xszheng2020/the_stack_dedup_python_hits_5 · Datasets at Hugging Face

bbee29ba0d95c4dca840b621137c9ff855af01a6

790

py

Python

test_python_toolbox/test_cute_iter_tools/test_pushback_iterator.py

hboshnak/python_toolbox

cb9ef64b48f1d03275484d707dc5079b6701ad0c

[ "MIT" ]

119

2015-02-05T17:59:47.000Z

2022-02-21T22:43:40.000Z

test_python_toolbox/test_cute_iter_tools/test_pushback_iterator.py

hboshnak/python_toolbox

cb9ef64b48f1d03275484d707dc5079b6701ad0c

[ "MIT" ]

4

2019-04-24T14:01:14.000Z

2020-05-21T12:03:29.000Z

test_python_toolbox/test_cute_iter_tools/test_pushback_iterator.py

hboshnak/python_toolbox

cb9ef64b48f1d03275484d707dc5079b6701ad0c

[ "MIT" ]

14

2015-03-30T06:30:42.000Z

2021-12-24T23:45:11.000Z

# Copyright 2009-2017 Ram Rachum. # This program is distributed under the MIT license. from python_toolbox import cute_testing from python_toolbox.cute_iter_tools import PushbackIterator def test_pushback_iterator(): pushback_iterator = PushbackIterator(iter([1, 2, 3])) assert next(pushback_iterator) == 1 assert next(pushback_iterator) == 2 pushback_iterator.push_back() assert next(pushback_iterator) == 2 assert next(pushback_iterator) == 3 pushback_iterator.push_back() assert next(pushback_iterator) == 3 with cute_testing.RaiseAssertor(StopIteration): next(pushback_iterator) pushback_iterator.push_back() assert next(pushback_iterator) == 3 with cute_testing.RaiseAssertor(StopIteration): next(pushback_iterator)

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5

a53d33fac1b5e0304d1f558a3b2f56778388ee19

78

py

Python

knowledge_graph/crawler/runner/jd/__init__.py

Syhen/knowledge-graph

35b35624f78ec58b3ca9f1e6eaf4a5e5ff80edc2

[ "MIT" ]

2

2019-07-01T02:18:33.000Z

2020-01-14T11:20:44.000Z

knowledge_graph/crawler/runner/jd/__init__.py

Syhen/knowledge-graph

35b35624f78ec58b3ca9f1e6eaf4a5e5ff80edc2

[ "MIT" ]

null

knowledge_graph/crawler/runner/jd/__init__.py

Syhen/knowledge-graph

35b35624f78ec58b3ca9f1e6eaf4a5e5ff80edc2

[ "MIT" ]

2

2020-03-19T10:22:34.000Z

2022-03-06T01:41:56.000Z

# -*- coding: utf-8 -*- """ Author: @heyao Created On: 2019/6/26 上午10:07 """

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null

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5

a56573d1cd986e2ca56dcd4354455eb4ec0362fb

129

py

Python

musicsort/web/admin.py

markoshorro/MusicSort

fc85ceeb8c06a3c7486645b4f4b924146de45734

[ "Apache-2.0" ]

null

musicsort/web/admin.py

markoshorro/MusicSort

fc85ceeb8c06a3c7486645b4f4b924146de45734

[ "Apache-2.0" ]

null

musicsort/web/admin.py

markoshorro/MusicSort

fc85ceeb8c06a3c7486645b4f4b924146de45734

[ "Apache-2.0" ]

null

from django.contrib import admin from web.models import UploadFile # Register your models here. admin.site.register(UploadFile)

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1

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5

a56a3991bd863b9a821850bf51c3bd8eb964d675

167

py

Python

boa3_test/test_sc/built_in_methods_test/StrSplit.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/built_in_methods_test/StrSplit.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/built_in_methods_test/StrSplit.py

hal0x2328/neo3-boa

6825a3533384cb01660773050719402a9703065b

[ "Apache-2.0" ]

15

2020-05-21T21:54:24.000Z

2021-11-18T06:17:24.000Z

from typing import List from boa3.builtin import public @public def main(string: str, sep: str, maxsplit: int) -> List[str]: return string.split(sep, maxsplit)

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null

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1

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5

a5753ba66364bfc00da52f102d50a9b75efcb714

72

py

Python

lib/scripts/convertPrintable.py

gideontong/Bot4Christ

4a503df857397cddebdc9e098c8ba7fd4ef3f17c

[ "MIT" ]

1

2020-09-10T09:27:32.000Z

lib/scripts/convertPrintable.py

gideontong/Bot4Christ

4a503df857397cddebdc9e098c8ba7fd4ef3f17c

[ "MIT" ]

11

2020-07-31T04:59:09.000Z

2021-02-23T18:21:30.000Z

lib/scripts/convertPrintable.py

gideontong/Bot4Christ

4a503df857397cddebdc9e098c8ba7fd4ef3f17c

[ "MIT" ]

1

2021-03-07T20:07:41.000Z

import json print(json.loads(open('CUV.json', encoding='utf-8').read()))

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null

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1

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1

0

1

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5

3c36737ac656039ff1fd242e645063e199a24177

180

py

Python

client/cart/errors.py

daniel-waruo/e-commerse-api

6b080039398fb4099a34335317d649dd67783f63

[ "Apache-2.0" ]

6

2019-11-21T10:09:49.000Z

2021-06-19T09:52:59.000Z

client/cart/errors.py

daniel-waruo/e-commerse-api

6b080039398fb4099a34335317d649dd67783f63

[ "Apache-2.0" ]

null

client/cart/errors.py

daniel-waruo/e-commerse-api

6b080039398fb4099a34335317d649dd67783f63

[ "Apache-2.0" ]

null

class NoUserIdOrSessionKeyError(Exception): pass class NoProductToDelete(Exception): pass class NoCart(Exception): pass class BadConfigError(Exception): pass

12

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16

180

8.375

0.4375

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0.402985

0

0.188889

180

14

44

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0

0.5

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true

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1

0

null

1

0

null

0

1

0

5

3c7f4418c06b101fe9a141bc5d7934b71d420b48

161

py

Python

scipy/io/arff/utils.py

lesserwhirls/scipy-cwt

ee673656d879d9356892621e23ed0ced3d358621

[ "BSD-3-Clause" ]

8

2015-10-07T00:37:32.000Z

2022-01-21T17:02:33.000Z

scipy/io/arff/utils.py

lesserwhirls/scipy-cwt

ee673656d879d9356892621e23ed0ced3d358621

[ "BSD-3-Clause" ]

null

scipy/io/arff/utils.py

lesserwhirls/scipy-cwt

ee673656d879d9356892621e23ed0ced3d358621

[ "BSD-3-Clause" ]

8

2015-05-09T14:23:57.000Z

2018-11-15T05:56:00.000Z

#! /usr/bin/env python # Last Change: Mon Aug 20 02:00 PM 2007 J try: from functools import partial except ImportError: from myfunctools import partial

20.125

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true

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null

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1

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1

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5

3c9fe7670ba972fdb98b8f44d78954a29771ab45

146

py

Python

learn_app/endpoints/index.py

alex-d-bondarev/learn-flask

dfa47821a3cf606f0535bbe79c373610afe1b957

[ "MIT" ]

null

learn_app/endpoints/index.py

alex-d-bondarev/learn-flask

dfa47821a3cf606f0535bbe79c373610afe1b957

[ "MIT" ]

null

learn_app/endpoints/index.py

alex-d-bondarev/learn-flask

dfa47821a3cf606f0535bbe79c373610afe1b957

[ "MIT" ]

null

""" Index Page """ from learn_app.main import app @app.route("/") def index(): """Default url :return: """ return "Index Page"

10.428571

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0.561644

18

146

4.5

0.666667

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0

0.253425

146

13

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11.230769

0.743119

0.212329

0

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true

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null

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null

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1

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1

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5

b1c9c012f6a0fafcca2c9cee054e1bf1c9410db4

77

py

Python

cherrypy/__main__.py

abancu/core

e110a1df32ec8bf67f007960e61df55f0a926219

[ "MIT" ]

674

2015-11-06T04:22:47.000Z

2022-02-26T17:31:43.000Z

cherrypy/__main__.py

abancu/core

e110a1df32ec8bf67f007960e61df55f0a926219

[ "MIT" ]

713

2015-11-06T10:48:58.000Z

2018-11-27T16:32:18.000Z

cherrypy/__main__.py

abancu/core

e110a1df32ec8bf67f007960e61df55f0a926219

[ "MIT" ]

115

2015-01-08T14:41:00.000Z

2022-02-13T12:31:17.000Z

import cherrypy.daemon if __name__ == '__main__': cherrypy.daemon.run()

15.4

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4

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true

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null

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1

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5

b1dbbd139bd089bb7853d3ab04dd3994a00683b3

50,191

py

Python

ibis/driver/tests/test_driver.py

shivaathreya/ibis

f99e3b7a677652a8a1c00a069e645d97682e839c

[ "Apache-2.0" ]

50

2018-09-27T13:03:45.000Z

2021-04-06T15:36:59.000Z

ibis/driver/tests/test_driver.py

shivaathreya/ibis

f99e3b7a677652a8a1c00a069e645d97682e839c

[ "Apache-2.0" ]

null

ibis/driver/tests/test_driver.py

shivaathreya/ibis

f99e3b7a677652a8a1c00a069e645d97682e839c

[ "Apache-2.0" ]

14

2018-10-03T20:36:15.000Z

2021-05-18T07:08:57.000Z

"""Driver tests.""" import copy import difflib import os import sys import time import unittest from mock import patch, Mock, MagicMock from ibis.driver.driver import Driver from ibis.inventor.tests.fixture_workflow_generator import * from ibis.inventory.cb_inventory import CheckBalancesInventory from ibis.inventory.automation_ids_inventory import AUTOInventory from ibis.inventory.export_it_inventory import ExportITInventory from ibis.inventory.inventory import Inventory from ibis.inventory.it_inventory import ITInventory from ibis.inventory.perf_inventory import PerfInventory from ibis.inventory.request_inventory import Request, RequestInventory from ibis.model.exporttable import ItTableExport from ibis.model.table import ItTable from ibis.settings import UNIT_TEST_ENV from ibis.utilities.config_manager import ConfigManager from ibis.utilities.file_parser import parse_file_by_sections from ibis.utilities.it_table_generation import Get_Auto_Split from ibis.utilities.utilities import Utilities from ibis.utilities.vizoozie import VizOozie BASE_DIR = os.path.dirname(os.path.abspath(__file__)) class DriverFunctionsTest(unittest.TestCase): """Tests the functionality of the Driver class.""" @patch('ibis.driver.driver.Utilities', autospec=True) @patch.object(Inventory, '_connect', autospec=True) def setUp(self, mock_connect, m_U): """Setup.""" mock_util_methods = MagicMock() mock_util_methods.run_subprocess = MagicMock() mock_util_methods.run_subprocess.return_value = 0 m_U.return_value = mock_util_methods self.cfg_mgr = ConfigManager(UNIT_TEST_ENV) self.driver = Driver(self.cfg_mgr) self.start_time = time.time() def tearDown(self): """Tear down.""" self.driver = None t2 = time.time() - self.start_time # print "%s: %.3f" % (self.id(), t2) def files_equal(self, test_file, expected_file): """Compares two files""" same = True test_fh = open(test_file, 'r') fo_gen = open(expected_file, 'r') test_str = test_fh.read() expected_str = fo_gen.read() if not self.strings_equal(test_str, expected_str): print "Generated test file:{0}".format(test_file) print "Fix the file:{0}".format(expected_file) same = False test_fh.close() fo_gen.close() return same def strings_equal(self, test_str, expected_str): """compare strings""" same = True test_str = [xml.strip().replace('\t', '') for xml in test_str.splitlines()] expected_str = [xml.strip().replace('\t', '') for xml in expected_str.splitlines()] if "".join(expected_str) != "".join(test_str): same = False print '\n' print '=' * 100 print "\nFiles don't match..." diff = difflib.unified_diff(expected_str, test_str) print '\n'.join(list(diff)) return same def test_submit_it_file_empty(self): """Test submit it file with an empty file.""" file_h = open(os.path.join(BASE_DIR, 'test_resources/empty_file.txt'), 'r') result = self.driver.submit_it_file(file_h) self.assertEquals(result, '') def test_submit_it_file_export_empty(self): """Test submit it file with an empty file.""" file_h = open(os.path.join(BASE_DIR, 'test_resources/empty_file.txt'), 'r') result = self.driver.submit_it_file_export(file_h) self.assertEquals(result, '') @patch.object(Get_Auto_Split, 'get_split_by_column', return_value='') def test_submit_it_file_insert(self, m1): """Test submit it file with a valid it table file.""" self.driver.req_inventory = MagicMock(spec=RequestInventory) self.driver.req_inventory.parse_file.return_value = \ ([Request(mock_table_mapping_val, self.cfg_mgr)], 'Parse File Success') self.driver.it_inventory.get_table_mapping = \ MagicMock(spec=ITInventory.get_table_mapping) self.driver.it_inventory.get_table_mapping.return_value = {} self.driver.it_inventory.insert = MagicMock(spec=ITInventory.insert) self.driver.it_inventory.insert.return_value = (True, 'Insert Success') result = self.driver.submit_it_file('test') self.assertEquals(result, 'Parse File Success\nInsert Success') def test_submit_it_file_export_insert(self): """Test submit it file with a valid it table file.""" self.driver.req_inventory = MagicMock(spec=RequestInventory) self.driver.req_inventory.parse_file_export.return_value = \ ([Request(mock_table_mapping_val_export, self.cfg_mgr)], 'Parse File Success') self.driver.export_it_inventory.get_table_mapping = \ MagicMock(spec=ExportITInventory.get_table_mapping) self.driver.export_it_inventory.get_table_mapping.return_value = {} self.driver.export_it_inventory.insert_export = \ MagicMock(spec=ExportITInventory.insert_export) self.driver.export_it_inventory.insert_export.return_value = \ (True, 'Insert Success') result = self.driver.submit_it_file_export('test') self.assertEquals(result, 'Parse File Success\nInsert Success') @patch.object(Get_Auto_Split, 'get_split_by_column', return_value='') def test_submit_it_file_update(self, m1): """Test submit it file with an updated it table file.""" self.driver.req_inventory = MagicMock(spec=RequestInventory) self.driver.req_inventory.parse_file.return_value = \ ([Request(mock_table_mapping_val, self.cfg_mgr)], 'Parse File Success') self.driver.it_inventory.insert = MagicMock(spec=ITInventory.insert) self.driver.it_inventory.update = MagicMock(spec=ITInventory.update) self.driver.it_inventory.get_table_mapping = MagicMock( spec=ITInventory.get_table_mapping) self.driver.it_inventory.update.return_value = (True, 'Update Success') updated_table = copy.deepcopy(mock_table_mapping_val) updated_table['db_username'] = 'updated_user' self.driver.it_inventory.get_table_mapping.return_value = updated_table result = self.driver.submit_it_file('test') self.assertEquals(result, 'Parse File Success\nUpdate Success') def test_submit_it_file_export_update(self): """Test submit it file with an updated it table file.""" self.driver.req_inventory = MagicMock(spec=RequestInventory) self.driver.req_inventory.parse_file_export.return_value = \ ([Request(mock_table_mapping_val_export, self.cfg_mgr)], 'Parse File Success\nUpdate Success') self.driver.export_it_inventory.insert_export = \ MagicMock(spec=ExportITInventory.insert_export) self.driver.export_it_inventory.update_export = \ MagicMock(spec=ExportITInventory.update_export) self.driver.export_it_inventory.get_table_mapping = \ MagicMock(spec=ExportITInventory.get_table_mapping) self.driver.export_it_inventory.update_export.return_value = \ (True, 'Update Success') updated_table = copy.deepcopy(mock_table_mapping_val_export) updated_table['db_username'] = 'updated_user' self.driver.export_it_inventory.get_table_mapping.return_value = \ updated_table result = self.driver.submit_it_file_export('test') self.assertEquals(result, 'Parse File Success\nUpdate Success') @patch('ibis.utilities.run_parallel.DryRunWorkflowManager.run_all', autospec=True) @patch('ibis.utilities.run_parallel.SqoopCacheManager.cache_ddl_views', autospec=True) @patch('ibis.utilities.run_parallel.SqoopCacheManager.cache_ddl_queries', autospec=True) @patch('ibis.driver.driver.Driver.update_it_table', autospec=True) @patch.object(sys, 'exit', autospec=True) @patch.object(Inventory, '_connect', autospec=True) @patch.object(Inventory, 'get_table_mapping', return_value=mock_table_mapping_val) @patch('ibis.inventor.action_builder.SqoopHelper.eval', autospec=True) @patch('ibis.driver.driver.RequestInventory.get_available_requests', autospec=True) def test_submit_request_valid(self, mock_get_available_requests, m_eval, m_get_t_m, mock_connect, mock_sys, m_s_it_file, m_sqoop_cache, m_sqoop_cache_view, m_dryrun): """Test submit request with a valid request file.""" m_eval.return_value = [['Col1', 'TIMESTAMP'], ['Col2', 'TIMESTAMP'], ['Col3', 'varchar']] mock_get_available_requests.return_value = \ ([ItTable(mock_table_mapping_val, self.cfg_mgr)], [], []) # self.driver.it_inventory = MagicMock(spec=ITInventory) self.driver.vizoozie = MagicMock(spec=VizOozie) self.driver.req_inventory.it_inventory = MagicMock(spec=ITInventory) file_h = open( os.path.join(BASE_DIR, 'test_resources/request_test_valid.txt'), 'r') result = self.driver.submit_request(file_h, True) self.assertIsNotNone(result) file_h = open( os.path.join(BASE_DIR, 'test_resources/request_test_valid.txt'), 'r') result = self.driver.submit_request(file_h, False) self.assertIsNotNone(result) def test_submit_request_invalid(self): """Test submit request with an invalid request file.""" file_h = open( os.path.join(BASE_DIR, 'test_resources/request_test_invalid.txt'), 'r') self.assertRaises(ValueError, self.driver.submit_request, file_h, True) @patch('ibis.driver.driver.Driver.update_it_table', autospec=True) def test_submit_request_empty(self, m_s_it_file): """Test submit request with an empty file.""" file_h = open(os.path.join(BASE_DIR, 'test_resources/empty_file.txt'), 'r') status, result = self.driver.submit_request(file_h, True) self.assertIn('Workflow not generated for request.', result) self.assertFalse(status) @patch.object(VizOozie, 'visualizeXML', autospec=True) @patch.object(VizOozie, 'convertDotToPDF', autospec=True) @patch('ibis.inventor.action_builder.SqoopHelper.eval', autospec=True) @patch('ibis.utilities.run_parallel.DryRunWorkflowManager.run_all', autospec=True) @patch('ibis.utilities.run_parallel.SqoopCacheManager.cache_ddl_views', autospec=True) @patch('ibis.utilities.run_parallel.SqoopCacheManager.cache_ddl_queries', autospec=True) @patch('ibis.driver.driver.Driver.update_it_table', autospec=True) @patch.object(Inventory, '_connect', autospec=True) @patch('ibis.driver.driver.RequestInventory.get_available_requests', autospec=True) def test_submit_request_unavailable(self, m_get_ar, m_con, m_s_it_file, m_sqoop_cache, m_sqoop_cache_view, m_dryrun, m_eval, m_convert_pdf, m_vi_xml): """Test submit request.""" m_eval.return_value = [['Col1', 'TIMESTAMP'], ['Col2', 'TIMESTAMP'], ['Col3', 'varchar']] m_get_ar.side_effect = [ ([ItTable(mock_table_mapping_val, self.cfg_mgr)], [ItTable(mock_table_mapping_val, self.cfg_mgr)], []), ([], [ItTable(mock_table_mapping_val, self.cfg_mgr)], [])] _path = os.path.join(BASE_DIR, 'test_resources/request_test_valid.txt') file_h = open(_path, 'r') _, result = self.driver.submit_request(file_h, True) self.assertIn('generated successfully', result) _path = os.path.join(BASE_DIR, 'test_resources/request_test_valid.txt') file_h = open(_path, 'r') _, result = self.driver.submit_request(file_h, True) self.assertIn('Workflow not generated for request.', result) def test_run_oozie_job(self): """Test run oozie.""" self.driver.utilities = Mock(spec=Utilities) self.driver.utilities.run_workflow.return_value = True self.assertTrue(self.driver.run_oozie_job("test_xml")) def test_save_it_table(self): """Test save it table.""" self.driver.it_inventory = Mock(spec=ITInventory) self.driver.it_inventory.save_all_tables.return_value = True self.assertTrue(self.driver.save_it_table(None)) def test_update_lifespan(self): """Test update lifespan using a mocked table from check and balances table.""" self.driver.cb_inventory = Mock(spec=CheckBalancesInventory) self.driver.cb_inventory.get.return_value = [ ['directory', 'pull_time', 'avro_size', 'ingest_timestamp', 'parquet_time', 'parquet_size', 'rows', 'lifespan', 'ack', 'cleaned', 'current_repull', 'domain', 'table']] self.assertIn('updated with new lifespan in checks_balances', self.driver.update_lifespan('db_name', 'tbl_name', 'lifespan')) def test_update_lifespan_no_tbl(self): """Test update lifespan using no table.""" self.driver.cb_inventory = Mock(spec=CheckBalancesInventory) self.driver.cb_inventory.get.return_value = None self.assertIn('ecord doesn\'t exist for table=', self.driver.update_lifespan('db_name', 'tbl_name', 'lifespan')) def test_update_all_lifespan(self): """Test update all lifespan.""" self.driver.it_inventory = Mock(spec=ITInventory) self.driver.cb_inventory = Mock(spec=CheckBalancesInventory) self.driver.it_inventory.get_all_tables.return_value = [ {'full_table_name': 'member.fake_database_fake_prog_tablename', 'domain': 'member', 'target_dir': 'mdm/member/fake_database/fake_prog_tablename', 'split_by': '', 'mappers': 10, 'db_username': 'fake_username', 'jdbcurl': 'jdbc:oracle:thin:@//fake.oracle:' '1521/fake_servicename', 'connection_factories': 'com.quest.oraoop.OraOopManagerFactory', 'password_file': 'jceks://hdfs/user/dev/fake.passwords.' 'jceks#fake.password.alias', 'load': '000100', 'fetch_size': 20000, 'hold': 0, 'source_database_name': 'fake_database', 'source_table_name': 'fake_prog_tablename', 'automation_appl_id': 'TEST01', 'views': 'fake_view_im'}, {'full_table_name': 'fake_domainfake_database_fake_job_tablename', 'domain': 'fake_domain', 'target_dir': 'mdm/fake_domain/fake_database/fake_job_tablename', 'split_by': '', 'mappers': 2, 'jdbcurl': 'jdbc:oracle:thin:@//fake.oracle:' '1521/fake_servicename', 'connection_factories': 'com.quest.oraoop.OraOopManagerFactory', 'password_file': 'jceks://hdfs/user/dev/fake.passwords.jceks' '#fake.password.alias', 'db_username': 'fake_username', 'load': '000100', 'fetch_size': 20000, 'hold': 0, 'source_database_name': 'fake_database', 'source_table_name': 'fake_job_tablename', 'automation_appl_id': 'TEST01', 'views': 'fake_view_im'}] self.driver.cb_inventory.get.return_value = [ ['directory', 'pull_time', 'avro_size', 'ingest_timestamp', 'parquet_time', 'parquet_size', 'rows', 'lifespan', 'ack', 'cleaned', 'current_repull', 'domain', 'table']] self.assertIsNotNone(self.driver.update_all_lifespan()) def test_update_all_lifespan_load_invalid(self): """ Tests update all lifespan using a mocked table and invalid load value """ self.driver.it_inventory = Mock(spec=ITInventory) self.driver.it_inventory.get_all_tables.return_value = [ {'full_table_name': 'member.fake_database_fake_prog_tablename', 'domain': 'member', 'target_dir': 'mdm/member/fake_database/fake_prog_tablename', 'split_by': '', 'jdbcurl': 'jdbc:oracle:thin:@//fake.oracle:' '1521/fake_servicename', 'connection_factories': 'com.quest.oraoop.OraOopManagerFactory', 'password_file': 'jceks://hdfs/user/dev/fake.passwords.jceks' '#fake.password.alias', 'load': '200100', 'db_username': 'fake_username', 'mappers': 10, 'fetch_size': 20000, 'hold': 0, 'source_database_name': 'fake_database', 'automation_appl_id': 'TEST01', 'source_table_name': 'fake_prog_tablename', 'views': 'fake_view_im'}] self.assertEquals(self.driver.update_all_lifespan(), '') def test_update_all_lifespan_no_tables(self): """ Tests update all lifespan using no tables """ self.driver.it_inventory = Mock(spec=ITInventory) self.driver.it_inventory.get_all_tables.return_value = [] self.assertEquals(self.driver.update_all_lifespan(), '') @patch.object(VizOozie, 'visualizeXML', autospec=True) @patch.object(VizOozie, 'convertDotToPDF', autospec=True) @patch('ibis.inventor.action_builder.SqoopHelper.eval', autospec=True) @patch('ibis.inventory.inventory.Inventory._connect', autospec=True) def test_gen_schedule_request(self, m_connect, m_eval, m_convert_pdf, m_vi_xml): """test gen wf for prod workflows""" m_eval.return_value = [['Col1', 'TIMESTAMP'], ['Col2', 'TIMESTAMP'], ['Col3', 'varchar']] tables = [ItTable(heavy_3_prop, self.cfg_mgr)] gen_files = self.driver.gen_schedule_request(tables, 'test_wf', 'test_appl') self.assertEquals(len(gen_files), 5) self.assertIn('test_wf.xml', gen_files) self.assertIn('test_wf.ksh', gen_files) self.assertIn('test_wf_job.properties', gen_files) @patch('ibis.utilities.run_parallel.DryRunWorkflowManager.run_all', autospec=True) @patch('ibis.utilities.run_parallel.SqoopCacheManager.cache_ddl_views', autospec=True) @patch('ibis.utilities.run_parallel.SqoopCacheManager.cache_ddl_queries', autospec=True) @patch('ibis.utilities.utilities.Utilities.put_dry_workflow', autospec=True) @patch('ibis.utilities.utilities.Utilities.dryrun_workflow', autospec=True) @patch.object(ITInventory, 'get_all_tables_for_automation', autospec=True) @patch.object(AUTOInventory, 'get_tables_by_id', autospec=True) @patch('ibis.inventory.inventory.Inventory._connect', autospec=True) @patch('ibis.inventor.action_builder.SqoopHelper.eval', autospec=True) @patch.object(VizOozie, 'visualizeXML', autospec=True) @patch.object(VizOozie, 'convertDotToPDF', autospec=True) @patch.object(PerfInventory, 'insert_freq_ingest', autospec=True) def test_gen_prod_workflow(self, m_freq_ingest, m_convert_pdf, m_v_xml, m_eval, m_c, m_get_id, m_get_t_automation, m_dryrun, m_put_w, m_sqoop_cache, m_sqoop_cache_view, m_dryrun_all): """ Tests generate_prod_workflows with 3 tables. One light, one medium and oen heavy.""" m_eval.return_value = [['Col1', 'varchar'], ['Col2', 'varchar']] m_get_id.side_effect = [appl_ref_id_tbl_01, appl_ref_id_tbl_02] _mock_automation_tables_02 = [ItTable(tbl, self.cfg_mgr) for tbl in mock_automation_tables_02] m_get_t_automation.return_value = _mock_automation_tables_02 self.cfg_mgr.env = 'perf' status, msg, git_files = self.driver.gen_prod_workflow('FAKED001') for file_name in git_files: git_file = 'full_fake_open_fake_prog_tablename.hql' if git_file in file_name: actual_hql_nm = os.path.join(self.cfg_mgr.files, file_name) with open(actual_hql_nm, 'r') as file_h: actual_hql = file_h.read() with open(BASE_DIR + '/test_resources/git_team_hql.hql', 'r') as file_h: expected_hql = file_h.read() self.assertTrue(expected_hql, actual_hql) self.assertEquals(len(git_files), 26) self.assertIn('Generated', msg) self.assertIn('workflow:', msg) self.assertIn('subworkflow:', msg) self.assertTrue(status) @patch('ibis.utilities.run_parallel.DryRunWorkflowManager.run_all', autospec=True) @patch('ibis.utilities.run_parallel.SqoopCacheManager.cache_ddl_views', autospec=True) @patch('ibis.utilities.run_parallel.SqoopCacheManager.cache_ddl_queries', autospec=True) @patch('ibis.utilities.utilities.Utilities.put_dry_workflow', autospec=True) @patch('ibis.utilities.utilities.Utilities.dryrun_workflow', autospec=True) @patch.object(ITInventory, 'get_all_tables_for_automation', autospec=True) @patch.object(AUTOInventory, 'get_tables_by_id', autospec=True) @patch('ibis.inventory.inventory.Inventory._connect', autospec=True) @patch('ibis.inventor.action_builder.SqoopHelper.eval', autospec=True) @patch.object(VizOozie, 'visualizeXML', autospec=True) @patch.object(VizOozie, 'convertDotToPDF', autospec=True) @patch.object(PerfInventory, 'insert_freq_ingest', autospec=True) def test_gen_prod_workflow_perf_nodomain(self, m_freq_ingest, m_convert_pdf, m_v_xml, m_eval, m_c, m_get_id, m_get_t_automation, m_dryrun, m_put_w, m_sqoop_cache, m_sqoop_cache_view, m_dryrun_all): """ Tests generate_prod_workflows with 3 tables. One light, one medium and oen heavy.""" m_eval.return_value = [['Col1', 'varchar'], ['Col2', 'varchar']] m_get_id.side_effect = [appl_ref_id_tbl_01, appl_ref_id_tbl_02] _mock_automation_tables = [ItTable(tbl, self.cfg_mgr) for tbl in mock_automation_tbl_perf_domain] m_get_t_automation.return_value = _mock_automation_tables self.cfg_mgr.env = 'perf' status, msg, git_files = self.driver.gen_prod_workflow('FAKED001') for file_name in git_files: git_file = 'full_fake_open_fake_prog_tablename.hql' if git_file in file_name: actual_hql_nm = os.path.join(self.cfg_mgr.files, file_name) with open(actual_hql_nm, 'r') as file_h: actual_hql = file_h.read() with open(BASE_DIR + '/test_resources/git_team_hql_nodomain.hql', 'r') as file_h: expected_hql = file_h.read() self.assertTrue(expected_hql, actual_hql) self.assertEquals(len(git_files), 23) self.assertIn('Generated', msg) self.assertIn('workflow:', msg) self.assertIn('subworkflow:', msg) self.assertTrue(status) @patch('ibis.utilities.run_parallel.DryRunWorkflowManager.run_all', autospec=True) @patch('ibis.utilities.run_parallel.SqoopCacheManager.cache_ddl_views', autospec=True) @patch('ibis.utilities.run_parallel.SqoopCacheManager.cache_ddl_queries', autospec=True) @patch('ibis.utilities.utilities.Utilities.put_dry_workflow', autospec=True) @patch('ibis.utilities.utilities.Utilities.dryrun_workflow', autospec=True) @patch.object(ITInventory, 'get_all_tables_for_automation', autospec=True) @patch.object(AUTOInventory, 'get_tables_by_id', autospec=True) @patch('ibis.inventory.inventory.Inventory._connect', autospec=True) @patch('ibis.inventor.action_builder.SqoopHelper.eval', autospec=True) @patch.object(VizOozie, 'visualizeXML', autospec=True) @patch.object(VizOozie, 'convertDotToPDF', autospec=True) @patch.object(Driver, 'gen_incr_workflow_files', autospec=True) @patch.object(PerfInventory, 'insert_freq_ingest', autospec=True) def test_gen_prod_workflow_2(self, m_freq_ingest, m_gen_incr, m_convert_pdf, m_v_xml, m_eval, m_c, m_get_id, m_get_t_automation, m_dryrun, m_put_w, m_sqoop_cache, m_sqoop_cache_view, m_dryrun_all): """ Tests generate_prod_workflows with five tables. 3 heavy, one medium and one light.""" m_eval.return_value = [['Col1', 'varchar'], ['Col2', 'varchar']] m_get_id.side_effect = [appl_ref_id_tbl_01, appl_ref_id_tbl_02] _mock_automation_tables_03 = [ItTable(tbl, self.cfg_mgr) for tbl in mock_automation_tables_03] m_get_t_automation.return_value = _mock_automation_tables_03 m_gen_incr.return_value = [] self.cfg_mgr.env = 'perf' status, msg, git_files = self.driver.gen_prod_workflow('FAKED001') self.assertEquals(len(git_files), 40) self.assertIn('Generated', msg) self.assertIn('workflow:', msg) self.assertIn('subworkflow:', msg) self.assertTrue(status) @patch('ibis.utilities.run_parallel.DryRunWorkflowManager.run_all', autospec=True) @patch('ibis.utilities.run_parallel.SqoopCacheManager.cache_ddl_views', autospec=True) @patch('ibis.utilities.run_parallel.SqoopCacheManager.cache_ddl_queries', autospec=True) @patch('ibis.utilities.utilities.Utilities.put_dry_workflow', autospec=True) @patch('ibis.utilities.utilities.Utilities.dryrun_workflow', autospec=True) @patch.object(ITInventory, 'get_all_tables_for_automation', autospec=True) @patch.object(AUTOInventory, 'get_tables_by_id', autospec=True) @patch('ibis.inventory.inventory.Inventory._connect', autospec=True) @patch('ibis.inventor.action_builder.SqoopHelper.eval', autospec=True) @patch.object(VizOozie, 'visualizeXML', autospec=True) @patch.object(VizOozie, 'convertDotToPDF', autospec=True) @patch.object(PerfInventory, 'insert_freq_ingest', autospec=True) def test_gen_prod_workflow_3(self, m_freq_ingest, m_convert_pdf, m_v_xml, m_eval, m_c, m_get_id, m_get_t_automation, m_dryrun, m_put_w, m_sqoop_cache, m_sqoop_cache_view, m_dryrun_all): """ Tests generate_prod_workflows with 6 tables. Two light, three medium and one heavy.""" m_eval.return_value = [['Col1', 'varchar'], ['Col2', 'varchar']] m_get_id.side_effect = [appl_ref_id_tbl_01, appl_ref_id_tbl_02] _mock_automation_tables_01 = [ItTable(tbl, self.cfg_mgr) for tbl in mock_automation_tables_01] m_get_t_automation.return_value = _mock_automation_tables_01 self.cfg_mgr.env = 'perf' status, msg, git_files = self.driver.gen_prod_workflow('FAKED001') self.assertEquals(len(git_files), 51) self.assertIn('Generated', msg) self.assertIn('workflow:', msg) self.assertIn('subworkflow:', msg) self.assertTrue(status) @patch.object(ITInventory, 'get_all_tables_for_automation', autospec=True) @patch.object(AUTOInventory, 'get_tables_by_id', autospec=True) @patch('ibis.inventory.inventory.Inventory._connect', autospec=True) def test_gen_prod_workflow_without_applrefs(self, m_c, m_get_tables_by_id, m_get_all_tables_for_automation): """ test_gen_prod_workflow_without_applrefs""" m_get_all_tables_for_automation.return_value = [mock_automation_tables_01, mock_automation_tables_02] m_get_tables_by_id.return_value = [] status, msg, git_files = self.driver.gen_prod_workflow('FAKED001') self.assertEquals(len(git_files), 0) self.assertIn( "No row found for automation_appl_id: 'FAKED001' " "in 'ibis.automation_ids' table", msg) self.assertFalse(status) @patch.object(ITInventory, 'get_all_tables_for_automation', autospec=True) @patch('ibis.inventory.inventory.Inventory._connect', autospec=True) def test_gen_prod_workflow_without_tables(self, m_c, m_get_all_tables_for_automation): """ test_gen_prod_workflow_without_applrefs""" m_get_all_tables_for_automation.return_value = [] status, msg, git_files = self.driver.gen_prod_workflow('FAKED001') self.assertEquals(len(git_files), 0) self.assertIn("No tables found for automation_appl_id: 'FAKED001'", msg) self.assertFalse(status) @patch.object(Inventory, '_connect', autospec=True) def test_export(self, m_con): """test export no table""" self.driver.it_inventory = MagicMock(spec=ITInventory) self.driver.it_inventory.get_table_mapping.return_value = light_3_prop result = self.driver.export('fake_database', 'fake_cens_tablename', 'fake_domain.fake_database_fake_cens_tablename') print result self.assertIn('_export.xml, generated to', result) def test_export_no_tbl(self): """test export no table""" self.driver.it_inventory = MagicMock(spec=ITInventory) self.driver.it_inventory.get_table_mapping.return_value = {} result = self.driver.export('fake_database', 'fake_fa_tablename', 'member.fake_database' '_fake_fa_tablename') self.assertIn( 'doesn\'t exist in the it_table export directory ' 'could not be found.', result) def test_export_fail(self): """test export no table""" self.driver.it_inventory = MagicMock(spec=ITInventory) self.driver.it_inventory.get_table_mapping.return_value = {} result = self.driver.export( 'fake_database', 'fake_fa_tablename', 'memberfake_database_fake_fa_tablename') self.assertIn('Please provide an appropriate --to', result) @patch.object(Driver, 'submit_it_file', autospec=True) @patch('ibis.driver.driver.create', autospec=True) def test_gen_it_table_with_split_by(self, mock_it_table_gen_create, mock_submit_it_file): """Test split by wrapper.""" with patch('__builtin__.open') as m_open: m_open.readlines.return_value = MagicMock(spec=file) self.driver.gen_it_table_with_split_by(m_open, 45) @patch.object(Inventory, '_connect', autospec=True) def test_generate_subworkflow(self, m_con): result = self.driver.generate_subworkflow('test_generate_subworkflow', ['files']) self.assertIn('Generated subworkflow', result) def test_group_workflows(self): """test _group_workflows""" ws_path = self.cfg_mgr.oozie_workspace generated_wfs = [ ws_path + 'file1.xml', ws_path + 'file2.xml', ws_path + 'file3.xml', ws_path + 'file4.xml', ws_path + 'file5.xml', ws_path + 'file6.xml', ws_path + 'file7.xml', ws_path + 'file8.xml', ws_path + 'file9.xml', ws_path + 'file10.xml', ws_path + 'file11.xml', ws_path + 'file12.xml', ws_path + 'file13.xml', ws_path + 'file14.xml', ws_path + 'file15.xml', ws_path + 'file16.xml', ws_path + 'file17.xml', ws_path + 'file18.xml'] chunks = self.driver._group_workflows(generated_wfs) self.assertEquals(len(chunks), 4) self.assertIn(ws_path + 'file18.xml', chunks[3]) def test_parse_kite_request(self): required_fields = ['source_database_name', 'source_table_name', 'hdfs_loc'] optional_fields = [] expected_request = [{'source_database_name': 'database_one', 'hdfs_loc': '/test/hdfs/loc', 'source_table_name': 'table_one'}, {'source_database_name': 'database_one', 'hdfs_loc': '/test/hdfs/loc', 'source_table_name': 'table_two'}] file_h = open(os.path.join(BASE_DIR, 'test_resources/kite_request.txt'), 'r') requests, msg, _ = parse_file_by_sections(file_h, '[Request]', required_fields, optional_fields) self.assertEquals(expected_request, requests) @patch.object(Driver, '_get_workflow_name', return_value="kite_request") def test_gen_kite_workflow(self, mock1): file_h = open(os.path.join(BASE_DIR, 'test_resources/kite_request.txt'), 'r') result = self.driver.gen_kite_workflow(file_h) self.assertIsNotNone(result) def test_gen_schedule_subworkflows_int_sys(self): """test gen schedule subworkflows for same table from sys and int""" sub_wf_file_name = 'test_subwf' tab1 = ItTable(sqls_int, self.cfg_mgr) tab2 = ItTable(sqls_sys, self.cfg_mgr) heavy_tables = [tab1, tab2] workflows_chunks = [[tab1, 'int_full_load'], [tab2, 'sys_full_load']] gen_files = self.driver.gen_schedule_subworkflows( sub_wf_file_name, workflows_chunks, heavy_tables, 'FAKED001') self.assertEquals(len(gen_files), 4) expected_file = os.path.join( BASE_DIR, 'test_resources/subwf_sys_int.xml') test_file = os.path.join(self.cfg_mgr.files, 'test_subwf.xml') self.assertTrue(self.files_equal(expected_file, test_file)) def test_gen_schedule_subworkflows_light(self): """test gen schedule subworkflows for 5 light tables""" sub_wf_file_name = 'test_subwf' tab1 = ItTable(fake_cens_tbl_prop, self.cfg_mgr) tab2 = ItTable(light_3_prop, self.cfg_mgr) tab3 = ItTable(light_4_prop, self.cfg_mgr) tab4 = ItTable(light_5_prop, self.cfg_mgr) tab5 = ItTable(fake_ben_tbl_prop, self.cfg_mgr) light_tables = [tab1, tab2, tab3, tab4, tab5] workflows_chunks = [ [tab1, 'tab1_full_load'], [tab2, 'tab2_full_load'], [tab3, 'tab3_full_load'], [tab4, 'tab4_full_load'], [tab5, 'tab5_full_load']] gen_files = self.driver.gen_schedule_subworkflows( sub_wf_file_name, workflows_chunks, light_tables, 'FAKED001') self.assertEquals(len(gen_files), 4) expected_file = os.path.join( BASE_DIR, 'test_resources/subwf_light.xml') test_file = os.path.join(self.cfg_mgr.files, 'test_subwf.xml') self.assertTrue(self.files_equal(expected_file, test_file)) def test_gen_schedule_subworkflows_heavy(self): """test gen schedule subworkflows for 3 heavy tables""" sub_wf_file_name = 'test_subwf' tab1 = ItTable(heavy_2_prop, self.cfg_mgr) tab2 = ItTable(heavy_3_prop, self.cfg_mgr) tab3 = ItTable(full_ingest_tbl_mysql, self.cfg_mgr) heavy_tables = [tab1, tab2, tab3] workflows_chunks = [ [tab1, 'tab1_full_load'], [tab2, 'tab2_full_load'], [tab3, 'tab3_full_load']] gen_files = self.driver.gen_schedule_subworkflows( sub_wf_file_name, workflows_chunks, heavy_tables, 'FAKED001') self.assertEquals(len(gen_files), 4) expected_file = os.path.join( BASE_DIR, 'test_resources/subwf_heavy.xml') test_file = os.path.join(self.cfg_mgr.files, 'test_subwf.xml') self.assertTrue(self.files_equal(expected_file, test_file)) def test_gen_schedule_subworkflows_mixed(self): """test gen schedule subworkflows for 5 light tables, 3 medium tables, 3 heavy tables """ sub_wf_file_name = 'test_subwf' # heavy tab1 = ItTable(heavy_2_prop, self.cfg_mgr) tab2 = ItTable(heavy_3_prop, self.cfg_mgr) tab3 = ItTable(full_ingest_tbl_mysql, self.cfg_mgr) # medium tab4 = ItTable(fake_fct_tbl_prop, self.cfg_mgr) tab5 = ItTable(fake_fact_tbl_prop, self.cfg_mgr) tab6 = ItTable(mock_table_mapping_val, self.cfg_mgr) # small tab7 = ItTable(fake_cens_tbl_prop, self.cfg_mgr) tab8 = ItTable(light_3_prop, self.cfg_mgr) tab9 = ItTable(light_4_prop, self.cfg_mgr) tab10 = ItTable(light_5_prop, self.cfg_mgr) tab11 = ItTable(fake_ben_tbl_prop, self.cfg_mgr) all_tables = [tab1, tab2, tab3, tab4, tab5, tab6, tab7, tab8, tab9, tab10, tab11] workflows_chunks = [ [tab1, 'tab1_heavy'], [tab2, 'tab2_heavy'], [tab3, 'tab3_heavy'], [tab4, 'tab4_medium'], [tab5, 'tab5_medium'], [tab6, 'tab6_medium'], [tab7, 'tab7_small'], [tab8, 'tab8_small'], [tab9, 'tab9_small'], [tab10, 'tab10_small'], [tab11, 'tab11_small']] gen_files = self.driver.gen_schedule_subworkflows( sub_wf_file_name, workflows_chunks, all_tables, 'FAKED001') self.assertEquals(len(gen_files), 4) expected_file = os.path.join( BASE_DIR, 'test_resources/subwf_mixed.xml') test_file = os.path.join(self.cfg_mgr.files, 'test_subwf.xml') self.assertTrue(self.files_equal(expected_file, test_file)) def test_determine_auto_domain(self): tab1 = ItTable(full_ingest_tbl_auto_domain, self.cfg_mgr) self.driver.add_env_to_domain(tab1) def test_determine_auto_domain_env(self): tab1 = ItTable(full_ingest_tbl_auto_domain_env, self.cfg_mgr) self.driver.add_env_to_domain(tab1) @patch('ibis.driver.driver.RequestInventory.get_available_requests', autospec=True) @patch('ibis.driver.driver.Driver.gen_prod_workflow', autospec=True) @patch('ibis.driver.driver.Driver.update_it_table', autospec=True) def test_gen_prod_workflow_tables(self, m_s_it_file, gen_prod_workflow, mock_get_available_requests): mock_get_available_requests.return_value = \ ([ItTable(mock_table_mapping_val, self.cfg_mgr)], [], []) gen_prod_workflow.return_value = (None, None, None) file_h = open( os.path.join(BASE_DIR, 'test_resources/request_test_valid.txt'), 'r') self.assertTrue(self.driver.gen_prod_workflow_tables(file_h)) @patch('ibis.driver.driver.subprocess', autospec=True) @patch.object(Inventory, 'get_table_mapping', return_value=fake_fct_tbl_prop) def test_retrieve_backup(self, mock_get_table_mapping, mock_subprocess): """test retrieve_backup""" tbl = fake_fct_tbl_prop msg = self.driver.retrieve_backup(tbl['source_database_name'], tbl['source_table_name']) self.assertEquals(msg, 'Retrieving backup for fake_database_fake_fct_tablename\n') @patch('ibis.inventory.it_inventory.ITInventory.update', autospec=True) @patch('ibis.driver.driver.RequestInventory.get_available_requests', autospec=True) @patch('ibis.driver.driver.Driver.gen_prod_workflow', autospec=True) @patch('ibis.driver.driver.Driver.update_it_table', autospec=True) def test_gen_prod_workflow_tables_noapp(self, m_s_it_file, gen_prod_workflow, mock_get_available_requests, mock_update): mock_get_available_requests.return_value = \ ([ItTable(mock_table_mapping_val_app, self.cfg_mgr)], [], []) gen_prod_workflow.return_value = (None, None, None) file_h = open( os.path.join(BASE_DIR, 'test_resources/request_test_valid.txt'), 'r') mock_update.return_value = (True, 'Update Success') self.assertTrue(self.driver.gen_prod_workflow_tables(file_h)) @patch('ibis.driver.driver.Driver.update_it_table_export') @patch('ibis.driver.driver.RequestInventory.get_available_requests_export', autospec=True) def test_export_request(self, mock_get_available_requests, mock_upate): mock_get_available_requests.return_value = \ ([ItTableExport(heavy_3_prop_exp, self.cfg_mgr)], []) request_file = open( os.path.join(BASE_DIR, 'test_resources/export_request.txt'), 'r') status, _ = self.driver.export_request(request_file, False) self.assertTrue(status) def test_export_oracle(self): self.driver.export_oracle("source_table_name", "source_database_name", "source_dir", "jdbc_url", "update_key", "target_table_name", "target_database_name", "user_name", "password_alias") def test_export_teradata(self): self.driver.export_teradata("source_table_name", "source_database_name", "source_dir", "jdbc_url", "target_table_name", "target_database_name", "user_name", "password_alias") @patch('subprocess.call') def test_retrieve_backup_notarget(self, mock_call): """ Given arguments for retrieve_back up expects print statement """ self.driver.it_inventory.get_table_mapping = MagicMock( spec=ITInventory.get_table_mapping) self.driver.it_inventory.get_table_mapping.return_value = \ mock_table_mapping_val self.driver.utilities.run_subprocess = \ MagicMock(spec=Utilities.run_subprocess) self.driver.utilities.run_subprocess.side_effect = [0, 1, 1, 1, 1, 1] msg = self.driver.retrieve_backup("db_name", "table_name") self.assertEqual(msg, "Retrieving backup for " + "db_name_table_name\nTarget directory doesn't " + "exist in it_table.\n") @patch('subprocess.call') def test_retrieve_backup_iftarget(self, mock_popen): """ Given arguments for retrieve_back up expects print statement """ self.driver.it_inventory.get_table_mapping = MagicMock( spec=ITInventory.get_table_mapping) self.driver.it_inventory.get_table_mapping.return_value = \ heavy_3_prop_exp self.driver.utilities.run_subprocess = \ MagicMock(spec=Utilities.run_subprocess) self.driver.utilities.run_subprocess.side_effect = [0, 1, 1, 1, 1, 1] msg = self.driver.retrieve_backup("db_name", "table_name") self.assertEqual(msg, "Retrieving backup for db_name_table_name\n" + "Failed to copy parquet_live.hql\nFailed to copy " + "avro_parquet.hql\nFailed to copy files to live.\n") @patch('subprocess.call') def test_retrieve_backup_iftable(self, mock_popen): """ Given arguments for retrieve_back up expects print statement """ self.driver.it_inventory.get_table_mapping = MagicMock( spec=ITInventory.get_table_mapping) self.driver.it_inventory.get_table_mapping.return_value = {} msg = self.driver.retrieve_backup("db_name", "table_name") self.assertEqual(msg, "Retrieving backup for db_name_table_name\n" + "db_name_{} does not exist in it_table. Directory " + "can't be found\n") @patch('getpass.getuser') def test_get_config_workflow_name(self, m_getuser): """test _get_config_workflow_name""" m_getuser.return_value = 'userId' mock_tmp = MagicMock() mock_tmp.name = '/path/to/requestFile.txt' val = self.driver._get_config_workflow_name(mock_tmp) self.assertEquals(val, 'userId_config_wf_requestFile') @patch('getpass.getuser') def test_get_workflow_name(self, m_getuser): """test _get_workflow_name""" m_getuser.return_value = 'userId' mock_tmp = MagicMock() mock_tmp.name = '/path/to/requestFile.txt' val = self.driver._get_workflow_name(mock_tmp) self.assertEquals(val, 'dev_userId_requestFile') @patch('getpass.getuser') def test_get_subworkflow_name_prefix(self, m_getuser): """test _get_subworkflow_name_prefix""" m_getuser.return_value = 'userId' mock_tmp = MagicMock() mock_tmp.name = '/path/to/requestFile.txt' val = self.driver._get_subworkflow_name_prefix(mock_tmp) self.assertEquals(val, 'userId_requestFile') @patch('getpass.getuser') def test_get_workflow_name_table(self, m_getuser): """test _get_workflow_name_table""" m_getuser.return_value = 'userId' tab1 = ItTable(fake_cens_tbl_prop, self.cfg_mgr) val = self.driver._get_workflow_name_table(tab1) self.assertEquals(val, 'dev_userId_fake_database_fake_cens_tablename') @patch('getpass.getuser') def test_get_workflow_name_table_export(self, m_getuser): """test _get_workflow_name_table_export""" m_getuser.return_value = 'userId' tab1 = ItTable(fake_cens_tbl_prop, self.cfg_mgr) val = self.driver._get_workflow_name_table_export(tab1) self.assertEquals(val, 'dev_userId_fake_database_fake_cens_tablename_export') def test_get_prod_table_workflow_name(self): """test _get_prod_table_workflow_name""" tab1 = ItTable(fake_cens_tbl_prop, self.cfg_mgr) val = self.driver._get_prod_table_workflow_name(tab1) self.assertEquals(val, 'fake_database_fake_cens_tablename') @patch('getpass.getuser') def test_get_incr_workflow_name(self, m_getuser): """test _get_incr_workflow_name""" m_getuser.return_value = 'userId' tab1 = ItTable(fake_cens_tbl_prop, self.cfg_mgr) val = self.driver._get_incr_workflow_name(tab1) self.assertEquals(val, 'dev_userId_incr_fake_cens_tablename') @patch.object(PerfInventory, 'insert_freq_ingest') def test_insert_freq_ingest_driver(self, m_freq_ingest): """ test freq_ingest_driver""" self.driver.insert_freq_ingest_driver(['mock_team_nm'], ['daily'], ['mock_table_nm'], ['no']) m_freq_ingest.assert_called_once_with(['mock_team_nm'], ['daily'], ['mock_table_nm'], ['no']) @patch.object(PerfInventory, 'insert_freq_ingest') def test_insert_freq_ingest_driver_fa(self, m_freq_ingest): """ test freq_ingest_driver for frequency and actovor are none""" with self.assertRaises(ValueError) as context: self.driver.insert_freq_ingest_driver(['mock_team_nm'], None, ['mock_table_nm'], None) error = "Either of frequency or activate column must contain value" self.assertTrue(error in str(context.exception)) @patch.object(PerfInventory, 'wipe_perf_env') def test_wipe_perf_env_driver(self, m_wipe_perf): """test wipe_perf_driver with team name""" self.driver.wipe_perf_env_driver(['fake_view_im'], False) m_wipe_perf.assert_called_once_with('fake_view_im', False) @patch.object(PerfInventory, 'wipe_perf_env', autospec=True) def test_wipe_perf_env_driver_ibis(self, m_wipe_perf): """test wipe_perf_driver with domain as ibis""" with self.assertRaises(ValueError) as context: self.driver.wipe_perf_env_driver(['ibis'], False) self.assertTrue('Cannot wipe Ibis database' in str(context.exception)) @patch.object(PerfInventory, 'wipe_perf_env', autospec=True) def test_wipe_perf_env_driver_domain(self, m_wipe_perf): """test wipe_perf_driver with team name as domain""" with self.assertRaises(ValueError) as context: self.driver.wipe_perf_env_driver(['domain1'], False) self.assertTrue('Team name provided is Domain, please \ provide your team name' in str(context.exception)) if __name__ == "__main__": unittest.main()

