xszheng2020/the_stack_dedup_python_hits_6 · Datasets at Hugging Face

6448ba5549fb9c70338edf64ad7ad0b04ae5f259

18,860

py

Python

test/test_md027.py

scop/pymarkdown

562ba8f7857d99ba09e86e42de5a37ec6d9b2c30

[ "MIT" ]

null

test/test_md027.py

scop/pymarkdown

562ba8f7857d99ba09e86e42de5a37ec6d9b2c30

[ "MIT" ]

null

test/test_md027.py

scop/pymarkdown

562ba8f7857d99ba09e86e42de5a37ec6d9b2c30

[ "MIT" ]

null

""" Module to provide tests related to the MD027 rule. """ from test.markdown_scanner import MarkdownScanner import pytest @pytest.mark.rules def test_md027_good_block_quote_empty(): """ Test to make sure we get the expected behavior after scanning a good file from the test/resources/rules/MD026 directory that has atx headings that do not end with punctuation. """ # Arrange scanner = MarkdownScanner() supplied_arguments = [ "scan", "test/resources/rules/md027/good_block_quote_empty.md", ] expected_return_code = 0 expected_output = "" expected_error = "" # Act execute_results = scanner.invoke_main(arguments=supplied_arguments) # Assert execute_results.assert_results( expected_output, expected_error, expected_return_code ) @pytest.mark.rules def test_md027_good_block_quote_empty_just_blank(): """ Test to make sure we get the expected behavior after scanning a good file from the test/resources/rules/MD026 directory that has atx headings that do not end with punctuation. """ # Arrange scanner = MarkdownScanner() supplied_arguments = [ "scan", "test/resources/rules/md027/good_block_quote_empty_just_blank.md", ] expected_return_code = 0 expected_output = "" expected_error = "" # Act execute_results = scanner.invoke_main(arguments=supplied_arguments) # Assert execute_results.assert_results( expected_output, expected_error, expected_return_code ) @pytest.mark.rules def test_md027_bad_block_quote_empty_too_many_spaces(): """ Test to make sure we get the expected behavior after scanning a good file from the test/resources/rules/MD026 directory that has atx headings that do not end with punctuation. """ # Arrange scanner = MarkdownScanner() supplied_arguments = [ "scan", "test/resources/rules/md027/bad_block_quote_empty_too_many_spaces.md", ] expected_return_code = 1 expected_output = ( "test/resources/rules/md027/bad_block_quote_empty_too_many_spaces.md:1:3: " + "MD027: Multiple spaces after blockquote symbol (no-multiple-space-blockquote)" ) expected_error = "" # Act execute_results = scanner.invoke_main(arguments=supplied_arguments) # Assert execute_results.assert_results( expected_output, expected_error, expected_return_code ) @pytest.mark.rules def test_md027_good_block_quote_simple_text(): """ Test to make sure we get the expected behavior after scanning a good file from the test/resources/rules/MD026 directory that has atx headings that do not end with punctuation. """ # Arrange scanner = MarkdownScanner() supplied_arguments = [ "scan", "test/resources/rules/md027/good_block_quote_simple_text.md", ] expected_return_code = 0 expected_output = "" expected_error = "" # Act execute_results = scanner.invoke_main(arguments=supplied_arguments) # Assert execute_results.assert_results( expected_output, expected_error, expected_return_code ) @pytest.mark.rules def test_md027_good_block_quote_followed_by_heading(): """ Test to make sure we get the expected behavior after scanning a good file from the test/resources/rules/MD026 directory that has atx headings that do not end with punctuation. """ # Arrange scanner = MarkdownScanner() supplied_arguments = [ "--disable-rules", "md022", "scan", "test/resources/rules/md027/good_block_quote_followed_by_heading.md", ] expected_return_code = 0 expected_output = "" expected_error = "" # Act execute_results = scanner.invoke_main(arguments=supplied_arguments) # Assert execute_results.assert_results( expected_output, expected_error, expected_return_code ) @pytest.mark.rules def test_md027_good_block_quote_indent(): """ Test to make sure we get the expected behavior after scanning a good file from the test/resources/rules/MD026 directory that has atx headings that do not end with punctuation. """ # Arrange scanner = MarkdownScanner() supplied_arguments = [ "scan", "test/resources/rules/md027/good_block_quote_indent.md", ] expected_return_code = 0 expected_output = "" expected_error = "" # Act execute_results = scanner.invoke_main(arguments=supplied_arguments) # Assert execute_results.assert_results( expected_output, expected_error, expected_return_code ) @pytest.mark.rules def test_md027_bad_block_quote_indent(): """ Test to make sure we get the expected behavior after scanning a good file from the test/resources/rules/MD026 directory that has atx headings that do not end with punctuation. """ # Arrange scanner = MarkdownScanner() supplied_arguments = [ "--stack-trace", "scan", "test/resources/rules/md027/bad_block_quote_indent.md", ] expected_return_code = 1 expected_output = ( "test/resources/rules/md027/bad_block_quote_indent.md:1:3: " + "MD027: Multiple spaces after blockquote symbol (no-multiple-space-blockquote)\n" + "test/resources/rules/md027/bad_block_quote_indent.md:2:3: " + "MD027: Multiple spaces after blockquote symbol (no-multiple-space-blockquote)" ) expected_error = "" # Act execute_results = scanner.invoke_main(arguments=supplied_arguments) # Assert execute_results.assert_results( expected_output, expected_error, expected_return_code ) @pytest.mark.rules def test_md027_bad_block_quote_indent_plus_one(): """ Test to make sure we get the expected behavior after scanning a good file from the test/resources/rules/MD026 directory that has atx headings that do not end with punctuation. """ # Arrange scanner = MarkdownScanner() supplied_arguments = [ "--stack-trace", "scan", "test/resources/rules/md027/bad_block_quote_indent_plus_one.md", ] expected_return_code = 1 expected_output = ( "test/resources/rules/md027/bad_block_quote_indent_plus_one.md:1:4: " + "MD027: Multiple spaces after blockquote symbol (no-multiple-space-blockquote)\n" + "test/resources/rules/md027/bad_block_quote_indent_plus_one.md:2:4: " + "MD027: Multiple spaces after blockquote symbol (no-multiple-space-blockquote)" ) expected_error = "" # Act execute_results = scanner.invoke_main(arguments=supplied_arguments) # Assert execute_results.assert_results( expected_output, expected_error, expected_return_code ) @pytest.mark.rules def test_md027_bad_block_quote_only_one_properly_indented(): """ Test to make sure we get the expected behavior after scanning a good file from the test/resources/rules/MD026 directory that has atx headings that do not end with punctuation. """ # Arrange scanner = MarkdownScanner() supplied_arguments = [ "scan", "test/resources/rules/md027/bad_block_quote_only_one_properly_indented.md", ] expected_return_code = 1 expected_output = ( "test/resources/rules/md027/bad_block_quote_only_one_properly_indented.md:2:3: " + "MD027: Multiple spaces after blockquote symbol (no-multiple-space-blockquote)" ) expected_error = "" # Act execute_results = scanner.invoke_main(arguments=supplied_arguments) # Assert execute_results.assert_results( expected_output, expected_error, expected_return_code ) @pytest.mark.rules def test_md027_bad_block_quote_only_one_properly_indented_plus_one(): """ Test to make sure we get the expected behavior after scanning a good file from the test/resources/rules/MD026 directory that has atx headings that do not end with punctuation. """ # Arrange scanner = MarkdownScanner() supplied_arguments = [ "scan", "test/resources/rules/md027/bad_block_quote_only_one_properly_indented_plus_one.md", ] expected_return_code = 1 expected_output = ( "test/resources/rules/md027/bad_block_quote_only_one_properly_indented_plus_one.md:2:4: " + "MD027: Multiple spaces after blockquote symbol (no-multiple-space-blockquote)" ) expected_error = "" # Act execute_results = scanner.invoke_main(arguments=supplied_arguments) # Assert execute_results.assert_results( expected_output, expected_error, expected_return_code ) @pytest.mark.rules def test_md027_good_block_quote_indent_with_blank(): """ Test to make sure we get the expected behavior after scanning a good file from the test/resources/rules/MD026 directory that has atx headings that do not end with punctuation. """ # Arrange scanner = MarkdownScanner() supplied_arguments = [ "scan", "test/resources/rules/md027/good_block_quote_indent_with_blank.md", ] expected_return_code = 0 expected_output = "" expected_error = "" # Act execute_results = scanner.invoke_main(arguments=supplied_arguments) # Assert execute_results.assert_results( expected_output, expected_error, expected_return_code ) @pytest.mark.rules def test_md027_good_block_quote_indent_with_blank_space(): """ Test to make sure we get the expected behavior after scanning a good file from the test/resources/rules/MD026 directory that has atx headings that do not end with punctuation. """ # Arrange scanner = MarkdownScanner() supplied_arguments = [ "scan", "test/resources/rules/md027/good_block_quote_indent_with_blank_space.md", ] expected_return_code = 0 expected_output = "" expected_error = "" # Act execute_results = scanner.invoke_main(arguments=supplied_arguments) # Assert execute_results.assert_results( expected_output, expected_error, expected_return_code ) @pytest.mark.rules def test_md027_bad_block_quote_indent_with_blank_two_spaces(): """ Test to make sure we get the expected behavior after scanning a good file from the test/resources/rules/MD026 directory that has atx headings that do not end with punctuation. """ # Arrange scanner = MarkdownScanner() supplied_arguments = [ "scan", "test/resources/rules/md027/bad_block_quote_indent_with_blank_two_spaces.md", ] expected_return_code = 1 expected_output = ( "test/resources/rules/md027/bad_block_quote_indent_with_blank_two_spaces.md:2:3: " + "MD027: Multiple spaces after blockquote symbol (no-multiple-space-blockquote)" ) expected_error = "" # Act execute_results = scanner.invoke_main(arguments=supplied_arguments) # Assert execute_results.assert_results( expected_output, expected_error, expected_return_code ) @pytest.mark.rules def test_md027_bad_block_quote_indent_with_blank_two_spaces_plus_one(): """ Test to make sure we get the expected behavior after scanning a good file from the test/resources/rules/MD026 directory that has atx headings that do not end with punctuation. """ # Arrange scanner = MarkdownScanner() supplied_arguments = [ "scan", "test/resources/rules/md027/bad_block_quote_indent_with_blank_two_spaces_plus_one.md", ] expected_return_code = 1 expected_output = ( "test/resources/rules/md027/bad_block_quote_indent_with_blank_two_spaces_plus_one.md:2:4: " + "MD027: Multiple spaces after blockquote symbol (no-multiple-space-blockquote)" ) expected_error = "" # Act execute_results = scanner.invoke_main(arguments=supplied_arguments) # Assert execute_results.assert_results( expected_output, expected_error, expected_return_code ) @pytest.mark.rules def test_md027_bad_block_quote_indent_with_blank_two_spaces_misaligned(): """ Test to make sure we get the expected behavior after scanning a good file from the test/resources/rules/MD026 directory that has atx headings that do not end with punctuation. """ # Arrange scanner = MarkdownScanner() supplied_arguments = [ "scan", "test/resources/rules/md027/bad_block_quote_indent_with_blank_two_spaces_misaligned.md", ] expected_return_code = 1 expected_output = ( "test/resources/rules/md027/bad_block_quote_indent_with_blank_two_spaces_misaligned.md:2:4: " + "MD027: Multiple spaces after blockquote symbol (no-multiple-space-blockquote)" ) expected_error = "" # Act execute_results = scanner.invoke_main(arguments=supplied_arguments) # Assert execute_results.assert_results( expected_output, expected_error, expected_return_code ) @pytest.mark.rules def test_md027_good_block_quote_indent_with_blank_space_no_start(): """ Test to make sure we get the expected behavior after scanning a good file from the test/resources/rules/MD026 directory that has atx headings that do not end with punctuation. """ # Arrange scanner = MarkdownScanner() supplied_arguments = [ "--disable-rules", "md028", "scan", "test/resources/rules/md027/good_block_quote_indent_with_blank_space_no_start.md", ] expected_return_code = 0 expected_output = "" expected_error = "" # Act execute_results = scanner.invoke_main(arguments=supplied_arguments) # Assert execute_results.assert_results( expected_output, expected_error, expected_return_code ) @pytest.mark.rules def test_md027_bad_two_block_quotes_space_top(): """ Test to make sure we get the expected behavior after scanning a good file from the test/resources/rules/MD026 directory that has atx headings that do not end with punctuation. """ # Arrange scanner = MarkdownScanner() supplied_arguments = [ "--disable-rules", "md028", "scan", "test/resources/rules/md027/bad_two_block_quotes_space_top.md", ] expected_return_code = 1 expected_output = ( "test/resources/rules/md027/bad_two_block_quotes_space_top.md:1:3: " + "MD027: Multiple spaces after blockquote symbol (no-multiple-space-blockquote)" ) expected_error = "" # Act execute_results = scanner.invoke_main(arguments=supplied_arguments) # Assert execute_results.assert_results( expected_output, expected_error, expected_return_code ) @pytest.mark.rules def test_md027_bad_two_block_quotes_space_bottom(): """ Test to make sure we get the expected behavior after scanning a good file from the test/resources/rules/MD026 directory that has atx headings that do not end with punctuation. """ # Arrange scanner = MarkdownScanner() supplied_arguments = [ "--disable-rules", "md028", "scan", "test/resources/rules/md027/bad_two_block_quotes_space_bottom.md", ] expected_return_code = 1 expected_output = ( "test/resources/rules/md027/bad_two_block_quotes_space_bottom.md:3:3: " + "MD027: Multiple spaces after blockquote symbol (no-multiple-space-blockquote)" ) expected_error = "" # Act execute_results = scanner.invoke_main(arguments=supplied_arguments) # Assert execute_results.assert_results( expected_output, expected_error, expected_return_code ) @pytest.mark.rules def test_md027_bad_misalligned_double_quote(): """ Test to make sure we get the expected behavior after scanning a good file from the test/resources/rules/MD026 directory that has atx headings that do not end with punctuation. """ # Arrange scanner = MarkdownScanner() supplied_arguments = [ "scan", "test/resources/rules/md027/bad_misalligned_double_quote.md", ] expected_return_code = 1 expected_output = ( "test/resources/rules/md027/bad_misalligned_double_quote.md:2:4: " + "MD027: Multiple spaces after blockquote symbol (no-multiple-space-blockquote)" ) expected_error = "" # Act execute_results = scanner.invoke_main(arguments=supplied_arguments) # Assert execute_results.assert_results( expected_output, expected_error, expected_return_code ) @pytest.mark.rules def test_md027_good_alligned_double_quote(): """ Test to make sure we get the expected behavior after scanning a good file from the test/resources/rules/MD026 directory that has atx headings that do not end with punctuation. """ # Arrange scanner = MarkdownScanner() supplied_arguments = [ "scan", "test/resources/rules/md027/good_alligned_double_quote.md", ] expected_return_code = 0 expected_output = "" expected_error = "" # Act execute_results = scanner.invoke_main(arguments=supplied_arguments) # Assert execute_results.assert_results( expected_output, expected_error, expected_return_code ) @pytest.mark.skip @pytest.mark.rules def test_md027_bad_misalligned_quote_within_list(): """ Test to make sure we get the expected behavior after scanning a good file from the test/resources/rules/MD026 directory that has atx headings that do not end with punctuation. """ # Arrange scanner = MarkdownScanner() supplied_arguments = [ "scan", "test/resources/rules/md027/bad_misalligned_quote_within_list.md", ] expected_return_code = 1 expected_output = "" expected_error = "" # Act execute_results = scanner.invoke_main(arguments=supplied_arguments) # Assert execute_results.assert_results( expected_output, expected_error, expected_return_code ) @pytest.mark.skip @pytest.mark.rules def test_md027_good_alligned_quote_within_list(): """ Test to make sure we get the expected behavior after scanning a good file from the test/resources/rules/MD026 directory that has atx headings that do not end with punctuation. """ # Arrange scanner = MarkdownScanner() supplied_arguments = [ "--stack-trace", "scan", "test/resources/rules/md027/good_alligned_quote_within_list.md", ] expected_return_code = 0 expected_output = "" expected_error = "" # Act execute_results = scanner.invoke_main(arguments=supplied_arguments) # Assert execute_results.assert_results( expected_output, expected_error, expected_return_code )

