169022

1,769

48

107

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0.111111

0

0.333333

0

null

0

1

0

null

0

5

b6fa50065e786193998fcc21cd22b25363a341a5

149

py

Python

__init__.py

jordanvrtanoski/block

18372826d7c2808a46152eeb588931ed088a1154

[ "MIT" ]

null

__init__.py

jordanvrtanoski/block

18372826d7c2808a46152eeb588931ed088a1154

[ "MIT" ]

null

__init__.py

jordanvrtanoski/block

18372826d7c2808a46152eeb588931ed088a1154

[ "MIT" ]

null

import sys, os from .block_utils import ( __version__, __name__, __author__, __email__, __description__, __license__ ) from .block import core

18.625

78

0.771812

17

149

5.294118

0.764706

0.2

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0.161074

149

7

79

21.285714

0.72

0

1

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true

0

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0

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0

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null

0

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0

null

0

1

0

1

0

1

0

5

8e435cf6f3657f0a0c68d75bb668ddf8fd2ba71f

3,830

py

Python

gg_manager/utilities/gg_stacks.py

petrichor-ai/gg-group-manager

21fdc50b33a1c56dad44b4537d3a8b0aa2db4b60

[ "MIT" ]

null

gg_manager/utilities/gg_stacks.py

petrichor-ai/gg-group-manager

21fdc50b33a1c56dad44b4537d3a8b0aa2db4b60

[ "MIT" ]

null

gg_manager/utilities/gg_stacks.py

petrichor-ai/gg-group-manager

21fdc50b33a1c56dad44b4537d3a8b0aa2db4b60

[ "MIT" ]

null

import boto3 import logging from botocore.exceptions import ClientError logging.basicConfig( format='%(asctime)s|%(name).10s|%(levelname).5s: %(message)s', level=logging.WARNING ) log = logging.getLogger('GroupStack') log.setLevel(logging.DEBUG) class Stack(object): def __init__(self, s): self._cfn = s.client('cloudformation') self._gg = s.client('greengrass') def create(self, config, cfntmp): ''' Create a Cloudformation Greengrass Resource Stack. ''' if config.get('Group', None): groupName = config['Group']['Name'] stackName = '{}-GG-Stack'.format(groupName) stackCaps = ['CAPABILITY_IAM'] if config.get('thingName', None): thingName = config['thingName'] stackName = '{}-Thing-Stack'.format(thingName) stackCaps = ['CAPABILITY_IAM'] if config.get('Alias', None): funcsName = config['Alias'] stackName = '{}-Funcs-Stack'.format(funcsName) stackCaps = ['CAPABILITY_IAM', 'CAPABILITY_AUTO_EXPAND'] response = self._cfn.create_stack( StackName=stackName, TemplateBody=cfntmp.json_dump(), Capabilities=stackCaps ) def update(self, config, cfntmp): ''' Update a Cloudformation Greengrass Resource Stack. ''' if config.get('Group', None): groupName = config['Group']['Name'] stackName = '{}-GG-Stack'.format(groupName) stackCaps = ['CAPABILITY_IAM'] if config.get('thingName', None): thingName = config['thingName'] stackName = '{}-Thing-Stack'.format(thingName) stackCaps = ['CAPABILITY_IAM'] if config.get('Alias', None): funcsName = config['Alias'] stackName = '{}-Funcs-Stack'.format(funcsName) stackCaps = ['CAPABILITY_IAM', 'CAPABILITY_AUTO_EXPAND'] response = self._cfn.update_stack( StackName=stackName, TemplateBody=cfntmp.json_dump(), Capabilities=stackCaps ) def delete(self, config, cfntmp): ''' Delete a Cloudformation Greengrass Resource Stack. ''' if config.get('Group', None): groupName = config['Group']['Name'] stackName = '{}-GG-Stack'.format(groupName) stackCaps = ['CAPABILITY_IAM'] if config.get('thingName', None): thingName = config['thingName'] stackName = '{}-Thing-Stack'.format(thingName) stackCaps = ['CAPABILITY_IAM'] if config.get('Alias', None): funcsName = config['Alias'] stackName = '{}-Funcs-Stack'.format(funcsName) stackCaps = ['CAPABILITY_IAM', 'CAPABILITY_AUTO_EXPAND'] response = self._cfn.delete_stack( StackName=stackName ) def output(self, config): ''' Retreive a Cloudformation Greengrass Resource Stack Output. ''' if config.get('Group', None): groupName = config['Group']['Name'] stackName = '{}-GG-Stack'.format(groupName) stackCaps = ['CAPABILITY_IAM'] if config.get('thingName', None): thingName = config['thingName'] stackName = '{}-Thing-Stack'.format(thingName) stackCaps = ['CAPABILITY_IAM'] if config.get('Alias', None): funcsName = config['Alias'] stackName = '{}-Funcs-Stack'.format(funcsName) stackCaps = ['CAPABILITY_IAM', 'CAPABILITY_AUTO_EXPAND'] response = self._cfn.describe_stacks( StackName=stackName ) outputs = response['Stacks'][0].get('Outputs', []) return {out['OutputKey']: out['OutputValue'] for out in outputs}

30.887097

72

0.575718

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3,830

6.109859

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0.060858

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123

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null

0

1

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null

0

5

6d014d3d0b767d1dfb62af7f16c5ae22c16bb9e3

438

py

Python

rumi/msg_rumi/__init__.py

rotationalio/rumi

313f4832e0e707443182f819268b509f651e7acb

[ "Apache-2.0" ]

null

rumi/msg_rumi/__init__.py

rotationalio/rumi

313f4832e0e707443182f819268b509f651e7acb

[ "Apache-2.0" ]

4

2021-12-13T06:54:19.000Z

2021-12-17T12:29:07.000Z

rumi/msg_rumi/__init__.py

rotationalio/rumi

313f4832e0e707443182f819268b509f651e7acb

[ "Apache-2.0" ]

null

# rumi.msg_rumi # Message-based translation monitoring # # Author: Tianshu Li # Created: Nov.15 2021 """ Reader and Reporter for message-based translation monitoring, especially for react app projects set up with lingui.js. """ ########################################################################## # Imports ########################################################################## from .reader import * from .reporter import *