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0.668262

0.632856

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0.257895

50,191

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0.792869

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0.206584

0.105498

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0.028302

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null

0

1

0

1

0

null

0

1

0

5

b1e9fff1193157930749bf5098beb644524c8007

2,825

py

Python

tests/test_sample_file.py

singer-io/singer-encodings

51256b6d2aa0dc492264a8f8094f30b3f7a26259

[ "Apache-2.0" ]

2

2021-10-06T00:14:40.000Z

2021-11-27T18:39:06.000Z

tests/test_sample_file.py

singer-io/singer-encodings

51256b6d2aa0dc492264a8f8094f30b3f7a26259

[ "Apache-2.0" ]

3

2019-10-31T15:51:49.000Z

2021-05-24T05:08:41.000Z

tests/test_sample_file.py

singer-io/singer-encodings

51256b6d2aa0dc492264a8f8094f30b3f7a26259

[ "Apache-2.0" ]

7

2020-05-27T13:22:59.000Z

2021-07-12T10:38:49.000Z

import unittest from unittest import mock from singer_encodings import json_schema from singer_encodings import csv import csv as _csv import io class Connection: def get_file_handle(self, f): if f.get("raise_error"): raise OSError("OSError") elif f.get("raise_permission_error"): raise PermissionError("Permission denied") else: return mock.mock_open() @mock.patch("singer_encodings.csv.get_row_iterators") class TestSampleFile(unittest.TestCase): def test_positive(self, mocked_csv_row_iterator): mocked_csv_row_iterator.return_value = [_csv.DictReader(io.StringIO("header\nvalue"))] conn = Connection() empty_file, samples = json_schema.sample_file(conn, {"table_name": "data", "key_properties": ["id"], "delimiter": ","}, {"filepath": "/root_dir/file.csv.gz", "last_modified": "2020-01-01"}, 1, 1000) # check if "csv.get_row_iterators" is called if it is called then error has not occurred # if it is not called then error has occured and function returned from the except block self.assertEquals(1, mocked_csv_row_iterator.call_count) # test if file is empty self.assertEquals(False, empty_file) # test if samples is not an empty list self.assertNotEquals([], samples) def test_negative_OSError(self, mocked_csv_row_iterator): conn = Connection() empty_file, samples = json_schema.sample_file(conn, {"table_name": "data", "key_properties": ["id"], "delimiter": ","}, {"filepath": "/root_dir/file.csv.gz", "last_modified": "2020-01-01", "raise_error": True}, 1, 1000) # check if "csv.get_row_iterators" is called if it is called then error has not occurred # if it is not called then error has occured and function returned from the except block self.assertEquals(0, mocked_csv_row_iterator.call_count) # test if file is empty self.assertEquals(False, empty_file) # test if samples is not an empty list self.assertEquals([], samples) def test_negative_PermisisonError(self, mocked_csv_row_iterator): conn = Connection() empty_file, samples = json_schema.sample_file(conn, {"table_name": "data", "key_properties": ["id"], "delimiter": ","}, {"filepath": "/root_dir/file.csv.gz", "last_modified": "2020-01-01", "raise_permission_error": True}, 1, 1000) # check if "csv.get_row_iterators" is called if it is called then error has not occurred # if it is not called then error has occured and function returned from the except block self.assertEquals(0, mocked_csv_row_iterator.call_count) # test if file is empty self.assertEquals(False, empty_file) # test if samples is not an empty list self.assertEquals([], samples)

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0

null

0

1

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null

0

5

b1fab5ca79bda60c6be92e0cf72089247b6368c1

9,914

py

Python

0000_students_work/2021tro/projection_liumethod_global_localrefine.py

takuya-ki/wrs

f6e1009b94332504042fbde9b39323410394ecde

[ "MIT" ]

23

2021-04-02T09:02:04.000Z

2022-03-22T05:31:03.000Z

0000_students_work/2021tro/projection_liumethod_global_localrefine.py

takuya-ki/wrs

f6e1009b94332504042fbde9b39323410394ecde

[ "MIT" ]

35

2021-04-12T09:41:05.000Z

2022-03-26T13:32:46.000Z

0000_students_work/2021tro/projection_liumethod_global_localrefine.py

takuya-ki/wrs

f6e1009b94332504042fbde9b39323410394ecde

[ "MIT" ]

16

2021-03-30T11:55:45.000Z

2022-03-30T07:10:59.000Z

import numpy as np import modeling.geometric_model as gm import modeling.collision_model as cm import visualization.panda.world as wd import basis.robot_math as rm import math from scipy.spatial import cKDTree import vision.depth_camera.surface.rbf_surface as rbfs base = wd.World(cam_pos=np.array([-.3,-.7,.42]), lookat_pos=np.array([0,0,0])) # gm.gen_frame().attach_to(base) bowl_model = cm.CollisionModel(initor="./objects/bowl.stl") bowl_model.set_rgba([.3,.3,.3,.3]) bowl_model.set_rotmat(rm.rotmat_from_euler(math.pi,0,0)) # bowl_model.attach_to(base) pn_direction = np.array([0, 0, -1]) bowl_samples, bowl_sample_normals = bowl_model.sample_surface(toggle_option='normals', radius=.002) selection = bowl_sample_normals.dot(-pn_direction)>.1 bowl_samples = bowl_samples[selection] bowl_sample_normals=bowl_sample_normals[selection] tree = cKDTree(bowl_samples) surface = rbfs.RBFSurface(bowl_samples[:, :2], bowl_samples[:,2]) surface.get_gometricmodel(rgba=[.3,.3,.3,.3]).attach_to(base) pt_direction = rm.orthogonal_vector(pn_direction, toggle_unit=True) tmp_direction = np.cross(pn_direction, pt_direction) plane_rotmat = np.column_stack((pt_direction, tmp_direction, pn_direction)) homomat=np.eye(4) homomat[:3,:3] = plane_rotmat homomat[:3,3] = np.array([-.07,-.03,.1]) twod_plane = gm.gen_box(np.array([.2, .2, .001]), homomat=homomat, rgba=[1,1,1,.3]) twod_plane.attach_to(base) circle_radius=.05 line_segs = [[homomat[:3,3], homomat[:3,3]+pt_direction*.05], [homomat[:3,3]+pt_direction*.05, homomat[:3,3]+pt_direction*.05+tmp_direction*.05], [homomat[:3,3]+pt_direction*.05+tmp_direction*.05, homomat[:3,3]+tmp_direction*.05], [homomat[:3,3]+tmp_direction*.05, homomat[:3,3]]] # gm.gen_linesegs(line_segs).attach_to(base) for sec in line_segs: gm.gen_stick(spos=sec[0], epos=sec[1], rgba=[0, 0, 0, 1], thickness=.002, type='round').attach_to(base) epos = (line_segs[0][1]-line_segs[0][0])*.7+line_segs[0][0] gm.gen_arrow(spos=line_segs[0][0], epos=epos, thickness=0.004).attach_to(base) spt = homomat[:3,3] # gm.gen_stick(spt, spt + pn_direction * 10, rgba=[0,1,0,1]).attach_to(base) # base.run() gm.gen_dasharrow(spt, spt-pn_direction*.07, thickness=.004).attach_to(base) # p0 cpt, cnrml = bowl_model.ray_hit(spt, spt + pn_direction * 10000, option='closest') gm.gen_dashstick(spt, cpt, rgba=[.57,.57,.57,.7], thickness=0.003).attach_to(base) gm.gen_sphere(pos=cpt, radius=.005).attach_to(base) gm.gen_dasharrow(cpt, cpt-pn_direction*.07, thickness=.004).attach_to(base) # p0 gm.gen_dasharrow(cpt, cpt+cnrml*.07, thickness=.004).attach_to(base) # p0 angle = rm.angle_between_vectors(-pn_direction, cnrml) vec = np.cross(-pn_direction, cnrml) rotmat = rm.rotmat_from_axangle(vec, angle) new_plane_homomat = np.eye(4) new_plane_homomat[:3,:3] = rotmat.dot(homomat[:3,:3]) new_plane_homomat[:3,3] = cpt twod_plane = gm.gen_box(np.array([.2, .2, .001]), homomat=new_plane_homomat, rgba=[1,1,1,.3]) twod_plane.attach_to(base) new_line_segs = [[cpt, cpt+rotmat.dot(pt_direction)*.05], [cpt+rotmat.dot(pt_direction)*.05, cpt+rotmat.dot(pt_direction)*.05+rotmat.dot(tmp_direction)*.05], [cpt+rotmat.dot(pt_direction)*.05+rotmat.dot(tmp_direction)*.05, cpt+rotmat.dot(tmp_direction)*.05], [cpt+rotmat.dot(tmp_direction)*.05, cpt]] # gm.gen_linesegs(new_line_segs).attach_to(base) for sec in new_line_segs: gm.gen_stick(spos=sec[0], epos=sec[1], rgba=[0, 0, 0, 1], thickness=.002, type='round').attach_to(base) epos = (new_line_segs[0][1]-new_line_segs[0][0])*.7+new_line_segs[0][0] gm.gen_arrow(spos=new_line_segs[0][0], epos=epos, thickness=0.004).attach_to(base) last_normal = cnrml direction = rotmat.dot(pt_direction) n=3 for tick in range(1, n+1): len = .05/n tmp_cpt = cpt extended_len = 0 for p in np.linspace(0, len, 1000): tmp_t_npt = cpt+direction*p tmp_z_surface = surface.get_zdata(np.array([tmp_t_npt[:2]])) tmp_projected_point = np.array([tmp_t_npt[0], tmp_t_npt[1], tmp_z_surface[0]]) tmp_len = np.linalg.norm(tmp_projected_point - tmp_cpt) extended_len += tmp_len tmp_cpt = tmp_projected_point print(tick, extended_len, len) if extended_len>len: break projected_point = tmp_projected_point t_npt = tmp_t_npt domain_grid = np.meshgrid(np.linspace(-.005, .005, 100, endpoint=True), np.linspace(-.005, .005, 100, endpoint=True)) domain_0, domain_1 = domain_grid domain = np.column_stack((domain_0.ravel()+t_npt[0], domain_1.ravel()+t_npt[1])) codomain = surface.get_zdata(domain) vertices = np.column_stack((domain, codomain)) plane_center, plane_normal = rm.fit_plane(vertices) new_normal = plane_normal if pn_direction.dot(new_normal) > .1: new_normal = -new_normal angle = rm.angle_between_vectors(-pn_direction, new_normal) vec = rm.unit_vector(np.cross(-pn_direction, new_normal)) new_rotmat = rm.rotmat_from_axangle(vec, angle) direction = new_rotmat.dot(direction) gm.gen_stick(spos=cpt, epos=projected_point, rgba=[1,.6,0,1], thickness=.002, type='round').attach_to(base) cpt=projected_point # last_normal = new_normal direction = new_rotmat.dot(tmp_direction) for tick in range(1, n+1): len = .05/n tmp_cpt = cpt extended_len = 0 for p in np.linspace(0, len, 1000): tmp_t_npt = cpt+direction*p tmp_z_surface = surface.get_zdata(np.array([tmp_t_npt[:2]])) tmp_projected_point = np.array([tmp_t_npt[0], tmp_t_npt[1], tmp_z_surface[0]]) tmp_len = np.linalg.norm(tmp_projected_point - tmp_cpt) extended_len += tmp_len tmp_cpt = tmp_projected_point print(tick, extended_len, len) if extended_len>len: break projected_point = tmp_projected_point t_npt = tmp_t_npt domain_grid = np.meshgrid(np.linspace(-.005, .005, 100, endpoint=True), np.linspace(-.005, .005, 100, endpoint=True)) domain_0, domain_1 = domain_grid domain = np.column_stack((domain_0.ravel()+t_npt[0], domain_1.ravel()+t_npt[1])) codomain = surface.get_zdata(domain) vertices = np.column_stack((domain, codomain)) plane_center, plane_normal = rm.fit_plane(vertices) new_normal = plane_normal if pn_direction.dot(new_normal) > .1: new_normal = -new_normal angle = rm.angle_between_vectors(-pn_direction, new_normal) vec = rm.unit_vector(np.cross(-pn_direction, new_normal)) new_rotmat = rm.rotmat_from_axangle(vec, angle) direction = new_rotmat.dot(tmp_direction) gm.gen_stick(spos=cpt, epos=projected_point, rgba=[1,.6,0,1], thickness=.002, type='round').attach_to(base) cpt=projected_point # last_normal = new_normal direction = new_rotmat.dot(-pt_direction) for tick in range(1, n+1): len = .05/n tmp_cpt = cpt extended_len = 0 for p in np.linspace(0, len, 1000): tmp_t_npt = cpt+direction*p tmp_z_surface = surface.get_zdata(np.array([tmp_t_npt[:2]])) tmp_projected_point = np.array([tmp_t_npt[0], tmp_t_npt[1], tmp_z_surface[0]]) tmp_len = np.linalg.norm(tmp_projected_point - tmp_cpt) extended_len += tmp_len tmp_cpt = tmp_projected_point print(tick, extended_len, len) if extended_len>len: break projected_point = tmp_projected_point t_npt = tmp_t_npt domain_grid = np.meshgrid(np.linspace(-.005, .005, 100, endpoint=True), np.linspace(-.005, .005, 100, endpoint=True)) domain_0, domain_1 = domain_grid domain = np.column_stack((domain_0.ravel()+t_npt[0], domain_1.ravel()+t_npt[1])) codomain = surface.get_zdata(domain) vertices = np.column_stack((domain, codomain)) plane_center, plane_normal = rm.fit_plane(vertices) new_normal = plane_normal if pn_direction.dot(new_normal) > .1: new_normal = -new_normal angle = rm.angle_between_vectors(-pn_direction, new_normal) vec = rm.unit_vector(np.cross(-pn_direction, new_normal)) new_rotmat = rm.rotmat_from_axangle(vec, angle) direction = new_rotmat.dot(-pt_direction) gm.gen_stick(spos=cpt, epos=projected_point, rgba=[1,.6,0,1], thickness=.002, type='round').attach_to(base) cpt=projected_point # last_normal = new_normal direction = new_rotmat.dot(-tmp_direction) for tick in range(1, n+1): len = .05/n tmp_cpt = cpt extended_len = 0 for p in np.linspace(0, len, 1000): tmp_t_npt = cpt+direction*p tmp_z_surface = surface.get_zdata(np.array([tmp_t_npt[:2]])) tmp_projected_point = np.array([tmp_t_npt[0], tmp_t_npt[1], tmp_z_surface[0]]) tmp_len = np.linalg.norm(tmp_projected_point - tmp_cpt) extended_len += tmp_len tmp_cpt = tmp_projected_point print(tick, extended_len, len) if extended_len>len: break projected_point = tmp_projected_point t_npt = tmp_t_npt domain_grid = np.meshgrid(np.linspace(-.005, .005, 100, endpoint=True), np.linspace(-.005, .005, 100, endpoint=True)) domain_0, domain_1 = domain_grid domain = np.column_stack((domain_0.ravel()+t_npt[0], domain_1.ravel()+t_npt[1])) codomain = surface.get_zdata(domain) vertices = np.column_stack((domain, codomain)) plane_center, plane_normal = rm.fit_plane(vertices) new_normal = plane_normal if pn_direction.dot(new_normal) > .1: new_normal = -new_normal angle = rm.angle_between_vectors(-pn_direction, new_normal) vec = rm.unit_vector(np.cross(-pn_direction, new_normal)) new_rotmat = rm.rotmat_from_axangle(vec, angle) direction = new_rotmat.dot(-tmp_direction) gm.gen_stick(spos=cpt, epos=projected_point, rgba=[1,.6,0,1], thickness=.002, type='round').attach_to(base) cpt=projected_point # last_normal = new_normal base.run()

