xszheng2020/the_stack_dedup_python_hits_6 · Datasets at Hugging Face

2541a12a12b71082c6ac8d4714f94e5033661e93

5,977

py

Python

{{cookiecutter.project_name}}/TensorFlow_imagenet/tensorflow_imagenet.py

Bhaskers-Blu-Org2/DistributedDeepLearning

2f407881b49415188ca2e38e5331781962939251

[ "MIT" ]

45

2019-06-13T17:38:11.000Z

2022-03-24T00:32:38.000Z

{{cookiecutter.project_name}}/TensorFlow_imagenet/tensorflow_imagenet.py

Hrashid789/DistributedDeepLearning

2f407881b49415188ca2e38e5331781962939251

[ "MIT" ]

11

2019-06-06T15:50:18.000Z

2019-10-21T08:45:26.000Z

{{cookiecutter.project_name}}/TensorFlow_imagenet/tensorflow_imagenet.py

Hrashid789/DistributedDeepLearning

2f407881b49415188ca2e38e5331781962939251

[ "MIT" ]

10

2019-07-01T04:57:37.000Z

2020-09-29T07:04:05.000Z

"""Module for running TensorFlow training on Imagenet data """ from invoke import task, Collection import os from config import load_config _BASE_PATH = os.path.dirname(os.path.abspath(__file__)) env_values = load_config() @task def submit_synthetic(c, node_count=int(env_values["CLUSTER_MAX_NODES"]), epochs=1): """Submit TensorFlow training job using synthetic imagenet data to remote cluster Args: node_count (int, optional): The number of nodes to use in cluster. Defaults to env_values['CLUSTER_MAX_NODES']. epochs (int, optional): Number of epochs to run training for. Defaults to 1. """ from aml_compute import TFExperimentCLI exp = TFExperimentCLI("synthetic_images_remote") run = exp.submit( os.path.join(_BASE_PATH, "src"), "resnet_main.py", {"--epochs": epochs}, node_count=node_count, dependencies_file=os.path.join(_BASE_PATH, "environment_gpu.yml"), wait_for_completion=True, ) print(run) @task def submit_synthetic_local(c, epochs=1): """Submit TensorFlow training job using synthetic imagenet data for local execution Args: epochs (int, optional): Number of epochs to run training for. Defaults to 1. """ from aml_compute import TFExperimentCLI exp = TFExperimentCLI("synthetic_images_local") run = exp.submit_local( os.path.join(_BASE_PATH, "src"), "resnet_main.py", {"--epochs": epochs}, dependencies_file=os.path.join(_BASE_PATH, "environment_gpu.yml"), wait_for_completion=True, ) print(run) @task def submit_images(c, node_count=int(env_values["CLUSTER_MAX_NODES"]), epochs=1): """Submit TensorFlow training job using real imagenet data to remote cluster Args: node_count (int, optional): The number of nodes to use in cluster. Defaults to env_values['CLUSTER_MAX_NODES']. epochs (int, optional): Number of epochs to run training for. Defaults to 1. """ from aml_compute import TFExperimentCLI exp = TFExperimentCLI("real_images_remote") run = exp.submit( os.path.join(_BASE_PATH, "src"), "resnet_main.py", { "--training_data_path": "{datastore}/train", "--validation_data_path": "{datastore}/validation", "--epochs": epochs, "--data_type": "images", "--data-format": "channels_first", }, node_count=node_count, dependencies_file=os.path.join(_BASE_PATH, "environment_gpu.yml"), wait_for_completion=True, ) print(run) @task def submit_images_local(c, epochs=1): """Submit TensorFlow training job using real imagenet data for local execution Args: epochs (int, optional): Number of epochs to run training for. Defaults to 1. """ from aml_compute import TFExperimentCLI exp = TFExperimentCLI("real_images_local") run = exp.submit_local( os.path.join(_BASE_PATH, "src"), "resnet_main.py", { "--training_data_path": "/data/train", "--validation_data_path": "/data/validation", "--epochs": epochs, "--data_type": "images", "--data-format": "channels_first", }, dependencies_file=os.path.join(_BASE_PATH, "environment_gpu.yml"), docker_args=["-v", f"{env_values['DATA']}:/data"], wait_for_completion=True, ) print(run) @task def submit_tfrecords(c, node_count=int(env_values["CLUSTER_MAX_NODES"]), epochs=1): """Submit TensorFlow training job using real imagenet data as tfrecords to remote cluster Args: node_count (int, optional): The number of nodes to use in cluster. Defaults to env_values['CLUSTER_MAX_NODES']. epochs (int, optional): Number of epochs to run training for. Defaults to 1. """ from aml_compute import TFExperimentCLI exp = TFExperimentCLI("real_tfrecords_remote") run = exp.submit( os.path.join(_BASE_PATH, "src"), "resnet_main.py", { "--training_data_path": "{datastore}/tfrecords/train", "--validation_data_path": "{datastore}/tfrecords/validation", "--epochs": epochs, "--data_type": "tfrecords", "--data-format": "channels_first", }, node_count=node_count, dependencies_file=os.path.join(_BASE_PATH, "environment_gpu.yml"), wait_for_completion=True, ) print(run) @task def submit_tfrecords_local(c, epochs=1): """Submit TensorFlow training job using real imagenet data as tfrecords for local execution Args: epochs (int, optional): Number of epochs to run training for. Defaults to 1. """ from aml_compute import TFExperimentCLI exp = TFExperimentCLI("real_tfrecords_local") run = exp.submit_local( os.path.join(_BASE_PATH, "src"), "resnet_main.py", { "--training_data_path": "/data/tfrecords/train", "--validation_data_path": "/data/tfrecords/validation", "--epochs": epochs, "--data_type": "tfrecords", "--data-format": "channels_first", }, dependencies_file=os.path.join(_BASE_PATH, "environment_gpu.yml"), docker_args=["-v", f"{env_values['DATA']}:/data"], wait_for_completion=True, ) print(run) remote_collection = Collection("remote") remote_collection.add_task(submit_images, "images") remote_collection.add_task(submit_tfrecords, "tfrecords") remote_collection.add_task(submit_synthetic, "synthetic") local_collection = Collection("local") local_collection.add_task(submit_images_local, "images") local_collection.add_task(submit_tfrecords_local, "tfrecords") local_collection.add_task(submit_synthetic_local, "synthetic") submit_collection = Collection("submit", local_collection, remote_collection) namespace = Collection("tf_imagenet", submit_collection)

33.768362

119

0.661034

723

5,977

5.217151

0.116183

0.022269

0.031813

0.044539

0.885207

0.791092

0.789502

0

0.002577

0.220847

5,977

176

120

33.960227

0.807387

0.237075

0

0.577586

0

0.247278

0.075771

0

1

0.051724

false

0

0.077586

0

0.12931

0.051724

0

null

0

1

0

null

0

6

c2a0ee99b15d496c33bf063ce58d2a62ede25145

30

py

Python

micropython.py

PaulskPt/micropython-mcp7940

f01582214d06a582eacde2db84bd53fead86a850

[ "MIT" ]

null

micropython.py

PaulskPt/micropython-mcp7940

f01582214d06a582eacde2db84bd53fead86a850

[ "MIT" ]

10

2019-06-20T22:32:48.000Z

2022-03-01T00:51:31.000Z

micropython.py

PaulskPt/micropython-mcp7940

f01582214d06a582eacde2db84bd53fead86a850

[ "MIT" ]

2

2019-07-16T09:38:51.000Z

2020-01-29T22:33:31.000Z

def const(val): return val

15

0.666667

5

30

4

0.8

0

0.233333

30

2

16

15

0.869565

0

1

0.5

false

0

0.5

1

0

1

0

null

0

1

0

null

0

1

0

1

0

6

c2c667e7a14b7fac157603c78ca5081db3f4c499

43

py

Python

Python/chinese2digits/__init__.py

lai-bluejay/chinese2digits

3da9c030a8a9ca5f82426e5719aff861109f51c1

[ "Apache-1.1" ]

271

2018-07-11T11:02:52.000Z

2022-03-31T01:12:08.000Z

Python/chinese2digits/__init__.py

Geekzhangwei/chinese2digits

921ac76f051e91768f42e68d77da040305d53cf0

[ "Apache-1.1" ]

22

2018-11-29T08:34:19.000Z

2022-03-16T08:20:06.000Z

Python/chinese2digits/__init__.py

Geekzhangwei/chinese2digits

921ac76f051e91768f42e68d77da040305d53cf0

[ "Apache-1.1" ]

52

2019-02-22T06:36:03.000Z

2022-03-10T07:05:08.000Z

from chinese2digits.chinese2digits import *

43

0.883721

4

43

9.5

0.75

0

0.05

0.069767

43

1

43

0.9

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

c2ca79fe5738a1745a937d67ebd5e853ec6cec9e

111

py

Python

pyxmeans/__init__.py

araceli252/pyxmeans

11c69f88939f44f73b49376c92e932d7c7e6f858

[ "MIT" ]

84

2015-01-22T22:50:27.000Z

2021-12-30T07:32:38.000Z

pyxmeans/__init__.py

araceli252/pyxmeans

11c69f88939f44f73b49376c92e932d7c7e6f858

[ "MIT" ]

13

2015-01-19T11:47:34.000Z

2017-12-03T20:24:55.000Z

pyxmeans/__init__.py

araceli252/pyxmeans

11c69f88939f44f73b49376c92e932d7c7e6f858

[ "MIT" ]

39

2015-01-13T07:10:01.000Z

2022-03-21T07:31:43.000Z

from . import _minibatch from . import benchmark from .mini_batch import MiniBatch from .xmeans import XMeans

18.5

33

0.810811

15

111

5.866667

0.466667

0.227273

0.431818

0

0.153153

111

5

34

22.2

0.93617

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

66a943c2bc1f4aecbfad9f25af6229384f949d24

135

py

Python

pyrobolearn/tools/interfaces/audio/__init__.py

Pandinosaurus/pyrobolearn

9cd7c060723fda7d2779fa255ac998c2c82b8436

[ "Apache-2.0" ]

2

2021-01-21T21:08:30.000Z

2022-03-29T16:45:49.000Z

pyrobolearn/tools/interfaces/audio/__init__.py

Pandinosaurus/pyrobolearn

9cd7c060723fda7d2779fa255ac998c2c82b8436

[ "Apache-2.0" ]

null

pyrobolearn/tools/interfaces/audio/__init__.py

Pandinosaurus/pyrobolearn

9cd7c060723fda7d2779fa255ac998c2c82b8436

[ "Apache-2.0" ]

1

2020-09-29T21:25:39.000Z

# -*- coding: utf-8 -*- # import audio interfaces # from .audio import * # from . import audio from .speaker import SpeakerInterface

16.875

37

0.696296

16

135

5.875

0.5625

0.234043

0

0.009091

0.185185

135

7

38

19.285714

0.845455

0.637037

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

dd0e23ac376765ebeebea98070e54299bb129f37

25

py

Python

python_modules/dagster/dagster/generate/new_repo/new_repo/solids/__init__.py

chasleslr/dagster

88907f9473fb8e7a9b1af9a0a8b349d42f4b8153

[ "Apache-2.0" ]

null

python_modules/dagster/dagster/generate/new_repo/new_repo/solids/__init__.py

chasleslr/dagster

88907f9473fb8e7a9b1af9a0a8b349d42f4b8153

[ "Apache-2.0" ]

null

python_modules/dagster/dagster/generate/new_repo/new_repo/solids/__init__.py

chasleslr/dagster

88907f9473fb8e7a9b1af9a0a8b349d42f4b8153

[ "Apache-2.0" ]

null

from .hello import hello

12.5

24

0.8

4

25

5

0.75

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

06d3c56d8f83d304750b8f3bcc0b0da70e8aa16e

16,431

py

Python

src/core/test/test_query_rewriter.py

RogerTangos/datahub-stub

8c3e89c792e45ccc9ad067fcf085ddd52f7ecd89

[ "MIT" ]

null

src/core/test/test_query_rewriter.py

RogerTangos/datahub-stub

8c3e89c792e45ccc9ad067fcf085ddd52f7ecd89

[ "MIT" ]

null

src/core/test/test_query_rewriter.py

RogerTangos/datahub-stub

8c3e89c792e45ccc9ad067fcf085ddd52f7ecd89

[ "MIT" ]

null

from core.db.query_rewriter import SQLQueryRewriter from django.db.models import signals from django.test import TestCase import factory import sqlparse from mock import patch class QueryRewriter(TestCase): """Tests all the query rewriter operations in query_rewriter.py.""" @factory.django.mute_signals(signals.pre_save) def setUp(self): self.repo_base = "test_repobase" self.user = "test_user" self.query_rewriter = SQLQueryRewriter(self.repo_base, self.user) self.mock_connection = self.create_patch( 'core.db.manager.DataHubConnection') def create_patch(self, name): # helper method for creating patches patcher = patch(name) thing = patcher.start() self.addCleanup(patcher.stop) return thing def test_extract_table_info(self): valid_table_token = "repo.table" expected_result = ["repo", "table", None] self.assertEqual( self.query_rewriter.extract_table_info(valid_table_token), expected_result) valid_table_token = "repobase.repo.table" expected_result = ["repo", "table", "repobase"] self.assertEqual( self.query_rewriter.extract_table_info(valid_table_token), expected_result) invalid_table_token = "testtable" exception_raised = False try: self.query_rewriter.extract_table_info(invalid_table_token) except Exception: exception_raised = True self.assertEquals(exception_raised, True) invalid_table_token = "table1.table2.table3.table4" exception_raised = False try: self.query_rewriter.extract_table_info(invalid_table_token) except Exception: exception_raised = True self.assertEquals(exception_raised, True) def test_extract_table_token(self): query = "SELECT * from repo1.table1 as tbl1" token = sqlparse.parse(query)[0].tokens[6] expected_result = [(["repo1", "table1", None], "as tbl1")] self.assertEqual( self.query_rewriter.extract_table_token(token), expected_result) query = ("SELECT * from repo1.table1 as tbl1, repo2.table2 as tbl2 " "where ... ") token = sqlparse.parse(query)[0].tokens[6] expected_result = [(["repo1", "table1", None], "as tbl1"), (["repo2", "table2", None], "as tbl2")] self.assertEqual( self.query_rewriter.extract_table_token(token), expected_result) query = "SELECT * from repo1.table1 tbl1, repo2.table2 tbl2 where ... " token = sqlparse.parse(query)[0].tokens[6] expected_result = [(["repo1", "table1", None], "tbl1"), (["repo2", "table2", None], "tbl2")] self.assertEqual( self.query_rewriter.extract_table_token(token), expected_result) def test_extract_table_string(self): valid_table_string = "repo.table" expected_result = (["repo", "table", None], '') self.assertEqual( self.query_rewriter.extract_table_string(valid_table_string), expected_result) valid_table_string = "repo.table test" expected_result = (["repo", "table", None], 'test') self.assertEqual( self.query_rewriter.extract_table_string(valid_table_string), expected_result) valid_table_string = "repo.table as test" expected_result = (["repo", "table", None], 'as test') self.assertEqual( self.query_rewriter.extract_table_string(valid_table_string), expected_result) valid_table_string = "repobase.repo.table test " expected_result = (["repo", "table", "repobase"], 'test') self.assertEqual( self.query_rewriter.extract_table_string(valid_table_string), expected_result) valid_table_string = "repobase.repo.table as test " expected_result = (["repo", "table", "repobase"], 'as test') self.assertEqual( self.query_rewriter.extract_table_string(valid_table_string), expected_result) invalid_table_string = "invalidtable" exception_raised = False try: self.query_rewriter.extract_table_string(invalid_table_string) except Exception: exception_raised = True self.assertEquals(exception_raised, True) def test_contains_subquery(self): query = ("select * from (select * from repo.table where " "repo.table.test = 'True')") subquery_token = sqlparse.parse(query)[0].tokens[6] no_subquery_token = sqlparse.parse(query)[0].tokens[0] self.assertEqual( self.query_rewriter.contains_subquery(subquery_token), True) self.assertEqual( self.query_rewriter.contains_subquery(no_subquery_token), False) def test_extract_subquery(self): query = ("select * from (select * from repo.table where " "repo.table.test='True')") subquery_token = sqlparse.parse(query)[0].tokens[6] expected_result = ('(', ('select * from repo.table where ' 'repo.table.test=\'True\''), ')') self.assertEqual( self.query_rewriter.extract_subquery(subquery_token), expected_result) def test_process_subquery(self): query = "select * from (select * from repo.table)" subquery_token = sqlparse.parse(query)[0].tokens[6] mock_table_policies = self.create_patch( 'core.db.query_rewriter.SQLQueryRewriter.find_table_policies') mock_table_policies.return_value = ["tester='Alice"] expected_result = ("(select * from (SELECT * FROM repo.table WHERE " "tester='Alice) AS repotable)") self.assertEqual( self.query_rewriter.process_subquery(subquery_token), expected_result) def test_apply_row_level_security_base(self): mock_find_table_policies = self.create_patch( 'core.db.query_rewriter.SQLQueryRewriter.find_table_policies') mock_find_table_policies.return_value = ["tester='Alice'"] query = "select * from repo.table" expected_result = ("select * from (SELECT * FROM repo.table WHERE " "tester='Alice') AS repotable") self.assertEqual( self.query_rewriter.apply_row_level_security_base(query), expected_result) query = "select * from hola.orders limit 3" expected_result = ("select * from (SELECT * FROM hola.orders WHERE " "tester='Alice') AS holaorders limit 3") self.assertEqual( self.query_rewriter.apply_row_level_security_base(query), expected_result) mock_find_table_policies = self.create_patch( 'core.db.query_rewriter.SQLQueryRewriter.find_table_policies') mock_find_table_policies.return_value = ["tester='Alice'", "tester='Bob'"] query = ("select * from hola.orders o, hola.customer t where " "o.customerid=t.customerid order by customer") expected_result = ("select * from (SELECT * FROM hola.orders " "WHERE tester='Alice' OR tester='Bob') o, " "(SELECT * FROM hola.customer WHERE tester='Alice' " "OR tester='Bob') t where o.customerid=t.customerid" " order by customer") self.assertEqual( self.query_rewriter.apply_row_level_security_base(query), expected_result) query = ("select * from test.orders right join test.customer " "on test.orders.customerid=test.customer.customerid") expected_result = ("select * from (SELECT * FROM test.orders WHERE " "tester='Alice' OR tester='Bob') AS testorders " "right join (SELECT * FROM test.customer WHERE " "tester='Alice' OR tester='Bob') AS testcustomer " "on testorders.customerid=testcustomer.customerid") self.assertEqual( self.query_rewriter.apply_row_level_security_base(query), expected_result) query = ("select * from test.orders right join test.customer on " "test.orders.customerid=test.customer.customerid") expected_result = ("select * from (SELECT * FROM test.orders WHERE " "tester='Alice' OR tester='Bob') AS testorders " "right join (SELECT * FROM test.customer WHERE " "tester='Alice' OR tester='Bob') AS testcustomer " "on testorders.customerid=testcustomer.customerid") self.assertEqual( self.query_rewriter.apply_row_level_security_base(query), expected_result) query = ("select * from test.orders right join test.customer on " "test.orders.customerid=test.customer.customerid") expected_result = ("select * from (SELECT * FROM test.orders WHERE " "tester='Alice' OR tester='Bob') AS testorders " "right join (SELECT * FROM test.customer WHERE " "tester='Alice' OR tester='Bob') AS testcustomer " "on testorders.customerid=testcustomer.customerid") self.assertEqual( self.query_rewriter.apply_row_level_security_base(query), expected_result) query = "select * from test.orders where test.orders.customerid='1'" expected_result = ("select * from (SELECT * FROM test.orders WHERE " "tester='Alice' OR tester='Bob') AS testorders " "where testorders.customerid='1'") self.assertEqual( self.query_rewriter.apply_row_level_security_base(query), expected_result) query = ("select count(*), visible from hola.grades_file " "group by visible") expected_result = ("select count(*), visible from (SELECT * FROM " "hola.grades_file WHERE tester='Alice' OR " "tester='Bob') AS holagrades_file group by visible") self.assertEqual( self.query_rewriter.apply_row_level_security_base(query), expected_result) query = ("select * from (select * from " "(select * from hola.orders) as i) as o") expected_result = ("select * from (select * from (select * from " "(SELECT * FROM hola.orders WHERE tester='Alice' " "OR tester='Bob') AS holaorders) as i) as o") self.assertEqual( self.query_rewriter.apply_row_level_security_base(query), expected_result) query = ("select * from hola.orders where customerid = " "(select customerid from hola.customer where customerid='3')") expected_result = ("select * from (SELECT * FROM hola.orders WHERE " "tester='Alice' OR tester='Bob') AS holaorders " "where customerid = (select customerid from " "(SELECT * FROM hola.customer WHERE tester='Alice' " "OR tester='Bob') AS holacustomer where " "customerid='3')") self.assertEqual( self.query_rewriter.apply_row_level_security_base(query), expected_result) query = ("select * from hola.orders as t, hola.orders_2, " "hola.customer where t.customerid=hola.orders_2.customerid " "and hola.orders_2.customerid=hola.customer.customerid") expected_result = ("select * from (SELECT * FROM hola.orders WHERE " "tester='Alice' OR tester='Bob') as t, " "(SELECT * FROM hola.orders_2 WHERE tester='Alice' " "OR tester='Bob') AS holaorders_2, " "(SELECT * FROM hola.customer WHERE tester='Alice' " "OR tester='Bob') AS holacustomer where " "t.customerid=holaorders_2.customerid and " "holaorders_2.customerid=holacustomer.customerid") self.assertEqual( self.query_rewriter.apply_row_level_security_base(query), expected_result) def test_apply_row_level_security_update(self): mock_find_table_policies = self.create_patch( 'core.db.query_rewriter.SQLQueryRewriter.find_table_policies') mock_find_table_policies.return_value = ["count > 10"] query = ("update hola.grades_file set firstname='Alice' " "where lastname='Abby'") expected_result = ("update hola.grades_file set firstname='Alice' " "where lastname='Abby' AND count > 10") self.assertEquals( self.query_rewriter.apply_row_level_security_update(query), expected_result) mock_find_table_policies = self.create_patch( 'core.db.query_rewriter.SQLQueryRewriter.find_table_policies') mock_find_table_policies.return_value = [] query = ("update hola.grades_file set firstname='Alice' " "where lastname='Abby'") expected_result = ("update hola.grades_file set firstname='Alice' " "where lastname='Abby'") self.assertEquals( self.query_rewriter.apply_row_level_security_update(query), expected_result) def test_apply_row_level_security_insert(self): query = "insert into repo.table values (a,b,c)" mock_find_table_policies = self.create_patch( 'core.db.query_rewriter.SQLQueryRewriter.find_table_policies') mock_find_table_policies.return_value = ["INSERT='True'"] expected_result = "insert into repo.table values (a,b,c)" self.assertEquals( self.query_rewriter.apply_row_level_security_insert(query), expected_result) mock_find_table_policies = self.create_patch( 'core.db.query_rewriter.SQLQueryRewriter.find_table_policies') mock_find_table_policies.return_value = ["INSERT='False'"] exception_raised = False try: self.query_rewriter.apply_row_level_security_insert(query) except Exception: exception_raised = True self.assertEquals(exception_raised, True) mock_find_table_policies = self.create_patch( 'core.db.query_rewriter.SQLQueryRewriter.find_table_policies') mock_find_table_policies.return_value = [] self.assertEquals( self.query_rewriter.apply_row_level_security_insert(query), expected_result) def test_apply_row_level_security(self): mock_apply_rls_base = self.create_patch( 'core.db.query_rewriter.SQLQueryRewriter.' 'apply_row_level_security_base') mock_apply_rls_base.return_value = "RLS for select called" mock_apply_rls_insert = self.create_patch( 'core.db.query_rewriter.SQLQueryRewriter.' 'apply_row_level_security_insert') mock_apply_rls_insert.return_value = "RLS for insert called" mock_apply_rls_update = self.create_patch( 'core.db.query_rewriter.SQLQueryRewriter.' 'apply_row_level_security_update') mock_apply_rls_update.return_value = "RLS for update called" select_query = "select * from repo.table" self.assertEquals( self.query_rewriter.apply_row_level_security(select_query), "RLS for select called") insert_query = "insert into repo.table values (a,b,c)" self.assertEquals( self.query_rewriter.apply_row_level_security(insert_query), "RLS for insert called") update_query = ("update repo.table set firstname='Alice' " "where lastname='Abby'") self.assertEquals( self.query_rewriter.apply_row_level_security(update_query), "RLS for update called")

46.154494

79

0.612805

1,762

16,431

5.461975

0.08059

0.057149

0.065357

0.056733

0.824501

0.799979

0.782938

0.747506

0.722984

0.674771

0

0.005577

0.29067

16,431

355

80

46.284507

0.820163

0.005903

0

0.559211

0

0.319206

0.080353

0

0.118421

1

0.039474

false

0

0.019737

0

0.065789

0

null

0

1

0

null

0

6

06dbbb053f3bfbd491b0175d66b52fe6c1c0c826

1,262

py

Python

test/test_skipint.py

hlatkydavid/vnmrjpy

48707a1000dc87e646e37c8bd686e695bd31a61e

[ "MIT" ]

null

test/test_skipint.py

hlatkydavid/vnmrjpy

48707a1000dc87e646e37c8bd686e695bd31a61e

[ "MIT" ]

null

test/test_skipint.py

hlatkydavid/vnmrjpy

48707a1000dc87e646e37c8bd686e695bd31a61e

[ "MIT" ]

null

import unittest import vnmrjpy as vj import glob import nibabel as nib class Test_SkipintGenerator(unittest.TestCase): def test_generate_gems(self): reduction = 4 gemsdir = sorted(glob.glob(vj.fids+'/gems*.fid'))[0] procpar = gemsdir+'/procpar' gen = vj.util.SkipintGenerator(procpar=procpar) kmask = gen.generate_kspace_mask() self.assertEqual(len(kmask.shape),4) #nib.viewers.OrthoSlicer3D(kmask).show() def test_generate_ge3d(self): reduction = 4 gemsdir = sorted(glob.glob(vj.fids+'/ge3d_s*.fid'))[0] procpar = gemsdir+'/procpar' gen = vj.util.SkipintGenerator(procpar=procpar) kmask = gen.generate_kspace_mask() self.assertEqual(len(kmask.shape),4) #nib.viewers.OrthoSlicer3D(kmask).show() def test_generate_mge3d(self): reduction = 4 gemsdir = sorted(glob.glob(vj.fids+'/mge3d*.fid'))[0] procpar = gemsdir+'/procpar' gen = vj.util.SkipintGenerator(procpar=procpar) kmask = gen.generate_kspace_mask() self.assertEqual(len(kmask.shape),4) nib.viewers.OrthoSlicer3D(kmask).show() def test_skiptab_ge3d(self): """ Generate skiptab""" pass

29.348837

62

0.637876

149

1,262

5.295302

0.268456

0.035488

0.057034

0.079848

0.776933

0.621039

0

0.017635

0.236133

1,262

42

63

30.047619

0.80083

0.075277

0

0.517241

1

0

0.04918

0

0.103448

1

0.137931

false

0.034483

0.137931

0

0.310345

0

null

0

1

0

1

0

null

0

6

66553e8ba96f41222ec939bb440eeb71b0ed9440

75

py

Python

5 kyu/A Chain adding function.py

mwk0408/codewars_solutions

9b4f502b5f159e68024d494e19a96a226acad5e5

[ "MIT" ]

6

2020-09-03T09:32:25.000Z

2020-12-07T04:10:01.000Z

5 kyu/A Chain adding function.py

mwk0408/codewars_solutions

9b4f502b5f159e68024d494e19a96a226acad5e5

[ "MIT" ]

1

2021-12-13T15:30:21.000Z

5 kyu/A Chain adding function.py

mwk0408/codewars_solutions

9b4f502b5f159e68024d494e19a96a226acad5e5

[ "MIT" ]

null

class add(int): def __call__(self, func): return add(self+func)

25

29

0.626667

11

75

3.909091

0.727273

0.372093

0

0.24

75

3

30

25

0.754386

0

1

0.333333

false

0

0.333333

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

b097456c525d2365c86b50159fcf720176508198

108

py

Python

pkgs/ops-pkg/src/genie/libs/ops/mld/ios/mld.py

miott/genielibs

6464642cdd67aa2367bdbb12561af4bb060e5e62

[ "Apache-2.0" ]

94

2018-04-30T20:29:15.000Z

2022-03-29T13:40:31.000Z

pkgs/ops-pkg/src/genie/libs/ops/mld/ios/mld.py

miott/genielibs

6464642cdd67aa2367bdbb12561af4bb060e5e62

[ "Apache-2.0" ]

67

2018-12-06T21:08:09.000Z

2022-03-29T18:00:46.000Z

pkgs/ops-pkg/src/genie/libs/ops/mld/ios/mld.py

miott/genielibs

6464642cdd67aa2367bdbb12561af4bb060e5e62

[ "Apache-2.0" ]

49

2018-06-29T18:59:03.000Z

2022-03-10T02:07:59.000Z

''' Mld Genie Ops Object for IOS - CLI. ''' from ..iosxe.mld import Mld as MldXE class Mld(MldXE): pass

15.428571

36

0.657407

18

108

3.944444

0.777778

0

0.212963

108

7

37

15.428571

0.835294

0.324074

0

1

0

true

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

6

b0c1d8e57f1678c811b93fc8d5752bce6a572793

164

py

Python

half/python/mutilthread.py

kong5664546498/half_a_wheel

d50c2359ac7dda55f54dd08bb588091eb6232b81

[ "MIT" ]

null

half/python/mutilthread.py

kong5664546498/half_a_wheel

d50c2359ac7dda55f54dd08bb588091eb6232b81

[ "MIT" ]

null

half/python/mutilthread.py

kong5664546498/half_a_wheel

d50c2359ac7dda55f54dd08bb588091eb6232b81

[ "MIT" ]

null

from threading import Thread from hello_world import hello t = Thread(target=hello, args=("kitty",)) c = Thread(target=hello, args=("kitty",)) t.start() c.start()

20.5

41

0.713415

25

164

4.64

0.48

0.206897

0.293103

0.362069

0.448276

0

0.115854

164

7

42

23.428571

0.8

0

0.060976

0

1

0

false

0

0.333333

0

0.333333

0

1

0

null

1

0

1

0

null

0

1

0

6

9fe801332cc30903478f4763357236cbc5b74a91

121

py

Python

app.py

yahya09/liga-badr

509a35249d7a0d2e068501082e6f8ab3ad45e55a

[ "MIT" ]

null

app.py

yahya09/liga-badr

509a35249d7a0d2e068501082e6f8ab3ad45e55a

[ "MIT" ]

null

app.py

yahya09/liga-badr

509a35249d7a0d2e068501082e6f8ab3ad45e55a

[ "MIT" ]

null

import coba print(coba.addGlobal(5)) print(coba.powerGlobal(2)) print(coba.addGlobal(12345)) print(coba.powerGlobal(-1))

20.166667

28

0.77686

18

121

5.222222

0.5

0.382979

0

0.069565

0.049587

121

6

29

20.166667

0.747826

0

1

0

true

0

0.2

0

0.2

0.8

1

0

null

1

0

1

0

null

0

1

0

1

0

6

b009258637a67ca5b092238713ad06eca208ef2c

38

py

Python

user_interface/__init__.py

DrunkBearEKB/console-hex

fc3499105835379f339fc844b2d4be125438e5ce

[ "Apache-2.0" ]

1

2021-06-02T19:05:59.000Z

user_interface/__init__.py

DrunkBearEKB/console-hex

fc3499105835379f339fc844b2d4be125438e5ce

[ "Apache-2.0" ]

null

user_interface/__init__.py

DrunkBearEKB/console-hex

fc3499105835379f339fc844b2d4be125438e5ce

[ "Apache-2.0" ]

null

from user_interface.win import Window

19

37

0.868421

6

38

5.333333

1

0

0.105263

38

1

38

0.941176

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

b02ed1b8c61843cbb061dbef658e4bd03b9fbb0f

39

py

Python

weibo_api/weibo_login/__init__.py

wdwind/weibo_api

2ca654f2a216bdf792d2aef7b04ab3da3c734b27

[ "MIT" ]

13

2019-07-01T02:28:28.000Z

2022-02-20T02:42:42.000Z

weibo_api/weibo_login/__init__.py

wdwind/weibo_api

2ca654f2a216bdf792d2aef7b04ab3da3c734b27

[ "MIT" ]

3

2020-04-11T22:33:13.000Z

2021-04-30T20:47:20.000Z

weibo_api/weibo_login/__init__.py

wdwind/weibo_api

2ca654f2a216bdf792d2aef7b04ab3da3c734b27

[ "MIT" ]

4

2019-08-31T07:32:36.000Z

2022-02-20T02:42:48.000Z

from .weibo_login import WeiboLoginApi

19.5

38

0.871795

5

39

6.6

1

0

0.102564

39

1

39

0.942857

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

c6668e86bf770869c38dffbacccdca3db8bad5ed

163

py

Python

plpred/models/__init__.py

Christiankun/plpred

a134fafaadc694d22f10806d634efa77213a476d

[ "MIT" ]

1

2021-04-09T19:25:47.000Z

plpred/models/__init__.py

Christiankun/plpred

a134fafaadc694d22f10806d634efa77213a476d

[ "MIT" ]

null

plpred/models/__init__.py

Christiankun/plpred

a134fafaadc694d22f10806d634efa77213a476d

[ "MIT" ]

null

from .plpred_rf import PlpredRF from .plpred_gb import PlpredGB from .base_model import BaseModel from .plpred_nn import PlpredNN from .plpred_svm import PlpredSVM

32.6

33

0.852761

25

163

5.36

0.56

0.298507

0

0.116564

163

5

34

32.6

0.930556

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

c66f47d41238be4ca674ec0bef3e26cf67bb85fe

89

py

Python

tests/cerami/datatype/__init__.py

gummybuns/dorm

e97c0baa42c4bdfb10bbe3b4b859873e3d50aa3a

[ "MIT" ]

null

tests/cerami/datatype/__init__.py

gummybuns/dorm

e97c0baa42c4bdfb10bbe3b4b859873e3d50aa3a

[ "MIT" ]

null

tests/cerami/datatype/__init__.py

gummybuns/dorm

e97c0baa42c4bdfb10bbe3b4b859873e3d50aa3a

[ "MIT" ]

null

from .dynamo_data_type_test import * from .translator import * from .expression import *

22.25

36

0.797753

12

89

5.666667

0.666667

0.294118

0

0.134831

89

3

37

29.666667

0.883117

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

c67fc7874e099b8b52fd1622ebeb2ff85dd8801e

263

py

Python

tests/test_integration.py

Yobmod/srimpy

3071a9d48fb2201810a863fb3d881248b365d2cf

[ "MIT" ]

1

2021-10-16T10:23:57.000Z

tests/test_integration.py

Yobmod/srimpy

3071a9d48fb2201810a863fb3d881248b365d2cf

[ "MIT" ]

null

tests/test_integration.py

Yobmod/srimpy

3071a9d48fb2201810a863fb3d881248b365d2cf

[ "MIT" ]

null

""" Integration Testing for pysrim #( c)2018 #( c)2018 """ #( c)2018 #( c)2018 import pytest #( c)2018 #( c)2018 from srim.core.target import Target #( c)2018 from srim.core.layer import Layer #( c)2018 from srim.core.element import Element #( c)2018

26.3

48

0.661597

42

263

4.142857

0.333333

0.258621

0.137931

0.229885

0.408046

0.114943

0

0.169014

0.190114

263

9

49

29.222222

0.647887

0.410646

0

1

0

true

0

1

0

1

0

null

1

0

1

0

1

0

null

0

1

0

1

0

1

0

6

c6b73f1b0e0a85e0b549ad8c4ef0809032d6e6a0

3,010

py

Python

AutoEncoder/ae_model.py

CsekM8/LVH-THESIS

b0dc60daaf0825ad43951e6895289da4e3ed911b

[ "MIT" ]

null

AutoEncoder/ae_model.py

CsekM8/LVH-THESIS

b0dc60daaf0825ad43951e6895289da4e3ed911b

[ "MIT" ]

null

AutoEncoder/ae_model.py

CsekM8/LVH-THESIS

b0dc60daaf0825ad43951e6895289da4e3ed911b

[ "MIT" ]

null

import torch.nn as nn class ConvAE(nn.Module): def __init__(self, variant='A'): super(ConvAE, self).__init__() if variant == 'A': self.encoder = nn.Sequential( nn.Conv2d(1, 12, 5, padding=2), nn.ReLU(inplace=True), nn.Conv2d(12, 6, 3, padding=1), nn.ReLU(inplace=True), nn.AvgPool2d(2), nn.Conv2d(6, 3, 3, padding=1) ) self.decoder = nn.Sequential( nn.ConvTranspose2d(3, 6, 3, padding=1), nn.ReLU(inplace=True), nn.UpsamplingBilinear2d(scale_factor=2), nn.ConvTranspose2d(6, 12, 3, padding=1), nn.ReLU(inplace=True), nn.ConvTranspose2d(12, 1, 5, padding=2) ) elif variant == 'B': self.encoder = nn.Sequential( nn.Conv2d(1, 24, 5, padding=2), nn.LeakyReLU(0.05, inplace=True), nn.MaxPool2d(2), nn.Conv2d(24, 12, 3, padding=1), nn.LeakyReLU(0.05, inplace=True), nn.MaxPool2d(2), nn.Conv2d(12, 6, 3, padding=1), nn.LeakyReLU(0.05, inplace=True), nn.MaxPool2d(2) ) self.decoder = nn.Sequential( nn.ConvTranspose2d(6, 6, 3, padding=1), nn.LeakyReLU(0.05, inplace=True), nn.UpsamplingBilinear2d(scale_factor=2), nn.ConvTranspose2d(6, 12, 3, padding=1), nn.LeakyReLU(0.05, inplace=True), nn.UpsamplingBilinear2d(scale_factor=2), nn.ConvTranspose2d(12, 24, 3, padding=1), nn.LeakyReLU(0.05, inplace=True), nn.UpsamplingBilinear2d(scale_factor=2), nn.ConvTranspose2d(24, 1, 5, padding=2), ) else: self.encoder = nn.Sequential( nn.Conv2d(1, 24, 7, padding=3), nn.ReLU(inplace=True), nn.AvgPool2d(2), nn.Conv2d(24, 12, 3, padding=1), nn.ReLU(inplace=True), nn.AvgPool2d(2), nn.Conv2d(12, 6, 3, padding=1), nn.ReLU(inplace=True), nn.AvgPool2d(2) ) self.decoder = nn.Sequential( nn.ConvTranspose2d(6, 6, 3, padding=1), nn.ReLU(inplace=True), nn.UpsamplingBilinear2d(scale_factor=2), nn.ConvTranspose2d(6, 12, 3, padding=1), nn.ReLU(inplace=True), nn.UpsamplingBilinear2d(scale_factor=2), nn.ConvTranspose2d(12, 24, 3, padding=1), nn.ReLU(inplace=True), nn.UpsamplingBilinear2d(scale_factor=2), nn.ConvTranspose2d(24, 1, 7, padding=3), ) def forward(self, x): x = self.encoder(x) x = self.decoder(x) return x

37.160494

57

0.47907

335

3,010

4.259701

0.134328

0.123336

0.14576

0.10021

0.83602

0.824107

0.796076

0.773651

0.725999

0.694464

0

0.091009

0.39402

3,010

80

58

37.625

0.691338

0

0.630137

0

0.000997

0

1

0.027397

false

0

0.013699

0

0.068493

0

null

0

1

0

null

0

6

af2e789d7fa56fb50e86563d6fbef6b454a4caeb

14

py

Python

requirements.py

Kromey/err-nanobot

af07232512b2fc04efb19d5271064decd4c14d08

[ "MIT" ]

1

2017-07-06T03:21:51.000Z

requirements.py

Kromey/err-nanobot

af07232512b2fc04efb19d5271064decd4c14d08

[ "MIT" ]

1

2015-11-19T17:36:03.000Z

requirements.py

Kromey/err-nanobot

af07232512b2fc04efb19d5271064decd4c14d08

[ "MIT" ]

null

pynano==0.1.1

7

13

0.642857

4

14

2.25

0.75

0

0.230769

0.071429

14

1

14

0.461538

0

null

0

null

0

1

0

null

0

1

0

1

0

1

0

null

0

1

0

6

a57633172a008d57320dfbd9cf14b1e54232db1b

165

py

Python

cytoself/components/layers/norm_mse.py

royerlab/cytoself

4c3b3a475f020b72540416ecd48198daccdfb2f1

[ "BSD-3-Clause" ]

16

2021-03-31T12:31:40.000Z

2022-03-17T16:19:00.000Z

cytoself/components/layers/norm_mse.py

royerlab/cytoself

4c3b3a475f020b72540416ecd48198daccdfb2f1

[ "BSD-3-Clause" ]

null

cytoself/components/layers/norm_mse.py

royerlab/cytoself

4c3b3a475f020b72540416ecd48198daccdfb2f1

[ "BSD-3-Clause" ]

null

from tensorflow.compat.v1.keras.losses import MSE def normalized_mse(var): def loss(y_true, y_pred): return MSE(y_true, y_pred) / var return loss

18.333333

49

0.69697

27

165

4.074074

0.592593

0.090909

0.109091

0.181818

0

0.007692

0.212121

165

8

50

20.625

0.838462

0

1

0.4

false

0

0.2

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

a592663ee49a52c64e37dbf24d8775a6fe50307b

5,421

py

Python

tests.py

Tauag/SPass

bbaba575d2922930ec4730f68e83adff4ab060ac

[ "MIT" ]

2

2018-04-27T17:52:10.000Z

2018-04-27T19:36:13.000Z

tests.py

Tauag/SPass

bbaba575d2922930ec4730f68e83adff4ab060ac

[ "MIT" ]

2

2018-04-27T13:56:53.000Z

2018-04-27T21:46:01.000Z

tests.py

Tauag/SPass

bbaba575d2922930ec4730f68e83adff4ab060ac

[ "MIT" ]

1

2018-04-27T20:55:39.000Z

import unittest import string from spass.generators import generate_random_password, generate_passphrase from spass.exceptions import ParameterError class TestGenerators(unittest.TestCase): def test_length(self): pass_set = generate_random_password() self.assertEqual(9, len(pass_set['password']), 'Generated password not of correct length: %s' % pass_set) pass_set = generate_random_password(length=40) self.assertEqual(40, len(pass_set['password']), 'Generated password not of correct length: %s' % pass_set) pass_set = generate_random_password(length=150) self.assertEqual(150, len(pass_set['password']), 'Generated password not of correct length: %s' % pass_set) def test_characters(self): target_chars = '<>,.?\\\'\"{}[]()=+-_^`~' pass_set = generate_random_password(length=150, ignored_chars=target_chars) for char in pass_set['password']: self.assertTrue(char not in target_chars, '<%s> was found and not expected' % char) pass_set = generate_random_password(length=150, letters=False) for char in pass_set['password']: self.assertTrue(char not in string.ascii_letters, '<%s> was found and not expected' % char) pass_set = generate_random_password(length=150, digits=False) for char in pass_set['password']: self.assertTrue(char not in string.digits, '<%s> was found and not expected' % char) pass_set = generate_random_password(length=150, punctuation=False) for char in pass_set['password']: self.assertTrue(char not in string.punctuation, '<%s> was found and not expected' % char) pass_set = generate_random_password(length=150, letters=False, punctuation=False) for char in pass_set['password']: self.assertTrue(char not in string.ascii_letters + string.punctuation, '<%s> was found and not expected' % char) pass_set = generate_random_password(length=150, letters=False, digits=False) for char in pass_set['password']: self.assertTrue(char not in string.ascii_letters + string.digits, '<%s> was found and not expected' % char) def test_padding_characters(self): pass_set = generate_passphrase(word_count=10, pad_length=10) pad_count, bank = 0, string.digits + string.punctuation for char in pass_set['password']: if char in bank: pad_count += 1 self.assertEqual(10, pad_count, 'Incorrect number of padding characters') pass_set = generate_passphrase(word_count=10, pad_length=10, punctuation=False) pad_count, bank = 0, string.digits for char in pass_set['password']: if char in bank: pad_count += 1 self.assertEqual(10, pad_count, 'Incorrect number of padding characters') pass_set = generate_passphrase(word_count=10, pad_length=10, digits=False) pad_count, bank = 0, string.punctuation for char in pass_set['password']: if char in bank: pad_count += 1 self.assertEqual(10, pad_count, 'Incorrect number of padding characters') def test_exception(self): with self.assertRaises(ParameterError): generate_random_password(letters=False, punctuation=False, digits=False) with self.assertRaises(ParameterError): generate_passphrase(pad_length=5, punctuation=False, digits=False) with self.assertRaises(ParameterError): generate_passphrase(pad_length=10, punctuation=False, digits=False) def test_entropy_random(self): pass_set = generate_random_password() self.assertEqual(58.99129966509874, pass_set['entropy'], 'Unexpected entropy value') pass_set = generate_random_password(letters=False, digits=False) self.assertEqual(45, pass_set['entropy'], 'Unexpected entropy value') pass_set = generate_random_password(length=15) self.assertEqual(98.31883277516458, pass_set['entropy'], 'Unexpected entropy value') pass_set = generate_random_password(length=150, letters=False, digits=False) self.assertEqual(750.0000000000001, pass_set['entropy'], 'Unexpected entropy value') pass_set = generate_random_password(length=20) self.assertEqual(131.09177703355275, pass_set['entropy'], 'Unexpected entropy value') pass_set = generate_random_password(length=20, ignored_chars='\'\":;<>,./?[]{}\\()') self.assertEqual(125.33573081389804, pass_set['entropy'], 'Unexpected entropy value') def test_entropy_passphrase(self): pass_set = generate_passphrase() self.assertEqual(69.62406251802891, pass_set['entropy'], 'Unexpected entropy value') pass_set = generate_passphrase(word_count=15) self.assertEqual(208.8721875540867, pass_set['entropy'], 'Unexpected entropy value') def test_entropy_deviation(self): pass_set = generate_passphrase(pad_length=3) self.assertEqual(16.176952268336283, pass_set['deviation'], 'Unexpected deviation value') pass_set = generate_passphrase(pad_length=10) self.assertEqual(53.923174227787605, pass_set['deviation'], 'Unexpected deviation value') pass_set = generate_passphrase(pad_length=10, punctuation=False) self.assertEqual(33.219280948873624, pass_set['deviation'], 'Unexpected deviation value')

48.837838

124

0.693968

663

5,421

5.470588

0.137255

0.094569

0.09512

0.086849

0.810587

0.778605

0.718224

0.697546

0.671078

0.618693

0

0.055158

0.200701

5,421

110

125

49.281818

0.781906

0

0.301205

1

0.072289

0.164176

0

0.313253

1

0.084337

false

0.626506

0.048193

0

0.144578

0

null

0

1

0

1

0

1

0

null

0

1

0

6

a5ad34e6ea0d935a875772260536808f7e5423ba

44

py

Python

tvae/models/__init__.py

khucnam/Efflux_TransVAE

7da1cc614f016d5520648f4853e34e2362181aa7

[ "MIT" ]

43

2019-05-15T21:58:56.000Z

2022-03-06T03:44:26.000Z

tvae/models/__init__.py

khucnam/Efflux_TransVAE

7da1cc614f016d5520648f4853e34e2362181aa7

[ "MIT" ]

1

2020-01-11T12:03:00.000Z

tvae/models/__init__.py

khucnam/Efflux_TransVAE

7da1cc614f016d5520648f4853e34e2362181aa7

[ "MIT" ]

6

2019-07-24T18:15:41.000Z

2022-01-13T22:17:58.000Z

from .transformer_vae import TransformerVAE

22

43

0.886364

5

44

7.6

1

0

0.090909

44

1

44

0.95

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

3c19d9cdc30a17972d1b43936e0248c1edc09a6f

140

py

Python

gallery/locale.py

lwairore/gallery

0ec163675e9e683f82ebfbb32e462c6e86198118

[ "MIT" ]

1

2020-08-15T11:39:14.000Z

gallery/locale.py

lwairore/django-explore-africa

0ec163675e9e683f82ebfbb32e462c6e86198118

[ "MIT" ]

4

2021-03-19T01:22:57.000Z

2021-09-08T01:03:46.000Z

gallery/locale.py

lwairore/django-explore-africa

0ec163675e9e683f82ebfbb32e462c6e86198118

[ "MIT" ]

1

2019-06-17T15:09:00.000Z

import os BASE_DIR= os.path.dirname(os.path.dirname(os.path.abspath(__file__))) print('Location of static',os.path.join(BASE_DIR, 'static'))

46.666667

69

0.771429

24

140

4.25

0.541667

0.235294

0.254902

0.294118

0.313725

0

0.05

140

3

70

46.666667

0.766917

0

0.170213

0

1

0

false

0

0.333333

0

0.333333

1

0

null

1

0

1

0

null

0

1

0

6

3c26e4745a87825db61fef2ee8fbaf4d96549e9d

87

py

Python

models/unet/utils/__init__.py

ustb-ai3d/automatic_inpainting

b50121fcd452f03e5a89e28a8154afa635cc027b

[ "MIT" ]

2

2020-04-21T07:17:01.000Z

2021-08-02T05:14:54.000Z

models/unet/utils/__init__.py

ustb-ai3d/automatic_inpainting

b50121fcd452f03e5a89e28a8154afa635cc027b

[ "MIT" ]

null

models/unet/utils/__init__.py

ustb-ai3d/automatic_inpainting

b50121fcd452f03e5a89e28a8154afa635cc027b

[ "MIT" ]

1

2020-07-17T09:22:00.000Z

from .load import * from .utils import * from .data_vis import * from .predict import *

21.75

23

0.735632

13

87

4.846154

0.538462

0.47619

0

0.172414

87

4

24

21.75

0.875

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

3c32c0ceceda8195359574885751daa0d0ba36c8

64

py

Python

acq4/modules/TaskRunner/__init__.py

aleonlein/acq4

4b1fcb9ad2c5e8d4595a2b9cf99d50ece0c0f555

[ "MIT" ]

47

2015-01-05T16:18:10.000Z

2022-03-16T13:09:30.000Z

acq4/modules/TaskRunner/__init__.py

aleonlein/acq4

4b1fcb9ad2c5e8d4595a2b9cf99d50ece0c0f555

[ "MIT" ]

48

2015-04-19T16:51:41.000Z

2022-03-31T14:48:16.000Z

acq4/modules/TaskRunner/__init__.py

sensapex/acq4

9561ba73caff42c609bd02270527858433862ad8

[ "MIT" ]

32

2015-01-15T14:11:49.000Z

2021-07-15T13:44:52.000Z

from __future__ import print_function from .TaskRunner import *

21.333333

37

0.84375

8

64

6.125

0.75

0

0.125

64

2

38

32

0.875

0

1

0

true

0

1

0

1

0.5

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

3c449589f89840855b4405837c43a0f5aa2d0967

68

py

Python

vedro/_context.py

iri6e4k0/vedro

dd51c16400993d0fe1fd34bba57edff710ac2638

[ "Apache-2.0" ]

2

2021-08-24T12:49:30.000Z

2022-01-23T07:21:25.000Z

vedro/_context.py

iri6e4k0/vedro

dd51c16400993d0fe1fd34bba57edff710ac2638

[ "Apache-2.0" ]

20

2015-12-09T11:04:23.000Z

2022-03-20T09:18:17.000Z

vedro/_context.py

iri6e4k0/vedro

dd51c16400993d0fe1fd34bba57edff710ac2638

[ "Apache-2.0" ]

3

2015-12-09T07:31:23.000Z

2022-01-28T11:03:24.000Z

from typing import Any def context(fn: Any) -> Any: return fn

11.333333

28

0.661765

11

68

4.090909

0.727273

0

0.25

68

5

29

13.6

0.882353

0

1

0.333333

false

0

0.333333

1

0

1

0

null

0

1

0

null

0

1

0

1

0

6

3c84b0ad6e9e695bae4b66f49c73036393efb790

125

py

Python

main.py

GuiCardosooo/yolo-gun-detection

184f2793319a4881ed9db2363dbbfc9fab329b10

[ "MIT" ]

null

main.py

GuiCardosooo/yolo-gun-detection

184f2793319a4881ed9db2363dbbfc9fab329b10

[ "MIT" ]

null

main.py

GuiCardosooo/yolo-gun-detection

184f2793319a4881ed9db2363dbbfc9fab329b10

[ "MIT" ]

null

import cfg.config as cfg import lib.prepare as lpp # 1 - Baixa base de dados # 2 - Divide base de dados lpp.divide_dataset()

20.833333

26

0.744

23

125

4

0.652174

0.130435

0.23913

0

0.019608

0.184

125

6

27

20.833333

0.882353

0.384

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

b1cfbf0c8516d2af5ad28dafcfa2ba7dc101fb37

81

py

Python

tests/modules/contrib/test_amixer.py

alexsr/bumblebee-status

f8d035c0798621d8f6b33b262aecb42236658bd0

[ "MIT" ]

null

tests/modules/contrib/test_amixer.py

alexsr/bumblebee-status

f8d035c0798621d8f6b33b262aecb42236658bd0

[ "MIT" ]

null

tests/modules/contrib/test_amixer.py

alexsr/bumblebee-status

f8d035c0798621d8f6b33b262aecb42236658bd0

[ "MIT" ]

null

import pytest def test_load_module(): __import__("modules.contrib.amixer")

13.5

40

0.753086

10

81

5.5

0.9

0

0.135802

81

5

41

16.2

0.785714

0

0.275

0

1

0.333333

true

0

0.666667

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

b1e17d2f82466a9b2101dcd8faf944f0f5307cee

111

py

Python

Solutions/7kyu/7kyu_insert_dashes.py

citrok25/Codewars-1

dc641c5079e2e8b5955eb027fd15427e5bdb2e26

[ "MIT" ]

46

2017-08-24T09:27:57.000Z

2022-02-25T02:24:33.000Z

Solutions/7kyu/7kyu_insert_dashes.py

abbhishek971/Codewars

9e761811db724da1e8aae44594df42b4ee879a16

[ "MIT" ]

null

Solutions/7kyu/7kyu_insert_dashes.py

abbhishek971/Codewars

9e761811db724da1e8aae44594df42b4ee879a16

[ "MIT" ]