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644a41a37c1677e5ef6f648ec510576394dc6a7a

231

py

Python

tests/models/fakes.py

lucaspanayiotou/OasisLMF_SQL

619244f6c5b2e1b6483d50ada045fc24e081de42

[ "BSD-3-Clause" ]

null

tests/models/fakes.py

lucaspanayiotou/OasisLMF_SQL

619244f6c5b2e1b6483d50ada045fc24e081de42

[ "BSD-3-Clause" ]

1

2021-03-31T19:01:15.000Z

tests/models/fakes.py

OasisLMF/OasisLMF_SQL

4c0edef7b346cf2a0b3cd0813320d063fa3e8b40

[ "BSD-3-Clause" ]

2

2019-03-21T09:22:34.000Z

2020-01-16T15:09:58.000Z

from oasislmf.model_preparation.manager import OasisManager as om def fake_model(supplier='supplier', model='model', version='version', resources=None): return om().create_model(supplier, model, version, resources=resources)

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174

py

Python

applications/messenger/models/__init__.py

dev-easyshares/mighty

a6cf473fb8cfbf5b92db68c7b068fc8ae2911b8b

[ "MIT" ]

null

applications/messenger/models/__init__.py

dev-easyshares/mighty

a6cf473fb8cfbf5b92db68c7b068fc8ae2911b8b

[ "MIT" ]

1

2022-03-12T00:57:37.000Z

applications/messenger/models/__init__.py

dev-easyshares/mighty

a6cf473fb8cfbf5b92db68c7b068fc8ae2911b8b

[ "MIT" ]

null

from mighty.applications.messenger.models.missive import Missive from mighty.applications.messenger.models.notification import Notification __all__ = (Missive, Notification)

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64c260c79d4873616e0acad12a9b46bce566cb60

1,816

py

Python

test/python/helpers.py

ryansun117/marius

c6a81b2ea6b6b468baf5277cf6955f9543b66c82

[ "Apache-2.0" ]

null

test/python/helpers.py

ryansun117/marius

c6a81b2ea6b6b468baf5277cf6955f9543b66c82

[ "Apache-2.0" ]

null

test/python/helpers.py

ryansun117/marius

c6a81b2ea6b6b468baf5277cf6955f9543b66c82

[ "Apache-2.0" ]

null

from pathlib import Path import random def dataset_generator(train_file, valid_file, test_file, train_len=1000, valid_len=100, test_len=100, delim="\t", start_col=0, num_line_skip=0): with open(str(Path(train_file)), "w") as f: for i in range(num_line_skip): f.write("This is a line needs to be skipped.\n") for i in range(train_len): src = random.randint(1, 100) dst = random.randint(1, 100) rel = random.randint(101, 110) for j in range(start_col): f.write("col_" + str(j) + delim) f.write(str(src) + delim + str(rel) + delim + str(dst) + "\n") f.close() with open(str(Path(valid_file)), "w") as f: for i in range(num_line_skip): f.write("This is a line needs to be skipped.\n") for i in range(valid_len): src = random.randint(1, 100) dst = random.randint(1, 100) rel = random.randint(101, 110) for j in range(start_col): f.write("col_" + str(j) + delim) f.write(str(src) + delim + str(rel) + delim + str(dst) + "\n") f.close() with open(str(Path(test_file)), "w") as f: for i in range(num_line_skip): f.write("This is a line needs to be skipped.\n") for i in range(test_len): src = random.randint(1, 100) dst = random.randint(1, 100) rel = random.randint(101, 110) for j in range(start_col): f.write("col_" + str(j) + delim) f.write(str(src) + delim + str(rel) + delim + str(dst) + "\n") f.close()

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b3971d3b3f40330353fab35a83f6a6295e8e3ce7

203

py

Python

formatter/identation.py

natansilva/sql_formatter

69cbd128db405c45b42694da4c4741ec664446e6

[ "MIT" ]

null

formatter/identation.py

natansilva/sql_formatter

69cbd128db405c45b42694da4c4741ec664446e6

[ "MIT" ]

null

formatter/identation.py

natansilva/sql_formatter

69cbd128db405c45b42694da4c4741ec664446e6

[ "MIT" ]

null

import re def remove_all_tabs(text_to_format): return re.sub('\t', '', text_to_format) def ident_in_select_from_clause(text_to_format): return re.sub('[\n]?,[\s]?', '\n\t, ', text_to_format)

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py

Python

epytope/Data/pssms/tepitopepan/mat/DRB1_0342_9.py

christopher-mohr/epytope

8ac9fe52c0b263bdb03235a5a6dffcb72012a4fd

[ "BSD-3-Clause" ]

7

2021-02-01T18:11:28.000Z

2022-01-31T19:14:07.000Z

epytope/Data/pssms/tepitopepan/mat/DRB1_0342_9.py

christopher-mohr/epytope

8ac9fe52c0b263bdb03235a5a6dffcb72012a4fd

[ "BSD-3-Clause" ]

22

2021-01-02T15:25:23.000Z

2022-03-14T11:32:53.000Z

epytope/Data/pssms/tepitopepan/mat/DRB1_0342_9.py

christopher-mohr/epytope

8ac9fe52c0b263bdb03235a5a6dffcb72012a4fd

[ "BSD-3-Clause" ]

4

2021-05-28T08:50:38.000Z

2022-03-14T11:45:32.000Z

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378bffc289e99fd3cd1a17217857aa61ff4ea49d

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py

Python

dgn/invertible_layer_helpers.py

matt-graham/differentiable-generator-networks

5dcef70fe73461d56f0b79628aaba2722b09e10c

[ "MIT" ]

1

2016-09-29T07:01:10.000Z

dgn/invertible_layer_helpers.py

matt-graham/differentiable-generator-networks

5dcef70fe73461d56f0b79628aaba2722b09e10c

[ "MIT" ]

null

dgn/invertible_layer_helpers.py

matt-graham/differentiable-generator-networks

5dcef70fe73461d56f0b79628aaba2722b09e10c

[ "MIT" ]

null

# -*- coding: utf-8 -*- """Invertible layer helper functions.""" __authors__ = 'Matt Graham' __license__ = 'MIT' import numpy as np import theano.tensor as tt import dgn.invertible_layers as layers def alt_lower_upper_tri_layers(n_layer, weights_inits, biases_inits, nl_fwd=tt.sinh, nl_inv=tt.arcsinh, weights_prec=0., biases_prec=0.): layers = [] for l in range(n_layer): if l % 2 == 0: layers.append(TriangularAffineLayer( weights_init=np.tril(weights_inits[l]), biases_init=biases_inits[l], lower=True, weights_prec=weights_prec, biases_prec=biases_prec)) layers.append(ElementwiseLayer(nl_fwd, nl_inv)) else: layers.append(TriangularAffineLayer( weights_init=np.triu(weights_inits[l]), biases_init=biases_inits[l], lower=False, weights_prec=weights_prec, biases_prec=biases_prec)) layers.append(ElementwiseLayer(nl_fwd, nl_inv)) return layers def alt_lower_upper_tri_with_fwd_diag_inv_nl_layers( n_layer, weights_inits, biases_inits, diag_weights_inits, nl_fwd=tt.sinh, nl_inv=tt.arcsinh, weights_prec=0., biases_prec=0., diag_weights_prec=0.): layers = [] for l in range(n_layer): if l % 2 == 0: layers.append(TriangularAffineLayer( weights_init=np.tril(weights_inits[l]), biases_init=biases_inits[2*l], lower=True, weights_prec=weights_prec, biases_prec=biases_prec)) layers.append(FwdDiagInvElementwiseLayer( forward_func=nl_fwd, inverse_func=nl_inv, diag_weights_init=diag_weights_inits[l], biases_init=biases_inits[2 * l + 1], diag_weights_prec=weights_prec, biases_prec=biases_prec)) else: layers.append(TriangularAffineLayer( weights_init=np.triu(weights_inits[l]), biases_init=biases_inits[l], lower=False, weights_prec=weights_prec, biases_prec=biases_prec)) layers.append(FwdDiagInvElementwiseLayer( forward_func=nl_fwd, inverse_func=nl_inv, diag_weights_init=diag_weights_inits[l], biases_init=biases_inits[2 * l + 1], diag_weights_prec=diag_weights_prec, biases_prec=biases_prec)) return layers def diag_plus_rank_1_with_fwd_diag_inv_nl_layers( n_layer, diag_weights_inits, u_vect_inits, v_vect_inits, biases_inits, nl_fwd=tt.sinh, nl_inv=tt.arcsinh, diag_weights_prec=0., u_vect_prec=0., v_vect_prec=0., biases_prec=0.): layers = [] for l in range(n_layer): layers.append(DiagPlusRank1AffineLayer( diag_weights_init=diag_weights_inits[2 * l], u_vect_init=u_vect_inits[l], v_vect_init=v_vect_inits[l], biases_init=biases_inits[2 * l], diag_weights_prec=diag_weights_prec, u_vect_prec=u_vect_prec, v_vect_prec=v_vect_prec, biases_prec=biases_prec)) layers.append(FwdDiagInvElementwiseLayer( forward_func=nl_fwd, inverse_func=nl_inv, diag_weights_init=diag_weights_inits[2 * l + 1], biases_init=biases_inits[2 * l + 1], diag_weights_prec=diag_weights_prec, biases_prec=biases_prec)) return layers

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1

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1

0

1

0

null

0

6

379c0560d6e691c7e425c1d2446591338132df8f

42

py

Python

xpd_client/__init__.py

moochannel/python-xpd-client

46b1f7202b6ca94f202e13386cb1ebf4bf335a80

[ "MIT" ]

null

xpd_client/__init__.py

moochannel/python-xpd-client

46b1f7202b6ca94f202e13386cb1ebf4bf335a80

[ "MIT" ]

1

2018-01-26T10:32:02.000Z

xpd_client/__init__.py

moochannel/python-xpd-client

46b1f7202b6ca94f202e13386cb1ebf4bf335a80

[ "MIT" ]

null

from .xpd_client import XPdClient # noqa

21

41

0.785714

6

42

5.333333

1

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0.166667

42

1

42

0.914286

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0

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0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

807dfc6eae951aff831d7e7d0545b58795bcb34a

161

py

Python

scripts/build_swift.py

1byte2bytes/SydChain

ac1fffd9f87c2afa6e2f6a0540d69dad0815ef4f

[ "MIT" ]

null

scripts/build_swift.py

1byte2bytes/SydChain

ac1fffd9f87c2afa6e2f6a0540d69dad0815ef4f

[ "MIT" ]

null

scripts/build_swift.py

1byte2bytes/SydChain

ac1fffd9f87c2afa6e2f6a0540d69dad0815ef4f

[ "MIT" ]

null

# Copyright (c) Sydney Erickson 2017 import buildlib import buildsettings buildlib.build_cmake("swift-swift-4.0.3-RELEASE.tar.gz", "-DCMAKE_BUILD_TYPE=Release")

32.2

86

0.801242

24

161

5.25

0.791667

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0.04698

0.074534

161

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32.2

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1

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6

80b2b92fc0821776db189806e44859a2f3da27e3

119

py

Python

backend/src/services/speech_translation/__init__.py

didi/MeetDot

a57009d30c1347a9b85950c2e02b77685ce63952

[ "Apache-2.0" ]

6

2021-09-23T14:53:58.000Z

2022-02-18T10:14:17.000Z

backend/src/services/speech_translation/__init__.py

didi/MeetDot

a57009d30c1347a9b85950c2e02b77685ce63952

[ "Apache-2.0" ]

null

backend/src/services/speech_translation/__init__.py

didi/MeetDot

a57009d30c1347a9b85950c2e02b77685ce63952

[ "Apache-2.0" ]

1

2021-09-24T02:48:50.000Z

from .interface import SpeechTranslationConfig, SpeechTranslationRequest from .service import SpeechTranslationService

39.666667

72

0.89916

9

119

11.888889

0.777778

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0.07563

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null

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1

0

1

0

6

03fb8a8f787f67eb14396a56f838f39708657f82

2,044

py

Python

spaceship_shooter/constant.py

ChinaAthena/EasierRehabitation

43b46f48602ca4627ab4e76e0f822dc3e1eaadf4

[ "MIT" ]

1

2020-03-09T19:47:10.000Z

spaceship_shooter/constant.py

ChinaAthena/EasierRehabitation

43b46f48602ca4627ab4e76e0f822dc3e1eaadf4

[ "MIT" ]

null

spaceship_shooter/constant.py

ChinaAthena/EasierRehabitation

43b46f48602ca4627ab4e76e0f822dc3e1eaadf4

[ "MIT" ]

null

# BLACK = (0, 0, 0) # WHITE = (255, 255, 255) # BRIGHT_RED = (255, 0, 0) # BRIGHT_GREEN = (0, 255, 0) # GREEN = (0, 128, 0) # MAROON = (128, 0, 0) # BRIGHT_BLUE = (0, 0, 255) # BLUE = (0, 0, 128) # # ASSETS_DIR = "../assets/" # BACKGROUND_IMG_PATH = ASSETS_DIR + "background.png" # SPACESHIP_IMG_PATH = ASSETS_DIR + "spaceship.png" # ASTEROID_IMG_PATH = [ASSETS_DIR + "asteroid0%d.png" % i for i in range(2)] # BULLET_IMG_PATH = ASSETS_DIR + "bullet.png" # EXPLOSION_IMG_PATHS = [ASSETS_DIR+"explosions/regularExplosion0%d.png" % i for i in range(9)] # # scale_of_player_image = [0.1, 0.1667] # scale_of_asteroid_image = [0.1428, 0.1428] # scale_of_bullet_image = [0.0125, 0.02] # lam_of_generating_asteroid = 1000 # lam_of_generating_bullet = 400 # scale_of_asteroid_vel = 0.00588 # scale_of_bullet_vel = 0.00667 # angle_variance_of_asteroid = 0.01 # player_relative_position = [0.5, 0.9] BLACK = (0, 0, 0) WHITE = (255, 255, 255) BRIGHT_RED = (255, 0, 0) BRIGHT_GREEN = (0, 255, 0) GREEN = (0, 128, 0) MAROON = (128, 0, 0) BRIGHT_BLUE = (0, 0, 255) BLUE = (0, 0, 128) ASSETS_DIR = "../assets/" BACKGROUND_IMG_PATH = ASSETS_DIR + "background.png" SPACESHIP_IMG_PATH = ASSETS_DIR + "spaceship.png" ASTEROID_IMG_PATH = [ASSETS_DIR + "asteroid0%d.png" % i for i in range(2)] BULLET_IMG_PATH = ASSETS_DIR + "bullet.png" EXPLOSION_IMG_PATHS = [ASSETS_DIR+"explosions/regularExplosion0%d.png" % i for i in range(9)] list_of_difficulty = [0.00188, 0.00288, 0.00388, 0.00488, 0.00588, 0.00688, 0.00788, 0.00888, 0.00988, 0.01088] scale_of_player_image = [0.1, 0.1667] scale_of_asteroid_image = [0.1428, 0.1428] scale_of_bullet_image = [0.0125, 0.02] lam_of_generating_asteroid = 1000 lam_of_generating_bullet = 400 f = open("difficulty.txt", "r") nums = [] if f.mode == 'r': nums = f.readlines() nums = [int(i) for i in nums] if nums: scale_of_asteroid_vel = list_of_difficulty[nums[0]-1] else: scale_of_asteroid_vel = 0.00588 scale_of_bullet_vel = 0.00667 angle_variance_of_asteroid = 0.01 player_relative_position = [0.5, 0.9]

31.9375

111

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null

0

1

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null

0

6

ff00cf667b7862346807f49ae3356366e41d2e1f

30

py

Python

src/pyphase/__init__.py

hsharrison/pyphase

adb3de4cb540553851c06b5d137a3a9c18cdf240

[ "MIT" ]

