xszheng2020/the_stack_dedup_python_hits_6 · Datasets at Hugging Face

aca0a92d2809b0aaf3b69c6f7b16cb9f71a76a62

3,103

py

Python

scanning/base_nonraster_scan.py

rbrenesh/ScopeFoundry

41981f79c0b6705e2e5d48a454a94f68ed294446

[ "BSD-3-Clause" ]

22

2017-01-05T19:58:44.000Z

2021-09-17T06:02:01.000Z

scanning/base_nonraster_scan.py

rbrenesh/ScopeFoundry

41981f79c0b6705e2e5d48a454a94f68ed294446

[ "BSD-3-Clause" ]

19

2017-01-18T21:46:15.000Z

2020-10-22T20:11:52.000Z

scanning/base_nonraster_scan.py

rbrenesh/ScopeFoundry

41981f79c0b6705e2e5d48a454a94f68ed294446

[ "BSD-3-Clause" ]

10

2017-07-21T08:42:22.000Z

2021-09-17T18:47:41.000Z

from ScopeFoundry import Measurement from ScopeFoundry.scanning.base_raster_scan import BaseRaster2DScan import time import numpy as np class BaseNonRaster2DScan(BaseRaster2DScan): name = "base_non_raster_2Dscan" def gen_raster_scan(self, gen_arrays=True): self.Npixels = self.Nh.val*self.Nv.val self.scan_shape = (1, self.Nv.val, self.Nh.val) if gen_arrays: #print "t0", time.time() - t0 self.create_empty_scan_arrays() #print "t1", time.time() - t0 # t0 = time.time() # pixel_i = 0 # for jj in range(self.Nv.val): # #print "tjj", jj, time.time() - t0 # self.scan_slow_move[pixel_i] = True # for ii in range(self.Nh.val): # self.scan_v_positions[pixel_i] = self.v_array[jj] # self.scan_h_positions[pixel_i] = self.h_array[ii] # self.scan_index_array[pixel_i,:] = [0, jj, ii] # pixel_i += 1 # print "for loop raster gen", time.time() - t0 t0 = time.time() H, V = np.meshgrid(self.h_array, self.v_array) self.scan_h_positions[:] = H.flat self.scan_v_positions[:] = V.flat II,JJ = np.meshgrid(np.arange(self.Nh.val), np.arange(self.Nv.val)) self.scan_index_array[:,1] = JJ.flat self.scan_index_array[:,2] = II.flat #self.scan_v_positions print("array flatten raster gen", time.time() - t0) def gen_spiral_scan(self, gen_arrays=True): #self.Npixels = self.Nh.val*self.Nv.val self.scan_shape = (1, Npixels) if gen_arrays: #print "t0", time.time() - t0 self.create_empty_scan_arrays() #print "t1", time.time() - t0 # t0 = time.time() # pixel_i = 0 # for jj in range(self.Nv.val): # #print "tjj", jj, time.time() - t0 # self.scan_slow_move[pixel_i] = True # for ii in range(self.Nh.val): # self.scan_v_positions[pixel_i] = self.v_array[jj] # self.scan_h_positions[pixel_i] = self.h_array[ii] # self.scan_index_array[pixel_i,:] = [0, jj, ii] # pixel_i += 1 # print "for loop raster gen", time.time() - t0 h = ix * np.cos(ix) v = ix * np.sin(ix) t0 = time.time() H, V = np.meshgrid(self.h_array, self.v_array) self.scan_h_positions[:] = H.flat self.scan_v_positions[:] = V.flat II,JJ = np.meshgrid(np.arange(self.Nh.val), np.arange(self.Nv.val)) self.scan_index_array[:,1] = JJ.flat self.scan_index_array[:,2] = II.flat #self.scan_v_positions print("array flatten raster gen", time.time() - t0)

39.782051

79

0.503706

395

3,103

3.762025

0.15443

0.107672

0.067295

0.072678

0.831763

0

0.017571

0.37641

3,103

77

80

40.298701

0.750388

0.411215

0

0.606061

0

0.038976

0.012249

0

1

0.060606

false

0

0.121212

0

0.242424

0.060606

0

null

0

1

0

null

0

6

c57b0be555bd1a46c4e7e148a0dd1c3e6287579f

291

py

Python

mozhi/dataset/idataset.py

gyan42/mozhi

ee54692b1913141e5fdfda486b7dcd2a37e9f39f

[ "Apache-2.0" ]

2

2021-08-16T11:06:36.000Z

2022-03-15T12:08:24.000Z

mozhi/dataset/idataset.py

gyan42/mozhi

ee54692b1913141e5fdfda486b7dcd2a37e9f39f

[ "Apache-2.0" ]

null

mozhi/dataset/idataset.py

gyan42/mozhi

ee54692b1913141e5fdfda486b7dcd2a37e9f39f

[ "Apache-2.0" ]

null

class IDataset(object): def __init__(self): pass def train(self): raise RuntimeError("No implementation found!") def val(self): raise RuntimeError("No implementation found!") def test(self): raise RuntimeError("No implementation found!")

19.4

54

0.635739

31

291

5.83871

0.483871

0.149171

0.348066

0.381215

0.729282

0.497238

0

0.261168

291

14

55

20.785714

0.84186

0

0.333333

0

0.249135

0

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0.444444

false

0.111111

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0.555556

0

null

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null

0

1

0

1

0

1

0

6

c5a480685972669790400495fcd2f9ab26657fe8

10,892

py

Python

Core/CrawlDetailInfo.py

RoboAdvisorProj/StockCrawling

9a7bbd359acdac902720579f994bdd16bea1bb33

[ "MIT" ]

3

2021-06-02T07:48:54.000Z

2021-11-01T15:22:39.000Z

Core/CrawlDetailInfo.py

RoboAdvisorProj/StockCrawling

9a7bbd359acdac902720579f994bdd16bea1bb33

[ "MIT" ]

null

Core/CrawlDetailInfo.py

RoboAdvisorProj/StockCrawling

9a7bbd359acdac902720579f994bdd16bea1bb33

[ "MIT" ]

2

2019-11-24T00:19:56.000Z

2020-01-30T15:31:50.000Z

from Setting import DefineManager from Utils import LogManager class CrawlDetailInfo(object): def __init__(self, webCrawler, crawlUrl): self.webCrawler = webCrawler self.crawlUrl = crawlUrl urlStatus = str(self.webCrawler.SetDriverUrl(crawlUrl)) crawlerStatus = str(self.webCrawler.GetDriverStatus()) msg = "web driver status: " + crawlerStatus + " url status: " + urlStatus LogManager.PrintLogMessage("CrawlDetailInfo", "__init__", msg, DefineManager.LOG_LEVEL_INFO) def Crawl3YearsBeforeSale(self): try: webDriver = self.webCrawler.GetDriver() subHtmlIframe = webDriver.find_element_by_id("coinfo_cp") webDriver = self.webCrawler.SwitchToFrame(subHtmlIframe) financialTable = webDriver.find_element_by_id(DefineManager.FINANCIAL_TABLE_ID_NAME) financialRows = financialTable.find_elements_by_tag_name(DefineManager.TAG_TR) financialSaleRow = financialRows[DefineManager.FINANCIAL_SALE_ROW_POINT] financialSaleStr = financialSaleRow.find_elements_by_tag_name(DefineManager.TAG_TD)[DefineManager.FINANCIAL_SALE_3_YEARS_BEFORE_COL_POINT].text LogManager.PrintLogMessage("CrawlDetailInfo", "Crawl3YearsBeforeSale", "crawl 3 years before sale successfully: " + financialSaleStr, DefineManager.LOG_LEVEL_INFO) webDriver = self.webCrawler.SwitchToDefault() return financialSaleStr except: LogManager.PrintLogMessage("CrawlDetailInfo", "Crawl3YearsBeforeSale", "crawl 3 years before sale failed", DefineManager.LOG_LEVEL_ERROR) return None def Crawl2YearsBeforeSale(self): try: webDriver = self.webCrawler.GetDriver() subHtmlIframe = webDriver.find_element_by_id("coinfo_cp") webDriver = self.webCrawler.SwitchToFrame(subHtmlIframe) financialTable = webDriver.find_element_by_id(DefineManager.FINANCIAL_TABLE_ID_NAME) financialRows = financialTable.find_elements_by_tag_name(DefineManager.TAG_TR) financialSaleRow = financialRows[DefineManager.FINANCIAL_SALE_ROW_POINT] financialSaleStr = financialSaleRow.find_elements_by_tag_name(DefineManager.TAG_TD)[DefineManager.FINANCIAL_SALE_2_YEARS_BEFORE_COL_POINT].text LogManager.PrintLogMessage("CrawlDetailInfo", "Crawl2YearsBeforeSale", "crawl 2 years before sale successfully: " + financialSaleStr, DefineManager.LOG_LEVEL_INFO) webDriver = self.webCrawler.SwitchToDefault() return financialSaleStr except: LogManager.PrintLogMessage("CrawlDetailInfo", "Crawl2YearsBeforeSale", "crawl 2 years before sale failed", DefineManager.LOG_LEVEL_ERROR) return None def Crawl1YearsBeforeSale(self): try: webDriver = self.webCrawler.GetDriver() subHtmlIframe = webDriver.find_element_by_id("coinfo_cp") webDriver = self.webCrawler.SwitchToFrame(subHtmlIframe) financialTable = webDriver.find_element_by_id(DefineManager.FINANCIAL_TABLE_ID_NAME) financialRows = financialTable.find_elements_by_tag_name(DefineManager.TAG_TR) financialSaleRow = financialRows[DefineManager.FINANCIAL_SALE_ROW_POINT] financialSaleStr = financialSaleRow.find_elements_by_tag_name(DefineManager.TAG_TD)[DefineManager.FINANCIAL_SALE_1_YEARS_BEFORE_COL_POINT].text LogManager.PrintLogMessage("CrawlDetailInfo", "Crawl1YearsBeforeSale", "crawl 1 years before sale successfully: " + financialSaleStr, DefineManager.LOG_LEVEL_INFO) webDriver = self.webCrawler.SwitchToDefault() return financialSaleStr except: LogManager.PrintLogMessage("CrawlDetailInfo", "Crawl1YearsBeforeSale", "crawl 1 years before sale failed", DefineManager.LOG_LEVEL_ERROR) return None def Crawl3YearsBeforeNetIncome(self): try: webDriver = self.webCrawler.GetDriver() subHtmlIframe = webDriver.find_element_by_id("coinfo_cp") webDriver = self.webCrawler.SwitchToFrame(subHtmlIframe) financialTable = webDriver.find_element_by_id(DefineManager.FINANCIAL_TABLE_ID_NAME) financialRows = financialTable.find_elements_by_tag_name(DefineManager.TAG_TR) financialNetIncomeRow = financialRows[DefineManager.FINANCIAL_NET_INCOME_ROW_POINT] financialNetIncomeStr = financialNetIncomeRow.find_elements_by_tag_name(DefineManager.TAG_TD)[DefineManager.FINANCIAL_NET_INCOME_3_YEARS_BEFORE_COL_POINT].text LogManager.PrintLogMessage("CrawlDetailInfo", "Crawl3YearsBeforeNetIncome", "crawl 3 years before net income successfully: " + financialNetIncomeStr, DefineManager.LOG_LEVEL_INFO) webDriver = self.webCrawler.SwitchToDefault() return financialNetIncomeStr except: LogManager.PrintLogMessage("CrawlDetailInfo", "Crawl3YearsBeforeNetIncome", "crawl 3 years before net income failed", DefineManager.LOG_LEVEL_ERROR) return None def Crawl2YearsBeforeNetIncome(self): try: webDriver = self.webCrawler.GetDriver() subHtmlIframe = webDriver.find_element_by_id("coinfo_cp") webDriver = self.webCrawler.SwitchToFrame(subHtmlIframe) financialTable = webDriver.find_element_by_id(DefineManager.FINANCIAL_TABLE_ID_NAME) financialRows = financialTable.find_elements_by_tag_name(DefineManager.TAG_TR) financialNetIncomeRow = financialRows[DefineManager.FINANCIAL_NET_INCOME_ROW_POINT] financialNetIncomeStr = financialNetIncomeRow.find_elements_by_tag_name(DefineManager.TAG_TD)[DefineManager.FINANCIAL_NET_INCOME_2_YEARS_BEFORE_COL_POINT].text LogManager.PrintLogMessage("CrawlDetailInfo", "Crawl2YearsBeforeNetIncome", "crawl 2 years before net income successfully: " + financialNetIncomeStr, DefineManager.LOG_LEVEL_INFO) webDriver = self.webCrawler.SwitchToDefault() return financialNetIncomeStr except: LogManager.PrintLogMessage("CrawlDetailInfo", "Crawl2YearsBeforeNetIncome", "crawl 2 years before net income failed", DefineManager.LOG_LEVEL_ERROR) return None def Crawl1YearsBeforeNetIncome(self): try: webDriver = self.webCrawler.GetDriver() subHtmlIframe = webDriver.find_element_by_id("coinfo_cp") webDriver = self.webCrawler.SwitchToFrame(subHtmlIframe) financialTable = webDriver.find_element_by_id(DefineManager.FINANCIAL_TABLE_ID_NAME) financialRows = financialTable.find_elements_by_tag_name(DefineManager.TAG_TR) financialNetIncomeRow = financialRows[DefineManager.FINANCIAL_NET_INCOME_ROW_POINT] financialNetIncomeStr = financialNetIncomeRow.find_elements_by_tag_name(DefineManager.TAG_TD)[DefineManager.FINANCIAL_NET_INCOME_1_YEARS_BEFORE_COL_POINT].text LogManager.PrintLogMessage("CrawlDetailInfo", "Crawl1YearsBeforeNetIncome", "crawl 1 years before net income successfully: " + financialNetIncomeStr, DefineManager.LOG_LEVEL_INFO) webDriver = self.webCrawler.SwitchToDefault() return financialNetIncomeStr except: LogManager.PrintLogMessage("CrawlDetailInfo", "Crawl1YearsBeforeNetIncome", "crawl 1 years before net income failed", DefineManager.LOG_LEVEL_ERROR) return None def CrawlActQ3(self): try: webDriver = self.webCrawler.GetDriver() subHtmlIframe = webDriver.find_element_by_id("coinfo_cp") webDriver = self.webCrawler.SwitchToFrame(subHtmlIframe) financialTable = webDriver.find_element_by_id(DefineManager.FINANCIAL_TABLE_ID_NAME) financialRows = financialTable.find_elements_by_tag_name(DefineManager.TAG_TR) financialActRow = financialRows[DefineManager.FINANCIAL_ACT_ROW_POINT] financialActStr = financialActRow.find_elements_by_tag_name(DefineManager.TAG_TD)[DefineManager.FINANCIAL_Q3_COL_POINT].text LogManager.PrintLogMessage("CrawlDetailInfo", "CrawlActQ3", "crawl ACT Q3 successfully: " + financialActStr, DefineManager.LOG_LEVEL_INFO) webDriver = self.webCrawler.SwitchToDefault() return financialActStr except: LogManager.PrintLogMessage("CrawlDetailInfo", "CrawlActQ3", "crawl ACT Q3 failed", DefineManager.LOG_LEVEL_ERROR) return None def CrawlDptQ3(self): try: webDriver = self.webCrawler.GetDriver() subHtmlIframe = webDriver.find_element_by_id("coinfo_cp") webDriver = self.webCrawler.SwitchToFrame(subHtmlIframe) financialTable = webDriver.find_element_by_id(DefineManager.FINANCIAL_TABLE_ID_NAME) financialRows = financialTable.find_elements_by_tag_name(DefineManager.TAG_TR) financialDptRow = financialRows[DefineManager.FINANCIAL_DPT_ROW_POINT] financialDptStr = financialDptRow.find_elements_by_tag_name(DefineManager.TAG_TD)[ DefineManager.FINANCIAL_Q3_COL_POINT].text LogManager.PrintLogMessage("CrawlDetailInfo", "CrawlDptQ3", "crawl DPT Q3 successfully: " + financialDptStr, DefineManager.LOG_LEVEL_INFO) webDriver = self.webCrawler.SwitchToDefault() return financialDptStr except: LogManager.PrintLogMessage("CrawlDetailInfo", "CrawlDptQ3", "crawl DPT Q3 failed", DefineManager.LOG_LEVEL_ERROR) return None def CrawlCapQ3(self): try: webDriver = self.webCrawler.GetDriver() subHtmlIframe = webDriver.find_element_by_id("coinfo_cp") webDriver = self.webCrawler.SwitchToFrame(subHtmlIframe) financialTable = webDriver.find_element_by_id(DefineManager.FINANCIAL_TABLE_ID_NAME) financialRows = financialTable.find_elements_by_tag_name(DefineManager.TAG_TR) financialCapRow = financialRows[DefineManager.FINANCIAL_CAP_ROW_POINT] financialCapStr = financialCapRow.find_elements_by_tag_name(DefineManager.TAG_TD)[ DefineManager.FINANCIAL_Q3_COL_POINT].text LogManager.PrintLogMessage("CrawlDetailInfo", "CrawlCapQ3", "crawl CAP Q3 successfully: " + financialCapStr, DefineManager.LOG_LEVEL_INFO) webDriver = self.webCrawler.SwitchToDefault() return financialCapStr except: LogManager.PrintLogMessage("CrawlDetailInfo", "CrawlCapQ3", "crawl CAP Q3 failed", DefineManager.LOG_LEVEL_ERROR) return None def __del__(self): return

49.963303

191

0.722732

999

10,892

7.571572

0.092092

0.057377

0.082099

0.052353

0.871761

0.840825

0.738498

0

0.006268

0.209053

10,892

218

192

49.963303

0.871735

0

0.647059

0

0.1243

0.025888

0

1

0.071895

false

0

0.013072

0.006536

0.215686

0

null

0

1

0

null

0

6

c5d840cde8c4fa75982fcd171b8a9c02ed0aca93

6,112

py

Python

test/test_smart_transfer.py

alice-health/airflow_smarttransfer

b572c647063d8f5f02942e4221c13cb7451642ce

[ "MIT" ]

8

2021-06-29T22:24:21.000Z

2022-01-30T08:42:26.000Z

test/test_smart_transfer.py

alice-health/airflow_smarttransfer

b572c647063d8f5f02942e4221c13cb7451642ce

[ "MIT" ]

null

test/test_smart_transfer.py

alice-health/airflow_smarttransfer

b572c647063d8f5f02942e4221c13cb7451642ce

[ "MIT" ]

null

import pytest from datetime import datetime from airflow import DAG from airflow.models import TaskInstance from smart_transfer.smart_transfer import SmartTransfer import os import psycopg2 PREOPERATOR1 = """ DROP TABLE IF EXISTS test_target; DROP TABLE IF EXISTS test_source; CREATE TABLE test_source ( texto text, numero numeric, updated_at timestamp without time zone, jsonobject jsonb ); INSERT INTO test_source (texto,numero,jsonobject, updated_at) values ('um' , 1,'[{"a":"foo"},{"b":"bar"},{"c":"baz"}]'::json, '2020-01-01'), ('dois',2,'[{"a":"foo"},{"b":"bar"},{"c":"baz"}]'::json, '2020-02-02'), ('tres',3,'[{"a":"foo"},{"b":"bar"},{"c":"baz"}]'::json, '2020-03-03'); """ PREOPERATOR2 = """ DROP TABLE IF EXISTS test_target2; DROP TABLE IF EXISTS test_source2; CREATE TABLE test_source2 ( id uuid PRIMARY KEY, texto text, numero numeric, updated_at timestamp without time zone, jsonobject jsonb ); INSERT INTO test_source2 (id, texto,numero,jsonobject, updated_at) values ('8320562f-2969-42f9-a852-249646afba02', 'um' , 1,'[{"a":"foo"},{"b":"bar"},{"c":"baz"}]'::json, '2020-01-01'), ('8320562f-2969-42f9-a852-249646afbb02', 'dois',2,'[{"a":"foo"},{"b":"bar"},{"c":"baz"}]'::json, '2020-02-02'), ('8320562f-2969-42f9-a852-249646afbc02', 'tres',3,'[{"a":"foo"},{"b":"bar"},{"c":"baz"}]'::json, '2020-03-03'); """ PREOPERATOR3 = """ DROP TABLE IF EXISTS public.test_target3; DROP TABLE IF EXISTS public.test_source3; CREATE TABLE public.test_source3 ( id uuid PRIMARY KEY, texto text, numero numeric, updated_at timestamp without time zone, jsonobject jsonb ); INSERT INTO public.test_source3 (id, texto,numero,jsonobject, updated_at) values ('8320562f-2969-42f9-a852-249646afba02', 'um' , 1,'[{"a":"foo"},{"b":"bar"},{"c":"baz"}]'::json, '2020-01-01'), ('8320562f-2969-42f9-a852-249646afbb02', 'dois',2,'[{"a":"foo"},{"b":"bar"},{"c":"baz"}]'::json, '2020-02-02'), ('8320562f-2969-42f9-a852-249646afbc02', 'tres',3,'[{"a":"foo"},{"b":"bar"},{"c":"baz"}]'::json, '2020-03-03'); """ def test_oracle_col_to_type(): actual = SmartTransfer.oracle_col_to_type(("", cx_Oracle.DB_TYPE_VARCHAR)) assert actual == ('text', '{column}') actual = SmartTransfer.oracle_col_to_type(("", cx_Oracle.DB_TYPE_NCHAR)) assert actual == ('text', '{column}') actual = SmartTransfer.oracle_col_to_type(("", cx_Oracle.DB_TYPE_DATE)) assert actual == ("timestamp without time zone", "{column}") actual = SmartTransfer.oracle_col_to_type( ("", cx_Oracle.DB_TYPE_TIMESTAMP)) assert actual == ("timestamp without time zone", "{column}") actual = SmartTransfer.oracle_col_to_type(("", cx_Oracle.DB_TYPE_NUMBER)) assert actual == ("numeric", "{column}") def test_smart_transfer_operator(): dag = DAG(dag_id="smart_transfer", start_date=datetime.now()) task = SmartTransfer( task_id="smart_transfer_test", source_table="test_source", source_conn_id="test_db", destination_table="test_target", destination_conn_id="test_db", updated_at_filter=False, commit_every=100, preoperator=PREOPERATOR1, dag=dag, ) ti = TaskInstance(task=task, execution_date=datetime.now()) result = task.execute(ti.get_template_context()) print("Result: {}".format(result)) assert result == 3 def test_smart_transfer_operator_with_filter(): dag = DAG(dag_id="smart_transfer", start_date=datetime.now()) task = SmartTransfer( task_id="smart_transfer_test", source_table="test_source", source_conn_id="test_db", destination_table="test_target", destination_conn_id="test_db", updated_at_filter=True, commit_every=100, preoperator=PREOPERATOR1, dag=dag, ) ti = TaskInstance(task=task, execution_date=datetime.now()) result = task.execute(ti.get_template_context()) print("Result: {}".format(result)) assert result == 3 def test_smart_transfer_operator_with_primary_key(): dag = DAG(dag_id="smart_transfer", start_date=datetime.now()) task = SmartTransfer( task_id="smart_transfer_test_pk", source_table="test_source2", source_conn_id="test_db", destination_table="test_target2", destination_conn_id="test_db", updated_at_filter=False, commit_every=100, preoperator=PREOPERATOR2, dag=dag, ) ti = TaskInstance(task=task, execution_date=datetime.now()) result = task.execute(ti.get_template_context()) print("Result: {}".format(result)) assert result == 3 def test_smart_transfer_operator_with_schema_name_pk(): dag = DAG(dag_id="smart_transfer", start_date=datetime.now()) task = SmartTransfer( task_id="smart_transfer_test_pk", source_table="public.test_source3", source_conn_id="test_db", destination_table="public.test_target3", destination_conn_id="test_db", updated_at_filter=False, commit_every=100, preoperator=PREOPERATOR3, dag=dag, ) ti = TaskInstance(task=task, execution_date=datetime.now()) result = task.execute(ti.get_template_context()) print("Result: {}".format(result)) assert result == 3 def test_smart_transfer_operator_skip_columns(): dag = DAG(dag_id="smart_transfer", start_date=datetime.now()) task = SmartTransfer( task_id="smart_transfer_test_pk", source_table="public.test_source3", source_conn_id="test_db", destination_table="public.test_target3", destination_conn_id="test_db", updated_at_filter=False, commit_every=100, skip_columns=['jsonobject'], preoperator=PREOPERATOR3, dag=dag, ) ti = TaskInstance(task=task, execution_date=datetime.now()) result = task.execute(ti.get_template_context()) print("Result: {}".format(result)) assert result == 3

35.74269

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0.65036

768

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0.05845

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0.8347

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0.800846

0

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0.191918

6,112

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false

0

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0.087248

0.033557

0

null

0

1

0

null

0

6

68002232808748695839cc471e063b003e7f2919

39

py

Python

lambdas/parser/__init__.py

oliverroick/nea

1ad4f76920e33890e9d0ab6d365566703d396878

[ "MIT" ]

1

2019-12-28T19:03:15.000Z

lambdas/parser/__init__.py

oliverroick/nea

1ad4f76920e33890e9d0ab6d365566703d396878

[ "MIT" ]

3

2018-02-20T16:57:46.000Z

2018-10-01T07:08:25.000Z

lambdas/parser/__init__.py

oliverroick/nea

1ad4f76920e33890e9d0ab6d365566703d396878

[ "MIT" ]

null

from .parser import parse_feed # NOQA

19.5

38

0.769231

6

39

4.833333

1

0

0.179487

39

1

39

0.90625

0.102564

0

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true

0

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0

1

0

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null

0

1

0

null

0

1

0

1

0

1

0

6

a85c78ccebd177f7c5d0170aacbec83491784e44

109

py

Python

t01primeshcflcm_producthcflcm.py

CherryNoddles/y1math

877b87ee60e7569b4fed4abdcb53a03df3d02ac3

[ "MIT" ]

1

2020-10-01T04:03:44.000Z

t01primeshcflcm_producthcflcm.py

CherryNoddles/y1math

877b87ee60e7569b4fed4abdcb53a03df3d02ac3

[ "MIT" ]

4

2019-10-20T03:52:11.000Z

2019-10-24T07:13:58.000Z

t01primeshcflcm_producthcflcm.py

CherryNoddles/y1math

877b87ee60e7569b4fed4abdcb53a03df3d02ac3

[ "MIT" ]

403

2019-10-17T05:30:21.000Z

2020-10-28T10:33:07.000Z

# Verify that the product of 2 numbers is equal to the product of their HCF and their LCM. num1 = num2 =

18.166667

90

0.715596

20

109

3.9

0.8

0.25641

0.307692

0

0.036585

0.247706

109

5

91

21.8

0.914634

0.807339

0

null

0

null

0

1

0

null

1

0

1

0

1

0

null

0

1

0

6

a89b698cc884647e577d03481bf1ed1452c99323

4,155

py

Python

src/blockchain_users/miner.py

gruyaume/my-blockchain

283f5ef0c8c09eff0478dfead3950c720cda2882

[ "Apache-2.0" ]

4

2021-11-14T17:16:03.000Z

2022-03-17T21:01:42.000Z

src/blockchain_users/miner.py

gruyaume/my-blockchain

283f5ef0c8c09eff0478dfead3950c720cda2882

[ "Apache-2.0" ]

null

src/blockchain_users/miner.py

gruyaume/my-blockchain

283f5ef0c8c09eff0478dfead3950c720cda2882

[ "Apache-2.0" ]

5

2021-07-30T14:27:37.000Z

2021-12-15T12:08:46.000Z

private_key = b'0\x82\x04\xa3\x02\x01\x00\x02\x82\x01\x01\x00\x97\xab\xedrE-l\xef?\xe2\xa1\xd0\xf1\xc9\x91\xac\x95\x14Ae\x18J\xba\x95\r\xca\xf3\xb1W\x94\x91tt4\x1f\xce\x07\xbbCG@\x93\x0b\x00q\x04\xfc\xf2\xcbYi\xa7\xa8\x88\xb5\xbfe\x05kE\xb6\xeb\xc0V\xf2\xa8\x88\xd0\x87\xbc\x97\xc5\x9aD\xc3{9U\xd2\x9ew/\xc5l\x98(\xdc\xc8\xc4(C\x08/\x8f\x1a9\x93\xbdDh\xf8\x0b\x0c\xa2\xc0\x12\xf4\xa4a\xc1\x19\x9a\x05\xcdx\xb3G;Yg)\xb1a\x18VbQ\x8d\x8f\x19\x9d\x08\xd5\x0e\xa43\x8d\x08\xf6\x8f\'\xd7Z\xb6U\xca>\x95v\x9b4\x81\x8cJ\xeb!`\xfc\xaf\xcf\xf2\xc6G.*\xb8%\x8e\t\xd7ga\x7f\xca<\xd9\xc4D\x8c\x18\xbdL\xf8\x81\xf98F\x8d\x98\x9d~\xfe\x8c\xb1\x15\xca)M\x00\x08\xf5I0\xdc\xb0\xce\x03_\x03z\xa0\xfd&)\x01\x91p\xff\x14\xa1\x15\x18\x93\x00Z<\xc5\x97\xf2N}\x92\x04\x11h\x0b\x9e\xb9\xefX\x82J~\xf3YP.\x01|P/ \xac\xae\xe8\xa5\x02\x03\x01\x00\x01\x02\x82\x01\x00\x1ep\x9a\x02"\x12 \xd7\xcf\x89\x8b\x92d\xc4`\xa13vR=\x98\xe7~\x94\xe5\xa4\xc2\xcd\xe7\xd6\xe0no\xd7\xfa>\x1e]\x1d\xfe\x91\xde1\n\x10\xa4\xc0\xa3u\xdeg\x0f\x08\x0b\x0f\xf6\xee\xael\xbe\x1c\x1d*\x88\x08\xc1|[\xe5\xb2\x1a\xff\xc4\x9bbd;\xb8\x96\xc1\xc9\x07\xe4f\x8c\x0f?hg\xbd\xf0\xde\x16\xb5p\xbc\xf7\x82\xb7\xd5\x1d[\x12Y#\x95jV\x07lz\xd7\xe7\xac::8\xe4\x97g\xd5\xfdL\x90V\xdd\xfa\xa1\xd0/L\xaa_se\xac[4+GR\xb3\x83\x07\x0c\x93a\x05XV\x04\xaauXf\xea\x90\xb3\x959\xd9hO\xeb\x91h7\xbf::\xcaf\xa8\xcd\x03/i\xd8LU6\xfc0\xff\x87\x0f\x81#\x1a\xa7H\xf2\x8at\xdd\x01 \x9c\x91\xe3\xf4\x80\x00\x8aMg\xa2w\x88\xaf\x02\x9e\xc3\x87\x00?\xf9<~\x96^K\x98\xcd.\xe2\xd47au\xeb\xfc\xc4\xc4\xd0\x12]\x04D\xf0\xe4\x96\xd5\xba\xc6\xd7\x93m\xb4zz\x08\xae\x15\x8fa\x02\x81\x81\x00\xb6s\xed\x08\x9d\xdf\xf3\xaf\xde\xd4\xd4\xbf\xaa\\\xf6qx\x0f_\xf1\xcc\x88\xb3\xb9\xf2\xa5\xe3_\x12\xd0\x91\xc4\xbc\xd8P\x0f\xef\x8b\x92U2\xc992\x86\x80\x1b\xd9\xd2\x84\x04zv\xa9\xd2\x03s|x\x83-\xa4\x0cS\xe0\xc3D\xa5\xfa\xac\t\xb9\x13\xf9\x1d\xa6\x91[u\x8b\xbd\x81#\x13\x8bE5\xb6?_\xdcg\xcf\xc7\x1e}\xa9I\xb1,\xb2\xc8\x9b]\xdaP&)\x87\x89a\x13\xce\xae\xed\xe3X\x1c\xa7\x19\xdd\x92\x8e\xc2\xc4\x81v\xe9\x02\x81\x81\x00\xd4\xcf\x8eL\x00\xcez\xf4\xd2\x9b\xf0\x8a\x1a\xa5\x9f\xf3\xacW\xe8.\x82\x16t<p\x12\xea\'\xf3(\xcaa\xe3:#WE\x83e\xc9A\xfco\xe6\xef\xb8T}\xc1\x0f\x87\x105!\xc7N\x13\xc6\x95i\x08b\xb1\x84R7\x0cTH\x85P\x88\x8c\xab\x01\xabJ\xe8\xfd\x85\x83\xc7| U2P\xbbu\x94\x17\x0f\x9f)B\xfb\x18\xce\x1d\xf8\xa7\xa5:\xecY6\xe5r\xe57\x18\xa1\x1fH\x9b\xads\x80\x84\xae4\x90\xb1\xffZ+F]\x02\x81\x81\x00\x82>\xbf7\'|w\xb9T\x99\x1aF\xb8\x97$V\\\x1e\x9d\x9f?#W)\xa6\xceEA\xd9l\x8a\xa2\xa7,\xc3\xfa\x9cFFp\x0b\x91\x1a\x03\xb7\x80<a\x82\xa6;\x8c0\xa9\x8a\x02l\xae\xaeX\xf6{\xe5P\xfbbi\xdf\x12\xd3=\x1d\xa1\xe4t\x064=\xd5\xeb\xab\x9cD-\xef}\xb8\x9a!#\x8ds\x8e\x1f\xd6\xe4]\xb4\xfd\x85\xab\x1b\t\xce5\xca\x81s\xa4\r\xad\xff5\xcc*\x85\xe0\xa0\x93\xa9#C<&M\xa1\x1e\x01\x02\x81\x80R\x89u}\x10\xcb\x04q3\xc5\xfbR\xf4\xe6\xcb\xd4\xacA#\xf1\xf5\xceS\xa2\xa5-\x10\xd8\x11\xbc\xfeQ\\\xd9\xcd\x9f)|\xbe\xdb\x81\xd60hw\r\x1c\xe7\xf1\x1a\xde\x9dp\x9d\xfb8\xd2\xfe\xb00\x1ejx\xc3\x03H\nf\x1e\x02f\xab\xaf3\x1b\xe0\x9d\x9b#\xc8\xc6\xc5u\xe4\xecf)]\xabJ\x96o\x1e\xa1\xd82\x9e\xfaB1\x90\x83\xcb\x08\xa3\xc9`\xd0a\n/i>B\xe0\xee2\xdf\xe3\x95\xd5\x19Q\x8f\xe2\x1e\xea\x99\x02\x81\x80r\x8fV@\xf9!\xbdv\xe4\x9et\x84\x84\xb4\xba;2/\xc7\x91H\xe5g\xbe%W\xfdBZL\xedX\xab6;\xcd\xa7\xc0K\x08\x1c\xe2\x1a\x94&4\xcd\x12bI=\x80\x08\x00\xd3\x91R\x1a\xd1y\x91z\x05\xdb\xbd\xaatl\xb5\x15}6\xd5\x03\x1fv.M?\xb7\xf2\xfd\x8b+i\x81\xc0.N\xe5\xcb\xb1J\x9c\xd2~$\xa1\x17n\xd8\xa4b\xef6\xf4>r4\xd1\xd8\xbbozV\xf1jSq]\xd0\xb3\xb3Rw\x9e\x02\xbd' public_key_hex = "30820122300d06092a864886f70d01010105000382010f003082010a028201010097abed72452d6cef3fe2a1d0f1c991ac95144165184aba950dcaf3b15794917474341fce07bb434740930b007104fcf2cb5969a7a888b5bf65056b45b6ebc056f2a888d087bc97c59a44c37b3955d29e772fc56c9828dcc8c42843082f8f1a3993bd4468f80b0ca2c012f4a461c1199a05cd78b3473b596729b161185662518d8f199d08d50ea4338d08f68f27d75ab655ca3e95769b34818c4aeb2160fcafcff2c6472e2ab8258e09d767617fca3cd9c4448c18bd4cf881f938468d989d7efe8cb115ca294d0008f54930dcb0ce035f037aa0fd2629019170ff14a1151893005a3cc597f24e7d920411680b9eb9ef58824a7ef359502e017c502f20acaee8a50203010001" public_key_hash = "4d9715dc8f9578ca2af159409be9c559c5eaceba"

1,038.75

3,485

0.777858

812

4,155

3.96798

0.455665

0.009311

0.00838

0.011173

0

0.313375

0.003129

4,155

3

3,486

1,385

0.46451

0

0.666667

0.334296

0.334055

0

1

0

false

0

null

0

1

0

1

0

1

0

null

0

6

a8cd4682ccb8a73ad3a3f2239da56cee53dedbe2

39

py

Python

tests/zip/source/helpers.py

fbiville/python3-function-invoker

12056d22dd4abf89377005fdad75c472a2c5a444

[ "Apache-2.0" ]

3

2018-03-25T08:25:26.000Z

2019-02-10T02:01:12.000Z

tests/zip/source/helpers.py

fbiville/python3-function-invoker

12056d22dd4abf89377005fdad75c472a2c5a444

[ "Apache-2.0" ]

11

2018-03-14T23:14:23.000Z

2019-11-08T16:33:40.000Z

tests/zip/source/helpers.py

fbiville/python3-function-invoker

12056d22dd4abf89377005fdad75c472a2c5a444

[ "Apache-2.0" ]

7

2018-02-22T16:18:45.000Z

2019-03-12T02:45:46.000Z

def upper(val): return val.upper()

13

22

0.641026

6

39

4.166667

0.666667

0

0.205128

39

2

23

19.5

0.806452

0

1

0.5

false

0

0.5

1

0

1

0

null

0

1

0

null

0

1

0

1

0

6

7637363bf05af6acdd9063dbcf5bf7677ce5027d

158

py

Python

privatekube/privatekube/privacy/__init__.py

DelphianCalamity/PrivateKube

14f575e77021ab7baca30f4061140ec83bdc96a7

[ "Apache-2.0" ]

9

2021-06-16T00:22:45.000Z

2021-11-25T07:19:11.000Z

privatekube/privatekube/privacy/__init__.py

DelphianCalamity/PrivateKube

14f575e77021ab7baca30f4061140ec83bdc96a7

[ "Apache-2.0" ]

2

2021-11-14T10:42:43.000Z

2022-03-16T03:43:22.000Z

privatekube/privatekube/privacy/__init__.py

DelphianCalamity/PrivateKube

14f575e77021ab7baca30f4061140ec83bdc96a7

[ "Apache-2.0" ]

3

2021-04-08T08:08:48.000Z

2021-12-24T01:42:20.000Z

import privatekube.privacy.mechanisms, privatekube.privacy.model_validation, privatekube.privacy.rdp, privatekube.privacy.text, privatekube.privacy.streaming

79

157

0.873418

17

158

8.058824

0.529412

0.656934

0

0.037975

158

1

158

0.901316

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

1

0

null

0

1

0

1

0

6

7665efd14219da92035509943129d77c014c9aab

77

py

Python

asynced/__init__.py

jorenham/asynced

4b406ad9626078e903fd012d3bcc40f4e27af41d

[ "MIT" ]

1

2022-03-15T19:56:13.000Z

asynced/__init__.py

jorenham/asynced

4b406ad9626078e903fd012d3bcc40f4e27af41d

[ "MIT" ]

null

asynced/__init__.py

jorenham/asynced

4b406ad9626078e903fd012d3bcc40f4e27af41d

[ "MIT" ]

null

from .asyncio_utils import * from .exceptions import * from .states import *

19.25

28

0.766234

10

77

5.8

0.6

0.344828

0

0.155844

77

3

29

25.666667

0.892308

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

76ba2808d7e40f4ff8feddb0b54d45b1316b72b8

139

py

Python

src/api/v1/dependencies/database.py

MaximVerlinskii/fastapi-project

52cb92780f54e83c47814967b97b15621d9dd7c7

[ "MIT" ]

1

2022-03-29T15:11:38.000Z

src/api/v1/dependencies/database.py

MaximVerlinskii/fastapi-project

52cb92780f54e83c47814967b97b15621d9dd7c7

[ "MIT" ]

null

src/api/v1/dependencies/database.py

MaximVerlinskii/fastapi-project

52cb92780f54e83c47814967b97b15621d9dd7c7

[ "MIT" ]

null

class UserRepositoryDependencyMarker: # pragma: no cover pass class ProductRepositoryDependencyMarker: # pragma: no cover pass