35

2017-08-01T22:09:48.000Z

2022-02-18T17:21:37.000Z

import re def insert_dash(num): return re.sub('[13579]+', lambda s: '-'.join(list(s.group(0))), str(num))

22.2

77

0.621622

19

111

3.578947

0.842105

0

0.0625

0.135135

111

4

78

27.75

0.645833

0

0.081081

0

1

0.333333

false

0

0.333333

1

0

1

0

null

0

1

0

null

0

1

0

1

0

6

5916b47592f752cb57ec0faf7de65db10fdbafe1

149

py

Python

libvirt_ebs/handlers/__init__.py

elprans/libvirt-ebs

a414711248db21de250d7740af06f8106cee57b8

[ "Apache-2.0" ]

null

libvirt_ebs/handlers/__init__.py

elprans/libvirt-ebs

a414711248db21de250d7740af06f8106cee57b8

[ "Apache-2.0" ]

null

libvirt_ebs/handlers/__init__.py

elprans/libvirt-ebs

a414711248db21de250d7740af06f8106cee57b8

[ "Apache-2.0" ]

null

from ._routing import handle_request as handle_request # NoQA from . import az # NoQA from . import instances # NoQA from . import volumes # NoQA

37.25

62

0.751678

21

149

5.190476

0.47619

0.220183

0.385321

0

0.194631

149

4

63

37.25

0.908333

0.127517

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

3cb7ca5ab1b4a32784b741c14790b4a27734d68e

27

py

Python

Text_to_Image/StyleGAN2_ada/__init__.py

talha-khalid-qureshi/Image-Captioning

4fce3efe39319c1eb111b8c4a3ca063a52e8f1bf

[ "Apache-2.0" ]

null

Text_to_Image/StyleGAN2_ada/__init__.py

talha-khalid-qureshi/Image-Captioning

4fce3efe39319c1eb111b8c4a3ca063a52e8f1bf

[ "Apache-2.0" ]

null

Text_to_Image/StyleGAN2_ada/__init__.py

talha-khalid-qureshi/Image-Captioning

4fce3efe39319c1eb111b8c4a3ca063a52e8f1bf

[ "Apache-2.0" ]

null

from StyleGAN2_ada import *

27

0.851852

4

27

5.5

1

0

0.041667

0.111111

27

1

27

0.875

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

5979faeab7725dd7b41ee09b56dfdf0927e1be64

62

py

Python

emojex/main.py

360macky/emojex

09d1dca4065924fa499f6c5309eb7c69afb02415

[ "MIT" ]

1

2021-05-14T15:49:27.000Z

emojex/main.py

360macky/emojex

09d1dca4065924fa499f6c5309eb7c69afb02415

[ "MIT" ]

null

emojex/main.py

360macky/emojex

09d1dca4065924fa499f6c5309eb7c69afb02415

[ "MIT" ]

null

import openai def set_api_key(key): openai.api_key = key

12.4

24

0.725806

11

62

3.818182

0.545455

0.285714

0.428571

0

0.193548

62

4

25

15.5

0.84

0

1

0.333333

false

0

0.333333

0

0.666667

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

5987cfc363efe54fcc27bd5a79cf2d13045a2065

35

py

Python

datasets/__init__.py

EagleMIT/m-i-d

943c9dc3c411fd0392ebca7b0c52a7bc4561503f

[ "MIT" ]

19

2021-07-27T06:08:39.000Z

2022-03-18T07:31:44.000Z

datasets/__init__.py

EagleMIT/mid

943c9dc3c411fd0392ebca7b0c52a7bc4561503f

[ "MIT" ]

3

2021-09-07T13:20:05.000Z

2021-10-11T01:51:29.000Z

datasets/__init__.py

EagleMIT/mid

943c9dc3c411fd0392ebca7b0c52a7bc4561503f

[ "MIT" ]

4

2021-07-27T06:20:32.000Z

2021-08-29T04:28:18.000Z

from .midataset import SliceDataset

35

0.885714

4

35

7.75

1

0

0.085714

35

1

35

0.96875

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

59a25cb3b22c47830c8e6d709ef537e3d40bcc90

45

py

Python

enthought/traits/ui/helper.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

3

2016-12-09T06:05:18.000Z

2018-03-01T13:00:29.000Z

enthought/traits/ui/helper.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

1

2020-12-02T00:51:32.000Z

2020-12-02T08:48:55.000Z

enthought/traits/ui/helper.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

null

# proxy module from traitsui.helper import *

15

29

0.777778

6

45

5.833333

1

0

0.155556

45

2

30

22.5

0.921053

0.266667

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

abbc23bd75d072c3314df0446c6e069b6faa861b

171

py

Python

apps/certificate/admin.py

LizanLycan/CertsGen

2e18d8ddea6adf90805face16cbb8f8fa06989c3

[ "MIT" ]

null

apps/certificate/admin.py

LizanLycan/CertsGen

2e18d8ddea6adf90805face16cbb8f8fa06989c3

[ "MIT" ]

1

2020-02-04T01:56:42.000Z

apps/certificate/admin.py

LizanLycan/CertsGen

2e18d8ddea6adf90805face16cbb8f8fa06989c3

[ "MIT" ]

null

from django.contrib import admin from .models import Certificate class CertificateAdmin(admin.ModelAdmin): pass admin.site.register(Certificate, CertificateAdmin)

17.1

50

0.812865

19

171

7.315789

0.684211

0

0.122807

171

9

51

19

0.926667

0

1

0

true

0.2

0.4

0

0.6

0

1

0

null

0

1

0

null

0

1

0

1

0

6

e61f03351c81978fb25204707fa849c17e42478c

5,773

py

Python

Ionburst/ionburst.py

ionburstcloud/ionburst-sdk-python

c5544bc26558aa2916186dcd0f0ea389234b9e28

[ "Apache-2.0" ]

3

2021-06-23T10:58:59.000Z

2021-07-01T18:27:51.000Z

Ionburst/ionburst.py

ionburstcloud/ionburst-sdk-python

c5544bc26558aa2916186dcd0f0ea389234b9e28

[ "Apache-2.0" ]

null

Ionburst/ionburst.py

ionburstcloud/ionburst-sdk-python

c5544bc26558aa2916186dcd0f0ea389234b9e28

[ "Apache-2.0" ]

1

2021-07-15T04:42:08.000Z

from .settings import Settings from .apiHandler import APIHandler class Ionburst: def __init__(self, server_url = None): self.settings = Settings(server_url) self.__apihandler = APIHandler(self.settings) def __check_token(self): if not self.settings.ionburst_id: raise ValueError('ionburst_id is not specified!') if not self.settings.ionburst_key: raise ValueError('ionburst_key is not specified!') if not self.settings.ionburst_uri: raise ValueError('ionburst_uri is not specified!') if not self.__apihandler.idToken: res = self.__apihandler.GetJWT() if res.status_code is not 200: raise SyntaxError('{}, status: {}. {}'.format(res.reason, res.status_code, res.text)) def get(self, id = None): self.__check_token() res = self.__apihandler.downloadData(id) if res.status_code is 200: return res.content else: raise SyntaxError('{}, status: {}. {}'.format(res.reason, res.status_code, res.text)) def getSecrets(self, id = None): self.__check_token() res = self.__apihandler.downloadSecrets(id) if res.status_code is 200: return res.content else: raise SyntaxError('{}, status: {}. {}'.format(res.reason, res.status_code, res.text)) def put(self, request = {}): self.__check_token() res = self.__apihandler.uploadData(request) if res.status_code is 200: return res.text else: raise SyntaxError('{}, status: {}. {}'.format(res.reason, res.status_code, res.text)) def putSecrets(self, request = {}): self.__check_token() res = self.__apihandler.uploadSecrets(request) if res.status_code is 200: return res.text else: raise SyntaxError('{}, status: {}. {}'.format(res.reason, res.status_code, res.text)) def delete(self, id = None): self.__check_token() res = self.__apihandler.deleteData(id) if res.status_code is 200: return res.text else: raise SyntaxError('{}, status: {}. {}'.format(res.reason, res.status_code, res.text)) def deleteSecrets(self, id = None): self.__check_token() res = self.__apihandler.deleteSecrets(id) if res.status_code is 200: return res.text else: raise SyntaxError('{}, status: {}. {}'.format(res.reason, res.status_code, res.text)) def getClassifications(self): if not self.settings.ionburst_uri: raise ValueError('ionburst_uri is not specified!') self.__check_token() res = self.__apihandler.classifications() if res.status_code is 200: return res.text else: raise SyntaxError('{}, status: {}. {}'.format(res.reason, res.status_code, res.text)) def startDeferred(self, request = {}): self.__check_token() if 'action' not in request: raise ValueError('action must be specified in the parameter!') if 'id' not in request: raise ValueError('id must be specified in the parameter!') if request['action'] is 'GET': res = self.__apihandler.downloadData(request['id'], True) elif request['action'] is 'PUT': res = self.__apihandler.uploadData(request, True) else: raise ValueError('Deferred action is only available for PUT or GET') if res.status_code is 200: return res.text else: raise SyntaxError('{}, status: {}. {}'.format(res.reason, res.status_code, res.text)) def startDeferredSecrets(self, request = {}): self.__check_token() if 'action' not in request: raise ValueError('action must be specified in the parameter!') if 'id' not in request: raise ValueError('id must be specified in the parameter!') if request['action'] is 'GET': res = self.__apihandler.downloadSecrets(request['id'], True) elif request['action'] is 'PUT': res = self.__apihandler.uploadSecrets(request, True) else: raise ValueError('Deferred action is only available for PUT or GET') if res.status_code is 200: return res.text else: raise SyntaxError('{}, status: {}. {}'.format(res.reason, res.status_code, res.text)) def checkDeferred(self, token = None): self.__check_token() res = self.__apihandler.checkDeferred(token) if res.status_code is 200 or res.status_code is 202: return res.text else: raise SyntaxError('{}, status: {}. {}'.format(res.reason, res.status_code, res.text)) def checkDeferredSecrets(self, token = None): self.__check_token() res = self.__apihandler.checkDeferredSecrets(token) if res.status_code is 200 or res.status_code is 202: return res.text else: raise SyntaxError('{}, status: {}. {}'.format(res.reason, res.status_code, res.text)) def fetch(self,token = None): self.__check_token() res = self.__apihandler.fetch(token) if res.status_code is 200: return res.content else: raise SyntaxError('{}, status: {}. {}'.format(res.reason, res.status_code, res.text)) def fetchSecrets(self,token = None): self.__check_token() res = self.__apihandler.fetchSecrets(token) if res.status_code is 200: return res.content else: raise SyntaxError('{}, status: {}. {}'.format(res.reason, res.status_code, res.text))

40.090278

101

0.602113

672

5,773

4.991071

0.104167

0.080501

0.116279

0.071556

0.832737

0.813953

0.792188

0.751342

0.64997

0

0.011558

0.280617

5,773

144

102

40.090278

0.796051

0

0.671875

0

0.118289

0

1

0.117188

false

0

0.015625

0

0.242188

0

null

0

1

0

null

0

6

e62ceeafa9f3a192093612680d0c268104d29a1d

25

py

Python

test_files_copy/file_2.py

A-Wei/multiprocess_copy_folder

de62c616bd5ac48a64aef2fea360951c443839ac

[ "MIT" ]

null

test_files_copy/file_2.py

A-Wei/multiprocess_copy_folder

de62c616bd5ac48a64aef2fea360951c443839ac

[ "MIT" ]

null

test_files_copy/file_2.py

A-Wei/multiprocess_copy_folder

de62c616bd5ac48a64aef2fea360951c443839ac

[ "MIT" ]

null

# Some comments import os

12.5

15

0.8

4

25

5

1

0

0.16

25

2

16

12.5

0.952381

0.52

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

e63e5f8fb1afd9440a0969d0c412dc63659cb387

53

py

Python

learn/__init__.py

starrysky9959/digital-recognition

a81c3fab5415cd037d362354116202d76006b755

[ "MIT" ]

null

learn/__init__.py

starrysky9959/digital-recognition

a81c3fab5415cd037d362354116202d76006b755

[ "MIT" ]

null

learn/__init__.py

starrysky9959/digital-recognition

a81c3fab5415cd037d362354116202d76006b755

[ "MIT" ]

null

import learn.mymodel from learn.mymodel import trans

26.5

31

0.849057

8

53

5.625

0.625

0.533333

0

0.113208

53

2

31

26.5

0.957447

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

050fe0e2db0d86deb4eeb2760ae579b4c0b6b006

11,675

py

Python

platform/radio/efr32_multiphy_configurator/pyradioconfig/parts/viper/calculators/calc_global.py

lmnotran/gecko_sdk

2e82050dc8823c9fe0e8908c1b2666fb83056230

[ "Zlib" ]

69

2021-12-16T01:34:09.000Z

2022-03-31T08:27:39.000Z

platform/radio/efr32_multiphy_configurator/pyradioconfig/parts/viper/calculators/calc_global.py

lmnotran/gecko_sdk

2e82050dc8823c9fe0e8908c1b2666fb83056230

[ "Zlib" ]

6

2022-01-12T18:22:08.000Z

2022-03-25T10:19:27.000Z

platform/radio/efr32_multiphy_configurator/pyradioconfig/parts/viper/calculators/calc_global.py

lmnotran/gecko_sdk

2e82050dc8823c9fe0e8908c1b2666fb83056230

[ "Zlib" ]

21

2021-12-20T09:05:45.000Z

2022-03-28T02:52:28.000Z

from pyradioconfig.parts.bobcat.calculators.calc_global import Calc_Global_Bobcat from pycalcmodel.core.variable import ModelVariableFormat from py_2_and_3_compatibility import * class Calc_Global_Viper(Calc_Global_Bobcat): def buildVariables(self, model): # Build variables from the Ocelot calculations super().buildVariables(model) self._addModelRegister(model, 'RAC.RX.SYPFDCHPLPENRX', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'MODEM.CTRL5.DEC2', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'FEFILT0.CFG.DEC1' , int, ModelVariableFormat.HEX ) self._addModelRegister(model, 'FEFILT0.CFG.CHFGAINREDUCTION' , int, ModelVariableFormat.HEX ) self._addModelRegister(model, 'FEFILT0.GAINCTRL.DEC1GAIN' , int, ModelVariableFormat.HEX ) self._addModelRegister(model, 'FEFILT0.GAINCTRL.BBSS' , int, ModelVariableFormat.HEX ) self._addModelRegister(model, 'FEFILT0.SRC2.SRC2RATIO', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'FEFILT0.SRC2.SRC2ENABLE', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'FEFILT0.SRC2.UPGAPS', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'FEFILT0.GAINCTRL.DEC0GAIN', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'FEFILT0.CHFCSDCOE00.SET0CSDCOEFF0', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'FEFILT0.CHFCSDCOE00.SET0CSDCOEFF1', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'FEFILT0.CHFCSDCOE00.SET0CSDCOEFF2', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'FEFILT0.CHFCSDCOE00.SET0CSDCOEFF3', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'FEFILT0.CHFCSDCOE01.SET0CSDCOEFF4', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'FEFILT0.CHFCSDCOE01.SET0CSDCOEFF5', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'FEFILT0.CHFCSDCOE01.SET0CSDCOEFF6', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'FEFILT0.CHFCSDCOE02.SET0CSDCOEFF7', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'FEFILT0.CHFCSDCOE02.SET0CSDCOEFF8', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'FEFILT0.CHFCSDCOE02.SET0CSDCOEFF9', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'FEFILT0.CHFCSDCOE03.SET0CSDCOEFF10', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'FEFILT0.CHFCSDCOE03.SET0CSDCOEFF11', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'FEFILT0.CHFCSDCOE10.SET1CSDCOEFF0', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'FEFILT0.CHFCSDCOE10.SET1CSDCOEFF1', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'FEFILT0.CHFCSDCOE10.SET1CSDCOEFF2', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'FEFILT0.CHFCSDCOE10.SET1CSDCOEFF3', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'FEFILT0.CHFCSDCOE11.SET1CSDCOEFF4', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'FEFILT0.CHFCSDCOE11.SET1CSDCOEFF5', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'FEFILT0.CHFCSDCOE11.SET1CSDCOEFF6', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'FEFILT0.CHFCSDCOE12.SET1CSDCOEFF7', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'FEFILT0.CHFCSDCOE12.SET1CSDCOEFF8', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'FEFILT0.CHFCSDCOE12.SET1CSDCOEFF9', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'FEFILT0.CHFCSDCOE13.SET1CSDCOEFF10', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'FEFILT0.CHFCSDCOE13.SET1CSDCOEFF11', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'FEFILT0.CHFCSDCOE00S.SET0CSDCOEFF0S', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'FEFILT0.CHFCSDCOE00S.SET0CSDCOEFF1S', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'FEFILT0.CHFCSDCOE00S.SET0CSDCOEFF2S', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'FEFILT0.CHFCSDCOE00S.SET0CSDCOEFF3S', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'FEFILT0.CHFCSDCOE00S.SET0CSDCOEFF4S', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'FEFILT0.CHFCSDCOE00S.SET0CSDCOEFF5S', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'FEFILT0.CHFCSDCOE00S.SET0CSDCOEFF6S', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'FEFILT0.CHFCSDCOE01S.SET0CSDCOEFF7S', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'FEFILT0.CHFCSDCOE01S.SET0CSDCOEFF8S', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'FEFILT0.CHFCSDCOE01S.SET0CSDCOEFF9S', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'FEFILT0.CHFCSDCOE01S.SET0CSDCOEFF10S', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'FEFILT0.CHFCSDCOE01S.SET0CSDCOEFF11S', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'FEFILT0.CHFCSDCOE10S.SET1CSDCOEFF0S', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'FEFILT0.CHFCSDCOE10S.SET1CSDCOEFF1S', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'FEFILT0.CHFCSDCOE10S.SET1CSDCOEFF2S', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'FEFILT0.CHFCSDCOE10S.SET1CSDCOEFF3S', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'FEFILT0.CHFCSDCOE10S.SET1CSDCOEFF4S', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'FEFILT0.CHFCSDCOE10S.SET1CSDCOEFF5S', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'FEFILT0.CHFCSDCOE10S.SET1CSDCOEFF6S', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'FEFILT0.CHFCSDCOE11S.SET1CSDCOEFF7S', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'FEFILT0.CHFCSDCOE11S.SET1CSDCOEFF8S', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'FEFILT0.CHFCSDCOE11S.SET1CSDCOEFF9S', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'FEFILT0.CHFCSDCOE11S.SET1CSDCOEFF10S', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'FEFILT0.CHFCSDCOE11S.SET1CSDCOEFF11S', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'FEFILT0.CFG.CHFCOEFFFWSWEN', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'FEFILT0.CFG.CHFCOEFFFWSWSEL', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'FEFILT0.CFG.CHFCOEFFSWEN', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'FEFILT0.CFG.CHFCOEFFSWSEL', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'FEFILT0.DIGMIXCTRL.DIGIQSWAPEN' , int, ModelVariableFormat.HEX ) self._addModelRegister(model, 'FEFILT0.DIGMIXCTRL.DIGMIXFREQ', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'FEFILT0.DIGMIXCTRL.MIXERCONJ', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'FEFILT0.DIGMIXCTRL.DIGMIXFBENABLE', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'FEFILT0.DCCOMP.DCGAINGEAREN', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'FEFILT0.DCCOMP.DCGAINGEAR', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'FEFILT0.DCCOMP.DCGAINGEARSMPS', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'FEFILT0.DCCOMP.DCESTIEN', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'FEFILT0.DCCOMP.DCCOMPEN', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'FEFILT0.DCCOMP.DCRSTEN', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'FEFILT0.DCCOMP.DCCOMPFREEZE', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'FEFILT0.DCCOMP.DCCOMPGEAR', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'FEFILT0.DCCOMP.DCLIMIT', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'FEFILT0.DCCOMPFILTINIT.DCCOMPINITVALI', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'FEFILT0.DCCOMPFILTINIT.DCCOMPINITVALQ', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'FEFILT0.DCCOMPFILTINIT.DCCOMPINIT', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'MODEM.TXCORR.TXDGAIN6DB', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'MODEM.TXCORR.TXDGAIN', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'MODEM.TXCORR.TXGAINIMB', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'MODEM.TXCORR.TXPHSIMB', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'MODEM.TXCORR.TXFREQCORR', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'MODEM.TXMISC.FORCECLKEN', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'MODEM.TXMISC.TXIQIMBEN', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'MODEM.TXMISC.TXINTPEN', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'MODEM.TXMISC.TXDSEN', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'MODEM.TXMISC.TXIQSWAP', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'MODEM.TXMISC.TXDACFORMAT', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'MODEM.TXMISC.TXDACFORCE', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'MODEM.TXMISC.TXDCI', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'MODEM.TXMISC.TXDCQ', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'MODEM.TXMISC.BR2M', int, ModelVariableFormat.HEX) self._addModelVariable(model, 'br2m', int, ModelVariableFormat.DECIMAL) self._addModelActual(model, 'shaping_filter_gain_iqmod', float, ModelVariableFormat.DECIMAL) def _add_SHAPING_regs(self, model): self._addModelRegister(model, 'MODEM.SHAPING2.COEFF9', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'MODEM.SHAPING2.COEFF10' , int, ModelVariableFormat.HEX ) self._addModelRegister(model, 'MODEM.SHAPING2.COEFF11' , int, ModelVariableFormat.HEX ) self._addModelRegister(model, 'MODEM.SHAPING3.COEFF12' , int, ModelVariableFormat.HEX ) self._addModelRegister(model, 'MODEM.SHAPING3.COEFF13' , int, ModelVariableFormat.HEX ) self._addModelRegister(model, 'MODEM.SHAPING3.COEFF14' , int, ModelVariableFormat.HEX ) self._addModelRegister(model, 'MODEM.SHAPING3.COEFF15' , int, ModelVariableFormat.HEX ) def _add_MODEM_RXRESTART(self, model): self._addModelRegister(model, 'MODEM.RXRESTART.RXRESTARTB4PREDET', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'MODEM.RXRESTART.RXRESTARTMATAP', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'MODEM.RXRESTART.RXRESTARTMALATCHSEL', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'MODEM.RXRESTART.RXRESTARTMACOMPENSEL', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'MODEM.RXRESTART.RXRESTARTMATHRESHOLD', int, ModelVariableFormat.HEX) self._addModelRegister(model, 'MODEM.RXRESTART.RXRESTARTUPONMARSSI', int, ModelVariableFormat.HEX) def _add_TXBR_regs(self, model): self._addModelRegister(model, 'MODEM.TXBR.TXBRNUM', int, ModelVariableFormat.HEX)

88.44697

111

0.75863

1,042

11,675

8.373321

0.170825

0.272321

0.30659

0.345673

0.796103

0.787049

0.782579

0.766418

0

0.03094

0.136274

11,675

131

112

89.122137

0.834292

0.003769

0

0.268983

0.254794

0

1

0.033898

false

0

0.025424

0

0.067797

0

null

1

0

1

0

null

0

6

058b5ca0c374af1e36cfbbaa49ba541aca6fde3d

96

py

Python

venv/lib/python3.8/site-packages/setuptools/command/bdist_egg.py

Retraces/UkraineBot

3d5d7f8aaa58fa0cb8b98733b8808e5dfbdb8b71

[ "MIT" ]

2

2022-03-13T01:58:52.000Z

2022-03-31T06:07:54.000Z

venv/lib/python3.8/site-packages/setuptools/command/bdist_egg.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

19

2021-11-20T04:09:18.000Z

2022-03-23T15:05:55.000Z

venv/lib/python3.8/site-packages/setuptools/command/bdist_egg.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

null

/home/runner/.cache/pip/pool/fa/ea/62/07a7c5b66f1c412423d4b4435691b5f93d78dc3b170af5747e1d37bbb5

96

0.895833

9

96

9.555556

1

0

0.416667

0

96

1

96

0.479167

0

1

0

null

0

null

0

1

0

null

0

1

0

1

0

1

0

null

1

0

1

0

6

554a76df40e86a905ec7beb050e88706b55abee9

29

py

Python

main.py

frimik/slack_status

2bf713cc69e227dad2e835b5c7d85ea2da9d6d92

[ "MIT" ]

null

main.py

frimik/slack_status

2bf713cc69e227dad2e835b5c7d85ea2da9d6d92

[ "MIT" ]

null

main.py

frimik/slack_status

2bf713cc69e227dad2e835b5c7d85ea2da9d6d92

[ "MIT" ]

null

from slack_status import app

14.5

28

0.862069

5

29

4.8

1

0

0.137931

29

1

29

0.96

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

55506f33557db0709d5ab33afde8c76d3c5f3913

208

py

Python

run_services.py

ojlangnes/digital_impersonator

cf2fa9cb9cfd78e1f2978ec7cfcebde3ef804d8b

[ "MIT" ]

null

run_services.py

ojlangnes/digital_impersonator

cf2fa9cb9cfd78e1f2978ec7cfcebde3ef804d8b

[ "MIT" ]

null

run_services.py

ojlangnes/digital_impersonator

cf2fa9cb9cfd78e1f2978ec7cfcebde3ef804d8b

[ "MIT" ]

null

from sys import executable from subprocess import Popen Popen([executable, "front_end_worker.py"]) Popen([executable, "back_end_worker.py"]) Popen([executable, "front_end.py"]) input("Press ENTER to exit.")

26

42

0.769231

30

208

5.166667

0.533333

0.290323

0.258065

0.296774

0.335484

0

0.091346

208

8

43

26

0.820106

0

0.330144

0

1

0

true

0

0.333333

0

0.333333

0

null

1

0

1

0

null

0

1

0

1

0

6

55aced861b5b51526e1b1f28379879d8801d5061

165

py

Python

teme/admin.py

MDS-PBSCB/teme

f750713801246bda523d372d3c953b3c2bed2e6c

[ "MIT" ]

null

teme/admin.py

MDS-PBSCB/teme

f750713801246bda523d372d3c953b3c2bed2e6c

[ "MIT" ]

null

teme/admin.py

MDS-PBSCB/teme

f750713801246bda523d372d3c953b3c2bed2e6c

[ "MIT" ]

null

from django.contrib import admin from .models import Teacher, Course, Rating admin.site.register(Teacher) admin.site.register(Course) admin.site.register(Rating)

18.333333

43

0.806061

23

165

5.782609

0.478261

0.203008

0.383459

0

0.09697

165

8

44

20.625

0.892617

0

1

0

true

0

0.4

0

0.4

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

e94fa3b4c36b58eef80105ba4526cf6de42b78a3

49

py

Python

sharetempus/__init__.py

ShareTempus/sharetempus-python

3e6285d013c00f0f466a03f5d2b8be45946d731a

[ "MIT" ]

1

2020-05-12T18:08:54.000Z

sharetempus/__init__.py

ShareTempus/sharetempus-python

3e6285d013c00f0f466a03f5d2b8be45946d731a

[ "MIT" ]

null

sharetempus/__init__.py

ShareTempus/sharetempus-python

3e6285d013c00f0f466a03f5d2b8be45946d731a

[ "MIT" ]

null

from sharetempus.ShareTempus import ShareTempus;

24.5

48

0.877551

5

49

8.6

0.6

0

0.081633

49

1

49

0.955556

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

e97ca67456f764501fac6fcb37278561294ca11b

6,191

py

Python

imcsdk/mometa/memory/MemoryPersistentMemoryLogicalConfiguration.py

ecoen66/imcsdk

b10eaa926a5ee57cea7182ae0adc8dd1c818b0ab

[ "Apache-2.0" ]

31

2016-06-14T07:23:59.000Z

2021-09-12T17:17:26.000Z

imcsdk/mometa/memory/MemoryPersistentMemoryLogicalConfiguration.py

sthagen/imcsdk

1831eaecb5960ca03a8624b1579521749762b932

[ "Apache-2.0" ]

109

2016-05-25T03:56:56.000Z

2021-10-18T02:58:12.000Z

imcsdk/mometa/memory/MemoryPersistentMemoryLogicalConfiguration.py

sthagen/imcsdk

1831eaecb5960ca03a8624b1579521749762b932

[ "Apache-2.0" ]

67

2016-05-17T05:53:56.000Z

2022-03-24T15:52:53.000Z

"""This module contains the general information for MemoryPersistentMemoryLogicalConfiguration ManagedObject.""" from ...imcmo import ManagedObject from ...imccoremeta import MoPropertyMeta, MoMeta from ...imcmeta import VersionMeta class MemoryPersistentMemoryLogicalConfigurationConsts: ADMIN_ACTION_DISABLE_SECURITY = "disable-security" ADMIN_ACTION_ENABLE_SECURITY = "enable-security" ADMIN_ACTION_MODIFY_PASSPHRASE = "modify-passphrase" ADMIN_ACTION_RESET_FACTORY_DEFAULT = "reset-factory-default" ADMIN_ACTION_SECURE_ERASE = "secure-erase" ADMIN_ACTION_UNLOCK_DIMMS = "unlock-dimms" FORCE_CONFIG_FALSE = "false" FORCE_CONFIG_NO = "no" FORCE_CONFIG_TRUE = "true" FORCE_CONFIG_YES = "yes" MGMT_MODE_HOST_MANAGED = "host-managed" MGMT_MODE_IMC_MANAGED = "imc-managed" REBOOT_ON_UPDATE_FALSE = "false" REBOOT_ON_UPDATE_NO = "no" REBOOT_ON_UPDATE_TRUE = "true" REBOOT_ON_UPDATE_YES = "yes" class MemoryPersistentMemoryLogicalConfiguration(ManagedObject): """This is MemoryPersistentMemoryLogicalConfiguration class.""" consts = MemoryPersistentMemoryLogicalConfigurationConsts() naming_props = set([]) mo_meta = { "classic": MoMeta("MemoryPersistentMemoryLogicalConfiguration", "memoryPersistentMemoryLogicalConfiguration", "pmemory-lconfig", VersionMeta.Version404b, "InputOutput", 0xff, [], ["admin", "read-only", "user"], ['computeBoard'], ['memoryPersistentMemoryDimms', 'memoryPersistentMemoryGoal', 'memoryPersistentMemoryLogicalNamespace', 'memoryPersistentMemorySecurity'], [None]), "modular": MoMeta("MemoryPersistentMemoryLogicalConfiguration", "memoryPersistentMemoryLogicalConfiguration", "pmemory-lconfig", VersionMeta.Version404b, "InputOutput", 0xff, [], ["admin", "read-only", "user"], ['computeBoard'], ['memoryPersistentMemoryDimms', 'memoryPersistentMemoryGoal', 'memoryPersistentMemoryLogicalNamespace', 'memoryPersistentMemorySecurity'], [None]) } prop_meta = { "classic": { "admin_action": MoPropertyMeta("admin_action", "adminAction", "string", VersionMeta.Version404b, MoPropertyMeta.READ_WRITE, 0x2, 0, 510, None, ["disable-security", "enable-security", "modify-passphrase", "reset-factory-default", "secure-erase", "unlock-dimms"], []), "dn": MoPropertyMeta("dn", "dn", "string", VersionMeta.Version404b, MoPropertyMeta.READ_WRITE, 0x4, 0, 255, None, [], []), "force_config": MoPropertyMeta("force_config", "forceConfig", "string", VersionMeta.Version404b, MoPropertyMeta.READ_WRITE, 0x8, None, None, None, ["No", "Yes", "false", "no", "true", "yes"], []), "mgmt_mode": MoPropertyMeta("mgmt_mode", "mgmtMode", "string", VersionMeta.Version404b, MoPropertyMeta.READ_WRITE, 0x10, None, None, None, ["host-managed", "imc-managed"], []), "reboot_on_update": MoPropertyMeta("reboot_on_update", "rebootOnUpdate", "string", VersionMeta.Version404b, MoPropertyMeta.READ_WRITE, 0x20, None, None, None, ["No", "Yes", "false", "no", "true", "yes"], []), "rn": MoPropertyMeta("rn", "rn", "string", VersionMeta.Version404b, MoPropertyMeta.READ_WRITE, 0x40, 0, 255, None, [], []), "status": MoPropertyMeta("status", "status", "string", VersionMeta.Version404b, MoPropertyMeta.READ_WRITE, 0x80, None, None, None, ["", "created", "deleted", "modified", "removed"], []), "child_action": MoPropertyMeta("child_action", "childAction", "string", VersionMeta.Version404b, MoPropertyMeta.INTERNAL, None, None, None, None, [], []), }, "modular": { "admin_action": MoPropertyMeta("admin_action", "adminAction", "string", VersionMeta.Version404b, MoPropertyMeta.READ_WRITE, 0x2, 0, 510, None, ["disable-security", "enable-security", "modify-passphrase", "reset-factory-default", "secure-erase", "unlock-dimms"], []), "dn": MoPropertyMeta("dn", "dn", "string", VersionMeta.Version404b, MoPropertyMeta.READ_WRITE, 0x4, 0, 255, None, [], []), "force_config": MoPropertyMeta("force_config", "forceConfig", "string", VersionMeta.Version404b, MoPropertyMeta.READ_WRITE, 0x8, None, None, None, ["No", "Yes", "no", "yes"], []), "mgmt_mode": MoPropertyMeta("mgmt_mode", "mgmtMode", "string", VersionMeta.Version404b, MoPropertyMeta.READ_WRITE, 0x10, None, None, None, ["host-managed", "imc-managed"], []), "reboot_on_update": MoPropertyMeta("reboot_on_update", "rebootOnUpdate", "string", VersionMeta.Version404b, MoPropertyMeta.READ_WRITE, 0x20, None, None, None, ["No", "Yes", "no", "yes"], []), "rn": MoPropertyMeta("rn", "rn", "string", VersionMeta.Version404b, MoPropertyMeta.READ_WRITE, 0x40, 0, 255, None, [], []), "status": MoPropertyMeta("status", "status", "string", VersionMeta.Version404b, MoPropertyMeta.READ_WRITE, 0x80, None, None, None, ["", "created", "deleted", "modified", "removed"], []), "child_action": MoPropertyMeta("child_action", "childAction", "string", VersionMeta.Version404b, MoPropertyMeta.INTERNAL, None, None, None, None, [], []), }, } prop_map = { "classic": { "adminAction": "admin_action", "dn": "dn", "forceConfig": "force_config", "mgmtMode": "mgmt_mode", "rebootOnUpdate": "reboot_on_update", "rn": "rn", "status": "status", "childAction": "child_action", }, "modular": { "adminAction": "admin_action", "dn": "dn", "forceConfig": "force_config", "mgmtMode": "mgmt_mode", "rebootOnUpdate": "reboot_on_update", "rn": "rn", "status": "status", "childAction": "child_action", }, } def __init__(self, parent_mo_or_dn, **kwargs): self._dirty_mask = 0 self.admin_action = None self.force_config = None self.mgmt_mode = None self.reboot_on_update = None self.status = None self.child_action = None ManagedObject.__init__(self, "MemoryPersistentMemoryLogicalConfiguration", parent_mo_or_dn, **kwargs)

60.696078

384

0.667259

569

6,191

7.047452

0.186292

0.04389

0.111721

0.167581

0.723441

0.714464

0.706733

0.705736

0.700249

0

0.022973

0.177354

6,191

101

385

61.29703

0.764382

0.02649

0

0.4

0

0.312552

0.085619

0

0.009643

0

1

0.0125

false

0.0375

0

0.3375

0

null

0

1

0

1

0

null

0

6

e994af040d1b678cef489263880d7232bdd9b630

69,486

py

Python

test/ibm_qiskit/circuit/TestQiskitQuantumCircuit.py

rubenandrebarreiro/semi-quantum-conference-key-agreement-prototype

adefc5a43e4fb1c2b7926af5da93e346f96497c0

[ "MIT" ]

null

test/ibm_qiskit/circuit/TestQiskitQuantumCircuit.py

rubenandrebarreiro/semi-quantum-conference-key-agreement-prototype

adefc5a43e4fb1c2b7926af5da93e346f96497c0

[ "MIT" ]

null

test/ibm_qiskit/circuit/TestQiskitQuantumCircuit.py

rubenandrebarreiro/semi-quantum-conference-key-agreement-prototype

adefc5a43e4fb1c2b7926af5da93e346f96497c0

[ "MIT" ]