23.052632

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438

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0.306977

0

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18

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0.497717

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null

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1

0

null

0

1

0

1

0

1

0

5

6d0ed5d15cdf65d9666258009bff578fb570c03c

399

py

Python

textwiser/transformations/__init__.py

vishalbelsare/textwiser

da500f24c8d35d29e3ff77702b7c5ece244562cc

[ "Apache-2.0" ]

24

2020-07-02T13:31:46.000Z

2022-03-25T02:27:24.000Z

textwiser/transformations/__init__.py

fmr-llc/textwiser

2c5bdd73c26bd3fb7bd2f324f57d99233aa9c17f

[ "Apache-2.0" ]

5

2020-07-23T16:43:02.000Z

2022-02-23T17:05:38.000Z

textwiser/transformations/__init__.py

fmr-llc/textwiser

2c5bdd73c26bd3fb7bd2f324f57d99233aa9c17f

[ "Apache-2.0" ]

6

2021-01-03T08:09:39.000Z

2022-03-25T02:18:59.000Z

# Copyright 2019 FMR LLC <opensource@fidelity.com> # SPDX-License-Identifer: Apache-2.0 from textwiser.transformations.pool import _PoolTransformation from textwiser.transformations.nmf import _NMFTransformation from textwiser.transformations.lda import _LDATransformation from textwiser.transformations.svd import _SVDTransformation from textwiser.transformations.umap_ import _UMAPTransformation

44.333333

63

0.87218

43

399

7.953488

0.627907

0.190058

0.409357

0

0.01626

0.075188

399

8

64

49.875

0.910569

0.20802

0

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0

true

0

1

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1

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null

0

1

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1

0

null

0

1

0

1

0

1

0

5

6d2147a86f023434d766c6ccac23cb017b63f75c

50

py

Python

amplpyfinance/efficient_frontier/__init__.py

ampl/amplpyfinance

9df026dbba9235b85a0a42d1c24768b8fb2c82e5

[ "MIT" ]

null

amplpyfinance/efficient_frontier/__init__.py

ampl/amplpyfinance

9df026dbba9235b85a0a42d1c24768b8fb2c82e5

[ "MIT" ]

null

amplpyfinance/efficient_frontier/__init__.py

ampl/amplpyfinance

9df026dbba9235b85a0a42d1c24768b8fb2c82e5

[ "MIT" ]

null

from .efficient_frontier import EfficientFrontier

25

49

0.9

5

50

8.8

1

0

0.08

50

1

50

0.956522

0

1

0

true

0

1

0

1

0

1

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null

0

1

0

null

0

1

0

1

0

1

0

5

6d24729caf18c9f840ce63eee05fa1eb7f2db864

127

py

Python

apigw/services.py

0x55AAh/anthill_gaming

475af798bd08d85fc0fbfce9d2ba710f73252c15

[ "MIT" ]

1

2018-11-30T21:56:14.000Z

apigw/services.py

0x55AAh/anthill_gaming

475af798bd08d85fc0fbfce9d2ba710f73252c15

[ "MIT" ]

null

apigw/services.py

0x55AAh/anthill_gaming

475af798bd08d85fc0fbfce9d2ba710f73252c15

[ "MIT" ]

null

from anthill.platform.services import APIGatewayService class Service(APIGatewayService): """Anthill default service."""

21.166667

55

0.787402

12

127

8.333333

0.75

0

0.11811

127

5

56

25.4

0.892857

0.188976

0

1

0

true

0

0.5

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

6d36107f07e45fe62007f715bf555f10f6ef82a6

128

py

Python

main/views.py

toskuef/kraft

e499fb5fd6c741c463bb49b5c223068be99d3521

[ "Apache-2.0" ]

null

main/views.py

toskuef/kraft

e499fb5fd6c741c463bb49b5c223068be99d3521

[ "Apache-2.0" ]

null

main/views.py

toskuef/kraft

e499fb5fd6c741c463bb49b5c223068be99d3521

[ "Apache-2.0" ]

null

from django.shortcuts import render def index(request): template = 'main/index.html' return render(request, template)

18.285714

36

0.734375

16

128

5.875

0.75

0.319149

0

0.171875

128

6

37

21.333333

0.886792

0

0.117188

0

1

0.25

false

0

0.25

0

0.75

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

6d3b54f9a6bd52a43531bef10c3e5cbb8114e23d

26

py

Python

exercises/poker/poker.py

haithamk/python-exercism

8166a98ba771e0d527efdda421d3d9e741f0459b

[ "MIT" ]

null

exercises/poker/poker.py

haithamk/python-exercism

8166a98ba771e0d527efdda421d3d9e741f0459b

[ "MIT" ]

null

exercises/poker/poker.py

haithamk/python-exercism

8166a98ba771e0d527efdda421d3d9e741f0459b

[ "MIT" ]

1

2021-12-29T19:26:23.000Z

def poker(hand): pass

8.666667

16

0.615385

4

26

4

1

0

0.269231

26

2

17

13

0.842105

0

1

0.5

false

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

1

0

5

6d43b05e32306f4234f4eecd76dc6a8ebf66d8bf

35,698

py

Python

selenium_login.py

JuanLeee/Kbot

1013f086bec682c93b9104b31748d7c4bcd5a957

[ "MIT" ]

null

selenium_login.py

JuanLeee/Kbot

1013f086bec682c93b9104b31748d7c4bcd5a957

[ "MIT" ]

null

selenium_login.py

JuanLeee/Kbot

1013f086bec682c93b9104b31748d7c4bcd5a957

[ "MIT" ]