1

2020-03-22T10:58:47.000Z

src/pyphase/__init__.py

hsharrison/pyphase

adb3de4cb540553851c06b5d137a3a9c18cdf240

[ "MIT" ]

null

src/pyphase/__init__.py

hsharrison/pyphase

adb3de4cb540553851c06b5d137a3a9c18cdf240

[ "MIT" ]

null

from pyphase.util import wrap

15

29

0.833333

5

30

5

1

0

0.133333

30

1

30

0.961538

0

1

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true

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1

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1

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null

0

1

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null

0

1

0

1

0

1

0

6

ff17f2c3801c4679b4cd027f4446ac3d72a8c0ad

25,015

py

Python

alphausblue/api/grouprootuser_pb2.py

alphauslabs/blue-sdk-python

24120a60cd153a69080661a687938b417b32f947

[ "Apache-2.0" ]

null

alphausblue/api/grouprootuser_pb2.py

alphauslabs/blue-sdk-python

24120a60cd153a69080661a687938b417b32f947

[ "Apache-2.0" ]

null

alphausblue/api/grouprootuser_pb2.py

alphauslabs/blue-sdk-python

24120a60cd153a69080661a687938b417b32f947

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # Generated by the protocol buffer compiler. DO NOT EDIT! # source: api/grouprootuser.proto """Generated protocol buffer code.""" from google.protobuf import descriptor as _descriptor from google.protobuf import message as _message from google.protobuf import reflection as _reflection from google.protobuf import symbol_database as _symbol_database # @@protoc_insertion_point(imports) _sym_db = _symbol_database.Default() DESCRIPTOR = _descriptor.FileDescriptor( name='api/grouprootuser.proto', package='blueapi.api', syntax='proto3', serialized_options=b'\n\031cloud.alphaus.blueapi.apiB\025ApiGroupRootUserProtoZ&github.com/alphauslabs/blue-sdk-go/api', create_key=_descriptor._internal_create_key, serialized_pb=b'\n\x17\x61pi/grouprootuser.proto\x12\x0b\x62lueapi.api\"\xb0\x02\n\rGroupRootUser\x12\r\n\x05\x65mail\x18\x01 \x01(\t\x12\x10\n\x08password\x18\x02 \x01(\t\x12\x0f\n\x07groupId\x18\x03 \x01(\t\x12\x11\n\tgroupName\x18\x04 \x01(\t\x12\x11\n\tgroupType\x18\x05 \x01(\t\x12\'\n\x04meta\x18\x06 \x01(\x0b\x32\x19.blueapi.api.FeatureFlags\x12\x1a\n\x12passwordUpdateTime\x18\x07 \x01(\t\x12\x12\n\nupdateTime\x18\x08 \x01(\t\x12\x14\n\x0cuserAccessId\x18\t \x01(\t\x12\x0e\n\x06userId\x18\n \x01(\t\x12\x1c\n\x14waveAvailabilityDays\x18\x0b \x01(\x05\x12\x16\n\x0ewaveRegistered\x18\x0c \x01(\t\x12\x12\n\nwaveStatus\x18\r \x01(\t\"\xbc\x07\n\x0c\x46\x65\x61tureFlags\x12\x17\n\x0f\x64\x61shboard_graph\x18\x01 \x01(\x08\x12\x15\n\rusage_account\x18\x02 \x01(\x08\x12\x1b\n\x13usage_account_graph\x18\x03 \x01(\x08\x12\'\n\x1fusage_account_menu_account_edit\x18\x04 \x01(\x08\x12!\n\x19usage_account_menu_budget\x18\x05 \x01(\x08\x12&\n\x1eusage_account_menu_budget_edit\x18\x06 \x01(\x08\x12#\n\x1busage_account_menu_fees_fee\x18\x07 \x01(\x08\x12&\n\x1eusage_account_menu_fees_credit\x18\x08 \x01(\x08\x12&\n\x1eusage_account_menu_fees_refund\x18\t \x01(\x08\x12*\n\"usage_account_menu_fees_other_fees\x18\n \x01(\x08\x12\x1d\n\x15usage_report_download\x18\x0b \x01(\x08\x12\x13\n\x0busage_group\x18\x0c \x01(\x08\x12\x19\n\x11usage_group_graph\x18\r \x01(\x08\x12\x11\n\tusage_tag\x18\x0e \x01(\x08\x12\x17\n\x0fusage_tag_graph\x18\x0f \x01(\x08\x12\x16\n\x0eusage_crosstag\x18\x10 \x01(\x08\x12\x1c\n\x14usage_crosstag_graph\x18\x11 \x01(\x08\x12\x14\n\x0cri_purchased\x18\x12 \x01(\x08\x12\x16\n\x0eri_utilization\x18\x13 \x01(\x08\x12\x19\n\x11ri_recommendation\x18\x14 \x01(\x08\x12\x14\n\x0csp_purchased\x18\x15 \x01(\x08\x12\x0f\n\x07invoice\x18\x16 \x01(\x08\x12%\n\x1dinvoice_download_csv_discount\x18\x17 \x01(\x08\x12#\n\x1binvoice_download_csv_merged\x18\x18 \x01(\x08\x12\x10\n\x08open_api\x18\x19 \x01(\x08\x12\x18\n\x10users_management\x18\x1a \x01(\x08\x12\x19\n\x11\x61q_coverage_ratio\x18\x1b \x01(\x08\x12\x18\n\x10\x61q_ri_management\x18\x1c \x01(\x08\x12\x18\n\x10\x61q_sp_management\x18\x1d \x01(\x08\x12\x1a\n\x12\x61q_ri_sp_instances\x18\x1e \x01(\x08\x12\x17\n\x0f\x61q_right_sizing\x18\x1f \x01(\x08\x12\x15\n\raq_scheduling\x18 \x01(\x08\x12\x16\n\x0ereport_filters\x18! \x01(\x08\x42Z\n\x19\x63loud.alphaus.blueapi.apiB\x15\x41piGroupRootUserProtoZ&github.com/alphauslabs/blue-sdk-go/apib\x06proto3' ) _GROUPROOTUSER = _descriptor.Descriptor( name='GroupRootUser', full_name='blueapi.api.GroupRootUser', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='email', full_name='blueapi.api.GroupRootUser.email', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='password', full_name='blueapi.api.GroupRootUser.password', index=1, number=2, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='groupId', full_name='blueapi.api.GroupRootUser.groupId', index=2, number=3, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='groupName', full_name='blueapi.api.GroupRootUser.groupName', index=3, number=4, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='groupType', full_name='blueapi.api.GroupRootUser.groupType', index=4, number=5, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='meta', full_name='blueapi.api.GroupRootUser.meta', index=5, number=6, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='passwordUpdateTime', full_name='blueapi.api.GroupRootUser.passwordUpdateTime', index=6, number=7, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='updateTime', full_name='blueapi.api.GroupRootUser.updateTime', index=7, number=8, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='userAccessId', full_name='blueapi.api.GroupRootUser.userAccessId', index=8, number=9, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='userId', full_name='blueapi.api.GroupRootUser.userId', index=9, number=10, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='waveAvailabilityDays', full_name='blueapi.api.GroupRootUser.waveAvailabilityDays', index=10, number=11, type=5, cpp_type=1, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='waveRegistered', full_name='blueapi.api.GroupRootUser.waveRegistered', index=11, number=12, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='waveStatus', full_name='blueapi.api.GroupRootUser.waveStatus', index=12, number=13, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=41, serialized_end=345, ) _FEATUREFLAGS = _descriptor.Descriptor( name='FeatureFlags', full_name='blueapi.api.FeatureFlags', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='dashboard_graph', full_name='blueapi.api.FeatureFlags.dashboard_graph', index=0, number=1, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='usage_account', full_name='blueapi.api.FeatureFlags.usage_account', index=1, number=2, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='usage_account_graph', full_name='blueapi.api.FeatureFlags.usage_account_graph', index=2, number=3, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='usage_account_menu_account_edit', full_name='blueapi.api.FeatureFlags.usage_account_menu_account_edit', index=3, number=4, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='usage_account_menu_budget', full_name='blueapi.api.FeatureFlags.usage_account_menu_budget', index=4, number=5, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='usage_account_menu_budget_edit', full_name='blueapi.api.FeatureFlags.usage_account_menu_budget_edit', index=5, number=6, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='usage_account_menu_fees_fee', full_name='blueapi.api.FeatureFlags.usage_account_menu_fees_fee', index=6, number=7, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='usage_account_menu_fees_credit', full_name='blueapi.api.FeatureFlags.usage_account_menu_fees_credit', index=7, number=8, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='usage_account_menu_fees_refund', full_name='blueapi.api.FeatureFlags.usage_account_menu_fees_refund', index=8, number=9, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='usage_account_menu_fees_other_fees', full_name='blueapi.api.FeatureFlags.usage_account_menu_fees_other_fees', index=9, number=10, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='usage_report_download', full_name='blueapi.api.FeatureFlags.usage_report_download', index=10, number=11, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='usage_group', full_name='blueapi.api.FeatureFlags.usage_group', index=11, number=12, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='usage_group_graph', full_name='blueapi.api.FeatureFlags.usage_group_graph', index=12, number=13, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='usage_tag', full_name='blueapi.api.FeatureFlags.usage_tag', index=13, number=14, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='usage_tag_graph', full_name='blueapi.api.FeatureFlags.usage_tag_graph', index=14, number=15, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='usage_crosstag', full_name='blueapi.api.FeatureFlags.usage_crosstag', index=15, number=16, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='usage_crosstag_graph', full_name='blueapi.api.FeatureFlags.usage_crosstag_graph', index=16, number=17, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='ri_purchased', full_name='blueapi.api.FeatureFlags.ri_purchased', index=17, number=18, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='ri_utilization', full_name='blueapi.api.FeatureFlags.ri_utilization', index=18, number=19, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='ri_recommendation', full_name='blueapi.api.FeatureFlags.ri_recommendation', index=19, number=20, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='sp_purchased', full_name='blueapi.api.FeatureFlags.sp_purchased', index=20, number=21, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='invoice', full_name='blueapi.api.FeatureFlags.invoice', index=21, number=22, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='invoice_download_csv_discount', full_name='blueapi.api.FeatureFlags.invoice_download_csv_discount', index=22, number=23, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='invoice_download_csv_merged', full_name='blueapi.api.FeatureFlags.invoice_download_csv_merged', index=23, number=24, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='open_api', full_name='blueapi.api.FeatureFlags.open_api', index=24, number=25, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='users_management', full_name='blueapi.api.FeatureFlags.users_management', index=25, number=26, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='aq_coverage_ratio', full_name='blueapi.api.FeatureFlags.aq_coverage_ratio', index=26, number=27, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='aq_ri_management', full_name='blueapi.api.FeatureFlags.aq_ri_management', index=27, number=28, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='aq_sp_management', full_name='blueapi.api.FeatureFlags.aq_sp_management', index=28, number=29, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='aq_ri_sp_instances', full_name='blueapi.api.FeatureFlags.aq_ri_sp_instances', index=29, number=30, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='aq_right_sizing', full_name='blueapi.api.FeatureFlags.aq_right_sizing', index=30, number=31, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='aq_scheduling', full_name='blueapi.api.FeatureFlags.aq_scheduling', index=31, number=32, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='report_filters', full_name='blueapi.api.FeatureFlags.report_filters', index=32, number=33, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=348, serialized_end=1304, ) _GROUPROOTUSER.fields_by_name['meta'].message_type = _FEATUREFLAGS DESCRIPTOR.message_types_by_name['GroupRootUser'] = _GROUPROOTUSER DESCRIPTOR.message_types_by_name['FeatureFlags'] = _FEATUREFLAGS _sym_db.RegisterFileDescriptor(DESCRIPTOR) GroupRootUser = _reflection.GeneratedProtocolMessageType('GroupRootUser', (_message.Message,), { 'DESCRIPTOR' : _GROUPROOTUSER, '__module__' : 'api.grouprootuser_pb2' # @@protoc_insertion_point(class_scope:blueapi.api.GroupRootUser) }) _sym_db.RegisterMessage(GroupRootUser) FeatureFlags = _reflection.GeneratedProtocolMessageType('FeatureFlags', (_message.Message,), { 'DESCRIPTOR' : _FEATUREFLAGS, '__module__' : 'api.grouprootuser_pb2' # @@protoc_insertion_point(class_scope:blueapi.api.FeatureFlags) }) _sym_db.RegisterMessage(FeatureFlags) DESCRIPTOR._options = None # @@protoc_insertion_point(module_scope)

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py

Python

myip/test_myip.py

orenhe/myip

bfaa8ba2090fc8bf933dfa031223500331fe6d62

[ "MIT" ]

2

2015-07-30T16:52:05.000Z

2018-03-01T12:56:57.000Z

myip/test_myip.py

orenhe/myip

bfaa8ba2090fc8bf933dfa031223500331fe6d62

[ "MIT" ]

1

2017-09-12T07:50:15.000Z

myip/test_myip.py

orenhe/myip

bfaa8ba2090fc8bf933dfa031223500331fe6d62

[ "MIT" ]