46.111628

147

0.694069

1,597

9,914

4.035066

0.097683

0.019863

0.039106

0.026071

0.788175

0.761949

0.761328

0.74612

0.729981

0.717877

0

0.04628

0.160884

9,914

214

148

46.327103

0.728333

0.034497

0

0.694301

0

0.006487

0

1

0

false

0

0.041451

0

0.041451

0.020725

0

null

0

1

0

null

0

5

594cb284abeb34f3dc359b047113ad375cf81c2f

214

py

Python

src/plexus/utils/logger.py

houseofbigseals/plexus

370a4a7af2dc4df1ce353a6b8c5f1c54c4f3a06a

[ "Plexus" ]

2

2022-02-25T17:46:49.000Z

2022-02-25T17:49:46.000Z

src/plexus/utils/logger.py

houseofbigseals/plexus

370a4a7af2dc4df1ce353a6b8c5f1c54c4f3a06a

[ "Plexus" ]

11

2022-03-09T21:15:44.000Z

2022-03-09T21:37:00.000Z

src/plexus/utils/logger.py

houseofbigseals/plexus

370a4a7af2dc4df1ce353a6b8c5f1c54c4f3a06a

[ "Plexus" ]

null

from datetime import datetime class PrintLogger(): def __init__(self, name): self.name = name def __call__(self, arg): print("{} | {} : {}".format(datetime.now(), self.name, arg))

23.777778

68

0.579439

24

214

4.833333

0.583333

0.206897

0

0.261682

214

9

68

23.777778

0.734177

0

0.056075

0

1

0.333333

false

0

0.166667

0

0.666667

0.166667

1

0

null

1

0

1

0

null

0

1

0

1

0

5

3ca197bc1347f2df110cdc0a84bc9933cd160740

33

py

Python

samples/src/main/resources/datasets/python/78.py

sritchie/kotlingrad

8165ed1cd77220a5347c58cded4c6f2bcf22ee30

[ "Apache-2.0" ]

11

2020-12-19T01:19:44.000Z

2021-12-25T20:43:33.000Z

src/main/resources/datasets/python/78.py

breandan/katholic

081c39f3acc73ff41f5865563debe78a36e1038f

[ "Apache-2.0" ]

null

src/main/resources/datasets/python/78.py

breandan/katholic

081c39f3acc73ff41f5865563debe78a36e1038f

[ "Apache-2.0" ]

2

2021-01-25T07:59:20.000Z

2021-08-07T07:13:49.000Z

def test15(x, y): {x.a, y.b}

11

17

0.454545

8

33

1.875

0.75

0

0.083333

0.272727

33

2

18

16.5

0.541667

0

1

0.5

false

0

0.5

0

1

null

0

1

0

null

0

1

0

5

3ce371b83b8d1ef63c4a66a5075f2d9fa2184cfd

160

py

Python

old-stuff-for-reference/nightjar-base/nightjar-src/python-src/nightjar/backend/impl/data_store_s3/tests/test_config.py

groboclown/nightjar-mesh

3655307b4a0ad00a0f18db835b3a0d04cb8e9615

[ "MIT" ]

3

2019-12-23T23:46:02.000Z

2020-08-07T23:10:20.000Z

old-stuff-for-reference/nightjar-base/nightjar-src/python-src/nightjar/backend/impl/data_store_s3/tests/test_config.py

groboclown/nightjar-mesh

3655307b4a0ad00a0f18db835b3a0d04cb8e9615

[ "MIT" ]

2

2020-02-07T15:59:15.000Z

2020-08-05T21:55:27.000Z

old-stuff-for-reference/nightjar-base/nightjar-src/python-src/nightjar/backend/impl/data_store_s3/tests/test_config.py

groboclown/nightjar-mesh

3655307b4a0ad00a0f18db835b3a0d04cb8e9615

[ "MIT" ]

1

2020-05-28T00:46:05.000Z

"""Test the config module""" import unittest # from .. import config class S3ConfigTest(unittest.TestCase): """Tests for the S3 configuration class."""

16

47

0.70625

19

160

5.947368

0.736842

0

0.015038

0.16875

160

9

48

17.777778

0.834586

0.51875

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

3ce857ee41613daf8cc5e2954e66bfa836c04fc6

57

py

Python

platon_account/__init__.py

awake006/platon-account

81041b0e34ac35ad4bb629dd2ece8aeabbe20468

[ "MIT" ]

null

platon_account/__init__.py

awake006/platon-account

81041b0e34ac35ad4bb629dd2ece8aeabbe20468

[ "MIT" ]

null

platon_account/__init__.py

awake006/platon-account

81041b0e34ac35ad4bb629dd2ece8aeabbe20468

[ "MIT" ]

null

from platon_account.account import Account # noqa: F401

28.5

56

0.807018

8

57

5.625

0.75

0

0.061224

0.140351

57

1

57

0.857143

0.175439

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

3cfc7c16ac4fc66a4ff92ed42200fd5dc386cf24

31

py

Python

ABC039/B.py

shimomura314/AtcoderCodes

db1d62a7715f5f1b3c40eceff8d34f0f34839f41

[ "MIT" ]

null

ABC039/B.py

shimomura314/AtcoderCodes

db1d62a7715f5f1b3c40eceff8d34f0f34839f41

[ "MIT" ]

null

ABC039/B.py

shimomura314/AtcoderCodes

db1d62a7715f5f1b3c40eceff8d34f0f34839f41

[ "MIT" ]

null

print(int(int(input())**(1/4)))

31

0.580645

6

31

3

0.833333

0

0.064516

0

31

1

31

0.516129

0

1

0

true

0

1

0

null

0

1

0

null

0

1

0

1

0

5

a721a509ce8bdb945a1089a48b0eac692d8f266b

33

py

Python

test/output/096.py

EliRibble/pyfmt

e84a5531a7c06703eddd9dbc2072b0c8deae8c57

[ "MIT" ]

null

test/output/096.py

EliRibble/pyfmt

e84a5531a7c06703eddd9dbc2072b0c8deae8c57

[ "MIT" ]

null

test/output/096.py

EliRibble/pyfmt

e84a5531a7c06703eddd9dbc2072b0c8deae8c57

[ "MIT" ]

null

print({i: i for i in range(10)})

16.5

32

0.606061

8

33

2.5

0.75

0

0.074074

0.181818

33

1

33

0.666667

0

1

0

true

0

1

0

null

0

1

0

null

0

1

0

1

0

5

59742bbb92403cbdaba5f5656ba53238695af28d

24

py

Python

tests/modules/ambiguous/pkg1/__init__.py

jouve/coveragepy

6aa3ae906a4d1fab8bbf5ef84e13ad7068ec361a

[ "Apache-2.0" ]

2,254

2015-01-05T01:28:03.000Z

2022-03-29T10:37:10.000Z

tests/modules/ambiguous/pkg1/__init__.py

jouve/coveragepy

6aa3ae906a4d1fab8bbf5ef84e13ad7068ec361a

[ "Apache-2.0" ]

707

2015-02-07T01:32:02.000Z

2022-03-31T18:00:14.000Z

tests/modules/ambiguous/pkg1/__init__.py

jouve/coveragepy

6aa3ae906a4d1fab8bbf5ef84e13ad7068ec361a

[ "Apache-2.0" ]

439

2015-01-16T15:06:08.000Z

2022-03-30T06:19:12.000Z

print("Ambiguous pkg1")

12

23

0.75

3

24

6

1

0

0.045455

0.083333

24

1

24

0.772727

0

0.583333

0

1

0

true

0

1

0

null

0

1

0

null

0

1

0

1

0

5

59c4f80d068cb2ddaeef67729553f8f9f270b91d

101

py

Python

CursoEmVideoPython/aula19.py

miguelabreuss/scripts_python

cf33934731a9d1b731672d4309aaea0a24ae151a

[ "MIT" ]

null

CursoEmVideoPython/aula19.py

miguelabreuss/scripts_python

cf33934731a9d1b731672d4309aaea0a24ae151a

[ "MIT" ]

1

2020-07-04T16:27:25.000Z

CursoEmVideoPython/aula19.py

miguelabreuss/scripts_python

cf33934731a9d1b731672d4309aaea0a24ae151a

[ "MIT" ]

null

teste = [0, 2, 3, 4, 5] print(teste) teste.insert(0, teste[3]) print(teste) teste.pop(4) print(teste)

16.833333

25

0.663366

20

101

3.35

0.45

0.447761

0

0.089888

0.118812

101

6

26

16.833333

0.662921

0

0.5

0

1

0

false

0

0.5

1

0

null

1

0

1

0

null

0

1

0

5

59e977bb1ef8b3dd72f60c247be6d06701b86f57

34

py

Python

test3.py

khlaiqjd/pyneta

0ee737308144fbf1b27984d561ca9d1ea2d2a680

[ "Apache-2.0" ]

null

test3.py

khlaiqjd/pyneta

0ee737308144fbf1b27984d561ca9d1ea2d2a680

[ "Apache-2.0" ]

null

test3.py

khlaiqjd/pyneta

0ee737308144fbf1b27984d561ca9d1ea2d2a680

[ "Apache-2.0" ]

null

for x in range (10) print(x)

6.8

20

0.558824

7

34

2.714286

0.857143

0

0.086957

0.323529

34

4

21

8.5

0.73913

0

null

0

null

0.5

1

0

null

0

1

0

null

0

1

0

1

0

5

ab7f7794263bca02df5cd980478b72f9c2b0d7ca

118

py

Python

mentalhacks/music/admin.py

ShubhamPatel33/mental-health-games

bd15fa5bc5627525455e690b679811707b9ddf40

[ "CC0-1.0" ]

null

mentalhacks/music/admin.py

ShubhamPatel33/mental-health-games

bd15fa5bc5627525455e690b679811707b9ddf40

[ "CC0-1.0" ]

null

mentalhacks/music/admin.py

ShubhamPatel33/mental-health-games

bd15fa5bc5627525455e690b679811707b9ddf40

[ "CC0-1.0" ]

null

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

29.5

32

0.822034

17

118

5.705882

0.647059

0

0.110169

118

4

33

29.5

0.92381

0.220339

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

ab81afccad793cc2cf0c06f8a0bcdf6dacf47836

5,808

py

Python

medvision/ops/cuda_fun_tools.py

TimothyZero/MedVision

92f5bc3f19f39542995f214818c93e3a870f8477

[ "Apache-2.0" ]

33

2021-06-16T08:49:34.000Z

2022-03-16T01:04:02.000Z

medvision/ops/cuda_fun_tools.py

TimothyZero/MedVision

92f5bc3f19f39542995f214818c93e3a870f8477

[ "Apache-2.0" ]

1

2021-12-04T08:30:24.000Z

2021-12-09T02:30:52.000Z

medvision/ops/cuda_fun_tools.py

TimothyZero/MedVision

92f5bc3f19f39542995f214818c93e3a870f8477

[ "Apache-2.0" ]

3

2021-08-23T05:48:58.000Z

2021-12-16T07:12:23.000Z

# -*- coding:utf-8 -*- import torch from medvision import _C def affine_2d(features, rois, out_size, spatial_scale, sampling_ratio=0, aligned=True, order=1): if isinstance(out_size, int): out_h = out_size out_w = out_size elif isinstance(out_size, tuple): assert len(out_size) == 2 assert isinstance(out_size[0], int) assert isinstance(out_size[1], int) out_h, out_w = out_size else: raise TypeError( '"out_size" must be an integer or tuple of integers') assert features.dtype in [torch.float32, torch.float16], \ f'input must be float16 or float32 nut get {features.dtype}' assert order in [0, 1, 3], f'order {order} is not supported!' if order == 0: # avoid sample average sampling_ratio = 1 batch_size, num_channels, data_height, data_width = features.size() num_rois = rois.size(0) output = features.new_zeros(num_rois, num_channels, out_h, out_w) _C.affine_2d( features, rois.type(features.type()), output, out_h, out_w, spatial_scale, sampling_ratio, aligned, order) return output def affine_3d(features, rois, out_size, spatial_scale, sampling_ratio=0, aligned=True, order=1): # clockwise is not used in 3d if isinstance(out_size, int): out_d = out_size out_h = out_size out_w = out_size elif isinstance(out_size, tuple): assert len(out_size) == 3 assert isinstance(out_size[0], int) assert isinstance(out_size[1], int) assert isinstance(out_size[2], int) out_d, out_h, out_w = out_size else: raise TypeError( '"out_size" must be an integer or tuple of integers') assert features.dtype in [torch.float32, torch.float16], \ f'input must be float16 or float32 nut get {features.dtype}' assert order in [0, 1, 3], f'order {order} is not supported!' if order == 0: # avoid sample average sampling_ratio = 1 batch_size, num_channels, data_depth, data_height, data_width = features.size() num_rois = rois.size(0) output = features.new_zeros(num_rois, num_channels, out_d, out_h, out_w) _C.affine_3d( features, rois.type(features.type()), output, out_d, out_h, out_w, spatial_scale, sampling_ratio, aligned, order) return output def apply_offset_2d(img, offset, order=1): """ image : b, c, d, h, w offset : b, 2, d, h, w """ assert img.shape[2:] == offset.shape[2:] assert offset.shape[1] == 2 channels = img.shape[1] kernel_size = [1, 1] stride = [1, 1] padding = [0, 0] dilation = [1, 1] group = 1 deformable_groups = 1 im2col_step = 64 weight = torch.eye(channels, channels).unsqueeze(-1).unsqueeze(-1).cuda() bias = torch.zeros(channels).cuda() offset = offset.cuda() if img.dtype == torch.float16: offset = offset.half() bias = bias.half() weight = weight.half() output = _C.deform_2d(img.contiguous(), weight.contiguous(), bias.contiguous(), offset.contiguous(), kernel_size[0], kernel_size[1], stride[0], stride[1], padding[0], padding[1], dilation[0], dilation[1], group, deformable_groups, im2col_step, order) assert img.shape == output.shape return output def apply_offset_3d(img, offset, order=1): assert img.shape[2:] == offset.shape[2:] assert offset.shape[1] == 3 channels = img.shape[1] kernel_size = [1, 1, 1] stride = [1, 1, 1] padding = [0, 0, 0] dilation = [1, 1, 1] group = 1 deformable_groups = 1 im2col_step = 64 weight = torch.eye(channels, channels).unsqueeze(-1).unsqueeze(-1).unsqueeze(-1).cuda() bias = torch.zeros(channels).cuda() offset = offset.cuda() if img.dtype == torch.float16: offset = offset.half() bias = bias.half() weight = weight.half() output = _C.deform_3d(img.contiguous(), weight.contiguous(), bias.contiguous(), offset.contiguous(), kernel_size[0], kernel_size[1], kernel_size[2], stride[0], stride[1], stride[2], padding[0], padding[1], padding[2], dilation[0], dilation[1], dilation[2], group, deformable_groups, im2col_step, order) assert img.shape == output.shape, f"input is {img.shape}, out is {output.shape}" return output def random_noise_2d(img, method, mean=0., std=1., inplace=False): """ method: 0 : uniform , U[-0.5, 0.5] 1 : normal, N(0, 1) mean: std * gen_noise + mean """ if not inplace: out = img.clone() _C.noise_2d(out, method, mean, std) return out else: _C.noise_2d(img, method, mean, std) return img def random_noise_3d(img, method, mean=0., std=1., inplace=False): if not inplace: out = img.clone() _C.noise_3d(out, method, mean, std) return out else: _C.noise_3d(img, method, mean, std) return img

29.784615

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0.534435

717

5,808

4.172943

0.157601

0.051471

0.051136

0.016043

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null

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1

0

1

0

null

0

5

abd46f3f796e0232106b0945fe70af10c2a75fcd

117

py

Python

app/src/services/__init__.py

beerjoa/flask-restplus-skeleton

efa221a00620746c9b7227d3840b2a43d788db1b

[ "Apache-2.0" ]

1

2021-02-03T03:30:00.000Z

app/src/services/__init__.py

beerjoa/flask-restplus-skeleton

efa221a00620746c9b7227d3840b2a43d788db1b

[ "Apache-2.0" ]

null

app/src/services/__init__.py

beerjoa/flask-restplus-skeleton

efa221a00620746c9b7227d3840b2a43d788db1b

[ "Apache-2.0" ]

null

from .calculation import ( create_calc, update_calc, delete_calc, select_calc, select_calc_list )

16.714286

26

0.692308

14

117

5.357143

0.642857

0.266667

0.373333

0

0.247863

117

7

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16.714286

0.852273

0

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true

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null

1

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1

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null

0

1

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5

f9ec00be1f12d6ec54ce82bc2487a372d6d7349c

215

py

Python

pyPowerCLI/__init__.py

arielsafari/pyPowerCLI

2dd2143199d32a0cc6256b9be9774a49c06e9d98

[ "MIT" ]

null

pyPowerCLI/__init__.py

arielsafari/pyPowerCLI

2dd2143199d32a0cc6256b9be9774a49c06e9d98

[ "MIT" ]

null

pyPowerCLI/__init__.py

arielsafari/pyPowerCLI

2dd2143199d32a0cc6256b9be9774a49c06e9d98

[ "MIT" ]

1

2020-10-13T12:14:04.000Z

from .install import is_installed from .install import install_powercli from .install import uninstall_powercli from .powershell import Powershell from .powershell import PowershellException __version__ = "0.0.1"

23.888889

43

0.837209

27

215

6.407407

0.444444

0.190751

0.294798

0

0.015789

0.116279

215

9

44

23.888889

0.894737

0

0.023256

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false

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0.833333

0

0.833333

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null

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1

0

1

0

null

0

1

0

1

0

5

e6064ab9f50b5bb33d70c208b56497aceb510bf2

150

py

Python

dtc/enums/unbundled_trade_indicator_enum.py

jseparovic/python-ws-dtc-client

fd3952cdaf7ab8c9d5a26ccf53b5e9acb3a9ea0f

[ "Apache-2.0" ]

15

2020-04-26T05:25:53.000Z

2022-02-11T19:38:42.000Z

dtc/enums/unbundled_trade_indicator_enum.py

jseparovic/python-ws-dtc-client

fd3952cdaf7ab8c9d5a26ccf53b5e9acb3a9ea0f

[ "Apache-2.0" ]

2

2021-01-08T19:58:08.000Z

2021-11-29T06:08:48.000Z

dtc/enums/unbundled_trade_indicator_enum.py

jseparovic/python-ws-dtc-client

fd3952cdaf7ab8c9d5a26ccf53b5e9acb3a9ea0f

[ "Apache-2.0" ]

4

2020-11-23T13:38:01.000Z

2021-12-27T13:21:06.000Z

class UnbundledTradeIndicatorEnum: UNBUNDLED_TRADE_NONE = 0 FIRST_SUB_TRADE_OF_UNBUNDLED_TRADE = 1 LAST_SUB_TRADE_OF_UNBUNDLED_TRADE = 2

25

42

0.813333

20

150

5.5

0.6

0.381818

0.181818

0.345455

0.436364

0

0.02381

0.16

150

5

43

30

0.849206

0

1

0

false

0

1

0

1

0

null

1

0

1

0

null

0

5

e647ab50e8c80fcfb4dd71878f67f512b7143656

138

py

Python

osf/files/addons-github-local.py

sifulan-access-federation/helm-charts

ea6a36cedc0e5743d6b04440816c9dd8071a23e2

[ "Apache-2.0" ]

null

osf/files/addons-github-local.py

sifulan-access-federation/helm-charts

ea6a36cedc0e5743d6b04440816c9dd8071a23e2

[ "Apache-2.0" ]

null

osf/files/addons-github-local.py

sifulan-access-federation/helm-charts

ea6a36cedc0e5743d6b04440816c9dd8071a23e2

[ "Apache-2.0" ]

null

import os # GitHub application credentials CLIENT_ID = os.environ['GITHUB_CLIENT_ID'] CLIENT_SECRET = os.environ['GITHUB_CLIENT_SECRET']

23

50

0.811594

19

138

5.578947

0.473684

0.150943

0.283019

0.396226

0

0.094203

138

5

51

27.6

0.848

0.217391

0

0.339623

0

1

0

false

0

0.333333

0

0.333333

0

1

0

null

0

1

0

1

0

null

0

1

0

5

0515ec51b6d64c74e28390fb0f0e6e1a807c4f68

267

py

Python

import_filter.py

jon2718/ipycool_2.0

34cf74ee99f4a725b997c50a7742ba788ac2dacd

[ "MIT" ]

null

import_filter.py

jon2718/ipycool_2.0

34cf74ee99f4a725b997c50a7742ba788ac2dacd

[ "MIT" ]

null

import_filter.py

jon2718/ipycool_2.0

34cf74ee99f4a725b997c50a7742ba788ac2dacd

[ "MIT" ]

null

from icoolobject import * from regularregioncontainer import * from regularregion import * from hard_edge_transport import * from icool_composite import * from container import * from cell import * from field import * from sregion import * from hard_edge_sol import *

26.7

36

0.816479

35

267

6.085714

0.428571

0.422535

0.131455

0.169014

0

0.146067

267

10

37

26.7

0.934211

0

1

0

true

0

1

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null

1

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1

0

null

0

1

0

1

0

1

0

5

55bcd00c2a99ae5705ec0b74e7d09173d52428fe

153

py

Python

exercicios/exercicio027.py

NicoCassio/cursoemvideo-python

2686ff74f4d45bdb0dc194f49f4dd19aae629d52

[ "MIT" ]

null

exercicios/exercicio027.py

NicoCassio/cursoemvideo-python

2686ff74f4d45bdb0dc194f49f4dd19aae629d52

[ "MIT" ]

null

exercicios/exercicio027.py

NicoCassio/cursoemvideo-python

2686ff74f4d45bdb0dc194f49f4dd19aae629d52

[ "MIT" ]

null

n = str(input('Nome Completo: ')).strip() print('Primeiro nome: {}'.format(n.split()[0])) print('Último nome: {}'.format(n.split()[len(n.split()) - 1]))

38.25

62

0.607843

23

153

4.043478

0.608696

0.193548

0.236559

0.344086

0

0.014286

0.084967

153

3

63

51

0.65

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1

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5

e99b58a5e9aa2ed895836703ecfa90a2e8eed06f

96

py

Python

enthought/blocks/ast_25/ast.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

3

2016-12-09T06:05:18.000Z

2018-03-01T13:00:29.000Z

enthought/blocks/ast_25/ast.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

1

2020-12-02T00:51:32.000Z

2020-12-02T08:48:55.000Z

enthought/blocks/ast_25/ast.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

null

# proxy module from __future__ import absolute_import from codetools.blocks.ast_25.ast import *

24

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5

e9b8b9aff145ccaafa1e7b2d2c25110a231c7df3

42,189

py

Python

optimization/first_sdEta_mjj_optimization/loose_analysis_sdeta_2.6_mjj_1250/Output/Histos/MadAnalysis5job_0/selection_7.py

sheride/axion_pheno

7d3fc08f5ae5b17a3500eba19a2e43f87f076ce5

[ "MIT" ]

null

optimization/first_sdEta_mjj_optimization/loose_analysis_sdeta_2.6_mjj_1250/Output/Histos/MadAnalysis5job_0/selection_7.py

sheride/axion_pheno

7d3fc08f5ae5b17a3500eba19a2e43f87f076ce5

[ "MIT" ]

null

optimization/first_sdEta_mjj_optimization/loose_analysis_sdeta_2.6_mjj_1250/Output/Histos/MadAnalysis5job_0/selection_7.py

sheride/axion_pheno

7d3fc08f5ae5b17a3500eba19a2e43f87f076ce5

[ "MIT" ]