19.857143

60

0.769784

12

139

8.916667

0.583333

0.149533

0.242991

0.317757

0

0.18705

139

6

61

23.166667

0.946903

0.23741

0

0.5

0

1

0

true

0.5

0

0.5

0

1

0

null

0

1

0

1

0

null

0

1

0

6

4f65dfe0dda3b2b927c0966dac6a9c50d5145b77

82

py

Python

zeus/modules/loss/__init__.py

wnov/vega

bf51cbe389d41033c4ae4bc02e5078c3c247c845

[ "MIT" ]

6

2020-11-13T15:44:47.000Z

2021-12-02T08:14:06.000Z

zeus/modules/loss/__init__.py

JacobLee121/vega

19256aca4d047bfad3b461f0a927e1c2abb9eb03

[ "MIT" ]

null

zeus/modules/loss/__init__.py

JacobLee121/vega

19256aca4d047bfad3b461f0a927e1c2abb9eb03

[ "MIT" ]

2

2021-06-25T09:42:32.000Z

2021-08-06T18:00:09.000Z

from .loss import * from .focal_loss import FocalLoss from .f1_loss import F1Loss

20.5

33

0.804878

13

82

4.923077

0.538462

0.46875

0

0.028571

0.146341

82

3

34

27.333333

0.885714

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

96c628b06e8c8324edca56b69d1717559c4258e2

21,451

py

Python

tests/functional_tests/test_invalid_packages.py

asellappen/conda-verify

8d99b80b9e19a193119f0790b521e806f7d3ad16

[ "BSD-3-Clause" ]

null

tests/functional_tests/test_invalid_packages.py

asellappen/conda-verify

8d99b80b9e19a193119f0790b521e806f7d3ad16

[ "BSD-3-Clause" ]

null

tests/functional_tests/test_invalid_packages.py

asellappen/conda-verify

8d99b80b9e19a193119f0790b521e806f7d3ad16

[ "BSD-3-Clause" ]

null

import os import pytest from conda_verify.errors import PackageError from conda_verify.verify import Verify @pytest.fixture def package_dir(): return os.path.join(os.path.dirname(__file__), 'test_packages') @pytest.fixture def verifier(): package_verifier = Verify() return package_verifier def test_invalid_package_sequence(package_dir, verifier): package = os.path.join(package_dir, 'test_-file.tar.bz2') with pytest.raises(PackageError) as excinfo: verifier.verify_package(path_to_package=package, exit_on_error=False) assert ('PackageError: ' 'Found invalid sequence "_-" ' 'in package in info/index.json' in str(excinfo)) def test_invalid_package_extension(package_dir, verifier): package = os.path.join(package_dir, 'testfile.zip') with pytest.raises(PackageError) as excinfo: verifier.verify_package(path_to_package=package, exit_on_error=False) assert ("PackageError: " 'Found package with invalid extension ".zip"' in str(excinfo)) def test_index_unicode(package_dir, verifier): package = os.path.join(package_dir, 'testfile-0.0.2-py36_0.tar.bz2') with pytest.raises(PackageError): verifier.verify_package(path_to_package=package, exit_on_error=True) package, errors = verifier.verify_package(path_to_package=package, exit_on_error=False) assert '[C1116] Found non-ascii characters inside info/index.json' in errors def test_info_in_files_file(package_dir, verifier): package = os.path.join(package_dir, 'testfile-0.0.3-py36_0.tar.bz2') with pytest.raises(PackageError): verifier.verify_package(path_to_package=package, exit_on_error=True) package, errors = verifier.verify_package(path_to_package=package, exit_on_error=False) assert '[C1120] Found filenames in info/files that start with "info"' in errors def test_duplicates_in_files_file(package_dir, verifier): package = os.path.join(package_dir, 'testfile-0.0.4-py36_0.tar.bz2') with pytest.raises(PackageError): verifier.verify_package(path_to_package=package, exit_on_error=True) package, errors = verifier.verify_package(path_to_package=package, exit_on_error=False) assert '[C1121] Found duplicate filenames in info/files' in errors def test_not_in_files_file(package_dir, verifier): package = os.path.join(package_dir, 'testfile-0.0.5-py36_0.tar.bz2') with pytest.raises(PackageError): verifier.verify_package(path_to_package=package, exit_on_error=True) package, errors = verifier.verify_package(path_to_package=package, exit_on_error=False) assert '[C1123] Found filename in tar archive missing from info/files: lib{}testfile.txt'.format(os.path.sep) in errors def test_not_in_tarball(package_dir, verifier): package = os.path.join(package_dir, 'testfile-0.0.6-py36_0.tar.bz2') with pytest.raises(PackageError): verifier.verify_package(path_to_package=package, exit_on_error=True) package, errors = verifier.verify_package(path_to_package=package, exit_on_error=False) assert '[C1122] Found filename in info/files missing from tar archive: testfile.txt' in errors def test_not_allowed_files(package_dir, verifier): package = os.path.join(package_dir, 'testfile-0.0.7-py36_0.tar.bz2') with pytest.raises(PackageError): verifier.verify_package(path_to_package=package, exit_on_error=True) package, errors = verifier.verify_package(path_to_package=package, exit_on_error=False) assert '[C1125] Found unallowed file in tar archive: info{}testfile~'.format(os.path.sep) in errors def test_file_not_allowed(package_dir, verifier): package = os.path.join(package_dir, 'testfile-0.0.8-py36_0.tar.bz2') with pytest.raises(PackageError): verifier.verify_package(path_to_package=package, exit_on_error=True) package, errors = verifier.verify_package(path_to_package=package, exit_on_error=False) assert '[C1126] Found info{}link.json however package is not a noarch package'.format(os.path.sep) in errors def test_invalid_package_name(package_dir, verifier): package = os.path.join(package_dir, 'testfile-0.0.9-py36_0.tar.bz2') with pytest.raises(PackageError): verifier.verify_package(path_to_package=package, exit_on_error=True) package, errors = verifier.verify_package(path_to_package=package, exit_on_error=False) assert '[C1102] Found package name in info/index.json "test-file" does not match filename "testfile"' in errors def test_invalid_build_number(package_dir, verifier): package = os.path.join(package_dir, 'testfile-0.0.10-py36_0.tar.bz2') with pytest.raises(PackageError): verifier.verify_package(path_to_package=package, exit_on_error=True) package, errors = verifier.verify_package(path_to_package=package, exit_on_error=False) assert '[C1108] Build number in info/index.json must be an integer' in errors def test_duplicates_in_bin(package_dir, verifier): package = os.path.join(package_dir, 'testfile-0.0.11-py36_0.tar.bz2') with pytest.raises(PackageError): verifier.verify_package(path_to_package=package, exit_on_error=True) package, errors = verifier.verify_package(path_to_package=package, exit_on_error=False) assert any('[C1127] Found both .bat and .exe files with same basename in same folder' in e for e in errors) def test_win_package_warning(package_dir, verifier): package = os.path.join(package_dir, 'testfile-0.0.12-py36_0.tar.bz2') with pytest.raises(PackageError): verifier.verify_package(path_to_package=package, exit_on_error=True) package, errors = verifier.verify_package(path_to_package=package, exit_on_error=False) assert '[C1129] Found filename "bin/testfile" in info/has_prefix not included in archive' in errors def test_win_package_binary_warning(package_dir, verifier): package = os.path.join(package_dir, 'testfile-0.0.13-py36_0.tar.bz2') with pytest.raises(PackageError): verifier.verify_package(path_to_package=package, exit_on_error=True) package, errors = verifier.verify_package(path_to_package=package, exit_on_error=False) assert '[C1132] Binary placeholder found in info/has_prefix not allowed in Windows package' in errors def test_package_placeholder_warning(package_dir, verifier): package = os.path.join(package_dir, 'testfile-0.0.14-py36_0.tar.bz2') with pytest.raises(PackageError): verifier.verify_package(path_to_package=package, exit_on_error=True) package, errors = verifier.verify_package(path_to_package=package, exit_on_error=False) assert any('[C1133] Binary placeholder' in err for err in errors) assert any('found in info/has_prefix does not have a length of 255 bytes' in e for e in errors) def test_invalid_prefix_mode(package_dir, verifier): package = os.path.join(package_dir, 'testfile-0.0.15-py36_0.tar.bz2') with pytest.raises(PackageError): verifier.verify_package(path_to_package=package, exit_on_error=True) package, errors = verifier.verify_package(path_to_package=package, exit_on_error=False) assert '[C1130] Found invalid mode "wrong_mode" in info/has_prefix' in errors def test_unicode_prefix(package_dir, verifier): package = os.path.join(package_dir, 'testfile-0.0.16-py36_0.tar.bz2') with pytest.raises(PackageError): verifier.verify_package(path_to_package=package, exit_on_error=True) package, errors = verifier.verify_package(path_to_package=package, exit_on_error=False) assert '[C1128] Found non-ascii characters in info/has_prefix' in errors def test_invalid_script_name(package_dir, verifier): package = os.path.join(package_dir, 'testfile-0.0.17-py36_0.tar.bz2') with pytest.raises(PackageError): verifier.verify_package(path_to_package=package, exit_on_error=True) package, errors = verifier.verify_package(path_to_package=package, exit_on_error=False) assert '[C1134] Found pre/post link file "bin{}test-pre-unlink.bat" in archive'.format(os.path.sep) in errors def test_invalid_setuptools(package_dir, verifier): package = os.path.join(package_dir, 'testfile-0.0.18-py36_0.tar.bz2') with pytest.raises(PackageError): verifier.verify_package(path_to_package=package, exit_on_error=True) package, errors = verifier.verify_package(path_to_package=package, exit_on_error=False) assert '[C1136] Found easy_install script "bin{}easy_install" in archive'.format(os.path.sep) in errors assert '[C1137] Found namespace file "bin{}easy_install.pth" in archive'.format(os.path.sep) in errors def test_invalid_eggfile(package_dir, verifier): package = os.path.join(package_dir, 'testfile-0.0.19-py36_0.tar.bz2') with pytest.raises(PackageError): verifier.verify_package(path_to_package=package, exit_on_error=True) package, errors = verifier.verify_package(path_to_package=package, exit_on_error=False) assert '[C1135] Found egg file "bin{}test.egg" in archive'.format(os.path.sep) in errors def test_invalid_namespace_file(package_dir, verifier): package = os.path.join(package_dir, 'testfile-0.0.21-py36_0.tar.bz2') with pytest.raises(PackageError): verifier.verify_package(path_to_package=package, exit_on_error=True) package, errors = verifier.verify_package(path_to_package=package, exit_on_error=False) assert '[C1137] Found namespace file "bin{}test-nspkg.pth" in archive'.format(os.path.sep) in errors def test_invalid_pyo_file(package_dir, verifier): package = os.path.join(package_dir, 'testfile-0.0.22-py36_0.tar.bz2') with pytest.raises(PackageError): verifier.verify_package(path_to_package=package, exit_on_error=True) package, errors = verifier.verify_package(path_to_package=package, exit_on_error=False) assert '[C1138] Found pyo file "bin{}test.pyo" in archive'.format(os.path.sep) in errors def test_invalid_pyc_and_so_files(package_dir, verifier): package = os.path.join(package_dir, 'testfile-0.0.23-py36_0.tar.bz2') with pytest.raises(PackageError): verifier.verify_package(path_to_package=package, exit_on_error=True) package, errors = verifier.verify_package(path_to_package=package, exit_on_error=False) assert '[C1138] Found pyo file "bin{}test.pyo" in archive'.format(os.path.sep) in errors assert '[C1139] Found pyc file "bin{}test.pyc" in invalid directory'.format(os.path.sep) in errors def test_invalid_pickle_file(package_dir, verifier): package = os.path.join(package_dir, 'testfile-0.0.24-py36_0.tar.bz2') with pytest.raises(PackageError): verifier.verify_package(path_to_package=package, exit_on_error=True) package, errors = verifier.verify_package(path_to_package=package, exit_on_error=False) assert '[C1140] Found lib2to3 .pickle file "lib{0}lib2to3{0}test.pickle"'.format(os.path.sep) in errors def test_missing_pyc_file(package_dir, verifier): package = os.path.join(package_dir, 'testfile-0.0.25-py27_0.tar.bz2') with pytest.raises(PackageError): verifier.verify_package(path_to_package=package, exit_on_error=True) package, errors = verifier.verify_package(path_to_package=package, exit_on_error=False) assert '[C1141] Found python file "lib{0}site-packages{0}python2.7{0}test.py" without a corresponding pyc file'.format(os.path.sep) in errors def test_invalid_windows_architecture(package_dir, verifier): package = os.path.join(package_dir, 'testfile-0.0.26-py27_0.tar.bz2') with pytest.raises(PackageError): verifier.verify_package(path_to_package=package, exit_on_error=True) package, errors = verifier.verify_package(path_to_package=package, exit_on_error=False) assert '[C1144] Found unrecognized Windows architecture "x84"' in errors def test_invalid_windows_dll(package_dir, verifier): package = os.path.join(package_dir, 'testfile-0.0.27-py27_0.tar.bz2') with pytest.raises(PackageError): verifier.verify_package(path_to_package=package, exit_on_error=True) package, errors = verifier.verify_package(path_to_package=package, exit_on_error=False) assert '[C1145] Found file "bin{}testfile.dll" with object type "None" but with arch "x86_64"'.format(os.path.sep) in errors def test_invalid_easy_install_file(package_dir, verifier): package = os.path.join(package_dir, 'testfile-0.0.31-py27_0.tar.bz2') with pytest.raises(PackageError): verifier.verify_package(path_to_package=package, exit_on_error=True) package, errors = verifier.verify_package(path_to_package=package, exit_on_error=False) assert '[C1137] Found namespace file "bin{}easy-install.pth" in archive'.format(os.path.sep) in errors def test_non_ascii_path(package_dir, verifier): package = os.path.join(package_dir, 'testfile-0.0.39-py36_0.tar.bz2') with pytest.raises(PackageError): verifier.verify_package(path_to_package=package, exit_on_error=True) package, errors = verifier.verify_package(path_to_package=package, exit_on_error=False) assert '[C1118] Found archive member names containing non-ascii characters' in errors def test_ascii_in_files_file(package_dir, verifier): package = os.path.join(package_dir, 'testfile-0.0.40-py36_0.tar.bz2') with pytest.raises(PackageError): verifier.verify_package(path_to_package=package, exit_on_error=True) package, errors = verifier.verify_package(path_to_package=package, exit_on_error=False) assert any('[C1119]' in e for e in errors) def test_missing_depends_key(package_dir, verifier): package = os.path.join(package_dir, 'testfile-0.0.41-py36_0.tar.bz2') with pytest.raises(PackageError): verifier.verify_package(path_to_package=package, exit_on_error=True) package, errors = verifier.verify_package(path_to_package=package, exit_on_error=False) assert '[C1112] Missing "depends" field in info/index.json' in errors def test_invalid_license_family(package_dir, verifier): package = os.path.join(package_dir, 'testfile-0.0.42-py36_0.tar.bz2') with pytest.raises(PackageError): verifier.verify_package(path_to_package=package, exit_on_error=True) package, errors = verifier.verify_package(path_to_package=package, exit_on_error=False) assert '[C1115] Found invalid license "FAKELICENSE" in info/index.json' in errors def test_invalid_file_hash(package_dir, verifier): package = os.path.join(package_dir, 'testfile-0.0.43-py36_0.tar.bz2') with pytest.raises(PackageError): verifier.verify_package(path_to_package=package, exit_on_error=True) package, errors = verifier.verify_package(path_to_package=package, exit_on_error=False) assert '[C1146] Found file "lib{0}python3.6{0}site-packages{0}test{0}__main__.py" with sha256 hash different than listed in paths.json'.format(os.path.sep) in errors def test_invalid_file_size(package_dir, verifier): package = os.path.join(package_dir, 'testfile-0.0.44-py36_0.tar.bz2') with pytest.raises(PackageError): verifier.verify_package(path_to_package=package, exit_on_error=True) package, errors = verifier.verify_package(path_to_package=package, exit_on_error=False) assert '[C1147] Found file "lib{0}python3.6{0}site-packages{0}test{0}__main__.py" with filesize different than listed in paths.json'.format(os.path.sep) in errors def test_duplicate_menu_json(package_dir, verifier): package = os.path.join(package_dir, 'testfile-0.0.45-py36_0.tar.bz2') with pytest.raises(PackageError): verifier.verify_package(path_to_package=package, exit_on_error=True) package, errors = verifier.verify_package(path_to_package=package, exit_on_error=False) assert '[C1143] Found more than one Menu json file' in errors def test_invalid_menu_json(package_dir, verifier): package = os.path.join(package_dir, 'testfile-0.0.46-py36_0.tar.bz2') with pytest.raises(PackageError): verifier.verify_package(path_to_package=package, exit_on_error=True) package, errors = verifier.verify_package(path_to_package=package, exit_on_error=False) assert '[C1142] Found invalid Menu json file "Menu{}wrongname.json"'.format(os.path.sep) in errors def test_python_binary_warning(package_dir, verifier): package = os.path.join(package_dir, 'python-0.0.1-py36_0.tar.bz2') with pytest.raises(PackageError): verifier.verify_package(path_to_package=package, exit_on_error=True) package, errors = verifier.verify_package(path_to_package=package, exit_on_error=False) assert '[C1131] Binary placeholder found in info/has_prefix not allowed when building Python' in errors def test_invalid_package_placeholder(package_dir, verifier): package = os.path.join(package_dir, 'testfile-0.0.47-py27_0.tar.bz2') with pytest.raises(PackageError): verifier.verify_package(path_to_package=package, exit_on_error=True) package, errors = verifier.verify_package(path_to_package=package, exit_on_error=False) assert '[C1129] Found filename "/opt/anaconda1anaconda2anaconda3 text testfile testfile" in info/has_prefix not included in archive' in errors def test_invalid_dependency_specs(package_dir, verifier): package = os.path.join(package_dir, 'testfile-0.0.48-py27_0.tar.bz2') with pytest.raises(PackageError): verifier.verify_package(path_to_package=package, exit_on_error=True) package, errors = verifier.verify_package(path_to_package=package, exit_on_error=False) assert '[C1114] Found invalid dependency "python 3.6@**&*&(&@!" in info/index.json' in errors def test_empty_dependencies(package_dir, verifier): package = os.path.join(package_dir, 'testfile-0.0.49-py27_0.tar.bz2') with pytest.raises(PackageError): verifier.verify_package(path_to_package=package, exit_on_error=True) package, errors = verifier.verify_package(path_to_package=package, exit_on_error=False) assert '[C1113] Found empty dependencies in info/index.json' in errors def test_invalid_build_string(package_dir, verifier): package = os.path.join(package_dir, 'testfile-0.0.50-py36_0.tar.bz2') with pytest.raises(PackageError): verifier.verify_package(path_to_package=package, exit_on_error=True) package, errors = verifier.verify_package(path_to_package=package, exit_on_error=False) assert '[C1110] Found invalid build string "py36_0!" in info/index.json' in errors def test_invalid_build_number_negative(package_dir, verifier): package = os.path.join(package_dir, 'testfile-0.0.51-py36_0.tar.bz2') with pytest.raises(PackageError): verifier.verify_package(path_to_package=package, exit_on_error=True) package, errors = verifier.verify_package(path_to_package=package, exit_on_error=False) assert '[C1109] Build number in info/index.json cannot be a negative integer' in errors def test_invalid_version_suffix(package_dir, verifier): package = os.path.join(package_dir, 'testfile-0.0.52-py36_0.tar.bz2') with pytest.raises(PackageError): verifier.verify_package(path_to_package=package, exit_on_error=True) package, errors = verifier.verify_package(path_to_package=package, exit_on_error=False) assert "[C1107] Package version in info/index.json cannot start or end with '_' or '.'" in errors def test_invalid_version(package_dir, verifier): package = os.path.join(package_dir, 'testfile-0.0.53-py36_0.tar.bz2') with pytest.raises(PackageError): verifier.verify_package(path_to_package=package, exit_on_error=True) package, errors = verifier.verify_package(path_to_package=package, exit_on_error=False) assert "[C1105] Found invalid version number in info/index.json" in errors def test_missing_version(package_dir, verifier): package = os.path.join(package_dir, 'testfile-0.0.54-py36_0.tar.bz2') with pytest.raises(PackageError): verifier.verify_package(path_to_package=package, exit_on_error=True) package, errors = verifier.verify_package(path_to_package=package, exit_on_error=False) assert "[C1104] Missing package version in info/index.json" in errors def test_invalid_package_name_pattern(package_dir, verifier): package = os.path.join(package_dir, 'testfile-0.0.55-py36_0.tar.bz2') with pytest.raises(PackageError): verifier.verify_package(path_to_package=package, exit_on_error=True) package, errors = verifier.verify_package(path_to_package=package, exit_on_error=False) assert "[C1103] Found invalid package name in info/index.json" in errors def test_missing_package_name(package_dir, verifier): package = os.path.join(package_dir, 'testfile-0.0.56-py36_0.tar.bz2') with pytest.raises(PackageError): verifier.verify_package(path_to_package=package, exit_on_error=True) package, errors = verifier.verify_package(path_to_package=package, exit_on_error=False) assert "[C1101] Missing package name in info/index.json" in errors def test_invalid_noarch_files(package_dir, verifier): package = os.path.join(package_dir, 'testfile-0.0.57-0.tar.bz2') with pytest.raises(PackageError): verifier.verify_package(path_to_package=package, exit_on_error=True) package, errors = verifier.verify_package(path_to_package=package, exit_on_error=False) assert '[C1148] Found architecture specific file "bin{}testfile.dll" in package.'.format(os.path.sep) in errors

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96ce9c03192563e46a2aaf6c94717bdc78b5faad

38,023

py

Python

instances/passenger_demand/pas-20210421-2109-int18e/78.py

LHcau/scheduling-shared-passenger-and-freight-transport-on-a-fixed-infrastructure

bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11

[ "BSD-3-Clause" ]

null

instances/passenger_demand/pas-20210421-2109-int18e/78.py

LHcau/scheduling-shared-passenger-and-freight-transport-on-a-fixed-infrastructure

bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11

[ "BSD-3-Clause" ]

null

instances/passenger_demand/pas-20210421-2109-int18e/78.py

LHcau/scheduling-shared-passenger-and-freight-transport-on-a-fixed-infrastructure

bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11

[ "BSD-3-Clause" ]