null

import unittest import myip_cmd import linux import darwin from mock import patch, Mock SAMPLE_OUTPUT_LINUX = """1: lo: <LOOPBACK,UP,LOWER_UP> mtu 16436 qdisc noqueue state UNKNOWN link/loopback 00:00:00:00:00:00 brd 00:00:00:00:00:00 inet 127.0.0.1/8 scope host lo inet6 ::1/128 scope host valid_lft forever preferred_lft forever 2: eth0: <NO-CARRIER,BROADCAST,MULTICAST,UP> mtu 1500 qdisc pfifo_fast state DOWN qlen 1000 link/ether 00:21:cc:b9:cb:d5 brd ff:ff:ff:ff:ff:ff inet 1.2.3.4/8 brd 1.255.255.255 scope global eth0 3: wlan0: <BROADCAST,MULTICAST,UP,LOWER_UP> mtu 1500 qdisc mq state UP qlen 1000 link/ether 10:0b:a9:81:ac:64 brd ff:ff:ff:ff:ff:ff inet 192.168.1.100/24 brd 192.168.1.255 scope global wlan0 inet6 fe80::120b:a9ff:fe81:ac64/64 scope link valid_lft forever preferred_lft forever 4: virbr0: <NO-CARRIER,BROADCAST,MULTICAST,UP> mtu 1500 qdisc noqueue state DOWN link/ether 6a:5f:ce:b7:85:a7 brd ff:ff:ff:ff:ff:ff inet 192.168.122.1/24 brd 192.168.122.255 scope global virbr0""" SAMPLE_OUTPUT_LINUX_NO_IP_ASSIGNED = """2: eth0: <NO-CARRIER,BROADCAST,MULTICAST,UP> mtu 1500 qdisc pfifo_fast state DOWN qlen 1000 link/ether 00:21:cc:b9:cb:d5 brd ff:ff:ff:ff:ff:ff""" SAMPLE_OUTPUT_DARWIN = """lo: flags=8049<UP,LOOPBACK,RUNNING,MULTICAST> mtu 16384 inet 127.0.0.1 netmask 0xff000000 inet6 ::1 prefixlen 128 inet6 fe80::1%lo0 prefixlen 64 scopeid 0x1 gif0: flags=8010<POINTOPOINT,MULTICAST> mtu 1280 stf0: flags=0<> mtu 1280 eth0: flags=8863<UP,BROADCAST,SMART,RUNNING,SIMPLEX,MULTICAST> mtu 1500 inet6 fe80::214:51ff:fe68:77e0%en0 prefixlen 64 scopeid 0x4 inet 1.2.3.4 netmask 0xffffff00 broadcast 192.168.1.255 ether 00:14:51:68:77:e0 media: autoselect (10baseT/UTP <half-duplex>) status: active supported media: none autoselect 10baseT/UTP <half-duplex> 10baseT/UTP <half-duplex,hw-loopback> 10baseT/UTP <full-duplex> 10baseT/UTP <full-duplex,hw-loopback> 10baseT/UTP <full-duplex,flow-control> 100baseTX <half-duplex> 100baseTX <half-duplex,hw-loopback> 100baseTX <full-duplex> 100baseTX <full-duplex,hw-loopback> 100baseTX <full-duplex,flow-control> 1000baseT <full-duplex> 1000baseT <full-duplex,hw-loopback> 1000baseT <full-duplex,flow-control> wlan0: flags=8863<UP,BROADCAST,SMART,RUNNING,SIMPLEX,MULTICAST> mtu 1500 inet6 fe80::214:51ff:fe68:77e0%en0 prefixlen 64 scopeid 0x4 inet 192.168.1.100 netmask 0xffffff00 broadcast 192.168.1.255 ether 00:14:51:68:77:e0 media: autoselect (10baseT/UTP <half-duplex>) status: active supported media: none autoselect 10baseT/UTP <half-duplex> 10baseT/UTP <half-duplex,hw-loopback> 10baseT/UTP <full-duplex> 10baseT/UTP <full-duplex,hw-loopback> 10baseT/UTP <full-duplex,flow-control> 100baseTX <half-duplex> 100baseTX <half-duplex,hw-loopback> 100baseTX <full-duplex> 100baseTX <full-duplex,hw-loopback> 100baseTX <full-duplex,flow-control> 1000baseT <full-duplex> 1000baseT <full-duplex,hw-loopback> 1000baseT <full-duplex,flow-control> en8: flags=8863<UP,BROADCAST,SMART,RUNNING,SIMPLEX,MULTICAST> mtu 1500 ether 00:14:51:68:77:e1 media: autoselect (<unknown type>) status: inactive supported media: none autoselect 10baseT/UTP <half-duplex> 10baseT/UTP <half-duplex,hw-loopback> 10baseT/UTP <full-duplex> 10baseT/UTP <full-duplex,hw-loopback> 10baseT/UTP <full-duplex,flow-control> 100baseTX <half-duplex> 100baseTX <half-duplex,hw-loopback> 100baseTX <full-duplex> 100baseTX <full-duplex,hw-loopback> 100baseTX <full-duplex,flow-control> 1000baseT <full-duplex> 1000baseT <full-duplex,hw-loopback> 1000baseT <full-duplex,flow-control> fw0: flags=8863<UP,BROADCAST,SMART,RUNNING,SIMPLEX,MULTICAST> mtu 4078 lladdr 00:14:51:ff:fe:a8:a2:d2 media: autoselect <full-duplex> status: inactive supported media: autoselect <full-duplex> virbr0: flags=8863<UP,BROADCAST,SMART,RUNNING,SIMPLEX,MULTICAST> mtu 1500 inet6 fe80::214:51ff:fe68:77e0%en0 prefixlen 64 scopeid 0x4 inet 192.168.122.1 netmask 0xffffff00 broadcast 192.168.1.255 ether 00:14:51:68:77:e0 media: autoselect (10baseT/UTP <half-duplex>) status: active supported media: none autoselect 10baseT/UTP <half-duplex> 10baseT/UTP <half-duplex,hw-loopback> 10baseT/UTP <full-duplex> 10baseT/UTP <full-duplex,hw-loopback> 10baseT/UTP <full-duplex,flow-control> 100baseTX <half-duplex> 100baseTX <half-duplex,hw-loopback> 100baseTX <full-duplex> 100baseTX <full-duplex,hw-loopback> 100baseTX <full-duplex,flow-control> 1000baseT <full-duplex> 1000baseT <full-duplex,hw-loopback> 1000baseT <full-duplex,flow-control>""" SAMPLE_IP_HASH2 = {"wlan0": "192.168.1.100", "eth0": "1.2.3.4", "virbr0": "192.168.122.1", "lo": "127.0.0.1", } class IpaddrLinuxParsingTests(unittest.TestCase): @patch("commands.getstatusoutput") def test_one_interface(self, mock_getoutput): mock_getoutput.return_value = (0, SAMPLE_OUTPUT_LINUX) self.assertEquals(SAMPLE_IP_HASH2, linux.parse_ip_addr_cmd(["wlan0"])) @patch("commands.getstatusoutput") def test_multiple_interfaces(self, mock_getoutput): mock_getoutput.return_value = (0, SAMPLE_OUTPUT_LINUX) self.assertEquals(SAMPLE_IP_HASH2, linux.parse_ip_addr_cmd([])) @patch("commands.getstatusoutput") def test_interface_with_no_ip_assigned(self, mock_getoutput): mock_getoutput.return_value = (0, SAMPLE_OUTPUT_LINUX_NO_IP_ASSIGNED) self.assertEquals({}, linux.parse_ip_addr_cmd([])) class ifconfigDarwinParsingTests(unittest.TestCase): @patch("commands.getstatusoutput") def test_one_interface(self, mock_getoutput): mock_getoutput.return_value = (0, SAMPLE_OUTPUT_DARWIN) self.assertEquals(SAMPLE_IP_HASH2, darwin.parse_ip_addr_cmd(["wlan0"])) @patch("commands.getstatusoutput") def test_multiple_interfaces(self, mock_getoutput): mock_getoutput.return_value = (0, SAMPLE_OUTPUT_DARWIN) self.assertEquals(SAMPLE_IP_HASH2, darwin.parse_ip_addr_cmd(["wlan0"])) class myipHighLevelTests(unittest.TestCase): def tearDown(self): reload(myip_cmd) def test_get_primary_ip(self): generate_ip_hash = Mock() generate_ip_hash.return_value = SAMPLE_IP_HASH2 myip_cmd.parse_ip_addr_cmd = generate_ip_hash config = myip_cmd.parse_args([]) ips = myip_cmd.get_ips(config) self.assertEquals(["1.2.3.4"], ips) def test_get_all_ips(self): generate_ip_hash = Mock() generate_ip_hash.return_value = SAMPLE_IP_HASH2 myip_cmd.parse_ip_addr_cmd = generate_ip_hash config = myip_cmd.parse_args(["--all"]) ips = myip_cmd.get_ips(config) self.assertEquals(["1.2.3.4", "192.168.1.100", "192.168.122.1"], ips) def test_specific_interface(self): generate_ip_hash = Mock() generate_ip_hash.return_value = SAMPLE_IP_HASH2 myip_cmd.parse_ip_addr_cmd = generate_ip_hash config = myip_cmd.parse_args(["wlan0"]) ips = myip_cmd.get_ips(config) self.assertEquals(["192.168.1.100"], ips)

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py

Python

src/app/managers/__init__.py

schwetzen/liblr

408235a4f539a05f54f0376dbf9dbcd83957db03

[ "Apache-2.0" ]

null

src/app/managers/__init__.py

schwetzen/liblr

408235a4f539a05f54f0376dbf9dbcd83957db03

[ "Apache-2.0" ]

1

2018-12-07T22:15:28.000Z

src/app/managers/__init__.py

schwetzen/liblr

408235a4f539a05f54f0376dbf9dbcd83957db03

[ "Apache-2.0" ]

2

2018-12-07T20:59:53.000Z

2018-12-17T21:02:21.000Z

from app.managers.user import UserManager

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py

Python

examples/flask_example/endpoints.py

exageraldo/connexion-auth-paths-extd

2a1d161e25a47fe5f391850e1809cab052d95aff

[ "BSD-3-Clause" ]

4

2022-02-07T03:44:24.000Z

2022-03-11T00:58:10.000Z

examples/flask_example/endpoints.py

exageraldo/connexion-auth-paths-extd

2a1d161e25a47fe5f391850e1809cab052d95aff

[ "BSD-3-Clause" ]

2

2022-02-08T18:51:08.000Z

2022-02-11T13:55:24.000Z

examples/flask_example/endpoints.py

exageraldo/connexion-auth-paths-extd

2a1d161e25a47fe5f391850e1809cab052d95aff

[ "BSD-3-Clause" ]

null

from http import HTTPStatus from flask import jsonify def get_index(): return jsonify({}), HTTPStatus.NO_CONTENT def get_welcome(): return jsonify({"welcome": "user"}), HTTPStatus.OK

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py

Python

migrations/0011_auto_20180424_1816.py

redditnfl/draft-cards

63779107a731ad741c8cf02b98a4b3d74cdcc3ac

[ "Apache-2.0", "0BSD" ]

null

migrations/0011_auto_20180424_1816.py

redditnfl/draft-cards

63779107a731ad741c8cf02b98a4b3d74cdcc3ac

[ "Apache-2.0", "0BSD" ]

10

2020-06-05T20:27:08.000Z

2022-02-10T10:47:58.000Z

migrations/0011_auto_20180424_1816.py

redditnfl/draft-cards

63779107a731ad741c8cf02b98a4b3d74cdcc3ac

[ "Apache-2.0", "0BSD" ]

1

2021-06-06T01:11:32.000Z

# -*- coding: utf-8 -*- # Generated by Django 1.11 on 2018-04-24 22:16 from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('draftcardposter', '0010_settings_layout'), ] operations = [ migrations.AddField( model_name='settings', name='last_submitted_overall', field=models.IntegerField(default=0), ), migrations.AlterField( model_name='player', name='position', field=models.CharField(choices=[('QB', 'Quarterback'), ('WR', 'Wide Reciever'), ('CB', 'Cornerback'), ('K', 'Kicker'), ('P', 'Punter'), ('LS', 'Long Snapper'), ('DE', 'Defensive End'), ('ILB', 'Inside Linebacker'), ('DT', 'Defensive Tackle'), ('RB', 'Runningback'), ('OT', 'Offensive Tackle'), ('OG', 'Offensive Guard'), ('TE', 'Tight end'), ('S', 'Safety'), ('LB', 'Linebacker'), ('C', 'Center'), ('FB', 'Fullback'), ('DB', 'Defensive Back'), ('OLB', 'Outside Linebacker'), ('OL', 'Offensive Lineman'), ('SS', 'Strong Safety'), ('DL', 'Defensive Lineman'), ('NT', 'Nose Tackle'), ('FS', 'Free Safety'), ('BL', 'Bandleader'), ('4-3 DT', '4-3 Defensive Tackle'), ('4-3 DE', '4-3 Defensive End'), ('4-3 MLB', '4-3 Middle Linebacker'), ('4-3 OLB', '4-3 Outside Linebacker'), ('3-4 DT', '3-4 Defensive Tackle'), ('3-4 DE', '3-4 Defensive End'), ('3-4 ILB', '3-4 Inside Linebacker'), ('3-4 OLB', '3-4 Outside Linebacker')], max_length=3), ), migrations.AlterField( model_name='priority', name='position', field=models.CharField(choices=[('QB', 'Quarterback'), ('WR', 'Wide Reciever'), ('CB', 'Cornerback'), ('K', 'Kicker'), ('P', 'Punter'), ('LS', 'Long Snapper'), ('DE', 'Defensive End'), ('ILB', 'Inside Linebacker'), ('DT', 'Defensive Tackle'), ('RB', 'Runningback'), ('OT', 'Offensive Tackle'), ('OG', 'Offensive Guard'), ('TE', 'Tight end'), ('S', 'Safety'), ('LB', 'Linebacker'), ('C', 'Center'), ('FB', 'Fullback'), ('DB', 'Defensive Back'), ('OLB', 'Outside Linebacker'), ('OL', 'Offensive Lineman'), ('SS', 'Strong Safety'), ('DL', 'Defensive Lineman'), ('NT', 'Nose Tackle'), ('FS', 'Free Safety'), ('BL', 'Bandleader'), ('4-3 DT', '4-3 Defensive Tackle'), ('4-3 DE', '4-3 Defensive End'), ('4-3 MLB', '4-3 Middle Linebacker'), ('4-3 OLB', '4-3 Outside Linebacker'), ('3-4 DT', '3-4 Defensive Tackle'), ('3-4 DE', '3-4 Defensive End'), ('3-4 ILB', '3-4 Inside Linebacker'), ('3-4 OLB', '3-4 Outside Linebacker')], max_length=3), ), ]

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py

Python

code/environment/__init__.py

OnlinePredictorTS/AOLForTimeSeries

ba2cd6aae7f367c6af879d0a4e58870050c00d04

[ "Apache-2.0" ]

null

code/environment/__init__.py

OnlinePredictorTS/AOLForTimeSeries

ba2cd6aae7f367c6af879d0a4e58870050c00d04

[ "Apache-2.0" ]

null

code/environment/__init__.py

OnlinePredictorTS/AOLForTimeSeries

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[ "Apache-2.0" ]

null

# utils init file import environment.RealCore import environment.RealExperiment

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py

Python

fec/fec/context.py

cnlucas/fec-cms

aa67a0d4c19a350420d2f8c4b4e6f93acb808639

[ "CC0-1.0" ]

39

2018-03-09T21:56:17.000Z

2022-01-20T02:31:38.000Z

fec/fec/context.py

rbtrsv/fec-cms

3136d1cf300ce1505d7035de38038e1c045937e6

[ "CC0-1.0" ]

3,183

2018-03-09T20:30:55.000Z

2022-03-30T21:27:49.000Z

fec/fec/context.py

rbtrsv/fec-cms

3136d1cf300ce1505d7035de38038e1c045937e6

[ "CC0-1.0" ]

19

2018-03-09T20:47:31.000Z

2022-03-10T02:54:33.000Z

from django.conf import settings def features(request): return {'features': settings.FEATURES} def show_settings(request): return {'settings': settings}

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45b364fd2d1de876b0ad19cced5f3a960d63326c

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py

Python

angr/engines/vex/__init__.py

Kyle-Kyle/angr

345b2131a7a67e3a6ffc7d9fd475146a3e12f837

[ "BSD-2-Clause" ]

6,132

2015-08-06T23:24:47.000Z

2022-03-31T21:49:34.000Z

angr/engines/vex/__init__.py

Kyle-Kyle/angr

345b2131a7a67e3a6ffc7d9fd475146a3e12f837

[ "BSD-2-Clause" ]

2,272

2015-08-10T08:40:07.000Z

2022-03-31T23:46:44.000Z

angr/engines/vex/__init__.py

Kyle-Kyle/angr

345b2131a7a67e3a6ffc7d9fd475146a3e12f837

[ "BSD-2-Clause" ]

1,155

2015-08-06T23:37:39.000Z

2022-03-31T05:54:11.000Z

from .claripy import * from .light import * from .heavy import * from .lifter import VEXLifter

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45c4d377ea00865edc156c28508f5bacc31a33d0

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py

Python

aau01/kmp.py

Micoael/3b1b-styled-video-code

036b339573e48f807e215bc7c7be9c6fe32b601d

[ "Apache-2.0" ]