null

from selenium import webdriver from selenium.webdriver.common.keys import Keys from selenium.webdriver.support.ui import Select from selenium.webdriver.common.by import By from time import sleep from selenium.webdriver.support.ui import WebDriverWait from selenium.webdriver.support import expected_conditions as EC, wait from selenium.webdriver.common.action_chains import ActionChains from tesseract_ocr import * from card_image import url_to_image import tesseract_ocr import sys import random import logging import json from discord_webhook import DiscordWebhook from good_stuffs import dict_good_stuff_static from good_stuffs import dict_good_stuff_addition from good_stuffs import char_numbers import time import math import time from dotenv import load_dotenv load_dotenv() class Discord_Scraper: def __init__(self, driver, action, user_name, password, server_name, channel_name, server_name_list, id_name, timer): self.hash_table = {} self.count_messages = 0 self.driver = driver self.action = action self.user_name = user_name self.password = password self.server_name = server_name self.server_name_list = server_name_list self.channel_name = channel_name global flag_debug global flag_cd_grab global flag_cd_grab_long global flag_global_clicked global flag_refresh global flag_stop flag_debug = False flag_refresh = False flag_global_clicked = False self.clicked_card = '' self.flag_clicked = False self.count_reset = 10 self.count_clicked = self.count_reset self.id_name = id_name self.curr_id = '' if timer: self.sleep_timer_grab = 300 self.sleep_timer_drop = 900 else: self.sleep_timer_grab = 600 self.sleep_timer_drop = 1800 flag_stop = False self.ocr = OCR_PyTes() self.dict_good_stuff = dict_good_stuff_static self.dict_good_stuff_addition = dict_good_stuff_addition for key in self.dict_good_stuff.keys(): temp_value = self.dict_good_stuff[key].split(' ', 1)[0] self.dict_good_stuff[key] = temp_value for key in self.dict_good_stuff_addition.keys(): temp_value = self.dict_good_stuff_addition[key].split(' ', 1)[0] self.dict_good_stuff_addition[key] = temp_value self.time = time.time() self.counter = 50 self.disc_webhook = os.getenv('DISCORD_LINK') def login_sign(self): error_count = 0 while error_count < 2: try: self.driver.get("https://discord.com/login") print('Login') username_input = WebDriverWait(self.driver, 120).until( EC.presence_of_element_located((By.NAME, 'email'))) username_input.send_keys(self.user_name) password_input = WebDriverWait(self.driver, 120).until( EC.presence_of_element_located((By.NAME, 'password'))) password_input.send_keys(self.password) print('Submit button') login_button = WebDriverWait(self.driver, 120).until( EC.presence_of_element_located((By.XPATH, "//button[@type='submit']"))) self.driver.execute_script( "arguments[0].click();", login_button) server_name = "//*[@data-list-item-id=\"" + \ self.server_name + "\"]" print("Trying if School Pop up") try: hcaptcha = WebDriverWait(self.driver, 5).until( EC.presence_of_element_located((By.CLASS_NAME, 'flexCenter-3_1bcw'))) print("HCaptcha") pop_up = WebDriverWait(self.driver, 120).until( EC.presence_of_element_located((By.CLASS_NAME, 'content-1LAB8Z'))) pop_up_button = WebDriverWait(pop_up, 20).until( EC.presence_of_element_located((By.CLASS_NAME, 'close-hZ94c6'))) self.driver.execute_script( "arguments[0].click();", pop_up_button) print("School Pop up") except: print("No HCaptcha") # logging.error("Exception occurred", exc_info=True) try: pop_up = WebDriverWait(self.driver, 5).until( EC.presence_of_element_located((By.CLASS_NAME, 'content-1LAB8Z'))) pop_up_button = WebDriverWait(pop_up, 5).until( EC.presence_of_element_located((By.CLASS_NAME, 'close-hZ94c6'))) self.driver.execute_script( "arguments[0].click();", pop_up_button) print("School Pop up") except: print("School No Pop up") # logging.error("Exception occurred", exc_info=True) print('Server') server_icon = WebDriverWait(self.driver, 120).until( EC.presence_of_element_located((By.XPATH, server_name))) self.driver.execute_script( "arguments[0].click();", server_icon) print("Trying to see if pop up") try: pop_up = WebDriverWait(self.driver, 5).until( EC.presence_of_element_located((By.CLASS_NAME, 'focusLock-Ns3yie'))) self.driver.execute_script("arguments[0].click();", WebDriverWait(pop_up, 5).until( EC.presence_of_element_located((By.CLASS_NAME, 'button-38aScr')))) print("Pop up") except: print("No Pop up") # logging.error("Exception occurred", exc_info=True) print('Channel Scroll') channel_list = WebDriverWait(self.driver, 120).until( EC.presence_of_element_located((By.CLASS_NAME, 'scroller-RmtA4e'))) self.action.move_to_element(channel_list.find_elements( By.CLASS_NAME, 'containerDefault--pIXnN')[-1]).perform() time.sleep(1) self.action.move_to_element(channel_list.find_elements( By.CLASS_NAME, 'containerDefault--pIXnN')[-1]).perform() channel_name = "//*[@data-list-item-id=\"" + \ self.channel_name + "\"]" print('Channel') logging.info("Logged into " + self.server_name_list) channel_icon = WebDriverWait(self.driver, 120).until( EC.presence_of_element_located((By.XPATH, channel_name))) self.driver.execute_script( "arguments[0].click();", channel_icon) webhook = DiscordWebhook(url=self.disc_webhook, rate_limit_retry=True, content='Logged into ' + self.user_name + ' and into server ' + self.server_name_list) response = webhook.execute() break except: logging.warning("Starting up") logging.error("Exception occurred", exc_info=True) time.sleep(5) def message_log(self, f_condition, f_action): global flag_debug global flag_refresh global flag_global_clicked global flag_cd_grab global flag_cd_grab_long global flag_stop flag_cd_grab_long = True flag_cd_grab = True refresh_counter = 0 logs = WebDriverWait(self.driver, 120, poll_frequency=0.05).until( EC.presence_of_all_elements_located((By.CLASS_NAME, 'message-2qnXI6'))) i = len(logs)-1 while i <= 0: logs = WebDriverWait(self.driver, 120, poll_frequency=0.05).until( EC.presence_of_all_elements_located((By.CLASS_NAME, 'message-2qnXI6'))) i = len(logs)-1 interval = 0.33 print('Message Scroll') self.action.move_to_element(WebDriverWait(self.driver, 120, poll_frequency=0.05).until( EC.presence_of_all_elements_located((By.CLASS_NAME, 'message-2qnXI6')))[-2]).perform() for n in range(1, 15): WebDriverWait(self.driver, 120, poll_frequency=0.05).until(EC.presence_of_all_elements_located( (By.CLASS_NAME, 'message-2qnXI6')))[-1].send_keys(Keys.ARROW_DOWN) n = 20 self.current_state_fill_hash(n) count_clear = 0 while not flag_stop: try: for j in range(1, n): z = 1 * j if flag_debug: print(z) try: logs = WebDriverWait(self.driver, 120, poll_frequency=0.05).until( EC.presence_of_all_elements_located((By.CLASS_NAME, 'message-2qnXI6'))) data_list_str = logs[z].get_attribute('id') self.curr_id = str(data_list_str) except: logging.info("NO ATTRIBUTES START" + " " + self.server_name_list) logging.info(data_list_str) logging.info(str(z) + " FAILED ") break if data_list_str not in self.hash_table: text = logs[z].text self.hash_table[data_list_str] = self.count_messages self.count_messages += 1 if self.flag_clicked: self.count_clicked -= 1 if self.id_name + ', your Evasion' in text: self.count_clicked = self.count_reset self.flag_clicked = False flag_cd_grab_long = True flag_cd_grab = True flag_global_clicked = False webhook = DiscordWebhook(url=self.disc_webhook, rate_limit_retry=True, content=self.user_name + ' got ' + self.clicked_card + ' in ' + self.server_name_list) logging.info("Evasion Proc") webhook.execute() break elif self.id_name + ' fought off' in text or self.id_name + ' took the' in text: self.count_clicked = self.count_reset self.flag_clicked = False flag_cd_grab_long = False logging.info("GOTTEM") print("Gottem") webhook = DiscordWebhook(url=self.disc_webhook, rate_limit_retry=True, content=self.user_name + ' got ' + self.clicked_card + ' in ' + self.server_name_list) webhook.execute() self.clean_hash_table() time.sleep(self.sleep_timer_grab) self.current_state_fill_hash(n) flag_cd_grab_long = True flag_cd_grab = True flag_global_clicked = False print("Exit: " + self.server_name_list) break elif self.count_clicked <= 0 or self.clicked_card in text.lower(): print("Fled") self.count_clicked = self.count_reset self.flag_clicked = False flag_cd_grab = False self.clean_hash_table() time.sleep(60) self.current_state_fill_hash(n) flag_cd_grab = True flag_cd_grab_long = True flag_global_clicked = False print("Exit: " + self.server_name_list) break elif f_condition(text): f_action() else: break if not flag_cd_grab: print("Enter: " + self.server_name_list) self.count_clicked = self.count_reset self.flag_clicked = False self.clean_hash_table() time.sleep(59) print("Exit: " + self.server_name_list) self.current_state_fill_hash(n) elif not flag_cd_grab_long: print("Enter: " + self.server_name_list) self.count_clicked = self.count_reset self.flag_clicked = False self.clean_hash_table() time.sleep(self.sleep_timer_grab-1) print("Exit: " + self.server_name_list) self.current_state_fill_hash(n) time.sleep(interval) except: logging.warning("Error ml " + self.server_name_list) logging.error("Exception occurred", exc_info=True) if flag_stop: self.driver.quit() print("Quit " + self.server_name_list) break def clean_hash_table(self): print("Cleaning Hash Table") for key, value in list(self.hash_table.items()): if value < (self.count_messages-100): del self.hash_table[key] def current_state_fill_hash(self, n): try: print("Filling hash table") logs = WebDriverWait(self.driver, 120, poll_frequency=0.05).until( EC.presence_of_all_elements_located((By.CLASS_NAME, 'message-2qnXI6'))) for j in range(1, n): z = -1 * j try: data_list_str = logs[z].get_attribute('id') if data_list_str not in self.hash_table: self.hash_table[data_list_str] = self.count_messages self.count_messages += 1 else: break except: logging.info("NO ATTRIBUTES START" + " " + self.server_name_list) logging.info(str(z) + " FAILED " + self.curr_id) logging.warning( "NO ATTRIBUTES START END", exc_info=True) break except: logging.warning( "Failed Filling", exc_info=True) def kd_every(self): global flag_refresh global flag_global_clicked global flag_cd_grab global flag_cd_grab_long global flag_stop flag_cd_grab_long = True flag_cd_grab = True flag_stop = False sleep(90) while not flag_stop: if not flag_cd_grab or not flag_cd_grab_long or flag_global_clicked: sleep(20) else: try: logging.warning('Drop') textbox = self.driver.find_element_by_class_name( 'markup-2BOw-j.slateTextArea-1Mkdgw.fontSize16Padding-3Wk7zP') textbox.send_keys('kd') textbox.send_keys(Keys.ENTER) sleep(float(random.randrange( self.sleep_timer_drop, self.sleep_timer_drop+300))) except: logging.error("Exception occurred", exc_info=True) self.driver.quit() print("Quit DROP " + self.server_name_list) def condition_BOT_droppping(self, text): try: if "dropping 4 cards" in text or "dropping 3 cards" in text: if self.driver.find_element_by_id(self.curr_id).find_element_by_class_name('anchor-3Z-8Bb'): return True except: return False return False def condition_BOT_droppping_Server(self, text): try: if "cards since this server is currently active!" in text: if self.driver.find_element_by_id(self.curr_id).find_element_by_class_name('anchor-3Z-8Bb'): return True except: return False return False def flag_stop_true(self): global flag_stop flag_stop = True self.driver.quit() print("Quit " + self.server_name_list) def action_href_img(self): global flag_global_clicked global flag_cd_grab error_count = 0 flag_finished = False while(error_count < 3 and not flag_finished): try: img_container = self.driver.find_element_by_id( self.curr_id).find_element_by_class_name('anchor-3Z-8Bb') href_link = img_container.get_attribute('href') error_count_image = 0 try: image = url_to_image(href_link) while image is None: error_count_image += 1 if error_count_image > 3: break logging.info("Cant get image:" + href_link + " " + str(error_count_image)) image = url_to_image(href_link) h, w, c = image.shape except: logging.warning("Cant get image:" + href_link) logging.warning("Unexpected error:", sys.exc_info()[0]) error_count += 1 if error_count_image > 3: break continue max = 4 if w > 900 else 3 pos = max-1 try: while pos >= 0 and not self.flag_clicked: print_num = self.ocr.get_print_num(image, pos) if (math.log10(print_num))+1 > 0 and (int(print_num) > 100) and not flag_global_clicked: name_card = print_num read_series = print_num series = print_num try: self.click_reactions(pos) name_card = self.ocr.get_names_single( image, pos) print(str(name_card) + str(series) + self.server_name_list) logging.info("Got Name: " + str(name_card) + " Series: " + str( read_series) + " Server: " + self.server_name_list + " URL: " + href_link) self.clicked_card = name_card.split(' ', 1)[0] self.flag_clicked = True except: error_count += 1 logging.warning( "Cant Click Edition" + self.server_name_list, exc_info=True) elif(not flag_global_clicked): name_card = self.ocr.get_names_single( image, pos) if (name_card in self.dict_good_stuff) and not flag_global_clicked: series = self.dict_good_stuff.get( name_card, '-1') read_series = self.ocr.get_names_bottom( image, pos).split(' ', 1)[0] if series == '123456' or read_series == series: try: self.click_reactions(pos) print(name_card + " " + series + " " + self.server_name_list) logging.info("Got Name: " + name_card + " Series: " + read_series + " Server: " + self.server_name_list + " URL: " + href_link) self.clicked_card = name_card.split(' ', 1)[ 0] self.flag_clicked = True except: error_count += 1 logging.warning( "Cant Click Edition " + self.server_name_list, exc_info=True) elif name_card in self.dict_good_stuff_addition and not flag_global_clicked: series = self.dict_good_stuff_addition.get( name_card, '-1') read_series = self.ocr.get_names_bottom( image, pos).split(' ', 1)[0] if series == '123456' or read_series == series: if name_card in char_numbers: edition = self.ocr.get_edition_number( image, pos) if edition == char_numbers[name_card]: try: self.click_reactions( pos) print( name_card + " " + series + " " + self.server_name_list) logging.info("Got Name: " + name_card + " Series: " + read_series + " Server: " + self.server_name_list + " URL: " + href_link) self.clicked_card = name_card.split(' ', 1)[ 0] self.flag_clicked = True except: error_count += 1 logging.warning( "Cant Click Edition " + self.server_name_list, exc_info=True) pos -= 1 flag_finished = True except: error_count += 1 logging.error("Exception occurred", exc_info=True) logging.warning( "Cant get card name:" + href_link + " pos:" + str(pos) + " w:" + str(w)) except: logging.warning("Error") logging.error("Exception occurred", exc_info=True) error_count += 1 def