null

""" Semi-Quantum Conference Key Agreement (SQCKA) Author: - Ruben Andre Barreiro (r.barreiro@campus.fct.unl.pt) Supervisors: - Andre Nuno Souto (ansouto@fc.ul.pt) - Antonio Maria Ravara (aravara@fct.unl.pt) Acknowledgments: - Paulo Alexandre Mateus (pmat@math.ist.utl.pt) """ # Import Packages and Libraries # Import Unittest for Python's Unitary Tests import unittest # Import N-Dimensional Arrays and Squared Roots from NumPy from numpy import array, sqrt # Import Assert_All_Close from NumPy.Testing from numpy.testing import assert_allclose # Import Aer and execute from Qiskit from qiskit import Aer, execute # Import QiskitQuantumCircuit from IBM_Qiskit.Circuit from src.ibm_qiskit.circuit import QiskitQuantumCircuit # Import QiskitQuantumRegister from IBM_Qiskit.Circuit.Quantum from src.ibm_qiskit.circuit.registers.quantum import QiskitQuantumRegister # Import QiskitClassicalRegister from IBM_Qiskit.Circuit.Classical from src.ibm_qiskit.circuit.registers.classical import QiskitClassicalRegister # Test Cases for the Prepare/Measure in the X-Basis (Diagonal Basis) class PrepareMeasureXBasisTests(unittest.TestCase): # Test #1 for the Prepare/Measure in the X-Basis (Diagonal Basis) # Description of the Test Case: # 1) The Quantum Circuit is created with a Quantum Register, # with 1 Qubit initialized in the state |0⟩; # 2) The Qubit is prepared/measured in the X-Basis (Diagonal Basis); def test_prepare_measure_x_basis_1(self): # The number of Qubits and Bits, for Quantum and Classical Registers, respectively num_qubits = num_bits = 1 # Creation of the IBM Qiskit's Quantum and Classical Registers qiskit_quantum_register_prepare_measure_x_basis_1 = \ QiskitQuantumRegister.QiskitQuantumRegister("qrmeasxbasis1", num_qubits) qiskit_classical_register_prepare_measure_x_basis_1 = \ QiskitClassicalRegister.QiskitClassicalRegister("crmeasxbasis1", num_bits) # Creation of the IBM Qiskit's Quantum Circuit with one Quantum and Classical Registers qiskit_quantum_circuit_prepare_measure_x_basis_1 = \ QiskitQuantumCircuit.QiskitQuantumCircuit("qcmeasxbasis1", qiskit_quantum_register_prepare_measure_x_basis_1, qiskit_classical_register_prepare_measure_x_basis_1, global_phase=0) # Prepare/Measure the Qubit in the X-Basis (Diagonal Basis) qiskit_quantum_circuit_prepare_measure_x_basis_1 \ .prepare_measure_single_qubit_in_x_basis(0, 0, 0, 0, is_final_measurement=False) # Getting the Backend for the State Vector Representation # (i.e., the Quantum State represented as State Vector) state_vector_backend = Aer.get_backend('statevector_simulator') # Execute the Quantum Circuit and store the Quantum State in a final state vector final_state_vector = execute(qiskit_quantum_circuit_prepare_measure_x_basis_1.quantum_circuit, state_vector_backend).result().get_statevector() # Assert All Close, from NumPy's Testing, for the State Vector of the Qubit, # after the Prepare/Measure in the X-Basis (Diagonal Basis) be performed assert_allclose(final_state_vector, array([((1. / sqrt(2.)) + 0.j), ((1. / sqrt(2.)) + 0.j)]), rtol=1e-7, atol=1e-7) # Dummy Assert Equal for Unittest self.assertEqual(True, True) # Test #2 for the Prepare/Measure in the X-Basis (Diagonal Basis) # Description of the Test Case: # 1) The Quantum Circuit is created with a Quantum Register, # with 1 Qubit initialized in the state |0⟩; # 2) It is applied the Pauli-X Gate to the 1st Qubit, then, |0⟩ ↦ |1⟩; # 3) The Qubit is prepared/measured in the X-Basis (Diagonal Basis); def test_prepare_measure_x_basis_2(self): # The number of Qubits and Bits, for Quantum and Classical Registers, respectively num_qubits = num_bits = 1 # Creation of the IBM Qiskit's Quantum and Classical Registers qiskit_quantum_register_prepare_measure_x_basis_2 = \ QiskitQuantumRegister.QiskitQuantumRegister("qrmeasxbasis2", num_qubits) qiskit_classical_register_prepare_measure_x_basis_2 = \ QiskitClassicalRegister.QiskitClassicalRegister("crmeasxbasis2", num_bits) # Creation of the IBM Qiskit's Quantum Circuit with one Quantum and Classical Registers qiskit_quantum_circuit_prepare_measure_x_basis_2 = \ QiskitQuantumCircuit.QiskitQuantumCircuit("qcmeasxbasis2", qiskit_quantum_register_prepare_measure_x_basis_2, qiskit_classical_register_prepare_measure_x_basis_2, global_phase=0) # Apply the Pauli-X Gate to the 1st Qubit of the Quantum Circuit (|0⟩ ↦ |1⟩) qiskit_quantum_circuit_prepare_measure_x_basis_2 \ .apply_pauli_x(qiskit_quantum_register_prepare_measure_x_basis_2.quantum_register[0]) # Prepare/Measure the Qubit in the X-Basis (Diagonal Basis) qiskit_quantum_circuit_prepare_measure_x_basis_2 \ .prepare_measure_single_qubit_in_x_basis(0, 0, 0, 0, is_final_measurement=False) # Getting the Backend for the State Vector Representation # (i.e., the Quantum State represented as State Vector) state_vector_backend = Aer.get_backend('statevector_simulator') # Execute the Quantum Circuit and store the Quantum State in a final state vector final_state_vector = execute(qiskit_quantum_circuit_prepare_measure_x_basis_2.quantum_circuit, state_vector_backend).result().get_statevector() # Assert All Close, from NumPy's Testing, for the State Vector of the Qubit, # after the Prepare/Measure in the X-Basis (Diagonal Basis) be performed assert_allclose(final_state_vector, array([((1. / sqrt(2.)) + 0.j), (-(1. / sqrt(2.)) + 0.j)]), rtol=1e-7, atol=1e-7) # Dummy Assert Equal for Unittest self.assertEqual(True, True) # Test #3 for the Prepare/Measure in the X-Basis (Diagonal Basis) # Description of the Test Case: # 1) The Quantum Circuit is created with a Quantum Register, # with 1 Qubit initialized in the state |0⟩; # 2) It is applied the Hadamard Gate to the 1st Qubit, then, |0⟩ ↦ |+⟩; # 3) The Qubit is prepared/measured in the X-Basis (Diagonal Basis); def test_prepare_measure_x_basis_3(self): # The number of Qubits and Bits, for Quantum and Classical Registers, respectively num_qubits = num_bits = 1 # Creation of the IBM Qiskit's Quantum and Classical Registers qiskit_quantum_register_prepare_measure_x_basis_3 = \ QiskitQuantumRegister.QiskitQuantumRegister("qrmeasxbasis3", num_qubits) qiskit_classical_register_prepare_measure_x_basis_3 = \ QiskitClassicalRegister.QiskitClassicalRegister("crmeasxbasis3", num_bits) # Creation of the IBM Qiskit's Quantum Circuit with one Quantum and Classical Registers qiskit_quantum_circuit_prepare_measure_x_basis_3 = \ QiskitQuantumCircuit.QiskitQuantumCircuit("qcmeasxbasis3", qiskit_quantum_register_prepare_measure_x_basis_3, qiskit_classical_register_prepare_measure_x_basis_3, global_phase=0) # Apply the Hadamard Gate to the 1st Qubit of the Quantum Circuit (|0⟩ ↦ |+⟩) qiskit_quantum_circuit_prepare_measure_x_basis_3 \ .apply_hadamard(qiskit_quantum_register_prepare_measure_x_basis_3.quantum_register[0]) # Prepare/Measure the Qubit in the X-Basis (Diagonal Basis) qiskit_quantum_circuit_prepare_measure_x_basis_3 \ .prepare_measure_single_qubit_in_x_basis(0, 0, 0, 0, is_final_measurement=False) # Getting the Backend for the State Vector Representation # (i.e., the Quantum State represented as State Vector) state_vector_backend = Aer.get_backend('statevector_simulator') # Execute the Quantum Circuit and store the Quantum State in a final state vector final_state_vector = execute(qiskit_quantum_circuit_prepare_measure_x_basis_3.quantum_circuit, state_vector_backend).result().get_statevector() # Assert All Close, from NumPy's Testing, for the State Vector of the Qubit, # after the Prepare/Measure in the X-Basis (Diagonal Basis) be performed assert_allclose(final_state_vector, array([(1. + 0.j), (0. + 0.j)]), rtol=1e-7, atol=1e-7) # Dummy Assert Equal for Unittest self.assertEqual(True, True) # Test #4 for the Prepare/Measure in the X-Basis # Description of the Test Case: # 1) The Quantum Circuit is created with a Quantum Register, # with 1 Qubit initialized in the state |0⟩; # 2) It is applied the Pauli-X Gate to the 1st Qubit, then, |0⟩ ↦ |1⟩; # 3) It is applied the Hadamard Gate to the 1st Qubit, then, |1⟩ ↦ |-⟩; # 4) The Qubit is prepared/measured in the X-Basis; def test_prepare_measure_x_basis_4(self): # The number of Qubits and Bits, for Quantum and Classical Registers, respectively num_qubits = num_bits = 1 # Creation of the IBM Qiskit's Quantum and Classical Registers qiskit_quantum_register_prepare_measure_x_basis_4 = \ QiskitQuantumRegister.QiskitQuantumRegister("qrmeasxbasis4", num_qubits) qiskit_classical_register_prepare_measure_x_basis_4 = \ QiskitClassicalRegister.QiskitClassicalRegister("crmeasxbasis4", num_bits) # Creation of the IBM Qiskit's Quantum Circuit with one Quantum and Classical Registers qiskit_quantum_circuit_prepare_measure_x_basis_4 = \ QiskitQuantumCircuit.QiskitQuantumCircuit("qcmeasxbasis4", qiskit_quantum_register_prepare_measure_x_basis_4, qiskit_classical_register_prepare_measure_x_basis_4, global_phase=0) # Apply the Pauli-X Gate to the 1st Qubit of the Quantum Circuit (|0⟩ ↦ |1⟩) qiskit_quantum_circuit_prepare_measure_x_basis_4 \ .apply_pauli_x(qiskit_quantum_register_prepare_measure_x_basis_4.quantum_register[0]) # Apply the Hadamard Gate to the 1st Qubit of the Quantum Circuit (|1⟩ ↦ |-⟩) qiskit_quantum_circuit_prepare_measure_x_basis_4 \ .apply_hadamard(qiskit_quantum_register_prepare_measure_x_basis_4.quantum_register[0]) # Prepare/Measure the Qubit in the X-Basis (Diagonal Basis) qiskit_quantum_circuit_prepare_measure_x_basis_4 \ .prepare_measure_single_qubit_in_x_basis(0, 0, 0, 0, is_final_measurement=False) # Getting the Backend for the State Vector Representation # (i.e., the Quantum State represented as State Vector) state_vector_backend = Aer.get_backend('statevector_simulator') # Execute the Quantum Circuit and store the Quantum State in a final state vector final_state_vector = execute(qiskit_quantum_circuit_prepare_measure_x_basis_4.quantum_circuit, state_vector_backend).result().get_statevector() # Assert All Close, from NumPy's Testing, for the State Vector of the Qubit, # after the Prepare/Measure in the X-Basis be performed assert_allclose(final_state_vector, array([(0. + 0.j), (1. + 0.j)]), rtol=1e-7, atol=1e-7) # Dummy Assert Equal for Unittest self.assertEqual(True, True) # Test Cases for the Prepare/Measure in the Y-Basis (Diagonal Basis) class PrepareMeasureYBasisTests(unittest.TestCase): # Test #1 for the Prepare/Measure in the Y-Basis (Diagonal Basis) # Description of the Test Case: # 1) The Quantum Circuit is created with a Quantum Register, # with 1 Qubit initialized in the state |0⟩; # 2) The Qubit is prepared/measured in the Y-Basis (Diagonal Basis); def test_prepare_measure_y_basis_1(self): # The number of Qubits and Bits, for Quantum and Classical Registers, respectively num_qubits = num_bits = 1 # Creation of the IBM Qiskit's Quantum and Classical Registers qiskit_quantum_register_prepare_measure_y_basis_1 = \ QiskitQuantumRegister.QiskitQuantumRegister("qrmeasybasis1", num_qubits) qiskit_classical_register_prepare_measure_y_basis_1 = \ QiskitClassicalRegister.QiskitClassicalRegister("crmeasybasis1", num_bits) # Creation of the IBM Qiskit's Quantum Circuit with one Quantum and Classical Registers qiskit_quantum_circuit_prepare_measure_y_basis_1 = \ QiskitQuantumCircuit.QiskitQuantumCircuit("qcmeasybasis1", qiskit_quantum_register_prepare_measure_y_basis_1, qiskit_classical_register_prepare_measure_y_basis_1, global_phase=0) # Prepare/Measure the Qubit in the Y-Basis (Computational Basis) qiskit_quantum_circuit_prepare_measure_y_basis_1 \ .prepare_measure_single_qubit_in_y_basis(0, 0, 0, 0, is_final_measurement=False) # Getting the Backend for the State Vector Representation # (i.e., the Quantum State represented as State Vector) state_vector_backend = Aer.get_backend('statevector_simulator') # Execute the Quantum Circuit and store the Quantum State in a final state vector final_state_vector = execute(qiskit_quantum_circuit_prepare_measure_y_basis_1.quantum_circuit, state_vector_backend).result().get_statevector() # Assert All Close, from NumPy's Testing, for the State Vector of the Qubit, # after the Prepare/Measure in the Y-Basis (Diagonal Basis) be performed assert_allclose(final_state_vector, array([((1. / sqrt(2.)) + 0.j), (1. / sqrt(2.)) * (0. + 1.j)]), rtol=1e-7, atol=1e-7) # Dummy Assert Equal for Unittest self.assertEqual(True, True) # Test #2 for the Prepare/Measure in the Y-Basis (Diagonal Basis) # Description of the Test Case: # 1) The Quantum Circuit is created with a Quantum Register, # with 1 Qubit initialized in the state |0⟩; # 2) It is applied the Pauli-X Gate to the 1st Qubit, then, |0⟩ ↦ |1⟩; # 3) The Qubit is prepared/measured in the Y-Basis (Diagonal Basis); def test_prepare_measure_y_basis_2(self): # The number of Qubits and Bits, for Quantum and Classical Registers, respectively num_qubits = num_bits = 1 # Creation of the IBM Qiskit's Quantum and Classical Registers qiskit_quantum_register_prepare_measure_y_basis_2 = \ QiskitQuantumRegister.QiskitQuantumRegister("qrmeasybasis2", num_qubits) qiskit_classical_register_prepare_measure_y_basis_2 = \ QiskitClassicalRegister.QiskitClassicalRegister("crmeasybasis2", num_bits) # Creation of the IBM Qiskit's Quantum Circuit with one Quantum and Classical Registers qiskit_quantum_circuit_prepare_measure_y_basis_2 = \ QiskitQuantumCircuit.QiskitQuantumCircuit("qcmeasybasis2", qiskit_quantum_register_prepare_measure_y_basis_2, qiskit_classical_register_prepare_measure_y_basis_2, global_phase=0) # Apply the Pauli-X Gate to the 1st Qubit of the Quantum Circuit (|0⟩ ↦ |1⟩) qiskit_quantum_circuit_prepare_measure_y_basis_2 \ .apply_pauli_x(qiskit_quantum_register_prepare_measure_y_basis_2.quantum_register[0]) # Prepare/Measure the Qubit in the Y-Basis (Diagonal Basis) qiskit_quantum_circuit_prepare_measure_y_basis_2 \ .prepare_measure_single_qubit_in_y_basis(0, 0, 0, 0, is_final_measurement=False) # Getting the Backend for the State Vector Representation # (i.e., the Quantum State represented as State Vector) state_vector_backend = Aer.get_backend('statevector_simulator') # Execute the Quantum Circuit and store the Quantum State in a final state vector final_state_vector = execute(qiskit_quantum_circuit_prepare_measure_y_basis_2.quantum_circuit, state_vector_backend).result().get_statevector() # Assert All Close, from NumPy's Testing, for the State Vector of the Qubit, # after the Prepare/Measure in the Y-Basis (Diagonal Basis) be performed assert_allclose(final_state_vector, array([((1. / sqrt(2.)) + 0.j), -(1. / sqrt(2.)) * (0. + 1.j)]), rtol=1e-7, atol=1e-7) # Dummy Assert Equal for Unittest self.assertEqual(True, True) # Test #3 for the Prepare/Measure in the Y-Basis (Diagonal Basis) # Description of the Test Case: # 1) The Quantum Circuit is created with a Quantum Register, # with 1 Qubit initialized in the state |0⟩; # 2) It is applied the Hadamard Gate to the 1st Qubit, then, |0⟩ ↦ |+⟩; # 3) The Qubit is prepared/measured in the Y-Basis (Diagonal Basis); def test_prepare_measure_y_basis_3(self): # The number of Qubits and Bits, for Quantum and Classical Registers, respectively num_qubits = num_bits = 1 # Creation of the IBM Qiskit's Quantum and Classical Registers qiskit_quantum_register_prepare_measure_y_basis_3 = \ QiskitQuantumRegister.QiskitQuantumRegister("qrmeasybasis3", num_qubits) qiskit_classical_register_prepare_measure_y_basis_3 = \ QiskitClassicalRegister.QiskitClassicalRegister("crmeasybasis3", num_bits) # Creation of the IBM Qiskit's Quantum Circuit with one Quantum and Classical Registers qiskit_quantum_circuit_prepare_measure_y_basis_3 = \ QiskitQuantumCircuit.QiskitQuantumCircuit("qcmeasybasis3", qiskit_quantum_register_prepare_measure_y_basis_3, qiskit_classical_register_prepare_measure_y_basis_3, global_phase=0) # Apply the Hadamard Gate to the 1st Qubit of the Quantum Circuit (|0⟩ ↦ |+⟩) qiskit_quantum_circuit_prepare_measure_y_basis_3 \ .apply_hadamard(qiskit_quantum_register_prepare_measure_y_basis_3.quantum_register[0]) # Prepare/Measure the Qubit in the Y-Basis (Diagonal Basis) qiskit_quantum_circuit_prepare_measure_y_basis_3 \ .prepare_measure_single_qubit_in_y_basis(0, 0, 0, 0, is_final_measurement=False) # Getting the Backend for the State Vector Representation # (i.e., the Quantum State represented as State Vector) state_vector_backend = Aer.get_backend('statevector_simulator') # Execute the Quantum Circuit and store the Quantum State in a final state vector final_state_vector = execute(qiskit_quantum_circuit_prepare_measure_y_basis_3.quantum_circuit, state_vector_backend).result().get_statevector() # Assert All Close, from NumPy's Testing, for the State Vector of the Qubit, # after the Prepare/Measure in the Y-Basis (Diagonal Basis) be performed assert_allclose(final_state_vector, array([(1. + 0.j), (0. + 0.j)]), rtol=1e-7, atol=1e-7) # Dummy Assert Equal for Unittest self.assertEqual(True, True) # Test #4 for the Prepare/Measure in the Y-Basis (Diagonal Basis) # Description of the Test Case: # 1) The Quantum Circuit is created with a Quantum Register, # with 1 Qubit initialized in the state |0⟩; # 2) It is applied the Pauli-X Gate to the 1st Qubit, then, |0⟩ ↦ |1⟩; # 3) It is applied the Hadamard Gate to the 1st Qubit, then, |1⟩ ↦ |-⟩; # 4) The Qubit is prepared/measured in the Y-Basis (Diagonal Basis); def test_prepare_measure_y_basis_4(self): # The number of Qubits and Bits, for Quantum and Classical Registers, respectively num_qubits = num_bits = 1 # Creation of the IBM Qiskit's Quantum and Classical Registers qiskit_quantum_register_prepare_measure_y_basis_4 = \ QiskitQuantumRegister.QiskitQuantumRegister("qrmeasybasis4", num_qubits) qiskit_classical_register_prepare_measure_y_basis_4 = \ QiskitClassicalRegister.QiskitClassicalRegister("crmeasybasis4", num_bits) # Creation of the IBM Qiskit's Quantum Circuit with one Quantum and Classical Registers qiskit_quantum_circuit_prepare_measure_y_basis_4 = \ QiskitQuantumCircuit.QiskitQuantumCircuit("qcmeasybasis4", qiskit_quantum_register_prepare_measure_y_basis_4, qiskit_classical_register_prepare_measure_y_basis_4, global_phase=0) # Apply the Pauli-X Gate to the 1st Qubit of the Quantum Circuit (|0⟩ ↦ |1⟩) qiskit_quantum_circuit_prepare_measure_y_basis_4 \ .apply_pauli_x(qiskit_quantum_register_prepare_measure_y_basis_4.quantum_register[0]) # Apply the Hadamard Gate to the 1st Qubit of the Quantum Circuit (|1⟩ ↦ |-⟩) qiskit_quantum_circuit_prepare_measure_y_basis_4 \ .apply_hadamard(qiskit_quantum_register_prepare_measure_y_basis_4.quantum_register[0]) # Prepare/Measure the Qubit in the Y-Basis (Diagonal Basis) qiskit_quantum_circuit_prepare_measure_y_basis_4 \ .prepare_measure_single_qubit_in_y_basis(0, 0, 0, 0, is_final_measurement=False) # Getting the Backend for the State Vector Representation # (i.e., the Quantum State represented as State Vector) state_vector_backend = Aer.get_backend('statevector_simulator') # Execute the Quantum Circuit and store the Quantum State in a final state vector final_state_vector = execute(qiskit_quantum_circuit_prepare_measure_y_basis_4.quantum_circuit, state_vector_backend).result().get_statevector() # Assert All Close, from NumPy's Testing, for the State Vector of the Qubit, # after the Prepare/Measure in the Y-Basis (Diagonal Basis) be performed assert_allclose(final_state_vector, array([(0. + 0.j), (0. + 1.j)]), rtol=1e-7, atol=1e-7) # Dummy Assert Equal for Unittest self.assertEqual(True, True) # Test Cases for the Prepare/Measure in the Z-Basis (Computational Basis) class PrepareMeasureZBasisTests(unittest.TestCase): # Test #1 for the Prepare/Measure in the Z-Basis (Computational Basis) # Description of the Test Case: # 1) The Quantum Circuit is created with a Quantum Register, # with 1 Qubit initialized in the state |0⟩; # 2) The Qubit is prepared/measured in the Z-Basis (Computational Basis); def test_prepare_measure_z_basis_1(self): # The number of Qubits and Bits, for Quantum and Classical Registers, respectively num_qubits = num_bits = 1 # Creation of the IBM Qiskit's Quantum and Classical Registers qiskit_quantum_register_prepare_measure_z_basis_1 = \ QiskitQuantumRegister.QiskitQuantumRegister("qrmeaszbasis1", num_qubits) qiskit_classical_register_prepare_measure_z_basis_1 = \ QiskitClassicalRegister.QiskitClassicalRegister("crmeaszbasis1", num_bits) # Creation of the IBM Qiskit's Quantum Circuit with one Quantum and Classical Registers qiskit_quantum_circuit_prepare_measure_z_basis_1 = \ QiskitQuantumCircuit.QiskitQuantumCircuit("qcmeaszbasis1", qiskit_quantum_register_prepare_measure_z_basis_1, qiskit_classical_register_prepare_measure_z_basis_1, global_phase=0) # Prepare/Measure the Qubit in the Z-Basis (Computational Basis) qiskit_quantum_circuit_prepare_measure_z_basis_1 \ .prepare_measure_single_qubit_in_z_basis(0, 0, 0, 0, is_final_measurement=False) # Getting the Backend for the State Vector Representation # (i.e., the Quantum State represented as State Vector) state_vector_backend = Aer.get_backend('statevector_simulator') # Execute the Quantum Circuit and store the Quantum State in a final state vector final_state_vector = execute(qiskit_quantum_circuit_prepare_measure_z_basis_1.quantum_circuit, state_vector_backend).result().get_statevector() # Assert All Close, from NumPy's Testing, for the State Vector of the Qubit, # after the Prepare/Measure in the Z-Basis (Computational Basis) be performed assert_allclose(final_state_vector, array([(1. + 0.j), (0. + 0.j)]), rtol=1e-7, atol=1e-7) # Dummy Assert Equal for Unittest self.assertEqual(True, True) # Test #2 for the Prepare/Measure in the Z-Basis (Computational Basis) # Description of the Test Case: # 1) The Quantum Circuit is created with a Quantum Register, # with 1 Qubit initialized in the state |0⟩; # 2) It is applied the Pauli-X Gate to the 1st Qubit, then, |0⟩ ↦ |1⟩; # 3) The Qubit is prepared/measured in the Z-Basis (Computational Basis); def test_prepare_measure_z_basis_2(self): # The number of Qubits and Bits, for Quantum and Classical Registers, respectively num_qubits = num_bits = 1 # Creation of the IBM Qiskit's Quantum and Classical Registers qiskit_quantum_register_prepare_measure_z_basis_2 = \ QiskitQuantumRegister.QiskitQuantumRegister("qrmeaszbasis2", num_qubits) qiskit_classical_register_prepare_measure_z_basis_2 = \ QiskitClassicalRegister.QiskitClassicalRegister("crmeaszbasis2", num_bits) # Creation of the IBM Qiskit's Quantum Circuit with one Quantum and Classical Registers qiskit_quantum_circuit_prepare_measure_z_basis_2 = \ QiskitQuantumCircuit.QiskitQuantumCircuit("qcmeaszbasis2", qiskit_quantum_register_prepare_measure_z_basis_2, qiskit_classical_register_prepare_measure_z_basis_2, global_phase=0) # Apply the Pauli-X Gate to the 1st Qubit of the Quantum Circuit (|0⟩ ↦ |1⟩) qiskit_quantum_circuit_prepare_measure_z_basis_2 \ .apply_pauli_x(qiskit_quantum_register_prepare_measure_z_basis_2.quantum_register[0]) # Prepare/Measure the Qubit in the Z-Basis (Computational Basis) qiskit_quantum_circuit_prepare_measure_z_basis_2 \ .prepare_measure_single_qubit_in_z_basis(0, 0, 0, 0, is_final_measurement=False) # Getting the Backend for the State Vector Representation # (i.e., the Quantum State represented as State Vector) state_vector_backend = Aer.get_backend('statevector_simulator') # Execute the Quantum Circuit and store the Quantum State in a final state vector final_state_vector = execute(qiskit_quantum_circuit_prepare_measure_z_basis_2.quantum_circuit, state_vector_backend).result().get_statevector() # Assert All Close, from NumPy's Testing, for the State Vector of the Qubit, # after the Prepare/Measure in the Z-Basis (Computational Basis) be performed assert_allclose(final_state_vector, array([(0. + 0.j), (1. + 0.j)]), rtol=1e-7, atol=1e-7) # Dummy Assert Equal for Unittest self.assertEqual(True, True) # Test #3 for the Prepare/Measure in the Z-Basis (Computational Basis) # Description of the Test Case: # 1) The Quantum Circuit is created with a Quantum Register, # with 1 Qubit initialized in the state |0⟩; # 2) It is applied the Hadamard Gate to the 1st Qubit, then, |0⟩ ↦ |+⟩; # 3) The Qubit is prepared/measured in the Z-Basis (Computational Basis); def test_prepare_measure_z_basis_3(self): # The number of Qubits and Bits, for Quantum and Classical Registers, respectively num_qubits = num_bits = 1 # Creation of the IBM Qiskit's Quantum and Classical Registers qiskit_quantum_register_prepare_measure_z_basis_3 = \ QiskitQuantumRegister.QiskitQuantumRegister("qrmeaszbasis3", num_qubits) qiskit_classical_register_prepare_measure_z_basis_3 = \ QiskitClassicalRegister.QiskitClassicalRegister("crmeaszbasis3", num_bits) # Creation of the IBM Qiskit's Quantum Circuit with one Quantum and Classical Registers qiskit_quantum_circuit_prepare_measure_z_basis_3 = \ QiskitQuantumCircuit.QiskitQuantumCircuit("qcmeaszbasis3", qiskit_quantum_register_prepare_measure_z_basis_3, qiskit_classical_register_prepare_measure_z_basis_3, global_phase=0) # Apply the Hadamard Gate to the 1st Qubit of the Quantum Circuit (|0⟩ ↦ |+⟩) qiskit_quantum_circuit_prepare_measure_z_basis_3 \ .apply_hadamard(qiskit_quantum_register_prepare_measure_z_basis_3.quantum_register[0]) # Prepare/Measure the Qubit in the Z-Basis (Computational Basis) qiskit_quantum_circuit_prepare_measure_z_basis_3 \ .prepare_measure_single_qubit_in_z_basis(0, 0, 0, 0, is_final_measurement=False) # Getting the Backend for the State Vector Representation # (i.e., the Quantum State represented as State Vector) state_vector_backend = Aer.get_backend('statevector_simulator') # Execute the Quantum Circuit and store the Quantum State in a final state vector final_state_vector = execute(qiskit_quantum_circuit_prepare_measure_z_basis_3.quantum_circuit, state_vector_backend).result().get_statevector() # Assert All Close, from NumPy's Testing, for the State Vector of the Qubit, # after the Prepare/Measure in the Z-Basis (Computational Basis) be performed assert_allclose(final_state_vector, array([((1. / sqrt(2.)) + 0.j), ((1. / sqrt(2.)) + 0.j)]), rtol=1e-7, atol=1e-7) # Dummy Assert Equal for Unittest self.assertEqual(True, True) # Test #4 for the Prepare/Measure in the Z-Basis (Computational Basis) # Description of the Test Case: # 1) The Quantum Circuit is created with a Quantum Register, # with 1 Qubit initialized in the state |0⟩; # 2) It is applied the Pauli-X Gate to the 1st Qubit, then, |0⟩ ↦ |1⟩; # 3) It is applied the Hadamard Gate to the 1st Qubit, then, |1⟩ ↦ |-⟩; # 4) The Qubit is prepared/measured in the Z-Basis (Computational Basis); def test_prepare_measure_z_basis_4(self): # The number of Qubits and Bits, for Quantum and Classical Registers, respectively num_qubits = num_bits = 1 # Creation of the IBM Qiskit's Quantum and Classical Registers qiskit_quantum_register_prepare_measure_z_basis_4 = \ QiskitQuantumRegister.QiskitQuantumRegister("qrmeaszbasis4", num_qubits) qiskit_classical_register_prepare_measure_z_basis_4 = \ QiskitClassicalRegister.QiskitClassicalRegister("crmeaszbasis4", num_bits) # Creation of the IBM Qiskit's Quantum Circuit with one Quantum and Classical Registers qiskit_quantum_circuit_prepare_measure_z_basis_4 = \ QiskitQuantumCircuit.QiskitQuantumCircuit("qcmeaszbasis4", qiskit_quantum_register_prepare_measure_z_basis_4, qiskit_classical_register_prepare_measure_z_basis_4, global_phase=0) # Apply the Pauli-X Gate to the 1st Qubit of the Quantum Circuit (|0⟩ ↦ |1⟩) qiskit_quantum_circuit_prepare_measure_z_basis_4 \ .apply_pauli_x(qiskit_quantum_register_prepare_measure_z_basis_4.quantum_register[0]) # Apply the Hadamard Gate to the 1st Qubit of the Quantum Circuit (|1⟩ ↦ |-⟩) qiskit_quantum_circuit_prepare_measure_z_basis_4 \ .apply_hadamard(qiskit_quantum_register_prepare_measure_z_basis_4.quantum_register[0]) # Prepare/Measure the Qubit in the Z-Basis (Computational Basis) qiskit_quantum_circuit_prepare_measure_z_basis_4 \ .prepare_measure_single_qubit_in_z_basis(0, 0, 0, 0, is_final_measurement=False) # Getting the Backend for the State Vector Representation # (i.e., the Quantum State represented as State Vector) state_vector_backend = Aer.get_backend('statevector_simulator') # Execute the Quantum Circuit and store the Quantum State in a final state vector final_state_vector = execute(qiskit_quantum_circuit_prepare_measure_z_basis_4.quantum_circuit, state_vector_backend).result().get_statevector() # Assert All Close, from NumPy's Testing, for the State Vector of the Qubit, # after the Prepare/Measure in the Z-Basis (Computational Basis) be performed assert_allclose(final_state_vector, array([((1. / sqrt(2.)) + 0.j), -((1. / sqrt(2.)) + 0.j)]), rtol=1e-7, atol=1e-7) # Dummy Assert Equal for Unittest self.assertEqual(True, True) # Test Cases for the Pauli-I Gate class PauliIGateTests(unittest.TestCase): # Test #1 for the Pauli-I Gate # Description of the Test Case: # 1) The Quantum Circuit is created with a Quantum Register, # with 1 Qubit initialized in the state |0⟩; # 2) It is applied the Pauli-I Gate to the 1st Qubit, then, |0⟩ ↦ |0⟩; def test_apply_pauli_i_1(self): # The number of Qubits and Bits, for Quantum and Classical Registers, respectively num_qubits = num_bits = 1 # Creation of the IBM Qiskit's Quantum and Classical Registers qiskit_quantum_register_pauli_i_1 = QiskitQuantumRegister.QiskitQuantumRegister("qrpaulii1", num_qubits) qiskit_classical_register_pauli_i_1 = QiskitClassicalRegister.QiskitClassicalRegister("crpaulii1", num_bits) # Creation of the IBM Qiskit's Quantum Circuit with one Quantum and Classical Registers qiskit_quantum_circuit_pauli_i_1 = \ QiskitQuantumCircuit.QiskitQuantumCircuit("qcpaulii1", qiskit_quantum_register_pauli_i_1, qiskit_classical_register_pauli_i_1, global_phase=0) # Apply the Pauli-I Gate to the 1st Qubit of the Quantum Circuit (|0⟩ ↦ |0⟩) qiskit_quantum_circuit_pauli_i_1.apply_pauli_i(qiskit_quantum_register_pauli_i_1.quantum_register[0]) # Getting the Backend for the State Vector Representation # (i.e., the Quantum State represented as State Vector) state_vector_backend = Aer.get_backend('statevector_simulator') # Execute the Quantum Circuit and store the Quantum State in a final state vector final_state_vector = \ execute(qiskit_quantum_circuit_pauli_i_1.quantum_circuit, state_vector_backend).result().get_statevector() # Assert All Close, from NumPy's Testing, for the State Vector of the Qubit, # after the Pauli-I Gate be applied assert_allclose(final_state_vector, array([(1. + 0.j), (0. + 0.j)]), rtol=1e-7, atol=1e-7) # Dummy Assert Equal for Unittest self.assertEqual(True, True) # Test #2 for the Pauli-I Gate # Description of the Test Case: # 1) The Quantum Circuit is created with a Quantum Register, # with 1 Qubit initialized in the state |0⟩; # 2) It is applied the Pauli-I Gate to the 1st Qubit, then, |0⟩ ↦ |0⟩; # 3) It is applied, again, the Pauli-I Gate to the 1st Qubit, then, |0⟩ ↦ |0⟩; def test_apply_pauli_i_2(self): # The number of Qubits and Bits, for Quantum and Classical Registers, respectively num_qubits = num_bits = 1 # Creation of the IBM Qiskit's Quantum and Classical Registers qiskit_quantum_register_pauli_i_2 = QiskitQuantumRegister.QiskitQuantumRegister("qrpaulii2", num_qubits) qiskit_classical_register_pauli_i_2 = QiskitClassicalRegister.QiskitClassicalRegister("crpaulii2", num_bits) # Creation of the IBM Qiskit's Quantum Circuit with one Quantum and Classical Registers qiskit_quantum_circuit_pauli_i_2 = \ QiskitQuantumCircuit.QiskitQuantumCircuit("qcpaulii2", qiskit_quantum_register_pauli_i_2, qiskit_classical_register_pauli_i_2, global_phase=0) # Apply the Pauli-I Gate to the 1st Qubit of the Quantum Circuit (|0⟩ ↦ |0⟩) qiskit_quantum_circuit_pauli_i_2.apply_pauli_i(qiskit_quantum_register_pauli_i_2.quantum_register[0]) # Apply the Pauli-I Gate to the 1st Qubit of the Quantum Circuit, again (|0⟩ ↦ |0⟩) qiskit_quantum_circuit_pauli_i_2.apply_pauli_i(qiskit_quantum_register_pauli_i_2.quantum_register[0]) # Getting the Backend for the State Vector Representation # (i.e., the Quantum State represented as State Vector) state_vector_backend = Aer.get_backend('statevector_simulator') # Execute the Quantum Circuit and store the Quantum State in a final state vector final_state_vector = \ execute(qiskit_quantum_circuit_pauli_i_2.quantum_circuit, state_vector_backend).result().get_statevector() # Assert All Close, from NumPy's Testing, for the State Vector of the Qubit, # after the two Pauli-I Gates be applied assert_allclose(final_state_vector, array([(1. + 0.j), (0. + 0.j)]), rtol=1e-7, atol=1e-7) # Dummy Assert Equal for Unittest self.assertEqual(True, True) # Test Cases for the Pauli-X Gate class PauliXGateTests(unittest.TestCase): # Test #1 for the Pauli-X Gate # Description of the Test Case: # 1) The Quantum Circuit is created with a Quantum Register, # with 1 Qubit initialized in the state |0⟩; # 2) It is applied the Pauli-X Gate to the 1st Qubit, then, |0⟩ ↦ |1⟩; def test_apply_pauli_x_1(self): # The number of Qubits and Bits, for Quantum and Classical Registers, respectively num_qubits = num_bits = 1 # Creation of the IBM Qiskit's Quantum and Classical Registers qiskit_quantum_register_pauli_x_1 = QiskitQuantumRegister.QiskitQuantumRegister("qrpaulix1", num_qubits) qiskit_classical_register_pauli_x_1 = QiskitClassicalRegister.QiskitClassicalRegister("crpaulix1", num_bits) # Creation of the IBM Qiskit's Quantum Circuit with one Quantum and Classical Registers qiskit_quantum_circuit_pauli_x_1 = \ QiskitQuantumCircuit.QiskitQuantumCircuit("qcpaulix1", qiskit_quantum_register_pauli_x_1, qiskit_classical_register_pauli_x_1, global_phase=0) # Apply the Pauli-X Gate to the 1st Qubit of the Quantum Circuit (|0⟩ ↦ |1⟩) qiskit_quantum_circuit_pauli_x_1.apply_pauli_x(qiskit_quantum_register_pauli_x_1.quantum_register[0]) # Getting the Backend for the State Vector Representation # (i.e., the Quantum State represented as State Vector) state_vector_backend = Aer.get_backend('statevector_simulator') # Execute the Quantum Circuit and store the Quantum State in a final state vector final_state_vector = \ execute(qiskit_quantum_circuit_pauli_x_1.quantum_circuit, state_vector_backend).result().get_statevector() # Assert All Close, from NumPy's Testing, for the State Vector of the Qubit, # after the Pauli-X Gate be applied assert_allclose(final_state_vector, array([(0. + 0.j), (1. + 0.j)]), rtol=1e-7, atol=1e-7) # Dummy Assert Equal for Unittest self.assertEqual(True, True) # Test #2 for the Pauli-X Gate # Description of the Test Case: # 1) The Quantum Circuit is created with a Quantum Register, # with 1 Qubit initialized in the state |0⟩; # 2) It is applied the Pauli-X Gate to the 1st Qubit, then, |0⟩ ↦ |1⟩; # 3) It is applied, again, the Pauli-X Gate to the 1st Qubit, then, |1⟩ ↦ |0⟩; def test_apply_pauli_x_2(self): # The number of Qubits and Bits, for Quantum and Classical Registers, respectively num_qubits = num_bits = 1 # Creation of the IBM Qiskit's Quantum and Classical Registers qiskit_quantum_register_pauli_x_2 = QiskitQuantumRegister.QiskitQuantumRegister("qrpaulix2", num_qubits) qiskit_classical_register_pauli_x_2 = QiskitClassicalRegister.QiskitClassicalRegister("crpaulix2", num_bits) # Creation of the IBM Qiskit's Quantum Circuit with one Quantum and Classical Registers qiskit_quantum_circuit_pauli_x_2 = \ QiskitQuantumCircuit.QiskitQuantumCircuit("qcpaulix2", qiskit_quantum_register_pauli_x_2, qiskit_classical_register_pauli_x_2, global_phase=0) # Apply the Pauli-X Gate to the 1st Qubit of the Quantum Circuit (|0⟩ ↦ |1⟩) qiskit_quantum_circuit_pauli_x_2.apply_pauli_x(qiskit_quantum_register_pauli_x_2.quantum_register[0]) # Apply the Pauli-X Gate to the 1st Qubit of the Quantum Circuit, again (|1⟩ ↦ |0⟩) qiskit_quantum_circuit_pauli_x_2.apply_pauli_x(qiskit_quantum_register_pauli_x_2.quantum_register[0]) # Getting the Backend for the State Vector Representation # (i.e., the Quantum State represented as State Vector) state_vector_backend = Aer.get_backend('statevector_simulator') final_state_vector = \ execute(qiskit_quantum_circuit_pauli_x_2.quantum_circuit, state_vector_backend).result().get_statevector() # Assert All Close, from NumPy's Testing, for the State Vector of the Qubit, # after the two Pauli-X Gates be applied assert_allclose(final_state_vector, array([(1. + 0.j), (0. + 0.j)]), rtol=1e-7, atol=1e-7) # Dummy Assert Equal for Unittest self.assertEqual(True, True) # Test #3 for the Pauli-X Gate # Description of the Test Case: # 1) The Quantum Circuit is created with a Quantum Register, # with 1 Qubit initialized in the state |0⟩; # 2) It is applied the Hadamard Gate to the 1st Qubit, then, |0⟩ ↦ |+⟩; # 3) It is applied the Pauli-X Gate to the 1st Qubit, then, |+⟩ ↦ |+⟩; def test_apply_pauli_x_3(self): # The number of Qubits and Bits, for Quantum and Classical Registers, respectively num_qubits = num_bits = 1 # Creation of the IBM Qiskit's Quantum and Classical Registers qiskit_quantum_register_pauli_x_3 = QiskitQuantumRegister.QiskitQuantumRegister("qrpaulix3", num_qubits) qiskit_classical_register_pauli_x_3 = QiskitClassicalRegister.QiskitClassicalRegister("crpaulix3", num_bits) # Creation of the IBM Qiskit's Quantum Circuit with one Quantum and Classical Registers qiskit_quantum_circuit_pauli_x_3 = \ QiskitQuantumCircuit.QiskitQuantumCircuit("qcpaulix3", qiskit_quantum_register_pauli_x_3, qiskit_classical_register_pauli_x_3, global_phase=0) # Apply the Hadamard Gate to the 1st Qubit of the Quantum Circuit (|0⟩ ↦ |+⟩) qiskit_quantum_circuit_pauli_x_3.apply_hadamard(qiskit_quantum_register_pauli_x_3.quantum_register[0]) # Apply the Pauli-X Gate to the 1st Qubit of the Quantum Circuit, again (|+⟩ ↦ |+⟩) qiskit_quantum_circuit_pauli_x_3.apply_pauli_x(qiskit_quantum_register_pauli_x_3.quantum_register[0]) # Getting the Backend for the State Vector Representation # (i.e., the Quantum State represented as State Vector) state_vector_backend = Aer.get_backend('statevector_simulator') # Execute the Quantum Circuit and store the Quantum State in a final state vector final_state_vector = \ execute(qiskit_quantum_circuit_pauli_x_3.quantum_circuit, state_vector_backend).result().get_statevector() # Assert All Close, from NumPy's Testing, for the State Vector of the Qubit, # after the Single Qubits (Hadamard and Pauli-X) Gates be applied assert_allclose(final_state_vector, array([((1. / sqrt(2.)) + 0.j), ((1. / sqrt(2.)) + 0.j)]), rtol=1e-7, atol=1e-7) # Dummy Assert Equal for Unittest self.assertEqual(True, True) # Test #4 for the Pauli-X Gate # Description of the Test Case: # 1) The Quantum Circuit is created with a Quantum Register, # with 1 Qubit initialized in the state |0⟩; # 2) It is applied the Hadamard Gate to the 1st Qubit, then, |0⟩ ↦ |+⟩; # 3) It is applied the Pauli-X Gate to the 1st Qubit, then, |+⟩ ↦ |+⟩; # 4) It is applied, again, the Hadamard Gate to the 1st Qubit, then, |+⟩ ↦ |0⟩; def test_apply_pauli_x_4(self): # The number of Qubits and Bits, for Quantum and Classical Registers, respectively num_qubits = num_bits = 1 # Creation of the IBM Qiskit's Quantum and Classical Registers qiskit_quantum_register_pauli_x_4 = QiskitQuantumRegister.QiskitQuantumRegister("qrpaulix4", num_qubits) qiskit_classical_register_pauli_x_4 = QiskitClassicalRegister.QiskitClassicalRegister("crpaulix4", num_bits) # Creation of the IBM Qiskit's Quantum Circuit with one Quantum and Classical Registers qiskit_quantum_circuit_pauli_x_4 = \ QiskitQuantumCircuit.QiskitQuantumCircuit("qcpaulix4", qiskit_quantum_register_pauli_x_4, qiskit_classical_register_pauli_x_4, global_phase=0) # Apply the Hadamard Gate to the 1st Qubit of the Quantum Circuit (|0⟩ ↦ |+⟩) qiskit_quantum_circuit_pauli_x_4.apply_hadamard(qiskit_quantum_register_pauli_x_4.quantum_register[0]) # Apply the Pauli-X Gate to the 1st Qubit of the Quantum Circuit, again (|+⟩ ↦ |+⟩) qiskit_quantum_circuit_pauli_x_4.apply_pauli_x(qiskit_quantum_register_pauli_x_4.quantum_register[0]) # Apply the Hadamard Gate to the 1st Qubit of the Quantum Circuit (|+⟩ ↦ |0⟩) qiskit_quantum_circuit_pauli_x_4.apply_hadamard(qiskit_quantum_register_pauli_x_4.quantum_register[0]) # Getting the Backend for the State Vector Representation # (i.e., the Quantum State represented as State Vector) state_vector_backend = Aer.get_backend('statevector_simulator') # Execute the Quantum Circuit and store the Quantum State in a final state vector final_state_vector = \ execute(qiskit_quantum_circuit_pauli_x_4.quantum_circuit, state_vector_backend).result().get_statevector() # Assert All Close, from NumPy's Testing, for the State Vector of the Qubit, # after the Single Qubits (Hadamard and Pauli-X) Gates be applied assert_allclose(final_state_vector, array([(1. + 0.j), (0. + 0.j)]), rtol=1e-7, atol=1e-7) # Dummy Assert Equal for Unittest self.assertEqual(True, True) # Test Cases for the Pauli-Y Gate class PauliYGateTests(unittest.TestCase): # Test #1 for the Pauli-Y Gate # Description of the Test Case: # 1) The Quantum Circuit is created with a Quantum Register, # with 1 Qubit initialized in the state |0⟩; # 2) It is applied the Pauli-Y Gate to the 1st Qubit, then, |0⟩ ↦ |+i⟩; def test_apply_pauli_y_1(self): # The number of Qubits and Bits, for Quantum and Classical Registers, respectively num_qubits = num_bits = 1 # Creation of the IBM Qiskit's Quantum and Classical Registers qiskit_quantum_register_pauli_y_1 = QiskitQuantumRegister.QiskitQuantumRegister("qrpauliy1", num_qubits) qiskit_classical_register_pauli_y_1 = QiskitClassicalRegister.QiskitClassicalRegister("crpauliy1", num_bits) # Creation of the IBM Qiskit's Quantum Circuit with one Quantum and Classical Registers qiskit_quantum_circuit_pauli_y_1 = \ QiskitQuantumCircuit.QiskitQuantumCircuit("qcpauliy1", qiskit_quantum_register_pauli_y_1, qiskit_classical_register_pauli_y_1, global_phase=0) # Apply the Pauli-Y Gate to the 1st Qubit of the Quantum Circuit (|0⟩ ↦ |+i⟩) qiskit_quantum_circuit_pauli_y_1.apply_pauli_y(qiskit_quantum_register_pauli_y_1.quantum_register[0]) # Getting the Backend for the State Vector Representation # (i.e., the Quantum State represented as State Vector) state_vector_backend = Aer.get_backend('statevector_simulator') # Execute the Quantum Circuit and store the Quantum State in a final state vector final_state_vector = \ execute(qiskit_quantum_circuit_pauli_y_1.quantum_circuit, state_vector_backend).result().get_statevector() # Assert All Close, from NumPy's Testing, for the State Vector of the Qubit, # after the Pauli-Y Gate be applied assert_allclose(final_state_vector, array([(0. + 0.j), (0. + 1.j)]), rtol=1e-7, atol=1e-7) # Dummy Assert Equal for Unittest self.assertEqual(True, True) # Test #2 for the Pauli-Y Gate # Description of the Test Case: # 1) The Quantum Circuit is created with a Quantum Register, # with 1 Qubit initialized in the state |0⟩; # 2) It is applied the Pauli-Y Gate to the 1st Qubit, then, |0⟩ ↦ |+i⟩; # 3) It is applied, again, the Pauli-Y Gate to the 1st Qubit, then, |+i⟩ ↦ |0⟩; def test_apply_pauli_y_2(self): # The number of Qubits and Bits, for Quantum and Classical Registers, respectively num_qubits = num_bits = 1 # Creation of the IBM Qiskit's Quantum and Classical Registers qiskit_quantum_register_pauli_y_2 = QiskitQuantumRegister.QiskitQuantumRegister("qrpauliy2", num_qubits) qiskit_classical_register_pauli_y_2 = QiskitClassicalRegister.QiskitClassicalRegister("crpauliy2", num_bits) # Creation of the IBM Qiskit's Quantum Circuit with one Quantum and Classical Registers qiskit_quantum_circuit_pauli_y_2 = \ QiskitQuantumCircuit.QiskitQuantumCircuit("qcpauliy2", qiskit_quantum_register_pauli_y_2, qiskit_classical_register_pauli_y_2, global_phase=0) # Apply the Pauli-Y Gate to the 1st Qubit of the Quantum Circuit (|0⟩ ↦ |+i⟩) qiskit_quantum_circuit_pauli_y_2.apply_pauli_y(qiskit_quantum_register_pauli_y_2.quantum_register[0]) # Apply the Pauli-X Gate to the 1st Qubit of the Quantum Circuit, again (|+i⟩ ↦ |0⟩) qiskit_quantum_circuit_pauli_y_2.apply_pauli_y(qiskit_quantum_register_pauli_y_2.quantum_register[0]) # Getting the Backend for the State Vector Representation # (i.e., the Quantum State represented as State Vector) state_vector_backend = Aer.get_backend('statevector_simulator') # Execute the Quantum Circuit and store the Quantum State in a final state vector final_state_vector = \ execute(qiskit_quantum_circuit_pauli_y_2.quantum_circuit, state_vector_backend).result().get_statevector() # Assert All Close, from NumPy's Testing, for the State Vector of the Qubit, # after the two Pauli-Y Gates be applied assert_allclose(final_state_vector, array([(1. + 0.j), (0. + 0.j)]), rtol=1e-7, atol=1e-7) # Dummy Assert Equal for Unittest self.assertEqual(True, True) # Test #3 for the Pauli-Y Gate # Description of the Test Case: # 1) The Quantum Circuit is created with a Quantum Register, # with 1 Qubit initialized in the state |0⟩; # 2) It is applied the Pauli-X Gate to the 1st Qubit, then, |0⟩ ↦ |1⟩; # 3) It is applied the Pauli-Y Gate to the 1st Qubit, then, |1⟩ ↦ -i|0⟩; def test_apply_pauli_y_3(self): # The number of Qubits and Bits, for Quantum and Classical Registers, respectively num_qubits = num_bits = 1 # Creation of the IBM Qiskit's Quantum and Classical Registers qiskit_quantum_register_pauli_y_3 = QiskitQuantumRegister.QiskitQuantumRegister("qrpauliy3", num_qubits) qiskit_classical_register_pauli_y_3 = QiskitClassicalRegister.QiskitClassicalRegister("crpauliy3", num_bits) # Creation of the IBM Qiskit's Quantum Circuit with one Quantum and Classical Registers qiskit_quantum_circuit_pauli_y_3 = \ QiskitQuantumCircuit.QiskitQuantumCircuit("qcpauliy3", qiskit_quantum_register_pauli_y_3, qiskit_classical_register_pauli_y_3, global_phase=0) # Apply the Pauli-X Gate to the 1st Qubit of the Quantum Circuit (|0⟩ ↦ |1⟩) qiskit_quantum_circuit_pauli_y_3.apply_pauli_x(qiskit_quantum_register_pauli_y_3.quantum_register[0]) # Apply the Pauli-Y Gate to the 1st Qubit of the Quantum Circuit, again (|1⟩ ↦ -i|0⟩) qiskit_quantum_circuit_pauli_y_3.apply_pauli_y(qiskit_quantum_register_pauli_y_3.quantum_register[0]) # Getting the Backend for the State Vector Representation # (i.e., the Quantum State represented as State Vector) state_vector_backend = Aer.get_backend('statevector_simulator') # Execute the Quantum Circuit and store the Quantum State in a final state vector final_state_vector = \ execute(qiskit_quantum_circuit_pauli_y_3.quantum_circuit, state_vector_backend).result().get_statevector() # Assert All Close, from NumPy's Testing, for the State Vector of the Qubit, # after the Single Qubits (Pauli-X and Pauli-Y) Gates be applied assert_allclose(final_state_vector, array([(0. - 1.j), (0. + 0.j)]), rtol=1e-7, atol=1e-7) # Dummy Assert Equal for Unittest self.assertEqual(True, True) # Test #4 for the Pauli-Y Gate # Description of the Test Case: # 1) The Quantum Circuit is created with a Quantum Register, # with 1 Qubit initialized in the state |0⟩; # 2) It is applied the Hadamard Gate to the 1st Qubit, then, |0⟩ ↦ |+⟩; # 3) It is applied the Pauli-Y Gate to the 1st Qubit, then, |+⟩ ↦ 1/sqrt(2)i x (-|0⟩ + |1⟩); def test_apply_pauli_y_4(self): # The number of Qubits and Bits, for Quantum and Classical Registers, respectively num_qubits = num_bits = 1 # Creation of the IBM Qiskit's Quantum and Classical Registers qiskit_quantum_register_pauli_y_4 = QiskitQuantumRegister.QiskitQuantumRegister("qrpauliy4", num_qubits) qiskit_classical_register_pauli_y_4 = QiskitClassicalRegister.QiskitClassicalRegister("crpauliy4", num_bits) # Creation of the IBM Qiskit's Quantum Circuit with one Quantum and Classical Registers qiskit_quantum_circuit_pauli_y_4 = \ QiskitQuantumCircuit.QiskitQuantumCircuit("qcpauliy4", qiskit_quantum_register_pauli_y_4, qiskit_classical_register_pauli_y_4, global_phase=0) # Apply the Hadamard Gate to the 1st Qubit of the Quantum Circuit (|0⟩ ↦ |+⟩) qiskit_quantum_circuit_pauli_y_4.apply_hadamard(qiskit_quantum_register_pauli_y_4.quantum_register[0]) # Apply the Pauli-Y Gate to the 1st Qubit of the Quantum Circuit, again (|+⟩ ↦ 1/sqrt(2)i x (-|0⟩ + |1⟩)) qiskit_quantum_circuit_pauli_y_4.apply_pauli_y(qiskit_quantum_register_pauli_y_4.quantum_register[0]) # Getting the Backend for the State Vector Representation # (i.e., the Quantum State represented as State Vector) state_vector_backend = Aer.get_backend('statevector_simulator') # Execute the Quantum Circuit and store the Quantum State in a final state vector final_state_vector = \ execute(qiskit_quantum_circuit_pauli_y_4.quantum_circuit, state_vector_backend).result().get_statevector() # Assert All Close, from NumPy's Testing, for the State Vector of the Qubit, # after the Single Qubits (Hadamard and Pauli-Y) Gates be applied assert_allclose(final_state_vector, array([(0. - ((1. / sqrt(2.)) * 1.j)), (0. + ((1. / sqrt(2.)) * 1.j))]), rtol=1e-7, atol=1e-7) # Dummy Assert Equal for Unittest self.assertEqual(True, True) # Test Cases for the Pauli-Z Gate class PauliZGateTests(unittest.TestCase): # Test #1 for the Pauli-Z Gate # Description of the Test Case: # 1) The Quantum Circuit is created with a Quantum Register, # with 1 Qubit initialized in the state |0⟩; # 2) It is applied the Pauli-Z Gate to the 1st Qubit, then, |0⟩ ↦ |0⟩; def test_apply_pauli_z_1(self): # The number of Qubits and Bits, for Quantum and Classical Registers, respectively num_qubits = num_bits = 1 # Creation of the IBM Qiskit's Quantum and Classical Registers qiskit_quantum_register_pauli_z_1 = QiskitQuantumRegister.QiskitQuantumRegister("qrpauliz1", num_qubits) qiskit_classical_register_pauli_z_1 = QiskitClassicalRegister.QiskitClassicalRegister("crpauliz1", num_bits) # Creation of the IBM Qiskit's Quantum Circuit with one Quantum and Classical Registers qiskit_quantum_circuit_pauli_z_1 = \ QiskitQuantumCircuit.QiskitQuantumCircuit("qcpauliz1", qiskit_quantum_register_pauli_z_1, qiskit_classical_register_pauli_z_1, global_phase=0) # Apply the Pauli-Z Gate to the 1st Qubit of the Quantum Circuit (|0⟩ ↦ |0⟩) qiskit_quantum_circuit_pauli_z_1.apply_pauli_z(qiskit_quantum_register_pauli_z_1.quantum_register[0]) # Getting the Backend for the State Vector Representation # (i.e., the Quantum State represented as State Vector) state_vector_backend = Aer.get_backend('statevector_simulator') # Execute the Quantum Circuit and store the Quantum State in a final state vector final_state_vector = \ execute(qiskit_quantum_circuit_pauli_z_1.quantum_circuit, state_vector_backend).result().get_statevector() # Assert All Close, from NumPy's Testing, for the State Vector of the Qubit, # after the Pauli-Z Gate be applied assert_allclose(final_state_vector, array([(1. + 0.j), (0. + 0.j)]), rtol=1e-7, atol=1e-7) # Dummy Assert Equal for Unittest self.assertEqual(True, True) # Test #2 for the Pauli-Z Gate # Description of the Test Case: # 1) The Quantum Circuit is created with a Quantum Register, # with 1 Qubit initialized in the state |0⟩; # 2) It is applied the Pauli-Z Gate to the 1st Qubit, then, |0⟩ ↦ |0⟩; # 3) It is applied, again, the Pauli-Z Gate to the 1st Qubit, then, |0⟩ ↦ |0⟩; def test_apply_pauli_z_2(self): # The number of Qubits and Bits, for Quantum and Classical Registers, respectively num_qubits = num_bits = 1 # Creation of the IBM Qiskit's Quantum and Classical Registers qiskit_quantum_register_pauli_z_2 = QiskitQuantumRegister.QiskitQuantumRegister("qrpauliz2", num_qubits) qiskit_classical_register_pauli_z_2 = QiskitClassicalRegister.QiskitClassicalRegister("crpauliz2", num_bits) # Creation of the IBM Qiskit's Quantum Circuit with one Quantum and Classical Registers qiskit_quantum_circuit_pauli_z_2 = \ QiskitQuantumCircuit.QiskitQuantumCircuit("qcpauliz2", qiskit_quantum_register_pauli_z_2, qiskit_classical_register_pauli_z_2, global_phase=0) # Apply the Pauli-Z Gate to the 1st Qubit of the Quantum Circuit (|0⟩ ↦ |0⟩) qiskit_quantum_circuit_pauli_z_2.apply_pauli_z(qiskit_quantum_register_pauli_z_2.quantum_register[0]) # Apply the Pauli-Z Gate to the 1st Qubit of the Quantum Circuit, again (|0⟩ ↦ |0⟩) qiskit_quantum_circuit_pauli_z_2.apply_pauli_z(qiskit_quantum_register_pauli_z_2.quantum_register[0]) # Getting the Backend for the State Vector Representation # (i.e., the Quantum State represented as State Vector) state_vector_backend = Aer.get_backend('statevector_simulator') # Execute the Quantum Circuit and store the Quantum State in a final state vector final_state_vector = \ execute(qiskit_quantum_circuit_pauli_z_2.quantum_circuit, state_vector_backend).result().get_statevector() # Assert All Close, from NumPy's Testing, for the State Vector of the Qubit, # after the two Pauli-Z Gates be applied assert_allclose(final_state_vector, array([(1. + 0.j), (0. + 0.j)]), rtol=1e-7, atol=1e-7) # Dummy Assert Equal for Unittest self.assertEqual(True, True) # Test Cases for the Hadamard Gate class HadamardGateTests(unittest.TestCase): # Test #1 for the Hadamard Gate # Description of the Test Case: # 1) The Quantum Circuit is created with a Quantum Register, # with 1 Qubit initialized in the state |0⟩; # 2) It is applied the Hadamard Gate to the 1st Qubit, then, |0⟩ ↦ |+⟩; def test_apply_hadamard_1(self): # The number of Qubits and Bits, for Quantum and Classical Registers, respectively num_qubits = num_bits = 1 # Creation of the IBM Qiskit's Quantum and Classical Registers qiskit_quantum_register_hadamard_1 = QiskitQuantumRegister.QiskitQuantumRegister("qrhadamard1", num_qubits) qiskit_classical_register_hadamard_1 = QiskitClassicalRegister.QiskitClassicalRegister("crhadamard1", num_bits) # Creation of the IBM Qiskit's Quantum Circuit with one Quantum and Classical Registers qiskit_quantum_circuit_hadamard_1 = \ QiskitQuantumCircuit.QiskitQuantumCircuit("qchadamard1", qiskit_quantum_register_hadamard_1, qiskit_classical_register_hadamard_1, global_phase=0) # Apply the Hadamard Gate to the 1st Qubit of the Quantum Circuit (|0⟩ ↦ |+⟩) qiskit_quantum_circuit_hadamard_1.apply_hadamard(qiskit_quantum_register_hadamard_1.quantum_register[0]) # Getting the Backend for the State Vector Representation # (i.e., the Quantum State represented as State Vector) state_vector_backend = Aer.get_backend('statevector_simulator') # Execute the Quantum Circuit and store the Quantum State in a final state vector final_state_vector = \ execute(qiskit_quantum_circuit_hadamard_1.quantum_circuit, state_vector_backend).result().get_statevector() # Assert All Close, from NumPy's Testing, for the State Vector of the Qubit, # after the Hadamard Gate be applied assert_allclose(final_state_vector, array([((1. / sqrt(2.)) + 0.j), ((1. / sqrt(2.)) + 0.j)]), rtol=1e-7, atol=1e-7) # Dummy Assert Equal for Unittest self.assertEqual(True, True) # Test #2 for the Hadamard Gate # Description of the Test Case: # 1) The Quantum Circuit is created with a Quantum Register, # with 1 Qubit initialized in the state |0⟩; # 2) It is applied the Hadamard Gate to the 1st Qubit, then, |0⟩ ↦ |+⟩; # 3) It is applied, again, the Hadamard Gate to the 1st Qubit, then, |+⟩ ↦ |1⟩; def test_apply_hadamard_2(self): # The number of Qubits and Bits, for Quantum and Classical Registers, respectively num_qubits = num_bits = 1 # Creation of the IBM Qiskit's Quantum and Classical Registers qiskit_quantum_register_hadamard_2 = QiskitQuantumRegister.QiskitQuantumRegister("qrhadamard2", num_qubits) qiskit_classical_register_hadamard_2 = QiskitClassicalRegister.QiskitClassicalRegister("crhadamard2", num_bits) # Creation of the IBM Qiskit's Quantum Circuit with one Quantum and Classical Registers qiskit_quantum_circuit_hadamard_2 = \ QiskitQuantumCircuit.QiskitQuantumCircuit("qchadamard2", qiskit_quantum_register_hadamard_2, qiskit_classical_register_hadamard_2, global_phase=0) # Apply the Hadamard Gate to the 1st Qubit of the Quantum Circuit (|0⟩ ↦ |+⟩) qiskit_quantum_circuit_hadamard_2.apply_hadamard(qiskit_quantum_register_hadamard_2.quantum_register[0]) # Apply the Hadamard Gate to the 1st Qubit of the Quantum Circuit, again (|+⟩ ↦ |0⟩) qiskit_quantum_circuit_hadamard_2.apply_hadamard(qiskit_quantum_register_hadamard_2.quantum_register[0]) # Getting the Backend for the State Vector Representation # (i.e., the Quantum State represented as State Vector) state_vector_backend = Aer.get_backend('statevector_simulator') # Execute the Quantum Circuit and store the Quantum State in a final state vector final_state_vector = \ execute(qiskit_quantum_circuit_hadamard_2.quantum_circuit, state_vector_backend).result().get_statevector() # Assert All Close, from NumPy's Testing, for the State Vector of the Qubit, # after the two Hadamard Gates be applied assert_allclose(final_state_vector, array([(1. + 0.j), (0. + 0.j)]), rtol=1e-7, atol=1e-7) # Dummy Assert Equal for Unittest self.assertEqual(True, True) # Configuration of the Test Suites if __name__ == '__main__': # Test Cases for the Measurements in the X-, Y- and Z-Basis prepare_measure_x_basis_tests_suite = unittest.TestLoader().loadTestsFromTestCase(PrepareMeasureXBasisTests) prepare_measure_y_basis_tests_suite = unittest.TestLoader().loadTestsFromTestCase(PrepareMeasureYBasisTests) prepare_measure_z_basis_tests_suite = unittest.TestLoader().loadTestsFromTestCase(PrepareMeasureZBasisTests) # Test Cases for the Pauli Gates pauli_i_gate_tests_suite = unittest.TestLoader().loadTestsFromTestCase(PauliIGateTests) pauli_x_gate_tests_suite = unittest.TestLoader().loadTestsFromTestCase(PauliXGateTests) pauli_y_gate_tests_suite = unittest.TestLoader().loadTestsFromTestCase(PauliYGateTests) pauli_z_gate_tests_suite = unittest.TestLoader().loadTestsFromTestCase(PauliZGateTests) # Test Cases for the Hadamard Gates hadamard_gate_tests_suite = unittest.TestLoader().loadTestsFromTestCase(HadamardGateTests) # Create a Global for all the Test Cases established all_test_cases = unittest.TestSuite([prepare_measure_x_basis_tests_suite, prepare_measure_y_basis_tests_suite, prepare_measure_z_basis_tests_suite, pauli_i_gate_tests_suite, pauli_x_gate_tests_suite, pauli_y_gate_tests_suite, pauli_z_gate_tests_suite, hadamard_gate_tests_suite])

55.767255

119

0.674035

9,162

69,486

4.892163

0.027505

0.074651

0.044621

0.048726

0.877962

0.869751

0.834412

0.820289

0.790549

0.764111

0

0.020276

0.260412

69,486

1,245

120

55.812048

0.847152

0.400714

0

0.404762

0

0.034309

0.013267

0

0.105159

1

0.051587

false

0

0.013889

0

0.081349

0

null

0

1

0

null

0

6

758adba3eff4379f74d281f36b829216f6f577d7

186

py

Python

blog/app/controller/admin/__init__.py

henrY2Young/flask-jwt

f1c47efee7fd7f271c02172371c2d9cec8adde5d

[ "MIT" ]

null

blog/app/controller/admin/__init__.py

henrY2Young/flask-jwt

f1c47efee7fd7f271c02172371c2d9cec8adde5d

[ "MIT" ]

null

blog/app/controller/admin/__init__.py

henrY2Young/flask-jwt

f1c47efee7fd7f271c02172371c2d9cec8adde5d

[ "MIT" ]

null

from flask import Flask from flask import Blueprint app = Flask(__name__) admin = Blueprint('admin', __name__) user = Blueprint('user', __name__) from .admin import * from .user import *

26.571429

36

0.758065

25

186

5.16

0.32

0.139535

0.232558

0

0.139785

186

7

37

26.571429

0.80625

0

0.048128

0

1

0

false

0

0.571429

0

0.571429

0.428571

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

f949d8f8f13e86557fd1fac026095a88589af0da

76

py

Python

p2pfs/__init__.py

yxwangcs/p2pfs

57e90d8f911de36da70f5977822cde609d1c3561

[ "MIT" ]

2

2020-07-02T12:09:19.000Z

2020-08-26T15:48:15.000Z

p2pfs/__init__.py

RyanWangGit/p2pfs

adcbf999010289e46c041aecc9af5c734c6de25e

[ "MIT" ]

null

p2pfs/__init__.py

RyanWangGit/p2pfs

adcbf999010289e46c041aecc9af5c734c6de25e

[ "MIT" ]

2

2020-07-19T04:15:53.000Z

2021-01-16T20:31:48.000Z

from p2pfs.core import * from p2pfs.ui import PeerTerminal, TrackerTerminal

25.333333

50

0.828947

10

76

6.3

0.7

0.285714

0

0.029851

0.118421

76

2

51

38

0.910448

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

ddc1f52b5cc0bf4b3c8a1c58078521f9458dff58

100

py

Python

terrascript/packet/__init__.py

amlodzianowski/python-terrascript

1111affe6cd30d9b8b7bc74ae4e27590f7d4dc49

[ "BSD-2-Clause" ]

null

terrascript/packet/__init__.py

amlodzianowski/python-terrascript

1111affe6cd30d9b8b7bc74ae4e27590f7d4dc49

[ "BSD-2-Clause" ]

null

terrascript/packet/__init__.py

amlodzianowski/python-terrascript

1111affe6cd30d9b8b7bc74ae4e27590f7d4dc49

[ "BSD-2-Clause" ]

null

# terrascript/packet/__init__.py import terrascript class packet(terrascript.Provider): pass

12.5

35

0.78

11

100

6.727273

0.727273

0

0.14

100

7

36

14.285714

0.860465

0.3

0

1

0

true

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

6

ddd443f5c797846a41f52199f95e41748d41bf04

43

py

Python

gym_DC/envs/__init__.py

Saeid-Rezaei-projects/Gym-DC

58124e79e17a4f537a4a16c67808b7fff76be4c6

[ "MIT" ]

null

gym_DC/envs/__init__.py

Saeid-Rezaei-projects/Gym-DC

58124e79e17a4f537a4a16c67808b7fff76be4c6

[ "MIT" ]

null

gym_DC/envs/__init__.py

Saeid-Rezaei-projects/Gym-DC

58124e79e17a4f537a4a16c67808b7fff76be4c6

[ "MIT" ]

null

from gym_DC.envs.DCgym_Env import DCGymEnv

21.5

42

0.860465

8

43

4.375

1

0

0.093023

43

1

43

0.897436

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

34b9e84cfe0060986816a172e20a835bbc5dd258

215

py

Python

jsonresume/exceptions.py

kelvintaywl/jsonresume-validator

73ac162cb30ca70699c942def629188f7dfd4d3c

[ "MIT" ]

42

2016-06-03T18:17:24.000Z

2021-12-09T04:13:14.000Z

jsonresume/exceptions.py

kelvintaywl/jsonresume-validator

73ac162cb30ca70699c942def629188f7dfd4d3c

[ "MIT" ]

3

2016-04-27T12:32:41.000Z

2020-09-29T16:43:35.000Z

jsonresume/exceptions.py

kelvintaywl/jsonresume-validator

73ac162cb30ca70699c942def629188f7dfd4d3c

[ "MIT" ]

9

2016-05-08T15:31:53.000Z

2021-04-28T09:17:47.000Z

# -*- coding: utf-8 -*- import colander class InvalidResumeError(colander.Invalid): """Exception when a JSON resume (as python object) has invalid schema. Subclass of colander.Invalid. """ pass

17.916667

74

0.674419

25

215

5.8

0.84

0.206897

0

0.005917

0.213953

215

11

75

19.545455

0.852071

0.572093

0

1

0

true

0.333333

0

0.666667

0

1

0

null

1

0

null

0

1

0

1

0

6

9b37db811e6bb60c829230acc03bbdeb31510d4d

135

py

Python

30-webpage/apptest/vue_app/views.py

AppTestBot/AppTestBot

035e93e662753e50d7dcc38d6fd362933186983b

[ "Apache-2.0" ]

null

30-webpage/apptest/vue_app/views.py

AppTestBot/AppTestBot

035e93e662753e50d7dcc38d6fd362933186983b

[ "Apache-2.0" ]

null

30-webpage/apptest/vue_app/views.py

AppTestBot/AppTestBot

035e93e662753e50d7dcc38d6fd362933186983b

[ "Apache-2.0" ]

null

from django.shortcuts import render # Create your views here. def test_vue(request): return render(request, 'vue_app/index.html')

22.5

48

0.762963

20

135

5.05

0.85

0

0.140741

135

5

49

27

0.87069

0.17037

0

0.163636

0

1

0.333333

false

0

0.333333

1

0

1

0

null

0

1

0

null

0

1

0

1

0

6

9b3a8a0ec45d224e54fcf75398f8b8b1308c34db

22

py

Python

whatsapp_api_service/__init__.py

em230418/whatsapp-api-service

6792cea86e1f76bfa68b526582391b8a685fa2c7

[ "MIT" ]

null

whatsapp_api_service/__init__.py

em230418/whatsapp-api-service

6792cea86e1f76bfa68b526582391b8a685fa2c7

[ "MIT" ]

null

whatsapp_api_service/__init__.py

em230418/whatsapp-api-service

6792cea86e1f76bfa68b526582391b8a685fa2c7

[ "MIT" ]

1

2022-01-21T13:13:27.000Z

from .base import app

11

21

0.772727

4

22

4.25

1

0

0.181818

22

1

22

0.944444

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

32ca356344d3b9131316284a815367005ce12b0a

279

py

Python

tests/test_cat.py

mloskot/python-package

4b24c22811052492d3af9d2f7d1ffa8f6ae8b412

[ "Unlicense" ]

null

tests/test_cat.py

mloskot/python-package

4b24c22811052492d3af9d2f7d1ffa8f6ae8b412

[ "Unlicense" ]

null

tests/test_cat.py

mloskot/python-package

4b24c22811052492d3af9d2f7d1ffa8f6ae8b412

[ "Unlicense" ]

null

def test_noise(): import pets.cat.noise assert pets.cat.noise.make() == 'meow!' def test_noise_from_cat(): from pets import cat assert cat.noise.make() == 'meow!' def test_noise_from_pets_cat(): from pets.cat import noise assert noise.make() == 'meow!'