7

2020-03-02T23:56:39.000Z

2020-06-08T15:05:46.000Z

my3b1b/old/kmp.py

Micoael/3b1b-styled-video-code

036b339573e48f807e215bc7c7be9c6fe32b601d

[ "Apache-2.0" ]

null

my3b1b/old/kmp.py

Micoael/3b1b-styled-video-code

036b339573e48f807e215bc7c7be9c6fe32b601d

[ "Apache-2.0" ]

null

from manimlib.imports import * from PrimoCreature import * class StartingScene(Scene): def construct(_): name = TextMobject("<","/",">").shift(2*UP).scale(2) mane = TextMobject("Micoael ","$\\rho$","rimo") name[0].shift(8*LEFT).set_color(BLUE) name[1].shift(8*UP).set_color(LIGHT_BROWN) name[2].shift(8*RIGHT).set_color(BLUE) _.play(name[0].shift,(8*RIGHT), name[1].shift,(8*DOWN), name[2].shift,(8*LEFT),) mane[1].shift(0.1*UP) _.play(FadeInFromDown(mane)) class StrMatcher: def gen_next(s2): k = -1 n = len(s2) j = 0 next_list = [0 for i in range(n)] next_list[0] = -1 while j < n-1: if k == -1 or s2[k] == s2[j]: k += 1 j += 1 next_list[j] = k else: k = next_list[k] return next_list def match(s1, s2, next_list): ans = -1 i = 0 j = 0 while i < len(s1): if s1[i] == s2[j] or j == -1: i += 1 j += 1 else: j = next_list[j] if j == len(s2): ans = i - len(s2) break return ans class Introduction(Scene): def construct(self): primo = PrimoCreature(color=BLUE).shift(2*DOWN+4*LEFT) self.play(FadeIn(primo)) texts = TextMobject("aaaaaaafsdiaaawsss\\\\dfsaaws","awsl","wsdawasa\\\\dwaawwaslwasawl").shift(2*RIGHT+DOWN) primo.look_at(texts) self.play(Write(texts)) palabras_ale = TextMobject("awsl ???") self.play(PrimoCreatureSays( primo, palabras_ale, bubble_kwargs={"height": 5, "width": 6}, target_mode="plain" )) self.wait(1.5) self.play(texts[1].set_color,YELLOW, texts[1].scale,2) self.play(texts[1].scale,0.5) palabras_ale = TextMobject("让计算机完成字符串查找?") primo.look_at(palabras_ale) self.play(PrimoCreatureSays( primo, palabras_ale, bubble_kwargs={"height": 5, "width": 6}, target_mode="plain" )) self.wait(5) palabras_ale = TextMobject("这不是很简单的吗?") primo.look_at(palabras_ale) self.play(PrimoCreatureSays( primo, palabras_ale, bubble_kwargs={"height": 5, "width": 6}, target_mode="plain" )) class BasicAlgorithm(Scene): def construct(_): _.str1="aaaaaaafsdiaaawslsdfsaawsawslwsda" _.str2="awsl" _.init(_.str1,_.str2) _.matchord(_.str1,_.str2) def init(_,str1,str2): _.a = 0 _.b = 0 _.comp = 0 _.len1 = len(str1) _.len2 = len(str2) _.moshi = VGroup() _.yuanlai = VGroup() _.next = VGroup() _.rect = Rectangle(width=0.5,height=1,fill_color=GREEN,fill_opacity=0.3).move_to(np.array([-6,2.75,0])) for i in range(0,len(str1)): pos = np.array((-6+0.5*i,3,0.0)) square = TextMobject(str1[i]) square.move_to(pos) _.moshi.add(square) for i in range(0,len(str2)): pos = np.array((-6+0.5*i,2.5,0.0)) square = TextMobject(str2[i]) _.yuanlai.add(square) square.move_to(pos) for i in range(0,len(str2)): pos = np.array((-6+0.5*i,2,0.0)) square = TextMobject(str(StrMatcher.gen_next(str2)[i])) _.next.add(square) square.move_to(pos) _.addTextsToScreen() _.add(_.rect) def addTextsToScreen(_): _.play(Write(_.yuanlai),Write(_.moshi)) def shifts(_,val): _.play(_.yuanlai.shift,(val*0.5*RIGHT), _.next.shift,(val*0.5*RIGHT), run_time=0.5) _.b += val def shiftto(_,val): _.play(_.yuanlai.shift,((val-_.b)*0.5*RIGHT), _.next.shift,((val-_.b)*0.5*RIGHT) ,run_time=0.5) _.b = val def shiftgreen(_,val): _.play(_.rect.shift,(val*0.5*RIGHT),run_time=0.5) _.comp += val def shiftgto(_,val): _.play(_.rect.shift,((val-_.comp)*0.5*RIGHT),run_time=0.5) _.comp = val def compare(_,dig): _.write(rect) def alignw(_,bb,aa): _.shiftto(bb-aa) _.shiftgto(bb) if _.str1[bb]==_.str2[aa]: _.play(_.rect.set_color,(GREEN),run_time = 0.5) else: _.play(_.rect.set_color,(RED),run_time = 0.5) def matchord(_,t, p): i, j = 0, 0 n, m = len(t), len(p) while i < n and j < m: _.alignw(i,j) if t[i] == p[j]: i, j = i+1, j+1 else: i, j = i-j+1, 0 if j == m: return i-j return -1 def match(_,s1, s2, next_list): ans = -1 i = 0 j = 0 while i < len(s1): _.alignw(i,j) if s1[i] == s2[j] or j == -1: i += 1 j += 1 else: j = next_list[j] if j == len(s2): ans = i - len(s2) break return ans class ProblemWithCommonAlgorithm(Scene): def construct(_): _.str1="aaaaaaafsdiaaawslsdfsaawsawslwsda" _.str2="aaaf" _.init(_.str1,_.str2) _.matchord(_.str1,_.str2) def init(_,str1,str2): _.a = 0 _.b = 0 _.comp = 0 _.len1 = len(str1) _.len2 = len(str2) _.moshi = VGroup() _.yuanlai = VGroup() _.next = VGroup() _.rect = Rectangle(width=0.5,height=1,fill_color=GREEN,fill_opacity=0.3).move_to(np.array([-6,2.75,0])) for i in range(0,len(str1)): pos = np.array((-6+0.5*i,3,0.0)) square = TextMobject(str1[i]) square.move_to(pos) _.moshi.add(square) for i in range(0,len(str2)): pos = np.array((-6+0.5*i,2.5,0.0)) square = TextMobject(str2[i]) _.yuanlai.add(square) square.move_to(pos) for i in range(0,len(str2)): pos = np.array((-6+0.5*i,2,0.0)) square = TextMobject(str(StrMatcher.gen_next(str2)[i])) _.next.add(square) square.move_to(pos) _.addTextsToScreen() _.add(_.rect) def addTextsToScreen(_): _.play(Write(_.yuanlai),Write(_.moshi)) def shifts(_,val): _.play(_.yuanlai.shift,(val*0.5*RIGHT), run_time=0.5) _.b += val def shiftto(_,val): _.play(_.yuanlai.shift,((val-_.b)*0.5*RIGHT), run_time=0.5) _.b = val def shiftgreen(_,val): _.play(_.rect.shift,(val*0.5*RIGHT),run_time=0.5) _.comp += val def shiftgto(_,val): _.play(_.rect.shift,((val-_.comp)*0.5*RIGHT),run_time=0.5) _.comp = val def compare(_,dig): _.write(rect) def alignw(_,bb,aa): _.shiftto(bb-aa) _.shiftgto(bb) if _.str1[bb]==_.str2[aa]: _.play(_.rect.set_color,(GREEN),run_time = 0.5) else: _.play(_.rect.set_color,(RED),run_time = 0.5) def matchord(_,t, p): i, j = 0, 0 n, m = len(t), len(p) while i < n and j < m: _.alignw(i,j) if t[i] == p[j]: i, j = i+1, j+1 else: i, j = i-j+1, 0 if j == m: return i-j return -1 def match(_,s1, s2, next_list): ans = -1 i = 0 j = 0 while i < len(s1): _.alignw(i,j) if s1[i] == s2[j] or j == -1: i += 1 j += 1 else: j = next_list[j] if j == len(s2): ans = i - len(s2) break return ans class HowToImprove(Scene): def construct(_): primo = PrimoCreature(color=BLUE).shift(2*DOWN+4*LEFT) _.play(FadeIn(primo)) palabras_ale = TextMobject("减少重复的移动?!") _.play(PrimoCreatureSays( primo, palabras_ale, bubble_kwargs={"height": 5, "width": 6}, target_mode="plain" )) _.wait(1.5) _.clear() ori = TextMobject("MicoaelPrim","p") patt = TextMobject("MicoaelPrim","o") al = VGroup(ori,patt).arrange(DOWN) _.play(Write(al)) _.play(ori[1].set_color,RED, patt[1].set_color,RED) _.play(FadeOut(al)) ori = TextMobject("MicoaelMico","p") patt = TextMobject("Mico","ael","Mico","o") al = VGroup(ori,patt).arrange(DOWN) _.play(Write(al)) _.play(ori[1].set_color,RED, patt[3].set_color,RED) _.play(patt[0].set_color,YELLOW, patt[2].set_color,YELLOW) _.play(patt.shift,1.8*RIGHT,) _.wait(3) _.clear() _.play(FadeIn(primo)) palabras_ale = TextMobject("也就是说找到前后长度对称\\\\的最大长度是吧？") _.play(PrimoCreatureSays( primo, palabras_ale, bubble_kwargs={"height": 5, "width": 6}, target_mode="plain" )) _.wait(3) _.clear() exam = TextMobject("M","i","c","o","a","M","i","c","o").scale(2) _.play(Write(exam)) _.play( exam[0].set_color,YELLOW, exam[5].set_color,YELLOW) _.wait(0.5) _.play( exam[1].set_color,YELLOW, exam[6].set_color,YELLOW) _.wait(0.5) _.play( exam[2].set_color,YELLOW, exam[7].set_color,YELLOW) _.wait(0.5) _.play( exam[3].set_color,YELLOW, exam[8].set_color,YELLOW) txt = TextMobject("$G=4$").shift(2*UP) _.play(Transform(exam.copy(),txt)) class TheConnectionBetweenPatternAndTheOrigin(Scene): def construct(_): pat = "MicoaMico" _.init("MicoaMickcMicoa",pat) _.shiftgreen(8) _.play(_.yuanlai[0].set_color,YELLOW,_.yuanlai[5].set_color,YELLOW) _.play(_.yuanlai[1].set_color,YELLOW,_.yuanlai[6].set_color,YELLOW) _.play(_.yuanlai[2].set_color,YELLOW,_.yuanlai[7].set_color,YELLOW) size = TextMobject("$G=3$") _.play(FadeInFromDown(size)) _.play(FocusOn(_.yuanlai[3])) _.shifts(5) _.wait(3) mask = TextMobject("----","我们不知道原来的字符串","----").add_background_rectangle().move_to(_.moshi) _.play(_.yuanlai[0].set_color,WHITE,_.yuanlai[5].set_color,WHITE,run_time=0.1) _.play(_.yuanlai[1].set_color,WHITE,_.yuanlai[6].set_color,WHITE,run_time=0.1) _.play(_.yuanlai[2].set_color,WHITE,_.yuanlai[7].set_color,WHITE,run_time=0.1) _.play(Write(mask)) gr = VGroup() for i in range (len(pat)+1): stri = "" for j in range(i): stri=stri+(pat[j]) gr.add(TextMobject(stri)) gr.arrange(DOWN).shift(0.5*DOWN) _.play(FadeOut(size)) _.play(Transform(_.yuanlai.copy(),gr)) primo = PrimoCreature(color=BLUE).shift(2*DOWN+4*LEFT) _.play(FadeIn(primo)) palabras_ale = TextMobject("也就是说我们把这一堆东西\\\\的最长公共前后缀算出来就好了吧") _.play(PrimoCreatureSays( primo, palabras_ale, bubble_kwargs={"height": 5, "width": 6}, target_mode="plain" ) ) _.wait(3) _.clear() _.add(gr) for i in range(1,len(gr)): _.play(ShowCreationThenDestructionAround(gr[i])) primo = PrimoCreature(color=BLUE).shift(2*DOWN+4*LEFT) _.play(FadeIn(primo)) palabras_ale = TextMobject("看上去好简单的样子！") _.play(PrimoCreatureSays( primo, palabras_ale, bubble_kwargs={"height": 5, "width": 6}, target_mode="plain" ) ) _.wait(3) def init(_,str1,str2): _.a = 0 _.b = 0 _.comp = 0 _.len1 = len(str1) _.len2 = len(str2) _.moshi = VGroup() _.yuanlai = VGroup() _.next = VGroup() _.rect = Rectangle(width=0.5,height=1,fill_color=GREEN,fill_opacity=0.3).move_to(np.array([-3,2.75,0])) for i in range(0,len(str1)): pos = np.array((-3+0.5*i,3,0.0)) square = TextMobject(str1[i]) square.move_to(pos) _.moshi.add(square) for i in range(0,len(str2)): pos = np.array((-3+0.5*i,2.5,0.0)) square = TextMobject(str2[i]) _.yuanlai.add(square) square.move_to(pos) for i in range(0,len(str2)): pos = np.array((-3+0.5*i,2,0.0)) square = TextMobject(str(i)) _.next.add(square) square.move_to(pos) _.addTextsToScreen() _.add(_.rect) _.play(_.rect.set_color,RED) def addTextsToScreen(_): _.play(Write(_.yuanlai),Write(_.moshi),Write(_.next)) def shifts(_,val): _.play(_.yuanlai.shift,(val*0.5*RIGHT), _.next.shift,(val*0.5*RIGHT), run_time=0.5) _.b += val def shiftto(_,val): _.play(_.yuanlai.shift,((val-_.b)*0.5*RIGHT), _.next.shift,((val-_.b)*0.5*RIGHT) ,run_time=0.5) _.b = val def shiftgreen(_,val): _.play(_.rect.shift,(val*0.5*RIGHT),run_time=0.5) _.comp += val def shiftgto(_,val): _.play(_.rect.shift,((val-_.comp)*0.5*RIGHT),run_time=0.5) _.comp = val def compare(_): _.play(Write(_.rect)) def alignw(_,bb,aa): _.shiftto(bb-aa) _.shiftgto(bb) if _.str1[bb]==_.str2[aa]: _.play(_.rect.set_color,(GREEN),run_time = 0.5) else: _.play(_.rect.set_color,(RED),run_time = 0.5) def matchord(_,t, p): i, j = 0, 0 n, m = len(t), len(p) while i < n and j < m: _.alignw(i,j) if t[i] == p[j]: i, j = i+1, j+1 else: i, j = i-j+1, 0 if j == m: return i-j return -1 def match(_,s1, s2, next_list): ans = -1 i = 0 j = 0 while i < len(s1): _.alignw(i,j) if s1[i] == s2[j] or j == -1: i += 1 j += 1 else: j = next_list[j] if j == len(s2): ans = i - len(s2) break return ans class HardToFigure(Scene): def construct(_): _.str1="AGCAxxx" _.str2="AGCT" _.init(_.str1,_.str2) _.match(_.str1,_.str2,StrMatcher.gen_next(_.str2)) def init(_,str1,str2): _.a = 0 _.b = 0 _.comp = 0 _.len1 = len(str1) _.len2 = len(str2) _.moshi = VGroup() _.yuanlai = VGroup() _.next = VGroup() _.rect = Rectangle(width=0.5,height=1,fill_color=GREEN,fill_opacity=0.3).move_to(np.array([-3,2.75,0])) for i in range(0,len(str1)): pos = np.array((-3+0.5*i,3,0.0)) square = TextMobject(str1[i]) square.move_to(pos) _.moshi.add(square) for i in range(0,len(str2)): pos = np.array((-3+0.5*i,2.5,0.0)) square = TextMobject(str2[i]) _.yuanlai.add(square) square.move_to(pos) for i in range(0,len(str2)): pos = np.array((-3+0.5*i,2,0.0)) square = TextMobject(str(StrMatcher.gen_next(str2)[i])) _.next.add(square) square.move_to(pos) _.addTextsToScreen() _.add(_.rect) def addTextsToScreen(_): _.play(Write(_.yuanlai),Write(_.moshi),Write(_.next)) def shifts(_,val): _.play(_.yuanlai.shift,(val*0.5*RIGHT), _.next.shift,(val*0.5*RIGHT), run_time=0.5) _.b += val def shiftto(_,val): _.play(_.yuanlai.shift,((val-_.b)*0.5*RIGHT), _.next.shift,((val-_.b)*0.5*RIGHT) ,run_time=0.5) _.b = val def shiftgreen(_,val): _.play(_.rect.shift,(val*0.5*RIGHT),run_time=0.5) _.comp += val def shiftgto(_,val): _.play(_.rect.shift,((val-_.comp)*0.5*RIGHT),run_time=0.5) _.comp = val def compare(_,dig): _.write(rect) def alignw(_,bb,aa): _.shiftto(bb-aa) _.shiftgto(bb) if _.str1[bb]==_.str2[aa]: _.play(_.rect.set_color,(GREEN),run_time = 0.5) else: _.play(_.rect.set_color,(RED),run_time = 0.5) def matchord(_,t, p): i, j = 0, 0 n, m = len(t), len(p) while i < n and j < m: _.alignw(i,j) if t[i] == p[j]: i, j = i+1, j+1 else: i, j = i-j+1, 0 if j == m: return i-j return -1 def match(_,s1, s2, next_list): ans = -1 i = 0 j = 0 while i < len(s1): _.alignw(i,j) if s1[i] == s2[j] or j == -1: i += 1 j += 1 else: j = next_list[j] if j == len(s2): ans = i - len(s2) break return ans class UnderstandRousThought(Scene): def construct(_): pat = "MicoaMico" gr = VGroup() for i in range (len(pat)+1): stri = "" for j in range(i): stri=stri+(pat[j]) gr.add(TextMobject(stri)) gr.arrange(DOWN).shift(0.5*DOWN) _.play(Write(gr)) primo = PrimoCreature(color=BLUE).shift(2*DOWN+4*LEFT) _.play(FadeIn(primo)) palabras_ale = TextMobject("遍历一遍不就好了吗？！") _.play(PrimoCreatureSays( primo, palabras_ale, bubble_kwargs={"height": 5, "width": 6}, target_mode="plain" )) _.wait(3) _.clear() _.add(gr) primo = PrimoCreature(color=LIGHT_BROWN).shift(2*DOWN+4*RIGHT).flip() _.play(FadeIn(primo)) palabras_ale = TextMobject("试着递推一下！") _.play(PrimoCreatureSays( primo, palabras_ale, bubble_kwargs={"height": 5, "width": 6}, target_mode="plain" )) m = ValueTracker(0) def upd(obj): obj.tex_string = "G="+str( int(m.get_value())) _.G = TextMobject("G=",str(int(m.get_value()))).add_updater(upd).shift(2*DOWN) _.wait(3) _.clear() _.txt0 = TextMobject("A","G","C","T","A","G","C","A","G","C","T","G","C","A"); _.show(0) _.add(_.G) _.moveto(0) _.wait(1) _.show(1) _.changeval(0) _.moveto(1) _.wait(1) _.show(2) _.changeval(0) _.moveto(2) _.wait(1) _.show(3) _.changeval(0) _.moveto(3) _.wait(1) _.show(4) _.compare(0,4) _.changeval(1) _.moveto(4) _.cls() _.cc(0,4) _.wait(1) _.show(5) _.compare(1,5) _.changeval(2) _.moveto(5) _.cc(1,5) _.wait(1) _.show(6) _.compare(2,6) _.changeval(3) _.moveto(6) _.cc(2,6) _.wait(1) _.show(7) _.compare(3,7) _.changeval("?") a = TextMobject("每检验到一个不匹配的就要归零吗？").add_background_rectangle() _.play(Write(a)) _.wait(3) _.play(Uncreate(a)) a = TextMobject("有没有更小的区间让他们相同呢？").add_background_rectangle() _.play(Write(a)) _.wait(3) _.play(Uncreate(a)) a = TextMobject("如果有，该怎么找到呢？").add_background_rectangle() _.play(Write(a)) _.wait(3) _.play(Uncreate(a)) _.compare(6,6) a = TextMobject("下一个公共前后缀有可能存在这里的next").add_background_rectangle().shift(2.5*UP) _.play(Write(a)) _.wait(1) a = TextMobject("如果发现他两个字符相等或$next$是$0$就不用继续下去了").add_background_rectangle().shift(2*UP) _.play(Write(a)) _.wait(1) a = TextMobject("(到头也没发现相同的)").add_background_rectangle().shift(1.5*UP) _.play(Write(a)) _.wait(1) _.compare(6,6) _.compare(3,3) _.compare(0,0) _.changeval(1) _.moveto(7) _.cls() _.cc(0,7) _.show(8) _.compare(1,8) _.changeval(2) _.moveto(8) _.cc(1,8) _.show(9) _.changeval(3) _.moveto(9) _.compare(2,9) _.cc(2,9) _.wait(1) _.show(10) _.changeval(4) _.compare(3,10) _.cc(3,10) _.moveto(10) _.wait(1) _.show(11) _.compare(10,10) _.compare(4,4) _.compare(0,0) _.changeval(0) _.moveto(11) _.wait(1) _.cls() _.show(12) _.compare(11,11) _.compare(0,0) _.changeval(0) _.moveto(12) _.wait(1) _.show(13) _.compare(12,12) _.compare(0,0) _.changeval(1) _.moveto(13) _.wait(1) _.txt0.shift(0.5*LEFT) _.wait(3) def cc(_,a,b): _.play(_.txt0[a].set_color,BLUE,_.txt0[b].set_color,BLUE) def cls(_): for i in range (len(_.txt0)): _.txt0[i].set_color(WHITE) def moveto(_,to): p = _.G[1].copy() _.play(p.move_to,_.txt0[to],p.shift,0.8*DOWN) def changeval(_,a): _.G.become(TextMobject("G=",str(a)).shift(2*DOWN)) def compare(_,a,b): _.play(ShowCreationThenDestructionAround(_.txt0[a]),ShowCreationThenDestructionAround(_.txt0[b])) def show(_,m): _.play(FadeInFromDown(_.txt0[m])) def init(_,str1,str2): _.a = 0 _.b = 0 _.comp = 0 _.len1 = len(str1) _.len2 = len(str2) _.moshi = VGroup() _.yuanlai = VGroup() _.next = VGroup() _.rect = Rectangle(width=0.5,height=1,fill_color=GREEN,fill_opacity=0.3).move_to(np.array([-3,2.75,0])) for i in range(0,len(str1)): pos = np.array((-3+0.5*i,3,0.0)) square = TextMobject(str1[i]) square.move_to(pos) _.moshi.add(square) for i in range(0,len(str2)): pos = np.array((-3+0.5*i,2.5,0.0)) square = TextMobject(str2[i]) _.yuanlai.add(square) square.move_to(pos) for i in range(0,len(str2)): pos = np.array((-3+0.5*i,2,0.0)) square = TextMobject(str(i)) _.next.add(square) square.move_to(pos) _.addTextsToScreen() _.add(_.rect) _.play(_.rect.set_color,RED) def addTextsToScreen(_): _.play(Write(_.yuanlai),Write(_.moshi),Write(_.next)) def shifts(_,val): _.play(_.yuanlai.shift,(val*0.5*RIGHT), _.next.shift,(val*0.5*RIGHT), run_time=0.5) _.b += val def shiftto(_,val): _.play(_.yuanlai.shift,((val-_.b)*0.5*RIGHT), _.next.shift,((val-_.b)*0.5*RIGHT) ,run_time=0.5) _.b = val def shiftgreen(_,val): _.play(_.rect.shift,(val*0.5*RIGHT),run_time=0.5) _.comp += val def shiftgto(_,val): _.play(_.rect.shift,((val-_.comp)*0.5*RIGHT),run_time=0.5) _.comp = val def alignw(_,bb,aa): _.shiftto(bb-aa) _.shiftgto(bb) if _.str1[bb]==_.str2[aa]: _.play(_.rect.set_color,(GREEN),run_time = 0.5) else: _.play(_.rect.set_color,(RED),run_time = 0.5) def matchord(_,t, p): i, j = 0, 0 n, m = len(t), len(p) while i < n and j < m: _.alignw(i,j) if t[i] == p[j]: i, j = i+1, j+1 else: i, j = i-j+1, 0 if j == m: return i-j return -1 def match(_,s1, s2, next_list): ans = -1 i = 0 j = 0 while i < len(s1): _.alignw(i,j) if s1[i] == s2[j] or j == -1: i += 1 j += 1 else: j = next_list[j] if j == len(s2): ans = i - len(s2) break return ans class AlmostDone(Scene): def construct(_): _.str1="1我们1我们11我们1我终于1完成了1next数组1的查找" _.str2="1我们1我终于1" _.init(_.str1,_.str2) _.match(_.str1,_.str2,StrMatcher.gen_next(_.str2)) def init(_,str1,str2): _.a = 0 _.b = 0 _.comp = 0 _.len1 = len(str1) _.len2 = len(str2) _.moshi = VGroup() _.yuanlai = VGroup() _.next = VGroup() _.rect = Rectangle(width=0.5,height=1,fill_color=GREEN,fill_opacity=0.3).move_to(np.array([-3,2.75,0])) for i in range(0,len(str1)): pos = np.array((-3+0.5*i,3,0.0)) square = TextMobject(str1[i]) square.move_to(pos) _.moshi.add(square) for i in range(0,len(str2)): pos = np.array((-3+0.5*i,2.5,0.0)) square = TextMobject(str2[i]) _.yuanlai.add(square) square.move_to(pos) for i in range(0,len(str2)): pos = np.array((-3+0.5*i,2,0.0)) square = TextMobject(str(StrMatcher.gen_next(str2)[i])) _.next.add(square) square.move_to(pos) _.addTextsToScreen() _.add(_.rect) def addTextsToScreen(_): _.play(Write(_.yuanlai),Write(_.moshi),Write(_.next)) def shifts(_,val): _.play(_.yuanlai.shift,(val*0.5*RIGHT), _.next.shift,(val*0.5*RIGHT), run_time=0.5) _.b += val def shiftto(_,val): _.play(_.yuanlai.shift,((val-_.b)*0.5*RIGHT), _.next.shift,((val-_.b)*0.5*RIGHT) ,run_time=0.5) _.b = val def shiftgreen(_,val): _.play(_.rect.shift,(val*0.5*RIGHT),run_time=0.5) _.comp += val def shiftgto(_,val): _.play(_.rect.shift,((val-_.comp)*0.5*RIGHT),run_time=0.5) _.comp = val def compare(_,dig): _.write(rect) def alignw(_,bb,aa): _.shiftto(bb-aa) _.shiftgto(bb) if _.str1[bb]==_.str2[aa]: _.play(_.rect.set_color,(GREEN),run_time = 0.5) else: _.play(_.rect.set_color,(RED),run_time = 0.5) def matchord(_,t, p): i, j = 0, 0 n, m = len(t), len(p) while i < n and j < m: _.alignw(i,j) if t[i] == p[j]: i, j = i+1, j+1 else: i, j = i-j+1, 0 if j == m: return i-j return -1 def match(_,s1, s2, next_list): ans = -1 i = 0 j = 0 while i < len(s1): _.alignw(i,j) if s1[i] == s2[j] or j == -1: i += 1 j += 1 else: j = next_list[j] if j == len(s2): ans = i - len(s2) break return ans class Demostrate3(Scene): def construct(_): _.str1="ji0de0san0lian0" _.str2="0san0lian0" _.init(_.str1,_.str2) _.match(_.str1,_.str2,StrMatcher.gen_next(_.str2)) def init(_,str1,str2): _.a = 0 _.b = 0 _.comp = 0 _.len1 = len(str1) _.len2 = len(str2) _.moshi = VGroup() _.yuanlai = VGroup() _.next = VGroup() _.rect = Rectangle(width=0.5,height=1,fill_color=GREEN,fill_opacity=0.3).move_to(np.array([-3,2.75,0])) for i in range(0,len(str1)): pos = np.array((-3+0.5*i,3,0.0)) square = TextMobject(str1[i]) square.move_to(pos) _.moshi.add(square) for i in range(0,len(str2)): pos = np.array((-3+0.5*i,2.5,0.0)) square = TextMobject(str2[i]) _.yuanlai.add(square) square.move_to(pos) for i in range(0,len(str2)): pos = np.array((-3+0.5*i,2,0.0)) square = TextMobject(str(StrMatcher.gen_next(str2)[i])) _.next.add(square) square.move_to(pos) _.addTextsToScreen() _.add(_.rect) def addTextsToScreen(_): _.play(Write(_.yuanlai),Write(_.moshi),Write(_.next)) def shifts(_,val): _.play(_.yuanlai.shift,(val*0.5*RIGHT), _.next.shift,(val*0.5*RIGHT), run_time=0.5) _.b += val def shiftto(_,val): _.play(_.yuanlai.shift,((val-_.b)*0.5*RIGHT), _.next.shift,((val-_.b)*0.5*RIGHT) ,run_time=0.5) _.b = val def shiftgreen(_,val): _.play(_.rect.shift,(val*0.5*RIGHT),run_time=0.5) _.comp += val def shiftgto(_,val): _.play(_.rect.shift,((val-_.comp)*0.5*RIGHT),run_time=0.5) _.comp = val def compare(_,dig): _.write(rect) def alignw(_,bb,aa): _.shiftto(bb-aa) _.shiftgto(bb) if _.str1[bb]==_.str2[aa]: _.play(_.rect.set_color,(GREEN),run_time = 0.5) else: _.play(_.rect.set_color,(RED),run_time = 0.5) def matchord(_,t, p): i, j = 0, 0 n, m = len(t), len(p) while i < n and j < m: _.alignw(i,j) if t[i] == p[j]: i, j = i+1, j+1 else: i, j = i-j+1, 0 if j == m: return i-j return -1 def match(_,s1, s2, next_list): ans = -1 i = 0 j = 0 while i < len(s1): _.alignw(i,j) if s1[i] == s2[j] or j == -1: i += 1 j += 1 else: j = next_list[j] if j == len(s2): ans = i - len(s2) break return ans