null

""" PASSENGERS """ numPassengers = 4084 passenger_arriving = ( (4, 8, 15, 6, 6, 0, 9, 7, 7, 4, 1, 0), # 0 (4, 11, 5, 5, 2, 0, 7, 10, 6, 8, 2, 0), # 1 (11, 10, 4, 1, 1, 0, 5, 9, 13, 5, 1, 0), # 2 (5, 5, 9, 5, 1, 0, 6, 8, 7, 3, 2, 0), # 3 (5, 10, 8, 7, 1, 0, 8, 16, 10, 3, 3, 0), # 4 (6, 12, 7, 1, 4, 0, 10, 6, 6, 5, 4, 0), # 5 (9, 12, 6, 2, 5, 0, 13, 17, 5, 3, 1, 0), # 6 (5, 5, 8, 2, 2, 0, 11, 17, 7, 8, 5, 0), # 7 (7, 9, 5, 4, 3, 0, 9, 15, 6, 6, 1, 0), # 8 (1, 8, 9, 6, 4, 0, 6, 14, 4, 5, 2, 0), # 9 (5, 2, 13, 10, 4, 0, 15, 9, 3, 9, 2, 0), # 10 (6, 16, 7, 6, 2, 0, 12, 12, 8, 7, 1, 0), # 11 (5, 12, 7, 4, 3, 0, 13, 11, 7, 3, 3, 0), # 12 (4, 17, 13, 6, 4, 0, 10, 8, 8, 5, 3, 0), # 13 (1, 15, 9, 5, 0, 0, 5, 9, 7, 6, 1, 0), # 14 (5, 11, 6, 3, 1, 0, 5, 10, 5, 5, 3, 0), # 15 (2, 5, 12, 7, 4, 0, 9, 17, 10, 10, 0, 0), # 16 (6, 5, 7, 7, 0, 0, 11, 9, 5, 5, 2, 0), # 17 (6, 11, 11, 5, 2, 0, 13, 8, 7, 5, 7, 0), # 18 (6, 12, 10, 7, 4, 0, 11, 12, 6, 3, 2, 0), # 19 (4, 12, 11, 3, 5, 0, 8, 15, 14, 10, 5, 0), # 20 (3, 4, 13, 4, 3, 0, 6, 7, 8, 7, 7, 0), # 21 (11, 13, 11, 5, 2, 0, 5, 14, 8, 2, 7, 0), # 22 (8, 10, 11, 6, 3, 0, 4, 7, 5, 7, 1, 0), # 23 (6, 12, 9, 3, 3, 0, 7, 12, 13, 5, 2, 0), # 24 (4, 10, 9, 5, 0, 0, 12, 11, 9, 6, 2, 0), # 25 (5, 9, 12, 0, 5, 0, 13, 14, 4, 8, 5, 0), # 26 (8, 8, 11, 0, 6, 0, 6, 9, 5, 6, 2, 0), # 27 (5, 15, 6, 3, 3, 0, 9, 2, 11, 3, 4, 0), # 28 (5, 14, 9, 5, 4, 0, 6, 10, 7, 1, 2, 0), # 29 (5, 14, 7, 2, 6, 0, 13, 6, 6, 7, 1, 0), # 30 (4, 10, 10, 2, 0, 0, 8, 12, 6, 3, 3, 0), # 31 (3, 12, 12, 4, 5, 0, 9, 16, 2, 9, 2, 0), # 32 (7, 13, 9, 3, 4, 0, 3, 11, 8, 5, 2, 0), # 33 (7, 10, 10, 5, 3, 0, 5, 13, 11, 7, 5, 0), # 34 (10, 18, 7, 7, 7, 0, 6, 9, 4, 3, 2, 0), # 35 (7, 7, 3, 6, 6, 0, 12, 13, 8, 3, 4, 0), # 36 (7, 19, 12, 3, 2, 0, 8, 8, 4, 4, 3, 0), # 37 (7, 13, 8, 1, 2, 0, 13, 11, 5, 4, 3, 0), # 38 (7, 11, 10, 7, 1, 0, 11, 10, 4, 9, 2, 0), # 39 (6, 14, 8, 4, 4, 0, 7, 16, 7, 7, 3, 0), # 40 (2, 24, 12, 4, 5, 0, 9, 17, 6, 8, 2, 0), # 41 (6, 11, 2, 3, 1, 0, 3, 11, 8, 6, 3, 0), # 42 (9, 10, 11, 4, 3, 0, 6, 11, 6, 6, 2, 0), # 43 (6, 14, 16, 3, 2, 0, 7, 5, 6, 3, 2, 0), # 44 (5, 11, 9, 7, 1, 0, 12, 16, 11, 9, 4, 0), # 45 (6, 11, 12, 6, 3, 0, 6, 21, 4, 10, 4, 0), # 46 (5, 10, 10, 6, 0, 0, 10, 12, 6, 8, 2, 0), # 47 (4, 11, 10, 3, 4, 0, 13, 9, 4, 7, 4, 0), # 48 (6, 16, 10, 5, 4, 0, 8, 7, 12, 3, 6, 0), # 49 (4, 10, 4, 7, 5, 0, 5, 14, 3, 3, 7, 0), # 50 (6, 14, 12, 1, 4, 0, 5, 13, 8, 3, 6, 0), # 51 (6, 18, 12, 5, 2, 0, 8, 11, 7, 8, 1, 0), # 52 (8, 9, 7, 5, 5, 0, 4, 11, 10, 6, 2, 0), # 53 (10, 12, 7, 3, 1, 0, 10, 14, 6, 9, 4, 0), # 54 (3, 16, 13, 7, 4, 0, 6, 11, 7, 6, 4, 0), # 55 (8, 18, 5, 3, 3, 0, 5, 13, 6, 4, 1, 0), # 56 (8, 10, 8, 3, 3, 0, 5, 18, 11, 9, 2, 0), # 57 (3, 16, 7, 7, 1, 0, 4, 14, 5, 8, 5, 0), # 58 (0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), # 59 ) station_arriving_intensity = ( (4.769372805092186, 12.233629261363635, 14.389624839331619, 11.405298913043477, 12.857451923076923, 8.562228260869567), # 0 (4.81413961808604, 12.369674877683082, 14.46734796754499, 11.46881589673913, 12.953819711538461, 8.559309850543478), # 1 (4.8583952589991215, 12.503702525252525, 14.54322622107969, 11.530934782608696, 13.048153846153847, 8.556302173913043), # 2 (4.902102161984196, 12.635567578125, 14.617204169344474, 11.591602581521737, 13.14036778846154, 8.553205638586958), # 3 (4.94522276119403, 12.765125410353535, 14.689226381748071, 11.650766304347826, 13.230375, 8.550020652173911), # 4 (4.987719490781387, 12.892231395991162, 14.759237427699228, 11.708372961956522, 13.318088942307691, 8.546747622282608), # 5 (5.029554784899035, 13.01674090909091, 14.827181876606687, 11.764369565217393, 13.403423076923078, 8.54338695652174), # 6 (5.0706910776997365, 13.138509323705808, 14.893004297879177, 11.818703125, 13.486290865384618, 8.5399390625), # 7 (5.1110908033362605, 13.257392013888888, 14.956649260925452, 11.871320652173912, 13.56660576923077, 8.536404347826087), # 8 (5.1507163959613695, 13.373244353693181, 15.018061335154243, 11.922169157608696, 13.644281249999999, 8.532783220108696), # 9 (5.1895302897278315, 13.485921717171717, 15.077185089974291, 11.971195652173915, 13.719230769230771, 8.529076086956522), # 10 (5.227494918788412, 13.595279478377526, 15.133965094794343, 12.018347146739131, 13.791367788461539, 8.525283355978262), # 11 (5.2645727172958745, 13.701173011363636, 15.188345919023137, 12.063570652173912, 13.860605769230768, 8.521405434782608), # 12 (5.3007261194029835, 13.803457690183082, 15.240272132069407, 12.106813179347826, 13.926858173076925, 8.51744273097826), # 13 (5.335917559262511, 13.90198888888889, 15.289688303341899, 12.148021739130433, 13.99003846153846, 8.513395652173912), # 14 (5.370109471027217, 13.996621981534089, 15.336539002249355, 12.187143342391304, 14.050060096153846, 8.509264605978261), # 15 (5.403264288849868, 14.087212342171718, 15.380768798200515, 12.224124999999999, 14.10683653846154, 8.50505), # 16 (5.4353444468832315, 14.173615344854797, 15.422322260604112, 12.258913722826087, 14.16028125, 8.500752241847827), # 17 (5.46631237928007, 14.255686363636363, 15.461143958868895, 12.291456521739132, 14.210307692307696, 8.496371739130435), # 18 (5.496130520193152, 14.333280772569443, 15.4971784624036, 12.321700407608695, 14.256829326923079, 8.491908899456522), # 19 (5.524761303775241, 14.40625394570707, 15.530370340616965, 12.349592391304348, 14.299759615384616, 8.487364130434782), # 20 (5.552167164179106, 14.47446125710227, 15.56066416291774, 12.375079483695652, 14.339012019230768, 8.482737839673913), # 21 (5.578310535557506, 14.537758080808082, 15.588004498714653, 12.398108695652175, 14.374499999999998, 8.47803043478261), # 22 (5.603153852063214, 14.595999790877526, 15.612335917416454, 12.418627038043478, 14.40613701923077, 8.473242323369567), # 23 (5.62665954784899, 14.649041761363636, 15.633602988431875, 12.43658152173913, 14.433836538461538, 8.468373913043479), # 24 (5.648790057067603, 14.696739366319445, 15.651750281169667, 12.451919157608696, 14.457512019230768, 8.463425611413044), # 25 (5.669507813871817, 14.738947979797977, 15.66672236503856, 12.464586956521739, 14.477076923076922, 8.458397826086957), # 26 (5.688775252414398, 14.77552297585227, 15.6784638094473, 12.474531929347828, 14.492444711538463, 8.453290964673915), # 27 (5.7065548068481124, 14.806319728535353, 15.68691918380463, 12.481701086956523, 14.503528846153845, 8.448105434782608), # 28 (5.722808911325724, 14.831193611900254, 15.69203305751928, 12.486041440217392, 14.510242788461538, 8.44284164402174), # 29 (5.7375, 14.85, 15.69375, 12.4875, 14.512500000000001, 8.4375), # 30 (5.751246651214834, 14.865621839488634, 15.692462907608693, 12.487236580882353, 14.511678590425532, 8.430077267616193), # 31 (5.7646965153452685, 14.881037215909092, 15.68863804347826, 12.486451470588234, 14.509231914893617, 8.418644565217393), # 32 (5.777855634590792, 14.896244211647728, 15.682330027173915, 12.485152389705883, 14.50518630319149, 8.403313830584706), # 33 (5.790730051150895, 14.91124090909091, 15.67359347826087, 12.483347058823531, 14.499568085106382, 8.38419700149925), # 34 (5.803325807225064, 14.926025390624996, 15.662483016304348, 12.481043198529411, 14.492403590425532, 8.361406015742128), # 35 (5.815648945012788, 14.940595738636366, 15.649053260869564, 12.478248529411767, 14.48371914893617, 8.335052811094453), # 36 (5.8277055067135555, 14.954950035511365, 15.63335883152174, 12.474970772058823, 14.47354109042553, 8.305249325337332), # 37 (5.839501534526853, 14.969086363636364, 15.615454347826088, 12.471217647058824, 14.461895744680852, 8.272107496251873), # 38 (5.851043070652174, 14.983002805397728, 15.595394429347825, 12.466996875000001, 14.44880944148936, 8.23573926161919), # 39 (5.862336157289003, 14.99669744318182, 15.573233695652176, 12.462316176470589, 14.434308510638296, 8.196256559220389), # 40 (5.873386836636828, 15.010168359374997, 15.549026766304348, 12.457183272058824, 14.418419281914893, 8.153771326836583), # 41 (5.88420115089514, 15.023413636363639, 15.522828260869566, 12.451605882352942, 14.401168085106384, 8.108395502248875), # 42 (5.894785142263428, 15.03643135653409, 15.494692798913043, 12.445591727941178, 14.38258125, 8.060241023238381), # 43 (5.905144852941176, 15.049219602272727, 15.464675, 12.439148529411764, 14.36268510638298, 8.009419827586207), # 44 (5.915286325127877, 15.061776455965909, 15.432829483695656, 12.43228400735294, 14.341505984042554, 7.956043853073464), # 45 (5.925215601023019, 15.074100000000003, 15.39921086956522, 12.425005882352941, 14.319070212765958, 7.90022503748126), # 46 (5.934938722826087, 15.086188316761364, 15.363873777173913, 12.417321874999999, 14.295404122340427, 7.842075318590705), # 47 (5.944461732736574, 15.098039488636365, 15.326872826086957, 12.409239705882353, 14.27053404255319, 7.7817066341829095), # 48 (5.953790672953963, 15.10965159801136, 15.288262635869566, 12.400767095588236, 14.24448630319149, 7.71923092203898), # 49 (5.96293158567775, 15.121022727272724, 15.248097826086958, 12.391911764705883, 14.217287234042553, 7.65476011994003), # 50 (5.971890513107417, 15.132150958806818, 15.206433016304347, 12.38268143382353, 14.188963164893616, 7.588406165667167), # 51 (5.980673497442456, 15.143034375, 15.163322826086954, 12.373083823529411, 14.159540425531915, 7.5202809970015), # 52 (5.989286580882353, 15.153671058238638, 15.118821875, 12.363126654411765, 14.129045345744682, 7.450496551724138), # 53 (5.9977358056266, 15.164059090909088, 15.072984782608694, 12.352817647058824, 14.09750425531915, 7.379164767616192), # 54 (6.00602721387468, 15.174196555397728, 15.02586616847826, 12.342164522058825, 14.064943484042553, 7.306397582458771), # 55 (6.014166847826087, 15.184081534090907, 14.977520652173913, 12.331175, 14.031389361702129, 7.232306934032984), # 56 (6.022160749680308, 15.193712109375003, 14.92800285326087, 12.319856801470587, 13.996868218085105, 7.15700476011994), # 57 (6.030014961636829, 15.203086363636363, 14.877367391304347, 12.308217647058825, 13.961406382978723, 7.0806029985007495), # 58 (0.0, 0.0, 0.0, 0.0, 0.0, 0.0), # 59 ) passenger_arriving_acc = ( (4, 8, 15, 6, 6, 0, 9, 7, 7, 4, 1, 0), # 0 (8, 19, 20, 11, 8, 0, 16, 17, 13, 12, 3, 0), # 1 (19, 29, 24, 12, 9, 0, 21, 26, 26, 17, 4, 0), # 2 (24, 34, 33, 17, 10, 0, 27, 34, 33, 20, 6, 0), # 3 (29, 44, 41, 24, 11, 0, 35, 50, 43, 23, 9, 0), # 4 (35, 56, 48, 25, 15, 0, 45, 56, 49, 28, 13, 0), # 5 (44, 68, 54, 27, 20, 0, 58, 73, 54, 31, 14, 0), # 6 (49, 73, 62, 29, 22, 0, 69, 90, 61, 39, 19, 0), # 7 (56, 82, 67, 33, 25, 0, 78, 105, 67, 45, 20, 0), # 8 (57, 90, 76, 39, 29, 0, 84, 119, 71, 50, 22, 0), # 9 (62, 92, 89, 49, 33, 0, 99, 128, 74, 59, 24, 0), # 10 (68, 108, 96, 55, 35, 0, 111, 140, 82, 66, 25, 0), # 11 (73, 120, 103, 59, 38, 0, 124, 151, 89, 69, 28, 0), # 12 (77, 137, 116, 65, 42, 0, 134, 159, 97, 74, 31, 0), # 13 (78, 152, 125, 70, 42, 0, 139, 168, 104, 80, 32, 0), # 14 (83, 163, 131, 73, 43, 0, 144, 178, 109, 85, 35, 0), # 15 (85, 168, 143, 80, 47, 0, 153, 195, 119, 95, 35, 0), # 16 (91, 173, 150, 87, 47, 0, 164, 204, 124, 100, 37, 0), # 17 (97, 184, 161, 92, 49, 0, 177, 212, 131, 105, 44, 0), # 18 (103, 196, 171, 99, 53, 0, 188, 224, 137, 108, 46, 0), # 19 (107, 208, 182, 102, 58, 0, 196, 239, 151, 118, 51, 0), # 20 (110, 212, 195, 106, 61, 0, 202, 246, 159, 125, 58, 0), # 21 (121, 225, 206, 111, 63, 0, 207, 260, 167, 127, 65, 0), # 22 (129, 235, 217, 117, 66, 0, 211, 267, 172, 134, 66, 0), # 23 (135, 247, 226, 120, 69, 0, 218, 279, 185, 139, 68, 0), # 24 (139, 257, 235, 125, 69, 0, 230, 290, 194, 145, 70, 0), # 25 (144, 266, 247, 125, 74, 0, 243, 304, 198, 153, 75, 0), # 26 (152, 274, 258, 125, 80, 0, 249, 313, 203, 159, 77, 0), # 27 (157, 289, 264, 128, 83, 0, 258, 315, 214, 162, 81, 0), # 28 (162, 303, 273, 133, 87, 0, 264, 325, 221, 163, 83, 0), # 29 (167, 317, 280, 135, 93, 0, 277, 331, 227, 170, 84, 0), # 30 (171, 327, 290, 137, 93, 0, 285, 343, 233, 173, 87, 0), # 31 (174, 339, 302, 141, 98, 0, 294, 359, 235, 182, 89, 0), # 32 (181, 352, 311, 144, 102, 0, 297, 370, 243, 187, 91, 0), # 33 (188, 362, 321, 149, 105, 0, 302, 383, 254, 194, 96, 0), # 34 (198, 380, 328, 156, 112, 0, 308, 392, 258, 197, 98, 0), # 35 (205, 387, 331, 162, 118, 0, 320, 405, 266, 200, 102, 0), # 36 (212, 406, 343, 165, 120, 0, 328, 413, 270, 204, 105, 0), # 37 (219, 419, 351, 166, 122, 0, 341, 424, 275, 208, 108, 0), # 38 (226, 430, 361, 173, 123, 0, 352, 434, 279, 217, 110, 0), # 39 (232, 444, 369, 177, 127, 0, 359, 450, 286, 224, 113, 0), # 40 (234, 468, 381, 181, 132, 0, 368, 467, 292, 232, 115, 0), # 41 (240, 479, 383, 184, 133, 0, 371, 478, 300, 238, 118, 0), # 42 (249, 489, 394, 188, 136, 0, 377, 489, 306, 244, 120, 0), # 43 (255, 503, 410, 191, 138, 0, 384, 494, 312, 247, 122, 0), # 44 (260, 514, 419, 198, 139, 0, 396, 510, 323, 256, 126, 0), # 45 (266, 525, 431, 204, 142, 0, 402, 531, 327, 266, 130, 0), # 46 (271, 535, 441, 210, 142, 0, 412, 543, 333, 274, 132, 0), # 47 (275, 546, 451, 213, 146, 0, 425, 552, 337, 281, 136, 0), # 48 (281, 562, 461, 218, 150, 0, 433, 559, 349, 284, 142, 0), # 49 (285, 572, 465, 225, 155, 0, 438, 573, 352, 287, 149, 0), # 50 (291, 586, 477, 226, 159, 0, 443, 586, 360, 290, 155, 0), # 51 (297, 604, 489, 231, 161, 0, 451, 597, 367, 298, 156, 0), # 52 (305, 613, 496, 236, 166, 0, 455, 608, 377, 304, 158, 0), # 53 (315, 625, 503, 239, 167, 0, 465, 622, 383, 313, 162, 0), # 54 (318, 641, 516, 246, 171, 0, 471, 633, 390, 319, 166, 0), # 55 (326, 659, 521, 249, 174, 0, 476, 646, 396, 323, 167, 0), # 56 (334, 669, 529, 252, 177, 0, 481, 664, 407, 332, 169, 0), # 57 (337, 685, 536, 259, 178, 0, 485, 678, 412, 340, 174, 0), # 58 (337, 685, 536, 259, 178, 0, 485, 678, 412, 340, 174, 0), # 59 ) passenger_arriving_rate = ( (4.769372805092186, 9.786903409090908, 8.63377490359897, 4.56211956521739, 2.5714903846153843, 0.0, 8.562228260869567, 10.285961538461537, 6.843179347826086, 5.755849935732647, 2.446725852272727, 0.0), # 0 (4.81413961808604, 9.895739902146465, 8.680408780526994, 4.587526358695651, 2.5907639423076922, 0.0, 8.559309850543478, 10.363055769230769, 6.881289538043478, 5.786939187017995, 2.4739349755366162, 0.0), # 1 (4.8583952589991215, 10.00296202020202, 8.725935732647814, 4.612373913043478, 2.609630769230769, 0.0, 8.556302173913043, 10.438523076923076, 6.918560869565217, 5.817290488431875, 2.500740505050505, 0.0), # 2 (4.902102161984196, 10.1084540625, 8.770322501606683, 4.636641032608694, 2.628073557692308, 0.0, 8.553205638586958, 10.512294230769232, 6.954961548913042, 5.846881667737789, 2.527113515625, 0.0), # 3 (4.94522276119403, 10.212100328282828, 8.813535829048842, 4.66030652173913, 2.6460749999999997, 0.0, 8.550020652173911, 10.584299999999999, 6.990459782608696, 5.875690552699228, 2.553025082070707, 0.0), # 4 (4.987719490781387, 10.313785116792928, 8.855542456619537, 4.6833491847826085, 2.663617788461538, 0.0, 8.546747622282608, 10.654471153846153, 7.025023777173913, 5.90369497107969, 2.578446279198232, 0.0), # 5 (5.029554784899035, 10.413392727272727, 8.896309125964011, 4.705747826086957, 2.680684615384615, 0.0, 8.54338695652174, 10.72273846153846, 7.058621739130436, 5.930872750642674, 2.603348181818182, 0.0), # 6 (5.0706910776997365, 10.510807458964646, 8.935802578727506, 4.72748125, 2.697258173076923, 0.0, 8.5399390625, 10.789032692307693, 7.0912218750000005, 5.95720171915167, 2.6277018647411614, 0.0), # 7 (5.1110908033362605, 10.60591361111111, 8.97398955655527, 4.7485282608695645, 2.7133211538461537, 0.0, 8.536404347826087, 10.853284615384615, 7.122792391304347, 5.982659704370181, 2.6514784027777774, 0.0), # 8 (5.1507163959613695, 10.698595482954543, 9.010836801092546, 4.768867663043478, 2.7288562499999993, 0.0, 8.532783220108696, 10.915424999999997, 7.153301494565217, 6.007224534061697, 2.6746488707386358, 0.0), # 9 (5.1895302897278315, 10.788737373737373, 9.046311053984574, 4.7884782608695655, 2.743846153846154, 0.0, 8.529076086956522, 10.975384615384616, 7.182717391304348, 6.030874035989716, 2.697184343434343, 0.0), # 10 (5.227494918788412, 10.87622358270202, 9.080379056876605, 4.807338858695652, 2.7582735576923074, 0.0, 8.525283355978262, 11.03309423076923, 7.2110082880434785, 6.053586037917737, 2.719055895675505, 0.0), # 11 (5.2645727172958745, 10.960938409090907, 9.113007551413881, 4.825428260869565, 2.7721211538461534, 0.0, 8.521405434782608, 11.088484615384614, 7.238142391304347, 6.0753383676092545, 2.740234602272727, 0.0), # 12 (5.3007261194029835, 11.042766152146465, 9.144163279241644, 4.8427252717391305, 2.7853716346153847, 0.0, 8.51744273097826, 11.141486538461539, 7.264087907608696, 6.096108852827762, 2.760691538036616, 0.0), # 13 (5.335917559262511, 11.121591111111112, 9.173812982005138, 4.859208695652173, 2.7980076923076918, 0.0, 8.513395652173912, 11.192030769230767, 7.288813043478259, 6.115875321336759, 2.780397777777778, 0.0), # 14 (5.370109471027217, 11.19729758522727, 9.201923401349612, 4.874857336956521, 2.810012019230769, 0.0, 8.509264605978261, 11.240048076923076, 7.312286005434782, 6.134615600899742, 2.7993243963068175, 0.0), # 15 (5.403264288849868, 11.269769873737372, 9.228461278920308, 4.88965, 2.8213673076923076, 0.0, 8.50505, 11.28546923076923, 7.334474999999999, 6.152307519280206, 2.817442468434343, 0.0), # 16 (5.4353444468832315, 11.338892275883836, 9.253393356362468, 4.903565489130434, 2.83205625, 0.0, 8.500752241847827, 11.328225, 7.3553482336956515, 6.168928904241644, 2.834723068970959, 0.0), # 17 (5.46631237928007, 11.40454909090909, 9.276686375321336, 4.916582608695652, 2.842061538461539, 0.0, 8.496371739130435, 11.368246153846156, 7.374873913043479, 6.184457583547558, 2.8511372727272724, 0.0), # 18 (5.496130520193152, 11.466624618055553, 9.298307077442159, 4.928680163043477, 2.8513658653846155, 0.0, 8.491908899456522, 11.405463461538462, 7.393020244565217, 6.198871384961439, 2.866656154513888, 0.0), # 19 (5.524761303775241, 11.525003156565655, 9.318222204370178, 4.939836956521739, 2.859951923076923, 0.0, 8.487364130434782, 11.439807692307692, 7.409755434782609, 6.212148136246785, 2.8812507891414136, 0.0), # 20 (5.552167164179106, 11.579569005681815, 9.336398497750643, 4.95003179347826, 2.8678024038461536, 0.0, 8.482737839673913, 11.471209615384614, 7.425047690217391, 6.224265665167096, 2.894892251420454, 0.0), # 21 (5.578310535557506, 11.630206464646465, 9.352802699228791, 4.95924347826087, 2.8748999999999993, 0.0, 8.47803043478261, 11.499599999999997, 7.438865217391305, 6.235201799485861, 2.907551616161616, 0.0), # 22 (5.603153852063214, 11.67679983270202, 9.367401550449872, 4.967450815217391, 2.8812274038461534, 0.0, 8.473242323369567, 11.524909615384614, 7.451176222826087, 6.244934366966581, 2.919199958175505, 0.0), # 23 (5.62665954784899, 11.719233409090908, 9.380161793059125, 4.974632608695652, 2.8867673076923075, 0.0, 8.468373913043479, 11.54706923076923, 7.461948913043478, 6.25344119537275, 2.929808352272727, 0.0), # 24 (5.648790057067603, 11.757391493055556, 9.391050168701799, 4.980767663043478, 2.8915024038461534, 0.0, 8.463425611413044, 11.566009615384614, 7.471151494565217, 6.260700112467866, 2.939347873263889, 0.0), # 25 (5.669507813871817, 11.79115838383838, 9.400033419023135, 4.985834782608695, 2.8954153846153843, 0.0, 8.458397826086957, 11.581661538461537, 7.478752173913043, 6.266688946015424, 2.947789595959595, 0.0), # 26 (5.688775252414398, 11.820418380681815, 9.40707828566838, 4.989812771739131, 2.8984889423076923, 0.0, 8.453290964673915, 11.593955769230769, 7.484719157608696, 6.271385523778919, 2.9551045951704538, 0.0), # 27 (5.7065548068481124, 11.84505578282828, 9.412151510282778, 4.992680434782609, 2.9007057692307687, 0.0, 8.448105434782608, 11.602823076923075, 7.489020652173913, 6.274767673521851, 2.96126394570707, 0.0), # 28 (5.722808911325724, 11.864954889520202, 9.415219834511568, 4.994416576086956, 2.902048557692307, 0.0, 8.44284164402174, 11.608194230769229, 7.491624864130435, 6.276813223007712, 2.9662387223800506, 0.0), # 29 (5.7375, 11.879999999999999, 9.41625, 4.995, 2.9025, 0.0, 8.4375, 11.61, 7.4925, 6.277499999999999, 2.9699999999999998, 0.0), # 30 (5.751246651214834, 11.892497471590906, 9.415477744565216, 4.994894632352941, 2.9023357180851064, 0.0, 8.430077267616193, 11.609342872340426, 7.492341948529411, 6.276985163043476, 2.9731243678977264, 0.0), # 31 (5.7646965153452685, 11.904829772727274, 9.413182826086956, 4.994580588235293, 2.901846382978723, 0.0, 8.418644565217393, 11.607385531914892, 7.49187088235294, 6.275455217391303, 2.9762074431818184, 0.0), # 32 (5.777855634590792, 11.916995369318181, 9.40939801630435, 4.994060955882353, 2.9010372606382977, 0.0, 8.403313830584706, 11.60414904255319, 7.491091433823529, 6.272932010869566, 2.9792488423295453, 0.0), # 33 (5.790730051150895, 11.928992727272727, 9.40415608695652, 4.993338823529412, 2.899913617021276, 0.0, 8.38419700149925, 11.599654468085104, 7.490008235294118, 6.269437391304347, 2.9822481818181816, 0.0), # 34 (5.803325807225064, 11.940820312499996, 9.39748980978261, 4.9924172794117645, 2.898480718085106, 0.0, 8.361406015742128, 11.593922872340425, 7.488625919117647, 6.264993206521739, 2.985205078124999, 0.0), # 35 (5.815648945012788, 11.952476590909091, 9.389431956521738, 4.9912994117647065, 2.896743829787234, 0.0, 8.335052811094453, 11.586975319148936, 7.486949117647059, 6.259621304347825, 2.988119147727273, 0.0), # 36 (5.8277055067135555, 11.96396002840909, 9.380015298913044, 4.989988308823529, 2.8947082180851056, 0.0, 8.305249325337332, 11.578832872340422, 7.484982463235293, 6.253343532608695, 2.9909900071022726, 0.0), # 37 (5.839501534526853, 11.97526909090909, 9.369272608695653, 4.988487058823529, 2.89237914893617, 0.0, 8.272107496251873, 11.56951659574468, 7.4827305882352935, 6.246181739130434, 2.9938172727272727, 0.0), # 38 (5.851043070652174, 11.986402244318182, 9.357236657608695, 4.98679875, 2.8897618882978717, 0.0, 8.23573926161919, 11.559047553191487, 7.480198125, 6.23815777173913, 2.9966005610795454, 0.0), # 39 (5.862336157289003, 11.997357954545455, 9.343940217391305, 4.984926470588235, 2.886861702127659, 0.0, 8.196256559220389, 11.547446808510635, 7.477389705882353, 6.22929347826087, 2.999339488636364, 0.0), # 40 (5.873386836636828, 12.008134687499997, 9.329416059782607, 4.982873308823529, 2.8836838563829783, 0.0, 8.153771326836583, 11.534735425531913, 7.474309963235294, 6.219610706521738, 3.002033671874999, 0.0), # 41 (5.88420115089514, 12.01873090909091, 9.31369695652174, 4.980642352941176, 2.880233617021277, 0.0, 8.108395502248875, 11.520934468085107, 7.4709635294117644, 6.209131304347826, 3.0046827272727277, 0.0), # 42 (5.894785142263428, 12.02914508522727, 9.296815679347825, 4.978236691176471, 2.8765162499999994, 0.0, 8.060241023238381, 11.506064999999998, 7.467355036764706, 6.1978771195652165, 3.0072862713068176, 0.0), # 43 (5.905144852941176, 12.03937568181818, 9.278805, 4.975659411764705, 2.8725370212765955, 0.0, 8.009419827586207, 11.490148085106382, 7.4634891176470575, 6.1858699999999995, 3.009843920454545, 0.0), # 44 (5.915286325127877, 12.049421164772726, 9.259697690217394, 4.972913602941176, 2.8683011968085106, 0.0, 7.956043853073464, 11.473204787234042, 7.459370404411764, 6.1731317934782615, 3.0123552911931815, 0.0), # 45 (5.925215601023019, 12.059280000000001, 9.239526521739132, 4.970002352941176, 2.8638140425531913, 0.0, 7.90022503748126, 11.455256170212765, 7.455003529411765, 6.159684347826087, 3.0148200000000003, 0.0), # 46 (5.934938722826087, 12.06895065340909, 9.218324266304347, 4.966928749999999, 2.859080824468085, 0.0, 7.842075318590705, 11.43632329787234, 7.450393124999999, 6.145549510869564, 3.0172376633522724, 0.0), # 47 (5.944461732736574, 12.07843159090909, 9.196123695652174, 4.9636958823529405, 2.854106808510638, 0.0, 7.7817066341829095, 11.416427234042551, 7.445543823529412, 6.130749130434782, 3.0196078977272727, 0.0), # 48 (5.953790672953963, 12.087721278409088, 9.17295758152174, 4.960306838235294, 2.8488972606382976, 0.0, 7.71923092203898, 11.39558904255319, 7.4404602573529415, 6.115305054347826, 3.021930319602272, 0.0), # 49 (5.96293158567775, 12.096818181818177, 9.148858695652175, 4.956764705882353, 2.8434574468085105, 0.0, 7.65476011994003, 11.373829787234042, 7.43514705882353, 6.099239130434783, 3.0242045454545443, 0.0), # 50 (5.971890513107417, 12.105720767045453, 9.123859809782608, 4.953072573529411, 2.837792632978723, 0.0, 7.588406165667167, 11.351170531914892, 7.429608860294118, 6.082573206521738, 3.026430191761363, 0.0), # 51 (5.980673497442456, 12.114427499999998, 9.097993695652173, 4.949233529411764, 2.8319080851063827, 0.0, 7.5202809970015, 11.32763234042553, 7.4238502941176465, 6.065329130434781, 3.0286068749999995, 0.0), # 52 (5.989286580882353, 12.122936846590909, 9.071293125, 4.945250661764706, 2.8258090691489364, 0.0, 7.450496551724138, 11.303236276595745, 7.417875992647058, 6.04752875, 3.030734211647727, 0.0), # 53 (5.9977358056266, 12.13124727272727, 9.043790869565216, 4.941127058823529, 2.8195008510638297, 0.0, 7.379164767616192, 11.278003404255319, 7.411690588235294, 6.0291939130434775, 3.0328118181818176, 0.0), # 54 (6.00602721387468, 12.139357244318182, 9.015519701086955, 4.93686580882353, 2.8129886968085103, 0.0, 7.306397582458771, 11.251954787234041, 7.405298713235295, 6.010346467391304, 3.0348393110795455, 0.0), # 55 (6.014166847826087, 12.147265227272724, 8.986512391304348, 4.9324699999999995, 2.8062778723404254, 0.0, 7.232306934032984, 11.225111489361701, 7.398705, 5.991008260869565, 3.036816306818181, 0.0), # 56 (6.022160749680308, 12.154969687500001, 8.95680171195652, 4.927942720588234, 2.7993736436170207, 0.0, 7.15700476011994, 11.197494574468083, 7.391914080882352, 5.9712011413043475, 3.0387424218750003, 0.0), # 57 (6.030014961636829, 12.16246909090909, 8.926420434782608, 4.923287058823529, 2.792281276595744, 0.0, 7.0806029985007495, 11.169125106382976, 7.384930588235295, 5.950946956521738, 3.0406172727272724, 0.0), # 58 (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0), # 59 ) passenger_allighting_rate = ( (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 0 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 1 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 2 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 3 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 4 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 5 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 6 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 7 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 8 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 9 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 10 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 11 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 12 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 13 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 14 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 15 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 16 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 17 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 18 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 19 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 20 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 21 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 22 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 23 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 24 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 25 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 26 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 27 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 28 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 29 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 30 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 31 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 32 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 33 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 34 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 35 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 36 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 37 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 38 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 39 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 40 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 41 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 42 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 43 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 44 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 45 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 46 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 47 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 48 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 49 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 50 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 51 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 52 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 53 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 54 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 55 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 56 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 57 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 58 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 59 ) """ parameters for reproducibiliy. More information: https://numpy.org/doc/stable/reference/random/parallel.html """ #initial entropy entropy = 258194110137029475889902652135037600173 #index for seed sequence child child_seed_index = ( 1, # 0 77, # 1 )

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6

8c07c5e794de555c7b4a48d74b0be3f39ee1771a

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py

Python

loss_functions/__init__.py

MPCAICDM/MPCA

c996435a0578ea4160f934bc01041c2ef23468f3

[ "MIT" ]

null

loss_functions/__init__.py

MPCAICDM/MPCA

c996435a0578ea4160f934bc01041c2ef23468f3

[ "MIT" ]

null

loss_functions/__init__.py

MPCAICDM/MPCA

c996435a0578ea4160f934bc01041c2ef23468f3

[ "MIT" ]

null

from .lsaloss import LSALoss

14.5

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6

8c36b2ed2bce96b861d5f4c12cbcd4922169b18e

2,221

py

Python

tests/test_class_oelint_vars_filessetting_double.py

HerrMuellerluedenscheid/oelint-adv

90ad0a9e385d863af85869f06750aa5d2440e986

[ "BSD-2-Clause" ]

22

2019-06-10T00:40:07.000Z

2022-01-18T19:59:47.000Z

tests/test_class_oelint_vars_filessetting_double.py

HerrMuellerluedenscheid/oelint-adv

90ad0a9e385d863af85869f06750aa5d2440e986

[ "BSD-2-Clause" ]

274

2019-03-07T06:00:27.000Z

2022-03-27T10:22:10.000Z

tests/test_class_oelint_vars_filessetting_double.py

HerrMuellerluedenscheid/oelint-adv

90ad0a9e385d863af85869f06750aa5d2440e986

[ "BSD-2-Clause" ]

17

2019-08-24T23:04:39.000Z

2021-11-02T19:18:19.000Z

import os import sys import pytest sys.path.insert(0, os.path.abspath(os.path.dirname(__file__))) from base import TestBaseClass class TestClassOelintVarsFileSettingsDouble(TestBaseClass): @pytest.mark.parametrize('id', ['oelint.vars.filessetting.double']) @pytest.mark.parametrize('occurrence', [1]) @pytest.mark.parametrize('input', [ { 'oelint_adv_test.bb': ''' FILES_${PN} += "${bindir}" FILES_${PN}-ping = "${base_bindir}/ping.${BPN}" ''' }, { 'oelint_adv_test.bbappend': ''' FILES_${PN} += "${bindir}" FILES_${PN}-ping = "${base_bindir}/ping.${BPN}" ''' }, { 'oelint_adv_test.bb': ''' FILES_${PN} += "${bindir}" ''' }, { 'oelint_adv_test.bb': ''' FILES_${PN}-doc += "${docdir}" ''' }, ], ) def test_bad(self, input, id, occurrence): self.check_for_id(self._create_args(input), id, occurrence) @pytest.mark.parametrize('id', ['oelint.vars.filessetting.double']) @pytest.mark.parametrize('occurrence', [2]) @pytest.mark.parametrize('input', [ { 'oelint_adv_test.bb': ''' FILES_${PN} += "/opt/other/path" FILES_${PN}-ping = "${base_bindir}/ping.${BPN}" FILES_${PN} += "/opt/other/path" ''' } ], ) def test_bad_non_default(self, input, id, occurrence): self.check_for_id(self._create_args(input), id, occurrence) @pytest.mark.parametrize('id', ['oelint.vars.filessetting.double']) @pytest.mark.parametrize('occurrence', [0]) @pytest.mark.parametrize('input', [ { 'oelint_adv_test.bb': ''' FILES_${PN} += "/opt/other/path" FILES_${PN}-ping = "${base_bindir}/ping.${BPN}" ''' } ], ) def test_good(self, input, id, occurrence): self.check_for_id(self._create_args(input), id, occurrence)

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2,221

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null

0

1

0

1

0

null

0

6

8c4306f1e8f0ebec857a43914778be64db056776

27

py

Python

src/masonite/orm/schema/__init__.py

Marlysson/orm

ec2f3e3c107135c95ecddc5034c809114344c880

[ "MIT" ]

null

src/masonite/orm/schema/__init__.py

Marlysson/orm

ec2f3e3c107135c95ecddc5034c809114344c880

[ "MIT" ]

null

src/masonite/orm/schema/__init__.py

Marlysson/orm

ec2f3e3c107135c95ecddc5034c809114344c880

[ "MIT" ]

null

from .Schema import Schema

13.5

26

0.814815

4

27

5.5

0.75

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27

1

27

0.956522

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null

0

1

0

1

0

1

0

6

4fb8421e7aef6cc851d1252e83bd2dac4c6ca2b9

2,475

py

Python

Hidden messages/Hamming.py

lovroselic/Coursera

1598b4fe02eb3addbc847f4f3ec21fb5b6e0be08

[ "MIT" ]

null

Hidden messages/Hamming.py

lovroselic/Coursera

1598b4fe02eb3addbc847f4f3ec21fb5b6e0be08

[ "MIT" ]

null

Hidden messages/Hamming.py

lovroselic/Coursera

1598b4fe02eb3addbc847f4f3ec21fb5b6e0be08

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Created on Wed Aug 26 12:58:44 2020 @author: SELICLO1 """ def HammingDistance(a,b): mm = 0 for i,s in enumerate(a): if s != b[i]: mm+= 1 return mm a = 'GTCTGGGCCGTGCGGACTTGTTGCGGATGATACAAGGGCTTCACTACGTCGAAGTAAGTTACCAATTATACAAGCTACCGGTGTAGAATGGTGAGTAGGTCCGCTTATAGCCCGCGCGGTGTACCTCGAGATAGATTGGGGTCAACCCAGTCCGACCCCACGCTACATGGTCATCCTATGTTGGACTGTTACCCCGACCAGTCCACACATCTACGTACTAACGAGCGACGTCGTCTAAGAGCTCATTTTAGACCTTTTGAGATGAAAGATACAAGTCAAACCGTGCGTTAGGCTGGCCCAACGCAAGTTTACAGATAGCGTTCTGGGGCACGTTTAATCGCTATGATTTAGTAGCACATAAGGAGCTGAAACGTTTATCGGCATTGGGGAGAGTAATCCGGAAGGGCTCATTCTCCCACGTTGTCCAATTACACACTGCTGTTCGATACTTGGCGCTCGGACTCACCGATAGCGCTGGCCCTGGTCGCCCCTAACACGTAACGGTTCTGGAGAATTGATCCGAACAACAGATAAGAAAGCTGATGAAGAAGTAACGGACGTAAACAGGACTGGTCGAGCAACACCCCTAGCTAGGGAAAGAGATAAAACGGGAATGCAGTTTTAATTTTCAGAATTATCTGGAATGCGCAAGCATTTTTTCGTTGGGGCCATGCGAGGCGAAGCAAATGTGTGAGAAAATTGCGGAGGTACGATACCATGGGAGTCTTGTGTTTTAGTCCGATGTCGCCCCTCACCCGCAAGAACGAGCACGGAACACTCTATCCCGAGGACTACTATGGCGAGAGCTCAATAACAGCACTGCTCTCCACAACGGCGCCTAGTGATCGTGAATCCCCCATTTCTGCCAATAGTAATATTCACCAGGGTATCCAATGCAACGTCTCATTAATGAAATATTTAGTGCGGTTGGCAAAAATCCCCGCTGAAAAGAGGTTTTACGTTGTAGAACTAGAATGTTCGTGGTTCGTTGCGACTTTTAACCTAAGCTGGTCGGTGGGCTTGAGACTAGCATGTTGCCCTCCTCGGGGCAGGGCGCGACAAACCATTGGTGTGAGGACACCAGCCAGACACGACAAATCGTCACGGCGCGCAT' b = 'TTTGTGGATCCCCCTTAATGCAGCCACAAAGCAGCACCCACGAGTATGCAGTGCTTAAGGGGTTTCGATCTTACGATCCCTGCCTAACACACGGTAGCTTGTTATGGCCGGGCTGTACTTAAACGTTACACTCCGAACCCCAATGGGGTTAAGCGTCGAACAGGAGTACAGTGTATCATATTCGACTATGTCCACGTGAGATCGTAACCGTCCGAAAACCGGCAGTAGAAGGGCAGCTAACAGGTATCTTGCACCAGCTTACCTATCTTTTAGTGGCGGGCCATCAGGAGGCCAGTCCCGCGTCCCTACACGCCCGGAGTGTAGAGTAGGGTACGGGCTCGTCTGGTGCGAAAAAGACCGCAGCCTACATAAGCTCGCACAACATAGTCGATGCCTCAAAGCAAGGGCGTCACGAACCTACCCTCTGCTATATTACGGACCTGGTACAATCTAGCAGATTTATAAAGACTACCAAGCTAAACTCACCGTTTACCGCTGTCCCACTGAATTCGGAGTCTCTATGTGCAACCGCAGGTCATGTACACACATTGTACACCTTAGCGGAAGCCTGGAGTTGCTTATCAAGGGCATCCAGGGATAAAGGACGCAAGCTACAAAAAACAATTTTCTCCCAGTACCCTCTTCACAAATTGCAACTGTTCCTGTCAAACTCCCTCTGCAACGGCCGGGCTTTACCTTTAGACTAAACAACTACGAGTGTTGGTATCCTAGGGGTGTGCTAACAACTACACCATAGTAGGGGCCTAATATATTCGATGCGCTAGCGTTGGTGCACATTCGTTGCAGTGCGTTTAAGCCGTGTCACCATTGATATCCGCCACTGGTGTCCCGGATGGCGCCGCTCGTCGGATAACACGGTTGGCGGAGCATGCTATGGATAGGAGAGTCACTGATGACGGGGTTGATCCAAGACGCATCTGGGGCATCCAAGACTGGCTCAGTTAACAGAACCCAGTTTTGCATTTCGAGGGCATAGGGGGGGTGTTTAAGTGTTCAAATCCGGTCCTAACCTGCCCGGCGACGCCCGCGGTTACGTTTAGATACTGGGCTAAGGTGTAGGCACTTGGCGGGAATCGTTCGCTTTGGATAATGC' hd = HammingDistance(a,b) print(hd)

145.588235

1,120

0.954747

44

2,475

53.704545

0.727273

0.013542

0.014388

0

0.006639

0.026263

2,475

17

1,121

145.588235

0.973859

0.031111

0

0.931828

0

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1

0.111111

false

0

0.222222

0.111111

0

1

null

0

1

0

1

null

1

0

6

4fe90fd39ab16cf658a1e95ce2fd8ba7ae601e59

234

py

Python

EventGAN/pytorch_utils/__init__.py

GEN418/EventGAN

372318bc8f285f513db4babf7786b5c04e97c86d

[ "MIT" ]

38

2019-12-19T10:01:49.000Z

2022-03-24T07:58:53.000Z

EventGAN/pytorch_utils/__init__.py

GEN418/EventGAN

372318bc8f285f513db4babf7786b5c04e97c86d

[ "MIT" ]

5

2020-01-16T06:26:29.000Z

2021-05-24T00:07:58.000Z

EventGAN/pytorch_utils/__init__.py

GEN418/EventGAN

372318bc8f285f513db4babf7786b5c04e97c86d

[ "MIT" ]

9

2020-01-22T03:31:50.000Z

2021-03-25T13:18:06.000Z

from .saver import CheckpointSaver from .data_loader import CheckpointDataLoader, CheckpointSampler from .data_loader import RandomSampler, SequentialSampler from .base_trainer import BaseTrainer from .base_options import BaseOptions

39

64

0.876068

26

234

7.730769

0.576923

0.079602

0.139303

0.199005

0

0.094017

234

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0.948113

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true

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null

0

1

0

1

0

1

0

6

4fec8e795f52f2953ba5bd06b9b3885773972575

29

py

Python

expvarpca/__init__.py

ddfabbro/extended-pca

ed340fbbf0d9443fe51847b0b6fcb9694e110a33

[ "MIT" ]

null

expvarpca/__init__.py

ddfabbro/extended-pca

ed340fbbf0d9443fe51847b0b6fcb9694e110a33

[ "MIT" ]

null

expvarpca/__init__.py

ddfabbro/extended-pca

ed340fbbf0d9443fe51847b0b6fcb9694e110a33

[ "MIT" ]

null

from expvarpca.pca import PCA

29

0.862069

5

29

5

0.8

0

0.103448

29

1

29

0.961538

0

1

0

true

0

1

0

1

0

1

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null

0

1

0

null

0

1

0

1

0

1

0

6

8b1abffb62a5c1e7dcd552a195e02d78fed77c43

101

py

Python

survol/sources_types/CIM_DiskDrive/__init__.py

rchateauneu/survol

ba66d3ec453b2d9dd3a8dabc6d53f71aa9ba8c78

[ "BSD-3-Clause" ]

9

2017-10-05T23:36:23.000Z

2021-08-09T15:40:03.000Z

survol/sources_types/CIM_DiskDrive/__init__.py

rchateauneu/survol

ba66d3ec453b2d9dd3a8dabc6d53f71aa9ba8c78

[ "BSD-3-Clause" ]

21

2018-01-02T09:33:03.000Z

2018-08-27T11:09:52.000Z

survol/sources_types/CIM_DiskDrive/__init__.py

rchateauneu/survol

ba66d3ec453b2d9dd3a8dabc6d53f71aa9ba8c78

[ "BSD-3-Clause" ]

4

2018-06-23T09:05:45.000Z

2021-01-22T15:36:50.000Z

""" Standard disk mounted on a CIM_ComputerSystem. """ def EntityOntology(): return (["Name"],)

14.428571

46

0.663366

11

101

6

1

0

0.168317

101

6

47

16.833333

0.785714

0.455446

0

0.085106

0

1

0.5

true

0

0.5

1

0

1

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null

0

1

0

null

0

1

0

1

0

6

8cc0b6f8f98220c14d241402a2032d3a1f4850f4

2,520

py

Python

python/anyascii/_data/_1f6.py

casept/anyascii

d4f426b91751254b68eaa84c6cd23099edd668e6

[ "ISC" ]

null

python/anyascii/_data/_1f6.py

casept/anyascii

d4f426b91751254b68eaa84c6cd23099edd668e6

[ "ISC" ]

null

python/anyascii/_data/_1f6.py

casept/anyascii

d4f426b91751254b68eaa84c6cd23099edd668e6

[ "ISC" ]

null

b=':grinning: :grin: :joy: :smiley: :smile: :sweat_smile: :laughing: :innocent: :smiling_imp: :wink: :blush: :yum: :relieved: :heart_eyes: :sunglasses: :smirk: :neutral_face: :expressionless: :unamused: :sweat: :pensive: :confused: :confounded: :kissing: :kissing_heart: :kissing_smiling_eyes: :kissing_closed_eyes: :stuck_out_tongue: :stuck_out_tongue_winking_eye: :stuck_out_tongue_closed_eyes: :disappointed: :worried: :angry: :rage: :cry: :persevere: :triumph: :disappointed_relieved: :frowning: :anguished: :fearful: :weary: :sleepy: :tired_face: :grimacing: :sob: :open_mouth: :hushed: :cold_sweat: :scream: :astonished: :flushed: :sleeping: :dizzy_face: :no_mouth: :mask: :smile_cat: :joy_cat: :smiley_cat: :heart_eyes_cat: :smirk_cat: :kissing_cat: :pouting_cat: :crying_cat_face: :scream_cat: :slight_frown: :slight_smile: :upside_down: :rolling_eyes: :person_gesturing_no: :person_gesturing_ok: :person_bowing: :see_no_evil: :hear_no_evil: :speak_no_evil: :person_raising_hand: :raised_hands: :person_frowning: :person_pouting: :pray: * * * * * * * * * * * * * * * * * * * * * * * * # # # # - | - | & & & & & & " " " !? !? !? / \\ / \\ :rocket: :helicopter: :steam_locomotive: :railway_car: :bullettrain_side: :bullettrain_front: :train2: :metro: :light_rail: :station: :tram: :train: :bus: :oncoming_bus: :trolleybus: :busstop: :minibus: :ambulance: :fire_engine: :police_car: :oncoming_police_car: :taxi: :oncoming_taxi: :red_car: :oncoming_automobile: :blue_car: :truck: :articulated_lorry: :tractor: :monorail: :mountain_railway: :suspension_railway: :mountain_cableway: :aerial_tramway: :ship: :person_rowing_boat: :speedboat: :traffic_light: :vertical_traffic_light: :construction: :rotating_light: :triangular_flag_on_post: :door: :no_entry_sign: :smoking: :no_smoking: :put_litter_in_its_place: :do_not_litter: :potable_water: :non_potable_water: :bike: :no_bicycles: :person_biking: :person_mountain_biking: :person_walking: :no_pedestrians: :children_crossing: :mens: :womens: :restroom: :baby_symbol: :toilet: :wc: :shower: :bath: :bathtub: :passport_control: :customs: :baggage_claim: :left_luggage: :couch: :sleeping_accommodation: :shopping_bags: :bellhop: :bed: :place_of_worship: :octagonal_sign: :shopping_cart: :hindu_temple: :tools: :shield: :oil: :motorway: :railway_track: :motorboat: :airplane_small: :airplane_departure: :airplane_arriving: :satellite_orbital: :cruise_ship: :scooter: :motor_scooter: :canoe: :sled: :flying_saucer: :skateboard: :auto_rickshaw:'

2,520

0.738095

301

2,520

5.770764

0.674419

0.013817

0.02418

0

0.000441

0.099206

2,520

1

2,520

0.764758

0

1

0.998017

0.140024

0

1

0

false

1

0

null

0

1

0

1

0

null

0

1

0

6

506d6929ec2de615400c55b1d76964ed02608d5d

177

py

Python

src/integration-tests/expecteds/py/json-schema.py

json-schema-tools/transpiler

28f21d327b299d6306706526cad45ca51e729f86

[ "Apache-2.0" ]

5

2020-10-26T23:32:25.000Z

2022-01-20T17:02:18.000Z

src/integration-tests/expecteds/py/json-schema.py

json-schema-tools/transpiler

28f21d327b299d6306706526cad45ca51e729f86

[ "Apache-2.0" ]

525

2020-04-20T08:58:52.000Z

2022-03-26T02:26:13.000Z

src/integration-tests/expecteds/py/json-schema.py

json-schema-tools/transpiler

28f21d327b299d6306706526cad45ca51e729f86

[ "Apache-2.0" ]

6

2020-10-26T23:32:45.000Z

2021-12-29T18:03:03.000Z

from typing import NewType from typing import Union from typing import TypedDict from typing import Optional class B(TypedDict): a: Optional[A] A = NewType("A", Union[B])

17.7

28

0.751412

27

177

4.925926

0.37037

0.300752

0.481203

0

0.169492

177

9

29

19.666667

0.904762

0

0.00565

0

1

0

false

0

0.571429

0

0.857143

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

508105c81ccc6bc80d25259fab2c34106c397fcf

83

py

Python

code/answer_4-2-23.py

KoyanagiHitoshi/AtCoder-Python-Introduction

6d014e333a873f545b4d32d438e57cf428b10b96

[ "MIT" ]

1

2022-03-29T13:50:12.000Z

code/answer_4-2-23.py

KoyanagiHitoshi/AtCoder-Python-Introduction

6d014e333a873f545b4d32d438e57cf428b10b96

[ "MIT" ]

null

code/answer_4-2-23.py

KoyanagiHitoshi/AtCoder-Python-Introduction

6d014e333a873f545b4d32d438e57cf428b10b96

[ "MIT" ]

null

N = input() print("Yes" if N[0] == N[1] == N[2] or N[1] == N[2] == N[3] else "No")

27.666667

70

0.433735

20

83

1.8

0.6

0.111111

0.166667

0.222222

0

0.092308

0.216867

83

2

71

41.5

0.461538

0

0.060241

0

1

0

false

0

0.5

1

0

1

null

0

1

0

1

0

1

0

null

0

1

0

6

50870279e8b9e4234ef4c89cbaa4eaae5c46e26c

34

py

Python

wickes_tools/cal4/__init__.py

1wickes/wickes-tools

ab8135c80183c2a3958cc84cf1a4a2edb3688c7b

[ "MIT" ]

null

wickes_tools/cal4/__init__.py

1wickes/wickes-tools

ab8135c80183c2a3958cc84cf1a4a2edb3688c7b

[ "MIT" ]

null

wickes_tools/cal4/__init__.py

1wickes/wickes-tools

ab8135c80183c2a3958cc84cf1a4a2edb3688c7b

[ "MIT" ]

null

from wickes_tools.cal4 import cal5

34

0.882353

6

34

4.833333

1

0

0.064516

0.088235

34

1

34

0.870968

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

509b5ac96ced1592f4e212201d0e6bd0700eee51

85

py

Python

mctsmol/__init__.py

lantunes/mctsmol

ba94706f7826b8ed3a2a52bbc88d7759d3bb78e3

[ "MIT" ]

1

2021-10-30T07:14:36.000Z

mctsmol/__init__.py

lantunes/mctsmol

ba94706f7826b8ed3a2a52bbc88d7759d3bb78e3

[ "MIT" ]

null

mctsmol/__init__.py

lantunes/mctsmol

ba94706f7826b8ed3a2a52bbc88d7759d3bb78e3

[ "MIT" ]

null

from .functions import * from .torsional_mcts import * from .molecular_mcts import *

21.25

29

0.788235

11

85

5.909091

0.545455

0.307692

0

0.141176

85

3

30

28.333333

0.890411

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

50f94cad6f5d2e05fad5bce0745822be11bb7e0f

67

py

Python

src/Problem0010.py

rrohrer/ProjectEuler

cb8bcce24a8c3ea4e539ac22c8fe0486c2f3554b

[ "MIT" ]

null

src/Problem0010.py

rrohrer/ProjectEuler

cb8bcce24a8c3ea4e539ac22c8fe0486c2f3554b

[ "MIT" ]

null

src/Problem0010.py

rrohrer/ProjectEuler

cb8bcce24a8c3ea4e539ac22c8fe0486c2f3554b

[ "MIT" ]

null

import prime_utils print sum(prime_utils.primes_below(2000000))