23.25

43

0.666667

42

279

4.238095

0.238095

0.157303

0.202247

0.179775

0.359551

0

0.193548

279

11

44

25.363636

0.791111

0

0.053763

0

0.333333

1

0.333333

true

0

0.333333

0

0.666667

0

null

0

1

0

null

0

1

0

1

0

1

0

6

fd107a5e926be2f15c8dbd8610cd275f5b315b19

48,154

py

Python

deploy/adapters/ansible/roles/moon/files/controllers.py

wtwde/compass-docker-osa-ocata

af14c185b70125740ea4801981085c740bf98ae0

[ "Apache-2.0" ]

null

deploy/adapters/ansible/roles/moon/files/controllers.py

wtwde/compass-docker-osa-ocata

af14c185b70125740ea4801981085c740bf98ae0

[ "Apache-2.0" ]

null

deploy/adapters/ansible/roles/moon/files/controllers.py

wtwde/compass-docker-osa-ocata

af14c185b70125740ea4801981085c740bf98ae0

[ "Apache-2.0" ]

null

# Copyright 2015 Open Platform for NFV Project, Inc. and its contributors # This software is distributed under the terms and conditions of the # 'Apache-2.0' license which can be found in the file 'LICENSE' in this # package distribution or at 'http://www.apache.org/licenses/LICENSE-2.0'. from keystone.common import controller from keystone import config from keystone import exception from keystone.models import token_model from keystone.contrib.moon.exception import * # noqa: F403 from oslo_log import log from uuid import uuid4 import requests CONF = config.CONF LOG = log.getLogger(__name__) @dependency.requires('configuration_api') # noqa: 405 class Configuration(controller.V3Controller): collection_name = 'configurations' member_name = 'configuration' def __init__(self): super(Configuration, self).__init__() def _get_user_id_from_token(self, token_id): response = self.token_provider_api.validate_token(token_id) token_ref = token_model.KeystoneToken( token_id=token_id, token_data=response) return token_ref.get('user') @controller.protected() def get_policy_templates(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) return self.configuration_api.get_policy_templates_dict(user_id) @controller.protected() def get_aggregation_algorithms(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) return self.configuration_api.get_aggregation_algorithms_dict(user_id) @controller.protected() def get_sub_meta_rule_algorithms(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) return self.configuration_api.get_sub_meta_rule_algorithms_dict( user_id) @dependency.requires('tenant_api', 'resource_api') # noqa: 405 class Tenants(controller.V3Controller): def __init__(self): super(Tenants, self).__init__() def _get_user_id_from_token(self, token_id): response = self.token_provider_api.validate_token(token_id) token_ref = token_model.KeystoneToken( token_id=token_id, token_data=response) return token_ref.get('user') @controller.protected() def get_tenants(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) return self.tenant_api.get_tenants_dict(user_id) def __get_keystone_tenant_dict( self, tenant_id="", tenant_name="", tenant_description="", domain="default"): # noqa tenants = self.resource_api.list_projects() for tenant in tenants: if tenant_id and tenant_id == tenant['id']: return tenant if tenant_name and tenant_name == tenant['name']: return tenant if not tenant_id: tenant_id = uuid4().hex if not tenant_name: tenant_name = tenant_id tenant = { "id": tenant_id, "name": tenant_name, "description": tenant_description, "enabled": True, "domain_id": domain } keystone_tenant = self.resource_api.create_project( tenant["id"], tenant) return keystone_tenant @controller.protected() def add_tenant(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) k_tenant_dict = self.__get_keystone_tenant_dict( tenant_name=kw.get('tenant_name'), tenant_description=kw.get( 'tenant_description', kw.get('tenant_name')), domain=kw.get('tenant_domain', "default"), ) tenant_dict = dict() tenant_dict['id'] = k_tenant_dict['id'] tenant_dict['name'] = kw.get('tenant_name', None) tenant_dict['description'] = kw.get('tenant_description', None) tenant_dict['intra_authz_extension_id'] = kw.get( 'tenant_intra_authz_extension_id', None) tenant_dict['intra_admin_extension_id'] = kw.get( 'tenant_intra_admin_extension_id', None) return self.tenant_api.add_tenant_dict( user_id, tenant_dict['id'], tenant_dict) @controller.protected() def get_tenant(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) tenant_id = kw.get('tenant_id', None) return self.tenant_api.get_tenant_dict(user_id, tenant_id) @controller.protected() def del_tenant(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) tenant_id = kw.get('tenant_id', None) return self.tenant_api.del_tenant(user_id, tenant_id) @controller.protected() def set_tenant(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) # Next line will raise an error if tenant doesn't exist k_tenant_dict = self.resource_api.get_project( kw.get('tenant_id', None)) tenant_id = kw.get('tenant_id', None) tenant_dict = dict() tenant_dict['name'] = k_tenant_dict.get('name', None) if 'tenant_description' in kw: tenant_dict['description'] = kw.get('tenant_description', None) if 'tenant_intra_authz_extension_id' in kw: tenant_dict['intra_authz_extension_id'] = kw.get( 'tenant_intra_authz_extension_id', None) if 'tenant_intra_admin_extension_id' in kw: tenant_dict['intra_admin_extension_id'] = kw.get( 'tenant_intra_admin_extension_id', None) self.tenant_api.set_tenant_dict(user_id, tenant_id, tenant_dict) def callback(self, context, prep_info, *args, **kwargs): token_ref = "" if context.get('token_id') is not None: token_ref = token_model.KeystoneToken( token_id=context['token_id'], token_data=self.token_provider_api.validate_token( context['token_id'])) if not token_ref: raise exception.Unauthorized @dependency.requires('authz_api') # noqa: 405 class Authz_v3(controller.V3Controller): def __init__(self): super(Authz_v3, self).__init__() @controller.protected(callback) def get_authz(self, context, tenant_id, subject_k_id, object_name, action_name): try: return self.authz_api.authz( tenant_id, subject_k_id, object_name, action_name) except Exception as e: return {'authz': False, 'comment': unicode(e)} @dependency.requires('admin_api', 'root_api') # noqa: 405 class IntraExtensions(controller.V3Controller): collection_name = 'intra_extensions' member_name = 'intra_extension' def __init__(self): super(IntraExtensions, self).__init__() def _get_user_id_from_token(self, token_id): response = self.token_provider_api.validate_token(token_id) token_ref = token_model.KeystoneToken( token_id=token_id, token_data=response) return token_ref.get('user')['id'] # IntraExtension functions @controller.protected() def get_intra_extensions(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) return self.admin_api.get_intra_extensions_dict(user_id) @controller.protected() def add_intra_extension(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) intra_extension_dict = dict() intra_extension_dict['name'] = kw.get('intra_extension_name', None) intra_extension_dict['model'] = kw.get('intra_extension_model', None) intra_extension_dict['genre'] = kw.get('intra_extension_genre', None) intra_extension_dict['description'] = kw.get( 'intra_extension_description', None) intra_extension_dict['subject_categories'] = kw.get( 'intra_extension_subject_categories', dict()) intra_extension_dict['object_categories'] = kw.get( 'intra_extension_object_categories', dict()) intra_extension_dict['action_categories'] = kw.get( 'intra_extension_action_categories', dict()) intra_extension_dict['subjects'] = kw.get( 'intra_extension_subjects', dict()) intra_extension_dict['objects'] = kw.get( 'intra_extension_objects', dict()) intra_extension_dict['actions'] = kw.get( 'intra_extension_actions', dict()) intra_extension_dict['subject_scopes'] = kw.get( 'intra_extension_subject_scopes', dict()) intra_extension_dict['object_scopes'] = kw.get( 'intra_extension_object_scopes', dict()) intra_extension_dict['action_scopes'] = kw.get( 'intra_extension_action_scopes', dict()) intra_extension_dict['subject_assignments'] = kw.get( 'intra_extension_subject_assignments', dict()) intra_extension_dict['object_assignments'] = kw.get( 'intra_extension_object_assignments', dict()) intra_extension_dict['action_assignments'] = kw.get( 'intra_extension_action_assignments', dict()) intra_extension_dict['aggregation_algorithm'] = kw.get( 'intra_extension_aggregation_algorithm', dict()) intra_extension_dict['sub_meta_rules'] = kw.get( 'intra_extension_sub_meta_rules', dict()) intra_extension_dict['rules'] = kw.get('intra_extension_rules', dict()) ref = self.admin_api.load_intra_extension_dict( user_id, intra_extension_dict=intra_extension_dict) return self.admin_api.populate_default_data(ref) @controller.protected() def get_intra_extension(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) intra_extension_id = kw.get('intra_extension_id', None) return self.admin_api.get_intra_extension_dict( user_id, intra_extension_id) @controller.protected() def del_intra_extension(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) intra_extension_id = kw.get('intra_extension_id', None) self.admin_api.del_intra_extension(user_id, intra_extension_id) @controller.protected() def set_intra_extension(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) intra_extension_id = kw.get('intra_extension_id', None) intra_extension_dict = dict() intra_extension_dict['name'] = kw.get('intra_extension_name', None) intra_extension_dict['model'] = kw.get('intra_extension_model', None) intra_extension_dict['genre'] = kw.get('intra_extension_genre', None) intra_extension_dict['description'] = kw.get( 'intra_extension_description', None) return self.admin_api.set_intra_extension_dict( user_id, intra_extension_id, intra_extension_dict) @controller.protected() def load_root_intra_extension(self, context, **kw): self.root_api.load_root_intra_extension_dict() # Metadata functions @controller.protected() def get_subject_categories(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) intra_extension_id = kw.get('intra_extension_id', None) return self.admin_api.get_subject_categories_dict( user_id, intra_extension_id) @controller.protected() def add_subject_category(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) intra_extension_id = kw.get('intra_extension_id', None) subject_category_dict = dict() subject_category_dict['name'] = kw.get('subject_category_name', None) subject_category_dict['description'] = kw.get( 'subject_category_description', None) return self.admin_api.add_subject_category_dict( user_id, intra_extension_id, subject_category_dict) @controller.protected() def get_subject_category(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) intra_extension_id = kw.get('intra_extension_id', None) subject_category_id = kw.get('subject_category_id', None) return self.admin_api.get_subject_category_dict( user_id, intra_extension_id, subject_category_id) @controller.protected() def del_subject_category(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) intra_extension_id = kw.get('intra_extension_id', None) subject_category_id = kw.get('subject_category_id', None) self.admin_api.del_subject_category( user_id, intra_extension_id, subject_category_id) @controller.protected() def set_subject_category(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) intra_extension_id = kw.get('intra_extension_id', None) subject_category_id = kw.get('subject_category_id', None) subject_category_dict = dict() subject_category_dict['name'] = kw.get('subject_category_name', None) subject_category_dict['description'] = kw.get( 'subject_category_description', None) return self.admin_api.set_subject_category_dict( user_id, intra_extension_id, subject_category_id, subject_category_dict) # noqa @controller.protected() def get_object_categories(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) intra_extension_id = kw.get('intra_extension_id', None) return self.admin_api.get_object_categories_dict( user_id, intra_extension_id) @controller.protected() def add_object_category(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) intra_extension_id = kw.get('intra_extension_id', None) object_category_dict = dict() object_category_dict['name'] = kw.get('object_category_name', None) object_category_dict['description'] = kw.get( 'object_category_description', None) return self.admin_api.add_object_category_dict( user_id, intra_extension_id, object_category_dict) @controller.protected() def get_object_category(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) intra_extension_id = kw.get('intra_extension_id', None) object_category_id = kw.get('object_category_id', None) return self.admin_api.get_object_categories_dict( user_id, intra_extension_id, object_category_id) @controller.protected() def del_object_category(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) intra_extension_id = kw.get('intra_extension_id', None) object_category_id = kw.get('object_category_id', None) self.admin_api.del_object_category( user_id, intra_extension_id, object_category_id) @controller.protected() def set_object_category(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) intra_extension_id = kw.get('intra_extension_id', None) object_category_id = kw.get('object_category_id', None) object_category_dict = dict() object_category_dict['name'] = kw.get('object_category_name', None) object_category_dict['description'] = kw.get( 'object_category_description', None) return self.admin_api.set_object_category_dict( user_id, intra_extension_id, object_category_id, object_category_dict) # noqa @controller.protected() def get_action_categories(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) intra_extension_id = kw.get('intra_extension_id', None) return self.admin_api.get_action_categories_dict( user_id, intra_extension_id) @controller.protected() def add_action_category(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) intra_extension_id = kw.get('intra_extension_id', None) action_category_dict = dict() action_category_dict['name'] = kw.get('action_category_name', None) action_category_dict['description'] = kw.get( 'action_category_description', None) return self.admin_api.add_action_category_dict( user_id, intra_extension_id, action_category_dict) @controller.protected() def get_action_category(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) intra_extension_id = kw.get('intra_extension_id', None) action_category_id = kw.get('action_category_id', None) return self.admin_api.get_action_categories_dict( user_id, intra_extension_id, action_category_id) @controller.protected() def del_action_category(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) intra_extension_id = kw.get('intra_extension_id', None) action_category_id = kw.get('action_category_id', None) self.admin_api.del_action_category( user_id, intra_extension_id, action_category_id) @controller.protected() def set_action_category(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) intra_extension_id = kw.get('intra_extension_id', None) action_category_id = kw.get('action_category_id', None) action_category_dict = dict() action_category_dict['name'] = kw.get('action_category_name', None) action_category_dict['description'] = kw.get( 'action_category_description', None) return self.admin_api.set_action_category_dict( user_id, intra_extension_id, action_category_id, action_category_dict) # noqa # Perimeter functions @controller.protected() def get_subjects(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) intra_extension_id = kw.get('intra_extension_id', None) return self.admin_api.get_subjects_dict(user_id, intra_extension_id) @controller.protected() def add_subject(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) intra_extension_id = kw.get('intra_extension_id', None) subject_dict = dict() subject_dict['name'] = kw.get('subject_name', None) subject_dict['description'] = kw.get('subject_description', None) subject_dict['password'] = kw.get('subject_password', None) subject_dict['email'] = kw.get('subject_email', None) return self.admin_api.add_subject_dict( user_id, intra_extension_id, subject_dict) @controller.protected() def get_subject(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) intra_extension_id = kw.get('intra_extension_id', None) subject_id = kw.get('subject_id', None) return self.admin_api.get_subject_dict( user_id, intra_extension_id, subject_id) @controller.protected() def del_subject(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) intra_extension_id = kw.get('intra_extension_id', None) subject_id = kw.get('subject_id', None) self.admin_api.del_subject(user_id, intra_extension_id, subject_id) @controller.protected() def set_subject(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) intra_extension_id = kw.get('intra_extension_id', None) subject_id = kw.get('subject_id', None) subject_dict = dict() subject_dict['name'] = kw.get('subject_name', None) subject_dict['description'] = kw.get('subject_description', None) return self.admin_api.set_subject_dict( user_id, intra_extension_id, subject_id, subject_dict) @controller.protected() def get_objects(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) intra_extension_id = kw.get('intra_extension_id', None) return self.admin_api.get_objects_dict(user_id, intra_extension_id) @controller.protected() def add_object(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) intra_extension_id = kw.get('intra_extension_id', None) object_dict = dict() object_dict['name'] = kw.get('object_name', None) object_dict['description'] = kw.get('object_description', None) return self.admin_api.add_object_dict( user_id, intra_extension_id, object_dict) @controller.protected() def get_object(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) intra_extension_id = kw.get('intra_extension_id', None) object_id = kw.get('object_id', None) return self.admin_api.get_object_dict( user_id, intra_extension_id, object_id) @controller.protected() def del_object(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) intra_extension_id = kw.get('intra_extension_id', None) object_id = kw.get('object_id', None) self.admin_api.del_object(user_id, intra_extension_id, object_id) @controller.protected() def set_object(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) intra_extension_id = kw.get('intra_extension_id', None) object_id = kw.get('object_id', None) object_dict = dict() object_dict['name'] = kw.get('object_name', None) object_dict['description'] = kw.get('object_description', None) return self.admin_api.set_object_dict( user_id, intra_extension_id, object_id, object_dict) @controller.protected() def get_actions(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) intra_extension_id = kw.get('intra_extension_id', None) return self.admin_api.get_actions_dict(user_id, intra_extension_id) @controller.protected() def add_action(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) intra_extension_id = kw.get('intra_extension_id', None) action_dict = dict() action_dict['name'] = kw.get('action_name', None) action_dict['description'] = kw.get('action_description', None) return self.admin_api.add_action_dict( user_id, intra_extension_id, action_dict) @controller.protected() def get_action(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) intra_extension_id = kw.get('intra_extension_id', None) action_id = kw.get('action_id', None) return self.admin_api.get_action_dict( user_id, intra_extension_id, action_id) @controller.protected() def del_action(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) intra_extension_id = kw.get('intra_extension_id', None) action_id = kw.get('action_id', None) self.admin_api.del_action(user_id, intra_extension_id, action_id) @controller.protected() def set_action(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) intra_extension_id = kw.get('intra_extension_id', None) action_id = kw.get('action_id', None) action_dict = dict() action_dict['name'] = kw.get('action_name', None) action_dict['description'] = kw.get('action_description', None) return self.admin_api.set_action_dict( user_id, intra_extension_id, action_id, action_dict) # Scope functions @controller.protected() def get_subject_scopes(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) intra_extension_id = kw.get('intra_extension_id', None) subject_category_id = kw.get('subject_category_id', None) return self.admin_api.get_subject_scopes_dict( user_id, intra_extension_id, subject_category_id) @controller.protected() def add_subject_scope(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) intra_extension_id = kw.get('intra_extension_id', None) subject_category_id = kw.get('subject_category_id', None) subject_scope_dict = dict() subject_scope_dict['name'] = kw.get('subject_scope_name', None) subject_scope_dict['description'] = kw.get( 'subject_scope_description', None) return self.admin_api.add_subject_scope_dict( user_id, intra_extension_id, subject_category_id, subject_scope_dict) # noqa @controller.protected() def get_subject_scope(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) intra_extension_id = kw.get('intra_extension_id', None) subject_category_id = kw.get('subject_category_id', None) subject_scope_id = kw.get('subject_scope_id', None) return self.admin_api.get_subject_scope_dict( user_id, intra_extension_id, subject_category_id, subject_scope_id) @controller.protected() def del_subject_scope(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) intra_extension_id = kw.get('intra_extension_id', None) subject_category_id = kw.get('subject_category_id', None) subject_scope_id = kw.get('subject_scope_id', None) self.admin_api.del_subject_scope( user_id, intra_extension_id, subject_category_id, subject_scope_id) @controller.protected() def set_subject_scope(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) intra_extension_id = kw.get('intra_extension_id', None) subject_category_id = kw.get('subject_category_id', None) subject_scope_id = kw.get('subject_scope_id', None) subject_scope_dict = dict() subject_scope_dict['name'] = kw.get('subject_scope_name', None) subject_scope_dict['description'] = kw.get( 'subject_scope_description', None) return self.admin_api.set_subject_scope_dict( user_id, intra_extension_id, subject_category_id, subject_scope_id, subject_scope_dict) # noqa @controller.protected() def get_object_scopes(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) intra_extension_id = kw.get('intra_extension_id', None) object_category_id = kw.get('object_category_id', None) return self.admin_api.get_object_scopes_dict( user_id, intra_extension_id, object_category_id) @controller.protected() def add_object_scope(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) intra_extension_id = kw.get('intra_extension_id', None) object_category_id = kw.get('object_category_id', None) object_scope_dict = dict() object_scope_dict['name'] = kw.get('object_scope_name', None) object_scope_dict['description'] = kw.get( 'object_scope_description', None) return self.admin_api.add_object_scope_dict( user_id, intra_extension_id, object_category_id, object_scope_dict) @controller.protected() def get_object_scope(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) intra_extension_id = kw.get('intra_extension_id', None) object_category_id = kw.get('object_category_id', None) object_scope_id = kw.get('object_scope_id', None) return self.admin_api.get_object_scope_dict( user_id, intra_extension_id, object_category_id, object_scope_id) @controller.protected() def del_object_scope(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) intra_extension_id = kw.get('intra_extension_id', None) object_category_id = kw.get('object_category_id', None) object_scope_id = kw.get('object_scope_id', None) self.admin_api.del_object_scope( user_id, intra_extension_id, object_category_id, object_scope_id) @controller.protected() def set_object_scope(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) intra_extension_id = kw.get('intra_extension_id', None) object_category_id = kw.get('object_category_id', None) object_scope_id = kw.get('object_scope_id', None) object_scope_dict = dict() object_scope_dict['name'] = kw.get('object_scope_name', None) object_scope_dict['description'] = kw.get( 'object_scope_description', None) return self.admin_api.set_object_scope_dict( user_id, intra_extension_id, object_category_id, object_scope_id, object_scope_dict) # noqa @controller.protected() def get_action_scopes(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) intra_extension_id = kw.get('intra_extension_id', None) action_category_id = kw.get('action_category_id', None) return self.admin_api.get_action_scopes_dict( user_id, intra_extension_id, action_category_id) @controller.protected() def add_action_scope(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) intra_extension_id = kw.get('intra_extension_id', None) action_category_id = kw.get('action_category_id', None) action_scope_dict = dict() action_scope_dict['name'] = kw.get('action_scope_name', None) action_scope_dict['description'] = kw.get( 'action_scope_description', None) return self.admin_api.add_action_scope_dict( user_id, intra_extension_id, action_category_id, action_scope_dict) @controller.protected() def get_action_scope(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) intra_extension_id = kw.get('intra_extension_id', None) action_category_id = kw.get('action_category_id', None) action_scope_id = kw.get('action_scope_id', None) return self.admin_api.get_action_scope_dict( user_id, intra_extension_id, action_category_id, action_scope_id) @controller.protected() def del_action_scope(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) intra_extension_id = kw.get('intra_extension_id', None) action_category_id = kw.get('action_category_id', None) action_scope_id = kw.get('action_scope_id', None) self.admin_api.del_action_scope( user_id, intra_extension_id, action_category_id, action_scope_id) @controller.protected() def set_action_scope(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) intra_extension_id = kw.get('intra_extension_id', None) action_category_id = kw.get('action_category_id', None) action_scope_id = kw.get('action_scope_id', None) action_scope_dict = dict() action_scope_dict['name'] = kw.get('action_scope_name', None) action_scope_dict['description'] = kw.get( 'action_scope_description', None) return self.admin_api.set_action_scope_dict( user_id, intra_extension_id, action_category_id, action_scope_id, action_scope_dict) # noqa # Assignment functions @controller.protected() def add_subject_assignment(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) intra_extension_id = kw.get('intra_extension_id', None) subject_id = kw.get('subject_id', None) subject_category_id = kw.get('subject_category_id', None) subject_scope_id = kw.get('subject_scope_id', None) return self.admin_api.add_subject_assignment_list( user_id, intra_extension_id, subject_id, subject_category_id, subject_scope_id) # noqa @controller.protected() def get_subject_assignment(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) intra_extension_id = kw.get('intra_extension_id', None) subject_id = kw.get('subject_id', None) subject_category_id = kw.get('subject_category_id', None) return self.admin_api.get_subject_assignment_list( user_id, intra_extension_id, subject_id, subject_category_id) @controller.protected() def del_subject_assignment(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) intra_extension_id = kw.get('intra_extension_id', None) subject_id = kw.get('subject_id', None) subject_category_id = kw.get('subject_category_id', None) subject_scope_id = kw.get('subject_scope_id', None) self.admin_api.del_subject_assignment( user_id, intra_extension_id, subject_id, subject_category_id, subject_scope_id) @controller.protected() def add_object_assignment(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) intra_extension_id = kw.get('intra_extension_id', None) object_id = kw.get('object_id', None) object_category_id = kw.get('object_category_id', None) object_scope_id = kw.get('object_scope_id', None) return self.admin_api.add_object_assignment_list( user_id, intra_extension_id, object_id, object_category_id, object_scope_id) # noqa @controller.protected() def get_object_assignment(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) intra_extension_id = kw.get('intra_extension_id', None) object_id = kw.get('object_id', None) object_category_id = kw.get('object_category_id', None) return self.admin_api.get_object_assignment_list( user_id, intra_extension_id, object_id, object_category_id) @controller.protected() def del_object_assignment(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) intra_extension_id = kw.get('intra_extension_id', None) object_id = kw.get('object_id', None) object_category_id = kw.get('object_category_id', None) object_scope_id = kw.get('object_scope_id', None) self.admin_api.del_object_assignment( user_id, intra_extension_id, object_id, object_category_id, object_scope_id) @controller.protected() def add_action_assignment(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) intra_extension_id = kw.get('intra_extension_id', None) action_id = kw.get('action_id', None) action_category_id = kw.get('action_category_id', None) action_scope_id = kw.get('action_scope_id', None) return self.admin_api.add_action_assignment_list( user_id, intra_extension_id, action_id, action_category_id, action_scope_id) # noqa @controller.protected() def get_action_assignment(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) intra_extension_id = kw.get('intra_extension_id', None) action_id = kw.get('action_id', None) action_category_id = kw.get('action_category_id', None) return self.admin_api.get_action_assignment_list( user_id, intra_extension_id, action_id, action_category_id) @controller.protected() def del_action_assignment(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) intra_extension_id = kw.get('intra_extension_id', None) action_id = kw.get('action_id', None) action_category_id = kw.get('action_category_id', None) action_scope_id = kw.get('action_scope_id', None) self.admin_api.del_action_assignment( user_id, intra_extension_id, action_id, action_category_id, action_scope_id) # Metarule functions @controller.protected() def get_aggregation_algorithm(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) intra_extension_id = kw.get('intra_extension_id', None) return self.admin_api.get_aggregation_algorithm_id( user_id, intra_extension_id) @controller.protected() def set_aggregation_algorithm(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) intra_extension_id = kw.get('intra_extension_id', None) aggregation_algorithm_id = kw.get('aggregation_algorithm_id', None) return self.admin_api.set_aggregation_algorithm_id( user_id, intra_extension_id, aggregation_algorithm_id) @controller.protected() def get_sub_meta_rules(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) intra_extension_id = kw.get('intra_extension_id', None) return self.admin_api.get_sub_meta_rules_dict( user_id, intra_extension_id) @controller.protected() def add_sub_meta_rule(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) intra_extension_id = kw.get('intra_extension_id', None) sub_meta_rule_dict = dict() sub_meta_rule_dict['name'] = kw.get('sub_meta_rule_name', None) sub_meta_rule_dict['algorithm'] = kw.get( 'sub_meta_rule_algorithm', None) sub_meta_rule_dict['subject_categories'] = kw.get( 'sub_meta_rule_subject_categories', None) sub_meta_rule_dict['object_categories'] = kw.get( 'sub_meta_rule_object_categories', None) sub_meta_rule_dict['action_categories'] = kw.get( 'sub_meta_rule_action_categories', None) return self.admin_api.add_sub_meta_rule_dict( user_id, intra_extension_id, sub_meta_rule_dict) @controller.protected() def get_sub_meta_rule(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) intra_extension_id = kw.get('intra_extension_id', None) sub_meta_rule_id = kw.get('sub_meta_rule_id', None) return self.admin_api.get_sub_meta_rule_dict( user_id, intra_extension_id, sub_meta_rule_id) @controller.protected() def del_sub_meta_rule(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) intra_extension_id = kw.get('intra_extension_id', None) sub_meta_rule_id = kw.get('sub_meta_rule_id', None) self.admin_api.del_sub_meta_rule( user_id, intra_extension_id, sub_meta_rule_id) @controller.protected() def set_sub_meta_rule(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) intra_extension_id = kw.get('intra_extension_id', None) sub_meta_rule_id = kw.get('sub_meta_rule_id', None) sub_meta_rule_dict = dict() sub_meta_rule_dict['name'] = kw.get('sub_meta_rule_name', None) sub_meta_rule_dict['algorithm'] = kw.get( 'sub_meta_rule_algorithm', None) sub_meta_rule_dict['subject_categories'] = kw.get( 'sub_meta_rule_subject_categories', None) sub_meta_rule_dict['object_categories'] = kw.get( 'sub_meta_rule_object_categories', None) sub_meta_rule_dict['action_categories'] = kw.get( 'sub_meta_rule_action_categories', None) return self.admin_api.set_sub_meta_rule_dict( user_id, intra_extension_id, sub_meta_rule_id, sub_meta_rule_dict) # Rules functions @controller.protected() def get_rules(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) intra_extension_id = kw.get('intra_extension_id', None) sub_meta_rule_id = kw.get('sub_meta_rule_id', None) return self.admin_api.get_rules_dict( user_id, intra_extension_id, sub_meta_rule_id) @controller.protected() def add_rule(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) intra_extension_id = kw.get('intra_extension_id', None) sub_meta_rule_id = kw.get('sub_meta_rule_id', None) subject_category_list = kw.get('subject_categories', []) object_category_list = kw.get('object_categories', []) action_category_list = kw.get('action_categories', []) enabled_bool = kw.get('enabled', True) rule_list = subject_category_list + action_category_list + \ object_category_list + [enabled_bool, ] return self.admin_api.add_rule_dict( user_id, intra_extension_id, sub_meta_rule_id, rule_list) @controller.protected() def get_rule(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) intra_extension_id = kw.get('intra_extension_id', None) sub_meta_rule_id = kw.get('sub_meta_rule_id', None) rule_id = kw.get('rule_id', None) return self.admin_api.get_rule_dict( user_id, intra_extension_id, sub_meta_rule_id, rule_id) @controller.protected() def del_rule(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) intra_extension_id = kw.get('intra_extension_id', None) sub_meta_rule_id = kw.get('sub_meta_rule_id', None) rule_id = kw.get('rule_id', None) self.admin_api.del_rule( user_id, intra_extension_id, sub_meta_rule_id, rule_id) @controller.protected() def set_rule(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) intra_extension_id = kw.get('intra_extension_id', None) sub_meta_rule_id = kw.get('sub_meta_rule_id', None) rule_id = kw.get('rule_id', None) rule_list = list() subject_category_list = kw.get('subject_categories', []) object_category_list = kw.get('object_categories', []) action_category_list = kw.get('action_categories', []) rule_list = subject_category_list + action_category_list + object_category_list # noqa return self.admin_api.set_rule_dict( user_id, intra_extension_id, sub_meta_rule_id, rule_id, rule_list) @dependency.requires('authz_api') # noqa: 405 class InterExtensions(controller.V3Controller): def __init__(self): super(InterExtensions, self).__init__() def _get_user_from_token(self, token_id): response = self.token_provider_api.validate_token(token_id) token_ref = token_model.KeystoneToken( token_id=token_id, token_data=response) return token_ref['user'] # @controller.protected() # def get_inter_extensions(self, context, **kw): # user = self._get_user_from_token(context.get('token_id')) # return { # 'inter_extensions': # self.interextension_api.get_inter_extensions() # } # @controller.protected() # def get_inter_extension(self, context, **kw): # user = self._get_user_from_token(context.get('token_id')) # return { # 'inter_extensions': # self.interextension_api.get_inter_extension(uuid=kw['inter_extension_id']) # } # @controller.protected() # def create_inter_extension(self, context, **kw): # user = self._get_user_from_token(context.get('token_id')) # return self.interextension_api.create_inter_extension(kw) # @controller.protected() # def delete_inter_extension(self, context, **kw): # user = self._get_user_from_token(context.get('token_id')) # if 'inter_extension_id' not in kw: # raise exception.Error # return # self.interextension_api.delete_inter_extension(kw['inter_extension_id']) @dependency.requires('moonlog_api', 'authz_api') # noqa: 405 class Logs(controller.V3Controller): def __init__(self): super(Logs, self).__init__() def _get_user_id_from_token(self, token_id): response = self.token_provider_api.validate_token(token_id) token_ref = token_model.KeystoneToken( token_id=token_id, token_data=response) return token_ref['user'] @controller.protected() def get_logs(self, context, **kw): user_id = self._get_user_id_from_token(context.get('token_id')) options = kw.get('options', '') return self.moonlog_api.get_logs(user_id, options) @dependency.requires('identity_api', "token_provider_api", "resource_api") # noqa: 405 class MoonAuth(controller.V3Controller): def __init__(self): super(MoonAuth, self).__init__() def _get_project(self, uuid="", name=""): projects = self.resource_api.list_projects() for project in projects: if uuid and uuid == project['id']: return project elif name and name == project['name']: return project def get_token(self, context, **kw): data_auth = { "auth": { "identity": { "methods": [ "password" ], "password": { "user": { "domain": { "id": "Default" }, "name": kw['username'], "password": kw['password'] } } } } } message = {} if "project" in kw: project = self._get_project(name=kw['project']) if project: data_auth["auth"]["scope"] = dict() data_auth["auth"]["scope"]['project'] = dict() data_auth["auth"]["scope"]['project']['id'] = project['id'] else: message = { "error": { "message": "Unable to find project {}".format(kw['project']), # noqa "code": 200, "title": "UnScopedToken" }} # req = requests.post("http://localhost:5000/v3/auth/tokens", # json=data_auth, # headers={"Content-Type": "application/json"} # ) req = requests.post("http://172.16.1.222:5000/v3/auth/tokens", json=data_auth, headers={"Content-Type": "application/json"} ) if req.status_code not in (200, 201): LOG.error(req.text) else: _token = req.headers['X-Subject-Token'] _data = req.json() _result = { "token": _token, 'message': message } try: _result["roles"] = map( lambda x: x['name'], _data["token"]["roles"]) except KeyError: pass return _result return {"token": None, 'message': req.json()}

45.300094

107

0.669809

6,274

48,154

4.707683

0.034428

0.127505

0.112134

0.084101

0.86833

0.819982

0.78748

0.75369

0.735712

0.71743

0

0.001893

0.221311

48,154

1,062

108

45.34275

0.785796

0.037671

0

0.574777

0

0.142814

0.032414

0

1

0.109375

false

0.00558

0.008929

0

0.217634

0

null

0

1

0

null

0

6

fd17fbfa4c9bc601605c850fd05b64b4b7b00bd8

20,316

py

Python

processing/calssification-fusion.py

gurkirt/actNet-inAct

1930bcb41553e50ddd83985a497a9d5ce4f1fcf2

[ "MIT" ]

27

2016-05-04T07:13:05.000Z

2021-12-05T04:45:45.000Z

processing/calssification-fusion.py

gurkirt/actNet-inAct

1930bcb41553e50ddd83985a497a9d5ce4f1fcf2

[ "MIT" ]

1

2017-12-28T08:29:00.000Z

processing/calssification-fusion.py

gurkirt/actNet-inAct

1930bcb41553e50ddd83985a497a9d5ce4f1fcf2

[ "MIT" ]

12

2016-05-15T21:40:06.000Z

2019-11-27T09:43:55.000Z

''' Autor: Gurkirt Singh Start data: 15th May 2016 purpose: of this file is read frame level predictions and process them to produce a label per video ''' from sklearn.svm import LinearSVC,SVC from sklearn.ensemble import RandomForestClassifier import numpy as np import pickle import os,h5py import time,json #import pylab as plt #######baseDir = "/mnt/sun-alpha/actnet/"; baseDir = "/data/shared/solar-machines/actnet/"; ########imgDir = "/mnt/sun-alpha/actnet/rgb-images/"; ######## imgDir = "/mnt/DATADISK2/ss-workspace/actnet/rgb-images/"; annotPklFile = "../Evaluation/data/actNet200-V1-3.pkl" def readannos(): with open(annotPklFile,'rb') as f: actNetDB = pickle.load(f) actionIDs = actNetDB['actionIDs']; taxonomy=actNetDB['taxonomy']; database = actNetDB['database']; return actionIDs,taxonomy,database def getnames(): fname = baseDir+'data/lists/gtnames.list' with open(fname,'rb') as f: lines = f.readlines() names = [] for name in lines: name = name.rstrip('\n') names.append(name) # print names return names def gettopklabel(preds,k,classtopk): scores = np.zeros(200) topk = min(classtopk,np.shape(preds)[1]); for i in range(200): values = preds[i,:]; values = np.sort(values); values = values[::-1] scores[i] = np.mean(values[:topk]) # print scores sortedlabel = np.argsort(scores)[::-1] # print sortedlabel sortedscores = scores[sortedlabel] # print sortedlabel[:k],sortedscores[:k] return sortedlabel[:k],sortedscores[:k] def gettopklabel4mp(scores,k): scores = scores - np.min(scores); scores = scores/np.sum(scores); sortedlabel = np.argsort(scores)[::-1] # print sortedlabel sortedscores = scores[sortedlabel] # print sortedlabel[:k],sortedscores[:k] ss = sortedscores[:20] ss = ss/np.sum(ss) ss = ss[:5] ss = ss/np.sum(ss) return sortedlabel[:k],ss[:k] def sumfuse(mbh,ims,k): mbh = mbh - np.min(mbh)+1.0; ims = ims - np.min(ims)+1.0; # mbh = mbh/np.sum(mbh) # ims = ims/np.sum(ims) scores = mbh*ims; scores = scores/np.sum(scores); sortedlabel = np.argsort(scores)[::-1] # print sortedlabel sortedscores = scores[sortedlabel] # print sortedlabel[:k],sortedscores[:k] ss = sortedscores[:5] ss = ss/np.sum(ss) return sortedlabel[:k],ss[:k] def wAPfuse(mbh,ims,wmbh,wims,k): for i in range(200): mbh[i] = (1+wmbh[i])*mbh[i] ims[i] = (1+wims[i])*ims[i] mbh = mbh - np.min(mbh)+1; ims = ims - np.min(ims)+1; # mbh = mbh/np.sum(mbh) # ims = ims/np.sum(ims) scores = mbh + ims; # scores = np.mean(wmbh)*mbh+np.mean(wims)*ims; # scores = np.zeros(200) # for i in range(200): # scores[i] = (mbh[i]*wmbh[i]+wims[i]*ims[i])/(wmbh[i]+wims[i]+1); scores = scores/np.sum(scores); sortedlabel = np.argsort(scores)[::-1] # print sortedlabel sortedscores = scores[sortedlabel] # print sortedlabel[:k],sortedscores[:k] ss = sortedscores[:5] ss = ss/np.sum(ss) return sortedlabel[:k],ss[:k] def fuseThree(mbh,ims,c3d,k): mbh = mbh - np.min(mbh)+1; ims = ims - np.min(ims)+1; #c3d = c3d - np.min(c3d)+1; # mbh = mbh/np.sum(mbh) # ims = ims/np.sum(ims) # print 'we are here in fuse three' scores = np.zeros_like(mbh);#*ims*c3d; for i in range(200): scores[i] = (mbh[i]*c3d[i]*ims[i])*(mbh[i]+ims[i]+c3d[i]) # scores = mbh*ims; # scores = np.mean(wmbh)*mbh+np.mean(wims)*ims; # scores = np.zeros(200) # for i in range(200): # scores[i] = (mbh[i]*wmbh[i]+wims[i]*ims[i])/(wmbh[i]+wims[i]+1); scores = scores/np.sum(scores); sortedlabel = np.argsort(scores)[::-1] # print sortedlabel sortedscores = scores[sortedlabel] # print sortedlabel[:k],sortedscores[:k] ss = sortedscores[:k] ss = ss/np.sum(ss[:5]) return sortedlabel[:k],ss[:k] def fuseCLF(clf,mbh,ims,c3d,k): mbh = mbh - np.min(mbh)+1; ims = ims - np.min(ims)+1; scores1 = mbh*ims*c3d scores2 = mbh+ims+c3d mbh = mbh/np.mean(mbh) ims = ims/np.mean(ims) c3d = c3d/np.mean(c3d) scores = scores1/np.mean(scores1) X = np.zeros((1,800)) count = 0; X[count,:200] =c3d; X[count,200:400] = mbh; X[count,400:600] = ims; X[count,600:] = scores; # print np.shape(X) clfScore = clf.decision_function(X); clfScore = clfScore - np.min(clfScore) +1; # print np.shape(clfScore) clfScore = scores2*scores1*clfScore[0] scores = clfScore/np.sum(clfScore); sortedlabel = np.argsort(scores)[::-1] # print scores sortedscores = scores[sortedlabel] # print sortedlabel[:k],sortedscores[:k] ss = sortedscores/np.sum(sortedscores[:3]) return sortedlabel[:k],ss[:k] def fuseCLFnEXT(clf,mbh,ims,c3d,ext,k): mbh = mbh - np.min(mbh)+0.9; ims = ims - np.min(ims)+1.4; scores1 = mbh*ims*c3d scores2 = mbh+ims+c3d+ext+1 mbh = mbh/np.mean(mbh) ims = ims/np.mean(ims) c3d = c3d/np.mean(c3d) scores = scores1/np.mean(scores1) X = np.zeros((1,800)) count = 0; X[count,:200] =c3d; X[count,200:400] = mbh; X[count,400:600] = ims; X[count,600:] = scores; # print np.shape(X) clfScore = clf.decision_function(X); clfScore = clfScore - np.min(clfScore) +1; # print np.shape(clfScore) clfScore = scores2*scores1*clfScore[0] scores = clfScore/np.sum(clfScore); sortedlabel = np.argsort(scores)[::-1] # print scores sortedscores = scores[sortedlabel] # print sortedlabel[:k],sortedscores[:k] ss = sortedscores/np.sum(sortedscores[:3]) return sortedlabel[:k],ss[:k] def fuseFour(mbh,ims,c3d,ext,k): mbh = mbh - np.min(mbh)+1; ims = ims - np.min(ims)+1; #c3d = c3d - np.min(c3d)+1; # mbh = mbh/np.sum(mbh) # ims = ims/np.sum(ims) scores = mbh*ims*c3d; scores = scores-min(scores); # scores = np.mean(wmbh)*mbh+np.mean(wims)*ims; # scores = np.zeros(200) # for i in range(200): # scores[i] = (mbh[i]*wmbh[i]+wims[i]*ims[i])/(wmbh[i]+wims[i]+1); scores = scores/np.sum(scores); sortedlabel = np.argsort(scores)[::-1] # print sortedlabel sortedscores = scores[sortedlabel] # print sortedlabel[:k],sortedscores[:k] ss = sortedscores ss = ss/np.sum(ss[:5]) return sortedlabel[:k],ss[:k] def getC3dMeanPreds(preds,classtopk=80): preds = preds - np.min(preds) + 0.9; scores = np.zeros(200) topk = min(classtopk,np.shape(preds)[0]); # for i in range(np.shape(preds)[0]): # preds[i,:] = preds[i,:] - np.min(preds[i,:])+1; # preds[i,:] = preds[i,:]/np.sum(preds[i,:]) ; for i in range(200): values = preds[:,i]; values = np.sort(values); values = values[::-1] scores[i] = np.mean(values[:topk]) return scores def getEXTMeanPreds(preds,classtopk=250): # preds = preds - np.min(preds) + 1; scores = np.zeros(200) topk = min(classtopk,np.shape(preds)[0]); # for i in range(np.shape(preds)[0]): # preds[i,:] = preds[i,:] - np.min(preds[i,:])+1; # preds[i,:] = preds[i,:]/np.sum(preds[i,:]) ; for i in range(200): values = preds[:,i]; values = np.sort(values); values = values[::-1] scores[i] = np.mean(values[:topk]) return scores def readpkl(filename): with open(filename) as f: data = pickle.load(f) return data def processOnePredictions(): ######################################### ######################################### names = getnames() gtlabels = readpkl('{}data/labels.pkl'.format(baseDir)) indexs = readpkl('{}data/indexs.pkl'.format(baseDir)) actionIDs,taxonomy,database = readannos() ######################################## ######################################## K = 5; subset = 'validation';#,'testing']: featType = 'MBH' savename = '{}data/predictions-{}-{}.pkl'.format(baseDir,subset,featType) with open(savename,'r') as f: data = pickle.load(f) outfilename = '{}results/classification/{}-{}-{}.json'.format(baseDir,subset,featType,str(K).zfill(3)) if True: #not os.path.isfile(outfilename): vcount = 0; vdata = {}; vdata['external_data'] = {'used':True, 'details':"We use extraction Net model with its weights pretrained on imageNet dataset and fine tuned on Activitty Net. Plus ImagenetShuffle, MBH features, C3D features privide on challenge page"} vdata['version'] = "VERSION 1.3" results = {} for videoId in database.keys(): videoInfo = database[videoId] if videoInfo['subset'] == subset: if vcount >-1: vidresults = [] vcount+=1 vidname = 'v_'+videoId print 'processing ', vidname, ' vcount ',vcount ind = data['vIndexs'][videoId] preds = data['scores'][ind,:] print 'shape of preds',np.shape(preds) labels,scores = gettopklabel4mp(preds,K) print labels print scores for idx in range(K): score = scores[idx] # if score>0.05: label = labels[idx] name = names[label] tempdict = {'label':name,'score':score} vidresults.append(tempdict) results[videoId] = vidresults vdata['results'] = results # print vdata print 'results saved in ', outfilename with open(outfilename,'wb') as f: json.dump(vdata,f) def getDATA(gtlabels,dataIMS,dataMBH,infileC3D,database,subset): X = np.zeros((11000,800)) Y = np.zeros(11000) count = 0; for videoId in database.keys(): videoInfo = database[videoId] if videoInfo['subset'] == subset: # if vcount >-1: vidresults = [] # vcount+=1 vidname = 'v_'+videoId # print 'processing ', vidname, ' vcount ',vcount ind = dataMBH['vIndexs'][videoId] predsMBH = dataMBH['scores'][ind,:] ind = dataIMS['vIndexs'][videoId] predsIMS = dataIMS['scores'][ind,:] preds = infileC3D[videoId]['scores'] predS3D = getC3dMeanPreds(preds) predsMBH = predsMBH - np.min(predsMBH)+1; predsIMS = predsIMS - np.min(predsIMS)+1; scores = predS3D*predsMBH*predsIMS predS3D = predS3D/np.mean(predS3D) predsMBH = predsMBH/np.mean(predsMBH) predsIMS = predsIMS/np.mean(predsIMS) scores = scores/np.mean(scores) X[count,:200] =predS3D; X[count,200:400] = predsMBH; X[count,400:600] = predsIMS; X[count,600:] = scores; Y[count] = gtlabels[videoId]; count+=1 #labels,scores = fuseThree(predsMBH,predsIMS,predS3D,K) return X[:count],Y[:count] def trainPreds(): ######################################### ######################################### names = getnames() gtlabels = readpkl('{}data/labels.pkl'.format(baseDir)) indexs = readpkl('{}data/indexs.pkl'.format(baseDir)) actionIDs,taxonomy,database = readannos() ######################################## ######################################## K = 5; subset = 'validation';#,'testing']: featType = 'IMS' savename = '{}data/ALLpredictions-{}.pkl'.format(baseDir,featType) with open(savename,'r') as f: dataIMS = pickle.load(f) featType = 'MBH' savename = '{}data/ALLpredictions-{}.pkl'.format(baseDir,featType) with open(savename,'r') as f: dataMBH = pickle.load(f) featType = 'C3D' savename = '{}data/ALLpredictions-SVM-{}.hdf5'.format(baseDir,featType) infileC3D = h5py.File(savename,'r'); xtrain,ytrain = getDATA(gtlabels,dataIMS,dataMBH,infileC3D,database,'training') print 'got training and shape is ',np.shape(xtrain) xval,yval = getDATA(gtlabels,dataIMS,dataMBH,infileC3D,database,'validation') print 'got validation and shape is ',np.shape(xval) numSamples = np.shape(xval)[0] bestclf = {}; bestscore = 0; Cs = [0.001,0.01,0.1,1,10,100]; for cc in Cs: clf = LinearSVC(C = cc)#,probability=True) clf = clf.fit(xtrain, ytrain) preds = clf.predict(xval) correctPreds = preds == yval; score = 100*float(np.sum(correctPreds))/numSamples print 'Overall Accuracy is ',score, '% ', ' C = ',str(cc),' features = ',featType if score>bestscore: bestclf = clf bestscore = score saveName = '{}data/LinearfusiontrainingSVM-{}.pkl'.format(baseDir,featType) with open(saveName,'w') as f: pickle.dump(bestclf,f) def processThreePredictions(): ######################################### ######################################### names = getnames() gtlabels = readpkl('{}data/labels.pkl'.format(baseDir)) indexs = readpkl('{}data/indexs.pkl'.format(baseDir)) actionIDs,taxonomy,database = readannos() ######################################## ######################################## K = 196; subset = 'testing' #'validation';#,'testing']: featType = 'IMS' savename = '{}data/ALLpredictions-{}.pkl'.format(baseDir,featType) with open(savename,'r') as f: dataIMS = pickle.load(f) featType = 'MBH' savename = '{}data/ALLpredictions-{}.pkl'.format(baseDir,featType) with open(savename,'r') as f: dataMBH = pickle.load(f) featType = 'C3D' savename = '{}data/ALLpredictions-SVM-{}.hdf5'.format(baseDir,featType) infileC3D = h5py.File(savename,'r'); featType = 'EXT' savename = '{}data/predictions-{}-{}.hdf5'.format(baseDir,subset,featType) infileEXT = h5py.File(savename,'r'); featType = 'C3D' savename = '{}data/LinearfusiontrainingSVM-{}.pkl'.format(baseDir,featType) with open(savename,'r') as f: clf = pickle.load(f) outfilename = '{}results/classification/{}-{}-{}.json'.format(baseDir,subset,'IMS-MBH-C3D-SUBMIT-OLD',str(K).zfill(3)) if True: #not os.path.isfile(outfilename): vcount = 0; vdata = {}; vdata['external_data'] = {'used':True, 'details':"We use ImagenetShuffle features, MBH features and C3D features provided on challenge page."} vdata['version'] = "VERSION 1.3" results = {} for videoId in database.keys(): videoInfo = database[videoId] if videoInfo['subset'] == subset: # if vcount >-1: vidresults = [] vcount+=1 vidname = 'v_'+videoId print 'processing ', vidname, ' vcount ',vcount ind = dataMBH['vIndexs'][videoId] predsMBH = dataMBH['scores'][ind,:] ind = dataIMS['vIndexs'][videoId] predsIMS = dataIMS['scores'][ind,:] preds = infileC3D[videoId]['scores'] predS3D = getC3dMeanPreds(preds,10) preds = np.transpose(infileEXT[videoId]['scores']) predEXT = getEXTMeanPreds(preds,20) #print 'shape of preds',np.shape(preds) # labels,scores = fuseThree(predsMBH,predsIMS,predS3D,K) # labels,scores = fuseFour(predsMBH,predsIMS,predS3D,predEXT,K) # labels,scores = fuseCLF(clf,predsMBH,predsIMS,predS3D,K) labels,scores = fuseCLFnEXT(clf,predsMBH,predsIMS,predS3D,predEXT,K) print labels,scores for idx in range(K): score = scores[idx] # if score>0.05: label = labels[idx] name = names[label] tempdict = {'label':name,'score':score} vidresults.append(tempdict) results[videoId] = vidresults vdata['results'] = results # print vdata print 'result saved in ',outfilename print 'process three result saved in ',outfilename with open(outfilename,'wb') as f: json.dump(vdata,f) def fuse2withAP(): ######################################### ######################################### names = getnames() gtlabels = readpkl('{}data/labels.pkl'.format(baseDir)) indexs = readpkl('{}data/indexs.pkl'.format(baseDir)) actionIDs,taxonomy,database = readannos() ######################################## ######################################## K = 5; subset = 'validation';#,'testing']: featType = 'IMS' savename = '{}data/predictions-{}-{}.pkl'.format(baseDir,subset,featType) with open(savename,'r') as f: dataIMS = pickle.load(f) savename = '{}data/weightAP-{}.pkl'.format(baseDir,featType) with open(savename,'r') as f: wIMS = pickle.load(f) featType = 'MBH' savename = '{}data/predictions-{}-{}.pkl'.format(baseDir,subset,featType) with open(savename,'r') as f: dataMBH = pickle.load(f) savename = '{}data/weightAP-{}.pkl'.format(baseDir,featType) with open(savename,'r') as f: wMBH = pickle.load(f) outfilename = '{}results/classification/{}-{}-{}.json'.format(baseDir,subset,'ap-fused-IMS-MBH',str(K).zfill(3)) if True: #not os.path.isfile(outfilename): vcount = 0; vdata = {}; vdata['external_data'] = {'used':True, 'details':"We use extraction Net model with its weights pretrained on imageNet dataset and fine tuned on Activitty Net. Plus ImagenetShuffle, MBH features, C3D features privide on challenge page"} vdata['version'] = "VERSION 1.3" results = {} for videoId in database.keys(): videoInfo = database[videoId] if videoInfo['subset'] == subset: if vcount >-1: vidresults = [] vcount+=1 vidname = 'v_'+videoId print 'processing ', vidname, ' vcount ',vcount ind = dataMBH['vIndexs'][videoId] predsMBH = dataMBH['scores'][ind,:] ind = dataIMS['vIndexs'][videoId] predsIMS = dataIMS['scores'][ind,:] #print 'shape of preds',np.shape(preds) # labels,scores = sumfuse(predsMBH[:201],predsIMS[:201],K) labels,scores = wAPfuse(predsMBH,predsIMS,wMBH,wIMS,K) print labels print scores for idx in range(K): score = scores[idx] # if score>0.05: label = labels[idx] name = names[label] tempdict = {'label':name,'score':score} vidresults.append(tempdict) results[videoId] = vidresults vdata['results'] = results # print vdata print 'Result saved in ',outfilename with open(outfilename,'wb') as f: json.dump(vdata,f) if __name__=="__main__": # processOnePredictions() # processTwoPredictions() # fuse2withAP() processThreePredictions() # trainPreds()