null

def selection_7(): # Library import import numpy import matplotlib import matplotlib.pyplot as plt import matplotlib.gridspec as gridspec # Library version matplotlib_version = matplotlib.__version__ numpy_version = numpy.__version__ # Histo binning xBinning = numpy.linspace(0.0,8000.0,161,endpoint=True) # Creating data sequence: middle of each bin xData = numpy.array([25.0,75.0,125.0,175.0,225.0,275.0,325.0,375.0,425.0,475.0,525.0,575.0,625.0,675.0,725.0,775.0,825.0,875.0,925.0,975.0,1025.0,1075.0,1125.0,1175.0,1225.0,1275.0,1325.0,1375.0,1425.0,1475.0,1525.0,1575.0,1625.0,1675.0,1725.0,1775.0,1825.0,1875.0,1925.0,1975.0,2025.0,2075.0,2125.0,2175.0,2225.0,2275.0,2325.0,2375.0,2425.0,2475.0,2525.0,2575.0,2625.0,2675.0,2725.0,2775.0,2825.0,2875.0,2925.0,2975.0,3025.0,3075.0,3125.0,3175.0,3225.0,3275.0,3325.0,3375.0,3425.0,3475.0,3525.0,3575.0,3625.0,3675.0,3725.0,3775.0,3825.0,3875.0,3925.0,3975.0,4025.0,4075.0,4125.0,4175.0,4225.0,4275.0,4325.0,4375.0,4425.0,4475.0,4525.0,4575.0,4625.0,4675.0,4725.0,4775.0,4825.0,4875.0,4925.0,4975.0,5025.0,5075.0,5125.0,5175.0,5225.0,5275.0,5325.0,5375.0,5425.0,5475.0,5525.0,5575.0,5625.0,5675.0,5725.0,5775.0,5825.0,5875.0,5925.0,5975.0,6025.0,6075.0,6125.0,6175.0,6225.0,6275.0,6325.0,6375.0,6425.0,6475.0,6525.0,6575.0,6625.0,6675.0,6725.0,6775.0,6825.0,6875.0,6925.0,6975.0,7025.0,7075.0,7125.0,7175.0,7225.0,7275.0,7325.0,7375.0,7425.0,7475.0,7525.0,7575.0,7625.0,7675.0,7725.0,7775.0,7825.0,7875.0,7925.0,7975.0]) # Creating weights for histo: y8_M_0 y8_M_0_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,91.4454449781,87.8999678868,84.8498903891,82.5039723137,79.5030147757,75.699617896,72.8624202236,68.6373036899,66.2177056749,63.7612676902,59.9824307904,57.4441128728,53.6365959965,51.3930378371,48.7196000304,45.2314428921,41.8988456262,40.3554068924,37.907124901,34.8897833764,32.486557348,30.8652986781,29.1580680787,27.499961439,25.2809712595,23.4386327709,21.6904582051,20.961710803,19.4141480726,18.005781228,16.5032544607,15.6598711526,14.5954080259,13.5227609059,12.4050738229,11.467530592,10.611867294,10.0059397911,9.19122045951,8.46656505402,7.60271376273,7.3611619609,6.72657848151,6.05924302899,5.62527138502,5.15445177128,4.85967601312,4.27831649007,4.06542466472,3.47997154503,3.27526731297,3.24660853648,2.81672968915,2.48510916121,2.25993454595,2.29678131572,2.10026507694,1.82596170198,1.56803431358,1.71132699603,1.43702362106,1.31420092183,1.18319022931,1.05217953679,1.07674431664,0.982579993891,0.814722931601,0.802440541677,0.749217385341,0.695994229006,0.659147459235,0.54041915664,0.454443227174,0.401220070839,0.429878847327,0.446255233892,0.33980900122,0.376655730991,0.30296223145,0.257927308396,0.216986461984,0.257927308396,0.180139692213,0.192421962137,0.188327885496,0.13510476916,0.126916615877,0.122822539236,0.151481115725,0.106446192671,0.10235211603,0.0818816928242,0.110540269313,0.0695994229006,0.0491289996945,0.0655053462594,0.0655053462594,0.0409408464121,0.0409408464121,0.0409408464121,0.0245645038473,0.0245645038473,0.0204704192061,0.00818816928242,0.0122822539236,0.00818816928242,0.00818816928242,0.0204704192061,0.0163763345648,0.00818816928242,0.00818816928242,0.00818816928242,0.00409408464121,0.0122822539236,0.0245645038473,0.00818816928242,0.00409408464121,0.00818816928242,0.0,0.0122822539236,0.00409408464121,0.00818816928242,0.0,0.0,0.0,0.00818816928242,0.0122822539236,0.0122822539236,0.00409408464121,0.0,0.0,0.00409408464121,0.0,0.0,0.0]) # Creating weights for histo: y8_M_1 y8_M_1_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,21.7137475141,18.7239646678,16.7192889547,16.5966432127,14.1680404358,12.6603321101,11.1051772131,8.6274015883,7.07200236908,6.2684987298,5.56554769663,5.7348269376,5.01689214425,4.75184260394,3.9978722879,3.72927887403,3.57229985426,2.87927270845,3.01340359671,2.50314187709,2.16243496185,2.00495408024,2.0054675574,1.91964838135,1.84664530683,1.48223272919,1.61554573885,1.40909868195,1.28775906248,1.14218308074,1.16635976431,0.984083383055,0.959476532193,0.789857643302,0.814075981805,0.486108266684,0.668383846883,0.631731510754,0.558847393114,0.570662574662,0.522333239217,0.449351392694,0.42502851633,0.473857310448,0.255485807905,0.303531729591,0.170176263942,0.255210925397,0.255139150742,0.243264610914,0.218714875374,0.206641913873,0.0850845272169,0.121545971602,0.133787235056,0.0970824697852,0.133591176499,0.145906938199,0.13367440626,0.0606758577112,0.109388458597,0.0363835496599,0.048696419546,0.0486149521089,0.0121471633567,0.0364699956626,0.0243169325254,0.0242461191386,0.0242698063767,0.0,0.0,0.0485277971713,0.0243416250893,0.0121530591319,0.0,0.0,0.0607590474196,0.0,0.0,0.0121471633567,0.0121205322363,0.0121471633567,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0121625276187,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0121863350153,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y8_M_2 y8_M_2_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,83.1212068927,76.824607181,69.3982532793,63.5827525776,60.2910899273,52.8836609889,50.9139359431,47.399801673,43.4944413076,39.3364905327,35.1412683237,31.092503843,27.0989523227,24.6183441763,19.7977841602,18.2718495015,17.7103111337,15.9945442186,13.3938369334,12.9606370879,11.80666473,11.2243295631,10.6323253102,9.83944372954,8.72532122868,8.20258316753,6.84748968868,6.59617196225,6.12477922703,5.13044182625,4.75881009248,4.79937480072,3.93553907034,3.32309007225,3.01214590225,3.12250281705,2.84131009412,2.42938141141,2.44955013311,2.23888595482,2.07849647845,1.73749963217,1.80718068555,1.64704326656,1.47577813448,1.47551987898,1.48584844623,1.30533570113,0.893687587195,1.10450795841,1.10456126235,0.793184049524,0.893564037762,0.873427546346,0.692699932667,0.672961361328,0.652713303535,0.512095455212,0.552208113029,0.471795613806,0.532079059371,0.462098016395,0.461943889511,0.431856916895,0.391730664509,0.341453896541,0.361380973736,0.361467169092,0.250996126019,0.291145642062,0.230908930413,0.210959994473,0.331238589819,0.21085284943,0.210896856167,0.18076897588,0.1204829271,0.0802791567416,0.160568147853,0.120489662404,0.0802927926321,0.140577932353,0.130570139092,0.100405235296,0.0903320724033,0.0702892967434,0.100369947264,0.0200759397984,0.0703052466032,0.0702111589586,0.0301292356122,0.060251132636,0.0200667830914,0.0602763383731,0.0602229931165,0.0501859446729,0.0301486977468,0.0200792413368,0.0501889610972,0.0401947210856,0.0200842659558,0.0200816792687,0.0,0.0100330444503,0.0200896996516,0.0100458580553,0.0,0.0,0.0,0.0201028396916,0.0,0.0401787051124,0.0100533743234,0.0100697952413,0.0,0.0301234630852,0.0100154872082,0.0100704150545,0.0,0.0100547792334,0.0,0.0100272843196,0.0,0.0,0.0,0.0,0.0,0.0100154872082,0.0,0.0,0.0200556388499,0.0,0.0,0.0,0.0]) # Creating weights for histo: y8_M_3 y8_M_3_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,79.5904910734,72.9289470262,66.3715664121,63.6979705878,59.3818100499,54.1827384781,50.1364116073,47.6928772189,45.4168818312,42.7770863096,38.757291864,35.916498292,33.2511169163,31.5063144119,29.3443909835,27.7343753114,25.9300038359,24.4113099194,23.1007681799,21.6164230086,19.7511622376,18.4819527434,17.3697602807,16.1376579939,15.3015189415,14.635608289,13.3114692403,12.8260083291,11.5174978579,10.917916237,9.71379670085,9.44371521904,8.48753227884,8.06977759921,7.64061937994,6.75987367797,5.85864873006,5.45108289171,4.86207199052,4.77379713728,4.62064357788,3.87219152971,3.5809714523,3.12942012618,2.57427655962,2.44782158312,2.25500019963,2.12876906889,2.08446507828,1.78208968219,1.78223512099,1.56755891673,1.38052136631,1.33634697061,1.15493684559,1.2320726303,1.14386968395,1.0834853618,0.940446299243,0.945851097658,0.753683376276,0.670950228926,0.654483550172,0.759017080303,0.599515401534,0.659978536895,0.57205590556,0.4839853979,0.467600376128,0.423597419462,0.467476062514,0.401526674884,0.429049749552,0.341017714112,0.36312693091,0.30244734361,0.291602280838,0.24201118349,0.214574640847,0.231050094679,0.18689828668,0.14298072453,0.148493383285,0.154001004496,0.120985218125,0.0880644544807,0.142968983799,0.120993383823,0.15407563329,0.060503760725,0.0990176213459,0.0384790081212,0.0880574669181,0.0604752823449,0.0660378696729,0.0495180935686,0.0604598040813,0.0384823190884,0.054997886405,0.0385418718093,0.0495465719487,0.0440156317792,0.0275110901863,0.0219811737759,0.0219859106934,0.0164965302711,0.0550405024117,0.0110186183891,0.033022172678,0.0274857318342,0.0219984314305,0.0385002145612,0.0164806782541,0.0109726954777,0.0109825796287,0.0165265077271,0.0110184762003,0.00550967279154,0.0110136824074,0.0219944623325,0.0,0.0109726954777,0.0109987038357,0.00551624597542,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0109941131696,0.0,0.0,0.0]) # Creating weights for histo: y8_M_4 y8_M_4_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,21.7282444078,20.1516136663,18.8290934357,17.8351540167,16.4726589592,15.612845446,14.7721675814,13.6774273055,12.9720457716,12.394805744,11.3105111112,10.8710766133,10.2453200605,9.57743221272,8.85698746827,8.40210095419,7.91957316805,7.40543187947,6.89525079635,6.5488049688,6.28830121118,5.7819119847,5.4492988181,5.23022490213,4.88673319545,4.66485747421,4.33517838695,4.03515674922,3.76679150279,3.58215975017,3.36709494097,3.15313401901,3.01894999289,2.68122343145,2.43659256208,2.22630525176,2.11786536688,1.93916590583,1.86218760862,1.8029468632,1.54545614854,1.55134314626,1.48226802424,1.33125472827,1.26518583509,1.16351116758,1.11127253036,1.06380938786,0.995836549747,0.94338787734,0.861497803045,0.764815479561,0.751938398554,0.643414339273,0.654317730842,0.563566897264,0.520056344429,0.505239644578,0.467712689616,0.421415162236,0.400638794093,0.397679302096,0.375968838529,0.333550348422,0.287169648711,0.285202292395,0.267429547689,0.24276279877,0.196381056899,0.204312771223,0.197381088942,0.175636033702,0.167760475733,0.17169763343,0.138171407885,0.14013547739,0.138168642154,0.120370003797,0.114477274201,0.100669224248,0.087808136379,0.0779597707988,0.0917720695547,0.0818955656725,0.0592263556062,0.0730177710924,0.0582057208752,0.0631581421748,0.0601987303436,0.0493430776884,0.0384867837044,0.0355326267611,0.0355319493576,0.0306009564571,0.0197422855481,0.025641252067,0.0256502907951,0.0256554254339,0.0197391510535,0.0207323930273,0.0187556773192,0.00986133244729,0.0108594445118,0.0147995282721,0.010860470638,0.00789214433616,0.016774283919,0.0128345488813,0.00790000863079,0.00690554011558,0.0128299152806,0.0128322922055,0.0108522696457,0.00789609251669,0.00888518188644,0.0029547950654,0.000986294378594,0.00887319705415,0.00197195865162,0.00296165167212,0.000986370536391,0.0,0.00691084711155,0.00296223287637,0.0,0.0049384363231,0.0029612532466,0.0,0.0,0.0,0.0,0.000986798222547,0.00295688900399,0.0,0.000985639421539]) # Creating weights for histo: y8_M_5 y8_M_5_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.91855460371,3.6174640517,4.20693917985,4.56541470639,4.77335347739,4.95461373448,4.9149800574,4.69500234693,4.36724632096,4.20386242862,3.93484074342,3.69797931057,3.60169940255,3.4032913586,3.16055009011,2.97640872956,2.79858531162,2.6523736121,2.49562453974,2.35541030458,2.20942506036,2.07029669032,2.01006998518,1.88732481532,1.77227032324,1.63877772926,1.56393105445,1.46436154324,1.39673903231,1.29437043766,1.22208358787,1.15098302773,1.08189695945,0.99338774876,0.9523469281,0.900648705295,0.855530489086,0.799559222961,0.744875071102,0.710885572116,0.664713498594,0.630227478376,0.564645865757,0.546731891824,0.523590241484,0.491037893397,0.448725162184,0.424027131833,0.395258587613,0.364749698464,0.369288486669,0.306029641185,0.295952260659,0.280313802323,0.271504891511,0.252317646691,0.245028947059,0.214254593093,0.201663398786,0.189805143437,0.182005879143,0.177967573137,0.158066874118,0.14847825293,0.145944114186,0.123512677254,0.117725144151,0.116209294036,0.104608501549,0.0899986943717,0.0932659721208,0.0857044058644,0.0801640931238,0.0703269312506,0.0710848763129,0.0564613057747,0.0599973291416,0.0549529774964,0.0456265803331,0.0461230615556,0.0418454571219,0.0342865035034,0.0342827305822,0.0332722838682,0.0312525187159,0.023946054746,0.0254629411139,0.0211764505105,0.0181537564798,0.0191579776737,0.0138627167657,0.0184041696215,0.0168915002832,0.0136135519288,0.0131082445513,0.0108440477177,0.00907850863798,0.0105871609954,0.0113437697314,0.00831828301883,0.00932457271778,0.00730944869859,0.00806695365345,0.00655363615696,0.00554359354169,0.00680535361711,0.00378330654434,0.00579944801195,0.00478946941231,0.00605198967334,0.00327663483374,0.00277243172588,0.00201775066051,0.00201705449054,0.00226745042802,0.00226795335081,0.00126151601582,0.0017645552395,0.000252039498778,0.00201699607627,0.00226813219448,0.00100769764271,0.000504526786889,0.000252008691256,0.000252077027941,0.000251635560151,0.000252211300725,0.000755091165496,0.000252349254409,0.00151264973344,0.000252440476682,0.0,0.000252177412451,0.000504245118116,0.0]) # Creating weights for histo: y8_M_6 y8_M_6_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0675709162443,0.0738641563791,0.081865323916,0.0950551519078,0.115955424904,0.1540528524,0.24905860087,0.54450075124,0.769578092043,0.97135674394,1.09050739761,1.21425990848,1.29635138657,1.37854682981,1.37148019896,1.41805758348,1.42397559941,1.40954643692,1.37937555198,1.37231391945,1.30825039732,1.27768864342,1.21645717182,1.20028959215,1.12467694197,1.05503928862,0.977390221163,0.937176703105,0.872525777908,0.838325543843,0.792227198727,0.739314738104,0.732858002782,0.663943107119,0.629830643628,0.601862220293,0.57142292534,0.508836807897,0.48620599508,0.47266213578,0.460110543814,0.387985524373,0.38016644579,0.374773153939,0.332379166903,0.317545440043,0.300352504129,0.276511497041,0.271459890637,0.249025811863,0.249635607424,0.219316172286,0.207579807008,0.202504908413,0.182351465036,0.157740616044,0.166048630707,0.14655876486,0.135969914867,0.131374755435,0.118509868504,0.113604912934,0.10992254748,0.0961775356186,0.0838908848285,0.0839287621298,0.0804638837613,0.0807336333254,0.0655627095136,0.0638522229696,0.0586969113341,0.057831601437,0.0463726431555,0.0486606260887,0.0486765707431,0.041802795238,0.0377761252152,0.0392342863536,0.0283599020885,0.0334874030478,0.0323437764589,0.0286264727183,0.0248971929928,0.026022465735,0.0229178765763,0.0220534463843,0.0186058622178,0.0171680342625,0.0160298058638,0.0154619362472,0.0103145819439,0.0123100529443,0.00802243040826,0.00772660358685,0.0083051526231,0.00830043920332,0.00686764855923,0.00801431912764,0.0080162344855,0.00601087579998,0.00487152077897,0.00544337505969,0.00572330720993,0.00429545191122,0.00457449036108,0.00400743447165,0.00400385767081,0.0037233587106,0.0034295712055,0.00229398991931,0.00143265169067,0.00343798138591,0.000855259166694,0.00229207156245,0.0025750866791,0.00171731625432,0.000861006639802,0.000572159678336,0.00200202780222,0.000570381774395,0.000860475717798,0.00085972337003,0.000858643432089,0.000572915225033,0.00057356520712,0.00057356520712,0.0,0.0,0.000283554335866,0.00028617065872,0.00028711554194,0.00028643716937,0.000858461792985,0.0,0.0]) # Creating weights for histo: y8_M_7 y8_M_7_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.00248200333718,0.00228607836919,0.00274382167614,0.00326061588718,0.00356079428486,0.00373654723971,0.00393078034521,0.0037965817458,0.00436280499548,0.00470882167684,0.00503061501125,0.00552396663822,0.00591817017829,0.00565894264782,0.00585045376996,0.00734143430237,0.00801140262673,0.00878636533021,0.00956328098866,0.0114680742377,0.0122667489995,0.0154201719664,0.0292202755919,0.0580403127615,0.0848045966161,0.104900670815,0.119703985612,0.132157971025,0.139424556023,0.148449806302,0.154279041593,0.153735944096,0.157583223463,0.160528078384,0.155438870533,0.154930264065,0.151875817358,0.149781755113,0.145160124913,0.135640517194,0.125316048959,0.118050260229,0.111407237825,0.102772788084,0.101684539554,0.0941787385266,0.0899360486431,0.085380257332,0.0794292347633,0.0727409507418,0.0698421956165,0.0653620917875,0.0632568817741,0.0581214484011,0.0543934417771,0.0507414579063,0.0482575171205,0.0463002874498,0.0431278252675,0.0390447203024,0.0339006453133,0.0324145304047,0.0302970620367,0.0278967169432,0.0267397880998,0.0241396925935,0.0247050269553,0.0221539606767,0.0202093665408,0.0197095316063,0.018398009195,0.0171704638471,0.0159319970386,0.0153326703641,0.0121598771014,0.0115514771491,0.0120716714296,0.0111885544155,0.00965121425265,0.00982154042343,0.00915725494656,0.00842389940373,0.00784253069495,0.00708062680619,0.00622029566369,0.00630578144764,0.00650244736506,0.00518212408378,0.00440692669043,0.00427655227857,0.00414750134993,0.00362707279867,0.00375672931098,0.0030870870379,0.00308767585801,0.00276532303973,0.00280651027317,0.002200073753,0.00224419586703,0.00218116152154,0.00159848609311,0.0016623200605,0.00144700216592,0.00131605402116,0.00127521714617,0.00151054109462,0.0011016074127,0.000948830176713,0.000884146715821,0.00114454015202,0.000583359381757,0.000756130098948,0.000691229549928,0.000691296604176,0.000454014252603,0.000410410509813,0.000323951140092,0.000259355184992,0.000453626595233,0.000302458661186,0.00034528507761,0.000237588915212,0.000172843764411,0.000151174136565,0.00019455157127,0.000172758060701,0.000215993298492,0.000108062404317,8.64719423925e-05,0.000194466077104,0.000107962074399,8.64273513178e-05,6.48593944747e-05,0.0,0.000151257786739]) # Creating weights for histo: y8_M_8 y8_M_8_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.000340529049739,0.000226834382902,0.000226190034121,0.000282455983469,0.000198556099336,0.000311927478019,0.000283915606464,0.000396934293661,0.000426309707626,0.000284036635331,0.000452470951268,0.000423984616851,0.000537416138714,0.000538236313483,0.000708132479939,0.000482514919788,0.000652958314929,0.000369270213036,0.00065283624655,0.000397530230896,0.000482461310682,0.000338402505707,0.000709753078753,0.000963777970429,0.00059755946688,0.000595947481163,0.000737284398878,0.00101727837302,0.000988734271454,0.000823323598187,0.000847222864267,0.000510676184032,0.000878811992748,0.000936591174197,0.000960263529713,0.00133213712567,0.00183731028301,0.00244296843741,0.00493542357407,0.0090128668223,0.0127721155247,0.0155716780719,0.0175810691295,0.0206949265685,0.0216567422378,0.0224287133616,0.0230155919938,0.023692729946,0.0237952258043,0.0244905403619,0.022532011129,0.025089804034,0.0235187156795,0.0237305236231,0.0217177467272,0.021652777243,0.0226582375554,0.0212580449322,0.0208667430102,0.019452754581,0.0174422349106,0.0181196995665,0.0180909941921,0.0161865195701,0.0156444884447,0.0146976981752,0.0153205023334,0.0139210462785,0.0123359245154,0.0118532628759,0.011694077987,0.011057299821,0.0106604647264,0.0100896094253,0.00947285968057,0.00915577147223,0.00821337388241,0.00714859754463,0.00739388667658,0.0062436743533,0.0068207325695,0.00547086270994,0.00654661629178,0.00572259532971,0.00525103122067,0.004399702285,0.00376636044776,0.0036318951482,0.00365377687048,0.00354583842569,0.00351659250519,0.00283266208625,0.00289430513286,0.00238034676474,0.00215560283996,0.00206969016408,0.00187019003958,0.0017550492634,0.00161363111527,0.00130419163436,0.00124945257927,0.00124738781194,0.00116378775269,0.00107706678218,0.000737893107257,0.00098884891475,0.000538168745219,0.000795156542378,0.000651599376072,0.00053781130168,0.000509176911089,0.000821214577354,0.000396265293601,0.00053726006345,0.000423722362887,0.000426096904721,0.000255661380169,0.0004539161696,0.000454472605388,0.000254088004889,0.000226904327193,0.000142027227766,0.000141940072132,0.000170191387793,8.47613423681e-05,0.000141584722466,8.52851226372e-05,0.00017019287281,0.000142127303047,8.4964953019e-05,0.000170293111443,5.67060637719e-05,5.67246858824e-05,0.0,0.000198725985256]) # Creating weights for histo: y8_M_9 y8_M_9_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,52.1413377758,33.8897872924,20.8552853687,15.6417553124,15.6333032133,18.2539922864,15.6320842345,7.82602851461,7.81914147662,7.80668251343,13.0190551166,5.22010562013,2.60351608428,5.20856569774,0.0,0.0,2.61218275442,0.0,0.0,2.60351608428,0.0,0.0,0.0,2.61303911587,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y8_M_10 y8_M_10_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,188.512260326,149.540774668,122.164038373,88.4754537986,69.5160542238,53.7174459245,52.6830352241,45.293106684,38.9715241329,43.1924276653,25.2817208533,11.5915943805,6.31543418471,13.6905573971,10.5254876588,7.37189897518,8.42519724155,11.5903208453,6.31768114714,5.26434440539,3.16002984769,4.21735610201,5.26718773627,3.15631158658,3.15886904535,1.05320784921,4.21410493206,2.1078104311,0.0,0.0,0.0,2.10380283509,0.0,1.05320784921,0.0,1.05407970141,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.05312782041,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y8_M_11 y8_M_11_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,257.521814883,205.008890842,172.758021752,146.025028807,124.15540243,105.714439494,96.7312821077,76.0072972442,68.6244876245,55.2815833135,51.3715475602,37.7713811019,32.7038578038,31.3289304508,23.0357784693,21.1951518373,19.3473207585,15.8948000327,14.5120496111,11.9749278081,10.3667684996,7.37337659746,8.52287969407,7.37318832126,4.60767595098,3.45462634539,4.60783348821,4.14604958583,1.84201027007,3.45644263444,2.07236236805,2.76433775729,0.459422380804,0.921741909787,2.99382916403,2.07329875401,0.921904442105,1.61187257642,0.920906578203,0.460790649327,0.460092490409,0.230531613584,0.230502872644,0.0,0.461045782801,0.0,0.0,0.460503239932,0.230000559406,0.230818869284,0.229683717445,0.0,0.460341476088,0.230166626707,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.230818869284,0.0,0.230732569618,0.0,0.0,0.230345104867,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y8_M_12 y8_M_12_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,146.926923549,122.644963462,104.311721772,90.6344052871,76.7066014652,66.37295046,57.9293227849,50.8111120566,42.0902978871,38.3802345653,32.5905646622,30.3782701949,23.7603108785,22.2359890583,18.6347151839,16.7247905945,14.8992568856,13.3482243668,11.7679920494,11.1867583237,8.77754026049,7.67130646617,7.03273110695,6.06453657151,5.23296939428,4.23688308807,4.68024298191,3.51696339915,3.04629688466,2.68672194664,2.10426406078,2.38175024736,2.13170071479,1.91074479977,1.68918219064,1.41222883306,0.83077697451,1.19038884508,0.60924783551,1.02469326045,0.664600497276,0.775557423815,0.415400028677,0.443286798162,0.498712940316,0.360059947062,0.360017359213,0.221452196183,0.332240502542,0.276862295761,0.276993290905,0.166050714201,0.138279589769,0.11072879109,0.110728829562,0.083081852363,0.0830894697018,0.055407291165,0.11079900141,0.0276649856646,0.0829972152655,0.0277334878536,0.0276723914107,0.0,0.0,0.027692458097,0.0276413026659,0.0,0.0276413026659,0.0276409217989,0.0276604729685,0.0277233275548,0.0276901036469,0.0277086391712,0.0276858910277,0.0276935968507,0.0,0.0276261987911,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0276409217989,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y8_M_13 y8_M_13_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,29.026318908,35.3250618544,39.2196044282,39.5429728949,39.2384950984,39.479977698,36.1842809013,32.1715335842,28.7040852165,24.6808397113,21.5952159063,18.983776446,16.7865663601,14.2453858718,12.9247866288,11.7354814885,10.0416443511,8.92265886615,8.36796616226,7.56194198062,6.55344233997,5.45462126494,4.87935029188,4.3853237649,3.88109518369,3.44845392916,3.08534466243,2.38958483158,2.48050763616,2.1373024486,1.71428433089,1.54283920993,1.40142740257,1.19999630088,1.18951024941,1.13920208248,0.836908273255,0.826855499611,0.917320753102,0.766500020518,0.594829582954,0.494083144374,0.453466322135,0.393126074517,0.423334459064,0.443669570777,0.302442543671,0.363097918981,0.262124343302,0.201567456774,0.191568266367,0.232050129241,0.252108707757,0.211650389953,0.19161887613,0.141113488899,0.141271932651,0.0907782572724,0.100839951342,0.100880487699,0.0704538257963,0.0605084552915,0.0504475318978,0.0503439821396,0.0807050940837,0.0503857382276,0.070658631489,0.0201428067537,0.0302693954036,0.030226498472,0.0201285280042,0.0,0.0201632023665,0.0201653869605,0.0101024428174,0.0503902287821,0.0201733485922,0.0100987896906,0.0,0.0,0.0201365867289,0.0,0.0201574131922,0.0100787945867,0.0,0.0,0.0100786186055,0.0100787945867,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y8_M_14 y8_M_14_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.543124652013,0.529074699622,0.514840841287,0.622532006575,0.636645056403,0.840329737806,1.66342771956,4.10233755586,6.40501310109,7.87374829344,8.73894496188,9.4154879895,9.53442281021,9.75768770349,9.68154371757,9.51718258987,9.08727743892,8.88915148779,8.36249180408,7.94727219678,7.36480206086,7.22585304103,6.51823452585,5.92998943552,5.28221730005,4.61711645394,4.20949547086,3.63827899362,3.28749841299,3.0810062804,2.76983434615,2.52082531398,2.22079161627,1.91255563653,1.8361500271,1.5872671898,1.46554261596,1.37500510441,1.15725931315,1.03840221001,0.797821687258,0.789382405104,0.746872430853,0.763996074838,0.650740408169,0.560155573546,0.472479761597,0.472422820008,0.393182442809,0.367883256259,0.32254028405,0.29428228941,0.31959013262,0.223468151663,0.251803979299,0.183900364715,0.178272111838,0.158434470121,0.186771413505,0.138571358584,0.124473390583,0.141509467638,0.132977270661,0.118823784611,0.084924648288,0.0848683607576,0.0594484041756,0.0622359258105,0.0565856273053,0.0537432032056,0.0197968784648,0.0565879742221,0.0226221162078,0.022638433051,0.0282886045919,0.0197985982547,0.0113217308044,0.0254694726802,0.0198233716934,0.0254744512218,0.0113217192622,0.0113298295913,0.00566533031535,0.00849214934162,0.00847760615195,0.00283038403144,0.00564845175104,0.0113323342517,0.0,0.00283438379334,0.00283013933651,0.00282797555612,0.00283099961619,0.00566126360861,0.00565840113953,0.00283009162869,0.00283013933651,0.00564985220634,0.00565938992253,0.0,0.00282876042667,0.0,0.0,0.00283011125045,0.0028292798263,0.0,0.0,0.00283619822939,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.00283094844571,0.0,0.0,0.0028292798263,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y8_M_15 y8_M_15_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0274077427031,0.0243797473086,0.0274037252112,0.021268767076,0.0303979145937,0.0198307058821,0.0182284119861,0.0229447228659,0.0274496545956,0.00910363762637,0.0273896403576,0.0350122284461,0.0152631367285,0.0152076126285,0.0213383051328,0.0289049319235,0.0167406047907,0.0228089552749,0.0273415131689,0.0228583940565,0.0335549069238,0.0503323662937,0.189002075139,0.37933252213,0.593795806108,0.743303455349,0.795113384234,0.881734170696,1.00665937835,0.945937234689,0.993189082791,1.0313414301,1.0024574364,0.906516541581,0.939829347359,0.840888922257,0.851375166692,0.762932565855,0.785767162181,0.738620950481,0.621521226068,0.604987120357,0.539364109332,0.452426295501,0.393033114499,0.37635496989,0.383920852263,0.327553078918,0.274384765154,0.280370237146,0.234636882787,0.201138917902,0.164498092804,0.167433225057,0.173553283024,0.162876444051,0.156934691823,0.129430352074,0.118827009569,0.109730545528,0.103670419086,0.0807514081679,0.0746512840499,0.0821665814746,0.0655582821422,0.0670738454797,0.0593457155181,0.044125341445,0.0487332273105,0.0350130555767,0.0365709089235,0.0365708616589,0.0167310100749,0.0273955366176,0.0243484226886,0.0274688676594,0.0121585017697,0.0152244506456,0.0121593289004,0.00455364027307,0.0212863967753,0.00761678990248,0.0106543894656,0.0106824930024,0.00758503162971,0.00456292304239,0.00456440833274,0.00611820401282,0.00305143509593,0.00153593320251,0.00303104632499,0.00455393804011,0.00612720201286,0.00455945145682,0.00305176122174,0.00306713167276,0.00304446238441,0.00304105342445,0.00459029279616,0.0,0.0,0.00152156948767,0.0,0.0,0.00153117011149,0.0,0.0,0.0,0.0,0.0015277280663,0.0,0.0030294109695,0.0,0.0,0.0,0.0,0.00154504581923,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y8_M_16 y8_M_16_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.00162502838019,0.00216863937262,0.00162359379128,0.00198514832545,0.00253011110502,0.00144329041994,0.00180668315355,0.00270776944101,0.00162486739814,0.00198780337404,0.00162450191495,0.00162484005429,0.00126353623619,0.00126474552725,0.00108374315487,0.00198528311904,0.00108362453651,0.000901856536544,0.000901657042031,0.000722885696117,0.00090354723328,0.00198681629984,0.00144446158367,0.00126382122836,0.000720861866653,0.00198573718088,0.000541847122028,0.00162538230965,0.00162537922865,0.00126355549242,0.00072170798527,0.00180596027479,0.00144432447933,0.00180585475066,0.00216756949665,0.00289131286512,0.00216796386418,0.00613874269101,0.0277971301606,0.0565147156772,0.0850477071499,0.109786913792,0.119894969526,0.128199332983,0.142473088317,0.139942974131,0.145165917622,0.142453023329,0.144814491478,0.148240328322,0.139916978225,0.138671562483,0.132703980692,0.14047729592,0.122607555719,0.123861636763,0.120429984538,0.111397620161,0.114653501545,0.103820872499,0.0957081085008,0.0982212386945,0.0756550972763,0.0801734169064,0.0736596514909,0.0715043790208,0.0660858307756,0.0592265316217,0.0547089822407,0.0556135242507,0.0516464718889,0.0471312717677,0.0420645733033,0.0422530147413,0.0359403189976,0.0319568949915,0.0330348739874,0.0321397213137,0.0245523057022,0.0283464255912,0.0229372551286,0.0231140773592,0.0178719277077,0.0194983394552,0.0182355777045,0.0151729402616,0.0106512084281,0.0119191346989,0.0117392045132,0.0117365356002,0.0120963613103,0.00650037848646,0.00848812948357,0.00650126042166,0.00740169315278,0.00632136489721,0.00758273634836,0.00541606034065,0.00324831099084,0.00342979091766,0.00379314167269,0.00289089038349,0.00307108977121,0.00234757709234,0.00198689486525,0.00234853489707,0.00234855530867,0.00144368825359,0.00234903941022,0.000902661831959,0.000902969546468,0.00108361105715,0.0010846632174,0.000902792389179,0.0012651306518,0.00126423947361,0.000723516144991,0.000721795793665,0.0007219344385,0.000542112857961,0.000901268451369,0.000541610270435,0.000903103184684,0.000722723558685,0.000541070325828,0.000181036147804,0.0,0.000722040347749,0.000360682034527,0.0,0.000180220184439,0.0,0.0,0.0,0.000180820863186]) # Creating a new Canvas fig = plt.figure(figsize=(12,6),dpi=80) frame = gridspec.GridSpec(1,1,right=0.7) pad = fig.add_subplot(frame[0]) # Creating a new Stack pad.hist(x=xData, bins=xBinning, weights=y8_M_0_weights+y8_M_1_weights+y8_M_2_weights+y8_M_3_weights+y8_M_4_weights+y8_M_5_weights+y8_M_6_weights+y8_M_7_weights+y8_M_8_weights+y8_M_9_weights+y8_M_10_weights+y8_M_11_weights+y8_M_12_weights+y8_M_13_weights+y8_M_14_weights+y8_M_15_weights+y8_M_16_weights,\ label="$bg\_dip\_1600\_inf$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#e5e5e5", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y8_M_0_weights+y8_M_1_weights+y8_M_2_weights+y8_M_3_weights+y8_M_4_weights+y8_M_5_weights+y8_M_6_weights+y8_M_7_weights+y8_M_8_weights+y8_M_9_weights+y8_M_10_weights+y8_M_11_weights+y8_M_12_weights+y8_M_13_weights+y8_M_14_weights+y8_M_15_weights,\ label="$bg\_dip\_1200\_1600$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#f2f2f2", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y8_M_0_weights+y8_M_1_weights+y8_M_2_weights+y8_M_3_weights+y8_M_4_weights+y8_M_5_weights+y8_M_6_weights+y8_M_7_weights+y8_M_8_weights+y8_M_9_weights+y8_M_10_weights+y8_M_11_weights+y8_M_12_weights+y8_M_13_weights+y8_M_14_weights,\ label="$bg\_dip\_800\_1200$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#ccc6aa", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y8_M_0_weights+y8_M_1_weights+y8_M_2_weights+y8_M_3_weights+y8_M_4_weights+y8_M_5_weights+y8_M_6_weights+y8_M_7_weights+y8_M_8_weights+y8_M_9_weights+y8_M_10_weights+y8_M_11_weights+y8_M_12_weights+y8_M_13_weights,\ label="$bg\_dip\_600\_800$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#ccc6aa", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y8_M_0_weights+y8_M_1_weights+y8_M_2_weights+y8_M_3_weights+y8_M_4_weights+y8_M_5_weights+y8_M_6_weights+y8_M_7_weights+y8_M_8_weights+y8_M_9_weights+y8_M_10_weights+y8_M_11_weights+y8_M_12_weights,\ label="$bg\_dip\_400\_600$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#c1bfa8", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y8_M_0_weights+y8_M_1_weights+y8_M_2_weights+y8_M_3_weights+y8_M_4_weights+y8_M_5_weights+y8_M_6_weights+y8_M_7_weights+y8_M_8_weights+y8_M_9_weights+y8_M_10_weights+y8_M_11_weights,\ label="$bg\_dip\_200\_400$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#bab5a3", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y8_M_0_weights+y8_M_1_weights+y8_M_2_weights+y8_M_3_weights+y8_M_4_weights+y8_M_5_weights+y8_M_6_weights+y8_M_7_weights+y8_M_8_weights+y8_M_9_weights+y8_M_10_weights,\ label="$bg\_dip\_100\_200$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#b2a596", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y8_M_0_weights+y8_M_1_weights+y8_M_2_weights+y8_M_3_weights+y8_M_4_weights+y8_M_5_weights+y8_M_6_weights+y8_M_7_weights+y8_M_8_weights+y8_M_9_weights,\ label="$bg\_dip\_0\_100$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#b7a39b", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y8_M_0_weights+y8_M_1_weights+y8_M_2_weights+y8_M_3_weights+y8_M_4_weights+y8_M_5_weights+y8_M_6_weights+y8_M_7_weights+y8_M_8_weights,\ label="$bg\_vbf\_1600\_inf$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#ad998c", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y8_M_0_weights+y8_M_1_weights+y8_M_2_weights+y8_M_3_weights+y8_M_4_weights+y8_M_5_weights+y8_M_6_weights+y8_M_7_weights,\ label="$bg\_vbf\_1200\_1600$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#9b8e82", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y8_M_0_weights+y8_M_1_weights+y8_M_2_weights+y8_M_3_weights+y8_M_4_weights+y8_M_5_weights+y8_M_6_weights,\ label="$bg\_vbf\_800\_1200$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#876656", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y8_M_0_weights+y8_M_1_weights+y8_M_2_weights+y8_M_3_weights+y8_M_4_weights+y8_M_5_weights,\ label="$bg\_vbf\_600\_800$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#afcec6", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y8_M_0_weights+y8_M_1_weights+y8_M_2_weights+y8_M_3_weights+y8_M_4_weights,\ label="$bg\_vbf\_400\_600$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#84c1a3", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y8_M_0_weights+y8_M_1_weights+y8_M_2_weights+y8_M_3_weights,\ label="$bg\_vbf\_200\_400$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#89a8a0", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y8_M_0_weights+y8_M_1_weights+y8_M_2_weights,\ label="$bg\_vbf\_100\_200$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#829e8c", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y8_M_0_weights+y8_M_1_weights,\ label="$bg\_vbf\_0\_100$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#adbcc6", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y8_M_0_weights,\ label="$signal$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#7a8e99", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") # Axis plt.rc('text',usetex=False) plt.xlabel(r"M [ j_{1} , j_{2} ] ( GeV ) ",\ fontsize=16,color="black") plt.ylabel(r"$\mathrm{Events}$ $(\mathcal{L}_{\mathrm{int}} = 40.0\ \mathrm{fb}^{-1})$ ",\ fontsize=16,color="black") # Boundary of y-axis ymax=(y8_M_0_weights+y8_M_1_weights+y8_M_2_weights+y8_M_3_weights+y8_M_4_weights+y8_M_5_weights+y8_M_6_weights+y8_M_7_weights+y8_M_8_weights+y8_M_9_weights+y8_M_10_weights+y8_M_11_weights+y8_M_12_weights+y8_M_13_weights+y8_M_14_weights+y8_M_15_weights+y8_M_16_weights).max()*1.1 ymin=0 # linear scale #ymin=min([x for x in (y8_M_0_weights+y8_M_1_weights+y8_M_2_weights+y8_M_3_weights+y8_M_4_weights+y8_M_5_weights+y8_M_6_weights+y8_M_7_weights+y8_M_8_weights+y8_M_9_weights+y8_M_10_weights+y8_M_11_weights+y8_M_12_weights+y8_M_13_weights+y8_M_14_weights+y8_M_15_weights+y8_M_16_weights) if x])/100. # log scale plt.gca().set_ylim(ymin,ymax) # Log/Linear scale for X-axis plt.gca().set_xscale("linear") #plt.gca().set_xscale("log",nonposx="clip") # Log/Linear scale for Y-axis plt.gca().set_yscale("linear") #plt.gca().set_yscale("log",nonposy="clip") # Legend plt.legend(bbox_to_anchor=(1.05,1), loc=2, borderaxespad=0.) # Saving the image plt.savefig('../../HTML/MadAnalysis5job_0/selection_7.png') plt.savefig('../../PDF/MadAnalysis5job_0/selection_7.png') plt.savefig('../../DVI/MadAnalysis5job_0/selection_7.eps') # Running! if __name__ == '__main__': selection_7()