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null

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6

45ccd4c8ee3c7889ccf5c6cefb898f24cf932ccd

150

py

Python

jira/jira_integration/doctype/jira_settings/test_jira_settings.py

hrwX/jira

f2d5f09584e246074199670d562591c933d07bb6

[ "MIT" ]

null

jira/jira_integration/doctype/jira_settings/test_jira_settings.py

hrwX/jira

f2d5f09584e246074199670d562591c933d07bb6

[ "MIT" ]

null

jira/jira_integration/doctype/jira_settings/test_jira_settings.py

hrwX/jira

f2d5f09584e246074199670d562591c933d07bb6

[ "MIT" ]

null

16.666667

48

0.78

19

150

6.157895

0.894737

0

0.03125

0.146667

150

8

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null

0

1

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6

b3139945a4e6ad11a8fd2459af9fb13d708aef7a

5,393

py

Python

client/tests/output_adapter_tests/test_slack_output_adapter.py

TheGuardianWolf/tellmefacts

79968e3d4284e307cc5a12d5147006aa3ba2a2ca

[ "MIT" ]

null

client/tests/output_adapter_tests/test_slack_output_adapter.py

TheGuardianWolf/tellmefacts

79968e3d4284e307cc5a12d5147006aa3ba2a2ca

[ "MIT" ]

null

client/tests/output_adapter_tests/test_slack_output_adapter.py

TheGuardianWolf/tellmefacts

79968e3d4284e307cc5a12d5147006aa3ba2a2ca

[ "MIT" ]

null

# -*- coding: utf-8 -*- import pytest from client.output import Slack from chatterbot.conversation import Statement from slackclient import SlackClient @pytest.fixture() def slack_adapter(mocker): """ Create and patches for an output slack adapter. """ # Patch methods in the slackclient library so that no real requests to # Slack are made mock_api_call = {'ok': True} mocker.patch( 'slackclient.SlackClient.api_call', return_value=mock_api_call) mocker.patch('slackclient.SlackClient.rtm_read') mocker.patch('slackclient.SlackClient.rtm_send_message', autospec=True) sc = SlackClient('xoxp-1234123412341234-12341234-1234') s = Slack(slack_client=sc, bot_name='tellmefacts') return s class TestSlackOutputAdapter(object): def test_slack(self, slack_adapter): """ Test object attributes. """ assert slack_adapter.default_channel == '#general' def test_send_message_api(self, slack_adapter, monkeypatch): """ Test sending a full response through Slack RTM after retrieving channel data from the last input statement. """ slack_adapter.send_message(Statement('hi'), 'abcd') # Check whether the call had the correct side effects assert slack_adapter.events.get('send').is_set() assert slack_adapter.slack_client.api_call.called # Check call args args, kwargs = slack_adapter.slack_client.api_call.call_args assert kwargs['text'] == 'hi' assert kwargs['channel'] == 'abcd' assert not kwargs['as_user'] # Clear send event as this method is a consumer of the event slack_adapter.events.get('send').clear() def test_send_message_rtm(self, slack_adapter, monkeypatch): """ Test sending a message through Slack RTM. """ # Pretend that websockets is connected monkeypatch.setattr(slack_adapter.slack_client.server, 'websocket', True) slack_adapter.send_message(Statement('hi'), 'abcd') # Check whether the call had the correct side effects assert slack_adapter.events.get('send').is_set() assert slack_adapter.slack_client.rtm_send_message.called # Check call args args, kwargs = slack_adapter.slack_client.rtm_send_message.call_args assert kwargs['message'] == 'hi' assert kwargs['channel'] == 'abcd' # Clear send event as this method is a consumer of the event slack_adapter.events.get('send').clear() def test_process_response_api(self, slack_adapter, mocker, monkeypatch): """ Test sending a full response through Slack Web API after retrieving channel data from the last input statement. """ # Create and set the chatbot object for this adapter to contain one # last input statement with a known channel. mock_sessions = mocker.Mock(conversation_sessions=mocker.Mock( get=mocker.Mock(return_value=mocker.Mock(conversation=mocker.Mock( get_last_input_statement=mocker.Mock(return_value=Statement( 'input', extra_data={'channel': 'abcd'}))))))) monkeypatch.setattr(slack_adapter, 'chatbot', mock_sessions) # Test adapter echo assert str(slack_adapter.process_response(Statement('test'))) == 'test' # Check that the call produced the right side effects assert slack_adapter.events.get('send').is_set() assert slack_adapter.slack_client.api_call.called # Check call args args, kwargs = slack_adapter.slack_client.api_call.call_args assert kwargs['text'] == 'test' assert kwargs['channel'] == 'abcd' assert not kwargs['as_user'] # Clear send event as this method is a consumer of the event slack_adapter.events.get('send').clear() def test_process_response_rtm(self, slack_adapter, mocker, monkeypatch): """ Test sending a full response through Slack RTM after retrieving channel data from the last input statement. """ # Create and set the chatbot object for this adapter to contain one # last input statement with a known channel. mock_sessions = mocker.Mock(conversation_sessions=mocker.Mock( get=mocker.Mock(return_value=mocker.Mock(conversation=mocker.Mock( get_last_input_statement=mocker.Mock(return_value=Statement( 'input', extra_data={'channel': 'abcd'}))))))) monkeypatch.setattr(slack_adapter, 'chatbot', mock_sessions) # Pretend websockets is connected monkeypatch.setattr(slack_adapter.slack_client.server, 'websocket', True) # Test adapter echo assert str(slack_adapter.process_response(Statement('test'))) == 'test' # Check that the call produced the right side effects assert slack_adapter.events.get('send').is_set() assert slack_adapter.slack_client.rtm_send_message.called # Check call args args, kwargs = slack_adapter.slack_client.rtm_send_message.call_args assert kwargs['message'] == 'test' assert kwargs['channel'] == 'abcd' # Clear send event as this method is a consumer of the event slack_adapter.events.get('send').clear()