11.166667

44

0.820896

10

67

5.2

0.8

0.384615

0

0.116667

0.104478

67

5

45

13.4

0.75

0

null

0

0.5

null

0.5

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

50fd834366a57aedde05bd2c490a468359820dd5

5,668

py

Python

post_stats/blink_data_creator.py

sajastu/transformers-sent-curr

6dc41545c4ac298a010090fbca4b454c2eaf3dbb

[ "Apache-2.0" ]

null

post_stats/blink_data_creator.py

sajastu/transformers-sent-curr

6dc41545c4ac298a010090fbca4b454c2eaf3dbb

[ "Apache-2.0" ]

null

post_stats/blink_data_creator.py

sajastu/transformers-sent-curr

6dc41545c4ac298a010090fbca4b454c2eaf3dbb

[ "Apache-2.0" ]

null

# import glob # import json # import os # import re # # from somajo import SoMaJo # import spacy # # nlp = spacy.load("en_core_web_lg") # nlp.disable_pipe("parser") # nlp.enable_pipe("senter") # tokenizer = SoMaJo("en_PTB") # # # def sentencizer(text): # sents = [] # doc = nlp(text) # sent_lst = doc.sents # for sent in sent_lst: # sents.append(sent.text) # return sents # # # cases = [] # src_tkns = [] # iter = 0 # # for f in glob.glob("blink/*.txt"): # str = '' # token_counter = 0 # size = os.path.getsize(f) # # with open(f) as fR: # # for line_num, l in enumerate(fR): # size -= len(l) # if len(l.strip()) > 0 : # str += l.strip() # summary_sents = sentencizer(str) # src_sentences_tkns = tokenizer.tokenize_text(summary_sents) # token_count = sum(sum(1 for t in s) for s in src_sentences_tkns) # # # if token_count > 350 or l.strip() == fR.readlines()[-1].strip(): # if not size: # print('yohoo last line') # if token_count > 300 or not size: # src_sentences_tkns = tokenizer.tokenize_text(summary_sents) # # # should store # for j, sentence in enumerate(src_sentences_tkns): # sent_tkns = [] # for token in sentence: # sent_tkns.append(token.text.lower()) # src_tkns.append(sent_tkns) # # ent = { # 'id': f + f'-{iter}', # 'document': '</s><s> '.join([' '.join(s) for s in src_tkns]), # 'summary': 'This is the gold', # 'ext_labels': [0 for s in range(len(src_tkns))], # 'rg_labels': [0 for s in range(len(src_tkns))] # } # cases.append(ent) # counter = 0 # iter += 1 # src_tkns = [] # str = '' # # else: # str += ' ' # src_tkns = [] # # continue # # # # summary_sents = sentencizer(str) # # src_sentences_tkns = tokenizer.tokenize_text(summary_sents) # # sent_num = sum(1 for _ in src_sentences_tkns) # # src_sentences_tkns = tokenizer.tokenize_text(summary_sents) # # # if not os.path.exists('../blink_test_segmented/'): # os.makedirs('../blink_test_segmented/') # # with open('../blink_test_segmented/test_300.json', mode='w') as fW: # for e in cases: # json.dump(e, fW) # fW.write('\n') # # # ############################################################################################## import glob import json import os import re from somajo import SoMaJo import spacy nlp = spacy.load("en_core_web_lg") nlp.disable_pipe("parser") nlp.enable_pipe("senter") tokenizer = SoMaJo("en_PTB") def sentencizer(text): sents = [] doc = nlp(text) sent_lst = doc.sents for sent in sent_lst: sents.append(sent.text) return sents bart_cases = [] cases = [] src_tkns = [] iter = 0 for f in glob.glob("blink/*.txt"): str = '' token_counter = 0 size = os.path.getsize(f) with open(f) as fR: for line_num, l in enumerate(fR): size -= len(l) if len(l.strip()) > 0 : str = l.strip() summary_sents = sentencizer(str) src_sentences_tkns = tokenizer.tokenize_text(summary_sents) token_count = sum(sum(1 for t in s) for s in src_sentences_tkns) if token_count > 30: src_sentences_tkns = tokenizer.tokenize_text(summary_sents) # should store for j, sentence in enumerate(src_sentences_tkns): sent_tkns = [] for token in sentence: sent_tkns.append(token.text.lower()) src_tkns.append(sent_tkns) ent = { 'id': f + f'-{iter}', 'document': '</s><s> '.join([' '.join(s) for s in src_tkns]), 'summary': 'This is the gold', 'ext_labels': [0 for s in range(len(src_tkns))], 'rg_labels': [0 for s in range(len(src_tkns))] } bart_cases.append(ent) ent = { 'id': f + f'-{iter}', 'document': '</s><s> '.join([' '.join(s) for s in src_tkns]), 'summary': 'This is the gold', 'ext_labels': [0 for s in range(len(src_tkns))], 'rg_labels': [0 for s in range(len(src_tkns))] } cases.append(ent) src_tkns = [] iter += 1 # summary_sents = sentencizer(str) # src_sentences_tkns = tokenizer.tokenize_text(summary_sents) # sent_num = sum(1 for _ in src_sentences_tkns) # src_sentences_tkns = tokenizer.tokenize_text(summary_sents) if not os.path.exists('../blink_test_segmented/'): os.makedirs('../blink_test_segmented/') with open('../blink_test_segmented/test_30_m1_bart.json', mode='w') as fW: for e in bart_cases: json.dump(e, fW) fW.write('\n') with open('../blink_test_segmented/test_30_m1_all.json', mode='w') as fW: for e in cases: json.dump(e, fW) fW.write('\n')

30.31016

94

0.486062

660

5,668

3.978788

0.162121

0.04265

0.085301

0.076161

0.931835

0.90099

0

0.010373

0.370677

5,668

186

95

30.473118

0.72582

0.489238

0

0.294118

0

0.118738

0.050093

0

1

0.014706

false

0

0.088235

0

0.117647

0

null

0

1

0

null

0

6

0faae4e385ef89555db20b9358db7ecb166d2f96

12,482

py

Python

tests/test_tasks_move_cube.py

prstolpe/rrc_simulation

b430fe4e575641cdd64945cf57d0dd67a0eea17a

[ "BSD-3-Clause" ]

null

tests/test_tasks_move_cube.py

prstolpe/rrc_simulation

b430fe4e575641cdd64945cf57d0dd67a0eea17a

[ "BSD-3-Clause" ]

null

tests/test_tasks_move_cube.py

prstolpe/rrc_simulation

b430fe4e575641cdd64945cf57d0dd67a0eea17a

[ "BSD-3-Clause" ]

null

#!/usr/bin/env python3 import unittest import numpy as np from scipy.spatial.transform import Rotation from rrc_simulation.tasks import move_cube class TestMoveCube(unittest.TestCase): """Test the functions of the "move cube" tasks module.""" def test_get_cube_corner_positions(self): # cube half width chw = move_cube._CUBE_WIDTH / 2 # no transformation expected_origin_corners = np.array( [ [-chw, -chw, -chw], [-chw, -chw, +chw], [-chw, +chw, -chw], [-chw, +chw, +chw], [+chw, -chw, -chw], [+chw, -chw, +chw], [+chw, +chw, -chw], [+chw, +chw, +chw], ] ) origin = move_cube.Pose() origin_corners = move_cube.get_cube_corner_positions(origin) np.testing.assert_array_almost_equal( expected_origin_corners, origin_corners ) # only translation expected_translated_corners = np.array( [ [-chw + 1, -chw + 2, -chw + 3], [-chw + 1, -chw + 2, +chw + 3], [-chw + 1, +chw + 2, -chw + 3], [-chw + 1, +chw + 2, +chw + 3], [+chw + 1, -chw + 2, -chw + 3], [+chw + 1, -chw + 2, +chw + 3], [+chw + 1, +chw + 2, -chw + 3], [+chw + 1, +chw + 2, +chw + 3], ] ) translated = move_cube.get_cube_corner_positions( move_cube.Pose([1, 2, 3], [0, 0, 0, 1]) ) np.testing.assert_array_almost_equal( expected_translated_corners, translated ) # only rotation rot_z90 = Rotation.from_euler("z", 90, degrees=True).as_quat() expected_rotated_corners = np.array( [ [+chw, -chw, -chw], [+chw, -chw, +chw], [-chw, -chw, -chw], [-chw, -chw, +chw], [+chw, +chw, -chw], [+chw, +chw, +chw], [-chw, +chw, -chw], [-chw, +chw, +chw], ] ) rotated = move_cube.get_cube_corner_positions( move_cube.Pose([0, 0, 0], rot_z90) ) np.testing.assert_array_almost_equal(expected_rotated_corners, rotated) # both rotation and translation expected_both_corners = np.array( [ [+chw + 1, -chw + 2, -chw + 3], [+chw + 1, -chw + 2, +chw + 3], [-chw + 1, -chw + 2, -chw + 3], [-chw + 1, -chw + 2, +chw + 3], [+chw + 1, +chw + 2, -chw + 3], [+chw + 1, +chw + 2, +chw + 3], [-chw + 1, +chw + 2, -chw + 3], [-chw + 1, +chw + 2, +chw + 3], ] ) both = move_cube.get_cube_corner_positions( move_cube.Pose([1, 2, 3], rot_z90) ) np.testing.assert_array_almost_equal(expected_both_corners, both) def test_sample_goal_difficulty_1_no_initial_pose(self): for i in range(1000): goal = move_cube.sample_goal(difficulty=1) # verify the goal is valid (i.e. within the allowed ranges) try: move_cube.validate_goal(goal) except move_cube.InvalidGoalError as e: self.fail( msg="Invalid goal: {} pose is {}, {}".format( e, e.position, e.orientation ), ) # verify the goal satisfies conditions of difficulty 1 # always on ground self.assertEqual(goal.position[2], move_cube._CUBE_WIDTH / 2) # no orientation np.testing.assert_array_equal(goal.orientation, [0, 0, 0, 1]) def test_sample_goal_difficulty_2_no_initial_pose(self): for i in range(1000): goal = move_cube.sample_goal(difficulty=2) # verify the goal is valid (i.e. within the allowed ranges) try: move_cube.validate_goal(goal) except move_cube.InvalidGoalError as e: self.fail( msg="Invalid goal: {} pose is {}, {}".format( e, e.position, e.orientation ), ) # verify the goal satisfies conditions of difficulty 2 self.assertLessEqual(goal.position[2], move_cube._max_height) self.assertGreaterEqual(goal.position[2], move_cube._min_height) # no orientation np.testing.assert_array_equal(goal.orientation, [0, 0, 0, 1]) def test_sample_goal_difficulty_3_no_initial_pose(self): for i in range(1000): goal = move_cube.sample_goal(difficulty=3) # verify the goal is valid (i.e. within the allowed ranges) try: move_cube.validate_goal(goal) except move_cube.InvalidGoalError as e: self.fail( msg="Invalid goal: {} pose is {}, {}".format( e, e.position, e.orientation ), ) # verify the goal satisfies conditions of difficulty 2 self.assertLessEqual(goal.position[2], move_cube._max_height) self.assertGreaterEqual(goal.position[2], move_cube._min_height) # no orientation np.testing.assert_array_equal(goal.orientation, [0, 0, 0, 1]) def test_sample_goal_difficulty_4_no_initial_pose(self): for i in range(1000): goal = move_cube.sample_goal(difficulty=4) # verify the goal is valid (i.e. within the allowed ranges) try: move_cube.validate_goal(goal) except move_cube.InvalidGoalError as e: self.fail( msg="Invalid goal: {} pose is {}, {}".format( e, e.position, e.orientation ), ) # verify the goal satisfies conditions of difficulty 2 self.assertLessEqual(goal.position[2], move_cube._max_height) self.assertGreaterEqual(goal.position[2], move_cube._min_height) def test_evaluate_state_difficulty_1(self): difficulty = 1 pose_origin = move_cube.Pose() pose_trans = move_cube.Pose(position=[1, 2, 3]) pose_rot = move_cube.Pose( orientation=Rotation.from_euler("z", 0.42).as_quat() ) pose_both = move_cube.Pose( [1, 2, 3], Rotation.from_euler("z", 0.42).as_quat() ) # needs to be zero for exact match cost = move_cube.evaluate_state(pose_origin, pose_origin, difficulty) self.assertEqual(cost, 0) # None-zero if there is translation, rotation is ignored self.assertNotEqual( move_cube.evaluate_state(pose_origin, pose_trans, difficulty), 0 ) self.assertEqual( move_cube.evaluate_state(pose_origin, pose_rot, difficulty), 0 ) self.assertNotEqual( move_cube.evaluate_state(pose_origin, pose_both, difficulty), 0 ) def test_evaluate_state_difficulty_2(self): difficulty = 2 pose_origin = move_cube.Pose() pose_trans = move_cube.Pose(position=[1, 2, 3]) pose_rot = move_cube.Pose( orientation=Rotation.from_euler("z", 0.42).as_quat() ) pose_both = move_cube.Pose( [1, 2, 3], Rotation.from_euler("z", 0.42).as_quat() ) # needs to be zero for exact match cost = move_cube.evaluate_state(pose_origin, pose_origin, difficulty) self.assertEqual(cost, 0) # None-zero if there is translation, rotation is ignored self.assertNotEqual( move_cube.evaluate_state(pose_origin, pose_trans, difficulty), 0 ) self.assertEqual( move_cube.evaluate_state(pose_origin, pose_rot, difficulty), 0 ) self.assertNotEqual( move_cube.evaluate_state(pose_origin, pose_both, difficulty), 0 ) def test_evaluate_state_difficulty_3(self): difficulty = 3 pose_origin = move_cube.Pose() pose_trans = move_cube.Pose(position=[1, 2, 3]) pose_rot = move_cube.Pose( orientation=Rotation.from_euler("z", 0.42).as_quat() ) pose_both = move_cube.Pose( [1, 2, 3], Rotation.from_euler("z", 0.42).as_quat() ) # needs to be zero for exact match cost = move_cube.evaluate_state(pose_origin, pose_origin, difficulty) self.assertEqual(cost, 0) # None-zero if there is translation, rotation is ignored self.assertNotEqual( move_cube.evaluate_state(pose_origin, pose_trans, difficulty), 0 ) self.assertEqual( move_cube.evaluate_state(pose_origin, pose_rot, difficulty), 0 ) self.assertNotEqual( move_cube.evaluate_state(pose_origin, pose_both, difficulty), 0 ) def test_evaluate_state_difficulty_4(self): difficulty = 4 pose_origin = move_cube.Pose() pose_trans = move_cube.Pose(position=[1, 2, 3]) pose_rot = move_cube.Pose( orientation=Rotation.from_euler("z", 0.42).as_quat() ) pose_both = move_cube.Pose( [1, 2, 3], Rotation.from_euler("z", 0.42).as_quat() ) # needs to be zero for exact match cost = move_cube.evaluate_state(pose_origin, pose_origin, difficulty) self.assertEqual(cost, 0) # None-zero if there is translation, rotation or both self.assertNotEqual( move_cube.evaluate_state(pose_origin, pose_trans, difficulty), 0 ) self.assertNotEqual( move_cube.evaluate_state(pose_origin, pose_rot, difficulty), 0 ) self.assertNotEqual( move_cube.evaluate_state(pose_origin, pose_both, difficulty), 0 ) def test_validate_goal(self): half_width = move_cube._CUBE_WIDTH / 2 yaw_rotation = Rotation.from_euler("z", 0.42).as_quat() full_rotation = Rotation.from_euler("zxz", [0.42, 0.1, -2.3]).as_quat() # test some valid goals try: move_cube.validate_goal( move_cube.Pose([0, 0, half_width], [0, 0, 0, 1]) ) except Exception as e: self.fail("Valid goal was considered invalid because %s" % e) try: move_cube.validate_goal( move_cube.Pose([0.05, -0.1, half_width], yaw_rotation) ) except Exception as e: self.fail("Valid goal was considered invalid because %s" % e) try: move_cube.validate_goal( move_cube.Pose([-0.12, 0.0, 0.06], full_rotation) ) except Exception as e: self.fail("Valid goal was considered invalid because %s" % e) # test some invalid goals # invalid values with self.assertRaises(ValueError): move_cube.validate_goal(move_cube.Pose([0, 0], [0, 0, 0, 1])) with self.assertRaises(ValueError): move_cube.validate_goal(move_cube.Pose([0, 0, 0], [0, 0, 1])) # invalid positions with self.assertRaises(move_cube.InvalidGoalError): move_cube.validate_goal( move_cube.Pose([0.3, 0, half_width], [0, 0, 0, 1]) ) with self.assertRaises(move_cube.InvalidGoalError): move_cube.validate_goal( move_cube.Pose([0, -0.3, half_width], [0, 0, 0, 1]) ) with self.assertRaises(move_cube.InvalidGoalError): move_cube.validate_goal(move_cube.Pose([0, 0, 0.3], [0, 0, 0, 1])) with self.assertRaises(move_cube.InvalidGoalError): move_cube.validate_goal(move_cube.Pose([0, 0, 0], [0, 0, 0, 1])) with self.assertRaises(move_cube.InvalidGoalError): move_cube.validate_goal( move_cube.Pose([0, 0, -0.01], [0, 0, 0, 1]) ) # valid CoM position but rotation makes it reach out of valid range with self.assertRaises(move_cube.InvalidGoalError): move_cube.validate_goal( move_cube.Pose([0, 0, half_width], full_rotation) ) if __name__ == "__main__": unittest.main()

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null

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0fe2d4059d7e19aa4a18dcce09d1d2fb973f8e82

157

py

Python

pycal/bisection.py

JanMaier97/PyCal

e57050187b246caf1cece36950c654e18df25063

[ "MIT" ]

1

2018-05-18T08:35:49.000Z

pycal/bisection.py

JanMaier97/PyCal

e57050187b246caf1cece36950c654e18df25063

[ "MIT" ]

null

pycal/bisection.py

JanMaier97/PyCal

e57050187b246caf1cece36950c654e18df25063

[ "MIT" ]

2

2018-05-17T20:46:02.000Z

2018-05-18T08:28:30.000Z

from calculator import Calculator def compute_zero(func, left, right): cal = calculator(func) def assert_grenzwertsatz(func, left, right): return

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0ff58aa5638eed1c736fbbad2273b0906b11a83c

217

py

Python

frappe/website/doctype/blog_post/test_blog_post.py

khatrijitendra/lumalock-frappe

b3864278dad21dde5c53604be65aa56c79e5d909

[ "MIT" ]

null

frappe/website/doctype/blog_post/test_blog_post.py

khatrijitendra/lumalock-frappe

b3864278dad21dde5c53604be65aa56c79e5d909

[ "MIT" ]

7

2020-03-24T17:07:47.000Z

2022-03-11T23:49:25.000Z

frappe/website/doctype/blog_post/test_blog_post.py

khatrijitendra/lumalock-frappe

b3864278dad21dde5c53604be65aa56c79e5d909

[ "MIT" ]

5

2016-11-12T12:14:58.000Z

2018-03-21T15:45:45.000Z

# Copyright (c) 2015, Frappe Technologies Pvt. Ltd. and Contributors # MIT License. See license.txt from __future__ import unicode_literals import frappe import unittest class TestBlogPost(unittest.TestCase): pass

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null

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6

ba178862dd3191b78173226363d918dea6ec7c92

11,005

py

Python

lego/apps/companies/tests/test_companies_api.py

HoboKristian/lego

2729dcef770ad1105f53e087c07ece3f9e9dbc67

[ "MIT" ]

null

lego/apps/companies/tests/test_companies_api.py

HoboKristian/lego

2729dcef770ad1105f53e087c07ece3f9e9dbc67

[ "MIT" ]

2

2021-02-02T23:09:32.000Z

2021-06-10T23:43:39.000Z

lego/apps/companies/tests/test_companies_api.py

HoboKristian/lego

2729dcef770ad1105f53e087c07ece3f9e9dbc67

[ "MIT" ]

null

from django.urls import reverse from lego.apps.users.models import AbakusGroup, User from lego.utils.test_utils import BaseAPITestCase _test_company_data = [{"name": "TEST"}, {"name": "TEST2"}] _test_semester_status_data = [ {"semester": 2, "company": 1, "contactedStatus": ["interested"]} ] _test_company_contact_data = [ { "name": "Test", "role": "Test", "mail": "test@test.no", "phone": "12345678", "company": 1, } ] def _get_list_url(): return reverse("api:v1:bdb-list") def _get_detail_url(pk): return reverse("api:v1:bdb-detail", kwargs={"pk": pk}) def _get_semester_status_list_url(company_pk): return reverse("api:v1:semester-status-list", kwargs={"company_pk": company_pk}) def _get_semester_status_detail_url(company_pk, pk): return reverse( "api:v1:semester-status-detail", kwargs={"company_pk": company_pk, "pk": pk} ) def _get_company_contacts_list_url(company_pk): return reverse("api:v1:company-contact-list", kwargs={"company_pk": company_pk}) def _get_company_contacts_detail_url(company_pk, pk): return reverse( "api:v1:company-contact-detail", kwargs={"company_pk": company_pk, "pk": pk} ) class ListCompaniesTestCase(BaseAPITestCase): fixtures = ["test_abakus_groups.yaml", "test_companies.yaml", "test_users.yaml"] def setUp(self): self.abakus_user = User.objects.all().first() def test_with_abakus_user(self): AbakusGroup.objects.get(name="Abakus").add_user(self.abakus_user) self.client.force_authenticate(self.abakus_user) company_response = self.client.get(_get_list_url()) self.assertEqual(company_response.status_code, 403) def test_with_bedkom_user(self): AbakusGroup.objects.get(name="Bedkom").add_user(self.abakus_user) self.client.force_authenticate(self.abakus_user) company_response = self.client.get(_get_list_url()) self.assertEqual(company_response.status_code, 200) self.assertEqual(len(company_response.data["results"]), 3) class RetrieveCompaniesTestCase(BaseAPITestCase): fixtures = ["test_abakus_groups.yaml", "test_companies.yaml", "test_users.yaml"] def setUp(self): self.abakus_user = User.objects.all().first() def test_with_abakus_user(self): AbakusGroup.objects.get(name="Abakus").add_user(self.abakus_user) self.client.force_authenticate(self.abakus_user) company_response = self.client.get(_get_detail_url(1)) self.assertEqual(company_response.status_code, 403) def test_with_bedkom_user(self): AbakusGroup.objects.get(name="Bedkom").add_user(self.abakus_user) self.client.force_authenticate(self.abakus_user) company_response = self.client.get(_get_detail_url(1)) self.assertEqual(company_response.status_code, 200) class CreateCompaniesTestCase(BaseAPITestCase): fixtures = ["test_abakus_groups.yaml", "test_companies.yaml", "test_users.yaml"] def setUp(self): self.abakus_user = User.objects.all().first() def test_company_creation_with_abakus_user(self): AbakusGroup.objects.get(name="Abakus").add_user(self.abakus_user) self.client.force_authenticate(self.abakus_user) company_response = self.client.post(_get_list_url(), _test_company_data[0]) self.assertEqual(company_response.status_code, 403) def test_company_creation_with_bedkom_user(self): AbakusGroup.objects.get(name="Bedkom").add_user(self.abakus_user) self.client.force_authenticate(self.abakus_user) company_response = self.client.post(_get_list_url(), _test_company_data[0]) self.assertEqual(company_response.status_code, 201) def test_company_update_with_abakus_user(self): AbakusGroup.objects.get(name="Abakus").add_user(self.abakus_user) self.client.force_authenticate(self.abakus_user) company_response = self.client.patch(_get_detail_url(1), _test_company_data[1]) self.assertEqual(company_response.status_code, 403) def test_company_update_with_bedkom_user(self): AbakusGroup.objects.get(name="Bedkom").add_user(self.abakus_user) self.client.force_authenticate(self.abakus_user) company_response = self.client.patch(_get_detail_url(1), _test_company_data[1]) self.assertEqual(company_response.status_code, 200) self.assertEqual(company_response.data["name"], _test_company_data[1]["name"]) class DeleteCompaniesTestCase(BaseAPITestCase): fixtures = ["test_abakus_groups.yaml", "test_companies.yaml", "test_users.yaml"] def setUp(self): self.abakus_user = User.objects.all().first() def test_company_delete_with_abakus_user(self): AbakusGroup.objects.get(name="Abakus").add_user(self.abakus_user) self.client.force_authenticate(self.abakus_user) delete_response = self.client.delete(_get_detail_url(1)) self.assertEqual(delete_response.status_code, 403) def test_company_delete_with_bedkom_user(self): AbakusGroup.objects.get(name="Bedkom").add_user(self.abakus_user) self.client.force_authenticate(self.abakus_user) delete_response = self.client.delete(_get_detail_url(1)) self.assertEqual(delete_response.status_code, 204) company_response = self.client.get(_get_detail_url(1)) self.assertEqual(company_response.status_code, 404) class CreateSemesterStatusTestCase(BaseAPITestCase): fixtures = ["test_abakus_groups.yaml", "test_companies.yaml", "test_users.yaml"] def setUp(self): self.abakus_user = User.objects.all().first() def test_semester_status_creation_with_abakus_user(self): AbakusGroup.objects.get(name="Abakus").add_user(self.abakus_user) self.client.force_authenticate(self.abakus_user) company_response = self.client.post( _get_semester_status_list_url(1), _test_semester_status_data[0] ) self.assertEqual(company_response.status_code, 403) def test_semester_status_creation_with_bedkom_user(self): AbakusGroup.objects.get(name="Bedkom").add_user(self.abakus_user) self.client.force_authenticate(self.abakus_user) response = self.client.post( _get_semester_status_list_url(1), _test_semester_status_data[0] ) self.assertEqual(response.status_code, 201) def test_semester_status_update_with_abakus_user(self): AbakusGroup.objects.get(name="Abakus").add_user(self.abakus_user) self.client.force_authenticate(self.abakus_user) response = self.client.patch( _get_semester_status_detail_url(1, 1), _test_semester_status_data[0] ) self.assertEqual(response.status_code, 403) def test_semester_status_update_with_bedkom_user(self): AbakusGroup.objects.get(name="Bedkom").add_user(self.abakus_user) self.client.force_authenticate(self.abakus_user) company_response = self.client.patch( _get_semester_status_detail_url(1, 1), _test_semester_status_data[0] ) self.assertEqual(company_response.status_code, 200) self.assertEqual( company_response.data["semester"], _test_semester_status_data[0]["semester"] ) class DeleteSemesterStatusTestCase(BaseAPITestCase): fixtures = ["test_abakus_groups.yaml", "test_companies.yaml", "test_users.yaml"] def setUp(self): self.abakus_user = User.objects.all().first() def test_semester_status_deletion_with_abakus_user(self): AbakusGroup.objects.get(name="Abakus").add_user(self.abakus_user) self.client.force_authenticate(self.abakus_user) response = self.client.delete(_get_semester_status_detail_url(1, 1)) self.assertEqual(response.status_code, 403) def test_semester_status_deletion_with_bedkom_user(self): AbakusGroup.objects.get(name="Bedkom").add_user(self.abakus_user) self.client.force_authenticate(self.abakus_user) response = self.client.delete(_get_semester_status_detail_url(1, 1)) self.assertEqual(response.status_code, 204) class CreateCompanyContactsTestCase(BaseAPITestCase): fixtures = ["test_abakus_groups.yaml", "test_companies.yaml", "test_users.yaml"] def setUp(self): self.abakus_user = User.objects.all().first() def test_company_contact_creation_with_abakus_user(self): AbakusGroup.objects.get(name="Abakus").add_user(self.abakus_user) self.client.force_authenticate(self.abakus_user) response = self.client.post( _get_company_contacts_list_url(1), _test_company_contact_data[0] ) self.assertEqual(response.status_code, 403) def test_company_contact_creation_with_bedkom_user(self): AbakusGroup.objects.get(name="Bedkom").add_user(self.abakus_user) self.client.force_authenticate(self.abakus_user) response = self.client.post( _get_company_contacts_list_url(1), _test_company_contact_data[0] ) self.assertEqual(response.status_code, 201) def test_company_contact_update_with_abakus_user(self): AbakusGroup.objects.get(name="Abakus").add_user(self.abakus_user) self.client.force_authenticate(self.abakus_user) response = self.client.patch( _get_company_contacts_detail_url(1, 1), _test_company_contact_data[0] ) self.assertEqual(response.status_code, 403) def test_company_contact_update_with_bedkom_user(self): AbakusGroup.objects.get(name="Bedkom").add_user(self.abakus_user) self.client.force_authenticate(self.abakus_user) company_response = self.client.patch( _get_company_contacts_detail_url(1, 1), _test_company_contact_data[0] ) self.assertEqual(company_response.status_code, 200) self.assertEqual( company_response.data["name"], _test_company_contact_data[0]["name"] ) class DeleteCompanyContacsTestCase(BaseAPITestCase): fixtures = ["test_abakus_groups.yaml", "test_companies.yaml", "test_users.yaml"] def setUp(self): self.abakus_user = User.objects.all().first() def test_company_contact_deletion_with_abakus_user(self): AbakusGroup.objects.get(name="Abakus").add_user(self.abakus_user) self.client.force_authenticate(self.abakus_user) response = self.client.delete(_get_company_contacts_detail_url(1, 1)) self.assertEqual(response.status_code, 403) def test_company_contact_deletion_with_bedkom_user(self): AbakusGroup.objects.get(name="Bedkom").add_user(self.abakus_user) self.client.force_authenticate(self.abakus_user) response = self.client.delete(_get_company_contacts_detail_url(1, 1)) self.assertEqual(response.status_code, 204) get_response = self.client.get(_get_company_contacts_detail_url(1, 1)) self.assertEqual(get_response.status_code, 404)

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0.060498

0.070541

0.097261

0.07642

0.911022

0.88644

0.883634

0.870675

0.841149

0.822712

0

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0.162562

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0.30198

0

null

0

1

0

null

0

6

e89001db6767a0773a371e5f76e0539d97f1d661

32

py

Python

nostr/cli.py

yuvadm/nostrpy

65db6c4c19862e93c3179b298774cbea9d216c5b

[ "MIT" ]

null

nostr/cli.py

yuvadm/nostrpy

65db6c4c19862e93c3179b298774cbea9d216c5b

[ "MIT" ]

1

2022-01-17T18:47:48.000Z

nostr/cli.py

yuvadm/nostrpy

65db6c4c19862e93c3179b298774cbea9d216c5b

[ "MIT" ]

null

def entry(): print("nostr")

10.666667

18

0.5625

4

32

4.5

1

0

0.21875

32

2

19

16

0.72

0

0.15625

0

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0.5

true

0

0.5

1

0

null

0

1

0

null

0

1

0

1

0

6

e8ad0bddec37cd7c51a78b653a8f2ca18e0d547e

101

py

Python

cemba_data/snm3C/__init__.py

jksr/cemba_data

c796c33a2fd262b2ef893df1951a90b8d0ba9289

[ "MIT" ]

4

2018-11-13T21:50:57.000Z

2020-11-25T18:42:57.000Z

cemba_data/snm3C/__init__.py

jksr/cemba_data

c796c33a2fd262b2ef893df1951a90b8d0ba9289

[ "MIT" ]

9

2020-10-25T01:58:07.000Z

2021-06-13T19:17:50.000Z

cemba_data/snm3C/__init__.py

jksr/cemba_data

c796c33a2fd262b2ef893df1951a90b8d0ba9289

[ "MIT" ]

3

2018-12-29T23:30:25.000Z

2020-10-14T18:00:03.000Z

from .prepare_impute import prepare_impute_dir from .prepare_dataset import prepare_dataset_commands

33.666667

53

0.90099

14

101

6.071429

0.5

0.258824

0

0.079208

101

2

54

50.5

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0

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true

0

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0

null

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0

1

0

1

0

6

e8d4c22b275221cc6e1a2f26808ab8008aed6f45

43

py

Python

riot_api/api/put/__init__.py

Alex-Weatherhead/riot_api

2d589f57cd46e0f7c54de29245078c730acd710f

[ "MIT" ]

null

riot_api/api/put/__init__.py

Alex-Weatherhead/riot_api

2d589f57cd46e0f7c54de29245078c730acd710f

[ "MIT" ]

null

riot_api/api/put/__init__.py

Alex-Weatherhead/riot_api

2d589f57cd46e0f7c54de29245078c730acd710f

[ "MIT" ]

null

from . import tournaments_v4 as tournaments

43

0.860465

6

43

6

0.833333

0

0.026316

0.116279

43

1

43

0.921053

0

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true

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1

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null

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null

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0

1

0

6

2ce2a4726b7acce407e04fa5f38eafa00db072c7

38

py

Python

python/imafly/imafly/__init__.py

willdickson/imafly

1152940a523f83c3e15e43eef0a698d3a23ca513

[ "MIT" ]

null

python/imafly/imafly/__init__.py

willdickson/imafly

1152940a523f83c3e15e43eef0a698d3a23ca513

[ "MIT" ]

null

python/imafly/imafly/__init__.py

willdickson/imafly

1152940a523f83c3e15e43eef0a698d3a23ca513

[ "MIT" ]

null

from .trial_runner import TrialRunner

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0

1

0

1

0

1

0

6

fa4b72eda7672cfa8948fc13b651b28ea0130fb9

21

py

Python

logbook.py

winksaville/cq-wing4

b87639601a807f82abb0228738e41f42b4fe9825

[ "MIT" ]

1

2020-05-21T11:53:24.000Z

logbook.py

winksaville/cq-wing4

b87639601a807f82abb0228738e41f42b4fe9825

[ "MIT" ]

2

2020-03-18T03:10:25.000Z

2021-07-14T22:15:34.000Z

audapter/command/info.py

borley1211/audapter

f1d389dd189cc31ad3ada8a17aee42a943075ebd

[ "MIT" ]

null

def info(): pass

7

11

0.52381

3

21

3.666667

1

0

0.333333

21

2

12

10.5

0.785714

0

1

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true

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0

1

0

null

0

1

0

null

0

1

0

6

fa5e2dbdd2f493e2b19767f84989f14c3c1c7a4b

211

py

Python

src/Exceptions/NoBranchSelected.py

flexiooss/flexio-flow

47491c7e5b49a02dc859028de0d486edc0014b26

[ "Apache-2.0" ]

null

src/Exceptions/NoBranchSelected.py

flexiooss/flexio-flow

47491c7e5b49a02dc859028de0d486edc0014b26

[ "Apache-2.0" ]

44

2019-04-05T06:08:15.000Z

2021-09-13T19:37:49.000Z

src/Exceptions/NoBranchSelected.py

flexiooss/flexio-flow

47491c7e5b49a02dc859028de0d486edc0014b26

[ "Apache-2.0" ]

null

class NoBranchSelected(Exception): def __init__(self, message: str = ''): self.message: str = message def __str__(self): return """ No git Branch Selected {0!s} """.format(self.message)

21.1

42

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211

5.04

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0

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211

9

43

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0

0.14218

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1

0.25

false

0

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0.5

0

1

0

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1

0

1

0

null

0

1

0

1

0

6

d744c4d42fb0478575697b13e4ff8058d38ccd17

28

py

Python

app/requests.py

kiira254/pitch-app

536058f590e024efcf62f728eec756157a3d5385

[ "Unlicense", "MIT" ]

null

app/requests.py

kiira254/pitch-app

536058f590e024efcf62f728eec756157a3d5385

[ "Unlicense", "MIT" ]

null

app/requests.py

kiira254/pitch-app

536058f590e024efcf62f728eec756157a3d5385

[ "Unlicense", "MIT" ]

null

import urllib.request,json

14

26

0.821429

4

28

5.75

1

0

0.107143

28

2

27

14

0.92

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

d78bd9080331113970e285d4de78adbd9ee3925b

155

py

Python

chopperhack19/sfr_history/__init__.py

ArgonneCPAC/bnlhack19

d399b2e200ec7dbd733c754b06c4bd368eb00e67

[ "BSD-3-Clause" ]

null

chopperhack19/sfr_history/__init__.py

ArgonneCPAC/bnlhack19

d399b2e200ec7dbd733c754b06c4bd368eb00e67

[ "BSD-3-Clause" ]

4

2019-09-23T18:56:16.000Z

2019-10-06T03:33:09.000Z

chopperhack19/sfr_history/__init__.py

ArgonneCPAC/bnlhack19

d399b2e200ec7dbd733c754b06c4bd368eb00e67

[ "BSD-3-Clause" ]

1

2019-09-25T19:13:30.000Z

""" """ from .halo_evolution import * from .main_sequence import * from .quenched_fraction import * from .mstar_at_z import * from .mean_sfr_vs_z import *

19.375

32

0.754839

23

155

4.73913

0.608696

0.366972

0

0.141935

155

7

33

22.142857

0.819549

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

ad70bdaca8932c9e3164f849906fb2dfc5e0b94b

159

py

Python

compiled/construct/valid_fail_range_bytes.py

smarek/ci_targets

c5edee7b0901fd8e7f75f85245ea4209b38e0cb3

[ "MIT" ]

4

2017-04-08T12:55:11.000Z

2020-12-05T21:09:31.000Z

compiled/construct/valid_fail_range_bytes.py

smarek/ci_targets

c5edee7b0901fd8e7f75f85245ea4209b38e0cb3

[ "MIT" ]

7

2018-04-23T01:30:33.000Z

2020-10-30T23:56:14.000Z

compiled/construct/valid_fail_range_bytes.py

smarek/ci_targets

c5edee7b0901fd8e7f75f85245ea4209b38e0cb3

[ "MIT" ]

6

2017-04-08T11:41:14.000Z

2020-10-30T22:47:31.000Z

from construct import * from construct.lib import * valid_fail_range_bytes = Struct( 'foo' / FixedSized(2, GreedyBytes), ) _schema = valid_fail_range_bytes

17.666667

36

0.773585

21

159

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0.241379

0.327586

0

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159

8

37

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null

1

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1

0

null

0

1

0

6

d127d0a8831ee0981ab9ae1c331180ef98bb9fc9

134

py

Python

bankroll/brokers/ibkr/__init__.py

bankroll-py/bankroll-broker-ibkr

e603c4d419d985ab8bc0fae9256919fa76b04de0

[ "MIT" ]

3

2019-10-08T18:18:54.000Z

2022-02-10T19:24:19.000Z

bankroll/brokers/ibkr/__init__.py

bankroll-py/bankroll-broker-ibkr

e603c4d419d985ab8bc0fae9256919fa76b04de0

[ "MIT" ]

7

2019-08-29T23:00:06.000Z

2019-09-08T00:14:17.000Z

bankroll/brokers/ibkr/__init__.py

bankroll-py/bankroll-broker-ibkr

e603c4d419d985ab8bc0fae9256919fa76b04de0

[ "MIT" ]

null

from .account import IBAccount, IBDataProvider, Settings, contract __all__ = ["IBAccount", "IBDataProvider", "Settings", "contract"]