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0

1

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1

0

1

0

null

0

5

e9cb61d999b3e8ff007f5af0d43063c443cb8c6b

62

py

Python

common/darts/api/__init__.py

j-woz/Benchmarks

d518162fdafb7cfa26071b6a30a3b456dad024f6

[ "MIT" ]

51

2017-01-24T20:57:27.000Z

2022-02-15T00:33:45.000Z

common/darts/api/__init__.py

j-woz/Benchmarks

d518162fdafb7cfa26071b6a30a3b456dad024f6

[ "MIT" ]

59

2017-08-21T22:19:44.000Z

2021-11-01T16:05:35.000Z

common/darts/api/__init__.py

j-woz/Benchmarks

d518162fdafb7cfa26071b6a30a3b456dad024f6

[ "MIT" ]

90

2016-11-22T03:57:07.000Z

2022-01-11T04:43:23.000Z

from .model import Model from .dataset import InMemoryDataset

20.666667

36

0.83871

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6.5

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0

1

0

1

0

1

0

5

e9cfa5403cc8e3974c33ff70c9186fd105e11ea1

101

py

Python

sunday/sponsorapp/admin.py

H0oxy/zxc-djangoprjct

e5c56e16a876af1ece2a6727df89bb924ae8813b

[ "MIT" ]

2

2020-10-18T21:02:54.000Z

2020-10-18T21:03:46.000Z

sunday/sponsorapp/admin.py

H0oxy/zxc-djangoprjct

e5c56e16a876af1ece2a6727df89bb924ae8813b

[ "MIT" ]

null

sunday/sponsorapp/admin.py

H0oxy/zxc-djangoprjct

e5c56e16a876af1ece2a6727df89bb924ae8813b

[ "MIT" ]

null

from django.contrib import admin from sponsorapp.models import Sponsor admin.site.register(Sponsor)

20.2

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null

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0

1

0

1

0

5

7590ef216c06a46f77e9be95921201f07c9fb14d

191

py

Python

test/test_del_contact.py

Sviatlana-Pi/python_training

ecd7fc7d53b3334d1b21d04c12f0355b3f29751c

[ "Apache-2.0" ]

null

test/test_del_contact.py

Sviatlana-Pi/python_training

ecd7fc7d53b3334d1b21d04c12f0355b3f29751c

[ "Apache-2.0" ]

null

test/test_del_contact.py

Sviatlana-Pi/python_training

ecd7fc7d53b3334d1b21d04c12f0355b3f29751c

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- def test_del_first_contact(app): app.session.login(username = "admin", password = "secret") app.contact.delete_first_contact() app.session.logout()

27.285714

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7

64

27.285714

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1

0

null

0

1

0

1

0

5

7592a8369f48ee5c95c640a8890cc582007d851a

55

py

Python

tinychat/__init__.py

alethea/udp-chat

12a699920cab7881aabce502e07bddbf34e51749

[ "Apache-2.0" ]

1

2015-02-15T22:58:47.000Z

tinychat/__init__.py

alethea/udp-chat

12a699920cab7881aabce502e07bddbf34e51749

[ "Apache-2.0" ]

null

tinychat/__init__.py

alethea/udp-chat

12a699920cab7881aabce502e07bddbf34e51749

[ "Apache-2.0" ]

null

# 9/23/2013 # Charles O. Goddard from . import server

11

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1

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null

0

1

0

1

0

1

0

5

759a39ea4e265f566a0116a7299bc0ad7ff6cad7

109

py

Python

library/favourite/admin.py

furkan-34/library-DRF-django-api

3634133b7c543d6d05845dd8fa1f206386c1badb

[ "MIT" ]

null

library/favourite/admin.py

furkan-34/library-DRF-django-api

3634133b7c543d6d05845dd8fa1f206386c1badb

[ "MIT" ]

null

library/favourite/admin.py

furkan-34/library-DRF-django-api

3634133b7c543d6d05845dd8fa1f206386c1badb

[ "MIT" ]

null

from django.contrib import admin from favourite.api.models import Favourite admin.site.register(Favourite)

18.166667

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109

6.066667

0.666667

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0

null

0

1

0

1

0

1

0

5

75e4a759c1c0db903673d30708c14c30316ea084

1,795

py

Python

bot/player_commands/__init__.py

UP929312/CommunityBot

c16294e8ff4f47d9a1e8c18c9cd4011e7ebbd67a

[ "Apache-2.0" ]

1

2021-06-15T07:31:13.000Z

bot/player_commands/__init__.py

UP929312/CommunityBot

c16294e8ff4f47d9a1e8c18c9cd4011e7ebbd67a

[ "Apache-2.0" ]

1

2021-06-01T10:14:32.000Z

2021-06-02T10:54:12.000Z

bot/player_commands/__init__.py

UP929312/CommunityBot

c16294e8ff4f47d9a1e8c18c9cd4011e7ebbd67a

[ "Apache-2.0" ]

2

2021-06-01T10:59:15.000Z

2021-06-03T18:29:36.000Z

from player_commands.bazaar import bazaar_cog from player_commands.sky import sky_cog from player_commands.wiki import wiki_cog from player_commands.dungeons import dungeons_cog from player_commands.kills import kills_cog from player_commands.lowest_bin import lowest_bin_cog from player_commands.skills import skills_cog from player_commands.slayer import slayer_cog from player_commands.invite import invite_cog from player_commands.auction_house import auction_house_cog from player_commands.missing import missing_cog from player_commands.weights import weights_cog from player_commands.leaderboard import leaderboard_cog from player_commands.price_check import price_check_cog from player_commands.minions import minions_cog from player_commands.rank import rank_cog from player_commands.guild_print import guild_print_cog from player_commands.maxer import maxer_cog from player_commands.set_prefix import set_prefix_cog from player_commands.link_account import link_account_cog from player_commands.help_command import help_cog from player_commands.regenerate_leaderboard import regenerate_leaderboard_cog #from player_commands._dev import _dev_cog assistant_commands = [set_prefix_cog, link_account_cog, help_cog, regenerate_leaderboard_cog] regular_commands = [sky_cog, wiki_cog, bazaar_cog, dungeons_cog, kills_cog, lowest_bin_cog, skills_cog, slayer_cog, invite_cog, auction_house_cog, missing_cog, weights_cog, leaderboard_cog, price_check_cog, minions_cog, rank_cog, guild_print_cog, maxer_cog] player_commands = regular_commands+assistant_commands

47.236842

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1,795

5.447257

0.156118

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0.320682

0.357862

0.049574

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0.197772

1,795

37

94

48.513514

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0

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false

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0.064516

0

null

1

0

null

0

1

0

1

0

5

ddf33eb8cc06c0b70baa29d7b075e961d0bad0f8

5,363

py

Python

tests/test_flask_twitter_oembedder.py

h3xh4wk/flask-twitter-oembedder

4115778a12affe97313e39d2bfa017dd4c032852

[ "MIT" ]

9

2015-03-30T16:15:11.000Z

2021-05-06T12:12:18.000Z

tests/test_flask_twitter_oembedder.py

h3xh4wk/flask-twitter-oembedder

4115778a12affe97313e39d2bfa017dd4c032852

[ "MIT" ]

null

tests/test_flask_twitter_oembedder.py

h3xh4wk/flask-twitter-oembedder

4115778a12affe97313e39d2bfa017dd4c032852

[ "MIT" ]

3

2016-10-24T13:39:10.000Z

2020-06-28T13:34:41.000Z

from flask import Flask, render_template_string, Markup from flask.ext.testing import TestCase, ContextVariableDoesNotExist from flask.ext.cache import Cache from flask.ext.twitter_oembedder import TwitterOEmbedder import types import httpretty class FlaskStaticTest(TestCase): def create_app(self): app = Flask(__name__) app.config['TESTING']=True app.config['CACHE_TYPE'] = 'simple' app.config['TWITTER_CONSUMER_KEY'] = 'twitter_consumer_key' app.config['TWITTER_CONSUMER_SECRET'] = 'twitter_consumer_secret' app.config['TWITTER_ACCESS_TOKEN'] = 'twitter_access_token' app.config['TWITTER_TOKEN_SECRET'] = 'twitter_token_secret' self.cache = Cache(app) self.twitter_oembedder = TwitterOEmbedder(app,self.cache) @app.route('/') def index(): return render_template_string('') return app def test_big_timeout_exception(self): try: self.twitter_oembedder.init(self.app, self.cache, timeout=60*60*24*365*2) assert False except Exception as e: self.assertIsInstance(e,Exception) def test_jinja_oembed_tweet_avaliable(self): response = self.client.get('/') self.assertIsInstance(self.get_context_variable('oembed_tweet'), types.FunctionType) @httpretty.activate def test_oembed_tweet_valid_id_debug_off(self): with open('tests/data/99530515043983360.json') as f: httpretty.register_uri(httpretty.GET, 'https://api.twitter.com/1.1/statuses/oembed.json?id=99530515043983360', body = f.read()) response = self.client.get('/') oembed_tweet = self.get_context_variable('oembed_tweet') valid = oembed_tweet('99530515043983360') self.assertIsInstance(valid, Markup) @httpretty.activate def test_oembed_tweet_invaild_id_debug_off(self): with open('tests/data/abc.json') as f: httpretty.register_uri(httpretty.GET, 'https://api.twitter.com/1.1/statuses/oembed.json?id=abc', body = f.read()) response = self.client.get('/') oembed_tweet = self.get_context_variable('oembed_tweet') invalid = oembed_tweet('abc') self.assertIs(invalid,'') @httpretty.activate def test_oembed_tweet_invalid_id_debug_on(self): self.twitter_oembedder.init(self.app, self.cache, debug=True) with open('tests/data/abc.json') as f: httpretty.register_uri(httpretty.GET, 'https://api.twitter.com/1.1/statuses/oembed.json?id=abc', body = f.read()) response = self.client.get('/') oembed_tweet = self.get_context_variable('oembed_tweet') try: invalid = oembed_tweet('abc') assert False except Exception as e: self.assertIsInstance(e, KeyError) @httpretty.activate def test_oembed_tweet_valid_id_app_debug_on(self): self.app.config['DEBUG'] = True self.twitter_oembedder.init(self.app, self.cache) with open('tests/data/99530515043983360.json') as f: httpretty.register_uri(httpretty.GET, 'https://api.twitter.com/1.1/statuses/oembed.json?id=99530515043983360', body = f.read()) response = self.client.get('/') oembed_tweet = self.get_context_variable('oembed_tweet') valid = oembed_tweet('99530515043983360') self.assertIsInstance(valid, Markup) @httpretty.activate def test_oembed_tweet_invalid_id_app_debug_on(self): self.app.config['DEBUG'] = True self.twitter_oembedder.init(self.app, self.cache) with open('tests/data/abc.json') as f: httpretty.register_uri(httpretty.GET, 'https://api.twitter.com/1.1/statuses/oembed.json?id=abc', body = f.read()) response = self.client.get('/') oembed_tweet = self.get_context_variable('oembed_tweet') try: invalid = oembed_tweet('abc') assert False except Exception as e: self.assertIsInstance(e, KeyError) @httpretty.activate def test_oembed_tweet_valid_id_app_debug_on_override(self): self.app.config['DEBUG'] = True self.twitter_oembedder.init(self.app, self.cache, debug=False) with open('tests/data/99530515043983360.json') as f: httpretty.register_uri(httpretty.GET, 'https://api.twitter.com/1.1/statuses/oembed.json?id=99530515043983360', body = f.read()) response = self.client.get('/') oembed_tweet = self.get_context_variable('oembed_tweet') valid = oembed_tweet('99530515043983360') self.assertIsInstance(valid, Markup) @httpretty.activate def test_oembed_tweet_invalid_id_app_debug_on_override(self): self.app.config['DEBUG'] = True self.twitter_oembedder.init(self.app, self.cache, debug=False) with open('tests/data/abc.json') as f: httpretty.register_uri(httpretty.GET, 'https://api.twitter.com/1.1/statuses/oembed.json?id=abc', body = f.read()) response = self.client.get('/') oembed_tweet = self.get_context_variable('oembed_tweet') invalid = oembed_tweet('abc') self.assertIs(invalid,'')

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0.042781

0.049911

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0.71123

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0

0.042878

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5,363

124

123

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0

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0

null

0

1

0

null

0

5

349b5c8834d9d9f9cd6eaf74debf6fec61ab3188

11,849

py

Python

SimModel_Python_API/simmodel_swig/Release/SimMaterialLayer_OpaqueMaterialLayer_Default.py

EnEff-BIM/EnEffBIM-Framework

6328d39b498dc4065a60b5cc9370b8c2a9a1cddf

[ "MIT" ]

3

2016-05-30T15:12:16.000Z

2022-03-22T08:11:13.000Z

SimModel_Python_API/simmodel_swig/Release/SimMaterialLayer_OpaqueMaterialLayer_Default.py

EnEff-BIM/EnEffBIM-Framework

6328d39b498dc4065a60b5cc9370b8c2a9a1cddf

[ "MIT" ]

21

2016-06-13T11:33:45.000Z

2017-05-23T09:46:52.000Z

SimModel_Python_API/simmodel_swig/Release/SimMaterialLayer_OpaqueMaterialLayer_Default.py

EnEff-BIM/EnEffBIM-Framework

6328d39b498dc4065a60b5cc9370b8c2a9a1cddf

[ "MIT" ]

null

# This file was automatically generated by SWIG (http://www.swig.org). # Version 3.0.7 # # Do not make changes to this file unless you know what you are doing--modify # the SWIG interface file instead. from sys import version_info if version_info >= (2, 6, 0): def swig_import_helper(): from os.path import dirname import imp fp = None try: fp, pathname, description = imp.find_module('_SimMaterialLayer_OpaqueMaterialLayer_Default', [dirname(__file__)]) except ImportError: import _SimMaterialLayer_OpaqueMaterialLayer_Default return _SimMaterialLayer_OpaqueMaterialLayer_Default if fp is not None: try: _mod = imp.load_module('_SimMaterialLayer_OpaqueMaterialLayer_Default', fp, pathname, description) finally: fp.close() return _mod _SimMaterialLayer_OpaqueMaterialLayer_Default = swig_import_helper() del swig_import_helper else: import _SimMaterialLayer_OpaqueMaterialLayer_Default del version_info try: _swig_property = property except NameError: pass # Python < 2.2 doesn't have 'property'. def _swig_setattr_nondynamic(self, class_type, name, value, static=1): if (name == "thisown"): return self.this.own(value) if (name == "this"): if type(value).__name__ == 'SwigPyObject': self.__dict__[name] = value return method = class_type.__swig_setmethods__.get(name, None) if method: return method(self, value) if (not static): if _newclass: object.__setattr__(self, name, value) else: self.__dict__[name] = value else: raise AttributeError("You cannot add attributes to %s" % self) def _swig_setattr(self, class_type, name, value): return _swig_setattr_nondynamic(self, class_type, name, value, 0) def _swig_getattr_nondynamic(self, class_type, name, static=1): if (name == "thisown"): return self.this.own() method = class_type.__swig_getmethods__.get(name, None) if method: return method(self) if (not static): return object.__getattr__(self, name) else: raise AttributeError(name) def _swig_getattr(self, class_type, name): return _swig_getattr_nondynamic(self, class_type, name, 0) def _swig_repr(self): try: strthis = "proxy of " + self.this.__repr__() except: strthis = "" return "<%s.%s; %s >" % (self.__class__.__module__, self.__class__.__name__, strthis,) try: _object = object _newclass = 1 except AttributeError: class _object: pass _newclass = 0 try: import weakref weakref_proxy = weakref.proxy except: weakref_proxy = lambda x: x import base class SimMaterialLayer(base.SimResourceObject): __swig_setmethods__ = {} for _s in [base.SimResourceObject]: __swig_setmethods__.update(getattr(_s, '__swig_setmethods__', {})) __setattr__ = lambda self, name, value: _swig_setattr(self, SimMaterialLayer, name, value) __swig_getmethods__ = {} for _s in [base.SimResourceObject]: __swig_getmethods__.update(getattr(_s, '__swig_getmethods__', {})) __getattr__ = lambda self, name: _swig_getattr(self, SimMaterialLayer, name) __repr__ = _swig_repr def LayerMaterial(self, *args): return _SimMaterialLayer_OpaqueMaterialLayer_Default.SimMaterialLayer_LayerMaterial(self, *args) def LayerThickness(self, *args): return _SimMaterialLayer_OpaqueMaterialLayer_Default.SimMaterialLayer_LayerThickness(self, *args) def IsVentilated(self, *args): return _SimMaterialLayer_OpaqueMaterialLayer_Default.SimMaterialLayer_IsVentilated(self, *args) def MaterialLayerName(self, *args): return _SimMaterialLayer_OpaqueMaterialLayer_Default.SimMaterialLayer_MaterialLayerName(self, *args) def __init__(self, *args): this = _SimMaterialLayer_OpaqueMaterialLayer_Default.new_SimMaterialLayer(*args) try: self.this.append(this) except: self.this = this def _clone(self, f=0, c=None): return _SimMaterialLayer_OpaqueMaterialLayer_Default.SimMaterialLayer__clone(self, f, c) __swig_destroy__ = _SimMaterialLayer_OpaqueMaterialLayer_Default.delete_SimMaterialLayer __del__ = lambda self: None SimMaterialLayer_swigregister = _SimMaterialLayer_OpaqueMaterialLayer_Default.SimMaterialLayer_swigregister SimMaterialLayer_swigregister(SimMaterialLayer) class SimMaterialLayer_OpaqueMaterialLayer(SimMaterialLayer): __swig_setmethods__ = {} for _s in [SimMaterialLayer]: __swig_setmethods__.update(getattr(_s, '__swig_setmethods__', {})) __setattr__ = lambda self, name, value: _swig_setattr(self, SimMaterialLayer_OpaqueMaterialLayer, name, value) __swig_getmethods__ = {} for _s in [SimMaterialLayer]: __swig_getmethods__.update(getattr(_s, '__swig_getmethods__', {})) __getattr__ = lambda self, name: _swig_getattr(self, SimMaterialLayer_OpaqueMaterialLayer, name) __repr__ = _swig_repr def SimMatLayer_MaterialLayerName(self, *args): return _SimMaterialLayer_OpaqueMaterialLayer_Default.SimMaterialLayer_OpaqueMaterialLayer_SimMatLayer_MaterialLayerName(self, *args) def SimMatLayer_MaterialName(self, *args): return _SimMaterialLayer_OpaqueMaterialLayer_Default.SimMaterialLayer_OpaqueMaterialLayer_SimMatLayer_MaterialName(self, *args) def SimMatLayer_LayerThickness(self, *args): return _SimMaterialLayer_OpaqueMaterialLayer_Default.SimMaterialLayer_OpaqueMaterialLayer_SimMatLayer_LayerThickness(self, *args) def __init__(self, *args): this = _SimMaterialLayer_OpaqueMaterialLayer_Default.new_SimMaterialLayer_OpaqueMaterialLayer(*args) try: self.this.append(this) except: self.this = this def _clone(self, f=0, c=None): return _SimMaterialLayer_OpaqueMaterialLayer_Default.SimMaterialLayer_OpaqueMaterialLayer__clone(self, f, c) __swig_destroy__ = _SimMaterialLayer_OpaqueMaterialLayer_Default.delete_SimMaterialLayer_OpaqueMaterialLayer __del__ = lambda self: None SimMaterialLayer_OpaqueMaterialLayer_swigregister = _SimMaterialLayer_OpaqueMaterialLayer_Default.SimMaterialLayer_OpaqueMaterialLayer_swigregister SimMaterialLayer_OpaqueMaterialLayer_swigregister(SimMaterialLayer_OpaqueMaterialLayer) class SimMaterialLayer_OpaqueMaterialLayer_Default(SimMaterialLayer_OpaqueMaterialLayer): __swig_setmethods__ = {} for _s in [SimMaterialLayer_OpaqueMaterialLayer]: __swig_setmethods__.update(getattr(_s, '__swig_setmethods__', {})) __setattr__ = lambda self, name, value: _swig_setattr(self, SimMaterialLayer_OpaqueMaterialLayer_Default, name, value) __swig_getmethods__ = {} for _s in [SimMaterialLayer_OpaqueMaterialLayer]: __swig_getmethods__.update(getattr(_s, '__swig_getmethods__', {})) __getattr__ = lambda self, name: _swig_getattr(self, SimMaterialLayer_OpaqueMaterialLayer_Default, name) __repr__ = _swig_repr def __init__(self, *args): this = _SimMaterialLayer_OpaqueMaterialLayer_Default.new_SimMaterialLayer_OpaqueMaterialLayer_Default(*args) try: self.this.append(this) except: self.this = this def _clone(self, f=0, c=None): return _SimMaterialLayer_OpaqueMaterialLayer_Default.SimMaterialLayer_OpaqueMaterialLayer_Default__clone(self, f, c) __swig_destroy__ = _SimMaterialLayer_OpaqueMaterialLayer_Default.delete_SimMaterialLayer_OpaqueMaterialLayer_Default __del__ = lambda self: None SimMaterialLayer_OpaqueMaterialLayer_Default_swigregister = _SimMaterialLayer_OpaqueMaterialLayer_Default.SimMaterialLayer_OpaqueMaterialLayer_Default_swigregister SimMaterialLayer_OpaqueMaterialLayer_Default_swigregister(SimMaterialLayer_OpaqueMaterialLayer_Default) class SimMaterialLayer_OpaqueMaterialLayer_Default_sequence(base.sequence_common): __swig_setmethods__ = {} for _s in [base.sequence_common]: __swig_setmethods__.update(getattr(_s, '__swig_setmethods__', {})) __setattr__ = lambda self, name, value: _swig_setattr(self, SimMaterialLayer_OpaqueMaterialLayer_Default_sequence, name, value) __swig_getmethods__ = {} for _s in [base.sequence_common]: __swig_getmethods__.update(getattr(_s, '__swig_getmethods__', {})) __getattr__ = lambda self, name: _swig_getattr(self, SimMaterialLayer_OpaqueMaterialLayer_Default_sequence, name) __repr__ = _swig_repr def __init__(self, *args): this = _SimMaterialLayer_OpaqueMaterialLayer_Default.new_SimMaterialLayer_OpaqueMaterialLayer_Default_sequence(*args) try: self.this.append(this) except: self.this = this def assign(self, n, x): return _SimMaterialLayer_OpaqueMaterialLayer_Default.SimMaterialLayer_OpaqueMaterialLayer_Default_sequence_assign(self, n, x) def begin(self, *args): return _SimMaterialLayer_OpaqueMaterialLayer_Default.SimMaterialLayer_OpaqueMaterialLayer_Default_sequence_begin(self, *args) def end(self, *args): return _SimMaterialLayer_OpaqueMaterialLayer_Default.SimMaterialLayer_OpaqueMaterialLayer_Default_sequence_end(self, *args) def rbegin(self, *args): return _SimMaterialLayer_OpaqueMaterialLayer_Default.SimMaterialLayer_OpaqueMaterialLayer_Default_sequence_rbegin(self, *args) def rend(self, *args): return _SimMaterialLayer_OpaqueMaterialLayer_Default.SimMaterialLayer_OpaqueMaterialLayer_Default_sequence_rend(self, *args) def at(self, *args): return _SimMaterialLayer_OpaqueMaterialLayer_Default.SimMaterialLayer_OpaqueMaterialLayer_Default_sequence_at(self, *args) def front(self, *args): return _SimMaterialLayer_OpaqueMaterialLayer_Default.SimMaterialLayer_OpaqueMaterialLayer_Default_sequence_front(self, *args) def back(self, *args): return _SimMaterialLayer_OpaqueMaterialLayer_Default.SimMaterialLayer_OpaqueMaterialLayer_Default_sequence_back(self, *args) def push_back(self, *args): return _SimMaterialLayer_OpaqueMaterialLayer_Default.SimMaterialLayer_OpaqueMaterialLayer_Default_sequence_push_back(self, *args) def pop_back(self): return _SimMaterialLayer_OpaqueMaterialLayer_Default.SimMaterialLayer_OpaqueMaterialLayer_Default_sequence_pop_back(self) def detach_back(self, pop=True): return _SimMaterialLayer_OpaqueMaterialLayer_Default.SimMaterialLayer_OpaqueMaterialLayer_Default_sequence_detach_back(self, pop) def insert(self, *args): return _SimMaterialLayer_OpaqueMaterialLayer_Default.SimMaterialLayer_OpaqueMaterialLayer_Default_sequence_insert(self, *args) def erase(self, *args): return _SimMaterialLayer_OpaqueMaterialLayer_Default.SimMaterialLayer_OpaqueMaterialLayer_Default_sequence_erase(self, *args) def detach(self, position, r, erase=True): return _SimMaterialLayer_OpaqueMaterialLayer_Default.SimMaterialLayer_OpaqueMaterialLayer_Default_sequence_detach(self, position, r, erase) def swap(self, x): return _SimMaterialLayer_OpaqueMaterialLayer_Default.SimMaterialLayer_OpaqueMaterialLayer_Default_sequence_swap(self, x) __swig_destroy__ = _SimMaterialLayer_OpaqueMaterialLayer_Default.delete_SimMaterialLayer_OpaqueMaterialLayer_Default_sequence __del__ = lambda self: None SimMaterialLayer_OpaqueMaterialLayer_Default_sequence_swigregister = _SimMaterialLayer_OpaqueMaterialLayer_Default.SimMaterialLayer_OpaqueMaterialLayer_Default_sequence_swigregister SimMaterialLayer_OpaqueMaterialLayer_Default_sequence_swigregister(SimMaterialLayer_OpaqueMaterialLayer_Default_sequence) # This file is compatible with both classic and new-style classes.

44.378277

181

0.770276

1,162

11,849

7.282272

0.129088

0.384661

0.382179

0.205625

0.738951

0.69239

0.652328

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0.502836

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0.001707

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11,849

266

182

44.545113

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0.024812

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0

1

0.169903

false

0.009709

0.053398

0.131068

0.558252

0

null

1

0

1

0

null

0

1

0

5

34aa5078e63278f98703ce0f89c002e42d8eb6f0

146

py

Python

python/testData/stubs/AttrsKwOnlyOnField.py

alexey-anufriev/intellij-community

ffcd46f14e630acdefcc76e2bfc7c43d2449013a

[ "Apache-2.0" ]

2

2019-04-28T07:48:50.000Z

2020-12-11T14:18:08.000Z

python/testData/stubs/AttrsKwOnlyOnField.py

alexey-anufriev/intellij-community

ffcd46f14e630acdefcc76e2bfc7c43d2449013a

[ "Apache-2.0" ]

1

2020-07-30T19:04:47.000Z

python/testData/stubs/AttrsKwOnlyOnField.py

bradleesand/intellij-community

750ff9c10333c9c1278c00dbe8d88c877b1b9749

[ "Apache-2.0" ]

1

2020-10-15T05:56:42.000Z

import attr @attr.s class Foo: bar1 = attr.ib(type=str) bar2 = attr.ib(type=str, kw_only=True) bar3 = attr.ib(type=str, kw_only=False)

24.333333

43

0.664384

27

146

3.518519

0.555556

0.189474

0.315789

0.410526

0.4

0

0.02521

0.184932

146

6

43

24.333333

0.773109

0

1

0

false

0

0.166667

0

0.833333

0

1

0

null

0

1

0

1

0

null

0

1

0

5

34ba58f7e7aeb18886f43477b76462ba7c38e674

13,198

py

Python

mi/dataset/parser/test/test_pco2w_abc_imodem.py

rmanoni/mi-dataset

c1012a0cd8f2ea075e008cdd1ab291ed54f44d43

[ "BSD-2-Clause" ]

null

mi/dataset/parser/test/test_pco2w_abc_imodem.py

rmanoni/mi-dataset

c1012a0cd8f2ea075e008cdd1ab291ed54f44d43

[ "BSD-2-Clause" ]

null

mi/dataset/parser/test/test_pco2w_abc_imodem.py

rmanoni/mi-dataset

c1012a0cd8f2ea075e008cdd1ab291ed54f44d43

[ "BSD-2-Clause" ]

null

#!/usr/bin/env python __author__ = 'mworden' """ @package mi.dataset.parser.test.test_pco2w_abc_imodem @author Mark Worden @brief Test code for the pco2w_abc_imodem parser """ from mi.logging import log import os from nose.plugins.attrib import attr from mi.core.exceptions import RecoverableSampleException from mi.dataset.test.test_parser import ParserUnitTestCase from mi.dataset.dataset_parser import DataSetDriverConfigKeys from mi.dataset.parser.pco2w_abc_imodem import Pco2wAbcImodemParser from mi.dataset.parser.pco2w_abc_particles import \ Pco2wAbcImodemMetadataTelemeteredDataParticle, \ Pco2wAbcImodemMetadataRecoveredDataParticle, \ Pco2wAbcImodemPowerTelemeteredDataParticle, \ Pco2wAbcImodemPowerRecoveredDataParticle, \ Pco2wAbcImodemInstrumentTelemeteredDataParticle, \ Pco2wAbcImodemInstrumentRecoveredDataParticle, \ Pco2wAbcImodemInstrumentBlankTelemeteredDataParticle, \ Pco2wAbcImodemInstrumentBlankRecoveredDataParticle, \ Pco2wAbcImodemControlTelemeteredDataParticle, \ Pco2wAbcImodemControlRecoveredDataParticle, \ Pco2wAbcParticleClassKey from mi.idk.config import Config RESOURCE_PATH = os.path.join( Config().base_dir(), 'mi', 'dataset', 'driver', 'pco2w_abc', 'imodem', 'resource') @attr('UNIT', group='mi') class Pco2wAbcParserUnitTestCase(ParserUnitTestCase): """ pco2w_abc Parser unit test suite """ def setUp(self): ParserUnitTestCase.setUp(self) self._telem_parser_config = { DataSetDriverConfigKeys.PARTICLE_MODULE: 'mi.dataset.parser.pco2w_abc_particles', DataSetDriverConfigKeys.PARTICLE_CLASS: None, DataSetDriverConfigKeys.PARTICLE_CLASSES_DICT: { Pco2wAbcParticleClassKey.METADATA_PARTICLE_CLASS: Pco2wAbcImodemMetadataTelemeteredDataParticle, Pco2wAbcParticleClassKey.POWER_PARTICLE_CLASS: Pco2wAbcImodemPowerTelemeteredDataParticle, Pco2wAbcParticleClassKey.INSTRUMENT_PARTICLE_CLASS: Pco2wAbcImodemInstrumentTelemeteredDataParticle, Pco2wAbcParticleClassKey.INSTRUMENT_BLANK_PARTICLE_CLASS: Pco2wAbcImodemInstrumentBlankTelemeteredDataParticle, Pco2wAbcParticleClassKey.CONTROL_PARTICLE_CLASS: Pco2wAbcImodemControlTelemeteredDataParticle, } } self._recov_parser_config = { DataSetDriverConfigKeys.PARTICLE_MODULE: 'mi.dataset.parser.pco2w_abc_particles', DataSetDriverConfigKeys.PARTICLE_CLASS: None, DataSetDriverConfigKeys.PARTICLE_CLASSES_DICT: { Pco2wAbcParticleClassKey.METADATA_PARTICLE_CLASS: Pco2wAbcImodemMetadataRecoveredDataParticle, Pco2wAbcParticleClassKey.POWER_PARTICLE_CLASS: Pco2wAbcImodemPowerRecoveredDataParticle, Pco2wAbcParticleClassKey.INSTRUMENT_PARTICLE_CLASS: Pco2wAbcImodemInstrumentRecoveredDataParticle, Pco2wAbcParticleClassKey.INSTRUMENT_BLANK_PARTICLE_CLASS: Pco2wAbcImodemInstrumentBlankRecoveredDataParticle, Pco2wAbcParticleClassKey.CONTROL_PARTICLE_CLASS: Pco2wAbcImodemControlRecoveredDataParticle, } } def test_happy_path(self): """ Read files and verify that all expected particles can be read. Verify that the contents of the particles are correct. There should be no exceptions generated. """ log.debug('===== START TEST HAPPY PATH =====') num_particles_to_request = 10 num_expected_particles = 7 with open(os.path.join(RESOURCE_PATH, 'pco2wXYZ_11212014_1624.DAT'), 'r') as file_handle: parser = Pco2wAbcImodemParser(self._telem_parser_config, file_handle, self.exception_callback) particles = parser.get_records(num_particles_to_request) self.assertEquals(len(particles), num_expected_particles) self.assert_particles(particles, "pco2wXYZ_11212014_1624.telem.yml", RESOURCE_PATH) self.assertEquals(len(self.exception_callback_value), 0) with open(os.path.join(RESOURCE_PATH, 'pco2wXYZ_11212014_1624.DAT'), 'r') as file_handle: parser = Pco2wAbcImodemParser(self._recov_parser_config, file_handle, self.exception_callback) particles = parser.get_records(num_particles_to_request) self.assertEquals(len(particles), num_expected_particles) self.assert_particles(particles, "pco2wXYZ_11212014_1624.recov.yml", RESOURCE_PATH) self.assertEquals(len(self.exception_callback_value), 0) log.debug('===== END TEST HAPPY PATH =====') def test_invalid_data_telem(self): """ Read files and verify that all expected particles can be read. Verify that invalid data is handled appropriately with the correct exceptions being reported. """ log.debug('===== START TEST INVALID DATA TELEMETERED =====') num_particles_to_request = 10 num_expected_particles = 7 with open(os.path.join(RESOURCE_PATH, 'pco2wXYZ_11212014_1625.DAT'), 'r') as file_handle: parser = Pco2wAbcImodemParser(self._telem_parser_config, file_handle, self.exception_callback) particles = parser.get_records(num_particles_to_request) self.assertEquals(len(particles), num_expected_particles) self.assert_particles(particles, "pco2wXYZ_11212014_1625.telem.yml", RESOURCE_PATH) self.assertEquals(len(self.exception_callback_value), 2) for exception in self.exception_callback_value: self.assertIsInstance(exception, RecoverableSampleException) log.debug('===== END TEST INVALID DATA TELEMETERED =====') def test_invalid_data_recov(self): """ Read files and verify that all expected particles can be read. Verify that invalid data is handled appropriately with the correct exceptions being reported. """ log.debug('===== START TEST INVALID DATA RECOVERED =====') num_particles_to_request = 10 num_expected_particles = 7 with open(os.path.join(RESOURCE_PATH, 'pco2wXYZ_11212014_1625.DAT'), 'r') as file_handle: parser = Pco2wAbcImodemParser(self._recov_parser_config, file_handle, self.exception_callback) particles = parser.get_records(num_particles_to_request) self.assertEquals(len(particles), num_expected_particles) self.assert_particles(particles, "pco2wXYZ_11212014_1625.recov.yml", RESOURCE_PATH) self.assertEquals(len(self.exception_callback_value), 2) for exception in self.exception_callback_value: self.assertIsInstance(exception, RecoverableSampleException) log.debug('===== END TEST INVALID DATA RECOVERED =====') def test_incomplete_metadata_one(self): """ Read a file containing insufficient data to create a metadata particle. In this case, the line specifying the sample count is missing. Verify that the contents of the particles are correct ensuring no metadata particle was generated. There should be no exceptions generated. """ log.debug('===== START TEST INCOMPLETE METADATA ONE =====') num_particles_to_request = 10 num_expected_particles = 7 with open(os.path.join(RESOURCE_PATH, 'pco2wXYZ_11212014_1626.DAT'), 'r') as file_handle: parser = Pco2wAbcImodemParser(self._telem_parser_config, file_handle, self.exception_callback) particles = parser.get_records(num_particles_to_request) self.assertEquals(len(particles), num_expected_particles) self.assert_particles(particles, "pco2wXYZ_11212014_1626.telem.yml", RESOURCE_PATH) self.assertEquals(len(self.exception_callback_value), 0) with open(os.path.join(RESOURCE_PATH, 'pco2wXYZ_11212014_1626.DAT'), 'r') as file_handle: parser = Pco2wAbcImodemParser(self._recov_parser_config, file_handle, self.exception_callback) particles = parser.get_records(num_particles_to_request) self.assertEquals(len(particles), num_expected_particles) self.assert_particles(particles, "pco2wXYZ_11212014_1626.recov.yml", RESOURCE_PATH) self.assertEquals(len(self.exception_callback_value), 0) log.debug('===== END TEST INCOMPLETE METADATA ONE =====') def test_incomplete_metadata_two(self): """ Read a file containing insufficient data to create a metadata particle. In this case, the line specifying the serial number is missing. Verify that the contents of the particles are correct ensuring no metadata particle was generated. There should be no exceptions generated. """ log.debug('===== START TEST INCOMPLETE METADATA TWO =====') num_particles_to_request = 10 num_expected_particles = 7 with open(os.path.join(RESOURCE_PATH, 'pco2wXYZ_11212014_1627.DAT'), 'r') as file_handle: parser = Pco2wAbcImodemParser(self._telem_parser_config, file_handle, self.exception_callback) particles = parser.get_records(num_particles_to_request) self.assertEquals(len(particles), num_expected_particles) self.assert_particles(particles, "pco2wXYZ_11212014_1627.telem.yml", RESOURCE_PATH) self.assertEquals(len(self.exception_callback_value), 0) with open(os.path.join(RESOURCE_PATH, 'pco2wXYZ_11212014_1627.DAT'), 'r') as file_handle: parser = Pco2wAbcImodemParser(self._recov_parser_config, file_handle, self.exception_callback) particles = parser.get_records(num_particles_to_request) self.assertEquals(len(particles), num_expected_particles) self.assert_particles(particles, "pco2wXYZ_11212014_1627.recov.yml", RESOURCE_PATH) self.assertEquals(len(self.exception_callback_value), 0) log.debug('===== END TEST INCOMPLETE METADATA TWO =====') def test_missing_file_time_telem(self): """ Read a file that is missing the file time metadata A RecoverableException should be reported. """ log.debug('===== START TEST MISSING FILE TIME TELEM =====') num_particles_to_request = 10 num_expected_particles = 6 with open(os.path.join(RESOURCE_PATH, 'pco2wXYZ_11212014_1628.DAT'), 'r') as file_handle: parser = Pco2wAbcImodemParser(self._telem_parser_config, file_handle, self.exception_callback) particles = parser.get_records(num_particles_to_request) self.assertEquals(len(particles), num_expected_particles) self.assert_particles(particles, "pco2wXYZ_11212014_1628.telem.yml", RESOURCE_PATH) self.assertEquals(len(self.exception_callback_value), 1) for exception in self.exception_callback_value: self.assertIsInstance(exception, RecoverableSampleException) log.debug('===== END TEST MISSING FILE TIME TELEM =====') def test_missing_file_time_recov(self): """ Read a file that is missing the file time metadata A RecoverableException should be reported. """ log.debug('===== START TEST MISSING FILE TIME RECOV =====') num_particles_to_request = 10 num_expected_particles = 6 with open(os.path.join(RESOURCE_PATH, 'pco2wXYZ_11212014_1628.DAT'), 'r') as file_handle: parser = Pco2wAbcImodemParser(self._recov_parser_config, file_handle, self.exception_callback) particles = parser.get_records(num_particles_to_request) self.assertEquals(len(particles), num_expected_particles) self.assert_particles(particles, "pco2wXYZ_11212014_1628.recov.yml", RESOURCE_PATH) self.assertEquals(len(self.exception_callback_value), 1) for exception in self.exception_callback_value: self.assertIsInstance(exception, RecoverableSampleException) log.debug('===== END TEST MISSING FILE TIME RECOV =====')