debug_on(self): global flag_debug flag_debug = not flag_debug def get_webelement_id(self): return self.driver.find_element_by_id(self.curr_id) def click_reactions(self, pos): global flag_global_clicked global flag_cd_grab try: reactions_container = WebDriverWait(self.driver.find_element_by_id( self.curr_id), 600, poll_frequency=0.05).until( EC.presence_of_element_located((By.CLASS_NAME, 'reactions-12N0jA'))) reactions = WebDriverWait(reactions_container, 600, poll_frequency=0.05).until( EC.presence_of_all_elements_located((By.CLASS_NAME, 'reaction-1hd86g'))) while len(reactions) < pos+1: reactions = reactions_container.find_elements( By.CLASS_NAME, 'reaction-1hd86g') if not flag_global_clicked: self.driver.execute_script("arguments[0].click();", reactions[pos].find_element( By.CLASS_NAME, 'reactionInner-15NvIl')) flag_global_clicked = True print("clicked 1") except: time.sleep(0.05) logging.info("Failed to Click 1st " + self.server_name_list, exc_info=True) try: logs = self.get_webelement_id() reactions_container = WebDriverWait(logs, 600, poll_frequency=0.05).until( EC.presence_of_element_located((By.CLASS_NAME, 'reactions-12N0jA'))) reactions = WebDriverWait(reactions_container, 600, poll_frequency=0.05).until( EC.presence_of_all_elements_located((By.CLASS_NAME, 'reaction-1hd86g'))) while len(reactions) < pos+1: reactions = reactions_container.find_elements( By.CLASS_NAME, 'reaction-1hd86g') if not flag_global_clicked: self.driver.execute_script("arguments[0].click();", reactions[pos].find_element( By.CLASS_NAME, 'reactionInner-15NvIl')) flag_global_clicked = True print("clicked 2") except: logging.info("Failed to Click 2nd " + self.server_name_list, exc_info=True) def button_click(self, pos): global flag_global_clicked global flag_cd_grab try: logs = self.driver.find_element_by_id( self.curr_id) reactions_container = WebDriverWait(logs, 600, poll_frequency=0.05).until( EC.presence_of_element_located((By.CLASS_NAME, 'children-2goeSq'))) reactions = WebDriverWait(reactions_container, 600, poll_frequency=0.05).until( EC.presence_of_all_elements_located((By.CLASS_NAME, 'button-38aScr'))) if not flag_global_clicked: WebDriverWait(logs, 600, poll_frequency=0.05).until( EC.element_to_be_clickable((By.CLASS_NAME, 'button-38aScr'))) self.driver.execute_script( "arguments[0].click();", reactions[pos]) flag_global_clicked = True print("clicked 1") except: time.sleep(0.05) logging.info("Failed to Click 1st " + self.server_name_list, exc_info=True) try: logs = self.get_webelement_id() reactions_container = WebDriverWait(logs, 600, poll_frequency=0.05).until( EC.presence_of_element_located((By.CLASS_NAME, 'children-2goeSq'))) reactions = WebDriverWait(reactions_container, 600, poll_frequency=0.05).until( EC.presence_of_all_elements_located((By.CLASS_NAME, 'button-38aScr'))) if not flag_global_clicked: WebDriverWait(logs, 600, poll_frequency=0.05).until( EC.element_to_be_clickable((By.CLASS_NAME, 'button-38aScr'))) self.driver.execute_script( "arguments[0].click();", reactions[pos]) flag_global_clicked = True print("clicked 2") except: logging.info("Failed to Click 2nd " + self.server_name_list, exc_info=True) def action_href_img_button(self): global flag_global_clicked global flag_cd_grab error_count = 0 flag_finished = False while(error_count < 5 and not flag_finished): try: img_container = self.driver.find_element_by_id( self.curr_id).find_element_by_class_name('anchor-3Z-8Bb') href_link = img_container.get_attribute('href') error_count_image = 0 try: image = url_to_image(href_link) while image is None: error_count_image += 1 if error_count_image > 10: break logging.info("Cant get image:" + href_link + " " + str(error_count_image)) image = url_to_image(href_link) h, w, c = image.shape except: time.sleep(0.1) logging.warning("Cant get image:" + href_link) logging.warning("Unexpected error:", sys.exc_info()[0]) error_count += 1 continue max = 4 if w > 900 else 3 pos = max-1 try: while pos >= 0 and not self.flag_clicked: print_num = self.ocr.get_print_num(image, pos) if (math.log10(print_num))+1 > 0 and (int(print_num) > 100) and not flag_global_clicked: name_card = print_num read_series = print_num series = print_num try: self.button_click(pos) name_card = self.ocr.get_names_single( image, pos) print(str(name_card) + str(series) + self.server_name_list) logging.info("Got Name: " + str(name_card) + " Series: " + str( read_series) + " Server: " + self.server_name_list + " URL: " + href_link) self.clicked_card = name_card.split(' ', 1)[0] self.flag_clicked = True except: error_count += 1 logging.warning( "Cant Click Edition " + self.server_name_list, exc_info=True) else: name_card = self.ocr.get_names_single( image, pos) if (name_card in self.dict_good_stuff and not flag_global_clicked): series = self.dict_good_stuff.get( name_card, '-1') read_series = self.ocr.get_names_bottom( image, pos).split(' ', 1)[0] if series == '123456' or read_series == series: try: self.button_click(pos) print(name_card + " " + series + " " + self.server_name_list) logging.info("Got Name: " + name_card + " Series: " + read_series + " Server: " + self.server_name_list + " URL: " + href_link) self.clicked_card = name_card.split(' ', 1)[ 0] self.flag_clicked = True except: error_count += 1 logging.warning( "Cant Click Edition " + self.server_name_list, exc_info=True) elif (name_card in self.dict_good_stuff_addition and not flag_global_clicked): series = self.dict_good_stuff_addition.get( name_card, '-1') read_series = self.ocr.get_names_bottom( image, pos).split(' ', 1)[0] if series == '123456' or read_series == series: if name_card in char_numbers: edition = self.ocr.get_edition_number( image, pos) if edition == char_numbers[name_card]: try: self.button_click( pos) print( name_card + " " + series + " " + self.server_name_list) logging.info("Got Name: " + name_card + " Series: " + read_series + " Server: " + self.server_name_list + " URL: " + href_link) self.clicked_card = name_card.split(' ', 1)[ 0] self.flag_clicked = True except: error_count += 1 logging.warning( "Cant Click Edition " + self.server_name_list, exc_info=True) pos -= 1 flag_finished = True except: error_count += 1 logging.error("Exception occurred", exc_info=True) logging.warning( "Cant get card name:" + href_link + " pos:" + str(pos) + " w:" + str(w)) except: logging.warning("Error") logging.error("Exception occurred", exc_info=True) error_count += 1 def action_href_img_button_dropping(self): if self.counter > 100: if (time.time() - self.time) > self.sleep_timer_drop: logging.warning('Drop') textbox = self.driver.find_element_by_class_name( 'markup-2BOw-j.slateTextArea-1Mkdgw.fontSize16Padding-3Wk7zP') textbox.send_keys('kd') textbox.send_keys(Keys.ENTER) self.time = time.time() self.counter = 0 self.counter += 1 self.action_href_img_button() def start_up(self): self.login_sign() self.message_log(self.condition_BOT_droppping, self.action_href_img) def start_up_button(self): self.login_sign() self.message_log(self.condition_BOT_droppping, self.action_href_img_button) def start_up_button_dropping(self): self.login_sign() self.message_log(self.condition_BOT_droppping, self.action_href_img_button_dropping) def start_up_server(self): self.login_sign() self.message_log(self.condition_BOT_droppping_Server, self.action_href_img) def start_up_kd(self): self.login_sign() self.kd_every()