33.5

66

0.761194

12

134

8.166667

0.666667

0.469388

0.632653

0.795918

0

0.104478

134

3

67

44.666667

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0

0.5

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0

null

1

0

1

0

null

0

1

0

6

d132c230238a8e8fc787dd414443f205e7beea00

6,602

py

Python

vcli/tests/unit_tests/models/test_vcli_credentials_file.py

vertica/vertica-accelerator-cli

706925f58a4bfc2876903396db72363f673be76a

[ "Apache-2.0" ]

null

vcli/tests/unit_tests/models/test_vcli_credentials_file.py

vertica/vertica-accelerator-cli

706925f58a4bfc2876903396db72363f673be76a

[ "Apache-2.0" ]

null

vcli/tests/unit_tests/models/test_vcli_credentials_file.py

vertica/vertica-accelerator-cli

706925f58a4bfc2876903396db72363f673be76a

[ "Apache-2.0" ]

null

# # (c) Copyright 2021 Micro Focus or one of its affiliates. # import unittest import pytest from unittest.mock import patch from vcli.models.vcli_credential_file import VcliCredentialFile from vcli.exceptions.vcli_custom_exception import ( ValidationFailedError, ProfileNotExistError, ProfilePermissionError ) from vcli.util.error_message import ErrorMessage class VcliCredentialFileTests(unittest.TestCase): """Vcli Credential File unit tests""" def setUp(self): self.username = "test_username" self.password = "test_password" self.client_id = "test_client_id" self.auth_endpont = "test_auth_endpoint" self.access_token_file = "test_token_file" self.default_profile = "default" self.test_obj = VcliCredentialFile(self.username, self.password, self.client_id, self.auth_endpont, self.access_token_file) def tearDown(self): pass @pytest.fixture(autouse=True) def inject_fixtures(self, capsys): self.capsys = capsys # -------------- tests -------------- # @patch('os.access') @patch('os.path.exists') @patch('argparse.Namespace') def test_post_init_username_none(self, mock_args, mock_os_path_exists, mock_os_access): mock_args.profile = self.default_profile mock_os_path_exists.return_value = True mock_os_access.return_value = True with pytest.raises(ValidationFailedError) as err: test_obj = VcliCredentialFile(username=None, password=self.password, client_id=self.client_id, auth_endpont=self.auth_endpont) stdout, stderr = self.capsys.readouterr() self.assertEqual('', stdout) self.assertEqual('', stderr) self.assertEqual( err.value.msg, ErrorMessage.USERNAME_CAN_NOT_BE_NONE) @patch('os.access') @patch('os.path.exists') @patch('argparse.Namespace') def test_post_init_password_none(self, mock_args, mock_os_path_exists, mock_os_access): mock_args.profile = self.default_profile mock_os_path_exists.return_value = True mock_os_access.return_value = True with pytest.raises(ValidationFailedError) as err: test_obj = VcliCredentialFile(username=self.username, password=None, client_id=self.client_id, auth_endpont=self.auth_endpont) stdout, stderr = self.capsys.readouterr() self.assertEqual('', stdout) self.assertEqual('', stderr) self.assertEqual( err.value.msg, ErrorMessage.PASSWORD_CAN_NOT_BE_NONE) @patch('os.access') @patch('os.path.exists') @patch('argparse.Namespace') def test_post_init_client_id_none(self, mock_args, mock_os_path_exists, mock_os_access): mock_args.profile = self.default_profile mock_os_path_exists.return_value = True mock_os_access.return_value = True with pytest.raises(ValidationFailedError) as err: test_obj = VcliCredentialFile(username=self.username, password=self.password, client_id=None, auth_endpont=self.auth_endpont) stdout, stderr = self.capsys.readouterr() self.assertEqual('', stdout) self.assertEqual('', stderr) self.assertEqual( err.value.msg, ErrorMessage.OKTA_CLIENT_CAN_NOT_BE_NONE) @patch('os.access') @patch('os.path.exists') @patch('argparse.Namespace') def test_post_init_auth_endpoint_none(self, mock_args, mock_os_path_exists, mock_os_access): mock_args.profile = self.default_profile mock_os_path_exists.return_value = True mock_os_access.return_value = True with pytest.raises(ValidationFailedError) as err: test_obj = VcliCredentialFile(username=self.username, password=self.password, client_id=self.client_id, auth_endpont=None) stdout, stderr = self.capsys.readouterr() self.assertEqual('', stdout) self.assertEqual('', stderr) self.assertEqual( err.value.msg, ErrorMessage.OKTA_AUTH_ENDPOINT_CAN_NOT_BE_NONE) @patch('os.access') @patch('os.path.exists') @patch('argparse.Namespace') def test_check_profile_file_profile_not_exist(self, mock_args, mock_os_path_exists, mock_os_access): mock_args.profile = "profile_not_exist" mock_os_path_exists.return_value = False mock_os_access.return_value = True with pytest.raises(ProfileNotExistError) as err: self.test_obj.check_profile_file(profile=mock_args) stdout, stderr = self.capsys.readouterr() self.assertEqual('', stdout) self.assertEqual('', stderr) self.assertEqual( err.value.msg, ErrorMessage.ERROR_CREDENTIAL_CONFIG_NOT_EXIST) @patch('os.access') @patch('os.path.exists') @patch('argparse.Namespace') def test_check_profile_file_profile_file_not_readable(self, mock_args, mock_os_path_exists, mock_os_access): mock_args.profile = self.default_profile mock_os_path_exists.return_value = True mock_os_access.return_value = False with pytest.raises(ProfilePermissionError) as err: self.test_obj.check_profile_file(profile=mock_args) stdout, stderr = self.capsys.readouterr() self.assertEqual('', stdout) self.assertEqual('', stderr) self.assertEqual( err.value.msg, ErrorMessage.ERROR_CREDENTIAL_CONFIG_NOT_READABLE) @patch('os.path.exists') @patch('argparse.Namespace') def test_get_access_token_profile_not_exist(self, mock_args, mock_os_path_exists): mock_args.profile = "profile_not_exist" mock_os_path_exists.return_value = False with pytest.raises(ProfileNotExistError) as err: self.test_obj.get_access_token(profile=mock_args) stdout, stderr = self.capsys.readouterr() self.assertEqual('', stdout) self.assertEqual('', stderr) self.assertEqual( err.value.msg, f"credential file section profile: {mock_args} does not exist") @patch('os.remove') @patch('os.path.exists') def test_delete_access_token_file_exists(self, mock_os_path_exists, mock_remove): mock_os_path_exists.return_value = True self.test_obj.delete_access_token(profile=self.default_profile) stdout, stderr = self.capsys.readouterr() self.assertEqual('', stdout) self.assertEqual('', stderr)

38.608187

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0.678431

783

6,602

5.416347

0.121328

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0.067909

0.060363

0.757604

0.742042

0.73709

0.729781

0.691818

0

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0.22372

6,602

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113

38.835294

0.826732

0.019085

0

0.631579

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0

0.172932

1

0.082707

false

0.067669

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0

0.135338

0

null

0

1

0

null

0

1

0

6

d135d8fcf3a13b28662e0dab4058c8c77750575b

116

py

Python

1330.py

pgw00/Beakjoon-python

677bf6ece5be4843a37aace02edaa29c532721fb

[ "MIT" ]

null

1330.py

pgw00/Beakjoon-python

677bf6ece5be4843a37aace02edaa29c532721fb

[ "MIT" ]

null

1330.py

pgw00/Beakjoon-python

677bf6ece5be4843a37aace02edaa29c532721fb

[ "MIT" ]

null

x,y=map(int,input().split()) if(x < y): print('<') elif(x > y): print('>') elif(x==y): print('==')

14.5

28

0.422414

18

116

2.722222

0.5

0.163265

0.428571

0.44898

0.591837

0

0.241379

116

7

29

16.571429

0.556818

0

0.034483

0

1

0

true

0

0.428571

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

d16089a51bc67e3b3effa77a3b1ff841c4c995ab

195

py

Python

ledmatrix/__init__.py

aliask/ledmatrix

a14fea890b92b70ebbafb67663146f557c027820

[ "MIT" ]

null

ledmatrix/__init__.py

aliask/ledmatrix

a14fea890b92b70ebbafb67663146f557c027820

[ "MIT" ]

8

2021-06-02T02:38:42.000Z

2021-08-22T12:08:03.000Z

ledmatrix/__init__.py

aliask/ledmatrix

a14fea890b92b70ebbafb67663146f557c027820

[ "MIT" ]

null

from ledmatrix.ledframe import LedFrame from ledmatrix.ledmatrix import LEDMatrix from ledmatrix.spinner import Spinner from ledmatrix.udpserver import UDPServer, NoDataException, FrameException

39

74

0.876923

22

195

7.772727

0.363636

0.304094

0

0.092308

195

4

75

48.75

0.966102

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

0f0eda57c48bcf3b2a85c50029e85e06bc1d5aa1

38

py

Python

models/__init__.py

Gero22/flask-react

540bd23216993e5a4042d6a133132daa7d34e726

[ "MIT" ]

19

2019-02-24T04:44:47.000Z

2022-03-12T15:26:40.000Z

models/__init__.py

Gero22/flask-react

540bd23216993e5a4042d6a133132daa7d34e726

[ "MIT" ]

2

2019-12-24T19:53:38.000Z

2019-12-24T19:53:39.000Z

models/__init__.py

Gero22/flask-react

540bd23216993e5a4042d6a133132daa7d34e726

[ "MIT" ]

8

2019-07-19T14:05:47.000Z

2021-01-17T18:29:51.000Z

from models.Component import Component

38

0.894737

5

38

6.8

0.8

0

0.078947

38

1

38

0.971429

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

0f1d264eda9afbbfe07f7538c712730a9bbdc19f

49

py

Python

TwoNN/__init__.py

fmottes/TWO-NN

ae48b074f1ee86e9524dc521fef8bb8af8343145

[ "MIT" ]

2

2019-10-15T12:22:08.000Z

2020-01-02T20:19:22.000Z

TwoNN/__init__.py

fmottes/TWO-NN

ae48b074f1ee86e9524dc521fef8bb8af8343145

[ "MIT" ]

null

TwoNN/__init__.py

fmottes/TWO-NN

ae48b074f1ee86e9524dc521fef8bb8af8343145

[ "MIT" ]

4

2020-01-02T20:19:11.000Z

2021-08-31T11:59:19.000Z

from TwoNN.twonn_dimension import twonn_dimension

49

0.918367

7

49

6.142857

0.571429

0.651163

0

0.061224

49

1

49

0.934783

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

0f249153732203f7dcd53408be21f51c05f650bb

73

py

Python

toontown/town/MMStreet.py

LittleNed/toontown-stride

1252a8f9a8816c1810106006d09c8bdfe6ad1e57

[ "Apache-2.0" ]

3

2020-01-02T08:43:36.000Z

2020-07-05T08:59:02.000Z

toontown/town/MMStreet.py

NoraTT/Historical-Commits-Project-Altis-Source

fe88e6d07edf418f7de6ad5b3d9ecb3d0d285179

[ "Apache-2.0" ]

null

toontown/town/MMStreet.py

NoraTT/Historical-Commits-Project-Altis-Source

fe88e6d07edf418f7de6ad5b3d9ecb3d0d285179

[ "Apache-2.0" ]

4

2019-06-20T23:45:23.000Z

2020-10-14T20:30:15.000Z

from toontown.town import Street class MMStreet(Street.Street): pass

18.25

32

0.780822

10

73

5.7

0.8

0

0.150685

73

4

33

18.25

0.919355

0

1

0

true

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

6

0f664fab555e3add9bee50731fb6bed1e681cb49

2,983

py

Python

GraphNorm_ws/gnn_ws/gnn_example/model/GIN/readout.py

vikasverma1077/GraphNorm

17723c2cc19795125c339dcc898039121d1fbdf2

[ "MIT" ]

null

GraphNorm_ws/gnn_ws/gnn_example/model/GIN/readout.py

vikasverma1077/GraphNorm

17723c2cc19795125c339dcc898039121d1fbdf2

[ "MIT" ]

null

GraphNorm_ws/gnn_ws/gnn_example/model/GIN/readout.py

vikasverma1077/GraphNorm

17723c2cc19795125c339dcc898039121d1fbdf2

[ "MIT" ]

null

import torch as th import torch.nn as nn import numpy as np from dgl import sum_nodes, mean_nodes, max_nodes class SumPooling(nn.Module): r"""Apply sum pooling over the nodes in the graph. .. math:: r^{(i)} = \sum_{k=1}^{N_i} x^{(i)}_k """ def __init__(self): super(SumPooling, self).__init__() def forward(self, graph, feat): r"""Compute sum pooling. Parameters ---------- graph : DGLGraph or BatchedDGLGraph The graph. feat : torch.Tensor The input feature with shape :math:`(N, *)` where :math:`N` is the number of nodes in the graph. Returns ------- torch.Tensor The output feature with shape :math:`(*)` (if input graph is a BatchedDGLGraph, the result shape would be :math:`(B, *)`. """ with graph.local_scope(): graph.ndata['h'] = feat readout = sum_nodes(graph, 'h') return readout class AvgPooling(nn.Module): r"""Apply average pooling over the nodes in the graph. .. math:: r^{(i)} = \frac{1}{N_i}\sum_{k=1}^{N_i} x^{(i)}_k """ def __init__(self): super(AvgPooling, self).__init__() def forward(self, graph, feat): r"""Compute average pooling. Parameters ---------- graph : DGLGraph or BatchedDGLGraph The graph. feat : torch.Tensor The input feature with shape :math:`(N, *)` where :math:`N` is the number of nodes in the graph. Returns ------- torch.Tensor The output feature with shape :math:`(*)` (if input graph is a BatchedDGLGraph, the result shape would be :math:`(B, *)`. """ with graph.local_scope(): graph.ndata['h'] = feat readout = mean_nodes(graph, 'h') return readout class MaxPooling(nn.Module): r"""Apply max pooling over the nodes in the graph. .. math:: r^{(i)} = \max_{k=1}^{N_i}\left( x^{(i)}_k \right) """ def __init__(self): super(MaxPooling, self).__init__() def forward(self, graph, feat): r"""Compute max pooling. Parameters ---------- graph : DGLGraph or BatchedDGLGraph The graph. feat : torch.Tensor The input feature with shape :math:`(N, *)` where :math:`N` is the number of nodes in the graph. Returns ------- torch.Tensor The output feature with shape :math:`(*)` (if input graph is a BatchedDGLGraph, the result shape would be :math:`(B, *)`. """ with graph.local_scope(): graph.ndata['h'] = feat readout = max_nodes(graph, 'h') return readout

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Python

Setup Rich Text Editor/mysite/main/views.py

AyemunHossain/Django

0b1ed21fd6bd2906a4a1a220c029a2193658320f

[ "MIT" ]

2

2020-02-14T19:23:50.000Z

2020-04-19T08:26:38.000Z

Setup Rich Text Editor/mysite/main/views.py

AyemunHossain/Django

0b1ed21fd6bd2906a4a1a220c029a2193658320f

[ "MIT" ]

42

2021-02-02T23:08:30.000Z

2022-03-12T00:54:55.000Z

Setup Rich Text Editor/mysite/main/views.py

AyemunHossain/Django

0b1ed21fd6bd2906a4a1a220c029a2193658320f

[ "MIT" ]

1

2022-03-07T08:09:41.000Z

from django.shortcuts import render from django.http import HttpResponse # Create your views here. def homepage(request): return HttpResponse("<strong>Happy Coding</strong>")

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86

py

Python

data/__init__.py

muzhial/memae-anomaly-detection

2225242b2e6bfe988c25704a55331aa81bea378b

[ "MIT" ]

297

2019-06-18T00:15:50.000Z

2022-03-27T14:19:42.000Z

data/__init__.py

muzhial/memae-anomaly-detection

2225242b2e6bfe988c25704a55331aa81bea378b

[ "MIT" ]

25

2019-07-29T18:46:55.000Z

2022-03-29T01:10:18.000Z

data/__init__.py

muzhial/memae-anomaly-detection

2225242b2e6bfe988c25704a55331aa81bea378b

[ "MIT" ]

84

2019-07-30T10:28:21.000Z

2022-03-27T23:04:19.000Z

from __future__ import print_function, absolute_import from .video_datasets import *

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py

Python

model/model.py

ryanhe312/ABAW2-FPNMAA

012ea1071647ae9d7ba65548792f40018644097b

[ "MIT" ]

12

2021-07-09T07:10:08.000Z

2022-03-18T01:52:23.000Z

model/model.py

ryanhe312/ABAW2-FPNMAA

012ea1071647ae9d7ba65548792f40018644097b

[ "MIT" ]

null

model/model.py

ryanhe312/ABAW2-FPNMAA

012ea1071647ae9d7ba65548792f40018644097b

[ "MIT" ]

1

2021-07-12T08:28:08.000Z

import torch import numpy as np import torchvision import torch.nn as nn import torchmetrics as tm import seaborn as sns import matplotlib.pyplot as plt import pytorch_lightning as pl from torch.nn import functional as F from .network import * from .metrics import * class MonoClassifier(pl.LightningModule): def __init__(self, params: dict): super().__init__() self.save_hyperparameters(params) self.backbone = torchvision.models.mobilenet_v2(pretrained=True) if self.hparams.data_type == 'VA_Set': self.head = ClassificationHead(input_dim=1000, target_dim=2) self.loss = nn.MSELoss() elif self.hparams.data_type == 'EXPR_Set': self.head = ClassificationHead(input_dim=1000, target_dim=7) self.loss = nn.CrossEntropyLoss() # self.loss = lambda x, y: F.cross_entropy(x, y) + F.multi_margin_loss(x, y) else: self.head = ClassificationHead(input_dim=1000, target_dim=12) self.loss = nn.BCEWithLogitsLoss() # self.loss = lambda x, y: F.binary_cross_entropy_with_logits(x, y) + F.l1_loss(x, y) def forward(self, x): # use forward for inference/predictions embedding = self.backbone(x) y_hat = self.head(embedding) return y_hat def training_step(self, batch, batch_idx): x, y = batch y_hat = self(x) loss = self.loss(y_hat, y) self.log('train/loss', loss) return loss def validation_step(self, batch, batch_idx): x, y = batch y_hat = self(x) loss = self.loss(y_hat, y) y_hat = y_hat.detach().cpu().numpy() y = y.detach().cpu().numpy() self.log_dict({'val/loss': loss}) return y_hat, y def validation_epoch_end(self, outputs) -> None: y_hat = [] y = [] for step in outputs: y_hat.append(step[0]) y.append(step[1]) y_hat = np.concatenate(y_hat, axis=0) y = np.concatenate(y, axis=0) if self.hparams.data_type == 'VA_Set': item, sum = VA_metric(y_hat, y) self.log_dict({'val/CCC-V': item[0], 'val/CCC-A': item[1], 'val/score': sum}) elif self.hparams.data_type == 'EXPR_Set': f1_acc, score, matrix = EXPR_metric(y_hat, y) self.log_dict({'val/f1': f1_acc[0], 'val/acc': f1_acc[1], 'val/score': score}) fig, ax = plt.subplots() ax = sns.heatmap(matrix, cmap='Blues') self.logger.experiment.add_figure('val/conf', fig) else: f1_acc, score = AU_metric(y_hat, y) self.log_dict({'val/f1': f1_acc[0], 'val/acc': f1_acc[1], 'val/score': score}) def predict_step(self, batch, batch_idx, dataloader_idx=None): y_hat = self(batch) y_hat = y_hat.detach().cpu().numpy() if self.hparams.data_type == 'EXPR_Set': y_hat = np.argmax(y_hat, axis=-1) elif self.hparams.data_type == 'AU_Set': y_hat = (y_hat > 0.5).astype(int) else: y_hat = np.clip(y_hat, -0.99, 0.99) return y_hat def configure_optimizers(self): # self.hparams available because we called self.save_hyperparameters() # optimizer = torch.optim.Adam(self.parameters(), lr=self.hparams.get('learning_rate', 1e-3)) optimizer = torch.optim.SGD(self.parameters(), lr=self.hparams.get('learning_rate', 3e-4), momentum=self.hparams.get('momentum', 0.9), weight_decay=self.hparams.get('weight_decay', 1e-4)) lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 50, self.hparams.get('gamma', 0.1)) return [optimizer], [lr_scheduler] def configure_callbacks(self): checkpoint = pl.callbacks.ModelCheckpoint( monitor='val/score', mode='max', filename='epoch{epoch}-loss{val/loss:.2f}-score{val/score:.2f}', save_top_k=3, auto_insert_metric_name=False ) return [checkpoint] TYPE = ['VA_Set', 'EXPR_Set', 'AU_Set'] class MultiClassifier(pl.LightningModule): def __init__(self, params: dict): super().__init__() self.save_hyperparameters(params) self.backbone = torchvision.models.mobilenet_v2(pretrained=True) self.VA_head = ClassificationHead(input_dim=1000, target_dim=2) self.EXPR_head = ClassificationHead(input_dim=1000, target_dim=7) self.AU_head = ClassificationHead(input_dim=1000, target_dim=12) self.VA_loss = nn.MSELoss() self.EXPR_loss = nn.CrossEntropyLoss() self.AU_loss = nn.BCEWithLogitsLoss() def forward(self, x): # use forward for inference/predictions embedding = self.backbone(x) y_hat = [self.VA_head(embedding), self.EXPR_head(embedding), self.AU_head(embedding)] y_hat = torch.cat(y_hat, dim=1) return y_hat def training_step(self, batch, batch_idx): x, y = batch y_hat = self(x) va_loss = self.VA_loss(y_hat[:, :2], y[:, :2]) expr_loss = self.EXPR_loss(y_hat[:, 2:9], y[:, 2].long()) au_loss = self.AU_loss(y_hat[:, 9:], y[:, 3:].float()) total_loss = va_loss + expr_loss + au_loss self.log_dict({'train/loss': total_loss, 'train/va_loss': va_loss, 'train/expr_loss': expr_loss, 'train/au_loss': au_loss}, on_epoch=True) return total_loss def validation_step(self, batch, batch_idx): x, y = batch y_hat = self(x) va_loss = self.VA_loss(y_hat[:, :2], y[:, :2]) expr_loss = self.EXPR_loss(y_hat[:, 2:9], y[:, 2].long()) au_loss = self.AU_loss(y_hat[:, 9:], y[:, 3:].float()) total_loss = va_loss + expr_loss + au_loss self.log_dict({'val/loss': total_loss, 'val/va_loss': va_loss, 'val/expr_loss': expr_loss, 'val/au_loss': au_loss}, on_epoch=True) y_hat = y_hat.detach().cpu().numpy() y = y.detach().cpu().numpy() item, sum = VA_metric(y_hat[:, :2], y[:, :2]) self.log_dict({'val/CCC-V': item[0], 'val/CCC-A': item[1], 'val/va_score': sum}, on_epoch=True) f1_acc, score, _ = EXPR_metric(y_hat[:, 2:9], y[:, 2].astype(int)) self.log_dict({'val/expr_f1': f1_acc[0], 'val/expr_acc': f1_acc[1], 'val/expr_score': score}, on_epoch=True) f1_acc, score = AU_metric(y_hat[:, 9:], y[:, 3:]) self.log_dict({'val/au_f1': f1_acc[0], 'val/au_acc': f1_acc[1], 'val/au_score': score}, on_epoch=True) def test_step(self, batch, batch_idx): pass def configure_optimizers(self): # self.hparams available because we called self.save_hyperparameters() optimizer = torch.optim.Adam(self.parameters(), lr=self.hparams.get('learning_rate', 1e-3)) # optimizer = torch.optim.SGD(self.parameters(), # lr=self.hparams.get('learning_rate', 3e-4), # momentum=self.hparams.get('momentum', 0.9), # weight_decay=self.hparams.get('weight_decay', 1e-4)) lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 50, self.hparams.get('gamma', 0.1)) return [optimizer], [lr_scheduler] def configure_callbacks(self): checkpoint = pl.callbacks.ModelCheckpoint( monitor='val/loss', filename='epoch{epoch}-loss{val/loss:.2f}-va{val/va_score:.2f}-expr{val/expr_score:.2f}-au{val/au_score:.2f}', save_top_k=3, auto_insert_metric_name=False ) return [checkpoint] class Mono3DClassifier(MonoClassifier): def __init__(self, params: dict): super().__init__(params) self.save_hyperparameters(params) self.backbone = torchvision.models.mobilenet_v2(pretrained=True) self.backbone_3d = torchvision.models.mobilenet_v2(pretrained=True) if self.hparams.data_type == 'VA_Set': self.head = ClassificationHead(input_dim=2000, target_dim=2) self.loss = nn.MSELoss() elif self.hparams.data_type == 'EXPR_Set': self.head = ClassificationHead(input_dim=2000, target_dim=7) self.loss = nn.CrossEntropyLoss() # self.loss = lambda x, y: F.cross_entropy(x, y) + F.multi_margin_loss(x, y) else: self.head = ClassificationHead(input_dim=2000, target_dim=12) self.loss = nn.BCEWithLogitsLoss() # self.loss = lambda x, y: F.binary_cross_entropy_with_logits(x, y) + F.l1_loss(x, y) def forward(self, x): # use forward for inference/predictions embedding = self.backbone(x[:, 0]) embedding_3d = self.backbone_3d(x[:, 1]) y_hat = self.head(torch.cat([embedding, embedding_3d], dim=-1)) return y_hat from torchvision.models.detection.backbone_utils import resnet_fpn_backbone class MonoPyramidClassifier(MonoClassifier): def __init__(self, params: dict): super().__init__(params) self.save_hyperparameters(params) self.backbone = resnet_fpn_backbone('resnet50', pretrained=True) if self.hparams.data_type == 'VA_Set': self.head = ClassificationHead(input_dim=1280, target_dim=2) self.loss = nn.MSELoss() elif self.hparams.data_type == 'EXPR_Set': self.head = ClassificationHead(input_dim=1280, target_dim=7) self.loss = nn.CrossEntropyLoss() # self.loss = lambda x, y: F.cross_entropy(x, y) + F.multi_margin_loss(x, y) else: self.head = ClassificationHead(input_dim=1280, target_dim=12) self.loss = nn.BCEWithLogitsLoss() # self.loss = lambda x, y: F.binary_cross_entropy_with_logits(x, y) + F.l1_loss(x, y) def forward(self, x): # use forward for inference/predictions layers = self.backbone(x) embedding = torch.cat([torch.mean(layer,dim=(-2,-1)) for layer in layers.values()], dim=-1) y_hat = self.head(embedding) return y_hat class MultiPyramidClassifier(MultiClassifier): def __init__(self, params: dict): super().__init__(params) self.save_hyperparameters(params) self.backbone = resnet_fpn_backbone('resnet50', pretrained=True) self.VA_head = ClassificationHead(input_dim=1280, target_dim=2) self.EXPR_head = ClassificationHead(input_dim=1280, target_dim=7) self.AU_head = ClassificationHead(input_dim=1280, target_dim=12) self.VA_loss = nn.MSELoss() self.EXPR_loss = nn.CrossEntropyLoss() self.AU_loss = nn.BCEWithLogitsLoss() def forward(self, x): # use forward for inference/predictions layers = self.backbone(x) embedding = torch.cat([torch.mean(layer, dim=(-2, -1)) for layer in layers.values()], dim=-1) y_hat = [self.VA_head(embedding), self.EXPR_head(embedding), self.AU_head(embedding)] y_hat = torch.cat(y_hat, dim=1) return y_hat if __name__ == '__main__': model = MonoPyramidClassifier({'data_type':'VA_Set'}) output = model.backbone(torch.rand(32, 3, 128, 128)) print([(k, v.shape) for k, v in output.items()]) ma = torch.cat([torch.mean(layer,dim=(-2,-1)) for layer in output.values()],dim=-1) print(ma.shape)

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6

0e48106e78765e163d20a2bf39c76cc3527b38f3

31

py

Python

python_graphics_engine/materials/__init__.py

DCC-Lab/python-graphic-engine

086817407ed4219272cf01612b96e5094a71eb94

[ "MIT" ]

1

2022-02-14T04:52:56.000Z

python_graphics_engine/materials/__init__.py

DCC-Lab/python-graphic-engine

086817407ed4219272cf01612b96e5094a71eb94

[ "MIT" ]

3

2020-03-24T18:01:09.000Z

2021-02-02T22:19:35.000Z

python_graphics_engine/materials/__init__.py

DCC-Lab/python-graphic-engine

086817407ed4219272cf01612b96e5094a71eb94

[ "MIT" ]

1

2020-08-07T22:49:48.000Z

from .material import Material

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py

Python

tests/nn/test_training.py

hectorcarrion/sleap

1150c2d0b64543e07d4b2429ea245a5afaa07cee

[ "BSD-3-Clause-Clear" ]

156

2020-05-01T18:43:43.000Z

2022-03-25T10:31:18.000Z

tests/nn/test_training.py

oeway/sleap

1eb06f81eb8f0bc1beedd1c3dd10902f8ff9e724

[ "BSD-3-Clause-Clear" ]

299

2020-04-20T16:37:52.000Z

2022-03-31T23:54:48.000Z

tests/nn/test_training.py

oeway/sleap

1eb06f81eb8f0bc1beedd1c3dd10902f8ff9e724

[ "BSD-3-Clause-Clear" ]

41

2020-05-14T15:25:21.000Z

2022-03-25T12:44:54.000Z

import pytest import sleap sleap.use_cpu_only() @pytest.fixture def training_labels(min_labels): labels = min_labels labels.append( sleap.LabeledFrame( video=labels.videos[0], frame_idx=1, instances=labels[0].instances ) ) return labels @pytest.fixture def cfg(): cfg = sleap.nn.config.TrainingJobConfig() cfg.data.instance_cropping.center_on_part = "A" cfg.model.backbone.unet = sleap.nn.config.UNetConfig( max_stride=8, output_stride=1, filters=8, filters_rate=1.0 ) cfg.optimization.preload_data = False cfg.optimization.batch_size = 1 cfg.optimization.batches_per_epoch = 2 cfg.optimization.epochs = 1 cfg.outputs.save_outputs = False return cfg def test_train_single_instance(min_labels_robot, cfg): cfg.model.heads.single_instance = sleap.nn.config.SingleInstanceConfmapsHeadConfig( sigma=1.5, output_stride=1, offset_refinement=False ) trainer = sleap.nn.training.SingleInstanceModelTrainer.from_config( cfg, training_labels=min_labels_robot ) trainer.setup() trainer.train() assert trainer.keras_model.output_names[0] == "SingleInstanceConfmapsHead_0" assert tuple(trainer.keras_model.outputs[0].shape) == (None, 320, 560, 2) def test_train_single_instance_with_offset(min_labels_robot, cfg): cfg.model.heads.single_instance = sleap.nn.config.SingleInstanceConfmapsHeadConfig( sigma=1.5, output_stride=1, offset_refinement=True ) trainer = sleap.nn.training.SingleInstanceModelTrainer.from_config( cfg, training_labels=min_labels_robot ) trainer.setup() trainer.train() assert trainer.keras_model.output_names[0] == "SingleInstanceConfmapsHead_0" assert tuple(trainer.keras_model.outputs[0].shape) == (None, 320, 560, 2) assert trainer.keras_model.output_names[1] == "OffsetRefinementHead_0" assert tuple(trainer.keras_model.outputs[1].shape) == (None, 320, 560, 4) def test_train_centroids(training_labels, cfg): cfg.model.heads.centroid = sleap.nn.config.CentroidsHeadConfig( sigma=1.5, output_stride=1, offset_refinement=False ) trainer = sleap.nn.training.CentroidConfmapsModelTrainer.from_config( cfg, training_labels=training_labels ) trainer.setup() trainer.train() assert trainer.keras_model.output_names[0] == "CentroidConfmapsHead_0" assert tuple(trainer.keras_model.outputs[0].shape) == (None, 384, 384, 1) def test_train_centroids_with_offset(training_labels, cfg): cfg.model.heads.centroid = sleap.nn.config.CentroidsHeadConfig( sigma=1.5, output_stride=1, offset_refinement=True ) trainer = sleap.nn.training.CentroidConfmapsModelTrainer.from_config( cfg, training_labels=training_labels ) trainer.setup() trainer.train() assert trainer.keras_model.output_names[0] == "CentroidConfmapsHead_0" assert trainer.keras_model.output_names[1] == "OffsetRefinementHead_0" assert tuple(trainer.keras_model.outputs[0].shape) == (None, 384, 384, 1) assert tuple(trainer.keras_model.outputs[1].shape) == (None, 384, 384, 2) def test_train_topdown(training_labels, cfg): cfg.model.heads.centered_instance = ( sleap.nn.config.CenteredInstanceConfmapsHeadConfig( sigma=1.5, output_stride=1, offset_refinement=False ) ) trainer = sleap.nn.training.TopdownConfmapsModelTrainer.from_config( cfg, training_labels=training_labels ) trainer.setup() trainer.train() assert trainer.keras_model.output_names[0] == "CenteredInstanceConfmapsHead_0" assert tuple(trainer.keras_model.outputs[0].shape) == (None, 96, 96, 2) def test_train_topdown_with_offset(training_labels, cfg): cfg.model.heads.centered_instance = ( sleap.nn.config.CenteredInstanceConfmapsHeadConfig( sigma=1.5, output_stride=1, offset_refinement=True ) ) trainer = sleap.nn.training.TopdownConfmapsModelTrainer.from_config( cfg, training_labels=training_labels ) trainer.setup() trainer.train() assert trainer.keras_model.output_names[0] == "CenteredInstanceConfmapsHead_0" assert trainer.keras_model.output_names[1] == "OffsetRefinementHead_0" assert tuple(trainer.keras_model.outputs[0].shape) == (None, 96, 96, 2) assert tuple(trainer.keras_model.outputs[1].shape) == (None, 96, 96, 4) def test_train_bottomup(training_labels, cfg): cfg.model.heads.multi_instance = sleap.nn.config.MultiInstanceConfig( confmaps=sleap.nn.config.MultiInstanceConfmapsHeadConfig( output_stride=1, offset_refinement=False ), pafs=sleap.nn.config.PartAffinityFieldsHeadConfig(output_stride=2), ) trainer = sleap.nn.training.TopdownConfmapsModelTrainer.from_config( cfg, training_labels=training_labels ) trainer.setup() trainer.train() assert trainer.keras_model.output_names[0] == "MultiInstanceConfmapsHead_0" assert trainer.keras_model.output_names[1] == "PartAffinityFieldsHead_0" assert tuple(trainer.keras_model.outputs[0].shape) == (None, 384, 384, 2) assert tuple(trainer.keras_model.outputs[1].shape) == (None, 192, 192, 2) def test_train_bottomup_with_offset(training_labels, cfg): cfg.model.heads.multi_instance = sleap.nn.config.MultiInstanceConfig( confmaps=sleap.nn.config.MultiInstanceConfmapsHeadConfig( output_stride=1, offset_refinement=True ), pafs=sleap.nn.config.PartAffinityFieldsHeadConfig(output_stride=2), ) trainer = sleap.nn.training.TopdownConfmapsModelTrainer.from_config( cfg, training_labels=training_labels ) trainer.setup() trainer.train() assert trainer.keras_model.output_names[0] == "MultiInstanceConfmapsHead_0" assert trainer.keras_model.output_names[1] == "PartAffinityFieldsHead_0" assert trainer.keras_model.output_names[2] == "OffsetRefinementHead_0" assert tuple(trainer.keras_model.outputs[0].shape) == (None, 384, 384, 2) assert tuple(trainer.keras_model.outputs[1].shape) == (None, 192, 192, 2) assert tuple(trainer.keras_model.outputs[2].shape) == (None, 384, 384, 4)

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null

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null

0

6

0e591128f79c32f6613f56d6a71b8c4ca6ca4982

69

py

Python

ais_wordnet_sim/database/__init__.py

minhdc/ais-wordnet-sim

f6c367a83781ae1a5dacd850eb628c882958175b

[ "MIT" ]

1

2019-09-26T13:49:29.000Z

ais_wordnet_sim/database/__init__.py

minhdc/ais-wordnet-sim

f6c367a83781ae1a5dacd850eb628c882958175b

[ "MIT" ]

null

ais_wordnet_sim/database/__init__.py

minhdc/ais-wordnet-sim

f6c367a83781ae1a5dacd850eb628c882958175b

[ "MIT" ]

1

2019-09-26T14:00:02.000Z

from .service import WordsService, SynonymsService, CategoriesService

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0.884058

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6

0e73b4bd3fdcecd738c6a672d7cdef6c217e846c

172

py

Python

clipper/scripts/stop_clipper.py

g2des/17700_project

ccb7ff64574aa8e649d8a3e743755e61b070d1f9

[ "MIT" ]

null

clipper/scripts/stop_clipper.py

g2des/17700_project

ccb7ff64574aa8e649d8a3e743755e61b070d1f9

[ "MIT" ]

null

clipper/scripts/stop_clipper.py

g2des/17700_project

ccb7ff64574aa8e649d8a3e743755e61b070d1f9

[ "MIT" ]

null

#!/usr/bin/env python from clipper_admin import ClipperConnection, DockerContainerManager clipper_conn = ClipperConnection(DockerContainerManager()) clipper_conn.stop_all()

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32,353

py

Python

gym_boirl/gym/envs/mujoco/assets/mjc_models.py

clear-nus/BOIRL

cc872111fda3c7b8118e1a864831013c30f63948

[ "MIT" ]