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null

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1

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null

0

6

b35579c295741a9a8f007749dc002e8f7a7d8717

119

py

Python

autoencoda/__init__.py

j-abc/autoencoda

c892afe52a18c9f7fca61116459190ae59ea76a0

[ "MIT" ]

null

autoencoda/__init__.py

j-abc/autoencoda

c892afe52a18c9f7fca61116459190ae59ea76a0

[ "MIT" ]

8

2019-06-16T20:19:21.000Z

2022-02-10T00:22:38.000Z

autoencoda/__init__.py

j-abc/autoencoda

c892afe52a18c9f7fca61116459190ae59ea76a0

[ "MIT" ]

1

2019-09-17T22:07:32.000Z

from . import billboard_query from . import ingest from . import models from . import predict from . import preprocess

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0

1

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6

2fa3e9fe5bef8d298b339926ed1c589e5b1ccc4a

76

py

Python

test_demo.py

kansasvirtual/Pytest201

55542f969f1b42ca02c9ba7de4881d8fb8941e95

[ "MIT" ]

null

test_demo.py

kansasvirtual/Pytest201

55542f969f1b42ca02c9ba7de4881d8fb8941e95

[ "MIT" ]

null

test_demo.py

kansasvirtual/Pytest201

55542f969f1b42ca02c9ba7de4881d8fb8941e95

[ "MIT" ]

null

def test_add(): assert demo.add(1, 2) == 3 def test_error(): pass

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null

1

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1

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null

0

1

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6

2fadd8a52ba6474897618ab03e7868ae5cba8343

35

py

Python

tests/import/import3a.py

sebastien-riou/micropython

116c15842fd48ddb77b0bc016341d936a0756573

[ "MIT" ]

13,648

2015-01-01T01:34:51.000Z

2022-03-31T16:19:53.000Z

tests/import/import3a.py

sebastien-riou/micropython

116c15842fd48ddb77b0bc016341d936a0756573

[ "MIT" ]

7,092

2015-01-01T07:59:11.000Z

2022-03-31T23:52:18.000Z

tests/import/import3a.py

sebastien-riou/micropython

116c15842fd48ddb77b0bc016341d936a0756573

[ "MIT" ]

4,942

2015-01-02T11:48:50.000Z

2022-03-31T19:57:10.000Z

from import1b import * print(var)

8.75

22

0.742857

5

35

5.2

1

0

0.034483

0.171429

35

3

23

11.666667

0.862069

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

6405098ef44818e0023454fa214d77df06257295

76

py

Python

scraper/main.py

PatchyVideo/PatchyVideo

cafbdfa34591d7292090d5e67bb633b974447b64

[ "MIT" ]

13

2020-06-04T00:25:24.000Z

2022-03-31T13:12:17.000Z

scraper/main.py

PatchyVideo/PatchyVideo

cafbdfa34591d7292090d5e67bb633b974447b64

[ "MIT" ]

1

2021-01-03T04:17:45.000Z

2021-02-07T14:19:04.000Z

scraper/main.py

PatchyVideo/PatchyVideo

cafbdfa34591d7292090d5e67bb633b974447b64

[ "MIT" ]

null

from .init import app from . import postVideo from . import postPlaylist

10.857143

26

0.763158

10

76

5.8

0.6

0.344828

0

0.197368

76

6

27

12.666667

0.95082

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

642ecbee000a91c3ce840f3048535c7ba2b37fd7

31

py

Python

ogb/nodeproppred/__init__.py

mufeili/ogb

0190bb642e44fec976a9e0686663d1dc939fedd2

[ "MIT" ]

9

2019-07-21T18:00:27.000Z

2020-08-21T08:26:30.000Z

ogb/nodeproppred/__init__.py

mufeili/ogb

0190bb642e44fec976a9e0686663d1dc939fedd2

[ "MIT" ]

2

2019-10-30T09:05:56.000Z

2020-09-18T10:41:34.000Z

ogb/nodeproppred/__init__.py

mufeili/ogb

0190bb642e44fec976a9e0686663d1dc939fedd2

[ "MIT" ]

3

2019-07-22T15:04:11.000Z

2021-06-21T09:38:56.000Z

from .evaluate import Evaluator

31

0.870968

4

31

6.75

1

0

0.096774

31

1

31

0.964286

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

6439beb248d4de7ff320993f92206801b605030b

10,885

py

Python

tests/testflows/rbac/tests/privileges/system/drop_cache.py

mcspring/ClickHouse

08f713f177f950c2f675c2c75d1261c91066888c

[ "Apache-2.0" ]

18

2021-05-29T01:12:33.000Z

2021-11-18T12:34:48.000Z

tests/testflows/rbac/tests/privileges/system/drop_cache.py

mcspring/ClickHouse

08f713f177f950c2f675c2c75d1261c91066888c

[ "Apache-2.0" ]

null

tests/testflows/rbac/tests/privileges/system/drop_cache.py

mcspring/ClickHouse

08f713f177f950c2f675c2c75d1261c91066888c

[ "Apache-2.0" ]

2

2021-07-13T06:42:45.000Z

2021-07-21T13:47:22.000Z

from testflows.core import * from testflows.asserts import error from rbac.requirements import * from rbac.helper.common import * import rbac.helper.errors as errors @TestSuite def dns_cache_privileges_granted_directly(self, node=None): """Check that a user is able to execute `SYSTEM DROP DNS CACHE` if and only if they have `SYSTEM DROP DNS CACHE` privilege granted directly. """ user_name = f"user_{getuid()}" if node is None: node = self.context.node with user(node, f"{user_name}"): Suite(run=dns_cache, flags=TE, examples=Examples("privilege grant_target_name user_name", [ tuple(list(row)+[user_name,user_name]) for row in dns_cache.examples ], args=Args(name="check privilege={privilege}", format_name=True))) @TestSuite def dns_cache_privileges_granted_via_role(self, node=None): """Check that a user is able to execute `SYSTEM DROP DNS CACHE` if and only if they have `SYSTEM DROP DNS CACHE` privilege granted via role. """ user_name = f"user_{getuid()}" role_name = f"role_{getuid()}" if node is None: node = self.context.node with user(node, f"{user_name}"), role(node, f"{role_name}"): with When("I grant the role to the user"): node.query(f"GRANT {role_name} TO {user_name}") Suite(run=dns_cache, flags=TE, examples=Examples("privilege grant_target_name user_name", [ tuple(list(row)+[role_name,user_name]) for row in dns_cache.examples ], args=Args(name="check privilege={privilege}", format_name=True))) @TestOutline(Suite) @Requirements( RQ_SRS_006_RBAC_Privileges_System_DropCache_DNS("1.0"), ) @Examples("privilege",[ ("SYSTEM",), ("SYSTEM DROP CACHE",), ("SYSTEM DROP DNS CACHE",), ("DROP CACHE",), ("DROP DNS CACHE",), ("SYSTEM DROP DNS",), ("DROP DNS",), ]) def dns_cache(self, privilege, grant_target_name, user_name, node=None): """Run checks for `SYSTEM DROP DNS CACHE` privilege. """ exitcode, message = errors.not_enough_privileges(name=user_name) if node is None: node = self.context.node with Scenario("SYSTEM DROP DNS CACHE without privilege"): with When("I check the user is unable to execute SYSTEM DROP DNS CACHE"): node.query("SYSTEM DROP DNS CACHE", settings = [("user", f"{user_name}")], exitcode=exitcode, message=message) with Scenario("SYSTEM DROP DNS CACHE with privilege"): with When(f"I grant {privilege} on the table"): node.query(f"GRANT {privilege} ON *.* TO {grant_target_name}") with Then("I check the user is bale to execute SYSTEM DROP DNS CACHE"): node.query("SYSTEM DROP DNS CACHE", settings = [("user", f"{user_name}")]) with Scenario("SYSTEM DROP DNS CACHE with revoked privilege"): with When(f"I grant {privilege} on the table"): node.query(f"GRANT {privilege} ON *.* TO {grant_target_name}") with And(f"I revoke {privilege} on the table"): node.query(f"REVOKE {privilege} ON *.* FROM {grant_target_name}") with Then("I check the user is unable to execute SYSTEM DROP DNS CACHE"): node.query("SYSTEM DROP DNS CACHE", settings = [("user", f"{user_name}")], exitcode=exitcode, message=message) @TestSuite def mark_cache_privileges_granted_directly(self, node=None): """Check that a user is able to execute `SYSTEM DROP MARK CACHE` if and only if they have `SYSTEM DROP MARK CACHE` privilege granted directly. """ user_name = f"user_{getuid()}" if node is None: node = self.context.node with user(node, f"{user_name}"): Suite(run=mark_cache, flags=TE, examples=Examples("privilege grant_target_name user_name", [ tuple(list(row)+[user_name,user_name]) for row in mark_cache.examples ], args=Args(name="check privilege={privilege}", format_name=True))) @TestSuite def mark_cache_privileges_granted_via_role(self, node=None): """Check that a user is able to execute `SYSTEM DROP MARK CACHE` if and only if they have `SYSTEM DROP MARK CACHE` privilege granted via role. """ user_name = f"user_{getuid()}" role_name = f"role_{getuid()}" if node is None: node = self.context.node with user(node, f"{user_name}"), role(node, f"{role_name}"): with When("I grant the role to the user"): node.query(f"GRANT {role_name} TO {user_name}") Suite(run=mark_cache, flags=TE, examples=Examples("privilege grant_target_name user_name", [ tuple(list(row)+[role_name,user_name]) for row in mark_cache.examples ], args=Args(name="check privilege={privilege}", format_name=True))) @TestOutline(Suite) @Requirements( RQ_SRS_006_RBAC_Privileges_System_DropCache_Mark("1.0"), ) @Examples("privilege",[ ("SYSTEM",), ("SYSTEM DROP CACHE",), ("SYSTEM DROP MARK CACHE",), ("DROP CACHE",), ("DROP MARK CACHE",), ("SYSTEM DROP MARK",), ("DROP MARKS",), ]) def mark_cache(self, privilege, grant_target_name, user_name, node=None): """Run checks for `SYSTEM DROP MARK CACHE` privilege. """ exitcode, message = errors.not_enough_privileges(name=user_name) if node is None: node = self.context.node with Scenario("SYSTEM DROP MARK CACHE without privilege"): with When("I check the user is unable to execute SYSTEM DROP MARK CACHE"): node.query("SYSTEM DROP MARK CACHE", settings = [("user", f"{user_name}")], exitcode=exitcode, message=message) with Scenario("SYSTEM DROP MARK CACHE with privilege"): with When(f"I grant {privilege} on the table"): node.query(f"GRANT {privilege} ON *.* TO {grant_target_name}") with Then("I check the user is bale to execute SYSTEM DROP MARK CACHE"): node.query("SYSTEM DROP MARK CACHE", settings = [("user", f"{user_name}")]) with Scenario("SYSTEM DROP MARK CACHE with revoked privilege"): with When(f"I grant {privilege} on the table"): node.query(f"GRANT {privilege} ON *.* TO {grant_target_name}") with And(f"I revoke {privilege} on the table"): node.query(f"REVOKE {privilege} ON *.* FROM {grant_target_name}") with Then("I check the user is unable to execute SYSTEM DROP MARK CACHE"): node.query("SYSTEM DROP MARK CACHE", settings = [("user", f"{user_name}")], exitcode=exitcode, message=message) @TestSuite def uncompressed_cache_privileges_granted_directly(self, node=None): """Check that a user is able to execute `SYSTEM DROP UNCOMPRESSED CACHE` if and only if they have `SYSTEM DROP UNCOMPRESSED CACHE` privilege granted directly. """ user_name = f"user_{getuid()}" if node is None: node = self.context.node with user(node, f"{user_name}"): Suite(run=uncompressed_cache, flags=TE, examples=Examples("privilege grant_target_name user_name", [ tuple(list(row)+[user_name,user_name]) for row in uncompressed_cache.examples ], args=Args(name="check privilege={privilege}", format_name=True))) @TestSuite def uncompressed_cache_privileges_granted_via_role(self, node=None): """Check that a user is able to execute `SYSTEM DROP UNCOMPRESSED CACHE` if and only if they have `SYSTEM DROP UNCOMPRESSED CACHE` privilege granted via role. """ user_name = f"user_{getuid()}" role_name = f"role_{getuid()}" if node is None: node = self.context.node with user(node, f"{user_name}"), role(node, f"{role_name}"): with When("I grant the role to the user"): node.query(f"GRANT {role_name} TO {user_name}") Suite(run=uncompressed_cache, flags=TE, examples=Examples("privilege grant_target_name user_name", [ tuple(list(row)+[role_name,user_name]) for row in uncompressed_cache.examples ], args=Args(name="check privilege={privilege}", format_name=True))) @TestOutline(Suite) @Requirements( RQ_SRS_006_RBAC_Privileges_System_DropCache_Uncompressed("1.0"), ) @Examples("privilege",[ ("SYSTEM",), ("SYSTEM DROP CACHE",), ("SYSTEM DROP UNCOMPRESSED CACHE",), ("DROP CACHE",), ("DROP UNCOMPRESSED CACHE",), ("SYSTEM DROP UNCOMPRESSED",), ("DROP UNCOMPRESSED",), ]) def uncompressed_cache(self, privilege, grant_target_name, user_name, node=None): """Run checks for `SYSTEM DROP UNCOMPRESSED CACHE` privilege. """ exitcode, message = errors.not_enough_privileges(name=user_name) if node is None: node = self.context.node with Scenario("SYSTEM DROP UNCOMPRESSED CACHE without privilege"): with When("I check the user is unable to execute SYSTEM DROP UNCOMPRESSED CACHE"): node.query("SYSTEM DROP UNCOMPRESSED CACHE", settings = [("user", f"{user_name}")], exitcode=exitcode, message=message) with Scenario("SYSTEM DROP UNCOMPRESSED CACHE with privilege"): with When(f"I grant {privilege} on the table"): node.query(f"GRANT {privilege} ON *.* TO {grant_target_name}") with Then("I check the user is bale to execute SYSTEM DROP UNCOMPRESSED CACHE"): node.query("SYSTEM DROP UNCOMPRESSED CACHE", settings = [("user", f"{user_name}")]) with Scenario("SYSTEM DROP UNCOMPRESSED CACHE with revoked privilege"): with When(f"I grant {privilege} on the table"): node.query(f"GRANT {privilege} ON *.* TO {grant_target_name}") with And(f"I revoke {privilege} on the table"): node.query(f"REVOKE {privilege} ON *.* FROM {grant_target_name}") with Then("I check the user is unable to execute SYSTEM DROP UNCOMPRESSED CACHE"): node.query("SYSTEM DROP UNCOMPRESSED CACHE", settings = [("user", f"{user_name}")], exitcode=exitcode, message=message) @TestFeature @Name("system drop cache") @Requirements( RQ_SRS_006_RBAC_Privileges_System_DropCache("1.0"), ) def feature(self, node="clickhouse1"): """Check the RBAC functionality of SYSTEM DROP CACHE. """ self.context.node = self.context.cluster.node(node) Suite(run=dns_cache_privileges_granted_directly, setup=instrument_clickhouse_server_log) Suite(run=dns_cache_privileges_granted_via_role, setup=instrument_clickhouse_server_log) Suite(run=mark_cache_privileges_granted_directly, setup=instrument_clickhouse_server_log) Suite(run=mark_cache_privileges_granted_via_role, setup=instrument_clickhouse_server_log) Suite(run=uncompressed_cache_privileges_granted_directly, setup=instrument_clickhouse_server_log) Suite(run=uncompressed_cache_privileges_granted_via_role, setup=instrument_clickhouse_server_log)