35.767606

243

0.532191

2,256

20,316

4.784574

0.118351

0.012044

0.028164

0.024458

0.764314

0.744673

0.722624

0.712896

0.706967

0.700852

0

0.023727

0.292577

20,316

567

244

35.830688

0.727317

0.12596

0

0.665803

0

0.005181

0.116229

0.037991

0

null

0

0.015544

null

0.041451

0

null

0

1

0

null

0

1

0

6

fd401a9f6126a85fdc3acd2797b86bfde3d81a7e

111

py

Python

orb_simulator/orbsim_language/orbsim_ast/stop_sim_node.py

dmguezjaviersnet/IA-Sim-Comp-Project

8165b9546efc45f98091a3774e2dae4f45942048

[ "MIT" ]

1

2022-01-19T22:49:09.000Z

orb_simulator/orbsim_language/orbsim_ast/stop_sim_node.py

dmguezjaviersnet/IA-Sim-Comp-Project

8165b9546efc45f98091a3774e2dae4f45942048

[ "MIT" ]

15

2021-11-10T14:25:02.000Z

2022-02-12T19:17:11.000Z

orb_simulator/orbsim_language/orbsim_ast/stop_sim_node.py

dmguezjaviersnet/IA-Sim-Comp-Project

8165b9546efc45f98091a3774e2dae4f45942048

[ "MIT" ]

null

from orbsim_language.orbsim_ast.statement_node import StatementNode class StopSimNode(StatementNode): pass

27.75

67

0.855856

13

111

7.076923

0.846154

0

0.099099

111

4

68

27.75

0.92

0

1

0

true

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

6

fd5ffd243fed4a807727d79e8870e953082b8338

171

py

Python

ovm/utils/compat23/__init__.py

lightcode/OVM

3c6c3528ef851f65d4bd75cafb8738c54fba7b6f

[ "MIT" ]

1

2018-03-20T14:54:10.000Z

ovm/utils/compat23/__init__.py

lightcode/OVM

3c6c3528ef851f65d4bd75cafb8738c54fba7b6f

[ "MIT" ]

null

ovm/utils/compat23/__init__.py

lightcode/OVM

3c6c3528ef851f65d4bd75cafb8738c54fba7b6f

[ "MIT" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- from ovm.utils.compat23.with_popen import Popen from ovm.utils.compat23.etree import etree __all__ = ['Popen', 'etree']

17.1

47

0.701754

25

171

4.6

0.64

0.121739

0.208696

0.347826

0

0.040541

0.134503

171

9

48

19

0.736486

0.251462

0

0.079365

0

1

0

false

0

0.666667

0

0.666667

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

b5e1cb419bda79854213d848aeaff1048d037cac

140

py

Python

tests/basics/bytearray_decode.py

sebastien-riou/micropython

116c15842fd48ddb77b0bc016341d936a0756573

[ "MIT" ]

13,648

2015-01-01T01:34:51.000Z

2022-03-31T16:19:53.000Z

tests/basics/bytearray_decode.py

sebastien-riou/micropython

116c15842fd48ddb77b0bc016341d936a0756573

[ "MIT" ]

7,092

2015-01-01T07:59:11.000Z

2022-03-31T23:52:18.000Z

tests/basics/bytearray_decode.py

sebastien-riou/micropython

116c15842fd48ddb77b0bc016341d936a0756573

[ "MIT" ]

4,942

2015-01-02T11:48:50.000Z

2022-03-31T19:57:10.000Z

try: print(bytearray(b'').decode()) print(bytearray(b'abc').decode()) except AttributeError: print("SKIP") raise SystemExit

20

37

0.657143

16

140

5.75

0.6875

0.304348

0.326087

0

0.171429

140

6

38

23.333333

0.793103

0

0.05

0

1

0

true

0

0.5

1

0

null

1

0

1

0

null

0

1

0

1

0

6

bd0551b1483aa6e1a5d31afcd1893e239690d37c

48

py

Python

platform/bq/third_party/inflection/__init__.py

google-cloud-sdk-unofficial/google-cloud-sdk

2a48a04df14be46c8745050f98768e30474a1aac

[ "Apache-2.0" ]

2

2019-11-10T09:17:07.000Z

2019-12-18T13:44:08.000Z

platform/bq/third_party/inflection/__init__.py

google-cloud-sdk-unofficial/google-cloud-sdk

2a48a04df14be46c8745050f98768e30474a1aac

[ "Apache-2.0" ]

null

platform/bq/third_party/inflection/__init__.py

google-cloud-sdk-unofficial/google-cloud-sdk

2a48a04df14be46c8745050f98768e30474a1aac

[ "Apache-2.0" ]

1

2020-07-25T01:40:19.000Z

#!/usr/bin/env python from .inflection import *

16

25

0.729167

7

48

5

1

0

0.125

48

2

26

24

0.833333

0.416667

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

1fefc92fec9274173e1160ecb53131a0d3ef84a5

32

py

Python

filament/locking.py

comstud/filament

be6dbd6bf76dbcb0655c7fae239333d64ee8bb5f

[ "MIT" ]

2

2017-03-08T20:29:52.000Z

2019-05-15T20:15:42.000Z

filament/locking.py

comstud/filament

be6dbd6bf76dbcb0655c7fae239333d64ee8bb5f

[ "MIT" ]

null

filament/locking.py

comstud/filament

be6dbd6bf76dbcb0655c7fae239333d64ee8bb5f

[ "MIT" ]

null

from _filament.locking import *

16

31

0.8125

4

32

6.25

1

0

0.125

32

1

32

0.892857

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

9527f61be2d1e19cb598533e897794282480fa9b

9,081

py

Python

tests/brainload/test_braindescriptors.py

dfsp-spirit/cogload

ff9d19803c2e0c9aea248a45380959c2758ba83a

[ "MIT" ]

8

2018-11-11T11:41:19.000Z

2022-02-09T10:50:34.000Z

tests/brainload/test_braindescriptors.py

dfsp-spirit/cogload

ff9d19803c2e0c9aea248a45380959c2758ba83a

[ "MIT" ]

8

2018-11-05T10:11:09.000Z

2019-11-05T20:34:19.000Z

tests/brainload/test_braindescriptors.py

dfsp-spirit/cogload

ff9d19803c2e0c9aea248a45380959c2758ba83a

[ "MIT" ]

1

2020-07-20T06:43:57.000Z

import os import pytest import numpy as np from numpy.testing import assert_array_equal, assert_allclose import brainload as bl import brainload.freesurferdata as fsd import brainload.braindescriptors as bd THIS_DIR = os.path.dirname(os.path.abspath(__file__)) TEST_DATA_DIR = os.path.join(THIS_DIR, os.pardir, 'test_data') # Respect the environment variable BRAINLOAD_TEST_DATA_DIR if it is set. If not, fall back to default. TEST_DATA_DIR = os.getenv('BRAINLOAD_TEST_DATA_DIR', TEST_DATA_DIR) def test_braindescriptors_init_nonempty(): expected_subject2_testdata_dir = os.path.join(TEST_DATA_DIR, 'subject2') if not os.path.isdir(expected_subject2_testdata_dir): pytest.skip("Test data missing: e.g., directory '%s' does not exist. You can get all test data by running './develop/get_test_data_all.bash' in the repo root." % expected_subject2_testdata_dir) subjects_list = ['subject1', 'subject2'] bdi = bd.BrainDescriptors(TEST_DATA_DIR, subjects_list) assert len(bdi.subjects_list) == 2 assert len(bdi.descriptor_names) == 0 assert len(bdi.hemis) == 2 assert bdi.descriptor_values.shape == (2, 0) def test_braindescriptors_init_with_hemi(): expected_subject2_testdata_dir = os.path.join(TEST_DATA_DIR, 'subject2') if not os.path.isdir(expected_subject2_testdata_dir): pytest.skip("Test data missing: e.g., directory '%s' does not exist. You can get all test data by running './develop/get_test_data_all.bash' in the repo root." % expected_subject2_testdata_dir) subjects_list = ['subject1', 'subject2'] bdi = bd.BrainDescriptors(TEST_DATA_DIR, subjects_list, hemi='lh') bdi.report_descriptors() bdi.check_for_hemi_dependent_file([]) assert len(bdi.subjects_list) == 2 assert len(bdi.descriptor_names) == 0 assert bdi.descriptor_values.shape == (2, 0) assert len(bdi.hemis) == 1 def test_check_for_NaNs_no_descriptors_yet(): expected_subject2_testdata_dir = os.path.join(TEST_DATA_DIR, 'subject2') if not os.path.isdir(expected_subject2_testdata_dir): pytest.skip("Test data missing: e.g., directory '%s' does not exist. You can get all test data by running './develop/get_test_data_all.bash' in the repo root." % expected_subject2_testdata_dir) subjects_list = ['subject1', 'subject2'] bdi = bd.BrainDescriptors(TEST_DATA_DIR, subjects_list, hemi='lh') subjects_over_threshold, descriptors_over_threshold, nan_share_per_subject, nan_share_per_descriptor = bdi.check_for_NaNs() assert len(subjects_over_threshold) == 0 assert len(descriptors_over_threshold) == 0 def test_check_for_NaNs_with_curv_descriptors(): expected_subject2_testdata_dir = os.path.join(TEST_DATA_DIR, 'subject2') if not os.path.isdir(expected_subject2_testdata_dir): pytest.skip("Test data missing: e.g., directory '%s' does not exist. You can get all test data by running './develop/get_test_data_all.bash' in the repo root." % expected_subject2_testdata_dir) subjects_list = ['subject1', 'subject2'] bdi = bd.BrainDescriptors(TEST_DATA_DIR, subjects_list, hemi='lh') bdi.add_curv_stats() subjects_over_threshold, descriptors_over_threshold, nan_share_per_subject, nan_share_per_descriptor = bdi.check_for_NaNs() assert len(subjects_over_threshold) == 0 assert len(descriptors_over_threshold) == 0 def test_check_for_custom_measure_stats_files_invalid_format(): expected_subject2_testdata_dir = os.path.join(TEST_DATA_DIR, 'subject2') if not os.path.isdir(expected_subject2_testdata_dir): pytest.skip("Test data missing: e.g., directory '%s' does not exist. You can get all test data by running './develop/get_test_data_all.bash' in the repo root." % expected_subject2_testdata_dir) subjects_list = ['subject1', 'subject2'] bdi = bd.BrainDescriptors(TEST_DATA_DIR, subjects_list, hemi='rh') with pytest.raises(ValueError) as exc_info: bdi.check_for_custom_measure_stats_files(["aparc"], ["area"], morph_file_format="nosuchformat") assert "nosuchformat" in str(exc_info.value) assert "morph_file_format must be one of" in str(exc_info.value) def test_check_for_custom_measure_stats_files_curv_format(): expected_subject2_testdata_dir = os.path.join(TEST_DATA_DIR, 'subject2') if not os.path.isdir(expected_subject2_testdata_dir): pytest.skip("Test data missing: e.g., directory '%s' does not exist. You can get all test data by running './develop/get_test_data_all.bash' in the repo root." % expected_subject2_testdata_dir) subjects_list = ['subject1', 'subject2'] bdi = bd.BrainDescriptors(TEST_DATA_DIR, subjects_list, hemi='rh') bdi.check_for_custom_measure_stats_files(["aparc"], ["area"], morph_file_format="curv") def test_check_for_custom_measure_stats_files_mgh_format(): expected_subject2_testdata_dir = os.path.join(TEST_DATA_DIR, 'subject2') if not os.path.isdir(expected_subject2_testdata_dir): pytest.skip("Test data missing: e.g., directory '%s' does not exist. You can get all test data by running './develop/get_test_data_all.bash' in the repo root." % expected_subject2_testdata_dir) subjects_list = ['subject1', 'subject2'] bdi = bd.BrainDescriptors(TEST_DATA_DIR, subjects_list, hemi='rh') bdi.check_for_custom_measure_stats_files(["aparc"], ["area"], morph_file_format="mgh") def test_braindescriptors_init_with_invalid_hemi(): expected_subject2_testdata_dir = os.path.join(TEST_DATA_DIR, 'subject2') if not os.path.isdir(expected_subject2_testdata_dir): pytest.skip("Test data missing: e.g., directory '%s' does not exist. You can get all test data by running './develop/get_test_data_all.bash' in the repo root." % expected_subject2_testdata_dir) subjects_list = ['subject1', 'subject2'] with pytest.raises(ValueError) as exc_info: bdi = bd.BrainDescriptors(TEST_DATA_DIR, subjects_list, hemi='nosuchhemi') assert "hemi must be one of {'lh', 'rh', 'both'} but is" in str(exc_info.value) assert "nosuchhemi" in str(exc_info.value) def test_braindescriptors_parcellation_stats(): expected_subject2_testdata_dir = os.path.join(TEST_DATA_DIR, 'subject2') if not os.path.isdir(expected_subject2_testdata_dir): pytest.skip("Test data missing: e.g., directory '%s' does not exist. You can get all test data by running './develop/get_test_data_all.bash' in the repo root." % expected_subject2_testdata_dir) subjects_list = ['subject1', 'subject2'] bdi = bd.BrainDescriptors(TEST_DATA_DIR, subjects_list) bdi.add_parcellation_stats(['aparc', 'aparc.a2009s']) bdi.add_segmentation_stats(['aseg']) bdi.add_custom_measure_stats(['aparc'], ['area']) bdi.add_curv_stats() assert len(bdi.descriptor_names) == 3089 assert bdi.descriptor_values.shape == (2, 3089) def test_braindescriptors_add_standard_stats(): expected_subject2_testdata_dir = os.path.join(TEST_DATA_DIR, 'subject2') if not os.path.isdir(expected_subject2_testdata_dir): pytest.skip("Test data missing: e.g., directory '%s' does not exist. You can get all test data by running './develop/get_test_data_all.bash' in the repo root." % expected_subject2_testdata_dir) subjects_list = ['subject1', 'subject2'] bdi = bd.BrainDescriptors(TEST_DATA_DIR, subjects_list) bdi.add_standard_stats() assert len(bdi.descriptor_names) == 3426 assert bdi.descriptor_values.shape == (2, 3426) def test_braindescriptors_standard_stats_have_unique_names(): expected_subject2_testdata_dir = os.path.join(TEST_DATA_DIR, 'subject2') if not os.path.isdir(expected_subject2_testdata_dir): pytest.skip("Test data missing: e.g., directory '%s' does not exist. You can get all test data by running './develop/get_test_data_all.bash' in the repo root." % expected_subject2_testdata_dir) subjects_list = ['subject1', 'subject2'] bdi = bd.BrainDescriptors(TEST_DATA_DIR, subjects_list) bdi.add_standard_stats() assert len(bdi.descriptor_names) == 3426 assert bdi.descriptor_values.shape == (2, 3426) assert len(bdi.descriptor_names) == len(list(set(bdi.descriptor_names))) dup_list = bdi._check_for_duplicate_descriptor_names() assert not dup_list def test_braindescriptors_file_checks(): expected_subject2_testdata_dir = os.path.join(TEST_DATA_DIR, 'subject2') if not os.path.isdir(expected_subject2_testdata_dir): pytest.skip("Test data missing: e.g., directory '%s' does not exist. You can get all test data by running './develop/get_test_data_all.bash' in the repo root." % expected_subject2_testdata_dir) subjects_list = ['subject1', 'subject2'] bdi = bd.BrainDescriptors(TEST_DATA_DIR, subjects_list) bdi.check_for_parcellation_stats_files(['aparc', 'aparc.a2009s']) bdi.check_for_segmentation_stats_files(['aseg', 'wmparc']) bdi.check_for_custom_measure_stats_files(['aparc'], ['area']) bdi.check_for_curv_stats_files() assert len(bdi.subjects_list) == 2 assert len(bdi.descriptor_names) == 0 assert bdi.descriptor_values.shape == (2, 0)

57.474684

201

0.755203

1,337

9,081

4.812266

0.103216

0.082064

0.134287

0.151072

0.827479

0.80572

0.788778

0.777432

0.751166

0.737022

0

0.015954

0.13721

9,081

157

202

57.840764

0.805233

0.011012

0

0.646154

0

0.092308

0.255596

0.048001

0

0.215385

1

0.092308

false

0

0.053846

0

0.146154

0

null

0

1

0

null

0

6

95310012f871b8978aa95e6de4f314ac1a3cf95d

73

py

Python

jacdac/gyroscope/__init__.py

microsoft/jacdac-python

712ad5559e29065f5eccb5dbfe029c039132df5a

[ "MIT" ]

1

2022-02-15T21:30:36.000Z

jacdac/gyroscope/__init__.py

microsoft/jacdac-python

712ad5559e29065f5eccb5dbfe029c039132df5a

[ "MIT" ]

null

jacdac/gyroscope/__init__.py

microsoft/jacdac-python

712ad5559e29065f5eccb5dbfe029c039132df5a

[ "MIT" ]

1

2022-02-08T19:32:45.000Z

# Autogenerated file. from .client import GyroscopeClient # type: ignore

24.333333

50

0.794521

8

73

7.25

1

0

0.136986

73

2

51

36.5

0.920635

0.438356

0

1

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

6

20fd5e0318d05107cbfe2ceb26e741c7fd70d53e

33

py

Python

deanslist/__init__.py

upeducationnetwork/deanslist-python

226eda2580055427119397bc28e7976f019d7301

[ "MIT" ]

null

deanslist/__init__.py

upeducationnetwork/deanslist-python

226eda2580055427119397bc28e7976f019d7301

[ "MIT" ]

2

2016-05-16T19:54:26.000Z

2016-05-20T12:02:20.000Z

deanslist/__init__.py

upeducationnetwork/deanslist-python

226eda2580055427119397bc28e7976f019d7301

[ "MIT" ]

null

from .deanslist import dl, dlall

16.5

32

0.787879

5

33

5.2

1

0

0.151515

33

1

33

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

1f1c111dfb17991d6534f57dfea9c5be22c90642

17,955

py

Python

tests/test_extract_fragments.py

phenylazide/MolecularSimilarity

429f64c3c18daa5d341110380f761aa003ad290b

[ "MIT" ]

1

2020-09-14T16:01:50.000Z

tests/test_extract_fragments.py

phenylazide/MolecularSimilarity

429f64c3c18daa5d341110380f761aa003ad290b

[ "MIT" ]

5

2019-04-20T06:23:01.000Z

2019-07-25T17:28:05.000Z

tests/test_extract_fragments.py

phenylazide/MolecularSimilarity

429f64c3c18daa5d341110380f761aa003ad290b

[ "MIT" ]

1

2020-07-07T14:55:14.000Z

#!/usr/bin/env python3 import unittest import rdkit import rdkit.Chem import rdkit.Chem.AtomPairs.Utils import extract_fragments class TestCalc(unittest.TestCase): def test_atom_pairs(self): molecule = rdkit.Chem.MolFromSmiles("c1ccccn1") result = [{"smiles": "CC", "index": 2509202, "type": "AP", "size": 2}, {"smiles": "CC", "index": 2509202, "type": "AP", "size": 2}, {"smiles": "CC", "index": 2509202, "type": "AP", "size": 2}, {"smiles": "CC", "index": 2509202, "type": "AP", "size": 2}, {"smiles": "CC", "index": 3557778, "type": "AP", "size": 3}, {"smiles": "CC", "index": 3557778, "type": "AP", "size": 3}, {"smiles": "CC", "index": 3557778, "type": "AP", "size": 3}, {"smiles": "CC", "index": 3557778, "type": "AP", "size": 3}, {"smiles": "CC", "index": 4606354, "type": "AP", "size": 4}, {"smiles": "CC", "index": 4606354, "type": "AP", "size": 4}, {"smiles": "CN", "index": 2574738, "type": "AP", "size": 2}, {"smiles": "CN", "index": 2574738, "type": "AP", "size": 2}, {"smiles": "CN", "index": 3623314, "type": "AP", "size": 3}, {"smiles": "CN", "index": 3623314, "type": "AP", "size": 3}, {"smiles": "CN", "index": 4671890, "type": "AP", "size": 4}] self.assertEqual(extract_fragments.extract_atompair_fragments(molecule), result) molecule = rdkit.Chem.MolFromSmiles("c1nccc2n1ccc2") result = [{"smiles": "CC", "index": 2509202, "type": "AP", "size": 2}, {"smiles": "CC", "index": 2509202, "type": "AP", "size": 2}, {"smiles": "CC", "index": 2509202, "type": "AP", "size": 2}, {"smiles": "CC", "index": 3557778, "type": "AP", "size": 3}, {"smiles": "CC", "index": 3557778, "type": "AP", "size": 3}, {"smiles": "CC", "index": 3557778, "type": "AP", "size": 3}, {"smiles": "CC", "index": 3557778, "type": "AP", "size": 3}, {"smiles": "CC", "index": 4606354, "type": "AP", "size": 4}, {"smiles": "CC", "index": 4606354, "type": "AP", "size": 4}, {"smiles": "CC", "index": 4606354, "type": "AP", "size": 4}, {"smiles": "CC", "index": 4606354, "type": "AP", "size": 4}, {"smiles": "CC", "index": 4606354, "type": "AP", "size": 4}, {"smiles": "CC", "index": 4606354, "type": "AP", "size": 4}, {"smiles": "CC", "index": 5654930, "type": "AP", "size": 5}, {"smiles": "CC", "index": 5654930, "type": "AP", "size": 5}, {"smiles": "CC", "index": 2510226, "type": "AP", "size": 2}, {"smiles": "CC", "index": 2510226, "type": "AP", "size": 2}, {"smiles": "CC", "index": 3558802, "type": "AP", "size": 3}, {"smiles": "CC", "index": 3558802, "type": "AP", "size": 3}, {"smiles": "CC", "index": 3558802, "type": "AP", "size": 3}, {"smiles": "CC", "index": 3558802, "type": "AP", "size": 3}, {"smiles": "CN", "index": 2574738, "type": "AP", "size": 2}, {"smiles": "CN", "index": 2574738, "type": "AP", "size": 2}, {"smiles": "CN", "index": 3623314, "type": "AP", "size": 3}, {"smiles": "CN", "index": 4671890, "type": "AP", "size": 4}, {"smiles": "CN", "index": 5720466, "type": "AP", "size": 5}, {"smiles": "CN", "index": 5720466, "type": "AP", "size": 5}, {"smiles": "CN", "index": 4671891, "type": "AP", "size": 4}, {"smiles": "CN", "index": 2575762, "type": "AP", "size": 2}, {"smiles": "CN", "index": 2575762, "type": "AP", "size": 2}, {"smiles": "CN", "index": 3624338, "type": "AP", "size": 3}, {"smiles": "CN", "index": 3624338, "type": "AP", "size": 3}, {"smiles": "CN", "index": 3624338, "type": "AP", "size": 3}, {"smiles": "CN", "index": 4672914, "type": "AP", "size": 4}, {"smiles": "CN", "index": 2575763, "type": "AP", "size": 2}, {"smiles": "NN", "index": 3624402, "type": "AP", "size": 3}] self.assertEqual(extract_fragments.extract_atompair_fragments(molecule), result) molecule = rdkit.Chem.MolFromSmiles("CCO") result = [{"smiles": "CC", "index": 2492801, "type": "AP", "size": 2}, {"smiles": "CO", "index": 3671425, "type": "AP", "size": 3}, {"smiles": "CO", "index": 2622850, "type": "AP", "size": 2}] self.assertEqual(extract_fragments.extract_atompair_fragments(molecule), result) def test_neighbourhood_fragments(self): #ECFP molecule = rdkit.Chem.MolFromSmiles("c1ccccn1") options = { "kekule": True, "isomeric": True } size = 6 result = [{"smiles": "N1:C:C:C:C:C:1", "index": 755035130, "type": "ECFP", "size": 3}] self.assertEqual(extract_fragments.extract_neighbourhood_fragments(molecule, size, options, True), result) size = 4 result = [{"smiles": "C(:C:C):C:N", "index": 1207774339, "type": "ECFP", "size": 2}, {"smiles": "C(:C:C):C:N", "index": 1207774339, "type": "ECFP", "size": 2}, {"smiles": "N(:C:C):C:C", "index": 1343371647, "type": "ECFP", "size": 2}, {"smiles": "C(:C:C):N:C", "index": 1821698485, "type": "ECFP", "size": 2}, {"smiles": "C(:C:C):N:C", "index": 1821698485, "type": "ECFP", "size": 2}, {"smiles": "C(:C:C):C:C", "index": 2763854213, "type": "ECFP", "size": 2}] self.assertEqual(extract_fragments.extract_neighbourhood_fragments(molecule, size, options, True), result) options = { "kekule": False, "isomeric": True } result = [{"smiles": "c(cc)cn", "index": 1207774339, "type": "ECFP", "size": 2}, {"smiles": "c(cc)cn", "index": 1207774339, "type": "ECFP", "size": 2}, {"smiles": "n(cc)cc", "index": 1343371647, "type": "ECFP", "size": 2}, {"smiles": "c(cc)nc", "index": 1821698485, "type": "ECFP", "size": 2}, {"smiles": "c(cc)nc", "index": 1821698485, "type": "ECFP", "size": 2}, {"smiles": "c(cc)cc", "index": 2763854213, "type": "ECFP", "size": 2}] self.assertEqual(extract_fragments.extract_neighbourhood_fragments(molecule, size, options, True), result) molecule = rdkit.Chem.MolFromSmiles("c1nccc2n1ccc2") options = { "kekule": False, "isomeric": False } result = [{"smiles": "c(cn)c(c)n", "index": 201245292, "type": "ECFP", "size": 2}, {"smiles": "c(cc)n(c)c", "index": 405194198, "type": "ECFP", "size": 2}, {"smiles": "n(cc)(cn)c(c)c", "index": 924977737, "type": "ECFP", "size": 2}, {"smiles": "c(cc)nc", "index": 1717044408, "type": "ECFP", "size": 2}, {"smiles": "c(cc)(cc)n(c)c", "index": 2345490282, "type": "ECFP", "size": 2}, {"smiles": "c(nc)n(c)c", "index": 2558786292, "type": "ECFP", "size": 2}, {"smiles": "n(cc)cn", "index": 2910395211, "type": "ECFP", "size": 2}, {"smiles": "c(cc)cn", "index": 3428161631, "type": "ECFP", "size": 2}, {"smiles": "c(cc)c(c)n", "index": 3896685563, "type": "ECFP", "size": 2}] self.assertEqual(extract_fragments.extract_neighbourhood_fragments(molecule, size, options, True), result) size = 6 molecule = rdkit.Chem.MolFromSmiles("C[C@H](O)c1ccccc1") options = { "kekule": False, "isomeric": True } result = [{"smiles": "c1ccccc1", "index": 742000539, "type": "ECFP", "size": 3}, {"smiles": "c1cccc(C)c1", "index": 997097697, "type": "ECFP", "size": 3}, {"smiles": "c1ccccc1[C@H](C)O", "index": 1566387358, "type": "ECFP", "size": 3}] self.assertEqual(extract_fragments.extract_neighbourhood_fragments(molecule, size, options, True), result) options = { "kekule": False, "isomeric": False } result = [{"smiles": "c1ccccc1", "index": 742000539, "type": "ECFP", "size": 3}, {"smiles": "c1cccc(C)c1", "index": 997097697, "type": "ECFP", "size": 3}, {"smiles": "c1ccccc1C(C)O", "index": 1566387358, "type": "ECFP", "size": 3}] self.assertEqual(extract_fragments.extract_neighbourhood_fragments(molecule, size, options, True), result) options = { "kekule": True, "isomeric": True } result = [{"smiles": "C1:C:C:C:C:C:1", "index": 742000539, "type": "ECFP", "size": 3}, {"smiles": "C1:C:C:C:C(C):C:1", "index": 997097697, "type": "ECFP", "size": 3}, {"smiles": "C1:C:C:C:C:C:1[C@H](C)O", "index": 1566387358, "type": "ECFP", "size": 3}] self.assertEqual(extract_fragments.extract_neighbourhood_fragments(molecule, size, options, True), result) options = { "kekule": True, "isomeric": False } result = [{"smiles": "C1:C:C:C:C:C:1", "index": 742000539, "type": "ECFP", "size": 3}, {"smiles": "C1:C:C:C:C(C):C:1", "index": 997097697, "type": "ECFP", "size": 3}, {"smiles": "C1:C:C:C:C:C:1C(C)O", "index": 1566387358, "type": "ECFP", "size": 3}] self.assertEqual(extract_fragments.extract_neighbourhood_fragments(molecule, size, options, True), result) # FCFP molecule = rdkit.Chem.MolFromSmiles("c1ccccn1") options = { "kekule": True, "isomeric": True } size = 6 result = [{"smiles": "C1:C:C:C:C:N:1", "index": 1067478186, "type": "FCFP", "size": 3}] self.assertEqual(extract_fragments.extract_neighbourhood_fragments(molecule, size, options, False), result) molecule = rdkit.Chem.MolFromSmiles("c1nccc2n1ccc2") options = { "kekule": False, "isomeric": False } size = 4 result = [{"smiles": "c(cc)(cc)n(c)c", "index": 435849959, "type": "FCFP", "size": 2}, {"smiles": "n(cc)cn", "index": 1127424909, "type": "FCFP", "size": 2}, {"smiles": "c(cc)cn", "index": 1230564256, "type": "FCFP", "size": 2}, {"smiles": "c(cc)nc", "index": 1251070542, "type": "FCFP", "size": 2}, {"smiles": "n(cc)(cn)c(c)c", "index": 1476508118, "type": "FCFP", "size": 2}, {"smiles": "c(nc)n(c)c", "index": 2154510652, "type": "FCFP", "size": 2}, {"smiles": "c(cc)n(c)c", "index": 2226952373, "type": "FCFP", "size": 2}, {"smiles": "c(cc)c(c)n", "index": 2460461453, "type": "FCFP", "size": 2}, {"smiles": "c(cn)c(c)n", "index": 2460461555, "type": "FCFP", "size": 2}] self.assertEqual(extract_fragments.extract_neighbourhood_fragments(molecule, size, options, False), result) molecule = rdkit.Chem.MolFromSmiles("CCO") size = 2 result = [{"smiles": "CC", "index": 3205495869, "type": "FCFP", "size": 1}, {"smiles": "OC", "index": 3205496825, "type": "FCFP", "size": 1}, {"smiles": "C(C)O", "index": 3766532901, "type": "FCFP", "size": 1}] self.assertEqual(extract_fragments.extract_neighbourhood_fragments(molecule, size, options, False), result) def test_path_fragments(self): molecule = rdkit.Chem.MolFromSmiles("c1ccccn1") options = { "kekule": False, "isomeric": False } size = 2 result = [{"smiles": "cc", "index": 83025, "type": "TT", "size": 2}, {"smiles": "cn", "index": 148561, "type": "TT", "size": 2}, {"smiles": "cc", "index": 83025, "type": "TT", "size": 2}, {"smiles": "cc", "index": 83025, "type": "TT", "size": 2}, {"smiles": "cc", "index": 83025, "type": "TT", "size": 2}, {"smiles": "cn", "index": 148561, "type": "TT", "size": 2}] self.assertEqual(extract_fragments.extract_path_fragments(molecule, size, options), result) size = 3 result = [{"smiles": "ccc", "index": 85016657, "type": "TT", "size": 3}, {"smiles": "cnc", "index": 85082193, "type": "TT", "size": 3}, {"smiles": "ccn", "index": 152125521, "type": "TT", "size": 3}, {"smiles": "ccc", "index": 85016657, "type": "TT", "size": 3}, {"smiles": "ccc", "index": 85016657, "type": "TT", "size": 3}, {"smiles": "ccn", "index": 152125521, "type": "TT", "size": 3}] self.assertEqual(extract_fragments.extract_path_fragments(molecule, size, options), result) molecule = rdkit.Chem.MolFromSmiles("c1nccc2n1ccc2") options = { "kekule": False, "isomeric": True } size = 5; result = [{"smiles": "cccnc", "index": 90245936857169, "type": "TT", "size": 5}, {"smiles": "cccnc", "index": 89216219430993, "type": "TT", "size": 5}, {"smiles": "cccnc", "index": 89216219430993, "type": "TT", "size": 5}, {"smiles": "cccnc", "index": 89216218382417, "type": "TT", "size": 5}, {"smiles": "ccncn", "index": 159515237499985, "type": "TT", "size": 5}, {"smiles": "ccncn", "index": 159515237499985, "type": "TT", "size": 5}, {"smiles": "ccncn", "index": 159515237498961, "type": "TT", "size": 5}, {"smiles": "ncccn", "index": 160615754711185, "type": "TT", "size": 5}, {"smiles": "ccccn", "index": 159515169342545, "type": "TT", "size": 5}, {"smiles": "cncnc", "index": 90315730075729, "type": "TT", "size": 5}, {"smiles": "cncnc", "index": 89216218447953, "type": "TT", "size": 5}, {"smiles": "cccnc", "index": 89216219430993, "type": "TT", "size": 5}, {"smiles": "ccccc", "index": 89146426212433, "type": "TT", "size": 5}, {"smiles": "ccncn", "index": 160614748078161, "type": "TT", "size": 5}, {"smiles": "ccncc", "index": 89147567063121, "type": "TT", "size": 5}, {"smiles": "ccccc", "index": 89147498905681, "type": "TT", "size": 5}, {"smiles": "c1ccnc1", "index": 90315730010193, "type": "TT", "size": 5}] self.assertEqual(extract_fragments.extract_path_fragments(molecule, size, options), result) options = { "kekule": True, "isomeric": False } size = 3 result = [{"smiles": "C:N:C", "index": 85082193, "type": "TT", "size": 3}, {"smiles": "C:N:C", "index": 86131793, "type": "TT", "size": 3}, {"smiles": "C:N:C", "index": 85083217, "type": "TT", "size": 3}, {"smiles": "N:C:N", "index": 153174161, "type": "TT", "size": 3}, {"smiles": "C:C:N", "index": 152125521, "type": "TT", "size": 3}, {"smiles": "C:C:C", "index": 86065233, "type": "TT", "size": 3}, {"smiles": "C:C:N", "index": 153175121, "type": "TT", "size": 3}, {"smiles": "C:C:C", "index": 85017681, "type": "TT", "size": 3}, {"smiles": "C:N:C", "index": 86131793, "type": "TT", "size": 3}, {"smiles": "C:C:C", "index": 86065233, "type": "TT", "size": 3}, {"smiles": "C:C:N", "index": 153175121, "type": "TT", "size": 3}, {"smiles": "C:C:N", "index": 153174097, "type": "TT", "size": 3}, {"smiles": "C:C:C", "index": 85016657, "type": "TT", "size": 3}] self.assertEqual(extract_fragments.extract_path_fragments(molecule, size, options), result) options = { "kekule": True, "isomeric": True } size = 8; result =[{"smiles": "C:C:N1:C:C:C:N:C:1", "index": 95794032134814304387153, "type": "TT", "size": 8}, {"smiles": "C:C:C:C:C:C:N:C", "index": 95794032134814236229713, "type": "TT", "size": 8}, {"smiles": "C:C:C1:C:C:C:N:1:C", "index": 95795185056317770383441, "type": "TT", "size": 8}, {"smiles": "C:C1:C:C:C:N:1:C:N", "index": 171278182067926592472145, "type": "TT", "size": 8}, {"smiles": "N:C:C:C1:C:C:C:N:1", "index": 171278108885601952546897, "type": "TT", "size": 8}, {"smiles": "C:N:C:N1:C:C:C:C:1", "index": 95794032206282492035153, "type": "TT", "size": 8}, {"smiles": "C:C:C1:C:C:N:C:N:1", "index": 95720317216182156476497, "type": "TT", "size": 8}, {"smiles": "C:C:C:N:C:N:C:C", "index": 95720317216182155427921, "type": "TT", "size": 8}, {"smiles": "C:C1:C:C:N:C:N:1:C", "index": 95795185126686513513553, "type": "TT", "size": 8}] self.assertEqual(extract_fragments.extract_path_fragments(molecule, size, options), result) molecule = rdkit.Chem.MolFromSmiles("CCO") options = { "kekule": False, "isomeric": True } size = 2 result = [{"smiles": "CC", "index": 66624, "type": "TT", "size": 2}, {"smiles": "CO", "index": 196673, "type": "TT", "size": 2}] self.assertEqual(extract_fragments.extract_path_fragments(molecule, size, options), result) if __name__ == "__main__": unittest.main()

57

115

0.482373

1,944

17,955

4.416667

0.085391

0.023993

0.062893

0.012113

0.854647

0.822735

0.787911

0.73515

0.710109

0.662124

0

0.134575

0.285269

17,955

314

116

57.181529

0.534481

0.001671

0

0.588679

0

0.003774

0.255957

0.001283

0

0.075472

1

0.011321

false

0

0.018868

0

0.033962

0

null

0

1

0

null

0

6

1f215fc68a5e08bb449a230f4e6f0374bb2d8e4e

1,413

py

Python

sane/dataset/__init__.py

AndreyBuyanov/Neuro-Evolutionary-Calculations

af6c06890a869a768ab6929f7d2ba6f12fb1b81a

[ "MIT" ]

null

sane/dataset/__init__.py

AndreyBuyanov/Neuro-Evolutionary-Calculations

af6c06890a869a768ab6929f7d2ba6f12fb1b81a

[ "MIT" ]

null

sane/dataset/__init__.py

AndreyBuyanov/Neuro-Evolutionary-Calculations

af6c06890a869a768ab6929f7d2ba6f12fb1b81a

[ "MIT" ]

null

from .dataset_loader import Cancer1Dataset from .dataset_loader import Cancer2Dataset from .dataset_loader import Cancer3Dataset from .dataset_loader import Diabetes1Dataset from .dataset_loader import Diabetes2Dataset from .dataset_loader import Diabetes3Dataset from .dataset_loader import Glass1Dataset from .dataset_loader import Glass2Dataset from .dataset_loader import Glass3Dataset from .dataset_loader import Card1Dataset from .dataset_loader import Card2Dataset from .dataset_loader import Card3Dataset from .dataset_loader import Flare1Dataset from .dataset_loader import Flare2Dataset from .dataset_loader import Flare3Dataset from .dataset_loader import Gene1Dataset from .dataset_loader import Gene2Dataset from .dataset_loader import Gene3Dataset from .dataset_loader import Heart1Dataset from .dataset_loader import Heart2Dataset from .dataset_loader import Heart3Dataset from .dataset_loader import Horse1Dataset from .dataset_loader import Horse2Dataset from .dataset_loader import Horse3Dataset from .dataset_loader import Mushroom1Dataset from .dataset_loader import Mushroom2Dataset from .dataset_loader import Mushroom3Dataset from .dataset_loader import Soybean1Dataset from .dataset_loader import Soybean2Dataset from .dataset_loader import Soybean3Dataset from .dataset_loader import Thyroid1Dataset from .dataset_loader import Thyroid2Dataset from .dataset_loader import Thyroid3Dataset

41.558824

44

0.883227

165

1,413

7.363636

0.224242

0.298765

0.461728

0.624691

0

0.025761

0.093418

1,413

33

45

42.818182

0.922717

0

1

0

true

0

1

0

1

0

null

1

0

null

0

1

0

1

0

1

0

6

2f3acf1de589e82fbac9f59cd6bffd9bf4046433

42

py

Python

ptb/monitor_utils/.ipynb_checkpoints/__init__-checkpoint.py

minhtannguyen/MomentumRNN

f185c5432a52533bb1625e2162ec044651e07d9a

[ "CC0-1.0" ]

13

2020-06-16T16:15:55.000Z

2021-11-24T05:24:48.000Z

mnist-timit/utils/__init__.py

minhtannguyen/MomentumRNN

f185c5432a52533bb1625e2162ec044651e07d9a

[ "CC0-1.0" ]

2

2020-12-07T08:26:01.000Z

2020-12-26T13:01:53.000Z

mnist-timit/utils/__init__.py

minhtannguyen/MomentumRNN

f185c5432a52533bb1625e2162ec044651e07d9a

[ "CC0-1.0" ]

5

2020-06-11T16:13:23.000Z

2022-03-07T14:28:46.000Z

"""Useful utils """ from .logger import *

10.5

21

0.642857

5

42

5.4

1

0

0.166667

42

3

22

14

0.771429

0.285714

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

2f4567b146067ec96c0feb23f68a7bf99f74f299

194

py

Python

custom_app/outpatient/doctype/outpatient_record/outpatient_record.py

benson-tseng/frappe_custom

c7830b1610c7c5a71e81d75f790410b919b2fbf2

[ "MIT" ]

1

2021-09-02T12:44:22.000Z

custom_app/outpatient/doctype/outpatient_record/outpatient_record.py

benson-tseng/frappe_custom

c7830b1610c7c5a71e81d75f790410b919b2fbf2

[ "MIT" ]

null

custom_app/outpatient/doctype/outpatient_record/outpatient_record.py

benson-tseng/frappe_custom

c7830b1610c7c5a71e81d75f790410b919b2fbf2

[ "MIT" ]

null

21.555556

49

0.793814

25

194

6.16

0.8

0

0.023952

0.139175

194

8

50

24.25

0.898204

0.525773

0

1

0

true

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

6

85eeafc7076dad3bc43cfc2e3e89c5d0cee7f864

369

py

Python

app.py

RomanGorelsky/project_mid

e08fd3f0feea30ee3618cbc4ce99ff141f484ca9

[ "MIT" ]

null

app.py

RomanGorelsky/project_mid

e08fd3f0feea30ee3618cbc4ce99ff141f484ca9

[ "MIT" ]

null

app.py

RomanGorelsky/project_mid

e08fd3f0feea30ee3618cbc4ce99ff141f484ca9

[ "MIT" ]

null

from flask import Flask, render_template app = Flask(__name__) @app.route('/') def hello_world(): return render_template('tables.html') @app.route('/charts') def render_charts(): return render_template('charts.html') @app.route('/tables') def render_tables(): return render_template('tables.html') if __name__ == '__main__': app.run(debug=True)

16.772727

41

0.701897

48

369

5

0.416667

0.233333

0.25

0.216667

0.25

0

0.140921

369

22

42

16.772727

0.757098

0

0.153846

0

0.151351

0

1

0.230769

false

0

0.076923

0.230769

0.538462

0

null

1

0

null

0

1

0

1

0

6

c83c73d3759f3357a637d8f1c262a00f0201b30b

174

py

Python

pysid/identification/__init__.py

lima-84/pysid

6038b9437e6f4bd23280c3541cb06c1cdf292d2a

[ "MIT" ]

5

2019-09-08T17:22:04.000Z

2022-01-08T18:09:56.000Z

pysid/identification/__init__.py

lima-84/pysid

6038b9437e6f4bd23280c3541cb06c1cdf292d2a

[ "MIT" ]

null

pysid/identification/__init__.py

lima-84/pysid

6038b9437e6f4bd23280c3541cb06c1cdf292d2a

[ "MIT" ]

4

2019-09-08T17:49:23.000Z

2022-01-10T11:44:50.000Z

#__init__.py for pysid # Load all the functions by default from .ivmethod import * from .pemethod import * from .tseries import * from .accr import * from .comcrit import *

19.333333

35

0.747126

25

174

5.04

0.68

0.31746

0

0.178161

174

8

36

21.75

0.881119

0.316092

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

c85dcbeadf179b1120b03a3cb0b74bc8ad296b89

339

py

Python

movie/start.py

fuey/spiders

4a39b0a0c55b23968e515d75a6946f3e8dc0991c

[ "Apache-2.0" ]

null

movie/start.py

fuey/spiders

4a39b0a0c55b23968e515d75a6946f3e8dc0991c

[ "Apache-2.0" ]

null

movie/start.py

fuey/spiders

4a39b0a0c55b23968e515d75a6946f3e8dc0991c

[ "Apache-2.0" ]

null

from scrapy import cmdline cmdline.execute("scrapy crawl douban".split()); # cmdline.execute("scrapy crawl imdb_movie_top250".split()) # cmdline.execute("scrapy crawl imdb_tv_top250 -s LOG_FILE=spider.log".split()) # cmdline.execute("scrapy crawl rotten_tomatoes_top100".split()) # cmdline.execute("scrapy crawl mtc_alltime_top".split())

42.375

79

0.781711

47

339

5.446809

0.468085

0.273438

0.390625

0.488281

0.5

0.265625

0

0.028754

0.076696

339

7

80

48.428571

0.789137

0.749263

0

0.2375

0

1

0

true

0

0.5

0

0.5

0

null

1

0

1

0

null

0

1

0

1

0

6

c0deee6815997981a4353d2570ca99f04eb912c3

83

py

Python

tests/__init__.py

gauravmk/rq-dashboard

d760276d075cd5e7879127c0155cad874c55e6fb

[ "BSD-2-Clause-FreeBSD" ]

null

tests/__init__.py

gauravmk/rq-dashboard

d760276d075cd5e7879127c0155cad874c55e6fb

[ "BSD-2-Clause-FreeBSD" ]

null

tests/__init__.py

gauravmk/rq-dashboard

d760276d075cd5e7879127c0155cad874c55e6fb

[ "BSD-2-Clause-FreeBSD" ]

null

from __future__ import absolute_import from .basic import * from .compat import *

16.6

38

0.795181

11

83

5.545455

0.545455

0.327869

0

0.156627

83

4

39

20.75

0.871429

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

c0e9fd5f20e04d63f2ad82e439285b5a6ab244f8

150

py

Python

python/testData/refactoring/move/cleanupImportsAfterMove/before/src/main.py

jnthn/intellij-community

8fa7c8a3ace62400c838e0d5926a7be106aa8557

[ "Apache-2.0" ]

2

2019-04-28T07:48:50.000Z

2020-12-11T14:18:08.000Z

python/testData/refactoring/move/cleanupImportsAfterMove/before/src/main.py

jnthn/intellij-community

8fa7c8a3ace62400c838e0d5926a7be106aa8557

[ "Apache-2.0" ]

173

2018-07-05T13:59:39.000Z

2018-08-09T01:12:03.000Z

python/testData/refactoring/move/cleanupImportsAfterMove/before/src/main.py

jnthn/intellij-community

8fa7c8a3ace62400c838e0d5926a7be106aa8557

[ "Apache-2.0" ]

2

2020-03-15T08:57:37.000Z

2020-04-07T04:48:14.000Z

from lib import B from lib import A from lib import D from lib import C class C1: print(A) class C2: print(B) class C3: print(C, D)

8.823529

17

0.64

29

150

3.310345

0.413793

0.291667

0.541667

0

0.028302

0.293333

150

16

18

9.375

0.877358

0

1

0

true

0

0.4

0

0.7

0.3

1

0

null

1

0

null

0

1

0

1

0

1

0

6

8d06f9f3a3597bcd5b776daef5a8973d139fe355

916

py

Python

arrays/numbers_even_number.py

wtlow003/leetcode-daily

e1d9c74b55e5b3106731a324d70a510e03b3b21f

[ "MIT" ]

null

arrays/numbers_even_number.py

wtlow003/leetcode-daily

e1d9c74b55e5b3106731a324d70a510e03b3b21f

[ "MIT" ]

null

arrays/numbers_even_number.py

wtlow003/leetcode-daily

e1d9c74b55e5b3106731a324d70a510e03b3b21f

[ "MIT" ]

1

2022-01-05T17:52:41.000Z

""" 1295. Find Numbers with Even Number of Digits https://leetcode.com/problems/find-numbers-with-even-number-of-digits/ Given an array nums of integers, return how many of them contain an even number of digits. Example: Input: nums = [12,345,2,6,7896] Output: 2 Explanation: 12 contains 2 digits (even number of digits). 345 contains 3 digits (odd number of digits). 2 contains 1 digit (odd number of digits). 6 contains 1 digit (odd number of digits). 7896 contains 4 digits (even number of digits). Therefore only 12 and 7896 contain an even number of digits. """ # Runtime: 56ms class Solution: def findNumbers(self, nums: List[int]) -> int: # Though process: # We need to find the length of each number within the array to check for even # We can use boolean to help us count the number of True events -> len(i) == even return sum([len(str(i)) % 2 == 0 for i in nums])

31.586207

90

0.70524

154

916

4.194805

0.480519

0.123839

0.195046

0.167183

0.356037

0.281734

0.198142

0

0.057692

0.20524

916

28

91

32.714286

0.82967

0.816594

0

1

0.333333

false

0

0.333333

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

239818a07781a6623c9a9fbf09464b5e67a01a5e

716

py

Python

cebulany/sql_utils.py

hackerspace-silesia/cebulany-manager

5965c39df15aca77a4a891a134762eb4230cbd51

[ "MIT" ]

4

2019-03-06T20:30:08.000Z

2020-01-23T19:25:20.000Z

cebulany/sql_utils.py

hackerspace-silesia/cebulany-manager

5965c39df15aca77a4a891a134762eb4230cbd51

[ "MIT" ]

9

2019-07-06T11:25:50.000Z

2022-01-22T05:18:39.000Z

cebulany/sql_utils.py

hackerspace-silesia/cebulany-manager

5965c39df15aca77a4a891a134762eb4230cbd51

[ "MIT" ]

3

2019-10-25T16:55:30.000Z

2019-10-26T19:55:57.000Z

from sqlalchemy import func as sql_func, Date from cebulany.models import db def get_year_month_col(column: Date): database_type = db.engine.dialect.name if database_type == 'postgresql': return sql_func.to_char(column, 'YYYY-MM') if database_type == 'sqlite': return sql_func.strftime('%Y-%m', column) raise AttributeError(f'Unknown database type: {database_type}') def get_year_col(column: Date): database_type = db.engine.dialect.name if database_type == 'postgresql': return sql_func.to_char(column, 'YYYY') if database_type == 'sqlite': return sql_func.strftime('%Y', column) raise AttributeError(f'Unknown database type: {database_type}')

29.833333

67

0.702514

98

716

4.928571

0.367347

0.248447

0.115942

0.086957

0.811594

0.401656

0

0.184358

716

23

68

31.130435

0.827055

0

0.5

0

0.175978

0

1

0.125

false

0

0.125

0

0.5

0

null

1

0

1

0

null

0

6

23d18e15e98aff62671326b817142b94c2e754be

57,734

py

Python

PNNL-Real-Time-Transactive-Energy/modificationScripts/supportFunctions/commercialLoads.py

GMLC-TDC/Use-Cases

14d687fe04af731c1ee466e05acfd5813095660a

[ "BSD-3-Clause" ]

1

2021-01-04T07:27:34.000Z

PNNL-Real-Time-Transactive-Energy/modificationScripts/supportFunctions/commercialLoads.py

GMLC-TDC/Use-Cases

14d687fe04af731c1ee466e05acfd5813095660a

[ "BSD-3-Clause" ]

null

PNNL-Real-Time-Transactive-Energy/modificationScripts/supportFunctions/commercialLoads.py

GMLC-TDC/Use-Cases

14d687fe04af731c1ee466e05acfd5813095660a

[ "BSD-3-Clause" ]