1

2021-02-26T10:09:15.000Z

gym_boirl/gym/envs/mujoco/assets/mjc_models.py

clear-nus/BOIRL

cc872111fda3c7b8118e1a864831013c30f63948

[ "MIT" ]

null

gym_boirl/gym/envs/mujoco/assets/mjc_models.py

clear-nus/BOIRL

cc872111fda3c7b8118e1a864831013c30f63948

[ "MIT" ]

null

from gym.envs.mujoco.assets.model_builder import MJCModel import numpy as np def block_push(object_pos=(0,0,0), goal_pos=(0,0,0)): mjcmodel = MJCModel('block_push') mjcmodel.root.compiler(inertiafromgeom="true",angle="radian",coordinate="local") mjcmodel.root.option(timestep="0.01",gravity="0 0 0",iterations="20",integrator="Euler") default = mjcmodel.root.default() default.joint(armature='0.04', damping=1, limited='true') default.geom(friction=".8 .1 .1",density="300",margin="0.002",condim="1",contype="1",conaffinity="1") worldbody = mjcmodel.root.worldbody() palm = worldbody.body(name='palm', pos=[0,0,0]) palm.geom(name='palm_geom', type='capsule', fromto=[0,-0.1,0,0,0.1,0], size=.12) proximal1 = palm.body(name='proximal_1', pos=[0,0,0]) proximal1.joint(name='proximal_j_1', type='hinge', pos=[0,0,0], axis=[0,1,0], range=[-2.5,2.3]) proximal1.geom(type='capsule', fromto=[0,0,0,0.4,0,0], size=0.06, contype=1, conaffinity=1) distal1 = proximal1.body(name='distal_1', pos=[0.4,0,0]) distal1.joint(name = "distal_j_1", type = "hinge", pos = "0 0 0", axis = "0 1 0", range = "-2.3213 2.3", damping = "1.0") distal1.geom(type="capsule", fromto="0 0 0 0.4 0 0", size="0.06", contype="1", conaffinity="1") distal2 = distal1.body(name='distal_2', pos=[0.4,0,0]) distal2.joint(name="distal_j_2",type="hinge",pos="0 0 0",axis="0 1 0",range="-2.3213 2.3",damping="1.0") distal2.geom(type="capsule",fromto="0 0 0 0.4 0 0",size="0.06",contype="1",conaffinity="1") distal4 = distal2.body(name='distal_4', pos=[0.4,0,0]) distal4.site(name="tip arml",pos="0.1 0 -0.2",size="0.01") distal4.site(name="tip armr",pos="0.1 0 0.2",size="0.01") distal4.joint(name="distal_j_3",type="hinge",pos="0 0 0",axis="1 0 0",range="-3.3213 3.3",damping="0.5") distal4.geom(type="capsule",fromto="0 0 -0.2 0 0 0.2",size="0.04",contype="1",conaffinity="1") distal4.geom(type="capsule",fromto="0 0 -0.2 0.2 0 -0.2",size="0.04",contype="1",conaffinity="1") distal4.geom(type="capsule",fromto="0 0 0.2 0.2 0 0.2",size="0.04",contype="1",conaffinity="1") object = worldbody.body(name='object', pos=object_pos) object.geom(rgba="1. 1. 1. 1",type="box",size="0.05 0.05 0.05",density='0.00001',contype="1",conaffinity="1") object.joint(name="obj_slidez",type="slide",pos="0.025 0.025 0.025",axis="0 0 1",range="-10.3213 10.3",damping="0.5") object.joint(name="obj_slidex",type="slide",pos="0.025 0.025 0.025",axis="1 0 0",range="-10.3213 10.3",damping="0.5") distal10 = object.body(name='distal_10', pos=[0,0,0]) distal10.site(name='obj_pos', pos=[0.025,0.025,0.025], size=0.01) goal = worldbody.body(name='goal', pos=goal_pos) goal.geom(rgba="1. 0. 0. 1",type="box",size="0.1 0.1 0.1",density='0.00001',contype="0",conaffinity="0") distal11 = goal.body(name='distal_11', pos=[0,0,0]) distal11.site(name='goal_pos', pos=[0.05,0.05,0.05], size=0.01) actuator = mjcmodel.root.actuator() actuator.motor(joint="proximal_j_1",ctrlrange="-2 2",ctrllimited="true") actuator.motor(joint="distal_j_1",ctrlrange="-2 2",ctrllimited="true") actuator.motor(joint="distal_j_2",ctrlrange="-2 2",ctrllimited="true") actuator.motor(joint="distal_j_3",ctrlrange="-2 2",ctrllimited="true") return mjcmodel EAST = 0 WEST = 1 NORTH = 2 SOUTH = 3 def twod_corridor(direction=EAST, length=1.2): mjcmodel = MJCModel('twod_corridor') mjcmodel.root.compiler(inertiafromgeom="true", angle="radian", coordinate="local") mjcmodel.root.option(timestep="0.01", gravity="0 0 0", iterations="20", integrator="Euler") default = mjcmodel.root.default() default.joint(damping=1, limited='false') default.geom(friction=".5 .1 .1", density="1000", margin="0.002", condim="1", contype="2", conaffinity="1") worldbody = mjcmodel.root.worldbody() particle = worldbody.body(name='particle', pos=[0,0,0]) particle.geom(name='particle_geom', type='sphere', size='0.03', rgba='0.0 0.0 1.0 1', contype=1) particle.site(name='particle_site', pos=[0,0,0], size=0.01) particle.joint(name='ball_x', type='slide', pos=[0,0,0], axis=[1,0,0]) particle.joint(name='ball_y', type='slide', pos=[0,0,0], axis=[0,1,0]) pos = np.array([0.0,0,0]) if direction == EAST or direction == WEST: pos[0] = length-0.1 else: pos[1] = length-0.1 if direction == WEST or direction == SOUTH: pos = -pos target = worldbody.body(name='target', pos=pos) target.geom(name='target_geom', conaffinity=2, type='sphere', size=0.02, rgba=[0,0.9,0.1,1]) # arena if direction == EAST: L = -0.1 R = length U = 0.1 D = -0.1 elif direction == WEST: L = -length R = 0.1 U = 0.1 D = -0.1 elif direction == SOUTH: L = -0.1 R = 0.1 U = 0.1 D = -length elif direction == NORTH: L = -0.1 R = 0.1 U = length D = -0.1 worldbody.geom(conaffinity=1, fromto=[L, D, .01, R, D, .01], name="sideS", rgba="0.9 0.4 0.6 1", size=.02, type="capsule") worldbody.geom(conaffinity=1, fromto=[R, D, .01, R, U, .01], name="sideE", rgba="0.9 0.4 0.6 1", size=".02", type="capsule") worldbody.geom(conaffinity=1, fromto=[L, U, .01, R, U, .01], name="sideN", rgba="0.9 0.4 0.6 1", size=".02", type="capsule") worldbody.geom(conaffinity=1, fromto=[L, D, .01, L, U, .01], name="sideW", rgba="0.9 0.4 0.6 1", size=".02", type="capsule") actuator = mjcmodel.root.actuator() actuator.motor(joint="ball_x", ctrlrange=[-1.0, 1.0], ctrllimited=True) actuator.motor(joint="ball_y", ctrlrange=[-1.0, 1.0], ctrllimited=True) return mjcmodel LEFT = 0 RIGHT = 1 def point_mass_maze(direction=RIGHT, length=1.2, initpos=None, borders=True): if initpos is None: initpos = [length/2, 0., 0.] else: assert(len(initpos) == 3) mjcmodel = MJCModel('twod_maze') mjcmodel.root.compiler(inertiafromgeom="true", angle="radian", coordinate="local") mjcmodel.root.option(timestep="0.01", gravity="0 0 0", iterations="20", integrator="Euler") default = mjcmodel.root.default() default.joint(damping=1, limited='false') default.geom(friction=".5 .1 .1", density="1000", margin="0.002", condim="1", contype="2", conaffinity="1") worldbody = mjcmodel.root.worldbody() particle = worldbody.body(name='particle', pos=initpos) particle.geom(name='particle_geom', type='sphere', size='0.03', rgba='0.0 0.0 1.0 1', contype=1) particle.site(name='particle_site', pos=[0,0,0], size=0.01) particle.joint(name='ball_x', type='slide', pos=[0,0,0], axis=[1,0,0]) particle.joint(name='ball_y', type='slide', pos=[0,0,0], axis=[0,1,0]) target = worldbody.body(name='target', pos=[length/2,length-0.1,0]) target.geom(name='target_geom', conaffinity=2, type='sphere', size=0.02, rgba=[0,0.9,0.1,1]) L = -0.1 R = length U = length D = -0.1 if borders: worldbody.geom(conaffinity=1, fromto=[L, D, .01, R, D, .01], name="sideS", rgba="0.9 0.4 0.6 1", size=".02", type="capsule") worldbody.geom(conaffinity=1, fromto=[R, D, .01, R, U, .01], name="sideE", rgba="0.9 0.4 0.6 1", size=".02", type="capsule") worldbody.geom(conaffinity=1, fromto=[L, U, .01, R, U, .01], name="sideN", rgba="0.9 0.4 0.6 1", size=".02", type="capsule") worldbody.geom(conaffinity=1, fromto=[L, D, .01, L, U, .01], name="sideW", rgba="0.9 0.4 0.6 1", size=".02", type="capsule") # arena if direction == LEFT: BL = -0.1 BR = length * 2/3 BH = length/2 else: BL = length * 1/3 BR = length BH = length/2 worldbody.geom(conaffinity=1, fromto=[BL, BH, .01, BR, BH, .01], name="barrier", rgba="0.9 0.4 0.6 1", size=".02", type="capsule") actuator = mjcmodel.root.actuator() actuator.motor(joint="ball_x", ctrlrange=[-1.0, 1.0], ctrllimited=True) actuator.motor(joint="ball_y", ctrlrange=[-1.0, 1.0], ctrllimited=True) return mjcmodel def ant_maze(direction=RIGHT, length=6.0): mjcmodel = MJCModel('ant_maze') mjcmodel.root.compiler(inertiafromgeom="true", angle="degree", coordinate="local") mjcmodel.root.option(timestep="0.01", gravity="0 0 -9.8", iterations="20", integrator="Euler") assets = mjcmodel.root.asset() assets.texture(builtin="gradient", height="100", rgb1="1 1 1", rgb2="0 0 0", type="skybox", width="100") assets.texture(builtin="flat", height="1278", mark="cross", markrgb="1 1 1", name="texgeom", random="0.01", rgb1="0.8 0.6 0.4", rgb2="0.8 0.6 0.4", type="cube", width="127") assets.texture(builtin="checker", height="100", name="texplane", rgb1="0 0 0", rgb2="0.8 0.8 0.8", type="2d", width="100") assets.material(name="MatPlane", reflectance="0.5", shininess="1", specular="1", texrepeat="60 60", texture="texplane") assets.material(name="geom", texture="texgeom", texuniform="true") default = mjcmodel.root.default() default.joint(armature="1", damping=1, limited='true') default.geom(friction="1 0.5 0.5", density="5.0", margin="0.01", condim="3", conaffinity="0") worldbody = mjcmodel.root.worldbody() ant = worldbody.body(name='ant', pos=[length/2, 1.0, 0.05]) ant.geom(name='torso_geom', pos=[0, 0, 0], size="0.25", type="sphere") ant.joint(armature="0", damping="0", limited="false", margin="0.01", name="root", pos=[0, 0, 0], type="free") front_left_leg = ant.body(name="front_left_leg", pos=[0, 0, 0]) front_left_leg.geom(fromto=[0.0, 0.0, 0.0, 0.2, 0.2, 0.0], name="aux_1_geom", size="0.08", type="capsule") aux_1 = front_left_leg.body(name="aux_1", pos=[0.2, 0.2, 0]) aux_1.joint(axis=[0, 0, 1], name="hip_1", pos=[0.0, 0.0, 0.0], range=[-30, 30], type="hinge") aux_1.geom(fromto=[0.0, 0.0, 0.0, 0.2, 0.2, 0.0], name="left_leg_geom", size="0.08", type="capsule") ankle_1 = aux_1.body(pos=[0.2, 0.2, 0]) ankle_1.joint(axis=[-1, 1, 0], name="ankle_1", pos=[0.0, 0.0, 0.0], range=[30, 70], type="hinge") ankle_1.geom(fromto=[0.0, 0.0, 0.0, 0.4, 0.4, 0.0], name="left_ankle_geom", size="0.08", type="capsule") front_right_leg = ant.body(name="front_right_leg", pos=[0, 0, 0]) front_right_leg.geom(fromto=[0.0, 0.0, 0.0, -0.2, 0.2, 0.0], name="aux_2_geom", size="0.08", type="capsule") aux_2 = front_right_leg.body(name="aux_2", pos=[-0.2, 0.2, 0]) aux_2.joint(axis=[0, 0, 1], name="hip_2", pos=[0.0, 0.0, 0.0], range=[-30, 30], type="hinge") aux_2.geom(fromto=[0.0, 0.0, 0.0, -0.2, 0.2, 0.0], name="right_leg_geom", size="0.08", type="capsule") ankle_2 = aux_2.body(pos=[-0.2, 0.2, 0]) ankle_2.joint(axis=[1, 1, 0], name="ankle_2", pos=[0.0, 0.0, 0.0], range=[-70, -30], type="hinge") ankle_2.geom(fromto=[0.0, 0.0, 0.0, -0.4, 0.4, 0.0], name="right_ankle_geom", size="0.08", type="capsule") back_left_leg = ant.body(name="back_left_leg", pos=[0, 0, 0]) back_left_leg.geom(fromto=[0.0, 0.0, 0.0, -0.2, -0.2, 0.0], name="aux_3_geom", size="0.08", type="capsule") aux_3 = back_left_leg.body(name="aux_3", pos=[-0.2, -0.2, 0]) aux_3.joint(axis=[0, 0, 1], name="hip_3", pos=[0.0, 0.0, 0.0], range=[-30, 30], type="hinge") aux_3.geom(fromto=[0.0, 0.0, 0.0, -0.2, -0.2, 0.0], name="backleft_leg_geom", size="0.08", type="capsule") ankle_3 = aux_3.body(pos=[-0.2, -0.2, 0]) ankle_3.joint(axis=[-1, 1, 0], name="ankle_3", pos=[0.0, 0.0, 0.0], range=[-70, -30], type="hinge") ankle_3.geom(fromto=[0.0, 0.0, 0.0, -0.4, -0.4, 0.0], name="backleft_ankle_geom", size="0.08", type="capsule") back_right_leg = ant.body(name="back_right_leg", pos=[0, 0, 0]) back_right_leg.geom(fromto=[0.0, 0.0, 0.0, 0.2, -0.2, 0.0], name="aux_4_geom", size="0.08", type="capsule") aux_4 = back_right_leg.body(name="aux_4", pos=[0.2, -0.2, 0]) aux_4.joint(axis=[0, 0, 1], name="hip_4", pos=[0.0, 0.0, 0.0], range=[-30, 30], type="hinge") aux_4.geom(fromto=[0.0, 0.0, 0.0, 0.2, -0.2, 0.0], name="backright_leg_geom", size="0.08", type="capsule") ankle_4 = aux_4.body(pos=[0.2, -0.2, 0]) ankle_4.joint(axis=[1, 1, 0], name="ankle_4", pos=[0.0, 0.0, 0.0], range=[30, 70], type="hinge") ankle_4.geom(fromto=[0.0, 0.0, 0.0, 0.4, -0.4, 0.0], name="backright_ankle_geom", size="0.08", type="capsule") target = worldbody.body(name='target', pos=[length/2,length-0.2,-0.5]) target.geom(name='target_geom', conaffinity=2, type='sphere', size=0.2, rgba=[0,0.9,0.1,1]) l = length/2 h = 0.75 w = 0.05 worldbody.geom(conaffinity=1, name="sideS", rgba="0.9 0.4 0.6 1", size=[l, w, h], pos=[length/2, 0, 0], type="box") worldbody.geom(conaffinity=1, name="sideE", rgba="0.9 0.4 0.6 1", size=[w, l, h], pos=[length, length/2, 0], type="box") worldbody.geom(conaffinity=1, name="sideN", rgba="0.9 0.4 0.6 1", size=[l, w, h], pos=[length/2, length, 0], type="box") worldbody.geom(conaffinity=1, name="sideW", rgba="0.9 0.4 0.6 1", size=[w, l, h], pos=[0, length/2, 0], type="box") # arena if direction == LEFT: bx, by, bz = (length/3, length/2, 0) else: bx, by, bz = (length*2/3, length/2, 0) worldbody.geom(conaffinity=1, name="barrier", rgba="0.9 0.4 0.6 1", size=[l * 2/3, w, h], pos=[bx, by, bz], type="box") worldbody.geom(conaffinity="1", condim="3", material="MatPlane", name="floor", pos=[length/2, length/2, -h + w], rgba="0.8 0.9 0.8 1", size="40 40 40", type="plane") actuator = mjcmodel.root.actuator() actuator.motor(ctrllimited="true", ctrlrange="-1.0 1.0", joint="hip_4", gear="50") actuator.motor(ctrllimited="true", ctrlrange="-1.0 1.0", joint="ankle_4", gear="50") actuator.motor(ctrllimited="true", ctrlrange="-1.0 1.0", joint="hip_1", gear="50") actuator.motor(ctrllimited="true", ctrlrange="-1.0 1.0", joint="ankle_1", gear="50") actuator.motor(ctrllimited="true", ctrlrange="-1.0 1.0", joint="hip_2", gear="50") actuator.motor(ctrllimited="true", ctrlrange="-1.0 1.0", joint="ankle_2", gear="50") actuator.motor(ctrllimited="true", ctrlrange="-1.0 1.0", joint="hip_3", gear="50") actuator.motor(ctrllimited="true", ctrlrange="-1.0 1.0", joint="ankle_3", gear="50") return mjcmodel def ant_maze_corridor(direction=RIGHT, height=6.0, width=10.0): mjcmodel = MJCModel('ant_maze_corridor') mjcmodel.root.compiler(inertiafromgeom="true", angle="degree", coordinate="local") mjcmodel.root.option(timestep="0.01", gravity="0 0 -9.8", iterations="20", integrator="Euler") assets = mjcmodel.root.asset() assets.texture(builtin="gradient", height="100", rgb1="1 1 1", rgb2="0 0 0", type="skybox", width="100") assets.texture(builtin="flat", height="1278", mark="cross", markrgb="1 1 1", name="texgeom", random="0.01", rgb1="0.8 0.6 0.4", rgb2="0.8 0.6 0.4", type="cube", width="127") assets.texture(builtin="checker", height="100", name="texplane", rgb1="0 0 0", rgb2="0.8 0.8 0.8", type="2d", width="100") assets.material(name="MatPlane", reflectance="0.5", shininess="1", specular="1", texrepeat="60 60", texture="texplane") assets.material(name="geom", texture="texgeom", texuniform="true") default = mjcmodel.root.default() default.joint(armature="1", damping=1, limited='true') default.geom(friction="1 0.5 0.5", density="5.0", margin="0.01", condim="3", conaffinity="0") worldbody = mjcmodel.root.worldbody() ant = worldbody.body(name='ant', pos=[height/2, 1.0, 0.05]) ant.geom(name='torso_geom', pos=[0, 0, 0], size="0.25", type="sphere") ant.joint(armature="0", damping="0", limited="false", margin="0.01", name="root", pos=[0, 0, 0], type="free") front_left_leg = ant.body(name="front_left_leg", pos=[0, 0, 0]) front_left_leg.geom(fromto=[0.0, 0.0, 0.0, 0.2, 0.2, 0.0], name="aux_1_geom", size="0.08", type="capsule") aux_1 = front_left_leg.body(name="aux_1", pos=[0.2, 0.2, 0]) aux_1.joint(axis=[0, 0, 1], name="hip_1", pos=[0.0, 0.0, 0.0], range=[-30, 30], type="hinge") aux_1.geom(fromto=[0.0, 0.0, 0.0, 0.2, 0.2, 0.0], name="left_leg_geom", size="0.08", type="capsule") ankle_1 = aux_1.body(pos=[0.2, 0.2, 0]) ankle_1.joint(axis=[-1, 1, 0], name="ankle_1", pos=[0.0, 0.0, 0.0], range=[30, 70], type="hinge") ankle_1.geom(fromto=[0.0, 0.0, 0.0, 0.4, 0.4, 0.0], name="left_ankle_geom", size="0.08", type="capsule") front_right_leg = ant.body(name="front_right_leg", pos=[0, 0, 0]) front_right_leg.geom(fromto=[0.0, 0.0, 0.0, -0.2, 0.2, 0.0], name="aux_2_geom", size="0.08", type="capsule") aux_2 = front_right_leg.body(name="aux_2", pos=[-0.2, 0.2, 0]) aux_2.joint(axis=[0, 0, 1], name="hip_2", pos=[0.0, 0.0, 0.0], range=[-30, 30], type="hinge") aux_2.geom(fromto=[0.0, 0.0, 0.0, -0.2, 0.2, 0.0], name="right_leg_geom", size="0.08", type="capsule") ankle_2 = aux_2.body(pos=[-0.2, 0.2, 0]) ankle_2.joint(axis=[1, 1, 0], name="ankle_2", pos=[0.0, 0.0, 0.0], range=[-70, -30], type="hinge") ankle_2.geom(fromto=[0.0, 0.0, 0.0, -0.4, 0.4, 0.0], name="right_ankle_geom", size="0.08", type="capsule") back_left_leg = ant.body(name="back_left_leg", pos=[0, 0, 0]) back_left_leg.geom(fromto=[0.0, 0.0, 0.0, -0.2, -0.2, 0.0], name="aux_3_geom", size="0.08", type="capsule") aux_3 = back_left_leg.body(name="aux_3", pos=[-0.2, -0.2, 0]) aux_3.joint(axis=[0, 0, 1], name="hip_3", pos=[0.0, 0.0, 0.0], range=[-30, 30], type="hinge") aux_3.geom(fromto=[0.0, 0.0, 0.0, -0.2, -0.2, 0.0], name="backleft_leg_geom", size="0.08", type="capsule") ankle_3 = aux_3.body(pos=[-0.2, -0.2, 0]) ankle_3.joint(axis=[-1, 1, 0], name="ankle_3", pos=[0.0, 0.0, 0.0], range=[-70, -30], type="hinge") ankle_3.geom(fromto=[0.0, 0.0, 0.0, -0.4, -0.4, 0.0], name="backleft_ankle_geom", size="0.08", type="capsule") back_right_leg = ant.body(name="back_right_leg", pos=[0, 0, 0]) back_right_leg.geom(fromto=[0.0, 0.0, 0.0, 0.2, -0.2, 0.0], name="aux_4_geom", size="0.08", type="capsule") aux_4 = back_right_leg.body(name="aux_4", pos=[0.2, -0.2, 0]) aux_4.joint(axis=[0, 0, 1], name="hip_4", pos=[0.0, 0.0, 0.0], range=[-30, 30], type="hinge") aux_4.geom(fromto=[0.0, 0.0, 0.0, 0.2, -0.2, 0.0], name="backright_leg_geom", size="0.08", type="capsule") ankle_4 = aux_4.body(pos=[0.2, -0.2, 0]) ankle_4.joint(axis=[1, 1, 0], name="ankle_4", pos=[0.0, 0.0, 0.0], range=[30, 70], type="hinge") ankle_4.geom(fromto=[0.0, 0.0, 0.0, 0.4, -0.4, 0.0], name="backright_ankle_geom", size="0.08", type="capsule") target = worldbody.body(name='target', pos=[height/2, width-1.0,-0.5]) target.geom(name='target_geom', conaffinity=2, type='sphere', size=0.2, rgba=[0,0.9,0.1,1]) l = height/2 h = 0.75 w = 0.05 worldbody.geom(conaffinity=1, name="sideS", rgba="0.9 0.4 0.6 1", size=[l, w, h], pos=[height/2, 0, 0], type="box") worldbody.geom(conaffinity=1, name="sideE", rgba="0.9 0.4 0.6 1", size=[w, width/2, h], pos=[height, width/2, 0], type="box") worldbody.geom(conaffinity=1, name="sideN", rgba="0.9 0.4 0.6 1", size=[l, w, h], pos=[height/2, width, 0], type="box") worldbody.geom(conaffinity=1, name="sideW", rgba="0.9 0.4 0.6 1", size=[w, width/2, h], pos=[0, width/2, 0], type="box") # arena wall_ratio = .55#2.0/3 if direction == LEFT: bx, by, bz = (height*(wall_ratio/2), width/2, 0) #bx, by, bz = (height/4, width/2, 0) # bx, by, bz = length * 5/3, length * 5/6 + w, 0 # bx1, by1, bz1 = bx - length/12, by-l/6, bz else: bx, by, bz = (height*(1-wall_ratio/2), width/2, 0) #bx, by, bz = (height*(3/4), width/2, 0) # bx, by, bz = length / 3, length * 5/6 + w, 0 # bx1, by1, bz1 = bx + length/12, by-l/6, bz worldbody.geom(conaffinity=1, name="barrier", rgba="0.9 0.4 0.6 1", size=[l*(wall_ratio), w, h], pos=[bx, by, bz], type="box") # worldbody.geom(conaffinity=1, name="barrier1", rgba="0.9 0.4 0.6 1", size=[w, l/2 - 2*w, h], pos=[length/2, length/2, bz], type="box") # worldbody.geom(conaffinity=1, name="barrier2", rgba="0.9 0.4 0.6 1", size=[l/6, w, h], pos=[bx1, by1, bz1], type="box") # worldbody.geom(conaffinity=1, name="barrier3", rgba="0.9 0.4 0.6 1", size=[w, l/6, h], pos=[bx, by, bz], type="box") # worldbody.geom(conaffinity=1, condim=3, name="floor", rgba="0.4 0.4 0.4 1", size=[l, l, w], pos=[length/2, length/2, -h], # type="box") worldbody.geom(conaffinity="1", condim="3", material="MatPlane", name="floor", pos=[height/2, height/2, -h + w], rgba="0.8 0.9 0.8 1", size="40 40 40", type="plane") actuator = mjcmodel.root.actuator() actuator.motor(ctrllimited="true", ctrlrange="-1.0 1.0", joint="hip_4", gear="30") actuator.motor(ctrllimited="true", ctrlrange="-1.0 1.0", joint="ankle_4", gear="30") actuator.motor(ctrllimited="true", ctrlrange="-1.0 1.0", joint="hip_1", gear="30") actuator.motor(ctrllimited="true", ctrlrange="-1.0 1.0", joint="ankle_1", gear="30") actuator.motor(ctrllimited="true", ctrlrange="-1.0 1.0", joint="hip_2", gear="30") actuator.motor(ctrllimited="true", ctrlrange="-1.0 1.0", joint="ankle_2", gear="30") actuator.motor(ctrllimited="true", ctrlrange="-1.0 1.0", joint="hip_3", gear="30") actuator.motor(ctrllimited="true", ctrlrange="-1.0 1.0", joint="ankle_3", gear="30") return mjcmodel def pusher(goal_pos=np.array([0.45, -0.05, -0.323])): mjcmodel = MJCModel('pusher') mjcmodel.root.compiler(inertiafromgeom="true", angle="radian", coordinate="local") mjcmodel.root.option(timestep="0.01", gravity="0 0 0", iterations="20", integrator="Euler") default = mjcmodel.root.default() default.joint(armature=0.04, damping=1, limited=False) default.geom(friction=[.8, .1, .1], density=300, margin=0.002, condim=1, contype=0, conaffinity=0) worldbody = mjcmodel.root.worldbody() worldbody.light(diffuse=[.5,.5,.5], pos=[0,0,3], dir=[0,0,-1]) worldbody.geom(name='table', type='plane', pos=[0,.5,-.325],size=[1,1,0.1], contype=1, conaffinity=1) r_shoulder_pan_link = worldbody.body(name='r_shoulder_pan_link', pos=[0,-.6,0]) r_shoulder_pan_link.geom(name='e1', type='sphere', rgba=[.6,.6,.6,1], pos=[-0.06,0.05,0.2], size=0.05) r_shoulder_pan_link.geom(name='e2', type='sphere', rgba=[.6,.6,.6,1], pos=[0.06,0.05,0.2], size=0.05) r_shoulder_pan_link.geom(name='e1p', type='sphere', rgba=[.1,.1,.1,1], pos=[-0.06,0.09,0.2], size=0.03) r_shoulder_pan_link.geom(name='e2p', type='sphere', rgba=[.1,.1,.1,1], pos=[0.06,0.09,0.2], size=0.03) r_shoulder_pan_link.geom(name='sp', type='capsule', fromto=[0,0,-0.4,0,0,0.2], size=0.1) r_shoulder_pan_link.joint(name='r_shoulder_pan_joint', type='hinge', pos=[0,0,0], axis=[0,0,1], range=[-2.2854, 1.714602], damping=1.0) r_shoulder_lift_link = r_shoulder_pan_link.body(name='r_shoulder_lift_link', pos=[0.1,0,0]) r_shoulder_lift_link.geom(name='s1', type='capsule', fromto="0 -0.1 0 0 0.1 0", size="0.1") r_shoulder_lift_link.joint(name="r_shoulder_lift_joint", type="hinge", pos="0 0 0", axis="0 1 0", range="-0.5236 1.3963", damping="1.0") r_upper_arm_roll_link = r_shoulder_lift_link.body(name='r_upper_arm_roll_link', pos=[0,0,0]) r_upper_arm_roll_link.geom(name="uar", type="capsule", fromto="-0.1 0 0 0.1 0 0", size="0.02") r_upper_arm_roll_link.joint(name="r_upper_arm_roll_joint", type="hinge", pos="0 0 0", axis="1 0 0", range="-1.5 1.7", damping="0.1") r_upper_arm_link = r_upper_arm_roll_link.body(name='r_upper_arm_link', pos=[0,0,0]) r_upper_arm_link.geom(name="ua", type="capsule", fromto="0 0 0 0.4 0 0", size="0.06") r_elbow_flex_link = r_upper_arm_link.body(name='r_elbow_flex_link', pos=[0.4,0,0]) r_elbow_flex_link.geom(name="ef", type="capsule", fromto="0 -0.02 0 0.0 0.02 0", size="0.06") r_elbow_flex_link.joint(name="r_elbow_flex_joint", type="hinge", pos="0 0 0", axis="0 1 0", range="-2.3213 0", damping="0.1") r_forearm_roll_link = r_elbow_flex_link.body(name='r_forearm_roll_link', pos=[0,0,0]) r_forearm_roll_link.geom(name="fr", type="capsule", fromto="-0.1 0 0 0.1 0 0", size="0.02") r_forearm_roll_link.joint(name="r_forearm_roll_joint", type="hinge", limited="true", pos="0 0 0", axis="1 0 0", damping=".1", range="-1.5 1.5") r_forearm_link = r_forearm_roll_link.body(name='r_forearm_link', pos=[0,0,0]) r_forearm_link.geom(name="fa", type="capsule", fromto="0 0 0 0.291 0 0", size="0.05") r_wrist_flex_link = r_forearm_link.body(name='r_wrist_flex_link', pos=[0.321,0,0]) r_wrist_flex_link.geom(name="wf", type="capsule", fromto="0 -0.02 0 0 0.02 0", size="0.01" ) r_wrist_flex_link.joint(name="r_wrist_flex_joint", type="hinge", pos="0 0 0", axis="0 1 0", range="-1.094 0", damping=".1") r_wrist_roll_link = r_wrist_flex_link.body(name='r_wrist_roll_link', pos=[0,0,0]) r_wrist_roll_link.joint(name="r_wrist_roll_joint", type="hinge", pos="0 0 0", limited="true", axis="1 0 0", damping="0.1", range="-1.5 1.5") r_wrist_roll_link.geom(type="capsule",fromto="0 -0.1 0. 0.0 +0.1 0",size="0.02",contype="1",conaffinity="1") r_wrist_roll_link.geom(type="capsule",fromto="0 -0.1 0. 0.1 -0.1 0",size="0.02",contype="1",conaffinity="1") r_wrist_roll_link.geom(type="capsule",fromto="0 +0.1 0. 0.1 +0.1 0",size="0.02",contype="1",conaffinity="1") tips_arm = r_wrist_roll_link.body(name='tips_arm', pos=[0,0,0]) tips_arm.geom(name="tip_arml",type="sphere",pos="0.1 -0.1 0.",size="0.01") tips_arm.geom(name="tip_armr",type="sphere",pos="0.1 0.1 0.",size="0.01") #object_ = worldbody.body(name="object", pos=[0.45, -0.05, -0.275]) object_ = worldbody.body(name="object", pos=[0.0, 0.0, -0.275]) #object_.geom(rgba="1 1 1 0",type="sphere",size="0.05 0.05 0.05",density="0.00001",conaffinity="0") object_.geom(rgba="1 1 1 1",type="cylinder",size="0.05 0.05 0.05",density="0.00001",conaffinity="0", contype=1) object_.joint(name="obj_slidey",type="slide",pos="0 0 0",axis="0 1 0",range="-10.3213 10.3",damping="0.5") object_.joint(name="obj_slidex",type="slide",pos="0 0 0",axis="1 0 0",range="-10.3213 10.3",damping="0.5") goal = worldbody.body(name='goal', pos=goal_pos) goal.geom(rgba="1 0 0 1",type="cylinder",size="0.08 0.001 0.1",density='0.00001',contype="0",conaffinity="0") goal.joint(name="goal_slidey",type="slide",pos="0 0 0",axis="0 1 0",range="-10.3213 10.3",damping="0.5") goal.joint(name="goal_slidex",type="slide",pos="0 0 0",axis="1 0 0",range="-10.3213 10.3",damping="0.5") actuator = mjcmodel.root.actuator() actuator.motor(joint="r_shoulder_pan_joint",ctrlrange=[-2.0,2.0], ctrllimited=True) actuator.motor(joint="r_shoulder_lift_joint",ctrlrange=[-2.0,2.0], ctrllimited=True) actuator.motor(joint="r_upper_arm_roll_joint",ctrlrange=[-2.0,2.0], ctrllimited=True) actuator.motor(joint="r_elbow_flex_joint",ctrlrange=[-2.0,2.0], ctrllimited=True) actuator.motor(joint="r_forearm_roll_joint",ctrlrange=[-2.0,2.0], ctrllimited=True) actuator.motor(joint="r_wrist_flex_joint",ctrlrange=[-2.0,2.0], ctrllimited=True) actuator.motor(joint="r_wrist_roll_joint",ctrlrange=[-2.0,2.0], ctrllimited=True) return mjcmodel def swimmer(): mjcmodel = MJCModel('swimmer') mjcmodel.root.compiler(inertiafromgeom="true", angle="degree", coordinate="local") mjcmodel.root.option(timestep=0.01, viscosity=0.1, density=4000, integrator="RK4", collision="predefined") default = mjcmodel.root.default() default.joint(armature=0.1) default.geom(rgba=[0.8, .6, .1, 1], condim=1, contype=1, conaffinity=1, material='geom') asset = mjcmodel.root.asset() asset.texture(builtin='gradient', height=100, rgb1=[1,1,1], rgb2=[0,0,0], type='skybox', width=100) asset.texture(builtin='flat', height=1278, mark='cross', markrgb=[1,1,1], name='texgeom', random=0.01, rgb1=[0.8,0.6,0.4], rgb2=[0.8,0.6,0.4], type='cube', width=127) asset.texture(builtin='checker', height=100, name='texplane',rgb1=[0,0,0], rgb2=[0.8,0.8,0.8], type='2d', width=100) asset.material(name='MatPlane', reflectance=0.5, shininess=1, specular=1, texrepeat=[30,30], texture='texplane') asset.material(name='geom', texture='texgeom', texuniform=True) worldbody = mjcmodel.root.worldbody() worldbody.light(cutoff=100, diffuse=[1,1,1], dir=[0,0,-1.3], directional=True, exponent=1, pos=[0,0,1.3], specular=[.1,.1,.1]) worldbody.geom(conaffinity=1, condim=3, material='MatPlane', name='floor', pos=[0,0,-0.1], rgba=[0.8,0.9,0.9,1], size=[40,40,0.1], type='plane') torso = worldbody.body(name='torso', pos=[0,0,0]) torso.geom(density=1000, fromto=[1.5,0,0,0.5,0,0], size=0.1, type='capsule') torso.joint(axis=[1,0,0], name='slider1', pos=[0,0,0], type='slide') torso.joint(axis=[0,1,0], name='slider2', pos=[0,0,0], type='slide') torso.joint(axis=[0,0,1], name='rot', pos=[0,0,0], type='hinge') mid = torso.body(name='mid', pos=[0.5,0,0]) mid.geom(density=1000, fromto=[0,0,0,-1,0,0], size=0.1, type='capsule') mid.joint(axis=[0,0,1], limited=True, name='rot2', pos=[0,0,0], range=[-100,100], type='hinge') back = mid.body(name='back', pos=[-1,0,0]) back.geom(density=1000, fromto=[0,0,0,-1,0,0], size=0.1, type='capsule') back.joint(axis=[0,0,1], limited=True, name='rot3', pos=[0,0,0], range=[-100,100], type='hinge') actuator = mjcmodel.root.actuator() actuator.motor(ctrllimited=True, ctrlrange=[-1,1], gear=150, joint='rot2') actuator.motor(ctrllimited=True, ctrlrange=[-1,1], gear=150, joint='rot3') return mjcmodel def swimmer_rllab(): mjcmodel = MJCModel('swimmer') mjcmodel.root.compiler(inertiafromgeom="true", angle="degree", coordinate="local") mjcmodel.root.option(timestep=0.01, viscosity=0.1, density=4000, integrator="Euler", iterations=1000, collision="predefined") custom = mjcmodel.root.custom() custom.numeric(name='frame_skip', data=50) default = mjcmodel.root.default() #default.joint(armature=0.1) default.geom(rgba=[0.8, .6, .1, 1], condim=1, contype=1, conaffinity=1, material='geom') asset = mjcmodel.root.asset() asset.texture(builtin='gradient', height=100, rgb1=[1,1,1], rgb2=[0,0,0], type='skybox', width=100) asset.texture(builtin='flat', height=1278, mark='cross', markrgb=[1,1,1], name='texgeom', random=0.01, rgb1=[0.8,0.6,0.4], rgb2=[0.8,0.6,0.4], type='cube', width=127) asset.texture(builtin='checker', height=100, name='texplane',rgb1=[0,0,0], rgb2=[0.8,0.8,0.8], type='2d', width=100) asset.material(name='MatPlane', reflectance=0.5, shininess=1, specular=1, texrepeat=[30,30], texture='texplane') asset.material(name='geom', texture='texgeom', texuniform=True) worldbody = mjcmodel.root.worldbody() worldbody.light(cutoff=100, diffuse=[1,1,1], dir=[0,0,-1.3], directional=True, exponent=1, pos=[0,0,1.3], specular=[.1,.1,.1]) worldbody.geom(conaffinity=1, condim=3, material='MatPlane', name='floor', pos=[0,0,-0.1], rgba=[0.8,0.9,0.9,1], size=[40,40,0.1], type='plane') torso = worldbody.body(name='torso', pos=[0,0,0]) torso.geom(density=1000, fromto=[1.5,0,0,0.5,0,0], size=0.1, type='capsule') torso.joint(axis=[1,0,0], name='slider1', pos=[0,0,0], type='slide') torso.joint(axis=[0,1,0], name='slider2', pos=[0,0,0], type='slide') torso.joint(axis=[0,0,1], name='rot', pos=[0,0,0], type='hinge') mid = torso.body(name='mid', pos=[0.5,0,0]) mid.geom(density=1000, fromto=[0,0,0,-1,0,0], size=0.1, type='capsule') mid.joint(axis=[0,0,1], limited=True, name='rot2', pos=[0,0,0], range=[-100,100], type='hinge') back = mid.body(name='back', pos=[-1,0,0]) back.geom(density=1000, fromto=[0,0,0,-1,0,0], size=0.1, type='capsule') back.joint(axis=[0,0,1], limited=True, name='rot3', pos=[0,0,0], range=[-100,100], type='hinge') actuator = mjcmodel.root.actuator() actuator.motor(ctrllimited=True, ctrlrange=[-50,50], joint='rot2') actuator.motor(ctrllimited=True, ctrlrange=[-50,50], joint='rot3') return mjcmodel

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py

Python

kindle_book/view/view_blueprint.py

CoderHito/kindle_book

b808435604f1d071ac335443dc951ee6274210c4

[ "MIT" ]

null

kindle_book/view/view_blueprint.py

CoderHito/kindle_book

b808435604f1d071ac335443dc951ee6274210c4

[ "MIT" ]

null

kindle_book/view/view_blueprint.py

CoderHito/kindle_book

b808435604f1d071ac335443dc951ee6274210c4

[ "MIT" ]

null

from flask import Blueprint view_blueprint = Blueprint("/", __name__, template_folder="templates", url_prefix='/') @view_blueprint.route("/") def index(): return "index"

22

86

0.721591

20

176

5.95

0.75

0.218487

0

0.119318

176

8

87

22

0.767742

0

0.096045

0

1

0.2

false

0

0.2

0.6

1

0

null

1

0

1

0

null

0

1

0

6

7ec81ce5d64c2393949b0f0d94825bf98b7d20b0

231

py

Python

py-appscript/tags/py-appscript-0.21.1/appscript_3x/sample/aem/filter_reference.py

BarracudaPff/code-golf-data-pythpn

42e8858c2ebc6a061012bcadb167d29cebb85c5e

[ "MIT" ]

null

py-appscript/tags/py-appscript-0.21.1/appscript_3x/sample/aem/filter_reference.py

BarracudaPff/code-golf-data-pythpn

42e8858c2ebc6a061012bcadb167d29cebb85c5e

[ "MIT" ]

null

py-appscript/tags/py-appscript-0.21.1/appscript_3x/sample/aem/filter_reference.py

BarracudaPff/code-golf-data-pythpn

42e8858c2ebc6a061012bcadb167d29cebb85c5e

[ "MIT" ]

null

from aem import * print(Application(findapp.byname("Finder")).event(b"coregetd", {b"----": app.property(b"home").elements(b"cobj").byfilter(its.property(b"pnam").beginswith("d").AND(its.property(b"pnam").ne("Documents")))}).send())

115.5

213

0.69697

34

231

4.735294

0.735294

0.167702

0.149068

0.198758

0

0.025974

231

2

213

115.5

0.715556

0

0.189655

0

1

0

true

0

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0

0.5

0

null

0

1

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1

0

1

0

null

0

1

0

1

0

1

0

6

7d122d88c194eb7a22976677877a7049504f543e

48

py

Python

AqEquil/.ipynb_checkpoints/__init__-checkpoint.py

worm-portal/AqEquil

44cf30e779f31ed3fcf57e431ba9e43df99dca94

[ "MIT" ]

5

2021-11-04T00:44:57.000Z

2022-03-14T18:57:43.000Z

AqEquil/__init__.py

worm-portal/AqEquil

44cf30e779f31ed3fcf57e431ba9e43df99dca94

[ "MIT" ]

null

AqEquil/__init__.py

worm-portal/AqEquil

44cf30e779f31ed3fcf57e431ba9e43df99dca94

[ "MIT" ]

null

from .AqSpeciation import AqEquil, load, compare

48

0.833333

6

48

6.666667

1

0

0.104167

48

1

48

0.930233

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

7d2e764ba6796a82abf3a421eafcca2c10eb7293

2,365

py

Python

dvh/modules/tools/auth.py

cutright/DVH-Analytics-Bokeh

4f96c14566081e3fe183fe52baeb01dc82923908

[ "BSD-3-Clause" ]

3

2020-05-30T05:11:48.000Z

2021-02-24T20:28:32.000Z

dvh/modules/tools/auth.py

cutright/DVH-Analytics-Bokeh

4f96c14566081e3fe183fe52baeb01dc82923908

[ "BSD-3-Clause" ]

1

2019-07-15T09:26:38.000Z

2019-08-07T22:07:20.000Z

dvh/modules/tools/auth.py

cutright/DVH-Analytics-Bokeh

4f96c14566081e3fe183fe52baeb01dc82923908

[ "BSD-3-Clause" ]