48.436906

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0.073134

0

null

0

1

0

null

0

5

edb5d3d8d8806423b588bab17da1e3091d722870

91

py

Python

autogluon/core/__init__.py

zhanghang1989/autogluon

8bfe6b0da8915020eeb9895fd18d7688c0d604c1

[ "Apache-2.0" ]

6

2020-06-16T19:17:36.000Z

2021-07-07T14:50:31.000Z

autogluon/core/__init__.py

zhanghang1989/autogluon

8bfe6b0da8915020eeb9895fd18d7688c0d604c1

[ "Apache-2.0" ]

null

autogluon/core/__init__.py

zhanghang1989/autogluon

8bfe6b0da8915020eeb9895fd18d7688c0d604c1

[ "Apache-2.0" ]

2

2020-12-13T16:40:04.000Z

2021-03-08T09:14:16.000Z

from .space import * from .task import * from .decorator import * from . import optimizer

15.166667

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5.583333

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null

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1

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5

edcf1173202e58c80335e9946bf92f418cbfb90c

177

py

Python

Scripts/using_name.py

StoneZhu2017/learning-python

f7aa7a0908cb4e156278494930e8be6a20aeba57

[ "bzip2-1.0.6" ]

null

Scripts/using_name.py

StoneZhu2017/learning-python

f7aa7a0908cb4e156278494930e8be6a20aeba57

[ "bzip2-1.0.6" ]

null

Scripts/using_name.py

StoneZhu2017/learning-python

f7aa7a0908cb4e156278494930e8be6a20aeba57

[ "bzip2-1.0.6" ]

null

#!/usr/bin/python3 #filename:using_name.py if __name__=='__main__': print('This program is being run by itself') else: print('I am being imported form another module')

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null

0

1

0

1

0

5

ede1ccae33fe50a0f877053257aac44a88ecfc97

159

py

Python

command_line_tools/__init__.py

NREL/rlmolecule

7e98dca49ea82bf9d14164955d82adfa8bbc2d64

[ "BSD-3-Clause" ]