2

2019-08-01T21:49:40.000Z

2019-09-23T19:30:36.000Z

""" This file contains four fuctions to add commercial load types to a feeder based on the use flags and cofiguration defined """ ################################################################################################################## # Modified April 11, 2018 by Jacob Hansen (jacob.hansen@pnnl.gov) # Created April 13, 2013 by Andy Fisher (andy.fisher@pnnl.gov) # Copyright (c) 2013 Battelle Memorial Institute. The Government retains a paid-up nonexclusive, irrevocable # worldwide license to reproduce, prepare derivative works, perform publicly and display publicly by or for the # Government, including the right to distribute to other Government contractors. ################################################################################################################## import math, random def append_commercial(glmCaseDict, use_flags, commercial_dict, last_object_key, config_data): """ This fucntion appends commercial houses to a feeder based on existing loads Inputs glmCaseDict - dictionary containing the full feeder use_flags - dictionary that contains the use flags commercial_dict - dictionary that contains information about commercial loads spots last_object_key - Last object key use_config_file - dictionary that contains the configurations of the feeder Outputs glmCaseDict - dictionary containing the full feeder last_object_key - Last object key """ # Initialize psuedo-random seed # random.seed(4) # Phase ABC - convert to "commercial buildings" # if number of "houses" > 15, then create a large office # if number of "houses" < 15 but > 6, create a big box commercial # else, create a residential strip mall # If using Configuration.m and load classifications, # building type is chosen according to classification # regardless of number of "houses" # Check if last_object_key exists in glmCaseDict if last_object_key in glmCaseDict: while last_object_key in glmCaseDict: last_object_key += 1 if len(commercial_dict) > 0 and use_flags["use_commercial"] == 1: # setup all of the line configurations we may need glmCaseDict[last_object_key] = {"object": "triplex_line_conductor", "name": "comm_line_cfg_cnd1", "resistance": "0.48", "geometric_mean_radius": "0.0158"} last_object_key += 1 glmCaseDict[last_object_key] = {"object": "triplex_line_conductor", "name": "comm_line_cfg_cnd2", "resistance": "0.48", "geometric_mean_radius": "0.0158"} last_object_key += 1 glmCaseDict[last_object_key] = {"object": "triplex_line_conductor", "name": "comm_line_cfg_cndN", "resistance": "0.48", "geometric_mean_radius": "0.0158"} last_object_key += 1 glmCaseDict[last_object_key] = {"object": "triplex_line_configuration", "name": "commercial_line_config", "conductor_1": "comm_line_cfg_cnd1", "conductor_2": "comm_line_cfg_cnd2", "conductor_N": "comm_line_cfg_cndN", "insulation_thickness": "0.08", "diameter": "0.522"} last_object_key += 1 glmCaseDict[last_object_key] = {"object": "line_spacing", "name": "line_spacing_commABC", "distance_AB": "53.19999999996 in", "distance_BC": "53.19999999996 in", "distance_AC": "53.19999999996 in", "distance_AN": "69.80000000004 in", "distance_BN": "69.80000000004 in", "distance_CN": "69.80000000004 in"} last_object_key += 1 glmCaseDict[last_object_key] = {"object": "overhead_line_conductor", "name": "overhead_line_conductor_comm", "rating.summer.continuous": "443.0", "geometric_mean_radius": "0.02270 ft", "resistance": "0.05230"} last_object_key += 1 glmCaseDict[last_object_key] = {"object": "line_configuration", "name": "line_configuration_commABC", "conductor_A": "overhead_line_conductor_comm", "conductor_B": "overhead_line_conductor_comm", "conductor_C": "overhead_line_conductor_comm", "conductor_N": "overhead_line_conductor_comm", "spacing": "line_spacing_commABC"} last_object_key += 1 glmCaseDict[last_object_key] = {"object": "line_configuration", "name": "line_configuration_commAB", "conductor_A": "overhead_line_conductor_comm", "conductor_B": "overhead_line_conductor_comm", "conductor_N": "overhead_line_conductor_comm", "spacing": "line_spacing_commABC"} last_object_key += 1 glmCaseDict[last_object_key] = {"object": "line_configuration", "name": "line_configuration_commAC", "conductor_A": "overhead_line_conductor_comm", "conductor_C": "overhead_line_conductor_comm", "conductor_N": "overhead_line_conductor_comm", "spacing": "line_spacing_commABC"} last_object_key += 1 glmCaseDict[last_object_key] = {"object": "line_configuration", "name": "line_configuration_commBC", "conductor_B": "overhead_line_conductor_comm", "conductor_C": "overhead_line_conductor_comm", "conductor_N": "overhead_line_conductor_comm", "spacing": "line_spacing_commABC"} last_object_key += 1 glmCaseDict[last_object_key] = {"object": "line_configuration", "name": "line_configuration_commA", "conductor_A": "overhead_line_conductor_comm", "conductor_N": "overhead_line_conductor_comm", "spacing": "line_spacing_commABC"} last_object_key += 1 glmCaseDict[last_object_key] = {"object": "line_configuration", "name": "line_configuration_commB", "conductor_B": "overhead_line_conductor_comm", "conductor_N": "overhead_line_conductor_comm", "spacing": "line_spacing_commABC"} last_object_key += 1 glmCaseDict[last_object_key] = {"object": "line_configuration", "name": "line_configuration_commC", "conductor_C": "overhead_line_conductor_comm", "conductor_N": "overhead_line_conductor_comm", "spacing": "line_spacing_commABC"} last_object_key += 1 # initializations for the commercial "house" list # print('iterating over commercial_dict') for iii in commercial_dict: total_comm_houses = commercial_dict[iii]['number_of_houses'][0] + commercial_dict[iii]['number_of_houses'][1] + commercial_dict[iii]['number_of_houses'][2] my_phases = 'ABC' # read through the phases and do some bit-wise math has_phase_A = 0 has_phase_B = 0 has_phase_C = 0 ph = '' if "A" in commercial_dict[iii]['phases']: has_phase_A = 1 ph += 'A' if "B" in commercial_dict[iii]['phases']: has_phase_B = 1 ph += 'B' if "C" in commercial_dict[iii]['phases']: has_phase_C = 1 ph += 'C' no_of_phases = has_phase_A + has_phase_B + has_phase_C if no_of_phases == 0: raise Exception('The phases in commercial buildings did not add up right.') # name of original load object if commercial_dict[iii]['parent'] != 'None': my_name = commercial_dict[iii]['parent'] #+ '_' + commercial_dict[iii]['name'] my_parent = commercial_dict[iii]['parent'] else: my_name = commercial_dict[iii]['name'] my_parent = commercial_dict[iii]['name'] nom_volt = int(float(commercial_dict[iii]['nom_volt'])) # Same for everyone # air_heat_fraction = 0 # mass_solar_gain_fraction = 0.5 # mass_internal_gain_fraction = 0.5 fan_type = 'ONE_SPEED' heat_type = 'GAS' cool_type = 'ELECTRIC' aux_type = 'NONE' # cooling_design_temperature = 100 # heating_design_temperature = 1 # over_sizing_factor = 0.3 no_of_stories = 1 surface_heat_trans_coeff = 0.59 # Office building - must have all three phases and enough load for 15 zones # *or* load is classified to be office buildings if total_comm_houses > 15 and no_of_phases == 3: no_of_offices = int(round(total_comm_houses / 15)) glmCaseDict[last_object_key] = {"object": "transformer_configuration", "name": "CTTF_config_A_{:s}".format(my_name), "connect_type": "SINGLE_PHASE_CENTER_TAPPED", "install_type": "POLETOP", "impedance": "0.00033+0.0022j", "shunt_impedance": "10000+10000j", "primary_voltage": "{:.3f}".format(nom_volt), "secondary_voltage": "120", "powerA_rating": "100 kVA"} last_object_key += 1 glmCaseDict[last_object_key] = {"object": "transformer_configuration", "name": "CTTF_config_B_{:s}".format(my_name), "connect_type": "SINGLE_PHASE_CENTER_TAPPED", "install_type": "POLETOP", "impedance": "0.00033+0.0022j", "shunt_impedance": "10000+10000j", "primary_voltage": "{:.3f}".format(nom_volt), "secondary_voltage": "120", "powerB_rating": "100 kVA"} last_object_key += 1 glmCaseDict[last_object_key] = {"object": "transformer_configuration", "name": "CTTF_config_C_{:s}".format(my_name), "connect_type": "SINGLE_PHASE_CENTER_TAPPED", "install_type": "POLETOP", "impedance": "0.00033+0.0022j", "shunt_impedance": "10000+10000j", "primary_voltage": "{:.3f}".format(nom_volt), "secondary_voltage": "120", "powerC_rating": "100 kVA"} last_object_key += 1 # print('iterating over number of offices') for jjj in range(no_of_offices): floor_area_choose = 40000. * (0.5 * random.random() + 0.5) # up to -50# #config_data.floor_area ceiling_height = 13. airchange_per_hour = 0.69 Rroof = 19. Rwall = 18.3 Rfloor = 46. Rdoors = 3. glazing_layers = 'TWO' glass_type = 'GLASS' glazing_treatment = 'LOW_S' window_frame = 'NONE' int_gains = 3.24 # W/sf glmCaseDict[last_object_key] = {"object": "overhead_line", "from": "{:s}".format(my_parent), "to": "{:s}_office_meter{:.0f}".format(my_name, jjj), "phases": "{:s}".format(commercial_dict[iii]['phases']), "length": "50ft", "configuration": "line_configuration_comm{:s}".format(ph)} last_object_key += 1 glmCaseDict[last_object_key] = {"object": "meter", "phases": "{:s}".format(commercial_dict[iii]['phases']), "name": "{:s}_office_meter{:.0f}".format(my_name, jjj), "groupid": "Commercial_Meter", "nominal_voltage": "{:f}".format(nom_volt)} last_object_key += 1 # for phind = 1:3 #for each of three floors (5 zones each) # for phind = 1:no_of_phases #jlh for phind in range(1,4): glmCaseDict[last_object_key] = {"object": "transformer", "name": "{:s}_CTTF_{:s}_{:.0f}".format(my_name, ph[phind-1], jjj), "phases": "{:s}S".format(ph[phind-1]), "from": "{:s}_office_meter{:.0f}".format(my_name, jjj), "to": "{:s}_tm_{:s}_{:.0f}".format(my_name, ph[phind-1], jjj), "groupid": "Distribution_Trans", "configuration": "CTTF_config_{:s}_{:s}".format(ph[phind-1], my_name)} last_object_key += 1 glmCaseDict[last_object_key] = {"object": "triplex_meter", "name": "{:s}_tm_{:s}_{:.0f}".format(my_name, ph[phind-1], jjj), "phases": "{:s}S".format(ph[phind-1]), "nominal_voltage": "120"} last_object_key += 1 # skew each office zone identically per floor sk = round(2 * random.normalvariate(0, 1)) skew_value = config_data["commercial_skew_std"] * sk if skew_value < -config_data["commercial_skew_max"]: skew_value = -config_data["commercial_skew_max"] elif skew_value > config_data["commercial_skew_max"]: skew_value = config_data["commercial_skew_max"] for zoneind in range(1, 6): total_depth = math.sqrt(floor_area_choose / (3. * 1.5)) total_width = 1.5 * total_depth if phind < 3: exterior_ceiling_fraction = 0 else: exterior_ceiling_fraction = 1 if zoneind == 5: exterior_wall_fraction = 0 w = total_depth - 30. d = total_width - 30. floor_area = w * d aspect_ratio = w / d else: window_wall_ratio = 0.33 if zoneind == 1 or zoneind == 3: w = total_width - 15. d = 15. floor_area = w * d exterior_wall_fraction = w / (2. * (w + d)) aspect_ratio = w / d else: w = total_depth - 15. d = 15. floor_area = w * d exterior_wall_fraction = w / (2. * (w + d)) aspect_ratio = w / d if phind > 1: exterior_floor_fraction = 0 else: exterior_floor_fraction = w / (2. * (w + d)) / (floor_area / (floor_area_choose / 3.)) thermal_mass_per_floor_area = 3.9 * (0.5 + 1. * random.random()) # +/- 50# interior_exterior_wall_ratio = (floor_area * (2. - 1.) + 0. * 20.) / (no_of_stories * ceiling_height * 2. * (w + d)) - 1. + window_wall_ratio * exterior_wall_fraction no_of_doors = 0.1 # will round to zero init_temp = 68. + 4. * random.random() os_rand = config_data["over_sizing_factor"] * (0.8 + 0.4 * random.random()) COP_A = config_data["cooling_COP"] * (0.8 + 0.4 * random.random()) glmCaseDict[last_object_key] = {"object": "house", "name": "office{:s}_{:s}{:.0f}_zone{:.0f}".format(my_name, my_phases[phind-1], jjj, zoneind), "parent": "{:s}_tm_{:s}_{:.0f}".format(my_name, my_phases[phind-1], jjj), "groupid": "Commercial", "motor_model" : "BASIC", "schedule_skew": "{:.0f}".format(skew_value), "floor_area": "{:.0f}".format(floor_area), "design_internal_gains": "{:.0f}".format(int_gains * floor_area * 3.413), "number_of_doors": "{:.0f}".format(no_of_doors), "aspect_ratio": "{:.2f}".format(aspect_ratio), "total_thermal_mass_per_floor_area": "{:1.2f}".format(thermal_mass_per_floor_area), "interior_surface_heat_transfer_coeff": "{:1.2f}".format(surface_heat_trans_coeff), "interior_exterior_wall_ratio": "{:2.1f}".format(interior_exterior_wall_ratio), "exterior_floor_fraction": "{:.3f}".format(exterior_floor_fraction), "exterior_ceiling_fraction": "{:.3f}".format(exterior_ceiling_fraction), "Rwall": "{:2.1f}".format(Rwall), "Rroof": "{:2.1f}".format(Rroof), "Rfloor": "{:.2f}".format(Rfloor), "Rdoors": "{:2.1f}".format(Rdoors), "exterior_wall_fraction": "{:.2f}".format(exterior_wall_fraction), "glazing_layers": "{:s}".format(glazing_layers), "glass_type": "{:s}".format(glass_type), "glazing_treatment": "{:s}".format(glazing_treatment), "window_frame": "{:s}".format(window_frame), "airchange_per_hour": "{:.2f}".format(airchange_per_hour), "window_wall_ratio": "{:0.3f}".format(window_wall_ratio), "heating_system_type": "{:s}".format(heat_type), "auxiliary_system_type": "{:s}".format(aux_type), "fan_type": "{:s}".format(fan_type), "cooling_system_type": "{:s}".format(cool_type), "air_temperature": "{:.2f}".format(init_temp), "mass_temperature": "{:.2f}".format(init_temp), "over_sizing_factor": "{:.1f}".format(os_rand), "cooling_COP": "{:2.2f}".format(COP_A), "cooling_setpoint" : "office_cooling", "heating_setpoint" : "office_heating"} parent_house = glmCaseDict[last_object_key] # if we do not use schedules we will assume the initial temp is the setpoint if use_flags['use_schedules'] == 0: del glmCaseDict[last_object_key]['cooling_setpoint'] del glmCaseDict[last_object_key]['heating_setpoint'] last_object_key += 1 # Need all of the "appliances" # Lights adj_lights = (0.9 + 0.1 * random.random()) * floor_area / 1000. # randomize 10# then convert W/sf -> kW glmCaseDict[last_object_key] = {"object": "ZIPload", "name": "lights_{:s}_{:s}_{:.0f}_zone{:.0f}".format(my_name, my_phases[phind-1], jjj, zoneind), "parent": parent_house["name"], "groupid": "Lights", # "groupid": "Commercial_zip", "schedule_skew": "{:.0f}".format(skew_value), "heatgain_fraction": "1.0", "power_fraction": "{:.2f}".format(config_data["c_pfrac"]), "impedance_fraction": "{:.2f}".format(config_data["c_zfrac"]), "current_fraction": "{:.2f}".format(config_data["c_ifrac"]), "power_pf": "{:.2f}".format(config_data["c_p_pf"]), "current_pf": "{:.2f}".format(config_data["c_i_pf"]), "impedance_pf": "{:.2f}".format(config_data["c_z_pf"]), "base_power": "office_lights*{:.2f}".format(adj_lights)} # if we do not use schedules we will assume adj_lights is the fixed value if use_flags['use_schedules'] == 0: glmCaseDict[last_object_key]['base_power'] = "{:.2f}".format(adj_lights) last_object_key += 1 # Plugs adj_plugs = (0.9 + 0.2 * random.random()) * floor_area / 1000. # randomize 20# then convert W/sf -> kW glmCaseDict[last_object_key] = {"object": "ZIPload", "name": "plugs_{:s}_{:s}_{:.0f}_zone{:.0f}".format(my_name, my_phases[phind-1], jjj, zoneind), "parent": parent_house["name"], "groupid": "Plugs", # "groupid": "Commercial_zip", "schedule_skew": "{:.0f}".format(skew_value), "heatgain_fraction": "1.0", "power_fraction": "{:.2f}".format(config_data["c_pfrac"]), "impedance_fraction": "{:.2f}".format(config_data["c_zfrac"]), "current_fraction": "{:.2f}".format(config_data["c_ifrac"]), "power_pf": "{:.2f}".format(config_data["c_p_pf"]), "current_pf": "{:.2f}".format(config_data["c_i_pf"]), "impedance_pf": "{:.2f}".format(config_data["c_z_pf"]), "base_power": "office_plugs*{:.2f}".format(adj_plugs)} # if we do not use schedules we will assume adj_plugs is the fixed value if use_flags['use_schedules'] == 0: glmCaseDict[last_object_key]['base_power'] = "{:.2f}".format(adj_plugs) last_object_key += 1 # Gas Waterheater adj_gas = (0.9 + 0.2 * random.random()) * floor_area / 1000. # randomize 20# then convert W/sf -> kW glmCaseDict[last_object_key] = {"object": "ZIPload", "name": "wh_{:s}_{:s}_{:.0f}_zone{:.0f}".format(my_name, my_phases[phind-1], jjj, zoneind), "parent": parent_house["name"], "groupid": "Gas_waterheater", # "groupid": "Commercial_zip", "schedule_skew": "{:.0f}".format(skew_value), "heatgain_fraction": "1.0", "power_fraction": "0.0", "impedance_fraction": "0.0", "current_fraction": "0.0", "power_pf": "1.0", "base_power": "office_gas*{:.2f}".format(adj_gas)} # if we do not use schedules we will assume adj_gas is the fixed value if use_flags['use_schedules'] == 0: glmCaseDict[last_object_key]['base_power'] = "{:.2f}".format(adj_gas) last_object_key += 1 # Exterior Lighting adj_ext = (0.9 + 0.1 * random.random()) * floor_area / 1000. # randomize 10# then convert W/sf -> kW glmCaseDict[last_object_key] = {"object": "ZIPload", "name": "ext_{:s}_{:s}_{:.0f}_zone{:.0f}".format(my_name, my_phases[phind-1], jjj, zoneind), "parent": parent_house["name"], "groupid": "Exterior_lighting", # "groupid": "Commercial_zip", "schedule_skew": "{:.0f}".format(skew_value), "heatgain_fraction": "0.0", "power_fraction": "{:.2f}".format(config_data["c_pfrac"]), "impedance_fraction": "{:.2f}".format(config_data["c_zfrac"]), "current_fraction": "{:.2f}".format(config_data["c_ifrac"]), "power_pf": "{:.2f}".format(config_data["c_p_pf"]), "current_pf": "{:.2f}".format(config_data["c_i_pf"]), "impedance_pf": "{:.2f}".format(config_data["c_z_pf"]), "base_power": "office_exterior*{:.2f}".format(adj_ext)} # if we do not use schedules we will assume adj_ext is the fixed value if use_flags['use_schedules'] == 0: glmCaseDict[last_object_key]['base_power'] = "{:.2f}".format(adj_ext) last_object_key += 1 # Occupancy adj_occ = (0.9 + 0.1 * random.random()) * floor_area / 1000. # randomize 10# then convert W/sf -> kW glmCaseDict[last_object_key] = {"object": "ZIPload", "name": "occ_{:s}_{:s}_{:.0f}_zone{:.0f}".format(my_name, my_phases[phind-1], jjj, zoneind), "parent": parent_house["name"], "groupid": "Occupancy", # "groupid": "Commercial_zip", "schedule_skew": "{:.0f}".format(skew_value), "heatgain_fraction": "1.0", "power_fraction": "0.0", "impedance_fraction": "0.0", "current_fraction": "0.0", "power_pf": "1.0", "base_power": "office_occupancy*{:.2f}".format(adj_occ)} # if we do not use schedules we will assume adj_occ is the fixed value if use_flags['use_schedules'] == 0: glmCaseDict[last_object_key]['base_power'] = "{:.2f}".format(adj_occ) last_object_key += 1 # end of house object # end # office zones (1-5) # end #office floors (1-3) # end # total offices needed # print('finished iterating over number of offices') # Big box - has at least 2 phases and enough load for 6 zones # *or* load is classified to be big boxes elif total_comm_houses > 6 and no_of_phases >= 2: no_of_bigboxes = int(round(total_comm_houses / 6.)) if has_phase_A == 1: glmCaseDict[last_object_key] = {"object": "transformer_configuration", "name": "CTTF_config_A_{:s}".format(my_name), "connect_type": "SINGLE_PHASE_CENTER_TAPPED", "install_type": "POLETOP", "impedance": "0.00033+0.0022j", "shunt_impedance": "10000+10000j", "primary_voltage": "{:.3f}".format(nom_volt), "secondary_voltage": "120", "powerA_rating": "100 kVA"} last_object_key += 1 if has_phase_B == 1: glmCaseDict[last_object_key] = {"object": "transformer_configuration", "name": "CTTF_config_B_{:s}".format(my_name), "connect_type": "SINGLE_PHASE_CENTER_TAPPED", "install_type": "POLETOP", "impedance": "0.00033+0.0022j", "shunt_impedance": "10000+10000j", "primary_voltage": "{:.3f}".format(nom_volt), "secondary_voltage": "120", "powerB_rating": "100 kVA"} last_object_key += 1 if has_phase_C == 1: glmCaseDict[last_object_key] = {"object": "transformer_configuration", "name": "CTTF_config_C_{:s}".format(my_name), "connect_type": "SINGLE_PHASE_CENTER_TAPPED", "install_type": "POLETOP", "impedance": "0.00033+0.0022j", "shunt_impedance": "10000+10000j", "primary_voltage": "{:.3f}".format(nom_volt), "secondary_voltage": "120", "powerC_rating": "100 kVA"} last_object_key += 1 # print('iterating over number of big boxes') for jjj in range(no_of_bigboxes): floor_area_choose = 20000. * (0.5 + 1. * random.random()) # +/- 50# ceiling_height = 14. airchange_per_hour = 1.5 Rroof = 19. Rwall = 18.3 Rfloor = 46. Rdoors = 3. glazing_layers = 'TWO' glass_type = 'GLASS' glazing_treatment = 'LOW_S' window_frame = 'NONE' int_gains = 3.6 # W/sf glmCaseDict[last_object_key] = {"object": "overhead_line", "from": "{:s}".format(my_parent), "to": "{:s}_bigbox_meter{:.0f}".format(my_name, jjj), "phases": "{:s}".format(commercial_dict[iii]["phases"]), "length": "50ft", "configuration": "line_configuration_comm{:s}".format(ph)} last_object_key += 1 glmCaseDict[last_object_key] = {"object": "meter", "phases": "{:s}".format(commercial_dict[iii]["phases"]), "name": "{:s}_bigbox_meter{:.0f}".format(my_name, jjj), "groupid": "Commercial_Meter", "nominal_voltage": "{:f}".format(nom_volt)} last_object_key += 1 # skew each big box zone identically sk = round(2 * random.normalvariate(0, 1)) skew_value = config_data["commercial_skew_std"] * sk if skew_value < -config_data["commercial_skew_max"]: skew_value = -config_data["commercial_skew_max"] elif skew_value > config_data["commercial_skew_max"]: skew_value = config_data["commercial_skew_max"] total_index = 0 for phind in range(no_of_phases): glmCaseDict[last_object_key] = {"object": "transformer", "name": "{:s}_CTTF_{:s}_{:.0f}".format(my_name, ph[phind], jjj), "phases": "{:s}S".format(ph[phind]), "from": "{:s}_bigbox_meter{:.0f}".format(my_name, jjj), "to": "{:s}_tm_{:s}_{:.0f}".format(my_name, ph[phind], jjj), "groupid": "Distribution_Trans", "configuration": "CTTF_config_{:s}_{:s}".format(ph[phind], my_name)} last_object_key += 1 glmCaseDict[last_object_key] = {"object": "triplex_meter", "name": "{:s}_tm_{:s}_{:.0f}".format(my_name, ph[phind], jjj), "phases": "{:s}S".format(ph[phind]), "nominal_voltage": "120"} last_object_key += 1 zones_per_phase = int(6. / no_of_phases) for zoneind in range(1,zones_per_phase+1): total_index += 1 thermal_mass_per_floor_area = 3.9 * (0.8 + 0.4 * random.random()) # +/- 20# floor_area = floor_area_choose / 6. exterior_ceiling_fraction = 1. aspect_ratio = 1.28301275561855 total_depth = math.sqrt(floor_area_choose / aspect_ratio) total_width = aspect_ratio * total_depth d = total_width / 3. w = total_depth / 2. if total_index == 2 or total_index == 5: exterior_wall_fraction = d / (2. * (d + w)) exterior_floor_fraction = (0. + d) / (2. * (total_width + total_depth)) / (floor_area / floor_area_choose) else: exterior_wall_fraction = 0.5 exterior_floor_fraction = (w + d) / (2. * (total_width + total_depth)) / (floor_area / floor_area_choose) if total_index == 2: window_wall_ratio = 0.76 else: window_wall_ratio = 0. if total_index < 4: no_of_doors = 0.1 # this will round to 0 elif total_index == 4 or total_index == 6: no_of_doors = 1. else: no_of_doors = 24. interior_exterior_wall_ratio = (floor_area * (2. - 1.) + no_of_doors * 20.) / (no_of_stories * ceiling_height * 2. * (w + d)) - 1. + window_wall_ratio * exterior_wall_fraction if total_index > 6: raise Exception('Something wrong in the indexing of the retail strip.') init_temp = 68. + 4. * random.random() os_rand = config_data["over_sizing_factor"] * (0.8 + 0.4 * random.random()) COP_A = config_data["cooling_COP"] * (0.8 + 0.4 * random.random()) glmCaseDict[last_object_key] = {"object": "house", "name": "bigbox{:s}_{:s}{:.0f}_zone{:.0f}".format(my_name, ph[phind], jjj, zoneind), "groupid": "Commercial", "motor_model": "BASIC", "schedule_skew": "{:.0f}".format(skew_value), "parent": "{:s}_tm_{:s}_{:.0f}".format(my_name, ph[phind],jjj), "floor_area": "{:.0f}".format(floor_area), "design_internal_gains": "{:.0f}".format(int_gains * floor_area * 3.413), "number_of_doors": "{:.0f}".format(no_of_doors), "aspect_ratio": "{:.2f}".format(aspect_ratio), "total_thermal_mass_per_floor_area": "{:1.2f}".format(thermal_mass_per_floor_area), "interior_surface_heat_transfer_coeff": "{:1.2f}".format(surface_heat_trans_coeff), "interior_exterior_wall_ratio": "{:2.1f}".format(interior_exterior_wall_ratio), "exterior_floor_fraction": "{:.3f}".format(exterior_floor_fraction), "exterior_ceiling_fraction": "{:.3f}".format(exterior_ceiling_fraction), "Rwall": "{:2.1f}".format(Rwall), "Rroof": "{:2.1f}".format(Rroof), "Rfloor": "{:.2f}".format(Rfloor), "Rdoors": "{:2.1f}".format(Rdoors), "exterior_wall_fraction": "{:.2f}".format(exterior_wall_fraction), "glazing_layers": "{:s}".format(glazing_layers), "glass_type": "{:s}".format(glass_type), "glazing_treatment": "{:s}".format(glazing_treatment), "window_frame": "{:s}".format(window_frame), "airchange_per_hour": "{:.2f}".format(airchange_per_hour), "window_wall_ratio": "{:0.3f}".format(window_wall_ratio), "heating_system_type": "{:s}".format(heat_type), "auxiliary_system_type": "{:s}".format(aux_type), "fan_type": "{:s}".format(fan_type), "cooling_system_type": "{:s}".format(cool_type), "air_temperature": "{:.2f}".format(init_temp), "mass_temperature": "{:.2f}".format(init_temp), "over_sizing_factor": "{:.1f}".format(os_rand), "cooling_COP": "{:2.2f}".format(COP_A), "cooling_setpoint": "bigbox_cooling", "heating_setpoint": "bigbox_heating"} parent_house = glmCaseDict[last_object_key] # cache this for a second... # if we do not use schedules we will assume the initial temp is the setpoint if use_flags['use_schedules'] == 0: del glmCaseDict[last_object_key]['cooling_setpoint'] del glmCaseDict[last_object_key]['heating_setpoint'] last_object_key += 1 # Need all of the "appliances" # Lights adj_lights = 1.2 * (0.9 + 0.1 * random.random()) * floor_area / 1000. # randomize 10# then convert W/sf -> kW glmCaseDict[last_object_key] = {"object": "ZIPload", "name": "lights_{:s}_{:s}_{:.0f}_zone{:.0f}".format(my_name, ph[phind], jjj, zoneind), "parent": parent_house["name"], "groupid": "Lights", # "groupid": "Commercial_zip", "schedule_skew": "{:.0f}".format(skew_value), "heatgain_fraction": "1.0", "power_fraction": "{:.2f}".format(config_data["c_pfrac"]), "impedance_fraction": "{:.2f}".format(config_data["c_zfrac"]), "current_fraction": "{:.2f}".format(config_data["c_ifrac"]), "power_pf": "{:.2f}".format(config_data["c_p_pf"]), "current_pf": "{:.2f}".format(config_data["c_i_pf"]), "impedance_pf": "{:.2f}".format(config_data["c_z_pf"]), "base_power": "bigbox_lights*{:.2f}".format(adj_lights)} # if we do not use schedules we will assume adj_lights is the fixed value if use_flags['use_schedules'] == 0: glmCaseDict[last_object_key]['base_power'] = "{:.2f}".format(adj_lights) last_object_key += 1 # Plugs adj_plugs = (0.9 + 0.2 * random.random()) * floor_area / 1000. # randomize 20# then convert W/sf -> kW glmCaseDict[last_object_key] = {"object": "ZIPload", "name": "plugs_{:s}_{:s}_{:.0f}_zone{:.0f}".format(my_name, ph[phind], jjj, zoneind), "parent": parent_house["name"], "groupid": "Plugs", # "groupid": "Commercial_zip", "schedule_skew": "{:.0f}".format(skew_value), "heatgain_fraction": "1.0", "power_fraction": "{:.2f}".format(config_data["c_pfrac"]), "impedance_fraction": "{:.2f}".format(config_data["c_zfrac"]), "current_fraction": "{:.2f}".format(config_data["c_ifrac"]), "power_pf": "{:.2f}".format(config_data["c_p_pf"]), "current_pf": "{:.2f}".format(config_data["c_i_pf"]), "impedance_pf": "{:.2f}".format(config_data["c_z_pf"]), "base_power": "bigbox_plugs*{:.2f}".format(adj_plugs)} # if we do not use schedules we will assume adj_plugs is the fixed value if use_flags['use_schedules'] == 0: glmCaseDict[last_object_key]['base_power'] = "{:.2f}".format(adj_plugs) last_object_key += 1 # Gas Waterheater adj_gas = (0.9 + 0.2 * random.random()) * floor_area / 1000. # randomize 20# then convert W/sf -> kW glmCaseDict[last_object_key] = {"object": "ZIPload", "name": "wh_{:s}_{:s}_{:.0f}_zone{:.0f}".format(my_name, ph[phind], jjj, zoneind), "parent": parent_house["name"], "groupid": "Gas_waterheater", # "groupid": "Commercial_zip", "schedule_skew": "{:.0f}".format(skew_value), "heatgain_fraction": "1.0", "power_fraction": "0.0", "impedance_fraction": "0.0", "current_fraction": "0.0", "power_pf": "1.0", "base_power": "bigbox_gas*{:.2f}".format(adj_gas)} # if we do not use schedules we will assume adj_gas is the fixed value if use_flags['use_schedules'] == 0: glmCaseDict[last_object_key]['base_power'] = "{:.2f}".format(adj_gas) last_object_key += 1 # Exterior Lighting adj_ext = (0.9 + 0.1 * random.random()) * floor_area / 1000. # randomize 10# then convert W/sf -> kW glmCaseDict[last_object_key] = {"object": "ZIPload", "name": "ext_{:s}_{:s}_{:.0f}_zone{:.0f}".format(my_name, ph[phind], jjj, zoneind), "parent": parent_house["name"], "groupid": "Exterior_lighting", # "groupid": "Commercial_zip", "schedule_skew": "{:.0f}".format(skew_value), "heatgain_fraction": "0.0", "power_fraction": "{:.2f}".format(config_data["c_pfrac"]), "impedance_fraction": "{:.2f}".format(config_data["c_zfrac"]), "current_fraction": "{:.2f}".format(config_data["c_ifrac"]), "power_pf": "{:.2f}".format(config_data["c_p_pf"]), "current_pf": "{:.2f}".format(config_data["c_i_pf"]), "impedance_pf": "{:.2f}".format(config_data["c_z_pf"]), "base_power": "bigbox_exterior*{:.2f}".format(adj_ext)} # if we do not use schedules we will assume adj_ext is the fixed value if use_flags['use_schedules'] == 0: glmCaseDict[last_object_key]['base_power'] = "{:.2f}".format(adj_ext) last_object_key += 1 # Occupancy adj_occ = (0.9 + 0.1 * random.random()) * floor_area / 1000. # randomize 10# then convert W/sf -> kW glmCaseDict[last_object_key] = {"object": "ZIPload", "name": "occ_{:s}_{:s}_{:.0f}_zone{:.0f}".format(my_name, ph[phind], jjj, zoneind), "parent": parent_house["name"], "groupid": "Occupancy", # "groupid": "Commercial_zip", "schedule_skew": "{:.0f}".format(skew_value), "heatgain_fraction": "1.0", "power_fraction": "0.0", "impedance_fraction": "0.0", "current_fraction": "0.0", "power_pf": "1.0", "base_power": "bigbox_occupancy*{:.2f}".format(adj_occ)} # if we do not use schedules we will assume adj_occ is the fixed value if use_flags['use_schedules'] == 0: glmCaseDict[last_object_key]['base_power'] = "{:.2f}".format(adj_occ) last_object_key += 1 # end #zone index # end #phase index # end #number of big boxes # print('finished iterating over number of big boxes') # Strip mall elif total_comm_houses > 0: # unlike for big boxes and offices, if total house number = 0, just don't populate anything. no_of_strip = total_comm_houses strip_per_phase = int(math.ceil(no_of_strip / no_of_phases)) if has_phase_A == 1: glmCaseDict[last_object_key] = {"object": "transformer_configuration", "name": "CTTF_config_A_{:s}".format(my_name), "connect_type": "SINGLE_PHASE_CENTER_TAPPED", "install_type": "POLETOP", "impedance": "0.00033+0.0022j", "shunt_impedance": "100000+100000j", "primary_voltage": "{:.3f}".format(nom_volt), "secondary_voltage": "120", "powerA_rating": "{:.0f} kVA".format(100. * strip_per_phase)} last_object_key += 1 if has_phase_B == 1: glmCaseDict[last_object_key] = {"object": "transformer_configuration", "name": "CTTF_config_B_{:s}".format(my_name), "connect_type": "SINGLE_PHASE_CENTER_TAPPED", "install_type": "POLETOP", "impedance": "0.00033+0.0022j", "shunt_impedance": "100000+100000j", "primary_voltage": "{:.3f}".format(nom_volt), "secondary_voltage": "120", "powerB_rating": "{:.0f} kVA".format(100. * strip_per_phase)} last_object_key += 1 if has_phase_C == 1: glmCaseDict[last_object_key] = {"object": "transformer_configuration", "name": "CTTF_config_C_{:s}".format(my_name), "connect_type": "SINGLE_PHASE_CENTER_TAPPED", "install_type": "POLETOP", "impedance": "0.00033+0.0022j", "shunt_impedance": "100000+100000j", "primary_voltage": "{:.3f}".format(nom_volt), "secondary_voltage": "120", "powerC_rating": "{:.0f} kVA".format(100. * strip_per_phase)} last_object_key += 1 glmCaseDict[last_object_key] = {"object": "overhead_line", "from": "{:s}".format(my_parent), "to": "{:s}_strip_node".format(my_name), "phases": "{:s}".format(commercial_dict[iii]["phases"]), "length": "50ft", "configuration": "line_configuration_comm{:s}".format(ph)} last_object_key += 1 glmCaseDict[last_object_key] = {"object": "node", "phases": "{:s}".format(commercial_dict[iii]["phases"]), "name": "{:s}_strip_node".format(my_name), "nominal_voltage": "{:f}".format(nom_volt)} last_object_key += 1 # print('iterating over number of stripmalls') for phind in range(no_of_phases): floor_area_choose = 2400. * (0.7 + 0.6 * random.random()) # +/- 30# # ceiling_height = 12 airchange_per_hour = 1.76 Rroof = 19. Rwall = 18.3 Rfloor = 40. Rdoors = 3. glazing_layers = 'TWO' glass_type = 'GLASS' glazing_treatment = 'LOW_S' window_frame = 'NONE' int_gains = 3.6 # W/sf thermal_mass_per_floor_area = 3.9 * (0.5 + 1. * random.random()) # +/- 50# exterior_ceiling_fraction = 1. for jjj in range(1, strip_per_phase+1): # skew each office zone identically per floor sk = round(2 * random.normalvariate(0, 1)) skew_value = config_data["commercial_skew_std"] * sk if skew_value < -config_data["commercial_skew_max"]: skew_value = -config_data["commercial_skew_max"] elif skew_value > config_data["commercial_skew_max"]: skew_value = config_data["commercial_skew_max"] if jjj == 1 or jjj == (math.floor(strip_per_phase / 2.) + 1.): floor_area = floor_area_choose aspect_ratio = 1.5 window_wall_ratio = 0.05 # if (j == jjj): # exterior_wall_fraction = 0.7; # exterior_floor_fraction = 1.4; # else: exterior_wall_fraction = 0.4 exterior_floor_fraction = 0.8 interior_exterior_wall_ratio = -0.05 else: floor_area = floor_area_choose / 2. aspect_ratio = 3.0 window_wall_ratio = 0.03 if jjj == strip_per_phase: exterior_wall_fraction = 0.63 exterior_floor_fraction = 2. else: exterior_wall_fraction = 0.25 exterior_floor_fraction = 0.8 interior_exterior_wall_ratio = -0.40 no_of_doors = 1 glmCaseDict[last_object_key] = {"object": "transformer", "name": "{:s}_CTTF_{:s}_{:.0f}".format(my_name, ph[phind], jjj), "phases": "{:s}S".format(ph[phind]), "from": "{:s}_strip_node".format(my_name), "to": "{:s}_tn_{:s}_{:.0f}".format(my_name, ph[phind], jjj), "groupid": "Distribution_Trans'", "configuration": "CTTF_config_{:s}_{:s}".format(ph[phind], my_name)} last_object_key += 1 glmCaseDict[last_object_key] = {"object": "triplex_node", "name": "{:s}_tn_{:s}_{:.0f}".format(my_name, ph[phind], jjj), "phases": "{:s}S".format(ph[phind]), "nominal_voltage": "120"} last_object_key += 1 glmCaseDict[last_object_key] = {"object": "triplex_meter", "parent": "{:s}_tn_{:s}_{:.0f}".format(my_name, ph[phind], jjj), "name": "{:s}_tm_{:s}_{:.0f}".format(my_name, ph[phind], jjj), "phases": "{:s}S".format(ph[phind]), "groupid": "Commercial_Meter", # was 'real(my_var), imag(my_var), but it's an int above "nominal_voltage": "120"} last_object_key += 1 init_temp = 68. + 4. * random.random() os_rand = config_data["over_sizing_factor"] * (0.8 + 0.4 * random.random()) COP_A = config_data["cooling_COP"] * (0.8 + 0.4 * random.random()) glmCaseDict[last_object_key] = {"object": "house", "name": "stripmall{:s}_{:s}{:.0f}".format(my_name, ph[phind], jjj), "groupid": "Commercial", "motor_model": "BASIC", "schedule_skew": "{:.0f}".format(skew_value), "parent": "{:s}_tm_{:s}_{:.0f}".format(my_name, ph[phind], jjj), "floor_area": "{:.0f}".format(floor_area), "design_internal_gains": "{:.0f}".format(int_gains * floor_area * 3.413), "number_of_doors": "{:.0f}".format(no_of_doors), "aspect_ratio": "{:.2f}".format(aspect_ratio), "total_thermal_mass_per_floor_area": "{:1.2f}".format(thermal_mass_per_floor_area), "interior_surface_heat_transfer_coeff": "{:1.2f}".format(surface_heat_trans_coeff), "interior_exterior_wall_ratio": "{:2.1f}".format(interior_exterior_wall_ratio), "exterior_floor_fraction": "{:.3f}".format(exterior_floor_fraction), "exterior_ceiling_fraction": "{:.3f}".format(exterior_ceiling_fraction), "Rwall": "{:2.1f}".format(Rwall), "Rroof": "{:2.1f}".format(Rroof), "Rfloor": "{:.2f}".format(Rfloor), "Rdoors": "{:2.1f}".format(Rdoors), "exterior_wall_fraction": "{:.2f}".format(exterior_wall_fraction), "glazing_layers": "{:s}".format(glazing_layers), "glass_type": "{:s}".format(glass_type), "glazing_treatment": "{:s}".format(glazing_treatment), "window_frame": "{:s}".format(window_frame), "airchange_per_hour": "{:.2f}".format(airchange_per_hour), "window_wall_ratio": "{:0.3f}".format(window_wall_ratio), "heating_system_type": "{:s}".format(heat_type), "auxiliary_system_type": "{:s}".format(aux_type), "fan_type": "{:s}".format(fan_type), "cooling_system_type": "{:s}".format(cool_type), "air_temperature": "{:.2f}".format(init_temp), "mass_temperature": "{:.2f}".format(init_temp), "over_sizing_factor": "{:.1f}".format(os_rand), "cooling_COP": "{:2.2f}".format(COP_A), "cooling_setpoint": "stripmall_cooling", "heating_setpoint": "stripmall_heating"} parent_house = glmCaseDict[last_object_key] # if we do not use schedules we will assume the initial temp is the setpoint if use_flags['use_schedules'] == 0: del glmCaseDict[last_object_key]['cooling_setpoint'] del glmCaseDict[last_object_key]['heating_setpoint'] last_object_key += 1 # Need all of the "appliances" # Lights adj_lights = (0.8 + 0.4 * random.random()) * floor_area / 1000. # randomize 10# then convert W/sf -> kW glmCaseDict[last_object_key] = {"object": "ZIPload", "name": "lights_{:s}_{:s}_{:.0f}".format(my_name, ph[phind], jjj), "parent": parent_house["name"], "groupid": "Lights", # "groupid": "Commercial_zip", "schedule_skew": "{:.0f}".format(skew_value), "heatgain_fraction": "1.0", "power_fraction": "{:.2f}".format(config_data["c_pfrac"]), "impedance_fraction": "{:.2f}".format(config_data["c_zfrac"]), "current_fraction": "{:.2f}".format(config_data["c_ifrac"]), "power_pf": "{:.2f}".format(config_data["c_p_pf"]), "current_pf": "{:.2f}".format(config_data["c_i_pf"]), "impedance_pf": "{:.2f}".format(config_data["c_z_pf"]), "base_power": "stripmall_lights*{:.2f}".format(adj_lights)} # if we do not use schedules we will assume adj_lights is the fixed value if use_flags['use_schedules'] == 0: glmCaseDict[last_object_key]['base_power'] = "{:.2f}".format(adj_lights) last_object_key += 1 # Plugs adj_plugs = (0.8 + 0.4 * random.random()) * floor_area / 1000. # randomize 20# then convert W/sf -> kW glmCaseDict[last_object_key] = {"object": "ZIPload", "name": "plugs_{:s}_{:s}_{:.0f}".format(my_name, ph[phind], jjj), "parent": parent_house["name"], "groupid": "Plugs", # "groupid": "Commercial_zip", "schedule_skew": "{:.0f}".format(skew_value), "heatgain_fraction": "1.0", "power_fraction": "{:.2f}".format(config_data["c_pfrac"]), "impedance_fraction": "{:.2f}".format(config_data["c_zfrac"]), "current_fraction": "{:.2f}".format(config_data["c_ifrac"]), "power_pf": "{:.2f}".format(config_data["c_p_pf"]), "current_pf": "{:.2f}".format(config_data["c_i_pf"]), "impedance_pf": "{:.2f}".format(config_data["c_z_pf"]), "base_power": "stripmall_plugs*{:.2f}".format(adj_plugs)} # if we do not use schedules we will assume adj_plugs is the fixed value if use_flags['use_schedules'] == 0: glmCaseDict[last_object_key]['base_power'] = "{:.2f}".format(adj_plugs) last_object_key += 1 # Gas Waterheater adj_gas = (0.8 + 0.4 * random.random()) * floor_area / 1000. # randomize 20# then convert W/sf -> kW glmCaseDict[last_object_key] = {"object": "ZIPload", "name": "wh_{:s}_{:s}_{:.0f}".format(my_name, ph[phind], jjj), "parent": parent_house["name"], "groupid": "Gas_waterheater", # "groupid": "Commercial_zip", "schedule_skew": "{:.0f}".format(skew_value), "heatgain_fraction": "1.0", "power_fraction": "0.0", "impedance_fraction": "0.0", "current_fraction": "0.0", "power_pf": "1.0", "base_power": "stripmall_gas*{:.2f}".format(adj_gas)} # if we do not use schedules we will assume adj_gas is the fixed value if use_flags['use_schedules'] == 0: glmCaseDict[last_object_key]['base_power'] = "{:.2f}".format(adj_gas) last_object_key += 1 # Exterior Lighting adj_ext = (0.8 + 0.4 * random.random()) * floor_area / 1000. # randomize 10# then convert W/sf -> kW glmCaseDict[last_object_key] = {"object": "ZIPload", "name": "ext_{:s}_{:s}_{:.0f}".format(my_name, ph[phind], jjj), "parent": parent_house["name"], "groupid": "Exterior_lighting", # "groupid": "Commercial_zip", "schedule_skew": "{:.0f}".format(skew_value), "heatgain_fraction": "0.0", "power_fraction": "{:.2f}".format(config_data["c_pfrac"]), "impedance_fraction": "{:.2f}".format(config_data["c_zfrac"]), "current_fraction": "{:.2f}".format(config_data["c_ifrac"]), "power_pf": "{:.2f}".format(config_data["c_p_pf"]), "current_pf": "{:.2f}".format(config_data["c_i_pf"]), "impedance_pf": "{:.2f}".format(config_data["c_z_pf"]), "base_power": "stripmall_exterior*{:.2f}".format(adj_ext)} # if we do not use schedules we will assume adj_ext is the fixed value if use_flags['use_schedules'] == 0: glmCaseDict[last_object_key]['base_power'] = "{:.2f}".format(adj_ext) last_object_key += 1 # Occupancy adj_occ = (0.8 + 0.4 * random.random()) * floor_area / 1000. # randomize 10# then convert W/sf -> kW glmCaseDict[last_object_key] = {"object": "ZIPload", "name": "occ_{:s}_{:s}_{:.0f}".format(my_name, ph[phind], jjj), "parent": parent_house["name"], "groupid": "Occupancy", # "groupid": "Commercial_zip", "schedule_skew": "{:.0f}".format(skew_value), "heatgain_fraction": "1.0", "power_fraction": "0.0", "impedance_fraction": "0.0", "current_fraction": "0.0", "power_pf": "1.0", "base_power": "stripmall_occupancy*{:.2f}".format(adj_occ)} # if we do not use schedules we will assume adj_occ is the fixed value if use_flags['use_schedules'] == 0: glmCaseDict[last_object_key]['base_power'] = "{:.2f}".format(adj_occ) last_object_key += 1 # end # end #number of strip zones # end #phase index # end #commercial selection # print('finished iterating over number of stripmalls') # add the "street light" loads # parent them to the METER as opposed to the node, so we don't # have any "grandchildren" elif total_comm_houses == 0 and sum(commercial_dict[iii]['load']) > 0: # print('writing street_light') glmCaseDict[last_object_key] = {"object": "load", "parent": "{:s}".format(my_parent), "name": "str_light_{:s}{:s}".format(ph, commercial_dict[iii]['name']), "nominal_voltage": "{:.2f}".format(nom_volt), "phases": "{:s}".format(ph) } if has_phase_A == 1 and commercial_dict[iii]['load'][0] > 0: if use_flags['use_schedules'] == 1: glmCaseDict[last_object_key]["base_power_A"] = "street_lighting*{:f}".format(config_data["light_scalar_comm"] * commercial_dict[iii]['load'][0]) else: glmCaseDict[last_object_key]["base_power_A"] = "{:f}".format(config_data["light_scalar_comm"] * commercial_dict[iii]['load'][0]) glmCaseDict[last_object_key]["power_pf_A"] = "{:f}".format(config_data["c_p_pf"]) glmCaseDict[last_object_key]["current_pf_A"] = "{:f}".format(config_data["c_i_pf"]) glmCaseDict[last_object_key]["impedance_pf_A"] = "{:f}".format(config_data["c_z_pf"]) glmCaseDict[last_object_key]["power_fraction_A"] = "{:f}".format(config_data["c_pfrac"]) glmCaseDict[last_object_key]["current_fraction_A"] = "{:f}".format(config_data["c_ifrac"]) glmCaseDict[last_object_key]["impedance_fraction_A"] = "{:f}".format(config_data["c_zfrac"]) if has_phase_B == 1 and commercial_dict[iii]['load'][1] > 0: if use_flags['use_schedules'] == 1: glmCaseDict[last_object_key]["base_power_B"] = "street_lighting*{:f}".format(config_data["light_scalar_comm"] * commercial_dict[iii]['load'][1]) else: glmCaseDict[last_object_key]["base_power_B"] = "{:f}".format(config_data["light_scalar_comm"] * commercial_dict[iii]['load'][1]) glmCaseDict[last_object_key]["power_pf_B"] = "{:f}".format(config_data["c_p_pf"]) glmCaseDict[last_object_key]["current_pf_B"] = "{:f}".format(config_data["c_i_pf"]) glmCaseDict[last_object_key]["impedance_pf_B"] = "{:f}".format(config_data["c_z_pf"]) glmCaseDict[last_object_key]["power_fraction_B"] = "{:f}".format(config_data["c_pfrac"]) glmCaseDict[last_object_key]["current_fraction_B"] = "{:f}".format(config_data["c_ifrac"]) glmCaseDict[last_object_key]["impedance_fraction_B"] = "{:f}".format(config_data["c_zfrac"]) if has_phase_C == 1 and commercial_dict[iii]['load'][2] > 0: if use_flags['use_schedules'] == 1: glmCaseDict[last_object_key]["base_power_C"] = "street_lighting*{:f}".format(config_data["light_scalar_comm"] * commercial_dict[iii]['load'][2]) else: glmCaseDict[last_object_key]["base_power_C"] = "{:f}".format(config_data["light_scalar_comm"] * commercial_dict[iii]['load'][2]) glmCaseDict[last_object_key]["power_pf_C"] = "{:f}".format(config_data["c_p_pf"]) glmCaseDict[last_object_key]["current_pf_C"] = "{:f}".format(config_data["c_i_pf"]) glmCaseDict[last_object_key]["impedance_pf_C"] = "{:f}".format(config_data["c_z_pf"]) glmCaseDict[last_object_key]["power_fraction_C"] = "{:f}".format(config_data["c_pfrac"]) glmCaseDict[last_object_key]["current_fraction_C"] = "{:f}".format(config_data["c_ifrac"]) glmCaseDict[last_object_key]["impedance_fraction_C"] = "{:f}".format(config_data["c_zfrac"]) last_object_key += 1 # end 'for each load' return glmCaseDict, last_object_key def add_normalized_commercial_ziploads(loadshape_dict, commercial_dict, config_data, last_key): """ This fucntion appends commercial zip loads to a feeder based on existing loads Inputs loadshape_dict - dictionary containing the full feeder commercial_dict - dictionary that contains information about commercial loads spots last_key - Last object key config_data - dictionary that contains the configurations of the feeder Outputs loadshape_dict - dictionary containing the full feeder last_key - Last object key """ for x in list(commercial_dict.keys()): load_name = commercial_dict[x]['name'] load_parent = commercial_dict[x].get('parent', 'None') phases = commercial_dict[x]['phases'] #nom_volt = commercial_dict[x]['nom_volt'] nom_volt = '120.0' bp_A = commercial_dict[x]['load'][0] * config_data['normalized_loadshape_scalar'] bp_B = commercial_dict[x]['load'][1] * config_data['normalized_loadshape_scalar'] bp_C = commercial_dict[x]['load'][2] * config_data['normalized_loadshape_scalar'] loadshape_dict[last_key] = {'object': 'load', 'name': '{:s}_loadshape'.format(load_name), 'phases': phases, 'nominal_voltage': nom_volt} if load_parent != 'None': loadshape_dict[last_key]['parent'] = load_parent else: loadshape_dict[last_key]['parent'] = load_parent if 'A' in phases and bp_A > 0.0: loadshape_dict[last_key]['base_power_A'] = 'norm_feeder_loadshape.value*{:f}'.format(bp_A) loadshape_dict[last_key]['power_pf_A'] = '{:f}'.format(config_data['c_p_pf']) loadshape_dict[last_key]['current_pf_A'] = '{:f}'.format(config_data['c_i_pf']) loadshape_dict[last_key]['impedance_pf_A'] = '{:f}'.format(config_data['c_z_pf']) loadshape_dict[last_key]['power_fraction_A'] = '{:f}'.format(config_data['c_pfrac']) loadshape_dict[last_key]['current_fraction_A'] = '{:f}'.format(config_data['c_ifrac']) loadshape_dict[last_key]['impedance_fraction_A'] = '{:f}'.format(config_data['c_zfrac']) if 'B' in phases and bp_B > 0.0: loadshape_dict[last_key]['base_power_B'] = 'norm_feeder_loadshape.value*{:f}'.format(bp_B) loadshape_dict[last_key]['power_pf_B'] = '{:f}'.format(config_data['c_p_pf']) loadshape_dict[last_key]['current_pf_B'] = '{:f}'.format(config_data['c_i_pf']) loadshape_dict[last_key]['impedance_pf_B'] = '{:f}'.format(config_data['c_z_pf']) loadshape_dict[last_key]['power_fraction_B'] = '{:f}'.format(config_data['c_pfrac']) loadshape_dict[last_key]['current_fraction_B'] = '{:f}'.format(config_data['c_ifrac']) loadshape_dict[last_key]['impedance_fraction_B'] = '{:f}'.format(config_data['c_zfrac']) if 'C' in phases and bp_C > 0.0: loadshape_dict[last_key]['base_power_C'] = 'norm_feeder_loadshape.value*{:f}'.format(bp_C) loadshape_dict[last_key]['power_pf_C'] = '{:f}'.format(config_data['c_p_pf']) loadshape_dict[last_key]['current_pf_C'] = '{:f}'.format(config_data['c_i_pf']) loadshape_dict[last_key]['impedance_pf_C'] = '{:f}'.format(config_data['c_z_pf']) loadshape_dict[last_key]['power_fraction_C'] = '{:f}'.format(config_data['c_pfrac']) loadshape_dict[last_key]['current_fraction_C'] = '{:f}'.format(config_data['c_ifrac']) loadshape_dict[last_key]['impedance_fraction_C'] = '{:f}'.format(config_data['c_zfrac']) last_key += last_key return loadshape_dict, last_key