1

2019-10-07T04:48:56.000Z

#!/usr/bin/env python # Users that wish to implement some form of user authentication should fill in this function, keeping the same input # parameters. The following example is for LDAP. Note that proper implementation also necessitates running your # Bokeh with a reverse proxy implementing SSL with something like Apache or NGINX. Please see Bokeh documentation for # some guidance on this. I (the DVH Analytics developer) do not claim to be a security expert by any stretch. The # end-user is entirely liable for proper security implementation. from __future__ import print_function # import ldap # This is from pip install python-ldap. Not necessary, just what we used for our implementation. # Place holder function. Edit this to implement. def check_credentials(username, password, usergroup): # ############################ # # Example Code # ############################ # """Verifies credentials for username and password. # Returns None on success or a string describing the error on failure # # Adapt to your needs # """ # LDAP_SERVER = 'ldaps://someserver:port' # # fully qualified AD user name # LDAP_USERNAME = '%s@somedomain' % username # # your password # LDAP_PASSWORD = password # base_dn = 'somebasedn' # ldap_filter = 'userPrincipalName=%s@somedomain' % username # try: # # build a client # ldap_client = ldap.initialize(LDAP_SERVER) # # # perform a synchronous bind # ldap_client.set_option(ldap.OPT_REFERRALS, 0) # ldap_client.simple_bind_s(LDAP_USERNAME, LDAP_PASSWORD) # # # get all user attributes # result = ldap_client.search_s(base_dn, ldap.SCOPE_SUBTREE, ldap_filter) # ldap_client.unbind() # except ldap.INVALID_CREDENTIALS: # ldap_client.unbind() # print("Login attempt: Invalid credentials") # return False # except ldap.SERVER_DOWN: # print("Login attempt: Server down") # return False # except ldap.LDAPError, e: # ldap_client.unbind() # if type(e.message) == dict and 'desc' in e.message: # print("Login error: %s" % e.message['desc']) # return False # else: # print("Login error: %s" % e.message) # return False # ldap_client.unbind() return True

40.084746

118

0.65074

290

2,365

5.196552

0.531034

0.053086

0.042468

0.02787

0.031851

0

0.000559

0.243975

2,365

58

119

40.775862

0.842282

0.813953

0

1

0.333333

false

0.333333

1

0.333333

0

null

0

1

0

null

0

1

0

1

0

6

7d3cf2a3479a35cfcfa8026de345107c8c277960

84

py

Python

shell.py

srbdev/shell.py

c83f1024b2aa7dec59c430cd617217ac1d985846

[ "MIT" ]

null

shell.py

srbdev/shell.py

c83f1024b2aa7dec59c430cd617217ac1d985846

[ "MIT" ]

null

shell.py

srbdev/shell.py

c83f1024b2aa7dec59c430cd617217ac1d985846

[ "MIT" ]

null

import subprocess def run(s): return subprocess.run(s, check=True, shell=True)

16.8

52

0.72619

13

84

4.692308

0.692308

0.131148

0

0.154762

84

4

53

21

0.859155

0

1

0.333333

false

0

0.333333

1

0

1

0

null

0

1

0

null

0

1

0

1

0

6

7d4419dcb685bcd06f468e55c8a6e8379e1da02b

27

py

Python

DSC 530 - Data Exploration and Analysis/ThinkStats2/thinkstats2/__init__.py

Hakuna-Patata/BU_MSDS_PTW

4759cb2db3e63ae5722bd42771e4d228dfbc733d

[ "MIT" ]

null

DSC 530 - Data Exploration and Analysis/ThinkStats2/thinkstats2/__init__.py

Hakuna-Patata/BU_MSDS_PTW

4759cb2db3e63ae5722bd42771e4d228dfbc733d

[ "MIT" ]

null

DSC 530 - Data Exploration and Analysis/ThinkStats2/thinkstats2/__init__.py

Hakuna-Patata/BU_MSDS_PTW

4759cb2db3e63ae5722bd42771e4d228dfbc733d

[ "MIT" ]

null

from .thinkstats2 import *

13.5

26

0.777778

3

27

7

1

0

0.043478

0.148148

27

1

27

0.869565

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

7d4f71a57ff88f722f1765113ac38211927c86b2

11,675

py

Python

cogs/guess.py

dave-kramer/Vignette

99ae6ae209ad9cb61079b8e09bd3b53a2c17293a

[ "MIT" ]

1

2021-11-23T22:17:12.000Z

cogs/guess.py

dave-kramer/vignette

99ae6ae209ad9cb61079b8e09bd3b53a2c17293a

[ "MIT" ]

null

cogs/guess.py

dave-kramer/vignette

99ae6ae209ad9cb61079b8e09bd3b53a2c17293a

[ "MIT" ]

null

import discord import aiosqlite import random from discord.ext import commands from discord_components import Button, ButtonStyle from load import * # set CHANNELID to channel u want to use guessgame in. class Guess(commands.Cog): def __init__(self, client): self.client = client description = "Guess game" @commands.command(name='guess') async def guess(self, ctx, arg=None): if ctx.channel.id != CHANNELID: await ctx.send("Please use this in `#guess-the-character` channel") else: if arg == "char": random_number = random.randint(0, 999) saved_number = random_number n = random.randint(1, 4) if n == 1: button_1 = Button(label=f"{chardata[saved_number]['name']}", style=ButtonStyle.green, custom_id="1") button_2 = Button(label=f"{chardata[random.randint(0, 999)]['name']}", style=ButtonStyle.green, custom_id="2") button_3 = Button(label=f"{chardata[random.randint(0, 999)]['name']}", style=ButtonStyle.green, custom_id="3") button_4 = Button(label=f"{chardata[random.randint(0, 999)]['name']}", style=ButtonStyle.green, custom_id="4") elif n == 2: button_1 = Button(label=f"{chardata[random.randint(0, 999)]['name']}", style=ButtonStyle.green, custom_id="1") button_2 = Button(label=f"{chardata[saved_number]['name']}", style=ButtonStyle.green, custom_id="2") button_3 = Button(label=f"{chardata[random.randint(0, 999)]['name']}", style=ButtonStyle.green, custom_id="3") button_4 = Button(label=f"{chardata[random.randint(0, 999)]['name']}", style=ButtonStyle.green, custom_id="4") elif n == 3: button_1 = Button(label=f"{chardata[random.randint(0, 999)]['name']}", style=ButtonStyle.green, custom_id="1") button_2 = Button(label=f"{chardata[random.randint(0, 999)]['name']}", style=ButtonStyle.green, custom_id="2") button_3 = Button(label=f"{chardata[saved_number]['name']}", style=ButtonStyle.green, custom_id="3") button_4 = Button(label=f"{chardata[random.randint(0, 999)]['name']}", style=ButtonStyle.green, custom_id="4") else: button_1 = Button(label=f"{chardata[random.randint(0, 999)]['name']}", style=ButtonStyle.green, custom_id="1") button_2 = Button(label=f"{chardata[random.randint(0, 999)]['name']}", style=ButtonStyle.green, custom_id="2") button_3 = Button(label=f"{chardata[random.randint(0, 999)]['name']}", style=ButtonStyle.green, custom_id="3") button_4 = Button(label=f"{chardata[saved_number]['name']}", style=ButtonStyle.green, custom_id="4") await ctx.send(f"{chardata[saved_number]['images']['jpg']['image_url']}", components = [[button_1, button_2, button_3, button_4]]) interaction = await self.client.wait_for("button_click") if int(interaction.custom_id) == n: async with aiosqlite.connect("main.db") as db: async with db.cursor() as cursor: await cursor.execute('SELECT id FROM users WHERE id = ?', (ctx.author.id,)) data = await cursor.fetchone() if data: await cursor.execute('UPDATE users SET points = points + 1 WHERE id = ?', (ctx.author.id,)) else: await cursor.execute('INSERT INTO users (id, user, guild, points) VALUES (?, ?, ?, ?)', (ctx.author.id, ctx.author.display_name, ctx.guild.id, 1)) await db.commit() cur = await db.execute("SELECT points FROM users WHERE id = ?", (ctx.author.id,)) points = await cur.fetchone() await interaction.send(content=f"`{chardata[saved_number]['name']}` was the **CORRECT ANSWER!!**\n{ctx.author.display_name} has `{points[0]}` points.", ephemeral=False) else: async with aiosqlite.connect("main.db") as db: async with db.cursor() as cursor: await cursor.execute('SELECT id FROM users WHERE id = ?', (ctx.author.id,)) data = await cursor.fetchone() if data: await cursor.execute('UPDATE users SET points = points - 2 WHERE id = ?', (ctx.author.id,)) else: await cursor.execute('INSERT INTO users (id, user, guild, points) VALUES (?, ?, ?, ?)', (ctx.author.id, ctx.author.display_name, ctx.guild.id, 0)) await db.commit() cur = await db.execute("SELECT points FROM users WHERE id = ?", (ctx.author.id,)) points = await cur.fetchone() await interaction.send(content=f"Woopsie, **wrong** answer! the correct answer was `{chardata[saved_number]['name']}`\n{ctx.author.display_name} has `{points[0]}` points.", ephemeral=False) elif arg == "anime": random_number = random.randint(0, 499) saved_number = random_number n = random.randint(1, 4) if n == 1: button_1 = Button(label=f"{animedata[saved_number]['title']}", style=ButtonStyle.green, custom_id="1") button_2 = Button(label=f"{animedata[random.randint(0, 499)]['title']}", style=ButtonStyle.green, custom_id="2") button_3 = Button(label=f"{animedata[random.randint(0, 499)]['title']}", style=ButtonStyle.green, custom_id="3") button_4 = Button(label=f"{animedata[random.randint(0, 499)]['title']}", style=ButtonStyle.green, custom_id="4") elif n == 2: button_1 = Button(label=f"{animedata[random.randint(0, 499)]['title']}", style=ButtonStyle.green, custom_id="1") button_2 = Button(label=f"{animedata[saved_number]['title']}", style=ButtonStyle.green, custom_id="2") button_3 = Button(label=f"{animedata[random.randint(0, 499)]['title']}", style=ButtonStyle.green, custom_id="3") button_4 = Button(label=f"{animedata[random.randint(0, 499)]['title']}", style=ButtonStyle.green, custom_id="4") elif n == 3: button_1 = Button(label=f"{animedata[random.randint(0, 499)]['title']}", style=ButtonStyle.green, custom_id="1") button_2 = Button(label=f"{animedata[random.randint(0, 499)]['title']}", style=ButtonStyle.green, custom_id="2") button_3 = Button(label=f"{animedata[saved_number]['title']}", style=ButtonStyle.green, custom_id="3") button_4 = Button(label=f"{animedata[random.randint(0, 499)]['title']}", style=ButtonStyle.green, custom_id="4") else: button_1 = Button(label=f"{animedata[random.randint(0, 499)]['title']}", style=ButtonStyle.green, custom_id="1") button_2 = Button(label=f"{animedata[random.randint(0, 499)]['title']}", style=ButtonStyle.green, custom_id="2") button_3 = Button(label=f"{animedata[random.randint(0, 499)]['title']}", style=ButtonStyle.green, custom_id="3") button_4 = Button(label=f"{animedata[saved_number]['title']}", style=ButtonStyle.green, custom_id="4") await ctx.send(f"{animedata[saved_number]['images']['jpg']['image_url']}", components = [[button_1, button_2, button_3, button_4]]) interaction = await self.client.wait_for("button_click") if int(interaction.custom_id) == n: async with aiosqlite.connect("main.db") as db: async with db.cursor() as cursor: await cursor.execute('SELECT id FROM users WHERE id = ?', (ctx.author.id,)) data = await cursor.fetchone() if data: await cursor.execute('UPDATE users SET points = points + 1 WHERE id = ?', (ctx.author.id,)) else: await cursor.execute('INSERT INTO users (id, user, guild, points) VALUES (?, ?, ?, ?)', (ctx.author.id, ctx.author.display_name, ctx.guild.id, 1)) await db.commit() cur = await db.execute("SELECT points FROM users WHERE id = ?", (ctx.author.id,)) points = await cur.fetchone() await interaction.send(content=f"`{animedata[saved_number]['title']}` was the **CORRECT ANSWER!!**\n{ctx.author.display_name} has `{points[0]}` points.", ephemeral=False) else: async with aiosqlite.connect("main.db") as db: async with db.cursor() as cursor: await cursor.execute('SELECT id FROM users WHERE id = ?', (ctx.author.id,)) data = await cursor.fetchone() if data: await cursor.execute('UPDATE users SET points = points - 2 WHERE id = ?', (ctx.author.id,)) else: await cursor.execute('INSERT INTO users (id, user, guild, points) VALUES (?, ?, ?, ?)', (ctx.author.id, ctx.author.display_name, ctx.guild.id, 0)) await db.commit() cur = await db.execute("SELECT points FROM users WHERE id = ?", (ctx.author.id,)) points = await cur.fetchone() await interaction.send(content=f"Woopsie, **wrong** answer! the correct answer was `{animedata[saved_number]['title']}`\n{ctx.author.display_name} has `{points[0]}` points.", ephemeral=False) else: await ctx.send("Dummy, you can only .guess `anime` or `char`") @commands.command(name='leaderboards') async def leaderboards(self, ctx): async with aiosqlite.connect("main.db") as db: cur = await db.execute("SELECT * FROM users ORDER BY points DESC LIMIT 10") row = await cur.fetchall() leaderboard = [] c = 0 count = 1 embed = discord.Embed(title="Top 10 Leaderboards",url="https://www.youtube.com/watch?v=dQw4w9WgXcQ", color=0x87CEEB) for i in row: if count == 10: continue else: leaderboard.append(f"{count}. **{row[c][1]}** with `{row[c][3]}` points.\n") c += 1 count += 1 string = ''.join([str(item) for item in leaderboard]) embed.add_field(name=f"Guess the Anime & Characters", value=f"{string}", inline=False) embed.set_image(url="https://i.pinimg.com/originals/d5/da/53/d5da5398e5a193120690d0f0ca64d2ed.gif") embed.set_footer(text="Requested by: {}".format(ctx.author.display_name), icon_url="https://cdn.discordapp.com/emojis/754736642761424986.png") await ctx.send(embed=embed) def setup(bot): bot.add_cog(Guess(bot))

62.433155

212

0.544069

1,347

11,675

4.62732

0.122494

0.043639

0.061608

0.138617

0.815659

0.795925

0.79031

0

0.031908

0.312805

11,675

186

213

62.768817

0.744983

0.004454

0

0.625

0

0.034722

0.280805

0.115697

0

0.0007

0

1

0.013889

false

0

0.041667

0

0.069444

0

null

0

1

0

null

0

6

add48aeff492bc5f7de0f1b92fb523d182eab88a

19

py

Python

access/__init__.py

dmcgrath/starmade-blueprint-library

488b264f247a470fb9998859c0ab7b676e489903

[ "Apache-2.0" ]

6

2015-03-10T20:00:04.000Z

2015-07-29T03:02:02.000Z

web/xss/models/__init__.py

greyshell/greyEnum

f6a26d62e6b45475d31273ab800073bd08429a10

[ "MIT" ]

3

2020-12-19T08:58:35.000Z

2021-06-02T03:12:54.000Z

access/__init__.py

dmcgrath/starmade-blueprint-library

488b264f247a470fb9998859c0ab7b676e489903

[ "Apache-2.0" ]

1

2018-01-30T05:07:57.000Z

from user import *

9.5

18

0.736842

3

19

4.666667

1

0

0.210526

19

1

19

0.933333

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

adefe7a5f7efa9ca70e88f4ead90062abe590cbc

8,675

py

Python

tf_quant_finance/volatility/black_scholes.py

0xflotus/tf-quant-finance

9b38bf2006889d66e9eebab00226807bb233843f

[ "Apache-2.0" ]

2

2019-07-26T21:28:16.000Z

2019-07-30T20:53:05.000Z

tf_quant_finance/volatility/black_scholes.py

SeptumCapital/tf-quant-finance

5aba5ddab3a4dd1efa87d5a12fec403315d2ac98

[ "Apache-2.0" ]

null

tf_quant_finance/volatility/black_scholes.py

SeptumCapital/tf-quant-finance

5aba5ddab3a4dd1efa87d5a12fec403315d2ac98

[ "Apache-2.0" ]

1

2020-04-24T22:20:18.000Z

# Copyright 2019 Google LLC # # 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 # # https://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. """Black Scholes prices of a batch of European options.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import tensorflow as tf import tensorflow_probability as tfp def option_price(forwards, strikes, volatilities, expiries, discount_factors=None, is_call_options=None, dtype=None, name=None): """Computes the Black Scholes price for a batch of European options. ## References: [1] Hull, John C., Options, Futures and Other Derivatives. Pearson, 2018. [2] Wikipedia contributors. Black-Scholes model. Available at: https://en.wikipedia.org/w/index.php?title=Black%E2%80%93Scholes_model Args: forwards: A real `Tensor` of any shape. The current forward prices to expiry. strikes: A real `Tensor` of the same shape and dtype as `forwards`. The strikes of the options to be priced. volatilities: A real `Tensor` of same shape and dtype as `forwards`. The volatility to expiry. expiries: A real `Tensor` of same shape and dtype as `forwards`. The expiry for each option. The units should be such that `expiry * volatility**2` is dimensionless. discount_factors: A real `Tensor` of same shape and dtype as the `forwards`. The discount factors to expiry (i.e. e^(-rT)). If not specified, no discounting is applied (i.e. the undiscounted option price is returned). Default value: None, interpreted as discount factors = 1. is_call_options: A boolean `Tensor` of a shape compatible with `forwards`. Indicates whether to compute the price of a call (if True) or a put (if False). If not supplied, it is assumed that every element is a call. dtype: Optional `tf.DType`. If supplied, the dtype to be used for conversion of any supplied non-`Tensor` arguments to `Tensor`. Default value: None which maps to the default dtype inferred by TensorFlow (float32). name: str. The name for the ops created by this function. Default value: None which is mapped to the default name `option_price`. Returns: option_prices: A `Tensor` of the same shape as `forwards`. The Black Scholes price of the options. #### Examples ```python forwards = np.array([1.0, 2.0, 3.0, 4.0, 5.0]) strikes = np.array([3.0, 3.0, 3.0, 3.0, 3.0]) volatilities = np.array([0.0001, 102.0, 2.0, 0.1, 0.4]) expiries = 1.0 computed_prices = option_price( forwards, strikes, volatilities, expiries, dtype=tf.float64) # Expected print output of computed prices: # [ 0. 2. 2.04806848 1.00020297 2.07303131] ``` """ with tf.compat.v1.name_scope( name, default_name='option_price', values=[ forwards, strikes, volatilities, expiries, discount_factors, is_call_options ]): forwards = tf.convert_to_tensor(forwards, dtype=dtype, name='forwards') strikes = tf.convert_to_tensor(strikes, dtype=dtype, name='strikes') volatilities = tf.convert_to_tensor( volatilities, dtype=dtype, name='volatilities') expiries = tf.convert_to_tensor(expiries, dtype=dtype, name='expiries') if discount_factors is None: discount_factors = 1 discount_factors = tf.convert_to_tensor( discount_factors, dtype=dtype, name='discount_factors') normal = tfp.distributions.Normal( loc=tf.zeros([], dtype=forwards.dtype), scale=1) sqrt_var = volatilities * tf.math.sqrt(expiries) d1 = (tf.math.log(forwards / strikes) + sqrt_var * sqrt_var / 2) / sqrt_var d2 = d1 - sqrt_var undiscounted_calls = forwards * normal.cdf(d1) - strikes * normal.cdf(d2) if is_call_options is None: return discount_factors * undiscounted_calls undiscounted_forward = forwards - strikes undiscounted_puts = undiscounted_calls - undiscounted_forward return discount_factors * tf.where(is_call_options, undiscounted_calls, undiscounted_puts) def binary_price(forwards, strikes, volatilities, expiries, discount_factors=None, is_call_options=None, dtype=None, name=None): """Computes the Black Scholes price for a batch of European binary options. The binary call (resp. put) option priced here is that which pays off a unit of cash if the underlying asset has a value greater (resp. smaller) than the strike price at expiry. Hence the binary option price is the discounted probability that the asset will end up higher (resp. lower) than the strike price at expiry. ## References: [1] Hull, John C., Options, Futures and Other Derivatives. Pearson, 2018. [2] Wikipedia contributors. Binary option. Available at: https://en.wikipedia.org/w/index.php?title=Binary_option Args: forwards: A real `Tensor` of any shape. The current forward prices to expiry. strikes: A real `Tensor` of the same shape and dtype as `forwards`. The strikes of the options to be priced. volatilities: A real `Tensor` of same shape and dtype as `forwards`. The volatility to expiry. expiries: A real `Tensor` of same shape and dtype as `forwards`. The expiry for each option. The units should be such that `expiry * volatility**2` is dimensionless. discount_factors: A real `Tensor` of same shape and dtype as the `forwards`. The discount factors to expiry (i.e. e^(-rT)). If not specified, no discounting is applied (i.e. the undiscounted option price is returned). Default value: None, interpreted as discount factors = 1. is_call_options: A boolean `Tensor` of a shape compatible with `forwards`. Indicates whether to compute the price of a call (if True) or a put (if False). If not supplied, it is assumed that every element is a call. dtype: Optional `tf.DType`. If supplied, the dtype to be used for conversion of any supplied non-`Tensor` arguments to `Tensor`. Default value: None which maps to the default dtype inferred by TensorFlow (float32). name: str. The name for the ops created by this function. Default value: None which is mapped to the default name `binary_price`. Returns: option_prices: A `Tensor` of the same shape as `forwards`. The Black Scholes price of the binary options with unit of cash payoff. #### Examples ```python forwards = np.array([1.0, 2.0, 3.0, 4.0, 5.0]) strikes = np.array([3.0, 3.0, 3.0, 3.0, 3.0]) volatilities = np.array([0.0001, 102.0, 2.0, 0.1, 0.4]) expiries = 1.0 prices = binary_price(forwards, strikes, volatilities, expiries, dtype=tf.float64) # Expected print output of prices: # [0. 0. 0.15865525 0.99764937 0.85927418] ``` """ with tf.compat.v1.name_scope( name, default_name='binary_price', values=[ forwards, strikes, volatilities, expiries, discount_factors, is_call_options ]): forwards = tf.convert_to_tensor(forwards, dtype=dtype, name='forwards') strikes = tf.convert_to_tensor(strikes, dtype=dtype, name='strikes') volatilities = tf.convert_to_tensor( volatilities, dtype=dtype, name='volatilities') expiries = tf.convert_to_tensor(expiries, dtype=dtype, name='expiries') if is_call_options is None: is_call_options = True if discount_factors is None: discount_factors = 1 discount_factors = tf.convert_to_tensor( discount_factors, dtype=dtype, name='discount_factors') sqrt_var = volatilities * tf.math.sqrt(expiries) d2 = (tf.math.log(forwards / strikes) - sqrt_var * sqrt_var / 2) / sqrt_var one = tf.ones_like(forwards) d2_signs = tf.where(is_call_options, one, -one) normal = tfp.distributions.Normal( loc=tf.zeros([], dtype=forwards.dtype), scale=1) return discount_factors * normal.cdf(d2_signs * d2)

43.375

80

0.678501

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8,675

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null

0

1

0

1

0

null

0

6

bc0eba9e36d0858396bba041e0f7909eeefd5132

526

py

Python

person_detector.py

RocketMan112/STEM_Mentorship

0deef1c87af45e3f9e465795251d096ce3975e4d

[ "MIT" ]

null

person_detector.py

RocketMan112/STEM_Mentorship

0deef1c87af45e3f9e465795251d096ce3975e4d

[ "MIT" ]

null

person_detector.py

RocketMan112/STEM_Mentorship

0deef1c87af45e3f9e465795251d096ce3975e4d

[ "MIT" ]

null

import logging log = logging.getLogger(__name__) class PersonDetectorBase: def initialize(self): pass def name(self): return "Error - Base PersonDetector class, not for direct use" class FakeFixedPersonDetector(PersonDetectorBase): def initialize(self): pass def name(self): return "FakeFixedPersonDetector" class ComputerVisionPersonDetector(PersonDetectorBase): def initialize(self): pass def name(self): return "ComputerVisionPersonDetector"

19.481481

70

0.69962

48

526

7.583333

0.4375

0.173077

0.255495

0.288462

0.461538

0

0.230038

526

26

71

20.230769

0.898765

0

0.529412

0

0.197719

0.096958

0

1

0.352941

false

0.176471

0.058824

0.176471

0.764706

0

null

0

1

0

null

0

1

0

1

0

1

0

6

bc1326683f367289a70949a21030659b628f79fe

38

py

Python

app/app.py

thanakijwanavit/samNbdevTemplate

85cb0ef31aca4cc243091f55fefe53d3a0b980e2

[ "Apache-2.0" ]

1

2020-11-23T09:17:18.000Z

app/app.py

thanakijwanavit/samNbdevTemplate

85cb0ef31aca4cc243091f55fefe53d3a0b980e2

[ "Apache-2.0" ]

null

app/app.py

thanakijwanavit/samNbdevTemplate

85cb0ef31aca4cc243091f55fefe53d3a0b980e2

[ "Apache-2.0" ]

1

2020-12-01T14:50:04.000Z

from src.helloworld import helloworld

19

37

0.868421

5

38

6.6

0.8

0

0.105263

38

1

38

0.970588

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

bc26d3cae94468ee30f8045b0ca6e9624eda04d1

137

py

Python

planet/admin.py

Dikutal/Dikutal

f09b15ab4bd20feafce464a82e0be82b7b5d94d6

[ "MIT" ]

null

planet/admin.py

Dikutal/Dikutal

f09b15ab4bd20feafce464a82e0be82b7b5d94d6

[ "MIT" ]

null

planet/admin.py

Dikutal/Dikutal

f09b15ab4bd20feafce464a82e0be82b7b5d94d6

[ "MIT" ]

null

from planet.models import PlanetFeed, PlanetFeedAdmin from django.contrib import admin admin.site.register(PlanetFeed, PlanetFeedAdmin)

27.4

53

0.854015

16

137

7.3125

0.6875

0.42735

0

0.087591

137

4

54

34.25

0.936

0

1

0

true

0

0.666667

0

0.666667

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

bc2c26090dd50ad808463a9507ffa38bb669f587

400

py

Python

scripts/reactor/chaoshontaleBoss.py

G00dBye/YYMS

1de816fc842b6598d5b4b7896b6ab0ee8f7cdcfb

[ "MIT" ]

54

2019-04-16T23:24:48.000Z

2021-12-18T11:41:50.000Z

scripts/reactor/chaoshontaleBoss.py

G00dBye/YYMS

1de816fc842b6598d5b4b7896b6ab0ee8f7cdcfb

[ "MIT" ]

3

2019-05-19T15:19:41.000Z

2020-04-27T16:29:16.000Z

scripts/reactor/chaoshontaleBoss.py

G00dBye/YYMS

1de816fc842b6598d5b4b7896b6ab0ee8f7cdcfb

[ "MIT" ]

49

2020-11-25T23:29:16.000Z

2022-03-26T16:20:24.000Z

# Easy horntail gem sm.spawnMob(8810102, 95, 260, False) sm.spawnMob(8810103, 95, 260, False) sm.spawnMob(8810104, 95, 260, False) sm.spawnMob(8810105, 95, 260, False) sm.spawnMob(8810106, 95, 260, False) sm.spawnMob(8810107, 95, 260, False) sm.spawnMob(8810108, 95, 260, False) sm.spawnMob(8810109, 95, 260, False) sm.spawnMob(8810118, 95, 260, False, 10000000000) #10,000,000,000 sm.removeReactor()

36.363636

65

0.7375

64

400

4.609375

0.328125

0.305085

0.325424

0.542373

0

0.363128

0.105

400

11

66

36.363636

0.460894

0.0775

0

1

0

true

0

null

1

0

1

0

1

0

null

0

1

0

6

70f0da6e6bac9d40d0cb741e67a9107f8f4085d4

4,883

py

Python

tests/test_model_loader_dumper.py

FlorianLudwig/json-five

b13cc74aa334b471473811e486746b9104e81fe0

[ "Apache-2.0" ]

13

2020-05-18T22:30:26.000Z

2022-02-15T13:15:34.000Z

tests/test_model_loader_dumper.py

FlorianLudwig/json-five

b13cc74aa334b471473811e486746b9104e81fe0

[ "Apache-2.0" ]

23

2020-05-19T06:22:16.000Z

2021-08-05T00:59:47.000Z

tests/test_model_loader_dumper.py

FlorianLudwig/json-five

b13cc74aa334b471473811e486746b9104e81fe0

[ "Apache-2.0" ]

1

2021-07-31T18:10:01.000Z

import math import pytest from json5.loader import loads, JsonIdentifier, ModelLoader from sly.lex import LexError from json5.dumper import dumps, ModelDumper @pytest.mark.parametrize('json_string', [ """{"foo":"bar"}""", """{"foo": "bar"}""", """{"foo":"bar","bacon":"eggs"}""", """{"foo": "bar", "bacon" : "eggs"}""", """["foo","bar","baz"]""", """[ "foo", "bar" , "baz" ]""", """{"foo":\n "bar"\n}""", """{"foo": {"bacon": "eggs"}}""", """ {"foo":"bar"}""", """{"foo": "bar"} """, """{'foo': 'bar'}""", """{"foo": 'bar'}""", """{"foo": "bar",}""", """["foo","bar", "baz",]""", """["foo", "bar", "baz", ]""", """["foo", "bar", "baz" ,]""", """[["foo"], ["foo","bar"], "baz"]""", """{unquoted: "foo"}""", """{unquoted: "foo"}""", """["foo"]""", """["foo" , ]""", ]) def test_round_trip_model_load_dump(json_string): assert dumps(loads(json_string, loader=ModelLoader()), dumper=ModelDumper()) == json_string def test_object_load_with_line_comment(): json_string = """{ // line comment "foo": "bar" }""" assert dumps(loads(json_string, loader=ModelLoader()), dumper=ModelDumper()) == json_string def test_object_with_multiline_comment(): json_string = """{ /* foo bar */ "foo": "bar" // Foobar }""" assert dumps(loads(json_string, loader=ModelLoader()), dumper=ModelDumper()) == json_string def test_array_load_with_line_comment(): json_string = """[ // line comment "foo", "bar" ]""" assert dumps(loads(json_string, loader=ModelLoader()), dumper=ModelDumper()) == json_string def test_array_with_multiline_comment(): json_string = """[ /* foo bar */ "foo", "bar" ]""" assert dumps(loads(json_string, loader=ModelLoader()), dumper=ModelDumper()) == json_string def test_nested_object(): json_string = """{"foo": {"bacon": "eggs"}}""" assert dumps(loads(json_string, loader=ModelLoader()), dumper=ModelDumper()) == json_string def test_single_quote_with_escape_single_quote(): json_string = r"""{'fo\'o': 'bar'}""" assert dumps(loads(json_string, loader=ModelLoader()), dumper=ModelDumper()) == json_string def test_double_quote_with_escape_double_quote(): json_string = r"""{"fo\"o": "bar"}""" assert dumps(loads(json_string, loader=ModelLoader()), dumper=ModelDumper()) == json_string def test_escape_sequence_strings(): json_string = r"""'\A\C\/\D\C'""" assert dumps(loads(json_string, loader=ModelLoader()), dumper=ModelDumper()) == json_string def test_line_continuations(): json_string = r"""'Hello \ world!'""" assert dumps(loads(json_string, loader=ModelLoader()), dumper=ModelDumper()) == json_string @pytest.mark.parametrize("terminator", ["\r\n", "\n", "\u2028", "\u2029"]) def test_line_continuations_alternate_terminators(terminator): json_string = f"""'Hello \\{terminator}world!'""" assert dumps(loads(json_string, loader=ModelLoader()), dumper=ModelDumper()) == json_string def test_number_literals_inf_nan(): json_string = """{ "positiveInfinity": Infinity, "negativeInfinity": -Infinity, "notANumber": NaN,}""" assert dumps(loads(json_string, loader=ModelLoader()), dumper=ModelDumper()) == json_string def test_number_literals(): json_string = """{ "integer": 123, "withFractionPart": 123.456, "onlyFractionPart": .456, "withExponent": 123e-2}""" assert dumps(loads(json_string, loader=ModelLoader()), dumper=ModelDumper()) == json_string def test_escape_sequences(): json_string = r"""{ "foo": "foo\nbar\nbaz", "bar": "foo\\bar\\baz", "baz": "foo\tbar\tbaz"}""" assert dumps(loads(json_string, loader=ModelLoader()), dumper=ModelDumper()) == json_string def test_empty_object(): json_string = "{}" assert dumps(loads(json_string, loader=ModelLoader()), dumper=ModelDumper()) == json_string def test_empty_array(): json_string = "[]" assert dumps(loads(json_string, loader=ModelLoader()), dumper=ModelDumper()) == json_string def test_hexadecimal_load(): json_string = """ { positiveHex: 0xdecaf, negativeHex: -0xC0FFEE,}""" assert dumps(loads(json_string, loader=ModelLoader()), dumper=ModelDumper()) == json_string def test_load_empty_array_with_whitespace(): json_string = "{ }" assert dumps(loads(json_string, loader=ModelLoader()), dumper=ModelDumper()) == json_string def test_load_empty_object_wtih_whitespace(): json_string = "[ ]" assert dumps(loads(json_string, loader=ModelLoader()), dumper=ModelDumper()) == json_string def test_load_empty_object_with_comments(): json_string = "{ // foo \n}" assert dumps(loads(json_string, loader=ModelLoader()), dumper=ModelDumper()) == json_string def test_load_empty_array_with_comments(): json_string = "[ // foo \n]" assert dumps(loads(json_string, loader=ModelLoader()), dumper=ModelDumper()) == json_string

31.301282

95

0.650625

563

4,883

5.387211

0.165187

0.211012

0.110781

0.138477

0.732608

0.715463

0.700626

0.674909

0

0.0069

0.139259

4,883

155

96

31.503226

0.714728

0

0.274336

0

0.162914

0.004825

0

0.003447

0

0.185841

1

0.185841

false

0

0.044248

0

0.230089

0

null

1

0

1

0

null

0

6

cb103ce609e52eda6749d0904732ea56ffe5405c

95

py

Python

mitest.py

tomasmerencio/citest

31a973b251eaad77363f183ec62b2fc3ac86f0b4

[ "MIT" ]

null

mitest.py

tomasmerencio/citest

31a973b251eaad77363f183ec62b2fc3ac86f0b4

[ "MIT" ]

null

mitest.py

tomasmerencio/citest

31a973b251eaad77363f183ec62b2fc3ac86f0b4

[ "MIT" ]

null

import server def test_webapp_index(): assert server.index() == 'Hola que tal como estas'

19

54

0.715789

14

95

4.714286

0.857143

0

0.178947

95

4

55

23.75

0.846154

0

0.242105

0

0.333333

1

0.333333

true

0

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

cb6331781052a7e3ef04bde332b46222ced78c4a

25

py

Python

src/nets/__init__.py

IngridNavarroA/SqueezeSeg

e1c0e4b850c20bba309fb857f3af6a1756cfceb1

[ "BSD-2-Clause" ]

5

2018-10-25T09:11:29.000Z

2019-06-18T09:48:46.000Z

src/nets/__init__.py

navarrs/squeeze-seg

e1c0e4b850c20bba309fb857f3af6a1756cfceb1

[ "BSD-2-Clause" ]

null

src/nets/__init__.py

navarrs/squeeze-seg

e1c0e4b850c20bba309fb857f3af6a1756cfceb1

[ "BSD-2-Clause" ]

null

from squeezeSeg import *

12.5

24

0.8

3

25

6.666667

1

0

0.16

25

1

25

0.952381

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

69839c18831cc3d6b228203c81990ea71f342c0a

97

py

Python

py/memoization_decorator.py

scoraig52/code

c9335071266267227b56e48861a4f188d16ca4a4

[ "MIT" ]

2

2021-02-18T04:42:40.000Z

2021-12-12T00:27:42.000Z

py/memoization_decorator.py

akar-0/code

be15d79e7c9de107cc66cbdfcb3ae91a799607dd

[ "MIT" ]

null

py/memoization_decorator.py

akar-0/code

be15d79e7c9de107cc66cbdfcb3ae91a799607dd

[ "MIT" ]

1

2021-11-20T10:24:09.000Z

# https://www.codewars.com/kata/reviews/54dbed5950602318ef0000bb/groups/54dc4a8303e88a91b40017c3

48.5

96

0.865979

9

97

9.333333

1

0

0.347368

0.020619

97

1

97

0.536842

0.969072

0

null

0

null

0

null

1

0

null

1

null

true

0

null

1

0

null

0

1

0

1

0

1

0

null

1

0

1

0

6

69ab46c1a858bc997210ee166811252ff5ba8f21

2,012

py

Python

CA-project/100-csr.py

simewu/openssl

b4b95c47e8efc25630bd3db91aa1977b591317a1

[ "OpenSSL" ]

null

CA-project/100-csr.py

simewu/openssl

b4b95c47e8efc25630bd3db91aa1977b591317a1

[ "OpenSSL" ]

null

CA-project/100-csr.py

simewu/openssl

b4b95c47e8efc25630bd3db91aa1977b591317a1

[ "OpenSSL" ]

null

import os import time count = input("Enter number of times to loop: \n") count = int(count) number = count alg = input("Enter alg to use: \n") if alg == "rsa": bits = input("enter bits for rsa") startTime = time.time() for i in range (count): current = i current = str(current) myCmd = '/home/pi/openssl/apps/openssl genpkey -algorithm rsa -out /home/pi/openssl/CA-project/csr/key_srv'+current+'.key -pkeyopt rsa_keygen_bits:'+bits+' > /dev/null 2>&1' os.system(myCmd) endTime = time.time() print ("Key generation time: ") print (endTime-startTime) startTime = time.time() for i in range (number): number = i number = str(number) myCmd = '/home/pi/openssl/apps/openssl req -new -key /home/pi/openssl/CA-project/csr/key_srv'+number+'.key -out /home/pi/openssl/CA-project/csr/key_srv'+number+'.csr -nodes -pkeyopt rsa_keygen_bits:2048 -subj "/CN=oqstest server" -config /home/pi/openssl/apps/openssl.cnf > /dev/null 2>&1' os.system(myCmd) endTime = time.time() print ("csr generation time") print (endTime-startTime) else: startTime = time.time() for i in range (count): current = i current = str(current) myCmd = '/home/pi/openssl/apps/openssl genpkey -algorithm '+alg+' -out /home/pi/openssl/CA-project/csr/key_srv'+current+'.key > /dev/null 2>&1' os.system(myCmd) endTime = time.time() print ("Key generation time: ") print (endTime-startTime) startTime = time.time() for i in range (number): number = i number = str(number) myCmd = '/home/pi/openssl/apps/openssl req -new -key /home/pi/openssl/CA-project/csr/key_srv'+number+'.key -out /home/pi/openssl/CA-project/csr/key_srv'+number+'.csr -nodes -subj "/CN=oqstest server" -config /home/pi/openssl/apps/openssl.cnf > /dev/null 2>&1' os.system(myCmd) endTime = time.time() print ("Csr generation time: ") print (endTime-startTime)

35.928571

297

0.63668

289

2,012

4.397924

0.204152

0.056648

0.122738

0.080252

0.851298

0

0.007571

0.212227

2,012

56

298

35.928571

0.794322

0

0.666667

0

0.111111

0.448584

0.206657

0

1

0

false

0

0.044444

0

0.044444

0.177778

0

null

0

1

0

null

0

6

69c9c17118da1b34e81dd625a9f2369d022c8f52

559

py

Python

fpipe/meta/__init__.py

vkvam/fpipe

2905095f46923c6c4c460c3d154544b654136df4

[ "MIT" ]

18

2019-12-16T17:55:57.000Z

2020-10-21T23:25:40.000Z

fpipe/meta/__init__.py

vkvam/fpipe

2905095f46923c6c4c460c3d154544b654136df4

[ "MIT" ]