16

2020-12-28T21:45:09.000Z

2022-03-19T12:03:58.000Z

command_line_tools/__init__.py

NREL/rlmolecule

7e98dca49ea82bf9d14164955d82adfa8bbc2d64

[ "BSD-3-Clause" ]

56

2020-12-30T16:12:33.000Z

2022-02-02T18:32:44.000Z

command_line_tools/__init__.py

NREL/rlmolecule

7e98dca49ea82bf9d14164955d82adfa8bbc2d64

[ "BSD-3-Clause" ]

7

2021-01-05T01:34:04.000Z

2021-09-29T13:42:44.000Z

from .command_line_config import parse_config_from_args, merge_configs from .run_tools import makedir_if_not_exists, setup_run, write_config_log, get_run_name

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null

0

1

0

1

0

1

0

5

610037c680f506aada84982212876a133919f396

106

py

Python

target_statistic_encoding/__init__.py

CircArgs/target_statistic_encoding

4ea812b15b595a9c4b53e9cddf41a9da1d48e3cb

[ "MIT" ]

1

2020-06-11T02:09:49.000Z

target_statistic_encoding/__init__.py

CircArgs/target_statistic_encoding

4ea812b15b595a9c4b53e9cddf41a9da1d48e3cb

[ "MIT" ]

null

target_statistic_encoding/__init__.py

CircArgs/target_statistic_encoding

4ea812b15b595a9c4b53e9cddf41a9da1d48e3cb

[ "MIT" ]

null

__package__ = "target_statistic_encoding" from .cat2num import Cat2Num from .stat_funcs import stat_funcs

26.5

41

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14

106

5.857143

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0

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106

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42

35.333333

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0

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0.666667

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1

0

1

0

null

0

1

0

1

0

5

610b3ed9c957a3fc962ef6888bf0559d39337b4b

68

py

Python

exceptions/CloudTrailBucketMissingLogsTableException.py

houey/SkyWrapper

26d4d74c9aa389b4a9d6681949bd48770f745953

[ "MIT" ]

106

2020-04-13T17:12:04.000Z

2022-02-16T13:39:46.000Z

exceptions/CloudTrailBucketMissingLogsTableException.py

houey/SkyWrapper

26d4d74c9aa389b4a9d6681949bd48770f745953

[ "MIT" ]

1

2021-05-14T22:49:30.000Z

exceptions/CloudTrailBucketMissingLogsTableException.py

houey/SkyWrapper

26d4d74c9aa389b4a9d6681949bd48770f745953

[ "MIT" ]

16

2020-04-15T15:58:20.000Z

2021-09-02T22:40:46.000Z

class CloudTrailBucketMissingLogsTableException(Exception): pass

34

59

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4

68

14.75

1

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68

2

60

34

0.951613

0

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true

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1

null

0

1

0

null

0

1

0

5

b626748e1ca5a1e9ce9ce72852c03f3f0aabefd8

323

py

Python

xga/samples/__init__.py

DavidT3/XGA

cde51c3f29f98b5f1e981fb6d327c04072b0ba38

[ "BSD-3-Clause" ]

12

2020-05-16T09:45:45.000Z

2022-02-14T14:41:46.000Z

xga/samples/__init__.py

DavidT3/XGA

cde51c3f29f98b5f1e981fb6d327c04072b0ba38

[ "BSD-3-Clause" ]

684

2020-05-28T08:52:09.000Z

2022-03-31T10:56:24.000Z

xga/samples/__init__.py

DavidT3/XGA

cde51c3f29f98b5f1e981fb6d327c04072b0ba38

[ "BSD-3-Clause" ]

2

2022-02-04T10:55:55.000Z

2022-02-04T11:30:56.000Z

# This code is a part of XMM: Generate and Analyse (XGA), a module designed for the XMM Cluster Survey (XCS). # Last modified by David J Turner (david.turner@sussex.ac.uk) 06/01/2021, 16:36. Copyright (c) David J Turner from .base import BaseSample from .extended import ClusterSample from .general import PointSample

35.888889

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0

1

0

1

0

5

b679abe384179501966dba4869a7f664f87c00b2

130

py

Python

avaandmed/utils/__init__.py

mihhail-m/avaandmed-py

c64b07db989b9aff4cfa7f4e18efc0c47ae5e219

[ "MIT" ]

null

avaandmed/utils/__init__.py

mihhail-m/avaandmed-py

c64b07db989b9aff4cfa7f4e18efc0c47ae5e219

[ "MIT" ]

5

2022-03-17T15:00:23.000Z

2022-03-26T08:33:19.000Z

avaandmed/utils/__init__.py

mihhail-m/avaandmed-py

c64b07db989b9aff4cfa7f4e18efc0c47ae5e219

[ "MIT" ]

null

from typing import List def build_endpoint(base_url: str, resources: List[str]): return f"{base_url}/{'/'.join(resources)}"

21.666667

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0.707692

19

130

4.684211

0.736842

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null

0

1

0

null

0

1

0

1

0

5

b697dfb37a0e69856dba889d05820e2f24985ad1

228

py

Python

mdtools/__init__.py

JGaed/openmm_constV

6564dacbb45870a4fd130fe778468d1fff871a61

[ "Unlicense" ]

null

mdtools/__init__.py

JGaed/openmm_constV

6564dacbb45870a4fd130fe778468d1fff871a61

[ "Unlicense" ]

null

mdtools/__init__.py

JGaed/openmm_constV

6564dacbb45870a4fd130fe778468d1fff871a61

[ "Unlicense" ]

null

#!/usr/local/bin/env python """ Various Python utilities for OpenMM. """ # Define global version. from mdtools import version __version__ = version.version # Import modules. from mdtools import vvintegrator, velres, ljtools

16.285714

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0.763158

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228

6.071429

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0.2

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0.144737

228

13

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17.538462

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0

1

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false

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1

0

null

1

0

null

0

1

0

1

0

5

fcf84ad2337911530a00875bd5988108936a64a5

159

py

Python

qhana/thirdparty/maxcut/_solvers/__init__.py

UST-QuAntiL/qhana

bf499d072dcc37f81efec1b8e17b7d5460db7a04

[ "Apache-2.0" ]