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0.654569

1,263

13,198

6.575614

0.125891

0.037568

0.060686

0.042986

0.755328

0.733895

0.726309

0.722095

0

0.035555

0.271177

13,198

330

98

39.993939

0.827841

0.095848

0

0.590164

0

0.114716

0.056879

0

0.185792

1

0.043716

false

0

0.04918

0

0.098361

0

null

0

1

0

null

0

5

9b2da1805113ec9795d284933fb092c7e5290b97

210

py

Python

Exercicios/Ex11.py

angeloridolfi/Python-CEV

fd11b7ea0725f83c84336b99304c50f183514245

[ "MIT" ]

null

Exercicios/Ex11.py

angeloridolfi/Python-CEV

fd11b7ea0725f83c84336b99304c50f183514245

[ "MIT" ]

null

Exercicios/Ex11.py

angeloridolfi/Python-CEV

fd11b7ea0725f83c84336b99304c50f183514245

[ "MIT" ]

null

n = float(input('Qual a largura da pardede? :')) n2 = float(input('Qual a altura da parede? :')) print(f'A área da parede é igual a {n*n2}, e será necessário {(n*n2)/2} litros de tinta para pintar a parede.')

42

111

0.671429

40

210

3.525

0.625

0.141844

0.198582

0.212766

0

0.022989

0.171429

210

4

112

52.5

0.787356

0

0.333333

0.738095

0

1

0

false

0

0.333333

0

null

0

1

0

1

0

1

0

null

0

5

9b635063d21cfdd4fba986ed81f94f977741e211

28

py

Python

public/cmd/echo.py

sgframework/cdn

28acfd0d1fcddc3179da9319d6c353ce95347e37

[ "MIT" ]

3

2019-05-21T22:54:01.000Z

2019-06-05T09:27:40.000Z

public/cmd/echo.py

sgframework/cdn

28acfd0d1fcddc3179da9319d6c353ce95347e37

[ "MIT" ]

10

2019-05-12T22:15:22.000Z

2022-02-26T10:14:35.000Z

public/cmd/echo.py

sgframework/cdn

28acfd0d1fcddc3179da9319d6c353ce95347e37

[ "MIT" ]

1

2019-05-23T16:41:33.000Z

#!/bin/python print('hello')

14

0.678571

4

28

4.75

1

0

0.035714

28

2

14

0.703704

0.428571

0

0.3125

0

1

0

true

0

1

0

null

0

1

0

null

0

1

0

1

0

5

9b8c82ba5563266d6395cb1aad2eb5e182fdfebd

13

py

Python

windows-virtual-desktop/archive/19H2/powershell/runbooks/Renew-RegistrationTokenAfterExpiration.py

faroukfriha/azure-as-code

687828825b4dbe2504e6e8f52500c751a4c8d452

[ "MIT" ]

1

2020-11-24T19:59:37.000Z

windows-virtual-desktop/archive/19H2/powershell/runbooks/Renew-RegistrationTokenAfterExpiration.py

faroukfriha/azure-as-code

687828825b4dbe2504e6e8f52500c751a4c8d452

[ "MIT" ]

null

windows-virtual-desktop/archive/19H2/powershell/runbooks/Renew-RegistrationTokenAfterExpiration.py

faroukfriha/azure-as-code

687828825b4dbe2504e6e8f52500c751a4c8d452

[ "MIT" ]

1

2020-11-24T19:59:58.000Z

print("Toto")

13

0.692308

2

13

4.5

1

0

13

1

13

0.692308

0

0.285714

0

1

0

true

0

1

0

null

0

1

0

null

0

1

0

1

0

5

fd007dd08e8e8e1d33f548f59b3130093cc2c95f

68

py

Python

nitorch/io/volumes/babel/__init__.py

liamchalcroft/nitorch

0de179aff97244a82213c528f0d6393725c868c9

[ "MIT" ]

46

2020-07-31T10:14:05.000Z

2022-03-24T12:51:46.000Z

nitorch/io/volumes/babel/__init__.py

liamchalcroft/nitorch

0de179aff97244a82213c528f0d6393725c868c9

[ "MIT" ]

36

2020-10-06T19:01:38.000Z

2022-02-03T18:07:35.000Z

nitorch/io/volumes/babel/__init__.py

liamchalcroft/nitorch

0de179aff97244a82213c528f0d6393725c868c9

[ "MIT" ]

6

2021-01-05T14:59:05.000Z

2021-11-18T18:26:45.000Z

from .array import BabelArray from . import array, metadata, utils

17

36

0.779412

9

68

5.888889

0.666667

0

0.161765

68

3

37

22.666667

0.929825

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

fd527a11499bbd59a17f1c13a3f4f3f0215322a0

34

py

Python

src/b2sum/__main__.py

karlrink/b2sum

2769aa8505583349aeb9b2c06c96fd95a7252e3c

[ "MIT" ]

1

2022-01-18T13:59:30.000Z

src/b2sum/__main__.py

karlrink/b2sum

2769aa8505583349aeb9b2c06c96fd95a7252e3c

[ "MIT" ]

null

src/b2sum/__main__.py

karlrink/b2sum

2769aa8505583349aeb9b2c06c96fd95a7252e3c

[ "MIT" ]

null

from .b2sum import main main()

5.666667

23

0.676471

5

34

4.6

0.8

0

0.038462

0.235294

34

5

24

6.8

0.846154

0

1

0

true

0

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

1

0

5

fd555d537bce1a776aed73925e9bbfc0f2b4c5ba

159

py

Python

src/common/models/line.py

wenksi/pren-robo-cube-ipcv

e2cf655a7e33aa63dae6e2b2a91abaa11d587f8f

[ "MIT" ]

null

src/common/models/line.py

wenksi/pren-robo-cube-ipcv

e2cf655a7e33aa63dae6e2b2a91abaa11d587f8f

[ "MIT" ]

null

src/common/models/line.py

wenksi/pren-robo-cube-ipcv

e2cf655a7e33aa63dae6e2b2a91abaa11d587f8f

[ "MIT" ]

null

from src.common.models.point import Point import logging class Line: def __init__(self, p1: Point, p2: Point): self.p1 = p1 self.p2 = p2

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py

Python

unrpyc/__main__.py

Dobby233Liu/unrpyc

d814a8a02d4fa658ec0e4476457c3832258aeca8

[ "MIT" ]

3

2021-12-05T09:26:41.000Z

2022-03-28T12:15:50.000Z

unrpyc/__main__.py

Dobby233Liu/unrpyc

d814a8a02d4fa658ec0e4476457c3832258aeca8

[ "MIT" ]

null

unrpyc/__main__.py

Dobby233Liu/unrpyc

d814a8a02d4fa658ec0e4476457c3832258aeca8

[ "MIT" ]

1

2022-02-11T22:49:50.000Z

#!/usr/bin/env python # FIXME: main is in __init__ from . import main if __name__ == '__main__': main()

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5

b5e2dcce54598d9e11a7a5a80d2478a94f24382d

856

py

Python

question2.py

gusenov/test-tech-mail-ru-python2

70e37a3de447b6f7c4da5add75f65df1b51405fe

[ "MIT" ]

null

question2.py

gusenov/test-tech-mail-ru-python2

70e37a3de447b6f7c4da5add75f65df1b51405fe

[ "MIT" ]

null

question2.py

gusenov/test-tech-mail-ru-python2

70e37a3de447b6f7c4da5add75f65df1b51405fe

[ "MIT" ]

null

l = ['a', 'b', 'c'] # print l.values() # AttributeError: 'list' object has no attribute 'values' # print l.contains() # AttributeError: 'list' object has no attribute 'contains' # print l.sorted() # AttributeError: 'list' object has no attribute 'sorted' # print l.type() # AttributeError: 'list' object has no attribute 'type' # print l.items() # AttributeError: 'list' object has no attribute 'items' # print l.len() # AttributeError: 'list' object has no attribute 'len' # print l.str() # AttributeError: 'list' object has no attribute 'str' # print values(l) # NameError: name 'values' is not defined # print contains(l) # NameError: name 'contains' is not defined # print items(l) # NameError: name 'items' is not defined print sorted(l) # ['a', 'b', 'c'] print type(l) # <type 'list'> print len(l) # 3 print str(l) # ['a', 'b', 'c']

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bd2ee5e4f091521676b07f93e09f40a8276df251

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py

Python

locintel/graphs/masks/apply/base.py

pedrofreitascampospro/locintel

eb9c56cdc308660c31d90abe9fe62bd3634ba273

[ "MIT" ]

null

locintel/graphs/masks/apply/base.py

pedrofreitascampospro/locintel

eb9c56cdc308660c31d90abe9fe62bd3634ba273

[ "MIT" ]

null

locintel/graphs/masks/apply/base.py

pedrofreitascampospro/locintel

eb9c56cdc308660c31d90abe9fe62bd3634ba273

[ "MIT" ]

null

class ApplyMaskBase(object): def __init__(self): pass def apply_mask(self, *arg, **kwargs): raise NotImplementedError("Please implement in subclass")

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1

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1

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5

1f9d52354f52bce8878cf8cf2e60643813c97ead

134

py

Python

lldb/test/API/dotest.py

medismailben/llvm-project

e334a839032fe500c3bba22bf976ab7af13ce1c1

[ "Apache-2.0" ]

2,338

2018-06-19T17:34:51.000Z

2022-03-31T11:00:37.000Z

test/dotest.py

DalavanCloud/lldb

e913eaf2468290fb94c767d474d611b41a84dd69

[ "Apache-2.0" ]

3,740

2019-01-23T15:36:48.000Z

2022-03-31T22:01:13.000Z

test/dotest.py

DalavanCloud/lldb

e913eaf2468290fb94c767d474d611b41a84dd69

[ "Apache-2.0" ]

500

2019-01-23T07:49:22.000Z

2022-03-30T02:59:37.000Z

#!/usr/bin/env python if __name__ == "__main__": import use_lldb_suite import lldbsuite.test lldbsuite.test.run_suite()

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5

1fe8abbc33a7710e8a33a6cda2fd803e8e6964c5

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py

Python

test/test_make_pdf.py

Konrad-Ziarko/DokiDokiMD

1f1707fe9b5861fb0407daf663f1a45de5a0fdfb

[ "MIT" ]

1

2019-06-30T10:08:01.000Z

test/test_make_pdf.py

Konrad-Ziarko/DokiDokiMD

1f1707fe9b5861fb0407daf663f1a45de5a0fdfb

[ "MIT" ]

17

2018-12-23T23:50:06.000Z

2019-12-09T19:17:20.000Z

test/test_make_pdf.py

Konrad-Ziarko/DokiDokiMD

1f1707fe9b5861fb0407daf663f1a45de5a0fdfb

[ "MIT" ]

1

2019-01-30T15:34:29.000Z

import os import shutil import unittest from dokidokimd.models import Chapter, Manga, MangaSite RESULTS_DIRECTORY = 'unittest_results_temp_dir' class TestMakePdfMethods(unittest.TestCase): def test_make_pdf1(self): """ Make pdf from previously downloaded images - simulated on copied files """ directory_name = os.path.dirname(__file__) dummy_manga_site = MangaSite('test_site') dummy_manga = Manga('test_manga', 'test_manga_url', dummy_manga_site) dummy_chapter = Chapter(dummy_manga, 'test_chapter_title') self.assertTrue(dummy_chapter.number_of_pages() == 0) source_images_dir = os.path.join(directory_name, 'images') test_tmp_dir = os.path.join(directory_name, RESULTS_DIRECTORY) images_dir = dummy_chapter.get_download_path(test_tmp_dir) os.makedirs(images_dir, exist_ok=True) for file in os.listdir(source_images_dir): file_path = os.path.join(source_images_dir, file) if os.path.isfile(file_path): shutil.copy(file_path, images_dir) result, path_to_pdf = dummy_chapter.make_pdf(test_tmp_dir) self.assertTrue(result) self.assertTrue(dummy_chapter.number_of_pages() == 0) self.assertTrue(os.path.isfile(path_to_pdf)) self.assertTrue(os.path.getsize(path_to_pdf) > 0) os.unlink(path_to_pdf) shutil.rmtree(test_tmp_dir, ignore_errors=True) def test_make_pdf2(self): """ Make pdf from pages in memory - simulated by manually added pages """ directory_name = os.path.dirname(__file__) dummy_manga_site = MangaSite('test_site') dummy_manga = Manga('test_manga', 'test_manga_url', dummy_manga_site) dummy_chapter = Chapter(dummy_manga, 'test_chapter_title') self.assertTrue(dummy_chapter.number_of_pages() == 0) source_images_dir = os.path.join(directory_name, 'images') test_tmp_dir = os.path.join(directory_name, RESULTS_DIRECTORY) for file in os.listdir(source_images_dir): file_path = os.path.join(source_images_dir, file) if os.path.isfile(file_path): with open(file_path, 'rb') as f: dummy_chapter.add_page(f.read()) result, path_to_pdf = dummy_chapter.make_pdf(test_tmp_dir) self.assertTrue(result) self.assertTrue(dummy_chapter.number_of_pages() == 4) self.assertTrue(os.path.isfile(path_to_pdf)) self.assertTrue(os.path.getsize(path_to_pdf) > 0) os.unlink(path_to_pdf) shutil.rmtree(test_tmp_dir, ignore_errors=True) if __name__ == '__main__': unittest.main()

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null

0

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null

0

5

951748b7de80db96124b09c7152b8db0c633f16b

123

py

Python

Ejercicio 1 - EspacioPorGuion - CP.py

2167-Team1/TeamProject

c2146bc03999677a09d1ee65b3c09ed5a10d9da0

[ "MIT" ]

null

Ejercicio 1 - EspacioPorGuion - CP.py

2167-Team1/TeamProject

c2146bc03999677a09d1ee65b3c09ed5a10d9da0

[ "MIT" ]

4

2021-11-16T02:36:24.000Z

2021-11-26T03:33:57.000Z

Ejercicio 1 - EspacioPorGuion - CP.py

2167-Team1/TeamProject

c2146bc03999677a09d1ee65b3c09ed5a10d9da0

[ "MIT" ]

4

2021-11-16T01:02:42.000Z

2021-11-27T03:07:36.000Z

texto = input("Ingrese su texto: ") def SpaceToDash(texto): return texto.replace(" ", "-") print (SpaceToDash(texto))

20.5

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null

0

1

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5

951e2d9bb1d117cc6eb13e5bd74c156839364891

79

py

Python

enthought/chaco/axis.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

3

2016-12-09T06:05:18.000Z

2018-03-01T13:00:29.000Z

enthought/chaco/axis.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

1

2020-12-02T00:51:32.000Z

2020-12-02T08:48:55.000Z

enthought/chaco/axis.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

null

# proxy module from __future__ import absolute_import from chaco.axis import *

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79

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0

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0

1

0

1

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5

1f5231c878cf2e7e850dff78cdbe053fc880ade1

1,133

py

Python

vjesala_art.py

njecolina/vjesala-igra-python

1c6d4b492bf6b5dfdd6032de7343089acfbd8335

[ "MIT" ]

null

vjesala_art.py

njecolina/vjesala-igra-python

1c6d4b492bf6b5dfdd6032de7343089acfbd8335

[ "MIT" ]

null

vjesala_art.py

njecolina/vjesala-igra-python

1c6d4b492bf6b5dfdd6032de7343089acfbd8335

[ "MIT" ]

null

stages = [''' +---+ | | O | /|\ | / \ | | ========= ''', ''' +---+ | | O | /|\ | / | | ========= ''', ''' +---+ | | O | /|\ | | | ========= ''', ''' +---+ | | O | /| | | | =========''', ''' +---+ | | O | | | | | ========= ''', ''' +---+ | | O | | | | ========= ''', ''' +---+ | | | | | | ========= '''] logo = ''' __ __ _ ______ __˘__ _ \ \ / / | | ____|/ ____| /\ | | /\ \ \ / / | | |__ | (___ / \ | | / \ \ \/ / | | __| \___ \ / /\ \ | | / /\ \ \ / |__| | |____ ____) / ____ \| |____ / ____ \ \/ \____/|______|_____/_/ \_\______/_/ \_\ "Vješala" igra, ver 0.0.1 na hrvatskom jeziku - Sonja Hranjec 2021. '''

15.736111

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0

1

0

null

0

5

2f0681496b5d81721c08f5a41bffb98565162e3a

97

py

Python

app/ws/admin.py

profefonso/ValuesArray

97cc0b82404d428d4a009e65aa52bdac0e129401

[ "MIT" ]

1

2020-09-07T19:42:58.000Z

app/ws/admin.py

profefonso/ValuesArray

97cc0b82404d428d4a009e65aa52bdac0e129401

[ "MIT" ]

null

app/ws/admin.py

profefonso/ValuesArray

97cc0b82404d428d4a009e65aa52bdac0e129401

[ "MIT" ]

null

from django.contrib import admin from .models import ValueArray admin.site.register(ValueArray)

19.4

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0.835052

13

97

6.230769

0.692308

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0.103093

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4

33

24.25

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true

0

0.666667

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0.666667

0

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null

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1

0

null

0

1

0

1

0

1

0

5

2f91534d6203d0dd0817a4758101a85db1d5248c

35

py

Python

addr2line/__init__.py

ramwin/addr2line

24553fd5096fe2983a699cb12f97e8d124aa2fb5

[ "MIT" ]

null

addr2line/__init__.py

ramwin/addr2line

24553fd5096fe2983a699cb12f97e8d124aa2fb5

[ "MIT" ]

null

addr2line/__init__.py

ramwin/addr2line

24553fd5096fe2983a699cb12f97e8d124aa2fb5

[ "MIT" ]

null

from .base import Addr2lineContext

17.5

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1

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0.114286

35

1

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null

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1

0

5

2f9d217917b5cd53b39a95aa30af0b5f70d42126

47

py

Python

src_old/nsessoracle/mixins/__init__.py

rishikesh67/django-tenant-oracle-schemas

918a64e842b678fc506eadbb4d7e51b0b38ab0a2

[ "MIT" ]

null

src_old/nsessoracle/mixins/__init__.py

rishikesh67/django-tenant-oracle-schemas

918a64e842b678fc506eadbb4d7e51b0b38ab0a2

[ "MIT" ]

8

2019-12-04T23:26:11.000Z

2022-02-10T09:42:18.000Z

src_old/nsessoracle/mixins/__init__.py

rishikesh67/django-tenant-oracle-schemas

918a64e842b678fc506eadbb4d7e51b0b38ab0a2

[ "MIT" ]

2

2019-06-26T05:31:16.000Z

2019-07-01T12:22:50.000Z

from .tenant_data_mixin import TenantDataMixin

23.5

46

0.893617

6

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6.666667

1

0

0.085106

47

1

47

0.930233

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5

85d8fe5218e7c4f0b78a62ea39a8268976962f92

196

py

Python

rsopt/conversion.py

radiasoft/rsopt

6d4d123dd61e30c7f562b2f5a28c3ccbbcddbde3

[ "Apache-2.0" ]

6

2020-11-03T16:51:50.000Z

2022-02-13T20:40:05.000Z

rsopt/conversion.py

radiasoft/rsopt

6d4d123dd61e30c7f562b2f5a28c3ccbbcddbde3

[ "Apache-2.0" ]

97

2020-05-18T18:24:49.000Z

2022-03-23T15:42:42.000Z

rsopt/conversion.py

radiasoft/rsopt

6d4d123dd61e30c7f562b2f5a28c3ccbbcddbde3

[ "Apache-2.0" ]

4

2020-08-18T23:19:55.000Z

2021-12-08T20:55:09.000Z

def create_switchyard(input_file, file_code): from rsbeams.rsdata.switchyard import Switchyard switchyard = Switchyard() switchyard.read(input_file, file_code) return switchyard

24.5

52

0.770408

23

196

6.347826

0.521739

0.410959

0.178082

0.232877

0

0.163265

196

8

53

24.5

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null

0

1

0

5

85fcae2a03f0c51bb31ec2fb7187d02c3d1ffd8e

1,609

py

Python

iguanas/rule_selection/tests/test_base_filter.py

Aditya-Kapadiya/Iguanas

dcc2c1e71f00574c3427fa530191e7079834c11b

[ "Apache-2.0" ]

20

2021-12-22T14:15:03.000Z

2022-03-31T22:46:42.000Z

iguanas/rule_selection/tests/test_base_filter.py

Aditya-Kapadiya/Iguanas

dcc2c1e71f00574c3427fa530191e7079834c11b

[ "Apache-2.0" ]

12

2022-01-18T16:55:56.000Z

2022-03-10T11:39:39.000Z

iguanas/rule_selection/tests/test_base_filter.py

Aditya-Kapadiya/Iguanas

dcc2c1e71f00574c3427fa530191e7079834c11b

[ "Apache-2.0" ]

5

2021-12-25T07:28:29.000Z

2022-02-23T09:40:03.000Z

import pytest import pandas as pd from iguanas.rule_selection._base_filter import _BaseFilter from iguanas.rules import Rules @pytest.fixture def _create_data(): X_rules = pd.DataFrame({ 'A': [1, 0, 1], 'B': [1, 1, 1] }) return X_rules def test_transform(_create_data): X_rules = _create_data bf = _BaseFilter(rules_to_keep=['A'], rules=None) X_rules_ = bf.transform(X_rules) pd.testing.assert_frame_equal(X_rules_, X_rules[['A']]) # With rules rules = Rules( rule_strings={ 'A': "X['a']>1", 'B': "X['b']>1" } ) bf = _BaseFilter(rules_to_keep=['A'], rules=rules) X_rules_ = bf.transform(X_rules) pd.testing.assert_frame_equal(X_rules_, X_rules[['A']]) assert bf.rules.rule_strings == {'A': "X['a']>1"} def test_fit_transform(_create_data): bf = _BaseFilter(rules_to_keep=['A'], rules=None) # Just create dummy fit method for testing bf.fit = lambda X_rules, y, sample_weight: None X_rules = _create_data bf.rules_to_keep = ['A'] X_rules_ = bf.fit_transform(X_rules) pd.testing.assert_frame_equal(X_rules_, X_rules[['A']]) # With rules rules = Rules( rule_strings={ 'A': "X['a']>1", 'B': "X['b']>1" } ) bf = _BaseFilter(rules_to_keep=['A'], rules=rules) # Just create dummy fit method for testing bf.fit = lambda X_rules, y, sample_weight: None X_rules_ = bf.fit_transform(X_rules) pd.testing.assert_frame_equal(X_rules_, X_rules[['A']]) assert bf.rules.rule_strings == {'A': "X['a']>1"}

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0.181818

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0

1

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null

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5

c809d932b9311dfb1f5787254fe055da5226a1c6

122

py

Python

take_a_number/admin.py

take-a-number/api

fd44f8b328e76511dcb248d330faa1d3faf0ff6f

[ "MIT" ]

1

2019-02-12T16:23:37.000Z

take_a_number/admin.py

take-a-number/api

fd44f8b328e76511dcb248d330faa1d3faf0ff6f

[ "MIT" ]

8

2019-02-17T23:06:29.000Z

2019-02-26T02:53:29.000Z

take_a_number/admin.py

take-a-number/api

fd44f8b328e76511dcb248d330faa1d3faf0ff6f

[ "MIT" ]

null

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

20.333333

32

0.778689

17

122

5.588235

0.647059

0

0.139344

122

5

33

24.4

0.904762

0.663934

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

c80fd5c863a9702cad2fa4b9b77f75606d32289d

38

py

Python

lesson01/xuegangqiang/hello.py

herrywen-nanj/51reboot

1130c79a360e1b548a6eaad176eb60f8bed22f40

[ "Apache-2.0" ]

null

lesson01/xuegangqiang/hello.py

herrywen-nanj/51reboot

1130c79a360e1b548a6eaad176eb60f8bed22f40

[ "Apache-2.0" ]

null

lesson01/xuegangqiang/hello.py

herrywen-nanj/51reboot

1130c79a360e1b548a6eaad176eb60f8bed22f40

[ "Apache-2.0" ]

null

#encoding: utf8 print("hello world")

9.5

20

0.710526

5

38

5.4

1

0

0.030303

0.131579

38

3

21

12.666667

0.787879

0.368421

0

0.478261

0

1

0

true

0

1

0

null

0

1

0

null

0

1

0

1

0

5

c816140cbe126f091d030f258bd5da93c340d0b9

78

py

Python

src/api/__init__.py

stephan-01010011/botty

d2d82602d1ecfb6be9d6af91b57895aae39e45de

[ "MIT" ]

5

2022-01-21T20:08:24.000Z

2022-01-28T14:37:17.000Z

src/api/__init__.py

stephan-01010011/botty

d2d82602d1ecfb6be9d6af91b57895aae39e45de

[ "MIT" ]

1

2022-02-10T08:21:22.000Z

2022-02-10T08:38:54.000Z

src/api/__init__.py

stephan-01010011/botty

d2d82602d1ecfb6be9d6af91b57895aae39e45de

[ "MIT" ]

6

2022-01-29T05:09:57.000Z

2022-02-11T21:48:52.000Z

from .generic_api import GenericApi from .discord_embeds import DiscordEmbeds

26

41

0.871795

10

78

6.6

0.8

0

0.102564

78

2

42

39

0.942857

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

c83d790655616d4075e57241f69eac74f562e716

384

py

Python

environments_utils/is_os.py

LucaCappelletti94/environments_utils

c6b8cc7a0fa07f770ed361f3bafaf1adee138f77

[ "MIT" ]

null

environments_utils/is_os.py

LucaCappelletti94/environments_utils

c6b8cc7a0fa07f770ed361f3bafaf1adee138f77

[ "MIT" ]

null

environments_utils/is_os.py

LucaCappelletti94/environments_utils

c6b8cc7a0fa07f770ed361f3bafaf1adee138f77

[ "MIT" ]

null

"""Utilities relative to operative systems.""" import sys def is_macos() -> bool: """Return whether OS is macOS.""" return sys.platform == "darwin" def is_windows() -> bool: """Return whether OS is Windows.""" return sys.platform in ("win32", "cygwin") def is_linux() -> bool: """Return whether OS is Linux.""" return sys.platform in ("linux", "linux2")

21.333333

46

0.630208

50

384

4.78

0.44

0.062762

0.213389

0.238494

0.263598

0

0.009804

0.203125

384

17

47

22.588235

0.771242

0.328125

0

0.118143

0

1

0.428571

true

0

0.142857

0

1

0

null

0

1

0

null

0

1

0

1

0

5

c84b2d6f8b394fc074927d883ef63a8dfb52867a

41

py

Python

test/com/facebook/buck/features/python/testdata/python_binary/preload_deps/preload_order.py

Unknoob/buck

2dfc734354b326f2f66896dde7746a11965d5a13

[ "Apache-2.0" ]

8,027

2015-01-02T05:31:44.000Z

2022-03-31T07:08:09.000Z

test/com/facebook/buck/features/python/testdata/python_binary/preload_deps/preload_order.py

Unknoob/buck

2dfc734354b326f2f66896dde7746a11965d5a13

[ "Apache-2.0" ]

2,355

2015-01-01T15:30:53.000Z

2022-03-30T20:21:16.000Z

test/com/facebook/buck/features/python/testdata/python_binary/preload_deps/preload_order.py

Unknoob/buck

2dfc734354b326f2f66896dde7746a11965d5a13

[ "Apache-2.0" ]

1,280

2015-01-09T03:29:04.000Z

2022-03-30T15:14:14.000Z

import ctypes ctypes.CDLL(None).func()

8.2

24

0.731707

6

41

5

0.833333

0

0.121951

41

4

25

10.25

0.833333

0

1

0

true

0

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

1

0

5

c08c0a07011156bc1e1739857200ee053b7961b1

115

py

Python

oiasg/define/_resource.py

will7101/OIASG

44badff57689da99a2c9896d176b32e7b51d42b5

[ "BSD-3-Clause" ]

1

2018-03-17T10:07:11.000Z

oiasg/define/_resource.py

will7101/OIASG

44badff57689da99a2c9896d176b32e7b51d42b5

[ "BSD-3-Clause" ]