23

2019-12-11T14:15:08.000Z

2020-02-17T12:53:21.000Z

fpipe/meta/__init__.py

vkvam/fpipe

2905095f46923c6c4c460c3d154544b654136df4

[ "MIT" ]

null

from .blocksize import BlockSize # noqa:F401 from .bucket import Bucket # noqa:F401 from .checksum import MD5 # noqa:F401 from .host import Host # noqa:F401 from .mime import Mime # noqa:F401 from .modified import Modified # noqa:F401 from .password import Password # noqa:F401 from .path import Path # noqa:F401 from .port import Port # noqa:F401 from .prefix import Prefix # noqa:F401 from .size import Size # noqa:F401 from .username import Username # noqa:F401 from .version import Version # noqa:F401 from .stream import Stream # noqa:F401

37.266667

45

0.749553

84

559

4.988095

0.22619

0.267303

0.372315

0

0.093275

0.175313

559

14

46

39.928571

0.815618

0.248658

0

1

0

true

0.071429

1

0

1

0

null

1

0

null

0

1

0

1

0

6

0e163640ffa42f2f6748233324772a7e206870e3

19

py

Python

decoder/__init__.py

gaoyiyeah/KWS-CTC

28fdc2062281996d6408e41a9b49febf3334d730

[ "MIT" ]

340

2017-12-15T22:41:59.000Z

2022-03-31T14:51:05.000Z

decoder/__init__.py

gaoyiyeah/KWS-CTC

28fdc2062281996d6408e41a9b49febf3334d730

[ "MIT" ]

12

2018-03-08T07:52:20.000Z

2022-02-09T23:26:52.000Z

decoder/__init__.py

gaoyiyeah/KWS-CTC

28fdc2062281996d6408e41a9b49febf3334d730

[ "MIT" ]

82

2017-12-21T01:05:04.000Z

2021-12-29T09:27:23.000Z

from kws import *

6.333333

17

0.684211

3

19

4.333333

1

0

0.263158

19

2

18

9.5

0.928571

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

384ed7e73bf6ba91620c00b58bd0e884a927dba3

210

py

Python

src/rl_with_teachers/envs/__init__.py

jacobphillips99/ac-teach

e01afee47c65b79922ba3a8cea5b0b4dd1e01c1e

[ "MIT" ]

19

2019-10-19T09:05:08.000Z

2022-01-27T13:36:37.000Z

src/rl_with_teachers/envs/__init__.py

jacobphillips99/ac-teach

e01afee47c65b79922ba3a8cea5b0b4dd1e01c1e

[ "MIT" ]

13

2019-12-07T12:47:20.000Z

2022-01-13T01:44:35.000Z

src/rl_with_teachers/envs/__init__.py

jacobphillips99/ac-teach

e01afee47c65b79922ba3a8cea5b0b4dd1e01c1e

[ "MIT" ]

8

2019-10-24T23:36:58.000Z

2022-01-27T13:36:39.000Z

# Import stuff to register envs from rl_with_teachers.envs.path import * from rl_with_teachers.envs.pick_place import * from rl_with_teachers.envs.reach import * from rl_with_teachers.envs.hook_sweep import *

30

46

0.828571

35

210

4.685714

0.428571

0.146341

0.243902

0.439024

0.646341

0.512195

0

0.109524

210

6

47

35

0.877005

0.138095

0

1

0

true

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

6

384ee32c51acb6d619685a01ae66b5d2c6559fbf

227

py

Python

sunpy/data/data_manager/__init__.py

mridullpandey/sunpy

65bf70731a8147899b8c0fca8b3b1a386e47c010

[ "BSD-2-Clause" ]

628

2015-01-14T17:34:10.000Z

2022-03-29T06:07:50.000Z

sunpy/data/data_manager/__init__.py

mridullpandey/sunpy

65bf70731a8147899b8c0fca8b3b1a386e47c010

[ "BSD-2-Clause" ]

3,983

2015-01-03T11:16:21.000Z

2022-03-31T16:55:38.000Z

sunpy/data/data_manager/__init__.py

mridullpandey/sunpy

65bf70731a8147899b8c0fca8b3b1a386e47c010

[ "BSD-2-Clause" ]

582

2015-01-14T10:09:24.000Z

2022-03-29T06:07:12.000Z

from sunpy.data.data_manager.cache import Cache from sunpy.data.data_manager.downloader import ParfiveDownloader from sunpy.data.data_manager.manager import DataManager from sunpy.data.data_manager.storage import SqliteStorage

45.4

64

0.876652

32

227

6.09375

0.34375

0.184615

0.266667

0.348718

0.492308

0

0.070485

227

4

65

56.75

0.924171

0

1

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true

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null

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1

0

null

0

1

0

1

0

1

0

6

38984dd422c12f9d474b54ce454d347b9dae2a76

43

py

Python

rpi_7segment/__init__.py

agjendem/7segment

a568f93173672f88e6fbd32243de2c52b2a717dc

[ "MIT" ]

null

rpi_7segment/__init__.py

agjendem/7segment

a568f93173672f88e6fbd32243de2c52b2a717dc

[ "MIT" ]

null

rpi_7segment/__init__.py

agjendem/7segment

a568f93173672f88e6fbd32243de2c52b2a717dc

[ "MIT" ]

1

2019-11-07T12:00:54.000Z

from rpi_7segment.segments import Segments

21.5

42

0.883721

6

43

6.166667

0.833333

0

0.025641

0.093023

43

1

43

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0

1

0

true

0

1

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1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

38a9e8668b179abb51ddf9663f9e537e89b5dba7

170

py

Python

spark/spark_streaming/src/main.py

jonathansantoscunha/data_engineering

f45c6238b27aca70f3ae25d8a377c790e656d5eb

[ "MIT" ]

null

spark/spark_streaming/src/main.py

jonathansantoscunha/data_engineering

f45c6238b27aca70f3ae25d8a377c790e656d5eb

[ "MIT" ]

null

spark/spark_streaming/src/main.py

jonathansantoscunha/data_engineering

f45c6238b27aca70f3ae25d8a377c790e656d5eb

[ "MIT" ]

null

from com.example.app.streaming_app import StreamingApp from com.example.handler.spark import Spark Spark.start_streaming(StreamingApp().handler) #StreamingApp().handler

28.333333

54

0.841176

22

170

6.409091

0.454545

0.099291

0.198582

0

0.064706

170

5

55

34

0.886792

0.129412

0

1

0

true

0

0.666667

0

0.666667

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

6

38d40a056df4b2fb7a6ee9225e80b18f6b5c5bdf

40

py

Python

src/notifications/__init__.py

LAC-Japan/ransomwatch

211b3bc8670d9fe38b915cac6befbe149cecae43

[ "MIT" ]

244

2021-04-14T18:20:55.000Z

2022-03-31T12:35:47.000Z

src/notifications/__init__.py

LAC-Japan/ransomwatch

211b3bc8670d9fe38b915cac6befbe149cecae43

[ "MIT" ]

62

2021-04-19T02:04:37.000Z

2022-03-29T17:56:53.000Z

src/notifications/__init__.py

LAC-Japan/ransomwatch

211b3bc8670d9fe38b915cac6befbe149cecae43

[ "MIT" ]

51

2021-04-20T04:54:45.000Z

2022-03-30T05:03:38.000Z

from .manager import NotificationManager

40

0.9

4

40

9

1

0

0.075

40

1

40

0.972973

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

38e354ac35ac5a3c819734747a16957289a01539

1,316

py

Python

nasse/utils/regex.py

Animenosekai/nasse

ba83187cab84b62338a5fab00db12f7d80a9fb5b

[ "MIT" ]

null

nasse/utils/regex.py

Animenosekai/nasse

ba83187cab84b62338a5fab00db12f7d80a9fb5b

[ "MIT" ]

null

nasse/utils/regex.py

Animenosekai/nasse

ba83187cab84b62338a5fab00db12f7d80a9fb5b

[ "MIT" ]

null

import re EMAIL_REGEX = re.compile('(?:[a-z0-9!#$%&\'*+\\/=?^_`{|}~-]+(?:\\.[a-z0-9!#$%&\'*+\\/=?^_`{|}~-]+)*|"(?:[\\x01-\\x08\\x0b\\x0c\\x0e-\\x1f\\x21\\x23-\\x5b\\x5d-\\x7f]|\\\\[\\x01-\\x09\\x0b\\x0c\\x0e-\\x7f])*")@(?:(?:[a-z0-9](?:[a-z0-9-]*[a-z0-9])?\\.)+[a-z0-9](?:[a-z0-9-]*[a-z0-9])?|\\[(?:(?:(2(5[0-5]|[0-4][0-9])|1[0-9][0-9]|[1-9]?[0-9]))\\.){3}(?:(2(5[0-5]|[0-4][0-9])|1[0-9][0-9]|[1-9]?[0-9])|[a-z0-9-]*[a-z0-9]:(?:[\\x01-\\x08\\x0b\\x0c\\x0e-\\x1f\\x21-\\x5a\\x53-\\x7f]|\\\\[\\x01-\\x09\\x0b\\x0c\\x0e-\\x7f])+)\\])') URL_REGEX = re.compile( r'^(?:(?:https?|ftp):\/\/)(?:\S+(?::\S*)?@)?(?:(?!(?:10|127)(?:\.\d{1,3}){3})(?!(?:169\.254|192\.168)(?:\.\d{1,3}){2})(?!172\.(?:1[6-9]|2\d|3[0-1])(?:\.\d{1,3}){2})(?:[1-9]\d?|1\d\d|2[01]\d|22[0-3])(?:\.(?:1?\d{1,2}|2[0-4]\d|25[0-5])){2}(?:\.(?:[1-9]\d?|1\d\d|2[0-4]\d|25[0-4]))|(?:(?:[a-z\u00a1-\uffff0-9]-*)*[a-z\u00a1-\uffff0-9]+)(?:\.(?:[a-z\u00a1-\uffff0-9]-*)*[a-z\u00a1-\uffff0-9]+)*(?:\.(?:[a-z\u00a1-\uffff]{2,}))\.?)(?::\d{2,5})?(?:[/?#]\S*)?$') def is_email(email: str): """Returns wether the given string is an email address or not""" return EMAIL_REGEX.fullmatch(str(email)) is not None def is_url(url: str): """Returns wether the given string is an URL or not""" return URL_REGEX.fullmatch(str(url).lower()) is not None

82.25

522

0.452128

256

1,316

2.292969

0.253906

0.040886

0.068143

0.541738

0.519591

0.448041

0.30494

0

0.15502

0.053951

1,316

15

523

87.733333

0.316466

0.081307

0

0.25

0.757095

0.74374

0

1

0.25

false

0

0.125

0

0.625

0

null

0

1

0

1

null

0

1

0

1

0

6

2a2f0979d0a5ab3d2cdf0df9e35452af7095c4b6

596

py

Python

sdk/python/pulumi_azure/hdinsight/__init__.py

henriktao/pulumi-azure

f1cbcf100b42b916da36d8fe28be3a159abaf022

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

109

2018-06-18T00:19:44.000Z

2022-02-20T05:32:57.000Z

sdk/python/pulumi_azure/hdinsight/__init__.py

henriktao/pulumi-azure

f1cbcf100b42b916da36d8fe28be3a159abaf022

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

663

2018-06-18T21:08:46.000Z

2022-03-31T20:10:11.000Z

sdk/python/pulumi_azure/hdinsight/__init__.py

henriktao/pulumi-azure

f1cbcf100b42b916da36d8fe28be3a159abaf022

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

41

2018-07-19T22:37:38.000Z

2022-03-14T10:56:26.000Z

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** from .. import _utilities import typing # Export this package's modules as members: from .get_cluster import * from .h_base_cluster import * from .hadoop_cluster import * from .interactive_query_cluster import * from .kafka_cluster import * from .ml_services_cluster import * from .r_server_cluster import * from .spark_cluster import * from .storm_cluster import * from ._inputs import * from . import outputs

31.368421

87

0.763423

88

596

5

0.602273

0.227273

0.347727

0

0.001984

0.154362

596

18

88

33.111111

0.871032

0.36745

0

1

0

1

0

true

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

2a4dc39d9ab8b1f9233eceacf9fc279b6652ead1

96

py

Python

venv/lib/python3.8/site-packages/clikit/api/args/__init__.py

Retraces/UkraineBot

3d5d7f8aaa58fa0cb8b98733b8808e5dfbdb8b71

[ "MIT" ]

2

2022-03-13T01:58:52.000Z

2022-03-31T06:07:54.000Z

venv/lib/python3.8/site-packages/clikit/api/args/__init__.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

19

2021-11-20T04:09:18.000Z

2022-03-23T15:05:55.000Z

venv/lib/python3.8/site-packages/clikit/api/args/__init__.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

null

/home/runner/.cache/pip/pool/b1/94/12/23f582e2d72ba6ab2ec6711c018792d596dc393c56258808f6c85e4dcb

96

0.895833

9

96

9.555556

1

0

0.447917

0

96

1

96

0.447917

0

1

0

null

0

null

0

1

0

null

0

1

0

1

0

1

0

null

1

0

1

0

6

2a9fc9d192a2e6e55c25f5aecf7255b6617aeb93

121

py

Python

bkgames/parsers/__init__.py

michalurbanski/bkgames

69b1d16ae27d3118dd78449ce7deecbd6e1b95e7

[ "MIT" ]

null

bkgames/parsers/__init__.py

michalurbanski/bkgames

69b1d16ae27d3118dd78449ce7deecbd6e1b95e7

[ "MIT" ]

null

bkgames/parsers/__init__.py

michalurbanski/bkgames

69b1d16ae27d3118dd78449ce7deecbd6e1b95e7

[ "MIT" ]

null

from bkgames.parsers.team_frequency_parser import TeamFrequencyParser from bkgames.parsers.file_parser import FileParser

40.333333

69

0.900826

15

121

7.066667

0.666667

0.207547

0.339623

0

0.066116

121

2

70

60.5

0.938053

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

2aae7959e955d4fadc70edb8155a25b49ecebc97

21

py

Python

mitielib/__init__.py

maxmert/nlp-mitie

ec3153ef2fe7a80e7cf3d80d14b388b8cd679343

[ "Unlicense" ]

2,695

2015-01-01T21:13:47.000Z

2022-03-31T04:45:32.000Z

mitielib/__init__.py

maxmert/nlp-mitie

ec3153ef2fe7a80e7cf3d80d14b388b8cd679343

[ "Unlicense" ]

208

2015-01-23T19:29:07.000Z

2022-02-08T02:55:17.000Z

mitielib/__init__.py

maxmert/nlp-mitie

ec3153ef2fe7a80e7cf3d80d14b388b8cd679343

[ "Unlicense" ]

567

2015-01-06T19:22:19.000Z

2022-03-21T17:01:04.000Z

from .mitie import *

10.5

20

0.714286

3

21

5

1

0

0.190476

21

1

21

0.882353

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

630a7eea1c65877f9f692dbc9219d3b20517b84d

88

py

Python

teste.py

Scarletmajor/Travis

f9ebabc614e63dcf60f75b139f4949a73d51b575

[ "Apache-2.0" ]

null

teste.py

Scarletmajor/Travis

f9ebabc614e63dcf60f75b139f4949a73d51b575

[ "Apache-2.0" ]

null

teste.py

Scarletmajor/Travis

f9ebabc614e63dcf60f75b139f4949a73d51b575

[ "Apache-2.0" ]

null

import pytest from principal import somar def teste_somar(): assert somar(2,3) == 5

17.6

27

0.727273

14

88

4.5

0.785714

0

0.041667

0.181818

88

5

28

17.6

0.833333

0

0.25

1

0.25

true

0

0.5

0

0.75

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

63249b1cbc08fbd2108ffd67c3746e3b10546b82

82

py

Python

python/testData/codeInsight/controlflow/MatchStatementSingleClauseDisjunctionConjunctionGuard.py

06needhamt/intellij-community

63d7b8030e4fdefeb4760e511e289f7e6b3a5c5b

[ "Apache-2.0" ]

null

python/testData/codeInsight/controlflow/MatchStatementSingleClauseDisjunctionConjunctionGuard.py

06needhamt/intellij-community

63d7b8030e4fdefeb4760e511e289f7e6b3a5c5b

[ "Apache-2.0" ]

null

python/testData/codeInsight/controlflow/MatchStatementSingleClauseDisjunctionConjunctionGuard.py

06needhamt/intellij-community

63d7b8030e4fdefeb4760e511e289f7e6b3a5c5b

[ "Apache-2.0" ]

null

match 42: case x if x % 4 == 0 and (x % 400 == 0 or x % 100 != 0): y z

20.5

60

0.414634

18

82

1.888889

0.722222

0

0.255319

0.426829

82

4

61

20.5

0.468085

0

1

0

true

0

1

0

1

null

0

1

0

1

0

1

0

null

0

1

0

6

2d854440ad5f57d03b8eb8bb2df67ff3325ddbd3

6,978

py

Python

etc/n-year_flood_depth/script/src/rp2flddph_dis.py

DirkEilander/CaMa-Flood_v4

a8e6a157a08c2a0144b8143bc2eb78d5d81eb9a7

[ "Apache-2.0" ]

22

2021-01-17T15:22:33.000Z

2022-01-22T15:14:50.000Z

etc/n-year_flood_depth/script/src/rp2flddph_dis.py

zhongwangwei/CaMa-Flood_v4

da1d1745568648858f02984b1e5b7ad05bc9bd3c

[ "Apache-2.0" ]

3

2021-01-19T08:30:50.000Z

2021-07-16T08:19:01.000Z

etc/n-year_flood_depth/script/src/rp2flddph_dis.py

zhongwangwei/CaMa-Flood_v4

da1d1745568648858f02984b1e5b7ad05bc9bd3c

[ "Apache-2.0" ]

25

2021-01-17T15:22:35.000Z

2022-01-15T08:32:48.000Z

#!/usr/bin/env python # coding=utf-8 import numpy as np import sys import lmoments as lmom years = sys.argv[1] yeare = sys.argv[2] ysize = int(sys.argv[3]) xsize = int(sys.argv[4]) outdir = sys.argv[5] var = sys.argv[6] rp = sys.argv[7] FUNC = sys.argv[8] rivhgt = np.fromfile(outdir+'/map/rivhgt.bin', np.float32).reshape(ysize,xsize) Nflddph = np.zeros((ysize,xsize), dtype = np.float64) if float(rp) > 1: RPP = 1.0 - (1.0/float(rp)) else: RPP = 1.0 - float(rp) Nrivdph = 0.0 # For GEV distribution if FUNC == "GEV": f_para1 = 'GEV_mu_'+var+'_'+years+'-'+yeare+'.bin' f_para2 = 'GEV_sigma_'+var+'_'+years+'-'+yeare+'.bin' f_para3 = 'GEV_theta_'+var+'_'+years+'-'+yeare+'.bin' para1 = np.fromfile(outdir+'/para/'+f_para1, np.float64).reshape(ysize,xsize) para2 = np.fromfile(outdir+'/para/'+f_para2, np.float64).reshape(ysize,xsize) para3 = np.fromfile(outdir+'/para/'+f_para3, np.float64).reshape(ysize,xsize) for i in range(ysize): for j in range(xsize): if para2[i,j] == -9999: Nflddph[i,j] = -9999 elif para2[i,j] == 0.0 or para2[i,j] == -999.0: Nflddph[i,j] = 0.0 else: if rivhgt[i,j] != -9999: Nrivdph = lmom.quagev(RPP, [para1[i,j], para2[i,j], para3[i,j]]) Nflddph[i,j] = Nrivdph - rivhgt[i,j] if Nflddph[i,j] < 0.0: Nflddph[i,j] = 0.0 fflddph = 'flddph_RP'+rp+'_'+ FUNC + '.bin' Nflddph.astype(np.float32).tofile(outdir+'/Nyear_flddph/'+fflddph) # For GAM distribution if FUNC == "GAM": f_para1 = 'GAM_alpha_'+var+'_'+years+'-'+yeare+'.bin' f_para2 = 'GAM_beta_'+var+'_'+years+'-'+yeare+'.bin' para1 = np.fromfile(outdir+'/para/'+f_para1, np.float64).reshape(ysize,xsize) para2 = np.fromfile(outdir+'/para/'+f_para2, np.float64).reshape(ysize,xsize) for i in range(ysize): for j in range(xsize): if para2[i,j] == -9999: Nflddph[i,j] = -9999 elif para2[i,j] == 0.0 or para2[i,j] == -999.0: Nflddph[i,j] = 0.0 else: if rivhgt[i,j] != -9999: Nrivdph = lmom.quagam(RPP, [para1[i,j], para2[i,j]]) Nflddph[i,j] = Nrivdph - rivhgt[i,j] if Nflddph[i,j] < 0.0: Nflddph[i,j] = 0.0 fflddph = 'flddph_RP'+rp+'_'+ FUNC + '.bin' Nflddph.astype(np.float32).tofile(outdir+'/Nyear_flddph/'+fflddph) # For PE3 distribution if FUNC == "PE3": f_para1 = 'PE3_para1_'+var+'_'+years+'-'+yeare+'.bin' f_para2 = 'PE3_para2_'+var+'_'+years+'-'+yeare+'.bin' f_para3 = 'PE3_gamma_'+var+'_'+years+'-'+yeare+'.bin' para1 = np.fromfile(outdir+'/para/'+f_para1, np.float64).reshape(ysize,xsize) para2 = np.fromfile(outdir+'/para/'+f_para2, np.float64).reshape(ysize,xsize) para3 = np.fromfile(outdir+'/para/'+f_para3, np.float64).reshape(ysize,xsize) for i in range(ysize): for j in range(xsize): if para2[i,j] == -9999. : Nflddph[i,j] = -9999. elif para2[i,j] == -999.0 : Nflddph[i,j] = 0.0 else: if rivhgt[i,j] != -9999: Nrivdph = lmom.quape3(RPP, [para1[i,j], para2[i,j], para3[i,j]]) Nflddph[i,j] = Nrivdph - rivhgt[i,j] if Nflddph[i,j] < 0.0: Nflddph[i,j] = 0.0 fflddph = 'flddph_RP'+rp+'_'+ FUNC + '.bin' Nflddph.astype(np.float32).tofile(outdir+'/Nyear_flddph/'+fflddph) # For GUM distribution if FUNC == "GUM": f_para1 = 'GUM_U_'+var+'_'+years+'-'+yeare+'.bin' f_para2 = 'GUM_A_'+var+'_'+years+'-'+yeare+'.bin' para1 = np.fromfile(outdir+'/para/'+f_para1, np.float64).reshape(ysize,xsize) para2 = np.fromfile(outdir+'/para/'+f_para2, np.float64).reshape(ysize,xsize) for i in range(ysize): for j in range(xsize): if para2[i,j] == -9999: Nflddph[i,j] = -9999 elif para2[i,j] == -999.0: Nflddph[i,j] = 0.0 else: if rivhgt[i,j] != -9999: Nrivdph = lmom.quagum(RPP, [para1[i,j], para2[i,j]]) Nflddph[i,j] = Nrivdph - rivhgt[i,j] if Nflddph[i,j] < 0.0: Nflddph[i,j] = 0.0 fflddph = 'flddph_RP'+rp+'_'+ FUNC + '.bin' Nflddph.astype(np.float32).tofile(outdir+'/Nyear_flddph/'+fflddph) # For WEI distribution if FUNC == "WEI": f_para1 = 'WEI_para1_'+var+'_'+years+'-'+yeare+'.bin' f_para2 = 'WEI_beta_'+var+'_'+years+'-'+yeare+'.bin' f_para3 = 'WEI_delta_'+var+'_'+years+'-'+yeare+'.bin' para1 = np.fromfile(outdir+'/para/'+f_para1, np.float64).reshape(ysize,xsize) para2 = np.fromfile(outdir+'/para/'+f_para2, np.float64).reshape(ysize,xsize) para3 = np.fromfile(outdir+'/para/'+f_para3, np.float64).reshape(ysize,xsize) for i in range(ysize): for j in range(xsize): if para2[i,j] == -9999. : Nflddph[i,j] = -9999. elif para2[i,j] == -999.0 : Nflddph[i,j] = 0.0 else: if rivhgt[i,j] != -9999: Nrivdph = lmom.quawei(RPP, [para1[i,j], para2[i,j], para3[i,j]]) Nflddph[i,j] = Nrivdph - rivhgt[i,j] if Nflddph[i,j] < 0.0: Nflddph[i,j] = 0.0 fflddph = 'flddph_RP'+rp+'_'+ FUNC + '.bin' Nflddph.astype(np.float32).tofile(outdir+'/Nyear_flddph/'+fflddph) # For WAK distribution if FUNC == "WAK": f_para1 = 'WAK_XI_'+var+'_'+years+'-'+yeare+'.bin' f_para2 = 'WAK_A_'+var+'_'+years+'-'+yeare+'.bin' f_para3 = 'WAK_B_'+var+'_'+years+'-'+yeare+'.bin' f_para4 = 'WAK_C_'+var+'_'+years+'-'+yeare+'.bin' f_para5 = 'WAK_D_'+var+'_'+years+'-'+yeare+'.bin' para1 = np.fromfile(outdir+'/para/'+f_para1, np.float64).reshape(ysize,xsize) para2 = np.fromfile(outdir+'/para/'+f_para2, np.float64).reshape(ysize,xsize) para3 = np.fromfile(outdir+'/para/'+f_para3, np.float64).reshape(ysize,xsize) para4 = np.fromfile(outdir+'/para/'+f_para4, np.float64).reshape(ysize,xsize) para5 = np.fromfile(outdir+'/para/'+f_para5, np.float64).reshape(ysize,xsize) for i in range(ysize): for j in range(xsize): if para2[i,j] == -9999. : Nflddph[i,j] = -9999. elif para2[i,j] == -999.0 : Nflddph[i,j] = 0.0 else: if rivhgt[i,j] != -9999: Nrivdph = lmom.quawak(RPP, [para1[i,j], para2[i,j], para3[i,j], para4[i,j], para5[i,j]]) Nflddph[i,j] = Nrivdph - rivhgt[i,j] if Nflddph[i,j] < 0.0: Nflddph[i,j] = 0.0 fflddph = 'flddph_RP'+rp+'_'+ FUNC + '.bin' Nflddph.astype(np.float32).tofile(outdir+'/Nyear_flddph/'+fflddph)

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py

Python

autodisc/gui/jupyter/__init__.py

flowersteam/holmes

e38fb8417ec56cfde8142eddd0f751e319e35d8c

[ "MIT" ]

6

2020-12-19T00:16:16.000Z

2022-01-28T14:59:21.000Z

autodisc/gui/jupyter/__init__.py

Evolutionary-Intelligence/holmes

e38fb8417ec56cfde8142eddd0f751e319e35d8c

[ "MIT" ]

null

autodisc/gui/jupyter/__init__.py

Evolutionary-Intelligence/holmes

e38fb8417ec56cfde8142eddd0f751e319e35d8c

[ "MIT" ]

1

2021-05-24T14:58:26.000Z

import autodisc.gui.jupyter.misc from autodisc.gui.jupyter.plotly_meanstd_scatter import plotly_meanstd_scatter from autodisc.gui.jupyter.plotly_meanstd_scatter_polar import plotly_meanstd_scatter_polar from autodisc.gui.jupyter.plot_scatter_per_datasource import plot_scatter_per_datasource from autodisc.gui.jupyter.plot_holmes_partitioning import plot_holmes_partitioning

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py

Python

signjoey/GCN/feeders/augmentations.py

Joe-Surrey/slt

4cc7a59f136502bf14aca003615cf51bc3ad5157

[ "Apache-2.0" ]

null

signjoey/GCN/feeders/augmentations.py

Joe-Surrey/slt

4cc7a59f136502bf14aca003615cf51bc3ad5157

[ "Apache-2.0" ]

null

signjoey/GCN/feeders/augmentations.py

Joe-Surrey/slt

4cc7a59f136502bf14aca003615cf51bc3ad5157

[ "Apache-2.0" ]

null

import random def random_mirror(data_numpy): # TODO random!!! if random.random() < 0.5: data_numpy[0] = - data_numpy[0] return data_numpy def augment(data_numpy): data_numpy = random_mirror(data_numpy) return data_numpy

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py

Python

books/tech/py/m_lutz-learning_py-5_ed/code/part_5-modules/ch_24-modules_packs/21-relative_imports_in_namespace_packages/ns/dir1/sub/mod1.py

ordinary-developer/education

1b1f40dacab873b28ee01dfa33a9bd3ec4cfed58

[ "MIT" ]

null

books/tech/py/m_lutz-learning_py-5_ed/code/part_5-modules/ch_24-modules_packs/21-relative_imports_in_namespace_packages/ns/dir1/sub/mod1.py

ordinary-developer/education

1b1f40dacab873b28ee01dfa33a9bd3ec4cfed58

[ "MIT" ]

null

books/tech/py/m_lutz-learning_py-5_ed/code/part_5-modules/ch_24-modules_packs/21-relative_imports_in_namespace_packages/ns/dir1/sub/mod1.py

ordinary-developer/education

1b1f40dacab873b28ee01dfa33a9bd3ec4cfed58

[ "MIT" ]

null

from . import mod2 print(r'dir1\sub\mod1')

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py

Python

plate_spinner/__init__.py

nowavailable/plate-spinner

6c375a8a859c0ee212862d9894f36852a8ae10f2

[ "MIT" ]

null

plate_spinner/__init__.py

nowavailable/plate-spinner

6c375a8a859c0ee212862d9894f36852a8ae10f2

[ "MIT" ]

null

plate_spinner/__init__.py

nowavailable/plate-spinner

6c375a8a859c0ee212862d9894f36852a8ae10f2

[ "MIT" ]

null

from .main_loop import main_loop

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py

Python

pymodelfit/operator.py

braidedlogix/pymodelfit

de8a02a27d13646b1f4b011d056edbed76540473

[ "Apache-1.1" ]

null

pymodelfit/operator.py

braidedlogix/pymodelfit

de8a02a27d13646b1f4b011d056edbed76540473

[ "Apache-1.1" ]

null

pymodelfit/operator.py

braidedlogix/pymodelfit

de8a02a27d13646b1f4b011d056edbed76540473

[ "Apache-1.1" ]

null

import collections.abc as abc def isMappingType(obj): return isinstance(obj, abc.Mapping) def isSequenceType(obj): return isinstance(obj, abc.Sequence)

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py

Python

tests/test_issues.py

chemelli74/pyuptimerobot

50da42dbb26347b4b5254515bebbee95878c8e9b

[ "MIT" ]

2

2021-11-14T18:24:08.000Z

2022-01-31T07:44:52.000Z

tests/test_issues.py

chemelli74/pyuptimerobot

50da42dbb26347b4b5254515bebbee95878c8e9b

[ "MIT" ]

24

2021-08-04T20:08:16.000Z

2022-03-29T06:15:51.000Z

tests/test_issues.py

chemelli74/pyuptimerobot

50da42dbb26347b4b5254515bebbee95878c8e9b

[ "MIT" ]

2

2021-09-01T21:11:40.000Z

2022-02-02T00:32:14.000Z

"""Tests for Container.""" import asyncio from unittest.mock import patch import aiohttp import pytest from pyuptimerobot import ( UptimeRobot, UptimeRobotAuthenticationException, UptimeRobotConnectionException, ) from pyuptimerobot.exceptions import UptimeRobotException from tests.common import TEST_API_TOKEN, TEST_RESPONSE_HEADERS, fixture @pytest.mark.asyncio async def test_api_key_error(aresponses): """test_api_key_error.""" aresponses.add( "api.uptimerobot.com", "/v2/getMonitors", "post", aresponses.Response( text=fixture("bad_api_key", False), status=200, headers=TEST_RESPONSE_HEADERS, ), ) aresponses.add( "api.uptimerobot.com", "/v2/getMonitors", "post", aresponses.Response( text=fixture("missing_api_key", False), status=200, headers=TEST_RESPONSE_HEADERS, ), ) async with aiohttp.ClientSession() as session: client = UptimeRobot(session=session, api_key=TEST_API_TOKEN) with pytest.raises(UptimeRobotAuthenticationException): await client.async_get_monitors() async with aiohttp.ClientSession() as session: client = UptimeRobot(session=session, api_key="") with pytest.raises(UptimeRobotAuthenticationException): await client.async_get_monitors() @pytest.mark.asyncio async def test_bad_status_code(aresponses): """test_bad_status_code.""" aresponses.add( "api.uptimerobot.com", "/v2/getMonitors", "post", aresponses.Response( text=fixture("getMonitors", False), status=500, headers=TEST_RESPONSE_HEADERS, ), ) async with aiohttp.ClientSession() as session: client = UptimeRobot(session=session, api_key=TEST_API_TOKEN) with pytest.raises( UptimeRobotConnectionException, match="Request for 'https://api.uptimerobot.com/v2/getMonitors' failed with status code '500'", ): result = await client.async_get_monitors() assert result is None @pytest.mark.asyncio async def test_client_error(): """test_bad_status_code.""" with patch("aiohttp.ClientSession._request", side_effect=aiohttp.ClientError): async with aiohttp.ClientSession() as session: client = UptimeRobot(session=session, api_key=TEST_API_TOKEN) with pytest.raises(UptimeRobotConnectionException): result = await client.async_get_monitors() assert result is None @pytest.mark.asyncio async def test_timeout_error(): """test_timeout_error.""" with patch("aiohttp.ClientSession._request", side_effect=asyncio.TimeoutError): async with aiohttp.ClientSession() as session: client = UptimeRobot(session=session, api_key=TEST_API_TOKEN) with pytest.raises(UptimeRobotConnectionException): result = await client.async_get_monitors() assert result is None @pytest.mark.asyncio async def test_uptime_robot_connection_exception(): """test_uptime_robot_connection_exception.""" with patch( "aiohttp.ClientSession._request", side_effect=UptimeRobotConnectionException ): async with aiohttp.ClientSession() as session: client = UptimeRobot(session=session, api_key=TEST_API_TOKEN) with pytest.raises(UptimeRobotConnectionException): result = await client.async_get_monitors() assert result is None @pytest.mark.asyncio async def test_uptime_robot_exception(): """test_uptime_robot_exception.""" with patch("aiohttp.ClientSession._request", side_effect=UptimeRobotException): async with aiohttp.ClientSession() as session: client = UptimeRobot(session=session, api_key=TEST_API_TOKEN) with pytest.raises(UptimeRobotException): result = await client.async_get_monitors() assert result is None @pytest.mark.asyncio async def test_exception(): """test_uptime_robot_exception.""" with patch("aiohttp.ClientSession._request", side_effect=Exception): async with aiohttp.ClientSession() as session: client = UptimeRobot(session=session, api_key=TEST_API_TOKEN) with pytest.raises(UptimeRobotException): result = await client.async_get_monitors() assert result is None

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6

93402c71c24dcf5de512ce3fa785535bf642fcc0

92

py

Python

start.py

vlakondra/py0

984d1383073f01e1894ebf138c704b79b6b5f311

[ "MIT" ]

null

start.py

vlakondra/py0

984d1383073f01e1894ebf138c704b79b6b5f311

[ "MIT" ]

null

start.py

vlakondra/py0

984d1383073f01e1894ebf138c704b79b6b5f311

[ "MIT" ]

null

from src_code.lib.funcs import multi_five,add_five print(add_five(33)) print(multi_five(33))

30.666667

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1

0

6

93593dd2d04726f2db2b779c759d7b5f4f6368f8

131,748

py

Python

userbot/plugins/rename.py

D0Tapk/HardcoreUserbot

d78b7d71a86b63465b694941577343298d727587

[ "MIT" ]

67

2020-03-04T09:34:55.000Z

2022-03-04T11:09:10.000Z

userbot/plugins/rename.py

D0Tapk/HardcoreUserbot

d78b7d71a86b63465b694941577343298d727587

[ "MIT" ]

3

2020-04-21T19:03:34.000Z

2020-07-23T11:30:53.000Z

userbot/plugins/rename.py

D0Tapk/HardcoreUserbot

d78b7d71a86b63465b694941577343298d727587

[ "MIT" ]

191

2020-02-06T11:02:24.000Z

2022-03-17T05:39:21.000Z

# Code from pro sar Spechide's fork of Uniborg. """Rename Telegram Files Syntax: .rnupload file.name""" import asyncio import time from datetime import datetime from hachoir.metadata import extractMetadata from hachoir.parser import createParser from base64 import b64decode import io import math import os from pySmartDL import SmartDL from telethon.tl.types import DocumentAttributeVideo from uniborg.util import progress, humanbytes, time_formatter, admin_cmd thumb_image_path = Config.TMP_DOWNLOAD_DIRECTORY + "/thumb_image.jpg" @borg.on(admin_cmd(pattern="rnupload (.*)")) async def _(event): if event.fwd_from: return thumb = None if os.path.exists(thumb_image_path): thumb = thumb_image_path await event.edit("`Downloading this lil baby, pouring some oil and reuploading.`👉🏻👌🏻💦💦\n\n__Might take time if file size is big or t h i c c__") input_str = event.pattern_match.group(1) if not os.path.isdir(Config.TMP_DOWNLOAD_DIRECTORY): os.makedirs(Config.TMP_DOWNLOAD_DIRECTORY) if event.reply_to_msg_id: start = datetime.now() end = datetime.now() file_name = input_str reply_message = await event.get_reply_message() to_download_directory = Config.TMP_DOWNLOAD_DIRECTORY downloaded_file_name = os.path.join(to_download_directory, file_name) downloaded_file_name = await borg.download_media( reply_message, downloaded_file_name, ) ms_one = (end - start).seconds if Config.NO_SONGS != True: await borg.send_file(event.chat_id, SONA, allow_cache=False) if os.path.exists(downloaded_file_name): c_time = time.time() await borg.send_file( event.chat_id, downloaded_file_name, force_document=True, supports_streaming=False, allow_cache=False, reply_to=event.message.id, thumb=thumb, ) end_two = datetime.now() os.remove(downloaded_file_name) ms_two = (end_two - end).seconds await event.edit("Downloaded in {} seconds. Uploaded in {} seconds.".format(ms_one, ms_two)) else: await event.edit("File Not Found {}".format(input_str)) else: await event.edit("Syntax // .rnupload file.name as reply to a Telegram media") SONA = io.BytesIO(b64decode("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SONA.name = "SONA.mp3"

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93822f784b43417aaed6b1247f6abaae66d961c6

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py

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common/commands/__init__.py

jmcollis/GitSavvy

153dca03bfd63db8248c1f9ee03bb6f2ebef545a

[ "MIT" ]

1

2019-06-19T14:58:32.000Z

common/commands/__init__.py

jmcollis/GitSavvy

153dca03bfd63db8248c1f9ee03bb6f2ebef545a

[ "MIT" ]

null

common/commands/__init__.py

jmcollis/GitSavvy

153dca03bfd63db8248c1f9ee03bb6f2ebef545a

[ "MIT" ]

null

from .debug import * from .log import * from .view_manipulation import * from .help import *

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fab2a7b70b3d4cfdada7390a02ff4905bde53a28

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py

Python

wouso/games/grandchallenge/admin.py

AlexandruGhergut/wouso

f26244ff58ae626808ae8c58ccc93d21f9f2666f

[ "Apache-2.0" ]

117

2015-01-02T18:07:33.000Z

2021-01-06T22:36:25.000Z

wouso/games/grandchallenge/admin.py

AlexandruGhergut/wouso

f26244ff58ae626808ae8c58ccc93d21f9f2666f

[ "Apache-2.0" ]

229

2015-01-12T07:07:58.000Z

2019-10-12T08:27:01.000Z

wouso/games/grandchallenge/admin.py

AlexandruGhergut/wouso

f26244ff58ae626808ae8c58ccc93d21f9f2666f

[ "Apache-2.0" ]

96

2015-01-07T05:26:09.000Z

2020-06-25T07:28:51.000Z

from django.contrib import admin from wouso.games.grandchallenge.models import GrandChallenge, GrandChallengeUser, GrandChallengeGame admin.site.register(GrandChallenge) admin.site.register(GrandChallengeGame) admin.site.register(GrandChallengeUser)

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