47.129796

182

0.594901

7,182

57,734

4.454748

0.065441

0.05251

0.068263

0.078015

0.844221

0.819122

0.794962

0.770082

0.760643

0.750016

0

0.031117

0.240205

57,734

1,225

183

47.129796

0.69824

0.11385

0

0.679039

0

0.290288

0.063417

0

1

0.002183

false

0

0.001092

0

0.005459

0

null

0

1

0

null

0

6

23d9368aebeeb12bab57e9e2287c481078587dba

23

py

Python

tests/__init__.py

newskylabs/newskylabs-collagen

3e2e331605745e6709f57dce8730ceb9ceaa002c

[ "Apache-2.0" ]

null

tests/__init__.py

newskylabs/newskylabs-collagen

3e2e331605745e6709f57dce8730ceb9ceaa002c

[ "Apache-2.0" ]

null

tests/__init__.py

newskylabs/newskylabs-collagen

3e2e331605745e6709f57dce8730ceb9ceaa002c

[ "Apache-2.0" ]

null

from . import collagen

11.5

22

0.782609

3

23

6

1

0

0.173913

23

1

23

0.947368

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

23fb877a355a3c0c8b3523dc2d232a403e7cb2d5

145

py

Python

env/models/robots/__init__.py

METU-KALFA/furniture

1f81e8a3a2543ac33c06ca61448d784c625d3ca0

[ "MIT" ]

null

env/models/robots/__init__.py

METU-KALFA/furniture

1f81e8a3a2543ac33c06ca61448d784c625d3ca0

[ "MIT" ]

null

env/models/robots/__init__.py

METU-KALFA/furniture

1f81e8a3a2543ac33c06ca61448d784c625d3ca0

[ "MIT" ]

null

from .robot import Robot from .sawyer_robot import Sawyer from .baxter_robot import Baxter from .cursor import Cursor from .ur5_robot import Ur5

24.166667

32

0.827586

23

145

5.086957

0.304348

0.376068

0

0.016

0.137931

145

5

33

29

0.92

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

9b1ca7bee9b8bed8320f7b949c141dce7be4a5d5

13,588

py

Python

lexleader.py

wenting-zhao/lex-leader

59be259aafb01f6c5b456d9d56f8c78ecaacc80f

[ "BSD-2-Clause-FreeBSD", "MIT" ]

null

lexleader.py

wenting-zhao/lex-leader

59be259aafb01f6c5b456d9d56f8c78ecaacc80f

[ "BSD-2-Clause-FreeBSD", "MIT" ]

null

lexleader.py

wenting-zhao/lex-leader

59be259aafb01f6c5b456d9d56f8c78ecaacc80f

[ "BSD-2-Clause-FreeBSD", "MIT" ]

null

import sys class LexLeader: def __init__(self, columns, rows, option, columns_enabled=True, rows_enabled=True): self.num_columns = columns self.num_rows = rows self.columns_enabled = columns_enabled self.rows_enabled = rows_enabled self.varmap = dict() self.num_var = 0 self.parse_option(option) for c in range(columns): for r in range(rows): self.num_var += 1 self.varmap[(c, r)] = self.num_var def parse_option(self, option): if option == "and": self.which_lex = self._and_helper elif option == "and-cse": self.which_lex = self._and_subexpr_helper elif option == "or": self.which_lex = self._or_helper elif option == "or-cse": self.which_lex = self._or_subexpr_helper elif option == "ror": self.which_lex = self._ror_helper elif option == "alpha": self.which_lex = self._alpha_helper elif option == "alpha-m": self.which_lex = self._alpha_m_helper elif option == "harvey": self.which_lex = self._harvey_helper def make_lexleader(self): """ return the row and column lex-leader constraints of the full matrix """ full = [] if self.columns_enabled: for c in range(self.num_columns-1, 0, -1): column1 = [self.varmap[(c, r)] for r in range(self.num_rows)] column2 = [self.varmap[(c-1, r)] for r in range(self.num_rows)] full.append(self.which_lex(column1, column2)) if self.rows_enabled: for r in range(self.num_rows-1, 0, -1): row1 = [self.varmap[(c, r)] for c in range(self.num_columns)] row2 = [self.varmap[(c, r-1)] for c in range(self.num_columns)] full.append(self.which_lex(row1, row2)) return "\n& ".join(full) def add_assumps(self, *variables): assumps = [] for var in variables: if var < 0: assumps.append("!x{}".format(abs(var))) else: assumps.append("x{}".format(var)) return "\n& "+"\n& ".join(assumps) def _and_helper(self, vector1, vector2): """ creates the lex-leader constraints between two vectors of variables via the plain AND decomposition encoding inputs: vector1, vector2: lists of integers, equivalent lengths, each representing a vector of variables returns: string containing the full expression of the lex-leader constraint """ # setup vectors with 1-based indexing to match constraints in the source paper A = [None] + vector1 B = [None] + vector2 res = [] res.append( "(!x{} | x{})".format(A[1], B[1]) ) assert len(vector1) == len(vector2) for i in range(1, len(vector1)): temp = [] for j in range(1, i+1): temp.append( "(x{} = x{})".format(A[j], B[j]) ) temp = " & ".join(temp) res.append( "({} -> (!x{} | x{}))".format(temp, A[i+1], B[i+1]) ) return "("+"\n& ".join(res)+")" def _and_subexpr_helper(self, vector1, vector2): """ creates the lex-leader constraints between two vectors of variables via the AND decomposition encoding using common sub-expression elimination inputs: vector1, vector2: lists of integers, equivalent lengths, each representing a vector of variables returns: string containing the full expression of the lex-leader constraint """ # setup vectors with 1-based indexing to match constraints in the source paper A = [None] + vector1 B = [None] + vector2 # creating the extra variables X = dict() assert len(vector1) == len(vector2) for i in range(1, len(vector1)): self.num_var += 1 X[i] = self.num_var res = [] # A[1] <= B[1] (thesis, 3.18) res.append( "(!x{} | x{})".format(A[1], B[1]) ) # X[1] <=> (A[1] = B[1]) (thesis, 3.19) res.append( "(x{} = (x{} = x{}))".format(X[1], A[1], B[1]) ) # 1 <= i <= n-2, X[i+1] <=> (X[i] & (A[i+1] = B[i+1])) (thesis, 3.20) for i in range(1, len(vector1)-1): res.append( "(x{} = (x{} & (x{} = x{})))".format(X[i+1], X[i], A[i+1], B[i+1]) ) # i <= i <= n-1, X[i] -> (A[i+1] <= B[i+1]) (thesis, 3.21) for i in range(1, len(vector1)): res.append( "(x{} -> (!x{} | x{}))".format(X[i], A[i+1], B[i+1]) ) return "("+"\n& ".join(res)+")" def _or_helper(self, vector1, vector2): """ creates the lex-leader constraints between two vectors of variables via the plain OR decomposition encoding inputs: vector1, vector2: lists of integers, equivalent lengths, each representing a vector of variables returns: string containing the full expression of the lex-leader constraint """ # setup vectors with 1-based indexing to match constraints in the source paper A = [None] + vector1 B = [None] + vector2 res = [] res.append( "(!x{} & x{})".format(A[1], B[1]) ) assert len(vector1) == len(vector2) for i in range(1, len(vector1)): temp = [] for j in range(1, i+1): temp.append( "(x{} = x{})".format(A[j], B[j]) ) temp = " & ".join(temp) res.append( "({} & (!x{} & x{}))".format(temp, A[i+1], B[i+1]) ) temp = [] for i in range(1, len(vector1)+1): temp.append( "(x{} = x{})".format(A[i], B[i]) ) res.append(" & ".join(temp)) return "("+"\n| ".join(res)+")" def _or_subexpr_helper(self, vector1, vector2): """ creates the lex-leader constraints between two vectors of variables via the OR decomposition encoding using common sub-expression elimination inputs: vector1, vector2: lists of integers, equivalent lengths, each representing a vector of variables returns: string containing the full expression of the lex-leader constraint """ # setup vectors with 1-based indexing to match constraints in the source paper A = [None] + vector1 B = [None] + vector2 # creating the extra variables X = dict() assert len(vector1) == len(vector2) for i in range(1, len(vector1)+1): self.num_var += 1 X[i] = self.num_var res = [] # for ANDing each element... temp = [] # for ORing each element... n = len(vector1) # A[1] < B[1] temp.append( "(!x{} & x{})".format(A[1], B[1]) ) # 1 <= i <= n-1, X[i] & (A[i+1] < B[i+1])) for i in range(1, n): temp.append( "(x{} & (!x{} & x{}))".format(X[i], A[i+1], B[i+1]) ) # X[n] temp.append( "x{}".format(X[n]) ) res.append( "("+" | ".join(temp)+")" ) # X[1] <=> A[1] = B[1] (thesis, 3.36) res.append( "(x{} = (x{} = x{}))".format(X[1], A[1], B[1]) ) # 1 <= i <= n−1, X[i+1] <=> (X[i] & (A[i+1] = B[i+1])) (thesis, 3.37) for i in range(1, n): res.append( "(x{} = (x{} & (x{} = x{})))".format(X[i+1], X[i], A[i+1], B[i+1]) ) return "("+"\n& ".join(res)+")" def _ror_helper(self, vector1, vector2): """ creates the lex-leader constraints between two vectors of variables via the recursive OR decomposition encoding using common sub-expression elimination inputs: vector1, vector2: lists of integers, equivalent lengths, each representing a vector of variables returns: string containing the full expression of the lex-leader constraint """ # setup vectors with 1-based indexing to match constraints in the source paper A = [None] + vector1 B = [None] + vector2 assert len(vector1) == len(vector2) n = len(vector1) # creating the extra variables X = dict() for i in range(1, len(vector1)+1): self.num_var += 1 X[i] = self.num_var res = [] # X[1] (thesis, 3.44) res.append( "(x{})".format(X[1]) ) # X[n] <=> (A[n] <= B[n]) (thesis, 3.45) res.append( "(x{} = (!x{} | x{}))".format(X[n], A[n], B[n]) ) # 1 <= i <= n−1, X[n−i] <=> (A[n−i]<B[n−i] | (A[n−i]=B[n−i] & X[n−i+1])) (thesis, 3.46) for i in range(1, n): res.append( "(x{0} = ((!x{1} & x{2}) | ((x{1}=x{2}) & x{3})))".format(X[n-i], A[n-i], B[n-i], X[n-i+1]) ) return "("+"\n& ".join(res)+")" def _alpha_helper(self, vector1, vector2): """ creates the lex-leader constraints between two vectors of variables via the Alpha encoding using common sub-expression elimination inputs: vector1, vector2: lists of integers, equivalent lengths, each representing a vector of variables returns: string containing the full expression of the lex-leader constraint """ # setup vectors with 1-based indexing to match constraints in the source paper A = [None] + vector1 B = [None] + vector2 assert len(vector1) == len(vector2) n = len(vector1) # creating the extra variables alpha = dict() for i in range(len(vector1)+1): self.num_var += 1 alpha[i] = self.num_var res = [] # alpha[0] (thesis, 3.66) res.append( "(x{})".format(alpha[0]) ) # 0 <= i <= n−1, -alpha[i] -> -a[i+1] (thesis, 3.67) for i in range(n): res.append( "(!x{} -> !x{})".format(alpha[i], alpha[i+1]) ) # 1 <= i <= n, alpha[i] -> (A[i] = B[i]) (thesis, 3.68) for i in range(1, n+1): res.append( "(x{} -> (x{} = x{}))".format(alpha[i], A[i], B[i]) ) # 0 <= i <= n−1, ((alpha[i]) & (!alpha[i+1])) -> (A[i+1] < B[i+1]) (thesis, 3.69) for i in range(n): res.append( "((x{} & !x{}) -> (!x{} & x{}))".format(alpha[i], alpha[i+1], A[i+1], B[i+1]) ) # 0 <= i <= n−1, alpha[i] -> (A[i+1] <= B[i+1]) (thesis, 3.70) for i in range(n): res.append( "(x{} -> (!x{} | x{}))".format(alpha[i], A[i+1], B[i+1]) ) return "("+"\n& ".join(res)+")" def _alpha_m_helper(self, vector1, vector2): """ creates the lex-leader constraints between two vectors of variables via the Alpha M encoding using common sub-expression elimination inputs: vector1, vector2: lists of integers, equivalent lengths, each representing a vector of variables returns: string containing the full expression of the lex-leader constraint """ # setup vectors with 1-based indexing to match constraints in the source paper A = [None] + vector1 B = [None] + vector2 assert len(vector1) == len(vector2) n = len(vector1) # creating the extra variables alpha = dict() for i in range(1, len(vector1)+2): self.num_var += 1 alpha[i] = self.num_var res = [] # alpha[1] (thesis, 3.81) res.append( "(x{})".format(alpha[1]) ) # 1 <= i <= n, alpha[i] <=> (((A[i] < B[i])|alpha[i+1]) & (A[i]<=B[i])) (thesis, 3.82) for i in range(1, n+1): res.append( "(x{0} = (((!x{1} & x{2})|x{3}) & (!x{1} | x{2})))".format(alpha[i], A[i], B[i], alpha[i+1]) ) return "("+"\n& ".join(res)+")" def _harvey_helper(self, vector1, vector2): """ creates the lex-leader constraints between two vectors of variables via the Harvey encoding inputs: vector1, vector2: lists of integers, equivalent lengths, each representing a vector of variables returns: string containing the full expression of the lex-leader constraint """ # setup vectors with 1-based indexing to match constraints in the source paper A = [None] + vector1 B = [None] + vector2 assert len(vector1) == len(vector2) n = len(vector1) # creating the extra variables X = dict() for i in range(1, len(vector1)+1): self.num_var += 1 X[i] = self.num_var res = [] # X[1] (thesis, 3.54) res.append( "(x{})".format(X[1]) ) # X[n] <=> (A[n] < (B[n]+1)) (thesis, 3.55) res.append( "(x{} = x{} -> x{})".format(X[n], A[n], B[n]) ) # 0 <= i <= n−2, X[n−i−1] <=> (A[n−i−1] < (B[n−i−1] + Bool2Int(X[n−i]))), # the right-hand side becomes (B+X)(!A+B)(!A+X) for i in range(0, len(vector1)-1): res.append( "(x{XX} = ((x{B} | x{X}) & (!x{A} | x{B}) & (!x{A} | x{X})))".format( X=X[n-i], XX=X[n-i-1], A=A[n-i-1], B=B[n-i-1] ) ) return "("+"\n& ".join(res)+")"

40.927711

118

0.497792

1,860

13,588

3.6

0.074731

0.013441

0.034349

0.032855

0.815711

0.778973

0.764486

0.733423

0.70236

0.683542

0

0.034379

0.338534

13,588

331

119

41.05136

0.7085

0.346703

0

0.521505

0

0.016129

0.082965

0

0.043011

1

0.064516

false

0

0.005376

0

0.129032

0

null

0

1

0

null

0

6

f1a6ddc83d96d40c68db915b61b446f708890502

156

py

Python

__init__.py

AbstractMonkey/flask_test

84a983c204234f471420a5041c28400c1193f762

[ "MIT" ]

null

__init__.py

AbstractMonkey/flask_test

84a983c204234f471420a5041c28400c1193f762

[ "MIT" ]

null

__init__.py

AbstractMonkey/flask_test

84a983c204234f471420a5041c28400c1193f762

[ "MIT" ]

null

import flask from Flask from flask_test import routes from flask_test import config app = Flask(__name__) # Config class app.config.from_object(Config)

14.181818

30

0.801282

24

156

4.916667

0.416667

0.228814

0.237288

0.322034

0

0.147436

156

10

31

15.6

0.887218

0.076923

0

null

0

0.6

null

0

1

0

null

1

0

null

0

1

0

1

0

6

f1e9262f2424f0e7346b55deb6ded28c5b3e17a6

16,337

py

Python

studd/studd_batch.py

vcerqueira/studd

c23dd2c81bb05abc47ef8e929b5c8a708f4b7923

[ "BSD-3-Clause" ]

2

2021-05-06T16:02:09.000Z

2021-05-26T02:38:02.000Z

studd/studd_batch.py

vcerqueira/studd

c23dd2c81bb05abc47ef8e929b5c8a708f4b7923

[ "BSD-3-Clause" ]

null

studd/studd_batch.py

vcerqueira/studd

c23dd2c81bb05abc47ef8e929b5c8a708f4b7923

[ "BSD-3-Clause" ]

1

2022-03-25T03:48:14.000Z

from skmultiflow.data.data_stream import DataStream from skmultiflow.drift_detection.page_hinkley import PageHinkley as PHT from ht_detectors.tracker_output import HypothesisTestDetector import copy import numpy as np class STUDD: def __init__(self, X, y, n_train): """ :param X: :param y: :param n_train: """ D = DataStream(X, y) D.prepare_for_use() self.datastream = D self.n_train = n_train self.W = n_train self.base_model = None self.student_model = None self.init_training_data = None def initial_fit(self, model, std_model): """ :return: """ X_tr, y_tr = self.datastream.next_sample(self.n_train) model.fit(X_tr, y_tr) yhat_tr = model.predict(X_tr) std_model.fit(X_tr, yhat_tr) self.base_model = model self.student_model = std_model self.init_training_data = dict({"X": X_tr, "y": y_tr, "y_hat": yhat_tr}) DETECTOR = PHT @staticmethod def drift_detection_std(datastream_, model_, std_model_, n_train_, delta, n_samples, upd_model=False, upd_std_model=True, detector=DETECTOR): datastream = copy.deepcopy(datastream_) base_model = copy.deepcopy(model_) student_model = copy.deepcopy(std_model_) n_train = copy.deepcopy(n_train_) std_detector = detector(delta=delta) std_alarms = [] iter = n_train n_updates = 0 samples_used = 0 y_hat_hist = [] y_buffer, y_hist = [], [] X_buffer, X_hist = [], [] while datastream.has_more_samples(): # print("Iteration: " + str(iter)) Xi, yi = datastream.next_sample() y_hist.append(yi[0]) y_buffer.append(yi[0]) X_hist.append(Xi[0]) X_buffer.append(Xi[0]) model_yhat = base_model.predict(Xi) y_hat_hist.append(model_yhat[0]) std_model_yhat = student_model.predict(Xi) std_err = int(model_yhat != std_model_yhat) std_detector.add_element(std_err) if std_detector.detected_change(): print("Found change std in iter: " + str(iter)) std_alarms.append(iter) if upd_model: X_buffer = np.array(X_buffer) y_buffer = np.array(y_buffer) samples_used_iter = len(y_buffer[-n_samples:]) print("Updating model with " + str(samples_used_iter), " Observations") base_model.fit(X_buffer[-n_samples:], y_buffer[-n_samples:]) yhat_buffer = base_model.predict(X_buffer) if upd_std_model: student_model.fit(X_buffer, yhat_buffer) else: student_model.fit(X_buffer[-n_samples:], yhat_buffer[-n_samples:]) # y_buffer = [] # X_buffer = [] y_buffer = list(y_buffer) X_buffer = list(X_buffer) n_updates += 1 samples_used += samples_used_iter print("Moving on") iter += 1 preds = dict({"y": y_hist, "y_hat": y_hat_hist}) output = dict({"alarms": std_alarms, "preds": preds, "n_updates": n_updates, "samples_used": samples_used}) return output @staticmethod def drift_detection_spv(datastream_, model_, n_train_, delay_time, observation_ratio, delta, n_samples, upd_model=False, detector=DETECTOR): import copy import numpy as np datastream = copy.deepcopy(datastream_) model = copy.deepcopy(model_) n_train = copy.deepcopy(n_train_) driftmodel = detector(delta=delta) alarms = [] iter = n_train j, n_updates, samples_used = 0, 0, 0 yhat_hist = [] y_buffer, y_hist = [], [] X_buffer, X_hist = [], [] while datastream.has_more_samples(): # print("Iteration: " + str(iter)) Xi, yi = datastream.next_sample() y_hist.append(yi[0]) y_buffer.append(yi[0]) X_hist.append(Xi[0]) X_buffer.append(Xi[0]) model_yhat = model.predict(Xi) yhat_hist.append(model_yhat[0]) put_i_available = np.random.binomial(1, observation_ratio) if put_i_available > 0: if j >= delay_time: err = int(y_hist[j - delay_time] != yhat_hist[j - delay_time]) driftmodel.add_element(err) if driftmodel.detected_change(): print("Found change in iter: " + str(iter)) alarms.append(iter) if upd_model: X_buffer = np.array(X_buffer) y_buffer = np.array(y_buffer) samples_used_iter = len(y_buffer[-n_samples:]) print("Updating model with " + str(samples_used_iter), " Observations") model.fit(X_buffer[-n_samples:], y_buffer[-n_samples:]) y_buffer = list(y_buffer) X_buffer = list(X_buffer) n_updates += 1 samples_used += samples_used_iter print("Moving on") iter += 1 j += 1 preds = dict({"y": y_hist, "y_hat": yhat_hist}) output = dict({"alarms": alarms, "preds": preds, "n_updates": n_updates, "samples_used": samples_used}) return output @staticmethod def BL2_retrain_after_w(datastream_, model_, n_train_, n_samples): import copy import numpy as np datastream = copy.deepcopy(datastream_) model = copy.deepcopy(model_) n_train = copy.deepcopy(n_train_) iter = copy.deepcopy(n_train_) j, n_updates, samples_used = 0, 0, 0 yhat_hist = [] y_buffer, y_hist = [], [] X_buffer, X_hist = [], [] while datastream.has_more_samples(): # print("Iteration: " + str(iter)) Xi, yi = datastream.next_sample() y_hist.append(yi[0]) y_buffer.append(yi[0]) X_hist.append(Xi[0]) X_buffer.append(Xi[0]) model_yhat = model.predict(Xi) yhat_hist.append(model_yhat[0]) if iter % n_train == 0 and iter > n_train + 1: X_buffer = np.array(X_buffer) y_buffer = np.array(y_buffer) samples_used_iter = len(y_buffer[-n_samples:]) print("Updating model with " + str(samples_used_iter), " Observations") model.fit(X_buffer[-n_samples:], y_buffer[-n_samples:]) y_buffer = list(y_buffer) X_buffer = list(X_buffer) n_updates += 1 samples_used += samples_used_iter print("Moving on") iter += 1 j += 1 preds = dict({"y": y_hist, "y_hat": yhat_hist}) output = dict({"alarms": [], "preds": preds, "n_updates": n_updates, "samples_used": samples_used}) return output @staticmethod def BL1_never_adapt(datastream_, model_): import copy datastream = copy.deepcopy(datastream_) model = copy.deepcopy(model_) yhat_hist, y_hist = [], [] while datastream.has_more_samples(): # print("Iteration: " + str(iter)) Xi, yi = datastream.next_sample() y_hist.append(yi[0]) model_yhat = model.predict(Xi) yhat_hist.append(model_yhat[0]) preds = dict({"y": y_hist, "y_hat": yhat_hist}) output = dict({"alarms": [], "preds": preds, "n_updates": 0, "samples_used": 0}) return output @staticmethod def drift_detection_uspv(datastream_, model_, n_train_, use_prob, method, pvalue, window_size, n_samples, upd_model=False): import copy import numpy as np assert method in ["wrs", "tt", "ks"] datastream = copy.deepcopy(datastream_) model = copy.deepcopy(model_) n_train = copy.deepcopy(n_train_) driftmodel = HypothesisTestDetector(method=method, window=window_size, thr=pvalue) alarms = [] y_buffer = [] y_hist = [] X_buffer = [] y_hat_hist = [] n_updates = 0 samples_used = 0 iter = n_train while datastream.has_more_samples(): # print("Iteration: " + str(iter)) Xi, yi = datastream.next_sample() y_buffer.append(yi[0]) y_hist.append(yi[0]) X_buffer.append(Xi[0]) y_hat_hist.append(model.predict(Xi)[0]) if use_prob: yprob_all = model.predict_proba(Xi) if len(yprob_all) < 2: yhat = yprob_all[0] elif len(yprob_all) == 2: yhat = yprob_all[1] else: yhat = np.max(yprob_all) else: yhat = model.predict(Xi)[0] driftmodel.add_element(yhat) if driftmodel.detected_change(): print("Found change in iter: " + str(iter)) alarms.append(iter) if upd_model: X_buffer = np.array(X_buffer) y_buffer = np.array(y_buffer) samples_used_iter = len(y_buffer[-n_samples:]) print("Updating model with " + str(samples_used_iter), " Observations") model.fit(X_buffer[-n_samples:], y_buffer[-n_samples:]) # y_buffer = [] # X_buffer = [] y_buffer = list(y_buffer) X_buffer = list(X_buffer) n_updates += 1 samples_used += samples_used_iter print("Moving on") iter += 1 preds = dict({"y": y_hist, "y_hat": y_hat_hist}) output = dict({"alarms": alarms, "preds": preds, "n_updates": n_updates, "samples_used": samples_used}) return output @staticmethod def drift_detection_uspv_f(datastream_, model_, n_train_, use_prob, method, pvalue, window_size, n_samples, upd_model=False): import copy import numpy as np from ht_detectors.tracker_output import FixedWindowDetector assert method in ["wrs", "tt", "ks"] datastream = copy.deepcopy(datastream_) model = copy.deepcopy(model_) n_train = copy.deepcopy(n_train_) driftmodel = FixedWindowDetector(ref_window=[], thr=pvalue, window_size=window_size) alarms = [] y_buffer = [] y_hist = [] X_buffer = [] y_hat_hist = [] n_updates = 0 samples_used = 0 iter = n_train while datastream.has_more_samples(): # print("Iteration: " + str(iter)) Xi, yi = datastream.next_sample() y_buffer.append(yi[0]) y_hist.append(yi[0]) X_buffer.append(Xi[0]) y_hat_hist.append(model.predict(Xi)[0]) if use_prob: yprob_all = model.predict_proba(Xi) if len(yprob_all) < 2: yhat = yprob_all[0] elif len(yprob_all) == 2: yhat = yprob_all[1] else: yhat = np.max(yprob_all) else: yhat = model.predict(Xi)[0] driftmodel.add_element(yhat) if driftmodel.detected_change(): print("Found change in iter: " + str(iter)) alarms.append(iter) if upd_model: X_buffer = np.array(X_buffer) y_buffer = np.array(y_buffer) samples_used_iter = len(y_buffer[-n_samples:]) print("Updating model with " + str(samples_used_iter), " Observations") model.fit(X_buffer[-n_samples:], y_buffer[-n_samples:]) # y_buffer = [] # X_buffer = [] y_buffer = list(y_buffer) X_buffer = list(X_buffer) n_updates += 1 samples_used += samples_used_iter print("Moving on") iter += 1 preds = dict({"y": y_hist, "y_hat": y_hat_hist}) output = dict({"alarms": alarms, "preds": preds, "n_updates": n_updates, "samples_used": samples_used}) return output @staticmethod def drift_detection_uspv_x(datastream_, model_, n_train_, X, pvalue, window_size, n_samples, upd_model=False): import copy import numpy as np from ht_detectors.tracker_covariates import XCTracker datastream = copy.deepcopy(datastream_) model = copy.deepcopy(model_) n_train = copy.deepcopy(n_train_) driftmodel = XCTracker(X=X, thr=pvalue, W=window_size) driftmodel.create_trackers() alarms = [] y_buffer = [] y_hist = [] X_buffer = [] y_hat_hist = [] n_updates = 0 samples_used = 0 iter = n_train while datastream.has_more_samples(): # print("Iteration: " + str(iter)) Xi, yi = datastream.next_sample() y_buffer.append(yi[0]) y_hist.append(yi[0]) X_buffer.append(Xi[0]) y_hat_hist.append(model.predict(Xi)[0]) # yhat = model.predict(Xi)[0] driftmodel.add_element(Xi) if driftmodel.detected_change(): print("Found change in iter: " + str(iter)) alarms.append(iter) if upd_model: X_buffer = np.array(X_buffer) y_buffer = np.array(y_buffer) samples_used_iter = len(y_buffer[-n_samples:]) print("Updating model with " + str(samples_used_iter), " Observations") model.fit(X_buffer[-n_samples:], y_buffer[-n_samples:]) # y_buffer = [] # X_buffer = [] y_buffer = list(y_buffer) X_buffer = list(X_buffer) n_updates += 1 samples_used += samples_used_iter print("Moving on") iter += 1 preds = dict({"y": y_hist, "y_hat": y_hat_hist}) output = dict({"alarms": alarms, "preds": preds, "n_updates": n_updates, "samples_used": samples_used}) return output

29.812044

92

0.483381

1,716

16,337

4.293124

0.081002

0.04941

0.038007

0.024433

0.787159

0.773992

0.749559

0.739514

0.72689

0

0.008038

0.421252

16,337

547

93

29.866545

0.771126

0.025464

0

0.806452

0

0.041222

0

0.005376

1

0.024194

false

0

0.048387

0

0.096774

0.045699

0

null

0

1

0

1

0

null

0

6

7b32fa1c33e00e06615c8ea7fda9d6cce271c330

68

py

Python

v2/backend/security/admins/__init__.py

jonfairbanks/rtsp-nvr

c770c77e74a062c63fb5e2419bc00a17543da332

[ "MIT" ]

558

2017-10-04T14:33:18.000Z

2022-03-24T21:25:08.000Z

v2/backend/security/admins/__init__.py

jonfairbanks/rtsp-nvr

c770c77e74a062c63fb5e2419bc00a17543da332

[ "MIT" ]

22

2018-04-29T04:25:49.000Z

2021-08-02T17:26:02.000Z

v2/backend/security/admins/__init__.py

jonfairbanks/rtsp-nvr

c770c77e74a062c63fb5e2419bc00a17543da332

[ "MIT" ]

127

2017-11-14T19:47:27.000Z

2022-03-24T21:25:12.000Z

from .role_admin import RoleAdmin from .user_admin import UserAdmin

22.666667

33

0.852941

10

68

5.6

0.7

0.392857

0

0.117647

68

2

34

0.933333

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

9e3c9e493814a8d39e13b9c3d6d3e69357c107f8

56

py

Python

hr_zk_attendance_integration/models/__init__.py

kelvzxu/odoo_hr_addons

b5e5af7b80c09697e857bc57eecd2126072501bc

[ "MIT" ]

null

hr_zk_attendance_integration/models/__init__.py

kelvzxu/odoo_hr_addons

b5e5af7b80c09697e857bc57eecd2126072501bc

[ "MIT" ]

null

hr_zk_attendance_integration/models/__init__.py

kelvzxu/odoo_hr_addons

b5e5af7b80c09697e857bc57eecd2126072501bc

[ "MIT" ]

null

from . import zk_machine from . import machine_analysis

18.666667

30

0.821429

8

56

5.5

0.625

0.454545

0

0.142857

56

2

31

28

0.916667

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

9e4b06f8a78c786241694bba14664c815f7fd5ec

195

py

Python

countries/admin.py

Thuhaa/geo_knowledge

c27e7740bd5ffa1e6f91fe738ad2f183da13e8c9

[ "MIT" ]

null

countries/admin.py

Thuhaa/geo_knowledge

c27e7740bd5ffa1e6f91fe738ad2f183da13e8c9

[ "MIT" ]

null

countries/admin.py

Thuhaa/geo_knowledge

c27e7740bd5ffa1e6f91fe738ad2f183da13e8c9

[ "MIT" ]

null

#from django.contrib import admin from django.contrib.gis import admin from .models import WorldBorders admin.site.register(WorldBorders, admin.GeoModelAdmin) # Register your models here.

27.857143

55

0.8

25

195

6.24

0.52

0.128205

0.217949

0

0.133333

195

6

56

32.5

0.923077

0.302564

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

7b45a77e384fe9c49f1834d5ec8afb42b3f1bccd

34,943

py

Python

vel/rl/buffers/tests/test_circular_vec_env_buffer_backend.py

galatolofederico/vel

0473648cffb3f34fb784d12dbb25844ab58ffc3c

[ "MIT" ]

273

2018-09-01T08:54:34.000Z

2022-02-02T13:22:51.000Z

vel/rl/buffers/tests/test_circular_vec_env_buffer_backend.py

braincorp/vel

bdf9d9eb6ed66278330e8cbece307f6e63ce53c6

[ "MIT" ]

47

2018-08-17T11:27:08.000Z

2022-03-11T23:26:55.000Z

vel/rl/buffers/tests/test_circular_vec_env_buffer_backend.py

braincorp/vel

bdf9d9eb6ed66278330e8cbece307f6e63ce53c6

[ "MIT" ]

37

2018-10-11T22:56:57.000Z

2020-10-06T19:53:05.000Z

import gym import gym.spaces import numpy as np import numpy.testing as nt import pytest from vel.exceptions import VelException from vel.rl.buffers.circular_replay_buffer import CircularVecEnvBufferBackend def get_half_filled_buffer(frame_history=1): """ Return simple preinitialized buffer """ observation_space = gym.spaces.Box(low=0, high=255, shape=(2, 2, 1), dtype=int) action_space = gym.spaces.Discrete(4) buffer = CircularVecEnvBufferBackend( 20, num_envs=2, observation_space=observation_space, action_space=action_space, frame_history=frame_history ) v1 = np.ones(8).reshape((2, 2, 2, 1)) for i in range(10): item = v1.copy() item[0] *= (i+1) item[1] *= 10 * (i+1) buffer.store_transition(item, 0, float(i)/2, False) return buffer def get_filled_buffer(frame_history=1): """ Return simple preinitialized buffer """ observation_space = gym.spaces.Box(low=0, high=255, shape=(2, 2, 1), dtype=int) action_space = gym.spaces.Discrete(4) buffer = CircularVecEnvBufferBackend( 20, num_envs=2, observation_space=observation_space, action_space=action_space, frame_history=frame_history ) v1 = np.ones(8).reshape((2, 2, 2, 1)) for i in range(30): item = v1.copy() item[0] *= (i+1) item[1] *= 10 * (i+1) buffer.store_transition(item, 0, float(i)/2, False) return buffer def get_filled_buffer1x1(frame_history=1): """ Return simple preinitialized buffer """ observation_space = gym.spaces.Box(low=0, high=255, shape=(2,), dtype=int) action_space = gym.spaces.Box(low=-1.0, high=1.0, shape=(2,), dtype=float) buffer = CircularVecEnvBufferBackend( 20, num_envs=2, observation_space=observation_space, action_space=action_space, frame_history=frame_history ) v1 = np.ones(4).reshape((2, 2)) a1 = np.arange(4).reshape((2, 2)) for i in range(30): item = v1.copy() item[:, 0] *= (i+1) item[:, 1] *= 10 * (i+1) buffer.store_transition(item, a1 * i, float(i)/2, False) return buffer def get_filled_buffer2x2(frame_history=1): """ Return simple preinitialized buffer """ observation_space = gym.spaces.Box(low=0, high=255, shape=(2, 2), dtype=int) action_space = gym.spaces.Box(low=-1.0, high=1.0, shape=(2, 2), dtype=float) buffer = CircularVecEnvBufferBackend( 20, num_envs=2, observation_space=observation_space, action_space=action_space, frame_history=frame_history ) v1 = np.ones(8).reshape((2, 2, 2)) a1 = np.arange(8).reshape((2, 2, 2)) for i in range(30): item = v1.copy() item[:, 0] *= (i+1) item[:, 1] *= 10 * (i+1) buffer.store_transition(item, a1 * i, float(i)/2, False) return buffer def get_filled_buffer3x3(frame_history=1): """ Return simple preinitialized buffer """ observation_space = gym.spaces.Box(low=0, high=255, shape=(2, 2, 2), dtype=int) action_space = gym.spaces.Box(low=-1.0, high=1.0, shape=(2, 2, 2), dtype=float) buffer = CircularVecEnvBufferBackend( 20, num_envs=2, observation_space=observation_space, action_space=action_space, frame_history=frame_history ) v1 = np.ones(16).reshape((2, 2, 2, 2)) a1 = np.arange(16).reshape((2, 2, 2, 2)) for i in range(30): item = v1.copy() item[:, 0] *= (i+1) item[:, 1] *= 10 * (i+1) buffer.store_transition(item, i * a1, float(i)/2, False) return buffer def get_filled_buffer1x1_history(frame_history=1): """ Return simple preinitialized buffer """ observation_space = gym.spaces.Box(low=0, high=255, shape=(2, 1), dtype=int) action_space = gym.spaces.Box(low=-1.0, high=1.0, shape=(2,), dtype=float) buffer = CircularVecEnvBufferBackend( 20, num_envs=2, observation_space=observation_space, action_space=action_space, frame_history=frame_history ) v1 = np.ones(4).reshape((2, 2, 1)) a1 = np.arange(4).reshape((2, 2)) for i in range(30): item = v1.copy() item[:, 0] *= (i+1) item[:, 1] *= 10 * (i+1) buffer.store_transition(item, a1 * i, float(i)/2, False) return buffer def get_filled_buffer2x2_history(frame_history=1): """ Return simple preinitialized buffer """ observation_space = gym.spaces.Box(low=0, high=255, shape=(2, 2, 1), dtype=int) action_space = gym.spaces.Box(low=-1.0, high=1.0, shape=(2, 2), dtype=float) buffer = CircularVecEnvBufferBackend( 20, num_envs=2, observation_space=observation_space, action_space=action_space, frame_history=frame_history ) v1 = np.ones(8).reshape((2, 2, 2, 1)) a1 = np.arange(8).reshape((2, 2, 2)) for i in range(30): item = v1.copy() item[:, 0] *= (i+1) item[:, 1] *= 10 * (i+1) buffer.store_transition(item, a1 * i, float(i)/2, False) return buffer def get_filled_buffer3x3_history(frame_history=1): """ Return simple preinitialized buffer """ observation_space = gym.spaces.Box(low=0, high=255, shape=(2, 2, 2, 1), dtype=int) action_space = gym.spaces.Box(low=-1.0, high=1.0, shape=(2, 2, 2), dtype=float) buffer = CircularVecEnvBufferBackend( 20, num_envs=2, observation_space=observation_space, action_space=action_space, frame_history=frame_history ) v1 = np.ones(16).reshape((2, 2, 2, 2, 1)) a1 = np.arange(16).reshape((2, 2, 2, 2)) for i in range(30): item = v1.copy() item[:, 0] *= (i+1) item[:, 1] *= 10 * (i+1) buffer.store_transition(item, i * a1, float(i)/2, False) return buffer def get_filled_buffer_extra_info(frame_history=1): """ Return simple preinitialized buffer """ observation_space = gym.spaces.Box(low=0, high=255, shape=(2, 2, 1), dtype=int) action_space = gym.spaces.Discrete(4) buffer = CircularVecEnvBufferBackend( 20, num_envs=2, observation_space=observation_space, action_space=action_space, frame_history=frame_history ) v1 = np.ones(8).reshape((2, 2, 2, 1)) for i in range(30): item = v1.copy() item[0] *= (i+1) item[1] *= 10 * (i+1) buffer.store_transition(item, 0, float(i)/2, False, extra_info={ 'neglogp': np.array([i / 30.0, (i+1) / 30.0]) }) return buffer def get_filled_buffer_with_dones(frame_history=1): """ Return simple preinitialized buffer with some done's in there """ observation_space = gym.spaces.Box(low=0, high=255, shape=(2, 2, 1), dtype=int) action_space = gym.spaces.Discrete(4) buffer = CircularVecEnvBufferBackend( 20, num_envs=2, observation_space=observation_space, action_space=action_space, frame_history=frame_history ) v1 = np.ones(8).reshape((2, 2, 2, 1)) done_set = {2, 5, 10, 13, 18, 22, 28} for i in range(30): item = v1.copy() item[0] *= (i+1) item[1] *= 10 * (i+1) done_array = np.array([i in done_set, (i+1) in done_set], dtype=bool) buffer.store_transition(item, 0, float(i)/2, done_array) return buffer def get_filled_buffer_frame_stack(frame_stack=4, frame_dim=1): """ Return a preinitialized buffer with frame stack implemented """ observation_space = gym.spaces.Box(low=0, high=255, shape=(2, 2, frame_dim * frame_stack), dtype=int) action_space = gym.spaces.Discrete(4) buffer = CircularVecEnvBufferBackend( buffer_capacity=20, num_envs=2, observation_space=observation_space, action_space=action_space, frame_stack_compensation=True, frame_history=frame_stack ) v1 = np.ones(8 * frame_dim).reshape((2, 2, 2, frame_dim)) done_set = {2, 5, 10, 13, 18, 22, 28} # simple buffer of previous frames to simulate frame stack item_array = [] for i in range(30): item = v1.copy() item[:, 0] *= (i+1) item[:, 1] *= 10 * (i+1) done_array = np.array([i in done_set, (i+1) in done_set], dtype=bool) item_array.append(item) if len(item_array) < frame_stack: item_concatenated = np.concatenate([item] * frame_stack, axis=-1) else: item_concatenated = np.concatenate(item_array[-frame_stack:], axis=-1) buffer.store_transition(item_concatenated, 0, float(i) / 2, done_array) return buffer def test_simple_get_frame(): """ Check if get_frame returns frames from a buffer partially full """ observation_space = gym.spaces.Box(low=0, high=255, shape=(2, 2, 1), dtype=int) action_space = gym.spaces.Discrete(4) buffer = CircularVecEnvBufferBackend( 20, num_envs=2, observation_space=observation_space, action_space=action_space, frame_history=4 ) v1 = np.ones(8).reshape((2, 2, 2, 1)) v1[1] *= 2 v2 = v1 * 2 v3 = v1 * 3 buffer.store_transition(v1, 0, 0, False) buffer.store_transition(v2, 0, 0, False) buffer.store_transition(v3, 0, 0, False) assert np.all(buffer.get_frame(0, 0).max(0).max(0) == np.array([0, 0, 0, 1])) assert np.all(buffer.get_frame(1, 0).max(0).max(0) == np.array([0, 0, 1, 2])) assert np.all(buffer.get_frame(2, 0).max(0).max(0) == np.array([0, 1, 2, 3])) assert np.all(buffer.get_frame(0, 1).max(0).max(0) == np.array([0, 0, 0, 2])) assert np.all(buffer.get_frame(1, 1).max(0).max(0) == np.array([0, 0, 2, 4])) assert np.all(buffer.get_frame(2, 1).max(0).max(0) == np.array([0, 2, 4, 6])) with pytest.raises(VelException): buffer.get_frame(3, 0) with pytest.raises(VelException): buffer.get_frame(4, 0) with pytest.raises(VelException): buffer.get_frame(3, 1) with pytest.raises(VelException): buffer.get_frame(4, 1) def test_full_buffer_get_frame(): """ Check if get_frame returns frames for full buffer """ buffer = get_filled_buffer(frame_history=4) nt.assert_array_equal(buffer.get_frame(0, 0).max(0).max(0), np.array([18, 19, 20, 21])) nt.assert_array_equal(buffer.get_frame(1, 0).max(0).max(0), np.array([19, 20, 21, 22])) nt.assert_array_equal(buffer.get_frame(9, 0).max(0).max(0), np.array([27, 28, 29, 30])) nt.assert_array_equal(buffer.get_frame(0, 1).max(0).max(0), np.array([180, 190, 200, 210])) nt.assert_array_equal(buffer.get_frame(1, 1).max(0).max(0), np.array([190, 200, 210, 220])) nt.assert_array_equal(buffer.get_frame(9, 1).max(0).max(0), np.array([270, 280, 290, 300])) with pytest.raises(VelException): buffer.get_frame(10, 0) with pytest.raises(VelException): buffer.get_frame(11, 0) with pytest.raises(VelException): buffer.get_frame(12, 0) with pytest.raises(VelException): buffer.get_frame(10, 1) with pytest.raises(VelException): buffer.get_frame(11, 1) with pytest.raises(VelException): buffer.get_frame(12, 1) nt.assert_array_equal(buffer.get_frame(13, 0).max(0).max(0), np.array([11, 12, 13, 14])) nt.assert_array_equal(buffer.get_frame(19, 0).max(0).max(0), np.array([17, 18, 19, 20])) nt.assert_array_equal(buffer.get_frame(13, 1).max(0).max(0), np.array([110, 120, 130, 140])) nt.assert_array_equal(buffer.get_frame(19, 1).max(0).max(0), np.array([170, 180, 190, 200])) def test_full_buffer_get_future_frame(): """ Check if get_frame_with_future works with full buffer """ buffer = get_filled_buffer(frame_history=4) nt.assert_array_equal(buffer.get_frame_with_future(0, 0)[1].max(0).max(0), np.array([19, 20, 21, 22])) nt.assert_array_equal(buffer.get_frame_with_future(1, 0)[1].max(0).max(0), np.array([20, 21, 22, 23])) nt.assert_array_equal(buffer.get_frame_with_future(0, 1)[1].max(0).max(0), np.array([190, 200, 210, 220])) nt.assert_array_equal(buffer.get_frame_with_future(1, 1)[1].max(0).max(0), np.array([200, 210, 220, 230])) with pytest.raises(VelException): buffer.get_frame_with_future(9, 0) with pytest.raises(VelException): buffer.get_frame_with_future(10, 0) with pytest.raises(VelException): buffer.get_frame_with_future(11, 0) with pytest.raises(VelException): buffer.get_frame_with_future(12, 0) with pytest.raises(VelException): buffer.get_frame_with_future(9, 1) with pytest.raises(VelException): buffer.get_frame_with_future(10, 1) with pytest.raises(VelException): buffer.get_frame_with_future(11, 1) with pytest.raises(VelException): buffer.get_frame_with_future(12, 1) nt.assert_array_equal(buffer.get_frame_with_future(13, 0)[1].max(0).max(0), np.array([12, 13, 14, 15])) nt.assert_array_equal(buffer.get_frame_with_future(19, 0)[1].max(0).max(0), np.array([18, 19, 20, 21])) nt.assert_array_equal(buffer.get_frame_with_future(13, 1)[1].max(0).max(0), np.array([120, 130, 140, 150])) nt.assert_array_equal(buffer.get_frame_with_future(19, 1)[1].max(0).max(0), np.array([180, 190, 200, 210])) def test_buffer_filling_size(): """ Check if buffer size is properly updated when we add items """ observation_space = gym.spaces.Box(low=0, high=255, shape=(2, 2, 1), dtype=int) action_space = gym.spaces.Discrete(4) buffer = CircularVecEnvBufferBackend(20, num_envs=2, observation_space=observation_space, action_space=action_space) v1 = np.ones(8).reshape((2, 2, 2, 1)) assert buffer.current_size == 0 buffer.store_transition(v1, 0, 0, False) buffer.store_transition(v1, 0, 0, False) assert buffer.current_size == 2 for i in range(30): buffer.store_transition(v1 * (i+1), 0, float(i)/2, False) assert buffer.current_size == buffer.buffer_capacity def test_get_frame_with_dones(): """ Check if get_frame works properly in case there are multiple sequences in buffer """ buffer = get_filled_buffer_with_dones(frame_history=4) nt.assert_array_equal(buffer.get_frame(0, 0).max(0).max(0), np.array([0, 0, 20, 21])) nt.assert_array_equal(buffer.get_frame(1, 0).max(0).max(0), np.array([0, 20, 21, 22])) nt.assert_array_equal(buffer.get_frame(2, 0).max(0).max(0), np.array([20, 21, 22, 23])) nt.assert_array_equal(buffer.get_frame(3, 0).max(0).max(0), np.array([0, 0, 0, 24])) nt.assert_array_equal(buffer.get_frame(8, 0).max(0).max(0), np.array([26, 27, 28, 29])) nt.assert_array_equal(buffer.get_frame(9, 0).max(0).max(0), np.array([0, 0, 0, 30])) nt.assert_array_equal(buffer.get_frame(0, 1).max(0).max(0), np.array([0, 190, 200, 210])) nt.assert_array_equal(buffer.get_frame(1, 1).max(0).max(0), np.array([190, 200, 210, 220])) nt.assert_array_equal(buffer.get_frame(2, 1).max(0).max(0), np.array([0, 0, 0, 230])) nt.assert_array_equal(buffer.get_frame(3, 1).max(0).max(0), np.array([0, 0, 230, 240])) nt.assert_array_equal(buffer.get_frame(8, 1).max(0).max(0), np.array([0, 0, 0, 290])) nt.assert_array_equal(buffer.get_frame(9, 1).max(0).max(0), np.array([0, 0, 290, 300])) with pytest.raises(VelException): buffer.get_frame(10, 0) with pytest.raises(VelException): buffer.get_frame(10, 1) nt.assert_array_equal(buffer.get_frame(11, 0).max(0).max(0), np.array([0, 0, 0, 12])) nt.assert_array_equal(buffer.get_frame(12, 0).max(0).max(0), np.array([0, 0, 12, 13])) with pytest.raises(VelException): buffer.get_frame(11, 1) with pytest.raises(VelException): buffer.get_frame(12, 1) def test_get_frame_future_with_dones(): """ Check if get_frame_with_future works properly in case there are multiple sequences in buffer """ buffer = get_filled_buffer_with_dones(frame_history=4) nt.assert_array_equal(buffer.get_frame_with_future(0, 0)[1].max(0).max(0), np.array([0, 20, 21, 22])) nt.assert_array_equal(buffer.get_frame_with_future(1, 0)[1].max(0).max(0), np.array([20, 21, 22, 23])) nt.assert_array_equal(buffer.get_frame_with_future(2, 0)[1].max(0).max(0), np.array([0, 0, 0, 0])) nt.assert_array_equal(buffer.get_frame_with_future(3, 0)[1].max(0).max(0), np.array([0, 0, 24, 25])) nt.assert_array_equal(buffer.get_frame_with_future(8, 0)[1].max(0).max(0), np.array([0, 0, 0, 0])) nt.assert_array_equal(buffer.get_frame_with_future(0, 1)[1].max(0).max(0), np.array([190, 200, 210, 220])) nt.assert_array_equal(buffer.get_frame_with_future(1, 1)[1].max(0).max(0), np.array([0, 0, 0, 0])) nt.assert_array_equal(buffer.get_frame_with_future(2, 1)[1].max(0).max(0), np.array([0, 0, 230, 240])) nt.assert_array_equal(buffer.get_frame_with_future(3, 1)[1].max(0).max(0), np.array([0, 230, 240, 250])) nt.assert_array_equal(buffer.get_frame_with_future(7, 1)[1].max(0).max(0), np.array([0, 0, 0, 0])) with pytest.raises(VelException): buffer.get_frame_with_future(9, 0) with pytest.raises(VelException): buffer.get_frame_with_future(10, 0) nt.assert_array_equal(buffer.get_frame_with_future(11, 0)[1].max(0).max(0), np.array([0, 0, 12, 13])) nt.assert_array_equal(buffer.get_frame_with_future(12, 0)[1].max(0).max(0), np.array([0, 12, 13, 14])) with pytest.raises(VelException): buffer.get_frame_with_future(9, 1) with pytest.raises(VelException): buffer.get_frame_with_future(10, 1) with pytest.raises(VelException): buffer.get_frame(11, 1) with pytest.raises(VelException): buffer.get_frame(12, 1) nt.assert_array_equal(buffer.get_frame_with_future(13, 1)[1].max(0).max(0), np.array([0, 0, 140, 150])) def test_get_batch(): """ Check if get_batch works properly for buffers """ buffer = get_filled_buffer_with_dones(frame_history=4) batch = buffer.get_transitions(np.array([ [0, 1, 2, 3, 4, 5, 6, 7], # Frames for env=0 [1, 2, 3, 4, 5, 6, 7, 8], # Frames for env=1 ]).T) obs = batch['observations'] act = batch['actions'] rew = batch['rewards'] obs_tp1 = batch['observations_next'] dones = batch['dones'] nt.assert_array_equal(dones[:, 0], np.array([False, False, True, False, False, False, False, False])) nt.assert_array_equal(dones[:, 1], np.array([True, False, False, False, False, False, True, False])) nt.assert_array_equal(obs[:, 0].max(1).max(1), np.array([ [0, 0, 20, 21], [0, 20, 21, 22], [20, 21, 22, 23], [0, 0, 0, 24], [0, 0, 24, 25], [0, 24, 25, 26], [24, 25, 26, 27], [25, 26, 27, 28], ])) nt.assert_array_equal(obs[:, 1].max(1).max(1), np.array([ [190, 200, 210, 220], [0, 0, 0, 230], [0, 0, 230, 240], [0, 230, 240, 250], [230, 240, 250, 260], [240, 250, 260, 270], [250, 260, 270, 280], [0, 0, 0, 290], ])) nt.assert_array_equal(act[:, 0], np.array([0, 0, 0, 0, 0, 0, 0, 0])) nt.assert_array_equal(rew[:, 0], np.array([10.0, 10.5, 11.0, 11.5, 12.0, 12.5, 13.0, 13.5])) nt.assert_array_equal(act[:, 1], np.array([0, 0, 0, 0, 0, 0, 0, 0])) nt.assert_array_equal(rew[:, 1], np.array([10.5, 11.0, 11.5, 12.0, 12.5, 13.0, 13.5, 14.0])) nt.assert_array_equal(obs_tp1[:, 0].max(1).max(1), np.array([ [0, 20, 21, 22], [20, 21, 22, 23], [0, 0, 0, 0], [0, 0, 24, 25], [0, 24, 25, 26], [24, 25, 26, 27], [25, 26, 27, 28], [26, 27, 28, 29] ])) nt.assert_array_equal(obs_tp1[:, 1].max(1).max(1), np.array([ [0, 0, 0, 0], [0, 0, 230, 240], [0, 230, 240, 250], [230, 240, 250, 260], [240, 250, 260, 270], [250, 260, 270, 280], [0, 0, 0, 0], [0, 0, 290, 300], ])) with pytest.raises(VelException): buffer.get_transitions(np.array([ [0, 1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 3, 4, 5, 6, 7, 8, 9] ]).T) def test_sample_and_get_batch(): """ Check if batch sampling works properly """ buffer = get_filled_buffer_with_dones(frame_history=4) for i in range(100): indexes = buffer.sample_batch_transitions(batch_size=5) batch = buffer.get_transitions(indexes) obs = batch['observations'] act = batch['actions'] rew = batch['rewards'] obs_tp1 = batch['observations_next'] dones = batch['dones'] with pytest.raises(AssertionError): nt.assert_array_equal(indexes[:, 0], indexes[:, 1]) assert obs.shape[0] == 5 assert act.shape[0] == 5 assert rew.shape[0] == 5 assert obs_tp1.shape[0] == 5 assert dones.shape[0] == 5 def test_storing_extra_info(): """ Make sure additional information are stored and recovered properly """ buffer = get_filled_buffer_extra_info(frame_history=4) indexes = np.array([ [0, 1, 2, 17, 18, 19], [0, 1, 2, 17, 18, 19], ]).T batch = buffer.get_transitions(indexes) nt.assert_equal(batch['neglogp'][0, 0], 20.0/30) nt.assert_equal(batch['neglogp'][1, 0], 21.0/30) nt.assert_equal(batch['neglogp'][2, 0], 22.0/30) nt.assert_equal(batch['neglogp'][3, 0], 17.0/30) nt.assert_equal(batch['neglogp'][4, 0], 18.0/30) nt.assert_equal(batch['neglogp'][5, 0], 19.0/30) nt.assert_equal(batch['neglogp'][0, 1], 21.0/30) nt.assert_equal(batch['neglogp'][1, 1], 22.0/30) nt.assert_equal(batch['neglogp'][2, 1], 23.0/30) nt.assert_equal(batch['neglogp'][3, 1], 18.0/30) nt.assert_equal(batch['neglogp'][4, 1], 19.0/30) nt.assert_equal(batch['neglogp'][5, 1], 20.0/30) def test_sample_rollout_half_filled(): """ Test if sampling rollout is correct and returns proper results """ buffer = get_half_filled_buffer(frame_history=4) indexes = [] for i in range(1000): rollout_idx = buffer.sample_batch_trajectories(rollout_length=5) rollout = buffer.get_trajectories(indexes=rollout_idx, rollout_length=5) assert rollout['observations'].shape[0] == 5 # Rollout length assert rollout['observations'].shape[-1] == 4 # History length indexes.append(rollout_idx) assert np.min(indexes) == 4 assert np.max(indexes) == 8 with pytest.raises(VelException): buffer.sample_batch_trajectories(rollout_length=10) rollout_idx = buffer.sample_batch_trajectories(rollout_length=9) rollout = buffer.get_trajectories(indexes=rollout_idx, rollout_length=9) nt.assert_array_equal(rollout_idx, np.array([8, 8])) nt.assert_array_equal(rollout['rewards'], np.array([ [0., 0.5, 1., 1.5, 2., 2.5, 3., 3.5, 4.], [0., 0.5, 1., 1.5, 2., 2.5, 3., 3.5, 4.], ]).T) def test_sample_rollout_filled(): """ Test if sampling rollout is correct and returns proper results """ buffer = get_filled_buffer(frame_history=4) indexes = [] for i in range(1000): rollout_idx = buffer.sample_batch_trajectories(rollout_length=5) rollout = buffer.get_trajectories(indexes=rollout_idx, rollout_length=5) assert rollout['observations'].shape[0] == 5 # Rollout length assert rollout['observations'].shape[-1] == 4 # History length indexes.append(rollout_idx) assert np.min(indexes) == 0 assert np.max(indexes) == 19 with pytest.raises(VelException): buffer.sample_batch_trajectories(rollout_length=17) max_rollout = buffer.sample_batch_trajectories(rollout_length=16) rollout = buffer.get_trajectories(max_rollout, rollout_length=16) nt.assert_array_equal(max_rollout, np.array([8, 8])) assert np.sum(rollout['rewards']) == pytest.approx(164.0 * 2, 1e-5) def test_buffer_flexible_obs_action_sizes(): b1x1 = get_filled_buffer1x1(frame_history=1) b2x2 = get_filled_buffer2x2(frame_history=1) b3x3 = get_filled_buffer3x3(frame_history=1) nt.assert_array_almost_equal(b1x1.get_frame(0, 0), np.array([21, 210])) nt.assert_array_almost_equal(b2x2.get_frame(0, 0), np.array([[21, 21], [210, 210]])) nt.assert_array_almost_equal(b3x3.get_frame(0, 0), np.array([[[21, 21], [21, 21]], [[210, 210], [210, 210]]])) nt.assert_array_almost_equal(b1x1.get_transition(0, 0)['actions'], np.array([0, 20])) nt.assert_array_almost_equal(b2x2.get_transition(0, 0)['actions'], np.array([[0, 20], [40, 60]])) nt.assert_array_almost_equal(b3x3.get_transition(0, 0)['actions'], np.array( [ [[0, 20], [40, 60]], [[80, 100], [120, 140]] ] )) def test_buffer_flexible_obs_action_sizes_with_history(): b1x1 = get_filled_buffer1x1_history(frame_history=2) b2x2 = get_filled_buffer2x2_history(frame_history=2) b3x3 = get_filled_buffer3x3_history(frame_history=2) nt.assert_array_almost_equal(b1x1.get_frame(0, 0), np.array([[20, 21], [200, 210]])) nt.assert_array_almost_equal(b2x2.get_frame(0, 0), np.array([[[20, 21], [20, 21]], [[200, 210], [200, 210]]])) nt.assert_array_almost_equal(b3x3.get_frame(0, 0), np.array( [[[[20, 21], [20, 21]], [[20, 21], [20, 21]]], [[[200, 210], [200, 210]], [[200, 210], [200, 210]]]] )) nt.assert_array_almost_equal(b1x1.get_transition(0, 0)['observations_next'], np.array([[21, 22], [210, 220]])) nt.assert_array_almost_equal(b2x2.get_transition(0, 0)['observations_next'], np.array( [[[21, 22], [21, 22]], [[210, 220], [210, 220]]] )) nt.assert_array_almost_equal(b3x3.get_transition(0, 0)['observations_next'], np.array( [[[[21, 22], [21, 22]], [[21, 22], [21, 22]]], [[[210, 220], [210, 220]], [[210, 220], [210, 220]]]] )) def test_frame_stack_compensation_single_dim(): buffer = get_filled_buffer_frame_stack(frame_stack=4, frame_dim=1) observations_1 = buffer.get_transition(frame_idx=0, env_idx=0)['observations'] observations_2 = buffer.get_transition(frame_idx=1, env_idx=0)['observations'] observations_3 = buffer.get_transition(frame_idx=2, env_idx=0)['observations'] nt.assert_array_almost_equal( observations_1, np.array([[[0, 0, 20, 21], [0, 0, 20, 21]], [[0, 0, 200, 210], [0, 0, 200, 210]]]) ) nt.assert_array_almost_equal( observations_2, np.array([[[0, 20, 21, 22], [0, 20, 21, 22]], [[0, 200, 210, 220], [0, 200, 210, 220]]]) ) nt.assert_array_almost_equal( observations_3, np.array([[[20, 21, 22, 23], [20, 21, 22, 23]], [[200, 210, 220, 230], [200, 210, 220, 230]]]) ) def test_frame_stack_compensation_multi_dim(): buffer = get_filled_buffer_frame_stack(frame_stack=4, frame_dim=2) observations_1 = buffer.get_transition(frame_idx=0, env_idx=0)['observations'] observations_2 = buffer.get_transition(frame_idx=1, env_idx=0)['observations'] observations_3 = buffer.get_transition(frame_idx=2, env_idx=0)['observations'] nt.assert_array_almost_equal( observations_1, np.array([[[0, 0, 0, 0, 20, 20, 21, 21], [0, 0, 0, 0, 20, 20, 21, 21]], [[0, 0, 0, 0, 200, 200, 210, 210], [0, 0, 0, 0, 200, 200, 210, 210]]]) ) nt.assert_array_almost_equal( observations_2, np.array([[[0, 0, 20, 20, 21, 21, 22, 22], [0, 0, 20, 20, 21, 21, 22, 22]], [[0, 0, 200, 200, 210, 210, 220, 220], [0, 0, 200, 200, 210, 210, 220, 220]]]) ) nt.assert_array_almost_equal( observations_3, np.array([[[20, 20, 21, 21, 22, 22, 23, 23], [20, 20, 21, 21, 22, 22, 23, 23]], [[200, 200, 210, 210, 220, 220, 230, 230], [200, 200, 210, 210, 220, 220, 230, 230]]]) ) def test_get_frame_with_future_forward_steps_exceptions(): """ Test function get_frame_with_future_forward_steps. Does it throw vel exception properly if and only if cannot provide enough future frames. """ buffer = get_filled_buffer_frame_stack(frame_stack=4, frame_dim=2) buffer.get_frame_with_future_forward_steps(0, 0, forward_steps=2, discount_factor=0.9) buffer.get_frame_with_future_forward_steps(1, 0, forward_steps=2, discount_factor=0.9) buffer.get_frame_with_future_forward_steps(2, 0, forward_steps=2, discount_factor=0.9) buffer.get_frame_with_future_forward_steps(3, 0, forward_steps=2, discount_factor=0.9) buffer.get_frame_with_future_forward_steps(4, 0, forward_steps=2, discount_factor=0.9) buffer.get_frame_with_future_forward_steps(5, 0, forward_steps=2, discount_factor=0.9) buffer.get_frame_with_future_forward_steps(6, 0, forward_steps=2, discount_factor=0.9) buffer.get_frame_with_future_forward_steps(7, 0, forward_steps=2, discount_factor=0.9) with pytest.raises(VelException): # No future for the frame buffer.get_frame_with_future_forward_steps(8, 0, forward_steps=2, discount_factor=0.9) with pytest.raises(VelException): # No future for the frame buffer.get_frame_with_future_forward_steps(9, 0, forward_steps=2, discount_factor=0.9) with pytest.raises(VelException): # No history for the frame buffer.get_frame_with_future_forward_steps(10, 0, forward_steps=2, discount_factor=0.9) buffer.get_frame_with_future_forward_steps(11, 0, forward_steps=2, discount_factor=0.9) buffer.get_frame_with_future_forward_steps(12, 0, forward_steps=2, discount_factor=0.9) buffer.get_frame_with_future_forward_steps(13, 0, forward_steps=2, discount_factor=0.9) buffer.get_frame_with_future_forward_steps(14, 0, forward_steps=2, discount_factor=0.9) buffer.get_frame_with_future_forward_steps(15, 0, forward_steps=2, discount_factor=0.9) buffer.get_frame_with_future_forward_steps(16, 0, forward_steps=2, discount_factor=0.9) buffer.get_frame_with_future_forward_steps(17, 0, forward_steps=2, discount_factor=0.9) buffer.get_frame_with_future_forward_steps(18, 0, forward_steps=2, discount_factor=0.9) buffer.get_frame_with_future_forward_steps(19, 0, forward_steps=2, discount_factor=0.9) with pytest.raises(VelException): # Index beyond buffer size buffer.get_frame_with_future_forward_steps(20, 0, forward_steps=2, discount_factor=0.9) def test_get_frame_with_future_forward_steps_with_dones(): """ Test function get_frame_with_future_forward_steps. Does it return empty frame if there is a done in between. Does it return correct rewards if there is a done in between Does it return correct rewards if there is no done in between """ buffer = get_filled_buffer_frame_stack(frame_stack=4, frame_dim=2) # Just a check to be sure nt.assert_array_equal( buffer.dones_buffer[:, 0], np.array([ False, False, True, False, False, False, False, False, True, False, True, False, False, True, False, False, False, False, True, False ]) ) nt.assert_array_equal( buffer.reward_buffer[:, 0], np.array([ 10., 10.5, 11., 11.5, 12., 12.5, 13., 13.5, 14., 14.5, 5., 5.5, 6., 6.5, 7., 7.5, 8., 8.5, 9., 9.5 ]) ) for i in [0, 3, 4, 5, 6, 11, 14, 15, 19]: result = buffer.get_frame_with_future_forward_steps(i, 0, forward_steps=2, discount_factor=0.9) next_frame = result[1] reward = result[2] done = result[3] assert next_frame.max() != 0 assert done is False assert reward == buffer.reward_buffer[i, 0] + 0.9 * buffer.reward_buffer[(i+1) % 20, 0] for i in [1, 2, 7, 12, 13, 17, 18]: result = buffer.get_frame_with_future_forward_steps(i, 0, forward_steps=2, discount_factor=0.9) next_frame = result[1] done = result[3] assert next_frame.max() == 0 assert done is True for i in [1, 7, 12, 17]: result = buffer.get_frame_with_future_forward_steps(i, 0, forward_steps=2, discount_factor=0.9) reward = result[2] assert reward == buffer.reward_buffer[i, 0] + 0.9 * buffer.reward_buffer[(i+1) % 20, 0] for i in [2, 13, 18]: result = buffer.get_frame_with_future_forward_steps(i, 0, forward_steps=2, discount_factor=0.9) reward = result[2] assert reward == buffer.reward_buffer[i, 0] def test_get_frame_with_future_forward_steps_without_dones(): """ Test function get_frame_with_future_forward_steps. Does it return correct frame if there is no done in between """ buffer = get_filled_buffer_frame_stack(frame_stack=4, frame_dim=2) result = buffer.get_frame_with_future_forward_steps(0, 0, forward_steps=2, discount_factor=0.9) frame = result[0] future_frame = result[1] nt.assert_array_equal( frame, np.array([[[0, 0, 0, 0, 20, 20, 21, 21], [0, 0, 0, 0, 20, 20, 21, 21]], [[0, 0, 0, 0, 200, 200, 210, 210], [0, 0, 0, 0, 200, 200, 210, 210]]]) ) nt.assert_array_equal( future_frame, np.array([[[20, 20, 21, 21, 22, 22, 23, 23], [20, 20, 21, 21, 22, 22, 23, 23]], [[200, 200, 210, 210, 220, 220, 230, 230], [200, 200, 210, 210, 220, 220, 230, 230]]]) ) result = buffer.get_frame_with_future_forward_steps(3, 0, forward_steps=4, discount_factor=0.9) frame = result[0] future_frame = result[1] nt.assert_array_equal( frame, np.array([[[0, 0, 0, 0, 0, 0, 24, 24], [0, 0, 0, 0, 0, 0, 24, 24]], [[0, 0, 0, 0, 0, 0, 240, 240], [0, 0, 0, 0, 0, 0, 240, 240]]]) ) nt.assert_array_equal( future_frame, np.array([[[25, 25, 26, 26, 27, 27, 28, 28], [25, 25, 26, 26, 27, 27, 28, 28]], [[250, 250, 260, 260, 270, 270, 280, 280], [250, 250, 260, 260, 270, 270, 280, 280]]]) ) result = buffer.get_frame_with_future_forward_steps(19, 0, forward_steps=2, discount_factor=0.9) frame = result[0] future_frame = result[1] nt.assert_array_equal( frame, np.array([[[0, 0, 0, 0, 0, 0, 20, 20], [0, 0, 0, 0, 0, 0, 20, 20]], [[0, 0, 0, 0, 0, 0, 200, 200], [0, 0, 0, 0, 0, 0, 200, 200]]]) ) nt.assert_array_equal( future_frame, np.array([[[0, 0, 20, 20, 21, 21, 22, 22], [0, 0, 20, 20, 21, 21, 22, 22]], [[0, 0, 200, 200, 210, 210, 220, 220], [0, 0, 200, 200, 210, 210, 220, 220]]]) )