1

2021-03-12T14:06:43.000Z

qhana/thirdparty/maxcut/_solvers/__init__.py

UST-QuAntiL/qhana

bf499d072dcc37f81efec1b8e17b7d5460db7a04

[ "Apache-2.0" ]

null

qhana/thirdparty/maxcut/_solvers/__init__.py

UST-QuAntiL/qhana

bf499d072dcc37f81efec1b8e17b7d5460db7a04

[ "Apache-2.0" ]

null

# coding: utf-8 """Max-cut problem solvers following a variety of approaches.""" from . import backend from ._bm import MaxCutBM from ._sdp import MaxCutSDP

19.875

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0.754717

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159

5.130435

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0

0.007463

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159

7

65

22.714286

0.873134

0.45912

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

1e4110bc6949cb0ab525311bb1a50d06196366b6

226

py

Python

tests/test_filesystem_misc.py

rave-engine/rave

0eeb956363f4d7eda92350775d7d386550361273

[ "BSD-2-Clause" ]

5

2015-03-18T01:19:56.000Z

2020-10-23T12:44:47.000Z

tests/test_filesystem_misc.py

rave-engine/rave

0eeb956363f4d7eda92350775d7d386550361273

[ "BSD-2-Clause" ]

null

tests/test_filesystem_misc.py

rave-engine/rave

0eeb956363f4d7eda92350775d7d386550361273

[ "BSD-2-Clause" ]

null

from rave import filesystem from .support.filesystem import * def test_filesystem_native_error(): native_err = RuntimeError('test') err = filesystem.NativeError('Error', native_err) assert err.native_error == native_err

22.6

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226

5.896552

0.448276

0.192982

0.245614

0.233918

0

0.119469

226

9

51

25.111111

0.859296

0

0.039823

0

0.166667

1

0.166667

false

0

0.333333

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0.5

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null

0

1

0

1

0

null

0

1

0

5

1eb7ca63675a4843a71d6b54c40a90c37e1652ed

60

py

Python

hero_db_utils/clients/__init__.py

AIO2020/hero-db-utils

037ace24a0934ca4df5354b6e90972a8b089e861

[ "Apache-2.0" ]

null

hero_db_utils/clients/__init__.py

AIO2020/hero-db-utils

037ace24a0934ca4df5354b6e90972a8b089e861

[ "Apache-2.0" ]

null

hero_db_utils/clients/__init__.py

AIO2020/hero-db-utils

037ace24a0934ca4df5354b6e90972a8b089e861

[ "Apache-2.0" ]

null

from .postgres import SQLBaseClient, PostgresDatabaseClient

30

59

0.883333

5

60

10.6

1

0

0.083333

60

1

60

0.963636

0

1

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true

0

1

0

1

0

1

0

1

null

0

1

0

null

0

1

0

1

0

5

1ebf538e1715f589ee6b617173750400b91b3c5e

1,823

py

Python

test/test_conjugate_gradient.py

JohnReid/nolina

23894517ac60d27d167447871ef85a4a78cad630

[ "MIT" ]

null

test/test_conjugate_gradient.py

JohnReid/nolina

23894517ac60d27d167447871ef85a4a78cad630

[ "MIT" ]

null

test/test_conjugate_gradient.py

JohnReid/nolina

23894517ac60d27d167447871ef85a4a78cad630

[ "MIT" ]

null

from codetiming import Timer import logging import pytest import numpy as np from nolina import random, conjugate_gradient as cg _logger = logging.getLogger(__name__) ds = [9, 21, 55] @pytest.mark.parametrize("d", ds) def test_steepest_descent(d, rng, seed): minimiser = cg.SteepestDescent(A=random.random_spsd_matrix(d=d, random_state=rng), b=random.random_vector(d=d, random_state=rng)) with Timer(text='Steepest descent minimiser done in {:.4f} seconds', logger=_logger.info): x_star = minimiser() _logger.info('Steepest descent minimiser took %d iterations.', minimiser.niter) np.testing.assert_allclose(minimiser.A @ x_star, minimiser.b) @pytest.mark.parametrize("d", ds) def test_conjugate_gradient_preliminary(d, rng, seed): minimiser = cg.ConjugateGradientPreliminary(A=random.random_spsd_matrix(d=d, random_state=rng), b=random.random_vector(d=d, random_state=rng)) with Timer(text='Conjugate gradient preliminary minimiser done in {:.4f} seconds', logger=_logger.info): x_star = minimiser() _logger.info('Conjugate gradient preliminary minimiser took %d iterations.', minimiser.niter) np.testing.assert_allclose(minimiser.A @ x_star, minimiser.b) @pytest.mark.parametrize("d", ds) def test_conjugate_gradient(d, rng, seed): minimiser = cg.ConjugateGradient(A=random.random_spsd_matrix(d=d, random_state=rng), b=random.random_vector(d=d, random_state=rng)) with Timer(text='Conjugate gradient minimiser done in {:.4f} seconds', logger=_logger.info): x_star = minimiser() _logger.info('Conjugate gradient minimiser took %d iterations.', minimiser.niter) np.testing.assert_allclose(minimiser.A @ x_star, minimiser.b)

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1,823

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0.258065

0

null

0

1

0

null

0

5

1edb65ff392560a68a6a63b67e394c7758d94c04

150

py

Python

ai_coin_identifier/urls.py

JohnGWebDev/coinloggr

36a6065b1a8f8582cc5b24917a2f89bca2dcc14b

[ "BSD-3-Clause" ]

null

ai_coin_identifier/urls.py

JohnGWebDev/coinloggr

36a6065b1a8f8582cc5b24917a2f89bca2dcc14b

[ "BSD-3-Clause" ]

1

2022-01-10T16:50:44.000Z

ai_coin_identifier/urls.py

JohnGWebDev/coinloggr

36a6065b1a8f8582cc5b24917a2f89bca2dcc14b

[ "BSD-3-Clause" ]

null

from django.urls import path from . import views urlpatterns = [ path('', views.ai_coin_identifier_home_page, name='ai-coin-identifier-home'), ]

21.428571

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