1

2018-03-17T11:35:54.000Z

oiasg/define/_resource.py

will7101/OIASG

44badff57689da99a2c9896d176b32e7b51d42b5

[ "BSD-3-Clause" ]

null

{ 'FONTS': [ ('杨任东竹石体-Regular.ttf', '杨任东竹石体-Regular'), ('杨任东竹石体-Semibold.ttf', '杨任东竹石体-Semibold') ], }

16.428571

45

0.565217

11

115

5.909091

0.454545

0.4

0

0.182609

115

6

46

19.166667

0.691489

0

0.651376

0

1

0

true

0

1

0

null

1

0

1

0

1

0

null

0

1

0

5

c0a17ee1377f938454f5acedd448736b70de37ee

154

py

Python

follows/admin.py

mohamed17717/Like-Reddit-Backend-Clone

d60d7a4625ee0f7354a21e53c26c7c52746d735f

[ "MIT" ]

1

2022-01-10T12:00:59.000Z

follows/admin.py

mohamed17717/Like-Reddit-Backend-Clone

d60d7a4625ee0f7354a21e53c26c7c52746d735f

[ "MIT" ]

null

follows/admin.py

mohamed17717/Like-Reddit-Backend-Clone

d60d7a4625ee0f7354a21e53c26c7c52746d735f

[ "MIT" ]

null

from django.contrib import admin from follows.models import UserFollow, ThreadFollow admin.site.register(UserFollow) admin.site.register(ThreadFollow)

19.25

51

0.837662

19

154

6.789474

0.578947

0.139535

0.263566

0

0.090909

154

7

52

22

0.921429

0

1

0

true

0

0.5

0

0.5

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

5

c0a4439eec24d573b0d6e5fdfb7e5e9388f02d65

134

py

Python

cpg-language-python/src/test/resources/python/issue473.py

anon767/cpg

985f981a94c1a43f4f4363f6d51a8087bad2430a

[ "Apache-2.0" ]

null

cpg-language-python/src/test/resources/python/issue473.py

anon767/cpg

985f981a94c1a43f4f4363f6d51a8087bad2430a

[ "Apache-2.0" ]

null

cpg-language-python/src/test/resources/python/issue473.py

anon767/cpg

985f981a94c1a43f4f4363f6d51a8087bad2430a

[ "Apache-2.0" ]

1

2021-12-17T09:16:39.000Z

if sys.version_info.minor > 9: phr = {"user_id": user_id} | content else: z = {"user_id": user_id} phr = {**z, **content}

22.333333

40

0.574627

21

134

3.428571

0.571429

0.333333

0.277778

0.333333

0

0.009709

0.231343

134

5

41

26.8

0.68932

0

0.104478

0

1

0

false

0

1

0

null

1

0

1

0

null

0

5

c0a6ad76b49e490ec9d2c5e7a6cbb2dd09728dbf

55

py

Python

gym_duckhunt/envs/__init__.py

borijang/gym-duckhunt

08fc5a5f6117ce782a696db5eb15f67bbc5ff8e6

[ "MIT" ]

null

gym_duckhunt/envs/__init__.py

borijang/gym-duckhunt

08fc5a5f6117ce782a696db5eb15f67bbc5ff8e6

[ "MIT" ]

null

gym_duckhunt/envs/__init__.py

borijang/gym-duckhunt

08fc5a5f6117ce782a696db5eb15f67bbc5ff8e6

[ "MIT" ]

null

from gym_duckhunt.envs.duckhunt_env import DuckHuntEnv

27.5

54

0.890909

8

55

5.875

0.875

0

0.072727

55

1

55

0.921569

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

c0e2a4256a320717e44188ca15cbdec7a0618d0d

231

py

Python

src/accent_analyser/__init__.py

stefantaubert/eng2ipa-accent-transformer

d620c70b06c83119402e255085046747ade87444

[ "MIT" ]

null

src/accent_analyser/__init__.py

stefantaubert/eng2ipa-accent-transformer

d620c70b06c83119402e255085046747ade87444

[ "MIT" ]

null

src/accent_analyser/__init__.py

stefantaubert/eng2ipa-accent-transformer

d620c70b06c83119402e255085046747ade87444

[ "MIT" ]

null

from accent_analyser.app import load_probabilities from accent_analyser.core import (ProbabilitiesDict, Symbols, check_probabilities_are_valid, replace_with_prob)

46.2

64

0.627706

21

231

6.52381

0.761905

0.145985

0.262774

0

0.341991

231

4

65

57.75

0.901316

0

1

0

true

0

0.5

0

0.5

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

5

2398a6803f4dd0c8aff6e76170e4cffab68edd4e

73

py

Python

backend/server/processor/__init__.py

shiv12095/realtimeviz

ee2bf10b5f9467212f9a9ce8957d80456ebd0259

[ "MIT" ]

1

2021-03-03T13:54:15.000Z

backend/server/processor/__init__.py

shiv12095/realtimeviz

ee2bf10b5f9467212f9a9ce8957d80456ebd0259

[ "MIT" ]

null

backend/server/processor/__init__.py

shiv12095/realtimeviz

ee2bf10b5f9467212f9a9ce8957d80456ebd0259

[ "MIT" ]

1

2021-03-03T13:59:48.000Z

from .lime_bike_socket_feed_processor import LimeBikeSocketFeedProcessor

36.5

72

0.931507

8

73

8

1

0

0.054795

73

1

73

0.927536

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

f1a7d25bbf44b54e295f1543f476ff76fa5c58f7

145

py

Python

tests2/config.py

Nlioxa/QA-Labs

211cbfb9e8be50e8192d4097c8c4f3f71c9bb59b

[ "MIT" ]

null

tests2/config.py

Nlioxa/QA-Labs

211cbfb9e8be50e8192d4097c8c4f3f71c9bb59b

[ "MIT" ]

null

tests2/config.py

Nlioxa/QA-Labs

211cbfb9e8be50e8192d4097c8c4f3f71c9bb59b

[ "MIT" ]

null

from exceptionManager import ExceptionManager from exception import TrueException, FalseException from server import Server, FactoryServerHandler

48.333333

51

0.896552

14

145

9.285714

0.571429

0

0.089655

145

3

52

48.333333

0.984848

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

f1b818c9af09b63a55ef05c345ca4dcd557a0323

167

py

Python

SimCalorimetry/EcalTrigPrimProducers/python/ecalTriggerPrimitiveDigis_readDBOffline_cff.py

ckamtsikis/cmssw

ea19fe642bb7537cbf58451dcf73aa5fd1b66250

[ "Apache-2.0" ]

852

2015-01-11T21:03:51.000Z

2022-03-25T21:14:00.000Z

SimCalorimetry/EcalTrigPrimProducers/python/ecalTriggerPrimitiveDigis_readDBOffline_cff.py

ckamtsikis/cmssw

ea19fe642bb7537cbf58451dcf73aa5fd1b66250

[ "Apache-2.0" ]

30,371

2015-01-02T00:14:40.000Z

2022-03-31T23:26:05.000Z

SimCalorimetry/EcalTrigPrimProducers/python/ecalTriggerPrimitiveDigis_readDBOffline_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 # Trigger Primitive Producer from SimCalorimetry.EcalTrigPrimProducers.ecalTriggerPrimitiveDigis_readDBOffline_cfi import *

27.833333

94

0.88024

16

167

9.0625

0.9375

0

0.083832

167

5

95

33.4

0.947712

0.155689

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

f1bed78838325dc43f0863523b3cefb43090b929

31

py

Python

src/data/__init__.py

super6liu/technical-analysis-julian

dd8868b65d80f78e536f3471d4dc09440de48e62

[ "MIT" ]

null

src/data/__init__.py

super6liu/technical-analysis-julian

dd8868b65d80f78e536f3471d4dc09440de48e62

[ "MIT" ]

null

src/data/__init__.py

super6liu/technical-analysis-julian

dd8868b65d80f78e536f3471d4dc09440de48e62

[ "MIT" ]

1

2021-10-03T13:18:09.000Z

from src.data.data import Data

15.5

30

0.806452

6

31

4.166667

0.666667

0

0.129032

31

1

31

0.925926

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

f1e3ccd1951c725074018b732f5020fc51f1a744

24

py

Python

src/test.py

MayD524/c69_shell

e89a2b6c90f59b5ef6db329dab39e595d28b5aa5

[ "MIT" ]

null

src/test.py

MayD524/c69_shell

e89a2b6c90f59b5ef6db329dab39e595d28b5aa5

[ "MIT" ]

null

src/test.py

MayD524/c69_shell

e89a2b6c90f59b5ef6db329dab39e595d28b5aa5

[ "MIT" ]

null

print("it worked again")

24

0.75

4

24

4.5

1

0

0.083333

24

1

24

0.818182

0

0.6

0

1

0

true

0

1

0

null

0

1

0

null

0

1

0

1

0

5

f1f7da6b4e80330bb82573cae9657385086edceb

22

py

Python

GetErDone/GetErDone.py

kdmundale/GetErDone

725fa14e1dce6476766ce44910034a5d95c1bd3b

[ "MIT" ]

null

GetErDone/GetErDone.py

kdmundale/GetErDone

725fa14e1dce6476766ce44910034a5d95c1bd3b

[ "MIT" ]

null

GetErDone/GetErDone.py

kdmundale/GetErDone

725fa14e1dce6476766ce44910034a5d95c1bd3b

[ "MIT" ]

null

print('initial test')

11

21

0.727273

3

22

5.333333

1

0

0.090909

22

1

22

0.8

0

0.545455

0

1

0

true

0

1

0

null

0

1

0

null

0

1

0

1

0

5

7b3164568dfb6b206fb5de8b12ea33c2f90e025c

77

py

Python

nicos_ess/commands/epics.py

ebadkamil/nicos

0355a970d627aae170c93292f08f95759c97f3b5

[ "CC-BY-3.0", "Apache-2.0", "CC-BY-4.0" ]

12

2019-11-06T15:40:36.000Z

2022-01-01T16:23:00.000Z

nicos_ess/commands/epics.py

ebadkamil/nicos

0355a970d627aae170c93292f08f95759c97f3b5

[ "CC-BY-3.0", "Apache-2.0", "CC-BY-4.0" ]

4

2019-11-08T10:18:16.000Z

2021-01-13T13:07:29.000Z

nicos_ess/commands/epics.py

ISISComputingGroup/nicos

94cb4d172815919481f8c6ee686f21ebb76f2068

[ "CC-BY-3.0", "Apache-2.0", "CC-BY-4.0" ]

6

2020-01-11T10:52:30.000Z

2022-02-25T12:35:23.000Z

from nicos.devices.epics import pvget, pvput # pylint:disable=unused-import

38.5

76

0.805195

11

77

5.636364

0.909091

0

0.103896

77

1

77

0.898551

0.363636

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

9e80571da093975821272bc18d47fdfac379f563

173

py

Python

katas/kyu_7/dropcaps.py

the-zebulan/CodeWars

1eafd1247d60955a5dfb63e4882e8ce86019f43a

[ "MIT" ]

40

2016-03-09T12:26:20.000Z

2022-03-23T08:44:51.000Z

katas/kyu_7/dropcaps.py

akalynych/CodeWars

1eafd1247d60955a5dfb63e4882e8ce86019f43a

[ "MIT" ]

null

katas/kyu_7/dropcaps.py

akalynych/CodeWars

1eafd1247d60955a5dfb63e4882e8ce86019f43a

[ "MIT" ]

36

2016-11-07T19:59:58.000Z

2022-03-31T11:18:27.000Z

from re import split def drop_cap(string): return ''.join(a.capitalize() if not a.isspace() and len(a) > 2 else a for a in split(r'(\s+)', string))

24.714286

74

0.578035

29

173

3.413793

0.793103

0

0.007937

0.271676

173

6

75

28.833333

0.777778

0

0.028902

0

1

0.25

false

0

0.25

0.75

0

1

0

null

0

1

0

null

0

1

0

1

0

5

9e985837b8b900c0e1e92ddff4a3a11c794d65c1

29

py

Python

example_snippets/multimenus_snippets/NewSnippets/SymPy/Manipulating expressions/Exponentials and Logarithms/Simplification, possibly to trig functions.py

kuanpern/jupyterlab-snippets-multimenus

477f51cfdbad7409eab45abe53cf774cd70f380c

[ "BSD-3-Clause" ]

null

example_snippets/multimenus_snippets/NewSnippets/SymPy/Manipulating expressions/Exponentials and Logarithms/Simplification, possibly to trig functions.py

kuanpern/jupyterlab-snippets-multimenus

477f51cfdbad7409eab45abe53cf774cd70f380c

[ "BSD-3-Clause" ]

null

example_snippets/multimenus_snippets/NewSnippets/SymPy/Manipulating expressions/Exponentials and Logarithms/Simplification, possibly to trig functions.py

kuanpern/jupyterlab-snippets-multimenus

477f51cfdbad7409eab45abe53cf774cd70f380c

[ "BSD-3-Clause" ]

1

2021-02-04T04:51:48.000Z

exptrigsimp(exp(z) + exp(-z))

29

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29

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1

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7b85e98cb7ac332ea84237eb0f930cdedca00d37

158

py

Python

Python/Python.py

JarryShaw/HelloWorld

669984fa415e9bb65f5b7c261ec4f87ffbe56c6d

[ "Apache-2.0" ]

1

2017-12-22T14:15:08.000Z

Python/Python.py

JarryShaw/HelloWorld

669984fa415e9bb65f5b7c261ec4f87ffbe56c6d

[ "Apache-2.0" ]

1

2018-01-16T09:22:52.000Z

Python/Python.py

JarryShaw/HelloWorld

669984fa415e9bb65f5b7c261ec4f87ffbe56c6d

[ "Apache-2.0" ]

1

2018-01-16T07:50:00.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- from __future__ import print_function print('Across the Great Wall, we can reach every corner in the world.')

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null

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0

1

0

1

0

5

7b8e36151963bb1d6549ff828fe4342c5da0a7a2

46

py

Python

tests/__init__.py

BenjaminDavison/aws_data_toolkit

3980b745a2cac032bd751fd1aa80f2d49f959faa

[ "MIT" ]

null

tests/__init__.py

BenjaminDavison/aws_data_toolkit

3980b745a2cac032bd751fd1aa80f2d49f959faa

[ "MIT" ]

147

2020-04-26T16:08:08.000Z

2022-03-27T18:32:18.000Z

tests/__init__.py

BenjaminDavison/aws_data_toolkit

3980b745a2cac032bd751fd1aa80f2d49f959faa

[ "MIT" ]

null

"""Unit test package for aws_data_toolkit."""

23

45

0.73913

7

46

4.571429

1

0

0.108696

46

1

46

0.780488

0.847826

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null

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null

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null

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null

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1

0

1

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null

0

1

0

5

7ba3bd6037ba5caed4f968e6f6c641b2d589d8ea

138

py

Python

flowcat/classifier/__init__.py

xiamaz/flowCat

5fea92eff3112ea3bb669595b469735b2bfa3938

[ "MIT" ]

4

2020-03-06T14:06:12.000Z

2021-06-25T15:03:54.000Z

flowcat/classifier/__init__.py

xiamaz/flowCat

5fea92eff3112ea3bb669595b469735b2bfa3938

[ "MIT" ]

3

2020-03-25T10:54:52.000Z

2020-11-26T19:06:23.000Z

flowcat/classifier/__init__.py

xiamaz/flowCat

5fea92eff3112ea3bb669595b469735b2bfa3938

[ "MIT" ]

2

2020-04-14T11:26:25.000Z

2021-04-02T19:25:52.000Z

from .classifier import SOMClassifier, SOMClassifierConfig from .saliency import SOMSaliency from .models import create_model_multi_input

34.5

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7.375

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138

3

59

46

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null

0

1

0

1

0

1

0

5

c8a61c626df704a345f1d0e0cafb9c3e98bf1a83

1,093

py

Python

Anotacoes/aula9.py

kaiquesouzasantos/python-solyd

0dffcc8f5a163bca15d0967dd243a9f703779936

[ "MIT" ]

null

Anotacoes/aula9.py

kaiquesouzasantos/python-solyd

0dffcc8f5a163bca15d0967dd243a9f703779936

[ "MIT" ]

null

Anotacoes/aula9.py

kaiquesouzasantos/python-solyd

0dffcc8f5a163bca15d0967dd243a9f703779936

[ "MIT" ]

null

# ===================== abrir texto(.txt) ===================== open('Caminho\NomeDoArvivo.[tipo_primitivo]') # open() => abrir arquivos em python # \ = \\ dentro open('Caminho\NomeDoArvivo.[tipo_primitivo]','w') # 'w' => (w = write) cria o arquivo ou sobreescreve open('Caminho\NomeDoArvivo.[tipo_primitivo]','r') # 'r' => (r = read) lê o arquivo open('Caminho\NomeDoArvivo.[tipo_primitivo]','r+') # 'r+' => escreve e lê o arquivo open('Caminho\NomeDoArvivo.[tipo_primitivo]','a') # 'a' => (a = append) cria(adiciona) o arquivo open('Caminho\NomeDoArvivo.png','rb') # => hexadecimal do arquivo arquivo = open('Caminho\NomeDoArvivo.[tipo_primitivo]','w') arquivo = open('Caminho\NomeDoArvivo.[tipo_primitivo]','r') arquivo.write("texto que sera escrito dento do arquivo") # write() => metodo que escreve print(arquivo.read()) # read() => metodo de leitura for i in range(0,1001): arquivo.write(str(i)+" - ") for linha in arquivo: print(linha) # ===================== abrir bytes ===================== open('Caminho\NomeDoArvivo.[tipo_primitivo]','b')

43.72

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1,093

5.091603

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1,093

24

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null

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1

null

0

1

0

1

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5

c8f0fb57dcd152c68fb189cf235fd2233078697d

89

py

Python

django_apollo_example/api/admin.py

jknaresh/django-graphql-ariadne

6583d76c45f0e7244925601926cbd1c017c4f74f

[ "Apache-2.0" ]

null

django_apollo_example/api/admin.py

jknaresh/django-graphql-ariadne

6583d76c45f0e7244925601926cbd1c017c4f74f

[ "Apache-2.0" ]

null

django_apollo_example/api/admin.py

jknaresh/django-graphql-ariadne

6583d76c45f0e7244925601926cbd1c017c4f74f

[ "Apache-2.0" ]

null

from django.contrib import admin from api.models import Post admin.site.register(Post)

14.833333

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0.808989

14

89

5.142857

0.714286

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0.123596

89

5

33

17.8

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true

0

0.666667

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0.666667

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null

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null

0

1

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1

0

1

0

5

a82d92eec56c0ad3e867e6cf1b2ea6843ef9862a

2,326

py

Python

detection/sample_tests/detection/unit_testing.py

Taicon42/CapCorrect

686851cba54b44b5edb6c2b56ff3b95f387ff4b1

[ "MIT" ]

null

detection/sample_tests/detection/unit_testing.py

Taicon42/CapCorrect

686851cba54b44b5edb6c2b56ff3b95f387ff4b1

[ "MIT" ]

null

detection/sample_tests/detection/unit_testing.py

Taicon42/CapCorrect

686851cba54b44b5edb6c2b56ff3b95f387ff4b1

[ "MIT" ]

null

import unittest import preparation_functions as pf import error_detection_functions as edf import error_correction_functions as ecf import exporting_functions as ef class ErrorTotalTests(unittest.TestCase): def test_filtered_total_errors_detected(self): """Test with profanity filter on""" text_list, timestamps = pf.get_file("GenerateSRT.txt") client = pf.initialize_api() sentences = pf.print_sentences(text_list) final_error_total = 0 for i, token in enumerate(sentences): sequence_switched, end_matches, offset_list, err_message, sentence_error_total = \ edf.detect_errors(str(sentences[i]), client, False) final_error_total += sentence_error_total self.assertEqual(final_error_total, 8) def test_unfiltered_total_errors_detected(self): """Test with profanity filter off""" text_list, timestamps = pf.get_file("GenerateSRT.txt") client = pf.initialize_api() sentences = pf.print_sentences(text_list) final_error_total = 0 for i, token in enumerate(sentences): sequence_switched, end_matches, offset_list, err_message, sentence_error_total = \ edf.detect_errors(str(sentences[i]), client, True) final_error_total += sentence_error_total self.assertEqual(final_error_total, 6) def test_error_type_detected(self): client = pf.initialize_api() test_str = "An eror in a short sentence." _, _, _, err_message, _ = \ edf.detect_errors(test_str, client, False) self.assertEqual(err_message, "Spelling mistake") def test_multiple_error_types_detected(self): client = pf.initialize_api() test_str = "An eror in a shit short sentence." _, _, _, err_message, _ = \ edf.detect_errors(test_str, client, False) self.assertEqual(err_message, "Spelling mistake, Profanity, ") def test_unfiltered_multiple_error_types_detected(self): client = pf.initialize_api() test_str = "An eror in a shit short sentence." _, _, _, err_message, _ = \ edf.detect_errors(test_str, client, True) self.assertEqual(err_message, "Spelling mistake, ") if __name__ == '__main__': unittest.main()

35.242424

94

0.675838

280

2,326

5.242857

0.271429

0.06812

0.061308

0.071526

0.79564

0.768392

0.705722

0

0.002256

0.237747

2,326

65

95

35.784615

0.825719

0.025795

0

0.521739

0

0.086475

0

0.108696

1

0.108696

false

0

0.108696

0

0.23913

0.043478

0

null

0

1

0

null

0

5

b575efaa222a5d25dc5d3faf4b1e7fe66c8e008c

507

py

Python

main/sitemaps.py

fideledev/Bhano-Blog

1ad541cb18bc9cb468d48136d17d058d2863fb59

[ "MIT" ]

null

main/sitemaps.py

fideledev/Bhano-Blog

1ad541cb18bc9cb468d48136d17d058d2863fb59

[ "MIT" ]

3

2021-09-08T03:40:19.000Z

2022-01-13T03:55:28.000Z

main/sitemaps.py

fidele000/Bhano-Blog

21810f6ce2d0e5ae32151673a6a42a7eb5168a7e

[ "MIT" ]

null

from django.contrib.sitemaps import Sitemap from .models import Post,Category class PostSitemap(Sitemap): changefreq = 'always' priority = 0.9 def items(self): return Post.published.all().order_by('-publish') def lastmod(self, obj): return obj.updated class CategorySitemap(Sitemap): changefreq = 'always' priority = 0.9 def items(self): return Category.objects.all().order_by('-publish') def lastmod(self, obj): return obj.updated

25.35

58

0.658777

61

507

5.442623

0.491803

0.10241

0.138554

0.186747

0.608434

0

0.010256

0.230769

507

20

59

25.35

0.841026

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0.625

0

0.055118

0

1

0.25

false

0

0.125

0.25

1

0

null

0

1

0

1

0

null

0

1

0

1

0

5

a90ba8de35136419270c289b2ea24ac17c583f67

153

py

Python

silasdk/__init__.py

xuru/Sila-Python

12fef8886580327779d32cf7596dae4516b36c11

[ "Apache-2.0" ]

null

silasdk/__init__.py

xuru/Sila-Python

12fef8886580327779d32cf7596dae4516b36c11

[ "Apache-2.0" ]

null

silasdk/__init__.py

xuru/Sila-Python

12fef8886580327779d32cf7596dae4516b36c11

[ "Apache-2.0" ]

null

from .ethwallet import EthWallet from .client import App from .processingTypes import ProcessingTypes from .registrationFields import RegistrationFields

30.6

50

0.869281

16

153

8.3125

0.4375

0

0.104575

153

4

51

38.25

0.970803

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

a90d5461929597e9f6c1a52129cdbaa7dc5181f2

3,755

py

Python

ray_tracer/tests/test_ray.py

jjason/RayTracerChallenge

ab3cea8968407426bddfa9e11319664fc0b595f6

[ "MIT" ]

1

2020-05-13T03:54:00.000Z

ray_tracer/tests/test_ray.py

jjason/RayTracerChallenge

ab3cea8968407426bddfa9e11319664fc0b595f6

[ "MIT" ]

null

ray_tracer/tests/test_ray.py

jjason/RayTracerChallenge

ab3cea8968407426bddfa9e11319664fc0b595f6

[ "MIT" ]

null

import unittest from matrix import Matrix from point import Point from vector import Vector from ray import Ray class TestRay(unittest.TestCase): def test_create(self): o = Point(x=1, y=2, z=3) d = Vector(x=4, y=5, z=6) r = Ray(origin=o, direction=d) self.assertEqual(r.origin, o) self.assertEqual(r.direction, d) def test_position(self): r = Ray(origin=Point(x=2, y=3, z=4), direction=Vector(x=1, y=0, z=0)) self.assertEqual(r.position(time=0), Point(x=2, y=3, z=4)) self.assertEqual(r.position(time=1), Point(x=3, y=3, z=4)) self.assertEqual(r.position(time=-1), Point(x=1, y=3, z=4)) self.assertEqual(r.position(time=2.5), Point(x=4.5, y=3, z=4)) def test_transform_by_identity(self): r1 = Ray(origin=Point(x=1, y=2, z=3), direction=Vector(x=0, y=1, z=0)) m = Matrix.identity() r2 = r1.transform(transformation=m) self.assertIsNot(r1, r2) self.assertEqual(r2.origin, r1.origin) self.assertEqual(r2.direction, r1.direction) def test_transform_by_translation(self): r1 = Ray(origin=Point(x=1, y=2, z=3), direction=Vector(x=0, y=1, z=0)) m = Matrix.translation_transform(x=3, y=4, z=5) r2 = r1.transform(transformation=m) self.assertIsNot(r1, r2) self.assertEqual(r2.origin, Point(x=4, y=6, z=8)) self.assertEqual(r2.direction, Vector(x=0, y=1, z=0)) def test_transform_by_scaling(self): r1 = Ray(origin=Point(x=1, y=2, z=3), direction=Vector(x=0, y=1, z=0)) m = Matrix.scaling_transform(x=2, y=3, z=4) r2 = r1.transform(transformation=m) self.assertIsNot(r1, r2) self.assertEqual(r2.origin, Point(x=2, y=6, z=12)) self.assertEqual(r2.direction, Vector(x=0, y=3, z=0)) def test_equal(self): r1 = Ray(origin=Point(x=1, y=2, z=3), direction=Vector(x=0, y=1, z=0)) r2 = Ray(origin=Point(x=1, y=2, z=3), direction=Vector(x=0, y=1, z=0)) self.assertTrue(r1 == r1) self.assertTrue(r1 == r2) r3 = Ray(origin=Point(x=0, y=2, z=3), direction=Vector(x=0, y=1, z=0)) self.assertFalse(r1 == r3) r3 = Ray(origin=Point(x=1, y=3, z=3), direction=Vector(x=0, y=1, z=0)) self.assertFalse(r1 == r3) r3 = Ray(origin=Point(x=1, y=2, z=4), direction=Vector(x=0, y=1, z=0)) self.assertFalse(r1 == r3) r3 = Ray(origin=Point(x=1, y=2, z=3), direction=Vector(x=1, y=1, z=0)) self.assertFalse(r1 == r3) r3 = Ray(origin=Point(x=1, y=2, z=3), direction=Vector(x=0, y=0, z=0)) self.assertFalse(r1 == r3) r3 = Ray(origin=Point(x=1, y=2, z=3), direction=Vector(x=0, y=1, z=1)) self.assertFalse(r1 == r3) def test_not_equal(self): r1 = Ray(origin=Point(x=1, y=2, z=3), direction=Vector(x=0, y=1, z=0)) r2 = Ray(origin=Point(x=1, y=2, z=3), direction=Vector(x=0, y=1, z=0)) self.assertFalse(r1 != r1) self.assertFalse(r1 != r2) r3 = Ray(origin=Point(x=0, y=2, z=3), direction=Vector(x=0, y=1, z=0)) self.assertTrue(r1 != r3) r3 = Ray(origin=Point(x=1, y=3, z=3), direction=Vector(x=0, y=1, z=0)) self.assertTrue(r1 != r3) r3 = Ray(origin=Point(x=1, y=2, z=4), direction=Vector(x=0, y=1, z=0)) self.assertTrue(r1 != r3) r3 = Ray(origin=Point(x=1, y=2, z=3), direction=Vector(x=1, y=1, z=0)) self.assertTrue(r1 != r3) r3 = Ray(origin=Point(x=1, y=2, z=3), direction=Vector(x=0, y=0, z=0)) self.assertTrue(r1 != r3) r3 = Ray(origin=Point(x=1, y=2, z=3), direction=Vector(x=0, y=1, z=1)) self.assertTrue(r1 != r3) if __name__ == '__main__': unittest.main()

37.929293

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3,755

3.171091

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0.030698

0.139535

0.744186

0.717674

0.714419

0.70093

0.666977

0.652093

0

0.080941

0.230093

3,755

98

79

38.316327

0.662746

0

0.493333

0

0.00213

0

0.413333

1

0.093333

false

0

0.066667

0

0.173333

0

null

0

1

0

1

0

null

0

1

0

5

a957be4a18b530737ff5086013ea9ddd64c3f8b7

316

py

Python

skills_utils/__init__.py

workforce-data-initiative/skills-utils

4cf9b7c2938984f34bbcc33d45482d23c52c7539

[ "MIT" ]

null

skills_utils/__init__.py

workforce-data-initiative/skills-utils

4cf9b7c2938984f34bbcc33d45482d23c52c7539

[ "MIT" ]

12

2017-04-06T22:34:14.000Z

2018-02-11T20:08:32.000Z

skills_utils/__init__.py

workforce-data-initiative/skills-utils

4cf9b7c2938984f34bbcc33d45482d23c52c7539

[ "MIT" ]

3

2018-03-05T18:36:26.000Z

2020-07-29T23:08:06.000Z

from skills_utils.io import stream_json_file from skills_utils.iteration import Batch from skills_utils.job_posting_import import JobPostingImportBase from skills_utils.s3 import split_s3_path from skills_utils.time import datetime_to_quarter, overlaps, quarter_to_daterange from skills_utils.common import safe_get

45.142857

81

0.892405

49

316

5.408163

0.510204

0.226415

0.339623

0

0.006897

0.082278

316

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52.666667

0.906897

0

1

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true

0

1

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1

0

null

1

0

1

0

null

0

1

0

1

0

5

a98ff71e1cf9356cebd4a942b3483bfcd14aa0df

15,673

py

Python

RL_forest/ddpg_plant/multi_ddpg/models.py

NoListen/RL-forest

6c43d43cc223a8be02256a60c38d72839b9d3fca

[ "MIT" ]