37.252665

120

0.624388

5,467

34,943

3.796415

0.044083

0.017249

0.07285

0.059841

0.898723

0.872416

0.849627

0.824331

0.755095

0.728355

0

0.11079

0.218613

34,943

937

121

37.292423

0.649355

0.054517

0

0.513302

0

0.011971

0

0.200313

1

0.045383

false

0

0.010955

0

0.073552

0

null

0

1

0

null

0

6

7b72186af6585eabbe5fd83946de8cfad8387a25

72

py

Python

CrashCourse/cOOP/hello.py

atabaksahraei/Python-for-Developer

6972b7c9a500312ce6a359817feb5f8461391078

[ "MIT" ]

null

CrashCourse/cOOP/hello.py

atabaksahraei/Python-for-Developer

6972b7c9a500312ce6a359817feb5f8461391078

[ "MIT" ]

null

CrashCourse/cOOP/hello.py

atabaksahraei/Python-for-Developer

6972b7c9a500312ce6a359817feb5f8461391078

[ "MIT" ]

null

def welt(): print("Hallo Welt") def mars(): print("Hallo Mars")

14.4

23

0.583333

10

72

4.2

0.5

0.47619

0

0.222222

72

5

24

14.4

0.75

0

0.273973

0

1

0.5

true

0

0.5

1

0

null

1

0

1

0

null

0

1

0

1

0

6

7b7d07626d7be6284893caf95bddb00d9476e477

1,681

py

Python

products/migrations/0002_auto_20200315_1517.py

gwoods22/beer-store-api

c21593734022718896720db916b73f0404840dc2

[ "MIT" ]

1

2020-09-10T16:56:56.000Z

products/migrations/0002_auto_20200315_1517.py

gwoods22/beer-store-api

c21593734022718896720db916b73f0404840dc2

[ "MIT" ]

null

products/migrations/0002_auto_20200315_1517.py

gwoods22/beer-store-api

c21593734022718896720db916b73f0404840dc2

[ "MIT" ]

1

2020-09-20T17:47:07.000Z

# Generated by Django 3.0.4 on 2020-03-15 15:17 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('products', '0001_initial'), ] operations = [ migrations.AddField( model_name='product', name='price_per_100ml', field=models.DecimalField(blank=True, decimal_places=2, default=None, max_digits=4, null=True), ), migrations.AddField( model_name='product', name='price_per_abv', field=models.DecimalField(blank=True, decimal_places=2, default=None, max_digits=4, null=True), ), migrations.AlterField( model_name='product', name='attributes', field=models.CharField(default='N/A', max_length=255), ), migrations.AlterField( model_name='product', name='brewer', field=models.CharField(default='N/A', max_length=255), ), migrations.AlterField( model_name='product', name='category', field=models.CharField(default='N/A', max_length=255), ), migrations.AlterField( model_name='product', name='country', field=models.CharField(default='N/A', max_length=255), ), migrations.AlterField( model_name='product', name='style', field=models.CharField(default='N/A', max_length=255), ), migrations.AlterField( model_name='product', name='type', field=models.CharField(default='N/A', max_length=255), ), ]

31.12963

107

0.559786

171

1,681

5.368421

0.321637

0.078431

0.139434

0.174292

0.793028

0.760349

0.671024

0.626362

0

0.038029

0.311719

1,681

53

108

31.716981

0.755402

0.02677

0

0.680851

1

0

0.099143

0

1

0

false

0

0.021277

0

0.085106

0

null

0

1

0

1

0

1

0

1

0

null

0

6

7b9ebd390fc8c0cc6c28cfcd6c25c2169c0a6990

4,672

py

Python

src/neural_nets.py

akensert/ddqn-isocratic-scouting-runs

ab8a87ce6dbf01cba9ffb92ea13c98ffc3a70e93

[ "MIT" ]

null

src/neural_nets.py

akensert/ddqn-isocratic-scouting-runs

ab8a87ce6dbf01cba9ffb92ea13c98ffc3a70e93

[ "MIT" ]

null

src/neural_nets.py

akensert/ddqn-isocratic-scouting-runs

ab8a87ce6dbf01cba9ffb92ea13c98ffc3a70e93

[ "MIT" ]

null

import tensorflow as tf from tensorflow.keras.initializers import TruncatedNormal RANDOM_SEED = 42 class QNetwork(tf.keras.Model): def __init__(self, hidden_sizes=[1024, 1024], dropout_rates=[0.2, 0.2], output_dims=11): super(QNetwork, self).__init__() self.dense_block = tf.keras.models.Sequential([ tf.keras.layers.Dense( units=hidden_sizes[0], kernel_initializer=TruncatedNormal(0.0, 0.05, seed=RANDOM_SEED)), tf.keras.layers.Activation('relu'), tf.keras.layers.Dropout(dropout_rates[0], seed=RANDOM_SEED), tf.keras.layers.Dense( units=hidden_sizes[1], kernel_initializer=TruncatedNormal(0.0, 0.05, seed=RANDOM_SEED)), tf.keras.layers.Activation('relu'), tf.keras.layers.Dropout(dropout_rates[1], seed=RANDOM_SEED), tf.keras.layers.Dense( units=output_dims, kernel_initializer=TruncatedNormal(0.0, 0.05, seed=RANDOM_SEED)), ]) def call(self, inputs): inputs = tf.where( inputs >= 0, tf.math.log(tf.math.maximum(inputs, 0.001)), -10) return self.dense_block(inputs) class DuelingNetwork(tf.keras.Model): def __init__(self, hidden_sizes=[1024, 1024], dropout_rates=[0.2, 0.2], output_dims=11): super(DuelingNetwork, self).__init__() self.feat_block = tf.keras.models.Sequential([ tf.keras.layers.Dense( units=hidden_sizes[0], kernel_initializer=TruncatedNormal(0.0, 0.05, seed=RANDOM_SEED)), tf.keras.layers.Activation('relu'), tf.keras.layers.Dropout(dropout_rates[0], seed=RANDOM_SEED), ]) self.val_block = tf.keras.models.Sequential([ tf.keras.layers.Dense( units=hidden_sizes[1], kernel_initializer=TruncatedNormal(0.0, 0.05, seed=RANDOM_SEED)), tf.keras.layers.Activation('relu'), tf.keras.layers.Dropout(dropout_rates[1], seed=RANDOM_SEED), tf.keras.layers.Dense( units=1, kernel_initializer=TruncatedNormal(0.0, 0.05, seed=RANDOM_SEED)), ]) self.adv_block = tf.keras.models.Sequential([ tf.keras.layers.Dense( units=hidden_sizes[1], kernel_initializer=TruncatedNormal(0.0, 0.05, seed=RANDOM_SEED)), tf.keras.layers.Activation('relu'), tf.keras.layers.Dropout(dropout_rates[1], seed=RANDOM_SEED), tf.keras.layers.Dense( units=output_dims, kernel_initializer=TruncatedNormal(0.0, 0.05, seed=RANDOM_SEED)), ]) def call(self, inputs): inputs = tf.where( inputs >= 0, tf.math.log(tf.math.maximum(inputs, 0.001)), -10) feat = self.feat_block(inputs) vals = self.val_block(feat) advs = self.adv_block(feat) qvals = vals + (advs - tf.math.reduce_mean(advs)) return qvals class ActorCriticNetwork(tf.keras.Model): def __init__(self, hidden_units=[1024, 1024], dropout_rates=[0.2, 0.2], output_dims=[11, 1]): super(ActorCriticNetwork, self).__init__() self.actor = tf.keras.models.Sequential([ tf.keras.layers.Dense( units=hidden_units[0], kernel_initializer=TruncatedNormal(0.0, 0.05, seed=RANDOM_SEED)), tf.keras.layers.Activation('relu'), tf.keras.layers.Dropout(dropout_rates[0], seed=RANDOM_SEED), tf.keras.layers.Dense( units=output_dims[0], kernel_initializer=TruncatedNormal(0.0, 0.05, seed=RANDOM_SEED)), tf.keras.layers.Activation('softmax'), ]) self.critic = tf.keras.models.Sequential([ tf.keras.layers.Dense( units=hidden_units[0], kernel_initializer=TruncatedNormal(0.0, 0.05, seed=RANDOM_SEED)), tf.keras.layers.Activation('relu'), tf.keras.layers.Dropout(dropout_rates[0], seed=RANDOM_SEED), tf.keras.layers.Dense( units=output_dims[1], kernel_initializer=TruncatedNormal(0.0, 0.05, seed=RANDOM_SEED)) ]) def call(self, inputs): inputs = tf.where( inputs >= 0, tf.math.log(tf.math.maximum(inputs, 0.001)), -10) policy_dist = self.actor(inputs) value = self.critic(inputs) return policy_dist, value

37.079365

81

0.579409

551

4,672

4.742287

0.117967

0.096441

0.134328

0.085725

0.819747

0.808649

0

0.043386

0.294521

4,672

125

82

37.376

0.749393

0

0.696078

0

0.007491

0

1

0.058824

false

0

0.019608

0

0.137255

0

null

0

1

0

null

0

6

a809f168ed42ee09b4353553a47ebb338b7f26d7

49

py

Python

examples/example_resolve.py

juancarlospaco/thatlib

37403983c228521b992ad592231957a1c7af01f2

[ "MIT" ]

31

2021-05-12T16:54:34.000Z

2022-02-17T12:36:52.000Z

examples/example_resolve.py

juancarlospaco/thatlib

37403983c228521b992ad592231957a1c7af01f2

[ "MIT" ]

1

2021-07-23T02:58:07.000Z

2021-09-03T21:53:29.000Z

examples/example_resolve.py

juancarlospaco/thatlib

37403983c228521b992ad592231957a1c7af01f2

[ "MIT" ]

1

2021-05-12T22:12:20.000Z

from thatlib import resolve print(resolve("./"))

16.333333

27

0.734694

6

49

6

0.833333

0

0.102041

49

2

28

24.5

0.818182

0

0.040816

0

1

0

true

0

0.5

0

0.5

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

b541aed235c3941ffbe34c1759517cd9f51a7549

18

py

Python

src/__init__.py

AlexanderFengler/ssm_simulators

cf650641647b7c049e60c48dde365607c8d3c54a

[ "MIT" ]

1

2021-10-31T15:08:11.000Z

src/__init__.py

AlexanderFengler/ssm_simulators

cf650641647b7c049e60c48dde365607c8d3c54a

[ "MIT" ]

3

2021-07-30T15:57:56.000Z

2022-02-25T02:47:09.000Z

src/__init__.py

AlexanderFengler/ssm_simulators

cf650641647b7c049e60c48dde365607c8d3c54a

[ "MIT" ]

null

from . import cssm

18

0.777778

3

18

4.666667

1

0

0.166667

18

1

18

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

b59e1b766deaf1e0fb27afacbceaea13cd456868

5,862

py

Python

tests/s3/test_s3_collections.py

paulhutchings/beartype-boto3-example

d69298d9444d578799e2a17cb63de11474b2278a

[ "MIT" ]

3

2021-11-16T06:21:11.000Z

2021-11-22T08:59:11.000Z

tests/s3/test_s3_collections.py

paulhutchings/beartype-boto3-example

d69298d9444d578799e2a17cb63de11474b2278a

[ "MIT" ]

9

2021-11-19T03:29:00.000Z

2021-12-30T23:54:47.000Z

tests/s3/test_s3_collections.py

paulhutchings/beartype-boto3-example

d69298d9444d578799e2a17cb63de11474b2278a

[ "MIT" ]

null

import pytest from bearboto3.s3 import ( ServiceResourceBucketsCollection, BucketMultipartUploadsCollection, BucketObjectVersionsCollection, BucketObjectsCollection, MultipartUploadPartsCollection, ) from beartype import beartype from beartype.roar import ( BeartypeCallHintPepParamException, BeartypeCallHintPepReturnException, BeartypeDecorHintPep484585Exception, ) # ============================ # ServiceResourceBucketsCollection # ============================ def test_buckets_arg_pass(gen_service_resource_buckets_collection): @beartype def func(param: ServiceResourceBucketsCollection): pass func(gen_service_resource_buckets_collection) def test_buckets_arg_fail(gen_bucket_objects_collection): with pytest.raises(BeartypeCallHintPepParamException): @beartype def func(param: ServiceResourceBucketsCollection): pass func(gen_bucket_objects_collection) def test_buckets_return_pass(gen_service_resource_buckets_collection): @beartype def func() -> ServiceResourceBucketsCollection: return gen_service_resource_buckets_collection func() def test_buckets_return_fail(gen_bucket_objects_collection): with pytest.raises( (BeartypeCallHintPepReturnException, BeartypeDecorHintPep484585Exception) ): @beartype def func() -> ServiceResourceBucketsCollection: return gen_bucket_objects_collection func() # ============================ # BucketMultipartUploadsCollection # ============================ def test_multipart_uploads_arg_pass(gen_bucket_multipart_uploads_collection): @beartype def func(param: BucketMultipartUploadsCollection): pass func(gen_bucket_multipart_uploads_collection) def test_multipart_uploads_arg_fail(gen_bucket_object_versions_collection): with pytest.raises(BeartypeCallHintPepParamException): @beartype def func(param: BucketMultipartUploadsCollection): pass func(gen_bucket_object_versions_collection) def test_multipart_uploads_return_pass(gen_bucket_multipart_uploads_collection): @beartype def func() -> BucketMultipartUploadsCollection: return gen_bucket_multipart_uploads_collection func() def test_multipart_uploads_return_fail(gen_bucket_object_versions_collection): with pytest.raises( (BeartypeCallHintPepReturnException, BeartypeDecorHintPep484585Exception) ): @beartype def func() -> BucketMultipartUploadsCollection: return gen_bucket_object_versions_collection func() # ============================ # BucketObjectVersionsCollection # ============================ def test_object_versions_arg_pass(gen_bucket_object_versions_collection): @beartype def func(param: BucketObjectVersionsCollection): pass func(gen_bucket_object_versions_collection) def test_object_versions_arg_fail(gen_bucket_objects_collection): with pytest.raises(BeartypeCallHintPepParamException): @beartype def func(param: BucketObjectVersionsCollection): pass func(gen_bucket_objects_collection) def test_object_versions_return_pass(gen_bucket_object_versions_collection): @beartype def func() -> BucketObjectVersionsCollection: return gen_bucket_object_versions_collection func() def test_object_versions_return_fail(gen_bucket_objects_collection): with pytest.raises( (BeartypeCallHintPepReturnException, BeartypeDecorHintPep484585Exception) ): @beartype def func() -> BucketObjectVersionsCollection: return gen_bucket_objects_collection func() # ============================ # BucketObjectsCollection # ============================ def test_objects_arg_pass(gen_bucket_objects_collection): @beartype def func(param: BucketObjectsCollection): pass func(gen_bucket_objects_collection) def test_objects_arg_fail(gen_service_resource_buckets_collection): with pytest.raises(BeartypeCallHintPepParamException): @beartype def func(param: BucketObjectsCollection): pass func(gen_service_resource_buckets_collection) def test_objects_return_pass(gen_bucket_objects_collection): @beartype def func() -> BucketObjectsCollection: return gen_bucket_objects_collection func() def test_objects_return_fail(gen_service_resource_buckets_collection): with pytest.raises( (BeartypeCallHintPepReturnException, BeartypeDecorHintPep484585Exception) ): @beartype def func() -> BucketObjectsCollection: return gen_service_resource_buckets_collection func() # ============================ # MultipartUploadPartsCollection # ============================ def test_parts_arg_pass(gen_multipart_upload_parts_collection): @beartype def func(param: MultipartUploadPartsCollection): pass func(gen_multipart_upload_parts_collection) def test_parts_arg_fail(gen_bucket_object_versions_collection): with pytest.raises(BeartypeCallHintPepParamException): @beartype def func(param: MultipartUploadPartsCollection): pass func(gen_bucket_object_versions_collection) def test_parts_return_pass(gen_multipart_upload_parts_collection): @beartype def func() -> MultipartUploadPartsCollection: return gen_multipart_upload_parts_collection func() def test_parts_return_fail(gen_bucket_object_versions_collection): with pytest.raises( (BeartypeCallHintPepReturnException, BeartypeDecorHintPep484585Exception) ): @beartype def func() -> MultipartUploadPartsCollection: return gen_bucket_object_versions_collection func()

25.486957

81

0.724497

498

5,862

8.094378

0.076305

0.062516

0.074423

0.0774

0.839742

0.76904

0.701563

0.60903

0.511784

0.243116

0

0.007882

0.177584

5,862

229

82

25.598253

0.828251

0.07523

0

0.716418

0

1

0.298507

false

0.149254

0.029851

0.074627

0.402985

0

null

0

1

0

1

0

null

0

1

0

1

0

6

b5a2999fd0d0979a0647b76e28afd188fe758e39

230

py

Python

CodeWars/2016/FindCapitals-7k.py

JLJTECH/TutorialTesting

f2dbbd49a86b3b086d0fc156ac3369fb74727f86

[ "MIT" ]

null

CodeWars/2016/FindCapitals-7k.py

JLJTECH/TutorialTesting

f2dbbd49a86b3b086d0fc156ac3369fb74727f86

[ "MIT" ]

null

CodeWars/2016/FindCapitals-7k.py

JLJTECH/TutorialTesting

f2dbbd49a86b3b086d0fc156ac3369fb74727f86

[ "MIT" ]

null

#Return list index of all capital letters in string def capitals(word): return [l for l, c in enumerate(word) if c.isupper()] #Alternate Solution def capitals(word): return [i for (i, c) in enumerate(word) if c.isupper()]

32.857143

59

0.708696

39

230

4.179487

0.538462

0.134969

0.184049

0.257669

0.319018

0

0.178261

230

7

59

32.857143

0.862434

0.295652

0

0.5

0

1

0.5

false

0

0.5

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

a912f357e6076933c0ebd7b06dfd11f21c274f62

10,427

py

Python

addons/project/tests/test_access_rights.py

SHIVJITH/Odoo_Machine_Test

310497a9872db7844b521e6dab5f7a9f61d365a4

[ "Apache-2.0" ]

null

addons/project/tests/test_access_rights.py

SHIVJITH/Odoo_Machine_Test

310497a9872db7844b521e6dab5f7a9f61d365a4

[ "Apache-2.0" ]

null

addons/project/tests/test_access_rights.py

SHIVJITH/Odoo_Machine_Test

310497a9872db7844b521e6dab5f7a9f61d365a4

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # Part of Odoo. See LICENSE file for full copyright and licensing details. from odoo.addons.mail.tests.common import mail_new_test_user from odoo.addons.project.tests.test_project_base import TestProjectCommon from odoo.exceptions import AccessError, ValidationError from odoo.tests.common import users class TestAccessRights(TestProjectCommon): def setUp(self): super().setUp() self.task = self.create_task('Make the world a better place') self.user = mail_new_test_user(self.env, 'Internal user', groups='base.group_user') self.portal = mail_new_test_user(self.env, 'Portal user', groups='base.group_portal') def create_task(self, name, *, with_user=None, **kwargs): values = dict(name=name, project_id=self.project_pigs.id, **kwargs) return self.env['project.task'].with_user(with_user or self.env.user).create(values) class TestCRUDVisibilityFollowers(TestAccessRights): def setUp(self): super().setUp() self.project_pigs.privacy_visibility = 'followers' @users('Internal user', 'Portal user') def test_project_no_write(self): with self.assertRaises(AccessError, msg="%s should not be able to write on the project" % self.env.user.name): self.project_pigs.with_user(self.env.user).name = "Take over the world" self.project_pigs.allowed_user_ids = self.env.user with self.assertRaises(AccessError, msg="%s should not be able to write on the project" % self.env.user.name): self.project_pigs.with_user(self.env.user).name = "Take over the world" @users('Internal user', 'Portal user') def test_project_no_unlink(self): self.project_pigs.task_ids.unlink() with self.assertRaises(AccessError, msg="%s should not be able to unlink the project" % self.env.user.name): self.project_pigs.with_user(self.env.user).unlink() self.project_pigs.allowed_user_ids = self.env.user self.project_pigs.task_ids.unlink() with self.assertRaises(AccessError, msg="%s should not be able to unlink the project" % self.env.user.name): self.project_pigs.with_user(self.env.user).unlink() @users('Internal user', 'Portal user') def test_project_no_read(self): self.project_pigs.invalidate_cache() with self.assertRaises(AccessError, msg="%s should not be able to read the project" % self.env.user.name): self.project_pigs.with_user(self.env.user).name @users('Portal user') def test_project_allowed_portal_no_read(self): self.project_pigs.allowed_user_ids = self.env.user self.project_pigs.invalidate_cache() with self.assertRaises(AccessError, msg="%s should not be able to read the project" % self.env.user.name): self.project_pigs.with_user(self.env.user).name @users('Internal user') def test_project_allowed_internal_read(self): self.project_pigs.allowed_user_ids = self.env.user self.project_pigs.invalidate_cache() self.project_pigs.with_user(self.env.user).name @users('Internal user', 'Portal user') def test_task_no_read(self): self.task.invalidate_cache() with self.assertRaises(AccessError, msg="%s should not be able to read the task" % self.env.user.name): self.task.with_user(self.env.user).name @users('Portal user') def test_task_allowed_portal_no_read(self): self.project_pigs.allowed_user_ids = self.env.user self.task.invalidate_cache() with self.assertRaises(AccessError, msg="%s should not be able to read the task" % self.env.user.name): self.task.with_user(self.env.user).name @users('Internal user') def test_task_allowed_internal_read(self): self.project_pigs.allowed_user_ids = self.env.user self.task.invalidate_cache() self.task.with_user(self.env.user).name @users('Internal user', 'Portal user') def test_task_no_write(self): with self.assertRaises(AccessError, msg="%s should not be able to write on the task" % self.env.user.name): self.task.with_user(self.env.user).name = "Paint the world in black & white" self.project_pigs.allowed_user_ids = self.env.user with self.assertRaises(AccessError, msg="%s should not be able to write on the task" % self.env.user.name): self.task.with_user(self.env.user).name = "Paint the world in black & white" @users('Internal user', 'Portal user') def test_task_no_create(self): with self.assertRaises(AccessError, msg="%s should not be able to create a task" % self.env.user.name): self.create_task("Archive the world, it's not needed anymore") self.project_pigs.allowed_user_ids = self.env.user with self.assertRaises(AccessError, msg="%s should not be able to create a task" % self.env.user.name): self.create_task("Archive the world, it's not needed anymore") @users('Internal user', 'Portal user') def test_task_no_unlink(self): with self.assertRaises(AccessError, msg="%s should not be able to unlink the task" % self.env.user.name): self.task.with_user(self.env.user).unlink() self.project_pigs.allowed_user_ids = self.env.user with self.assertRaises(AccessError, msg="%s should not be able to unlink the task" % self.env.user.name): self.task.with_user(self.env.user).unlink() class TestCRUDVisibilityPortal(TestAccessRights): def setUp(self): super().setUp() self.project_pigs.privacy_visibility = 'portal' @users('Portal user') def test_task_portal_no_read(self): self.task.invalidate_cache() with self.assertRaises(AccessError, msg="%s should not be able to read the task" % self.env.user.name): self.task.with_user(self.env.user).name @users('Portal user') def test_task_allowed_portal_read(self): self.project_pigs.allowed_user_ids = self.env.user self.task.invalidate_cache() self.task.with_user(self.env.user).name @users('Internal user') def test_task_internal_read(self): self.task.with_user(self.env.user).name class TestCRUDVisibilityEmployees(TestAccessRights): def setUp(self): super().setUp() self.project_pigs.privacy_visibility = 'employees' @users('Portal user') def test_task_portal_no_read(self): self.task.invalidate_cache() with self.assertRaises(AccessError, msg="%s should not be able to read the task" % self.env.user.name): self.task.with_user(self.env.user).name self.project_pigs.allowed_user_ids = self.env.user self.task.invalidate_cache() with self.assertRaises(AccessError, msg="%s should not be able to read the task" % self.env.user.name): self.task.with_user(self.env.user).name @users('Internal user') def test_task_allowed_portal_read(self): self.task.invalidate_cache() self.task.with_user(self.env.user).name class TestAllowedUsers(TestAccessRights): def setUp(self): super().setUp() self.project_pigs.privacy_visibility = 'followers' def test_project_permission_added(self): self.project_pigs.allowed_user_ids = self.user self.assertIn(self.user, self.task.allowed_user_ids) def test_project_default_permission(self): self.project_pigs.allowed_user_ids = self.user task = self.create_task("Review the end of the world") self.assertIn(self.user, task.allowed_user_ids) def test_project_default_customer_permission(self): self.project_pigs.privacy_visibility = 'portal' self.project_pigs.partner_id = self.portal.partner_id self.assertIn(self.portal, self.task.allowed_user_ids) self.assertIn(self.portal, self.project_pigs.allowed_user_ids) def test_project_permission_removed(self): self.project_pigs.allowed_user_ids = self.user self.project_pigs.allowed_user_ids -= self.user self.assertNotIn(self.user, self.task.allowed_user_ids) def test_project_specific_permission(self): self.project_pigs.allowed_user_ids = self.user john = mail_new_test_user(self.env, login='John') self.task.allowed_user_ids |= john self.project_pigs.allowed_user_ids -= self.user self.assertIn(john, self.task.allowed_user_ids, "John should still be allowed to read the task") def test_project_specific_remove_mutliple_tasks(self): self.project_pigs.allowed_user_ids = self.user john = mail_new_test_user(self.env, login='John') task = self.create_task('task') self.task.allowed_user_ids |= john self.project_pigs.allowed_user_ids -= self.user self.assertIn(john, self.task.allowed_user_ids) self.assertNotIn(john, task.allowed_user_ids) self.assertNotIn(self.user, task.allowed_user_ids) self.assertNotIn(self.user, self.task.allowed_user_ids) def test_no_portal_allowed(self): with self.assertRaises(ValidationError, msg="It should not allow to add portal users"): self.task.allowed_user_ids = self.portal def test_visibility_changed(self): self.project_pigs.privacy_visibility = 'portal' self.task.allowed_user_ids |= self.portal self.assertNotIn(self.user, self.task.allowed_user_ids, "Internal user should have been removed from allowed users") self.project_pigs.privacy_visibility = 'employees' self.assertNotIn(self.portal, self.task.allowed_user_ids, "Portal user should have been removed from allowed users") def test_write_task(self): self.user.groups_id |= self.env.ref('project.group_project_user') self.assertNotIn(self.user, self.project_pigs.allowed_user_ids) self.task.allowed_user_ids = self.user self.project_pigs.invalidate_cache() self.task.invalidate_cache() self.task.with_user(self.user).name = "I can edit a task!" def test_no_write_project(self): self.user.groups_id |= self.env.ref('project.group_project_user') self.assertNotIn(self.user, self.project_pigs.allowed_user_ids) with self.assertRaises(AccessError, msg="User should not be able to edit project"): self.project_pigs.with_user(self.user).name = "I can't edit a task!"

45.532751

124

0.702215

1,482

10,427

4.744265

0.082321

0.055753

0.076661

0.070403

0.831461

0.816527

0.785379

0.759351

0.714408

0.659366

0

0.000118

0.189892

10,427

228

125

45.732456

0.832248

0.009015

0

0.677966

0

0.161084

0.005034

0

0.186441

1

0.180791

false

0

0.022599

0

0.237288

0

null

0

1

0

null

0

6

a97d7a9e568e6bff482fe88025d5c5252ad6273c

95

py

Python

julee/intents/news.py

riczfe/SEPM_GROUP6

9c1f44958121f36b09c20be53be28d4744322c58

[ "MIT" ]

null

julee/intents/news.py

riczfe/SEPM_GROUP6

9c1f44958121f36b09c20be53be28d4744322c58

[ "MIT" ]

null

julee/intents/news.py

riczfe/SEPM_GROUP6

9c1f44958121f36b09c20be53be28d4744322c58

[ "MIT" ]

null

import webbrowser def open_news(): webbrowser.open_new_tab("https://abcnews.go.com/")

19

54

0.705263

13

95

4.923077

0.846154

0

0.147368

95

5

54

19

0.790123

0

0.25

0

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

8d5456d91b24e383d2251d3243505d616011a834

27

py

Python

randomstate/prng/xorshift1024/__init__.py

bashtage/ng-numpy-randomstate

b397db9cb8688b291fc40071ab043009dfa05a85

[ "Apache-2.0", "BSD-3-Clause" ]

43

2016-02-11T03:38:16.000Z

2022-02-03T10:00:15.000Z

randomstate/prng/xorshift1024/__init__.py

bashtage/pcg-python

b397db9cb8688b291fc40071ab043009dfa05a85

[ "Apache-2.0", "BSD-3-Clause" ]

31

2015-12-26T19:47:36.000Z

2018-12-10T15:55:46.000Z

randomstate/prng/xorshift1024/__init__.py

bashtage/ng-numpy-randomstate

b397db9cb8688b291fc40071ab043009dfa05a85

[ "Apache-2.0", "BSD-3-Clause" ]

11

2016-04-28T02:00:38.000Z

2020-08-07T10:33:10.000Z

from .xorshift1024 import *

27

0.814815

3

27

7.333333

1

0

0.166667

0.111111

27

1

27

0.75

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

8d63629bc59b322e97e3adbda6a9e5d92002e264

35

py

Python

MovieKit/__init__.py

muellermax/Movie-Diary

a5ff2f70d545d95ec708813fd4656c4d3ccd7c31

[ "Unlicense" ]

1

2020-05-24T17:15:21.000Z

MovieKit/__init__.py

muellermax/Movie-Diary

a5ff2f70d545d95ec708813fd4656c4d3ccd7c31

[ "Unlicense" ]

null

MovieKit/__init__.py

muellermax/Movie-Diary

a5ff2f70d545d95ec708813fd4656c4d3ccd7c31

[ "Unlicense" ]

null

from .MovieDiary import MovieDiary

17.5

34

0.857143

4

35

7.5

0.75

0

0.114286

35

1

35

0.967742

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

5750ec498f1879e7273ef4163f27245e5416c4f8

3,977

py

Python

tests/test_autodiff/test_autodiff.py

VIVelev/nujo

56c3058b14c4e0b7ae86d0f22dbe4c4dc81e8e71

[ "MIT" ]

5

2020-03-02T22:14:38.000Z

2022-03-09T11:13:13.000Z

tests/test_autodiff/test_autodiff.py

VIVelev/nujo

56c3058b14c4e0b7ae86d0f22dbe4c4dc81e8e71

[ "MIT" ]

30

2020-03-09T10:43:54.000Z

2020-06-09T20:05:45.000Z

tests/test_autodiff/test_autodiff.py

VIVelev/nujo

56c3058b14c4e0b7ae86d0f22dbe4c4dc81e8e71

[ "MIT" ]

3

2020-03-20T13:54:23.000Z

2020-10-17T01:03:17.000Z

import pytest import torch from numpy import allclose, random import nujo as nj # ==================================================================================================== def test_scalar_diff(scalar_tensors): (X_nj, y_nj, W1_nj, W2_nj, X_torch, y_torch, W1_torch, W2_torch) = scalar_tensors # Test Forward loss_nj = nj.mean((X_nj * W1_nj * W2_nj - y_nj)**2) loss_torch = torch.mean((X_torch * W1_torch * W2_torch - y_torch)**2) assert allclose(loss_nj.value, loss_torch.detach().numpy()) # Test Backward loss_nj.backward() loss_torch.backward() assert allclose(W1_nj.grad.value, W1_torch.grad.detach().numpy()) assert allclose(W2_nj.grad.value, W2_torch.grad.detach().numpy()) # ==================================================================================================== def test_matrix_diff(matrix_tensors): (X_nj, y_nj, W1_nj, W2_nj, X_torch, y_torch, W1_torch, W2_torch) = matrix_tensors # Test Forward loss_nj = nj.mean((X_nj @ W1_nj @ W2_nj - y_nj)**2) loss_torch = torch.mean((X_torch @ W1_torch @ W2_torch - y_torch)**2) assert allclose(loss_nj.value, loss_torch.detach().numpy()) # Test Backward loss_nj.backward() loss_torch.backward() assert allclose(W1_nj.grad.value, W1_torch.grad.detach().numpy()) assert allclose(W2_nj.grad.value, W2_torch.grad.detach().numpy()) # ==================================================================================================== def test_prod_log(matrix_tensors): (X_nj, y_nj, W1_nj, W2_nj, X_torch, y_torch, W1_torch, W2_torch) = matrix_tensors # Test Forward loss_nj = nj.prod(nj.log(X_nj @ W1_nj @ W2_nj) + y_nj) loss_torch = torch.prod(torch.log(X_torch @ W1_torch @ W2_torch) + y_torch) assert allclose(loss_nj.value, loss_torch.detach().numpy()) # Test Backward loss_nj.backward() loss_torch.backward() assert allclose(W1_nj.grad.value, W1_torch.grad.detach().numpy()) assert allclose(W2_nj.grad.value, W2_torch.grad.detach().numpy()) # ==================================================================================================== def test_aggregate_by_dim(matrix_tensors): (X_nj, y_nj, W1_nj, _, X_torch, y_torch, W1_torch, _) = matrix_tensors # Test Forward loss_nj = nj.prod(nj.mean(X_nj @ W1_nj, dim=1, keepdim=True) + y_nj) loss_torch = torch.prod( torch.mean(X_torch @ W1_torch, axis=1, keepdim=True) + y_torch) assert allclose(loss_nj.value, loss_torch.detach().numpy()) # Test Backward loss_nj.backward() loss_torch.backward() assert allclose(W1_nj.grad.value, W1_torch.grad.detach().numpy()) # ==================================================================================================== # Unit Test fixtures - generate the same nujo and PyTorch tensors @pytest.fixture def scalar_tensors(): X = random.rand() y = random.rand() W1 = random.rand() W2 = random.rand() X_nj = nj.Tensor(X) y_nj = nj.Tensor(y) W1_nj = nj.Tensor(W1, diff=True) W2_nj = nj.Tensor(W2, diff=True) X_torch = torch.tensor(X) y_torch = torch.tensor(y) W1_torch = torch.tensor(W1, requires_grad=True) W2_torch = torch.tensor(W2, requires_grad=True) return X_nj, y_nj, W1_nj, W2_nj, X_torch, y_torch, W1_torch, W2_torch @pytest.fixture def matrix_tensors(): X = random.rand(3, 3) y = random.rand(3, 1) W1 = random.rand(3, 2) W2 = random.rand(2, 1) X_nj = nj.Tensor(X) y_nj = nj.Tensor(y) W1_nj = nj.Tensor(W1, diff=True) W2_nj = nj.Tensor(W2, diff=True) X_torch = torch.tensor(X) y_torch = torch.tensor(y) W1_torch = torch.tensor(W1, requires_grad=True) W2_torch = torch.tensor(W2, requires_grad=True) return X_nj, y_nj, W1_nj, W2_nj, X_torch, y_torch, W1_torch, W2_torch # ====================================================================================================

29.029197

102

0.570279

557

3,977

3.793537

0.091562

0.030289

0.028396

0.030289

0.823474

0.810222

0.780407

0.753431

0

0.025339

0.166457

3,977

136

103

29.242647

0.612066

0.195373

0

0.60274

0

0.150685

1

0.082192

false

0

0.054795

0

0.164384

0

null

0

1

0

null

0

6

f5672d3d5ed0ca947fa69be1d52f58e3f2039412

71

py

Python

CodeWars/8 Kyu/Merge two sorted arrays into one.py

anubhab-code/Competitive-Programming

de28cb7d44044b9e7d8bdb475da61e37c018ac35

[ "MIT" ]

null

CodeWars/8 Kyu/Merge two sorted arrays into one.py

anubhab-code/Competitive-Programming

de28cb7d44044b9e7d8bdb475da61e37c018ac35

[ "MIT" ]

null

CodeWars/8 Kyu/Merge two sorted arrays into one.py

anubhab-code/Competitive-Programming

de28cb7d44044b9e7d8bdb475da61e37c018ac35

[ "MIT" ]

null

def merge_arrays(arr1, arr2): return sorted(list(set(arr1 + arr2)))

35.5

41

0.704225

11

71

4.454545

0.818182

0.326531

0

0.065574

0.140845

71

2

41

35.5

0.737705

0

1

0.5

false

0

0.5

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

f593d25e862fd843feb96c153977c78f30f8bc50

1,870

py

Python

tests_flstudio/stubs/collections.py

rjuang/rum-library

e7c61407c31832e46ddf1335f98f47c4b82652d0

[ "MIT" ]

3

2021-04-03T09:15:46.000Z

2022-01-10T10:53:13.000Z

tests_flstudio/stubs/collections.py

rjuang/rum-library

e7c61407c31832e46ddf1335f98f47c4b82652d0

[ "MIT" ]

1

2022-01-30T04:06:24.000Z

tests_flstudio/stubs/collections.py

rjuang/rum

e7c61407c31832e46ddf1335f98f47c4b82652d0

[ "MIT" ]

null

def channelCount(*args, **kwargs): pass def channelNumber(*args, **kwargs): pass def closeGraphEditor(*args, **kwargs): pass def deselectAll(*args, **kwargs): pass def focusEditor(*args, **kwargs): pass def getChannelColor(*args, **kwargs): pass def getChannelIndex(*args, **kwargs): pass def getChannelMidiInPort(*args, **kwargs): pass def getChannelName(*args, **kwargs): pass def getChannelPan(*args, **kwargs): pass def getChannelPitch(*args, **kwargs): pass def getChannelVolume(*args, **kwargs): pass def getCurrentStepParam(*args, **kwargs): pass def getGridBit(*args, **kwargs): pass def getGridBitWithLoop(*args, **kwargs): pass def getRecEventId(*args, **kwargs): pass def getStepParam(*args, **kwargs): pass def getTargetFxTrack(*args, **kwargs): pass def incEventValue(*args, **kwargs): pass def isChannelMuted(*args, **kwargs): pass def isChannelSelected(*args, **kwargs): pass def isChannelSolo(*args, **kwargs): pass def isGraphEditorVisible(*args, **kwargs): pass def isGridBitAssigned(*args, **kwargs): pass def isHighLighted(*args, **kwargs): pass def midiNoteOn(*args, **kwargs): pass def muteChannel(*args, **kwargs): pass def processRECEvent(*args, **kwargs): pass def selectAll(*args, **kwargs): pass def selectChannel(*args, **kwargs): pass def selectOneChannel(*args, **kwargs): pass def selectedChannel(*args, **kwargs): pass def setChannelColor(*args, **kwargs): pass def setChannelName(*args, **kwargs): pass def setChannelPan(*args, **kwargs): pass def setChannelPitch(*args, **kwargs): pass def setChannelVolume(*args, **kwargs): pass def setGridBit(*args, **kwargs): pass def setStepParameterByIndex(*args, **kwargs): pass def showCSForm(*args, **kwargs): pass def showEditor(*args, **kwargs): pass def showGraphEditor(*args, **kwargs): pass def soloChannel(*args, **kwargs): pass def updateGraphEditor(*args, **kwargs): pass version = 1.0

40.652174

50

0.738503

223

1,870

6.192825

0.2287

0.31861

0.446054

0.529327

0

0.001183

0.095722

1,870

45

51

41.555556

0.815494

0

1

0.977778

false

0.977778

0

0.977778

0

null

1

0

null

0

1

0

1

0

1

0

6

194ff170922d2fe2cff8e77d4db31dac8b6737c5

190

py

Python

Accounts/admin.py

Shreya549/AchieveVIT

4623f80a4e38914f2d759fc0c3591bd642486a5b

[ "MIT" ]

3

2020-08-29T20:23:27.000Z

2021-05-20T05:44:01.000Z

Accounts/admin.py

Shreya549/AchieveVIT

4623f80a4e38914f2d759fc0c3591bd642486a5b

[ "MIT" ]

1

2020-09-29T16:28:24.000Z

Accounts/admin.py

Shreya549/AchieveVIT

4623f80a4e38914f2d759fc0c3591bd642486a5b

[ "MIT" ]

null

from django.contrib import admin from .models import User, Faculty, HR, OTPStore admin.site.register(User) admin.site.register(Faculty) admin.site.register(HR) admin.site.register(OTPStore)

27.142857

47

0.810526

28

190

5.5

0.428571

0.233766

0.441558

0

0.078947

190

7

48

27.142857

0.88

0

1

0

true

0

0.333333

0

0.333333

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6