2

2017-08-14T09:11:14.000Z

2018-07-16T06:19:39.000Z

RL_forest/ddpg_plant/multi_ddpg/models.py

NoListen/RL-forest

6c43d43cc223a8be02256a60c38d72839b9d3fca

[ "MIT" ]

null

RL_forest/ddpg_plant/multi_ddpg/models.py

NoListen/RL-forest

6c43d43cc223a8be02256a60c38d72839b9d3fca

[ "MIT" ]

null

# https://github.com/openai/baselines/baselines/ddpg/models.py import tensorflow as tf import tensorflow.contrib as tc from tensorflow.contrib import rnn import numpy as np # cover 2d and 3d def get_w_bound(filter_shape): # return np.sqrt(6./(np.prod(filter_shape[:-2]))*np.sum(filter_shape[-2:])) return np.sqrt(6./((np.prod(filter_shape[:-2]))*np.sum(filter_shape[-2:]))) # modified from https://github.com/openai/universe-starter-agent/model.py def conv2d(x, num_filters, name, filter_size=(3, 3), stride=(1, 1), pad="SAME", dtype=tf.float32, collections=None): with tf.variable_scope(name): stride_shape = [1, stride[0], stride[1], 1] filter_shape = [filter_size[0], filter_size[1], int(x.get_shape()[3]), num_filters] w_bound = get_w_bound(filter_shape) w = tf.get_variable("W", filter_shape, dtype, tf.random_uniform_initializer(-w_bound, w_bound), collections=collections) b = tf.get_variable("b", [1, 1, 1, num_filters], initializer=tf.constant_initializer(0.0), collections=collections) return tf.nn.conv2d(x, w, stride_shape, pad) + b # TODO check about the conv3 layer. # Doubt about some problems # modified from https://github.com/openai/universe-starter-agent/model.py def conv3d(x, num_filters, name, filter_size=(1, 3, 3), stride=(1, 1, 1), pad="SAME", dtype=tf.float32, collections=None): with tf.variable_scope( name): stride_shape = [1, stride[0], stride[1], stride[2], 1] filter_shape = [filter_size[0], filter_size[1], filter_size[2], int(x.get_shape()[4]), num_filters] w_bound = get_w_bound(filter_shape) w = tf.get_variable("W", filter_shape, dtype, tf.random_uniform_initializer(-w_bound, w_bound), collections=collections) b = tf.get_variable("b", [1, 1, 1, 1, num_filters], initializer=tf.constant_initializer(0.0), collections=collections) return tf.nn.conv3d(x, w, stride_shape, pad) + b class Model(object): def __init__(self, name): self.name = name @property def vars(self): vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=self.name) vars_without_optimizer = [var for var in vars if 'optimizer' not in var.name] return vars_without_optimizer @property def trainable_vars(self): return tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=self.name) @property def perturbable_vars(self): return [var for var in self.trainable_vars if 'LayerNorm' not in var.name] # Initialization can't be determined temporally # simple class Dynamic_Actor(Model): def __init__(self, nb_unit_actions, name='actor', layer_norm=True, time_step=5): super(Dynamic_Actor, self).__init__(name=name) self.nb_unit_actions = nb_unit_actions self.layer_norm = layer_norm self.time_step = time_step # au alive units. def __call__(self, ud, mask, au, n_hidden=64, reuse=False): with tf.variable_scope(self.name) as scope: if reuse: scope.reuse_variables() x = ud if self.layer_norm: x = tc.layers.layer_norm(x, center=True, scale=True) x = tf.layers.dense(x, 64) if self.layer_norm: x = tc.layers.layer_norm(x, center=True, scale=True) x = tf.nn.relu(x) # no need to extend one dimension shape = x.get_shape().as_list() x = tf.reshape(x, [-1, self.time_step, shape[-1]]) # build bidirection lstm lstm_fw_cell = rnn.BasicLSTMCell(n_hidden, forget_bias=1.0) lstm_bw_cell = rnn.BasicLSTMCell(n_hidden, forget_bias=1.0) x, _ = tf.nn.bidirectional_dynamic_rnn(lstm_fw_cell, lstm_bw_cell, x, dtype=tf.float32, sequence_length=au) x = tf.concat(x, 2) # TODO v2 turn on the batch_norm after lstm if self.layer_norm: x = tc.layers.layer_norm(x, center=True, scale=True) x = tf.nn.relu(x) x = tf.reshape(x, [-1, self.time_step * n_hidden * 2, 1]) x = tf.layers.conv1d(x, self.nb_unit_actions, kernel_size=n_hidden * 2, strides=n_hidden * 2, kernel_initializer=tf.random_uniform_initializer(minval=-3e-3, maxval=3e-3)) x = tf.nn.tanh(x) return x class Dynamic_Critic(Model): def __init__(self, name='critic', layer_norm=True, time_step=5): super(Dynamic_Critic, self).__init__(name=name) self.layer_norm = layer_norm self.time_step = time_step def __call__(self, ud, action, mask, au, n_hidden=64, reuse=False, unit_data = False): with tf.variable_scope(self.name) as scope: if reuse: scope.reuse_variables() # x [ batch_size*time_step, DATA_NUM] x = ud if self.layer_norm: x = tc.layers.layer_norm(x, center=True, scale=True) x = tf.layers.dense(x, 64) if self.layer_norm: x = tc.layers.layer_norm(x, center=True, scale=True) x = tf.nn.relu(x) # format action to be [ batch_size*time_step, nb_actions] x = tf.concat([x, action], axis=-1) # another dense layer x = tf.layers.dense(x, 64) if self.layer_norm: x = tc.layers.layer_norm(x, center=True, scale=True) x = tf.nn.relu(x) shape = x.get_shape().as_list() x = tf.reshape(x, [-1, self.time_step, shape[-1]]) lstm_fw_cell = rnn.BasicLSTMCell(n_hidden, forget_bias=1.0) lstm_bw_cell = rnn.BasicLSTMCell(n_hidden, forget_bias=1.0) x, _ = tf.nn.bidirectional_dynamic_rnn(lstm_fw_cell, lstm_bw_cell, x, dtype=tf.float32, sequence_length=au) x = tf.concat(x, 2) # TODO v2 turn on the batch_norm after lstm if self.layer_norm: x = tc.layers.layer_norm(x, center=True, scale=True) x = tf.nn.relu(x) x = tf.reshape(x, [-1, self.time_step * n_hidden * 2, 1]) # Q value of each q = tf.layers.conv1d(x, 1, kernel_size=n_hidden*2, strides=n_hidden*2, kernel_initializer=tf.random_uniform_initializer(minval=-3e-3, maxval=3e-3)) q = tf.squeeze(q, [-1]) #q = tf.multiply(q, mask) Q = tf.reduce_sum(q, axis=1, keep_dims=True) #print(Q.get_shape().as_list, "Q") if unit_data: return Q, q return Q @property def output_vars(self): output_vars = [var for var in self.trainable_vars if 'output' in var.name] return output_vars class Dynamic_Conv_Actor(Model): def __init__(self, nb_unit_actions, name='actor', layer_norm=True, time_step=5): super(Dynamic_Conv_Actor, self).__init__(name=name) self.nb_unit_actions = nb_unit_actions self.layer_norm = layer_norm self.time_step = time_step # the differences between static ones and dynamic ones. # static [1, 0, 0, 0, 1] dynamic [1, 1, 0, 0, 0] # different arrangements of alive units. # not use mask temproally def __call__(self, s, ul, mask, au, n_hidden=256, reuse=False): with tf.variable_scope(self.name) as scope: if reuse: scope.reuse_variables() # embedding x = s # [batch_size, myself_num, ms, ms, 1] u = ul u_shape = u.get_shape().as_list() # tf.shape maybe also ok assert (u_shape[1] == self.time_step) # 40 -> 20 # different soldiers share the same gerneral state x = conv2d(x, 24, "conv1", (3, 3), (2, 2)) # 4 map # u_shape = u.get_shape u = tf.reshape(u, []) # apply conv2d but multiple timesteps simultaneously u = conv3d(u, 8, "u_conv1", (1, 3, 3), (1, 2, 2)) # 1 map # u [batch_size*myself_num, ms/2, ms/2, 1] -> [batch_size, myself, ms/2, ms/2, 1] # x [batch_size, ms/2, ms/2, c] u_list = tf.split(u, self.time_step, axis=1) u_list = [tf.squeeze(unit, [1]) for unit in u_list] # Concat the unit location with general state along the channel axis ux = tf.stack([tf.concat([x, unit], -1) for unit in u_list], axis=1) if self.layer_norm: ux = tc.layers.layer_norm(ux, center=True, scale=True) ux = tf.nn.relu(ux) # 20 -> 10 ux = conv3d(ux, 32, "conv2", (1, 3, 3), (1, 2, 2)) if self.layer_norm: ux = tc.layers.layer_norm(ux, center=True, scale=True) ux = tf.nn.relu(ux) # 10 -> 5. ux = conv3d(ux, 32, "conv3", (1, 3, 3), (1, 2, 2)) if self.layer_norm: ux = tc.layers.layer_norm(ux, center=True, scale=True) ux = tf.nn.relu(ux) #TODO add one (1,1,1) layer to reorganize the features. ux_shape = ux.get_shape().as_list() ux = tf.reshape(ux, (-1, self.time_step, int(np.prod(ux_shape[2:])))) # TODO is it necessary to add one layer here??? # ux = tf.layers.dense(ux, n_hidden) # # if self.layer_norm: # ux = tc.layers.layer_norm(ux, center=True, scale=True) # ux = tf.nn.relu(ux) # no need to extend one dimension # build bidirection lstm lstm_fw_cell = rnn.BasicLSTMCell(n_hidden, forget_bias=1.0) lstm_bw_cell = rnn.BasicLSTMCell(n_hidden, forget_bias=1.0) # TODO use output_states for process like policy iteration ---- WOW exciting ideas. ux, _ = tf.nn.bidirectional_dynamic_rnn(lstm_fw_cell, lstm_bw_cell, ux, dtype=tf.float32, sequence_length=au) ux = tf.concat(ux, 2) # TODO v2 turn on the batch_norm after lstm if self.layer_norm: ux = tc.layers.layer_norm(ux, center=True, scale=True) ux = tf.nn.relu(ux) ux = tf.layers.dense(ux, 256, kernel_initializer=tf.random_uniform_initializer(minval=-3e-3, maxval=3e-3)) if self.layer_norm: ux = tc.layers.layer_norm(ux, center=True, scale=True) ux = tf.nn.relu(ux) # number of convolution kernel. --> num_actions. default (-1, 1) # convert to [batch_size, time_step*n_hidden], channels_last ux = tf.reshape(ux, [-1, self.time_step*256, 1]) ux = tf.layers.conv1d(ux, self.nb_unit_actions, kernel_size = 256, strides = 256, kernel_initializer=tf.random_uniform_initializer(minval=-3e-3, maxval=3e-3)) # [ batch_size, time_step, nb_actions] ux = tf.nn.tanh(ux) return ux class Dynamic_Conv_Critic(Model): def __init__(self, name='critic', layer_norm=True, time_step=5): super(Dynamic_Conv_Critic, self).__init__(name=name) self.layer_norm = layer_norm self.time_step = time_step # the parameter's location has been changed def __call__(self, s, ul, mask, au, action, n_hidden=64, reuse=False, unit_data=False): with tf.variable_scope(self.name) as scope: if reuse: scope.reuse_variables() # x [ batch_size*time_step, DATA_NUM] x = s u = ul u_shape = u.get_shape().as_list() assert (u_shape[1] == self.time_step) x = conv2d(x, 24, "conv1", (3, 3), (2, 2)) # 4 map # TODO Notice! Fixing! the conv3 get wrong implementation u = conv3d(u, 8, "u_conv1", (1, 3, 3), (1, 2, 2)) # 1 map u_list = tf.split(u, self.time_step, axis=1) u_list = [tf.squeeze(unit, [1]) for unit in u_list] # stack in the time_step axis # TODO extract the weight for unit location ux = tf.stack([tf.concat([x, unit], -1) for unit in u_list], axis=1) if self.layer_norm: ux = tc.layers.layer_norm(ux, center=True, scale=True) ux = tf.nn.relu(ux) # [batch_size, myself_num, ms/2, ms/2, ???] ux = conv3d(ux, 32, "conv2", (1, 3, 3), (1, 2, 2)) if self.layer_norm: ux = tc.layers.layer_norm(ux, center=True, scale=True) ux = tf.nn.relu(ux) # 10 -> 5. ux = conv3d(ux, 32, "conv3", (1, 3, 3), (1, 2, 2)) if self.layer_norm: ux = tc.layers.layer_norm(ux, center=True, scale=True) ux = tf.nn.relu(ux) #TODO add one (1,1,1) layer to reorganize the features. ux_shape = ux.get_shape().as_list() ux = tf.reshape(ux, (-1, self.time_step, int(np.prod(ux_shape[2:])))) ux = tf.layers.dense(ux, n_hidden) if self.layer_norm: ux = tc.layers.layer_norm(ux, center=True, scale=True) ux = tf.nn.relu(ux) # Maybe I need an embedding. ux = tf.concat([ux, action], axis=-1) lstm_fw_cell = rnn.BasicLSTMCell(n_hidden, forget_bias=1.0) lstm_bw_cell = rnn.BasicLSTMCell(n_hidden, forget_bias=1.0) ux, _ = tf.nn.bidirectional_dynamic_rnn(lstm_fw_cell, lstm_bw_cell, ux, dtype=tf.float32, sequence_length=au) ux = tf.concat(ux, 2) # TODO v2 turn on the batch_norm after lstm if self.layer_norm: ux = tc.layers.layer_norm(ux, center=True, scale=True) ux = tf.nn.relu(ux) ux = tf.layers.dense(ux, 256, kernel_initializer=tf.random_uniform_initializer(minval=-3e-3, maxval=3e-3)) if self.layer_norm: ux = tc.layers.layer_norm(ux, center=True, scale=True) #ux = tf.nn.relu(ux) ux = tf.nn.dropout(ux, keep_prob=0.5) ux = tf.reshape(ux, [-1, self.time_step * 256, 1]) # Q value of each # some computations are wasted q = tf.layers.conv1d(ux, 1, kernel_size=256, strides=256, kernel_initializer=tf.random_uniform_initializer(minval=-3e-3, maxval=3e-3)) q = tf.squeeze(q, [-1]) """ kill the gradient using the mask """ #print(p.get_shape().as_list(), q.get_shape().as_list(), "pq") #pQ = tf.multiply(p,q) Q_mask = tf.multiply(q, mask) #print(mask.get_shape().as_list(), pQ_mask.get_shape().as_list(), "mask") Q = tf.reduce_sum(Q_mask, axis=1, keep_dims=True) if unit_data: return Q,Q_mask # if mask_loss: # #TODO check this putput # qm = tf.multiply(1-mask, q) # return Q,qm # #print(Q.get_shape().as_list, "Q") return Q @property def output_vars(self): output_vars = [var for var in self.trainable_vars if 'output' in var.name] return output_vars

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169

py

Python

kompassi/wsgi.py

darkismus/kompassi

35dea2c7af2857a69cae5c5982b48f01ba56da1f

[ "CC-BY-3.0" ]

13

2015-11-29T12:19:12.000Z

2021-02-21T15:42:11.000Z

kompassi/wsgi.py

darkismus/kompassi

35dea2c7af2857a69cae5c5982b48f01ba56da1f

[ "CC-BY-3.0" ]

23

2015-04-29T19:43:34.000Z

2021-02-10T05:50:17.000Z

kompassi/wsgi.py

darkismus/kompassi

35dea2c7af2857a69cae5c5982b48f01ba56da1f

[ "CC-BY-3.0" ]

11

2015-09-20T18:59:00.000Z

2020-02-07T08:47:34.000Z

import os os.environ.setdefault("DJANGO_SETTINGS_MODULE", "kompassi.settings") from django.core.wsgi import get_wsgi_application application = get_wsgi_application()

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8d328c4601a037477cdc36cefc6cb622fe71093d

11,701

py

Python

build/lib/Scopuli/Interfaces/MySQL/Schema/Core/__init__.py

MaxOnNet/scopuli-core

17d72d7f67286ae84b21aa2541f3e1f03b6154ca

[ "Apache-2.0" ]

null

build/lib/Scopuli/Interfaces/MySQL/Schema/Core/__init__.py

MaxOnNet/scopuli-core

17d72d7f67286ae84b21aa2541f3e1f03b6154ca

[ "Apache-2.0" ]

null

build/lib/Scopuli/Interfaces/MySQL/Schema/Core/__init__.py

MaxOnNet/scopuli-core

17d72d7f67286ae84b21aa2541f3e1f03b6154ca

[ "Apache-2.0" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- # # Copyright [2017] Tatarnikov Viktor [viktor@tatarnikov.org] # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ """ from Scopuli.Interfaces.MySQL.SQLAlchemy import * class Image(Base, Schema): """ Базовая таблица храниения изображений """ __tablename__ = 'image' __table_args__ = { 'mysql_engine': 'InnoDB', 'mysql_charset': 'utf8', 'mysql_collate': 'utf8_general_ci', 'mysql_comment': 'Таблица со списком и информацией об используемых изображениях' } id = Column(Integer(), primary_key=True, autoincrement=True, doc="Row ID - Сурогатный ключ") uuid = Column(String(64), index=True, nullable=False, doc="UUID файла в кэше") file = Column(String(256), nullable=False, doc="Относительный путь до изображения") md5sum = Column(String(64), index=True, nullable=False, doc="Контрольная сумма изображения") file_thumbnail = Column(String(256), nullable=False, doc="Относительный путь до изображения") md5sum_thumbnail = Column(String(64), index=True, nullable=False, doc="Контрольная сумма изображения") size = Column(Integer(), ColumnDefault(0), nullable=False, doc="Размер изображения") height = Column(Integer(), ColumnDefault(0), nullable=False, doc="Высота изображения") width = Column(Integer(), ColumnDefault(0), nullable=False, doc="Ширина изображения") # Automatic Logger date_create = Column(DateTime(), nullable=False, default=func.utc_timestamp(), doc="AutoLogger - Время создания") date_change = Column(DateTime(), nullable=False, default=func.utc_timestamp(), onupdate=func.utc_timestamp(), doc="AutoLogger - Время последнего изменения") class File(Base, Schema): """ Базовая таблица хранения файлов """ __tablename__ = 'file' __table_args__ = { 'mysql_engine': 'InnoDB', 'mysql_charset': 'utf8', 'mysql_collate': 'utf8_general_ci', 'mysql_comment': 'Таблица со списком и информацией об используемых файлов' } id = Column(Integer(), primary_key=True, autoincrement=True, doc="Row ID - Сурогатный ключ") uuid = Column(String(64), index=True, nullable=False, doc="UUID файла в кэше") file = Column(String(256), nullable=False, doc="Относительный путь до файла") type = Column(String(16), nullable=False, doc="Тип файла") md5sum = Column(String(64), index=True, nullable=False, doc="Сонтрольная сумма изображения") file_thumbnail = Column(String(256), nullable=False, doc="Относительный путь до изображения") md5sum_thumbnail = Column(String(64), index=True, nullable=False, doc="Сонтрольная сумма изображения") size = Column(Integer(), ColumnDefault(0), nullable=False, doc="Размер файла") # Automatic Logger date_create = Column(DateTime(), nullable=False, default=func.utc_timestamp(), doc="AutoLogger - Время создания") date_change = Column(DateTime(), nullable=False, default=func.utc_timestamp(), onupdate=func.utc_timestamp(), doc="AutoLogger - Время последнего изменения") class Address(Base, Schema): """ Базовая таблица хранения адресов """ __tablename__ = 'address' __table_args__ = { 'mysql_engine': 'InnoDB', 'mysql_charset': 'utf8', 'mysql_collate': 'utf8_general_ci', 'mysql_comment': 'Таблица со списком и информацией об используемых файлов' } id = Column(Integer(), primary_key=True, autoincrement=True, doc="Row ID - Сурогатный ключ") country = Column(String(64), nullable=False, doc="Страна") city = Column(String(64), nullable=False, doc="Город") street = Column(String(64), nullable=False, doc="Улица") house = Column(String(8), nullable=False, doc="Дом") room = Column(String(8), nullable=False, doc="Кабинет \ квартира") floor = Column(String(256), nullable=False, doc="Этаж") index = Column(String(64), nullable=False, doc="Почтовый индекс") type = Column(String(16), nullable=False, doc="Тип файла") latitude = Column(String(16), nullable=False, doc="Широта") longitude = Column(String(16), nullable=False, doc="Долгота") # Automatic Logger date_create = Column(DateTime(), nullable=False, default=func.utc_timestamp(), doc="AutoLogger - Время создания") date_change = Column(DateTime(), nullable=False, default=func.utc_timestamp(), onupdate=func.utc_timestamp(), doc="AutoLogger - Время последнего изменения") # Rela phones = association_proxy('address_phones', 'phone') urls = association_proxy('address_urls', 'url') emails = association_proxy('address_emails', 'email') class Phone(Base, Schema): """ Базовая таблица хранения телефонов """ __tablename__ = 'phone' __table_args__ = { 'mysql_engine': 'InnoDB', 'mysql_charset': 'utf8', 'mysql_collate': 'utf8_general_ci', 'mysql_comment': 'Таблица со списком телефонов' } id = Column(Integer(), primary_key=True, autoincrement=True, doc="Row ID - Сурогатный ключ") country = Column(String(16), ColumnDefault(""), nullable=False, doc="Код страны") city = Column(String(16), ColumnDefault(""), nullable=True, doc="Код города") number = Column(String(16), ColumnDefault(""), nullable=False, doc="Номер телефона") title = Column(String(128), ColumnDefault(""), nullable=False, doc="Название") description = Column(String(256), ColumnDefault(""), nullable=False, doc="Краткое описание") is_enable = Column(Boolean(), ColumnDefault(False), default=False, nullable=False, doc="Метка использования") is_published = Column(Boolean(), ColumnDefault(False), default=False, nullable=False, doc="Метка использования в интернете") # Automatic Logger date_create = Column(DateTime(), nullable=False, default=func.utc_timestamp(), doc="AutoLogger - Время создания") date_change = Column(DateTime(), nullable=False, default=func.utc_timestamp(), onupdate=func.utc_timestamp(), doc="AutoLogger - Время последнего изменения") @hybrid_property def phone(self): if self.city is None: return "+{}{}".format(self.country, self.number) else: return "+{}{}{}".format(self.country, self.city, self.number) class AddressPhone(Base, Schema): """ Базовая таблица хранения телефонов """ __tablename__ = 'address_phone' __table_args__ = { 'mysql_engine': 'InnoDB', 'mysql_charset': 'utf8', 'mysql_collate': 'utf8_general_ci', 'mysql_comment': 'Таблица со списком телефонов у адреса' } id = Column(Integer(), primary_key=True, autoincrement=True, doc="Row ID - Сурогатный ключ") cd_address = Column(Integer(), ForeignKey(Address.id), nullable=False, doc="Ссылка на Address") cd_phone = Column(Integer(), ForeignKey(Phone.id), nullable=False, doc="Ссылка на Phone") phone = relationship(Phone) address = relationship(Address, backref=backref("address_phones", cascade="all, delete-orphan")) class Email(Base, Schema): """ Базовая таблица хранения телефонов """ __tablename__ = 'email' __table_args__ = { 'mysql_engine': 'InnoDB', 'mysql_charset': 'utf8', 'mysql_collate': 'utf8_general_ci', 'mysql_comment': 'Таблица со списком почтовых адресов' } id = Column(Integer(), primary_key=True, autoincrement=True, doc="Row ID - Сурогатный ключ") email = Column(String(128), ColumnDefault(""), nullable=False, doc="Адрес") title = Column(String(128), ColumnDefault(""), nullable=False, doc="Название") description = Column(String(256), ColumnDefault(""), nullable=False, doc="Краткое описание") is_enable = Column(Boolean(), ColumnDefault(False), default=False, nullable=False, doc="Метка использования") is_published = Column(Boolean(), ColumnDefault(False), default=False, nullable=False, doc="Метка использования в интернете") # Automatic Logger date_create = Column(DateTime(), nullable=False, default=func.utc_timestamp(), doc="AutoLogger - Время создания") date_change = Column(DateTime(), nullable=False, default=func.utc_timestamp(), onupdate=func.utc_timestamp(), doc="AutoLogger - Время последнего изменения") class AddressEmail(Base, Schema): """ Базовая таблица хранения почтовых адресов """ __tablename__ = 'address_email' __table_args__ = { 'mysql_engine': 'InnoDB', 'mysql_charset': 'utf8', 'mysql_collate': 'utf8_general_ci', 'mysql_comment': 'Таблица со списком электронных почтовых адресов у адреса' } id = Column(Integer(), primary_key=True, autoincrement=True, doc="Row ID - Сурогатный ключ") cd_address = Column(Integer(), ForeignKey(Address.id), nullable=False, doc="Ссылка на Address") cd_email = Column(Integer(), ForeignKey(Email.id), nullable=False, doc="Ссылка на Email") email = relationship(Email) address = relationship(Address, backref=backref("address_emails", cascade="all, delete-orphan")) class Url(Base, Schema): """ Базовая таблица хранения телефонов """ __tablename__ = 'url' __table_args__ = { 'mysql_engine': 'InnoDB', 'mysql_charset': 'utf8', 'mysql_collate': 'utf8_general_ci', 'mysql_comment': 'Таблица со списком почтовых адресов' } id = Column(Integer(), primary_key=True, autoincrement=True, doc="Row ID - Сурогатный ключ") url = Column(String(256), ColumnDefault(""), nullable=False, doc="Адрес") title = Column(String(128), ColumnDefault(""), nullable=False, doc="Название") description = Column(String(256), ColumnDefault(""), nullable=False, doc="Краткое описание") is_enable = Column(Boolean(), ColumnDefault(False), default=False, nullable=False, doc="Метка использования") is_published = Column(Boolean(), ColumnDefault(False), default=False, nullable=False, doc="Метка использования в интернете") # Automatic Logger date_create = Column(DateTime(), nullable=False, default=func.utc_timestamp(), doc="AutoLogger - Время создания") date_change = Column(DateTime(), nullable=False, default=func.utc_timestamp(), onupdate=func.utc_timestamp(), doc="AutoLogger - Время последнего изменения") class AddressUrl(Base, Schema): """ Базовая таблица хранения почтовых адресов """ __tablename__ = 'address_url' __table_args__ = { 'mysql_engine': 'InnoDB', 'mysql_charset': 'utf8', 'mysql_collate': 'utf8_general_ci', 'mysql_comment': 'Таблица со списком электронных адресов у адреса' } id = Column(Integer(), primary_key=True, autoincrement=True, doc="Row ID - Сурогатный ключ") cd_address = Column(Integer(), ForeignKey(Address.id), nullable=False, doc="Ссылка на Address") cd_url = Column(Integer(), ForeignKey(Url.id), nullable=False, doc="Ссылка на Email") url = relationship(Url) address = relationship(Address, backref=backref("address_urls", cascade="all, delete-orphan"))

43.988722

160

0.678574

1,329

11,701

5.82167

0.177577

0.099134

0.097195

0.041877

0.825126

0.798371

0.74357

0.685149

0.657231

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0.011575

0.187847

11,701

265

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null

0

1

0

1

0

null

0

1

0

5

8d3efcf59f1ae679322dc231bb9a8466aabe9ee1

89

py

Python

irekia/__init__.py

eillarra/irekia

bbceb8d26c9dddf93f5015459bf53887703fb87e

[ "MIT" ]

1

2017-04-29T11:37:59.000Z

irekia/__init__.py

eillarra/irekia

bbceb8d26c9dddf93f5015459bf53887703fb87e

[ "MIT" ]

1

2021-03-31T18:50:59.000Z

irekia/__init__.py

eillarra/irekia

bbceb8d26c9dddf93f5015459bf53887703fb87e

[ "MIT" ]

null

from __future__ import absolute_import from .client import Client, get_metadata # noqa

22.25

48

0.820225

12

89

5.583333

0.666667

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0.146067

89

3

49

29.666667

0.881579

0.044944

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true

0

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0

1

0

null

0

1

0

1

0

1

0

5

8d6df2bbf0e86020114e72dfe033f37b704f09e3

1,682

py

Python

modules/runnable/script5.py

Asichurter/Few-Shot-Project

865cd6aa7b996c518dfa48dcc9ffad90445f9efe

[ "MIT" ]

null

modules/runnable/script5.py

Asichurter/Few-Shot-Project

865cd6aa7b996c518dfa48dcc9ffad90445f9efe

[ "MIT" ]

null

modules/runnable/script5.py

Asichurter/Few-Shot-Project

865cd6aa7b996c518dfa48dcc9ffad90445f9efe

[ "MIT" ]

null

import requests import os # save_path = 'C:/Users/Asichurter/Desktop/dl_malwares/' # # url = 'https://www.virustotal.com/vtapi/v2/file/download' # apikey = 'c424abc9c8d7102cfaf9cf2d8f01fb95f4ddfd81a563d6e07738fa960b501d87' # hashes = [ "63956d6417f8f43357d9a8e79e52257e" # "6f7bde7a1126debf0cc359a54953efc1" # "7520c8f9534ca818726a4feaebf49e2b" # "e435a536968941854bcec3b902c439f6" # "e93049e2df82ab26f35ad0049173cb14" # "4235e2d487958ff377f0f92b266591f0" # "e4647acec12b82944f5df603dc682660" # "6524a10da9701301b2582f12cc66f90c" # "14a3f5108958b61c6bdc2de17c785a89" # "1515a80662d5bd0d8a6fb9ecfeedb652" # "94e4b62861ab7a4d3246a4888e9025b5" # "cfb9fbcd2bb1ca2d326720971f385a4b" # "a89153d58a70f143ed1fd3b89f26a90f" # "323037966ab54ce841f528870908e259" # "5af2ee5a9e61b194f6cb076775237980" # "ac79fefb5ddfe4f20061bca398884233" # "52411226cbdd24441966c08f959ad5dc" # "3742f0a58ca91a0c56c74f49dd22ab0b" # "485a4912b2d639694f836451a2b30435" # "b29bea9ae0292d8a6a18219b63a62787"] # # for i,hash_val in enumerate(hashes): # print(i, hash_val) # params = {'apikey': apikey, 'hash': hash_val} # # response = requests.get(url, params=params) # print(response) # downloaded_file = response.content # # with open(save_path+str(i)+'.pe', 'wb') as f: # f.write(downloaded_file) url = 'https://www.virustotal.com/gui/file/968c37e74571c6f3bf8f2749c9e1d0ea6999eb503de2a9a6cc78c68530559c6d/detection.html' response = requests.get(url)

39.116279

123

0.685493

101

1,682

11.336634

0.663366

0.018341

0.019214

0.036681

0.041921

0

0.381749

0.218193

1,682

42

124

40.047619

0.488973

0.849584

0

0.532407

0

1

0

false

0

0.5

0

0.5

0

1

null

0

1

0

1

0

null

0

1

0

5

8da0ed0bb0b0f2ad312d1b93fba7462bedf16e43

164

py

Python

django_review/review/admin.py

edfranceschini/django_review

6a03bd41d81a84949d635f4cc64efdfaff51e10c

[ "MIT" ]

null

django_review/review/admin.py

edfranceschini/django_review

6a03bd41d81a84949d635f4cc64efdfaff51e10c

[ "MIT" ]

null

django_review/review/admin.py

edfranceschini/django_review

6a03bd41d81a84949d635f4cc64efdfaff51e10c

[ "MIT" ]

null

from django.contrib import admin from .models import Company, Profile, Review admin.site.register(Company) admin.site.register(Profile) admin.site.register(Review)

27.333333

44

0.823171

23

164

5.869565

0.478261

0.2

0.377778

0

0.079268

164

6

45

27.333333

0.89404

0

1

0

true

0

0.4

0

0.4

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

5

a5f2a97d7d498665423cabcef1dc59adede23787

382

py

Python

Server/Data/scripts/player/skills/prayer.py

CoderMMK/RSPS

5cf72f4203626e3bf3ab8790072547e260afa3f5

[ "WTFPL" ]

null

Server/Data/scripts/player/skills/prayer.py

CoderMMK/RSPS

5cf72f4203626e3bf3ab8790072547e260afa3f5

[ "WTFPL" ]

null

Server/Data/scripts/player/skills/prayer.py

CoderMMK/RSPS

5cf72f4203626e3bf3ab8790072547e260afa3f5

[ "WTFPL" ]

2

2019-07-19T21:28:47.000Z

2020-01-07T14:23:31.000Z

# Bone Bury Functions # Author: Lmctruck30 # from server.util import ScriptManager def itemClick_526(player, itemId, itemSlot): player.getPA().buryBone(5, 1600, itemId, itemSlot) def itemClick_532(player, itemId, itemSlot): player.getPA().buryBone(10, 1600, itemId, itemSlot) def itemClick_536(player, itemId, itemSlot): player.getPA().buryBone(15, 1600, itemId, itemSlot)

22.470588

52

0.759162

49

382

5.857143

0.489796

0.292683

0.209059

0.271777

0.616725

0.407666

0

0.083086

0.117801

382

16

53

23.875

0.768546

0.099476

0

1

0.428571

false

0

0.142857

0

0.571429

0

null

1

0

null

0

1

0

1

0

5

a5f4e46f6d82fde46b3af94a9cc6258e191f0f2f

245

py

Python

ch01/c118.py

claudiordgz/GoodrichTamassiaGoldwasser

0e434caa5bf6f2adefaeff9c17b4f3910c1cff7b

[ "MIT" ]

2

2017-01-04T02:00:39.000Z

2018-10-10T17:43:51.000Z

ch01/c118.py

claudiordgz/GoodrichTamassiaGoldwasser

0e434caa5bf6f2adefaeff9c17b4f3910c1cff7b

[ "MIT" ]

null

ch01/c118.py

claudiordgz/GoodrichTamassiaGoldwasser

0e434caa5bf6f2adefaeff9c17b4f3910c1cff7b

[ "MIT" ]

null

__author__ = 'Claudio' """Demonstrate how to use Python’s list comprehension syntax to produce the list [0, 2, 6, 12, 20, 30, 42, 56, 72, 90]. """ def demonstration_list_comprehension(): return [idx*x for idx, x in enumerate(range(1,11))]

27.222222

71

0.693878

40

245

4.1

0.85

0.207317

0

0.098039

0.167347

245

8

72

30.625

0.705882

0

0.057377

0

1

0.333333

false

0

0.333333

0.666667

0

null

1

0

1

0

null

0

1

0

1

0

5

a5ff164795bd2b1745e48b05cf366f3770ea9640

89

py

Python

cart/admin.py

geraldofada/flea-market

174a269657dc148f52e2785933da73758e6e86be

[ "MIT" ]

null

cart/admin.py

geraldofada/flea-market

174a269657dc148f52e2785933da73758e6e86be

[ "MIT" ]

null

cart/admin.py

geraldofada/flea-market

174a269657dc148f52e2785933da73758e6e86be

[ "MIT" ]

null

from cart.models import Cart from django.contrib import admin admin.site.register(Cart)

17.8

32

0.820225

14

89

5.214286

0.642857

0

0.11236

89

5

33

17.8

0.924051

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

572392deacfc0e33849ccd26e9c8fa2d3b4a5aac

434

py

Python

Bot/ExchangeBase.py

JHJohny/CryptoBuyInBot

4a7ab4ff1c5dd92fc3853e4de75c54053f66871e

[ "MIT" ]

null

Bot/ExchangeBase.py

JHJohny/CryptoBuyInBot

4a7ab4ff1c5dd92fc3853e4de75c54053f66871e

[ "MIT" ]

null

Bot/ExchangeBase.py

JHJohny/CryptoBuyInBot

4a7ab4ff1c5dd92fc3853e4de75c54053f66871e

[ "MIT" ]

null

from abc import ABC, abstractmethod class Exchange(ABC): @abstractmethod def get_current_minute_candle(self, symbol): """Takes keyword of cryptocurrency and returns dict of - Open, High, Low, Close values""" pass @abstractmethod def create_buy_order(self): pass @abstractmethod def set_stop_loss(self): pass @abstractmethod def set_stop_profit(self): pass

19.727273

97

0.66129

51

434

5.45098

0.647059

0.244604

0.226619

0.179856

0.230216

0

0.267281

434

21

98

20.666667

0.874214

0.191244

0

0.571429

0

1

0.285714

false

0.285714

0.071429

0

0.428571

0

null

1

0

null

0

1

0

1

0

5

573ad505fac4d4515eb374d85148e9fb951de2e4

80

py

Python

01_basic/exercise_042.py

sideroff/python-exercises

6a9cc55735d977a71697204c734b3ade84a0c4fd

[ "MIT" ]

null

01_basic/exercise_042.py

sideroff/python-exercises

6a9cc55735d977a71697204c734b3ade84a0c4fd

[ "MIT" ]

4

2020-03-24T18:00:07.000Z

2021-06-02T00:51:22.000Z

01_basic/exercise_042.py

sideroff/python-exercises

6a9cc55735d977a71697204c734b3ade84a0c4fd

[ "MIT" ]

null

import struct print("python is running on %i bit os" %(struct.calcsize("P")*8))

26.666667

65

0.7

14

80

4

0.928571

0

0.014286

0.125

80

3

65

26.666667

0.785714

0

0.382716

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