40.314815

101

0.66927

1,470

10,885

4.80068

0.068027

0.075103

0.03826

0.040385

0.927023

0.92433

0.912569

0.907184

0.900241

0

0.002445

0.210932

10,885

269

102

40.464684

0.819187

0.100873

0

0.624339

0

0.330304

0.013014

0

0.005291

1

0.05291

false

0

0.026455

0

0.079365

0

null

0

1

0

null

0

6

ff52ab48b937ab8bbc64551038e7b7708865c9cd

104

py

Python

cloudnetpy/categorize/__init__.py

saveriogzz/cloudnetpy

baa3ed5f254425c5a9c787556ec652ea659b38ba

[ "MIT" ]

13

2020-02-16T06:52:51.000Z

2022-03-10T09:43:19.000Z

cloudnetpy/categorize/__init__.py

saveriogzz/cloudnetpy

baa3ed5f254425c5a9c787556ec652ea659b38ba

[ "MIT" ]

17

2020-01-15T10:47:08.000Z

2022-03-28T13:08:23.000Z

cloudnetpy/categorize/__init__.py

saveriogzz/cloudnetpy

baa3ed5f254425c5a9c787556ec652ea659b38ba

[ "MIT" ]

12

2020-03-03T16:45:13.000Z

2022-03-23T08:02:43.000Z

from .datasource import DataSource from .categorize import generate_categorize from .radar import Radar

26

43

0.855769

13

104

6.769231

0.461538

0

0.115385

104

3

44

34.666667

0.956522

0

1

0

1

0

true

0

1

0

1

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1

0

null

0

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1

0

null

0

1

0

1

0

1

0

6

ff743e5c69e5f42f90d38907674b688dc4f85200

137

py

Python

pyccx/bc/__init__.py

drlukeparry/pyccx

7f9eaebeda334da895da4c7593f5fe40936554b0

[ "BSD-2-Clause" ]

10

2020-04-09T11:22:13.000Z

2022-02-14T08:07:52.000Z

pyccx/bc/__init__.py

drlukeparry/pyccx

7f9eaebeda334da895da4c7593f5fe40936554b0

[ "BSD-2-Clause" ]

4

2020-04-10T15:56:42.000Z

2021-04-08T12:34:47.000Z

pyccx/bc/__init__.py

drlukeparry/pyccx

7f9eaebeda334da895da4c7593f5fe40936554b0

[ "BSD-2-Clause" ]

3

2020-04-22T16:14:26.000Z

2021-06-26T23:14:48.000Z

from .boundarycondition import BoundaryCondition, BoundaryConditionType, Acceleration, Film, Fixed, Force, HeatFlux, Pressure, Radiation

68.5

136

0.846715

12

137

9.666667

0.916667

0

0.087591

137

1

137

0.928

0

1

0

true

0

1

0

1

0

1

0

null

0

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0

1

0

null

0

1

0

1

0

1

0

6

ff99110afbfbb1426f428dc875738e33e778c206

373

py

Python

src/pynumerals/__init__.py

numeralbank/pynumerals

7c827ba7e7892b2779573cd3047ab44da027243d

[ "Apache-2.0" ]

null

src/pynumerals/__init__.py

numeralbank/pynumerals

7c827ba7e7892b2779573cd3047ab44da027243d

[ "Apache-2.0" ]

5

2020-07-06T13:53:57.000Z

2020-10-23T13:33:18.000Z

src/pynumerals/__init__.py

numeralbank/pynumerals

7c827ba7e7892b2779573cd3047ab44da027243d

[ "Apache-2.0" ]

null

__version__ = "1.0.0.dev0" from pynumerals.errorcheck import * # noqa: F401, F403 from pynumerals.mappings import * # noqa: F401, F403 from pynumerals.numerals_html import * # noqa: F401, F403 from pynumerals.numerals_utils import * # noqa: F401, F403 from pynumerals.process_html import * # noqa: F401, F403 from pynumerals.value_parser import * # noqa: F401, F403

41.444444

59

0.747989

51

373

5.313725

0.352941

0.309963

0.398524

0.678967

0.442804

0

0.126984

0.155496

373

8

60

46.625

0.733333

0.270777

0

0.037736

0

1

0

false

0

0.857143

0

0.857143

0

null

1

0

1

0

null

0

1

0

1

0

6

44191d4ed3dc518aef5c78e93d49d21e7a6ac72f

311

py

Python

algoneer/result/__init__.py

algoneer/algoneer-py

5f300543116278c91a9cf8c9ef5a1375e3f1e75d

[ "MIT" ]

10

2019-08-05T16:06:12.000Z

2020-12-19T16:40:48.000Z

algoneer/result/__init__.py

algoneer/algoneer-py

5f300543116278c91a9cf8c9ef5a1375e3f1e75d

[ "MIT" ]

null

algoneer/result/__init__.py

algoneer/algoneer-py

5f300543116278c91a9cf8c9ef5a1375e3f1e75d

[ "MIT" ]

1

2020-04-27T08:50:14.000Z

from .algorithm_result import AlgorithmResult from .model_result import ModelResult from .datapoint_model_result import DatapointModelResult from .dataset_result import DatasetResult from .dataset_model_result import DatasetModelResult from .result import Result from .result_collection import ResultCollection

38.875

56

0.88746

36

311

7.444444

0.388889

0.268657

0.190299

0

0.090032

311

7

57

44.428571

0.946996

0

1

0

true

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

4426e21da693bb0d7c45320a2398e0e631a2a88d

6,143

py

Python

discordbot.py

Kuraplayz04/kuradayobot_heroku

9440ef58a58a5c54ea962b595955e23ac456b38d

[ "MIT" ]

null

discordbot.py

Kuraplayz04/kuradayobot_heroku

9440ef58a58a5c54ea962b595955e23ac456b38d

[ "MIT" ]

null

discordbot.py

Kuraplayz04/kuradayobot_heroku

9440ef58a58a5c54ea962b595955e23ac456b38d

[ "MIT" ]

null

# Discord.pyの読み込み import discord # Discordへ接続するのに必要 client = discord.Client(activity=discord.Game(name='青鬼基幹システム v1.7')) # 自分のBotのアクセストークンを記入 TOKEN = "ODQyNzM1NDQxOTQxMTAyNjAy.YJ5oig.3UZkcAZSP7cNJKcQg2enzzanSpo" @client.event async def on_member_join(member): channel = client.get_channel(825992683701010450) await channel.send(f'{member} joined on {member.joined_at}') # Bot起動時に実行される @client.event async def on_ready(): print('ログインしました') # メッセージを取得した時に実行される @client.event async def on_message(message, lastmessage=None): # Botのメッセージは除外 if message.author.bot: return # 条件に当てはまるメッセージかチェックし正しい場合は返す def check(msg): return msg.author == message.author # /getとチャンネル上に打ち込むとBotが反応を示す if message.content.startswith("/f3"): await message.delete() # /getと打ち込まれたチャンネル上に下記の文章を出力 # ユーザーからのメッセージを待つ wait_message = await client.wait_for("message", check=check) an0 = '<@&825992683465080845>' an1 = '\n青鬼ごっこやります。（' an2 = '）\nサーバー: EventServer\nID:KuraPlayz04\n\nver1.16.2' # メッセージを打ち込まれたのを確認すると下記の文章を出力 channel = client.get_channel(825992683701010449) await channel.send(an0 + an1 + wait_message.content + an2) embed_r_3 = discord.Embed(title="アナウンス完了!", description="Tier3チャット\nメンションアナウンス",color=discord.Colour.dark_blue()) await message.channel.send(embed=embed_r_3) if message.content.startswith("/f2"): await message.delete() # /getと打ち込まれたチャンネル上に下記の文章を出力 # ユーザーからのメッセージを待つ wait_message = await client.wait_for("message", check=check) an0 = '<@&825992683465080845>' an1 = '\n青鬼ごっこやります。（' an2 = '）\nサーバー: EventServer\nID:KuraPlayz04\n\nver1.16.2' # メッセージを打ち込まれたのを確認すると下記の文章を出力 channel = client.get_channel(825992683701010450) await channel.send(an0 + an1 + wait_message.content + an2) embed_r_2 = discord.Embed(title="アナウンス完了!", description="Tier2チャット\nメンションアナウンス",color=discord.Colour.red()) await message.channel.send(embed=embed_r_2) if message.content.startswith("/f1"): await message.delete() # /getと打ち込まれたチャンネル上に下記の文章を出力 # ユーザーからのメッセージを待つ wait_message = await client.wait_for("message", check=check) an0 = '<@&825992683465080845>' an1 = '\n青鬼ごっこやります。（' an2 = '）\nサーバー: EventServer\nID:KuraPlayz04\n\nver1.16.2' # メッセージを打ち込まれたのを確認すると下記の文章を出力 channel = client.get_channel(825992683701010451) await channel.send(an0 + an1 + wait_message.content + an2) embed_r_1 = discord.Embed(title="アナウンス完了!", description="Tier1チャット\nメンションアナウンス",color=discord.Colour.purple()) await message.channel.send(embed=embed_r_1) if message.content.startswith("/n3"): await message.delete() # ユーザーからのメッセージを待つ wait_message = await client.wait_for("message", check=check) ans1 = '\n次どぞ（' ans2 = '）' # メッセージを打ち込まれたのを確認すると下記の文章を出力 channel = client.get_channel(825992683701010449) await channel.send(ans1 + wait_message.content + ans2) embed_r_3 = discord.Embed(title="アナウンス完了!", description="Tier3チャット\nネクストアナウンス",color=discord.Colour.dark_blue()) await message.channel.send(embed=embed_r_3) if message.content.startswith("/n2"): await message.delete() # ユーザーからのメッセージを待つ wait_message = await client.wait_for("message", check=check) ans1 = '\n次どぞ（' ans2 = '）' # メッセージを打ち込まれたのを確認すると下記の文章を出力 channel = client.get_channel(825992683701010450) await channel.send(ans1 + wait_message.content + ans2) embed_r_2 = discord.Embed(title="アナウンス完了!", description="Tier2チャット\nネクストアナウンス",color=discord.Colour.red()) await message.channel.send(embed=embed_r_2) if message.content.startswith("/n1"): await message.delete() # ユーザーからのメッセージを待つ wait_message = await client.wait_for("message", check=check) ans1 = '\n次どぞ（' ans2 = '）' # メッセージを打ち込まれたのを確認すると下記の文章を出力 channel = client.get_channel(825992683701010451) await channel.send(ans1 + wait_message.content + ans2) embed_r_1 = discord.Embed(title="アナウンス完了!", description="Tier1チャット\nネクストアナウンス",color=discord.Colour.purple()) await message.channel.send(embed=embed_r_1) if message.content.startswith("/l3"): await message.delete() # ユーザーからのメッセージを待つ wait_message = await client.wait_for("message", check=check) ansl1 = 'ラストどうぞ（' ansl2 = '）' # メッセージを打ち込まれたのを確認すると下記の文章を出力 channel = client.get_channel(825992683701010449) await channel.send(ansl1 + wait_message.content + ansl2) embed_r_3 = discord.Embed(title="アナウンス完了!", description="Tier3チャット\nラストアナウンス",color=discord.Colour.dark_blue()) await message.channel.send(embed=embed_r_3) if message.content.startswith("/l2"): await message.delete() # ユーザーからのメッセージを待つ wait_message = await client.wait_for("message", check=check) ansl1 = '\nラストどうぞ（' ansl2 = '）' # メッセージを打ち込まれたのを確認すると下記の文章を出力 channel = client.get_channel(825992683701010450) await channel.send(ansl1 + wait_message.content + ansl2) embed_r_2 = discord.Embed(title="アナウンス完了!", description="Tier2チャット\nラストアナウンス",color=discord.Colour.red()) await message.channel.send(embed=embed_r_2) if message.content.startswith("/l1"): await message.delete() # ユーザーからのメッセージを待つ wait_message = await client.wait_for("message", check=check) ansl1 = '\nラストどうぞ（' ansl2 = '）' # メッセージを打ち込まれたのを確認すると下記の文章を出力 channel = client.get_channel(825992683701010451) await channel.send(ansl1 + wait_message.content + ansl2) embed_r_1 = discord.Embed(title="アナウンス完了!", description="Tier1チャット\nラストアナウンス",color=discord.Colour.purple()) await message.channel.send(embed=embed_r_1) # Botの実行 client.run(TOKEN)

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6

9297cb2af9e010c4967d2cdadf4a06a7947b0ae5

31

py

Python

notebook_image_tabs/__init__.py

oscar6echo/notebook-image-tabs

3b628d1d672d9bdf0716ccf88cd8f527021c06ef

[ "MIT" ]

4

2020-04-18T13:09:06.000Z

2022-02-03T07:42:30.000Z

notebook_image_tabs/__init__.py

oscar6echo/notebook-image-tabs

3b628d1d672d9bdf0716ccf88cd8f527021c06ef

[ "MIT" ]

null

notebook_image_tabs/__init__.py

oscar6echo/notebook-image-tabs

3b628d1d672d9bdf0716ccf88cd8f527021c06ef

[ "MIT" ]

null

from .viewer import ImageTabs

10.333333

29

0.806452

4

31

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1

0

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1

0

1

0

6

920be80d45e568a068c83aaf230e81c851c81461

95,596

py

Python

testing/test_mappo.py

rallen10/ergo_particle_gym

5bb8073d880ab1da60ee333d892ea8a4720f3396

[ "FSFULLR", "FSFUL" ]

null

testing/test_mappo.py

rallen10/ergo_particle_gym

5bb8073d880ab1da60ee333d892ea8a4720f3396

[ "FSFULLR", "FSFUL" ]

null

testing/test_mappo.py

rallen10/ergo_particle_gym

5bb8073d880ab1da60ee333d892ea8a4720f3396

[ "FSFULLR", "FSFUL" ]

3

2019-12-08T08:36:23.000Z

2021-11-07T17:35:53.000Z

#!/usr/bin/env python # suite of unit, integration, system, and/or acceptance tests for train.py. # To run test, simply call: # # in a shell with conda environment ergo_particle_gym activated: # nosetests test_train.py # # in ipython: # run test_train.py import sys import os.path sys.path.append(os.path.join(os.path.dirname(__file__), '..')) import unittest import numpy as np import tensorflow as tf from gym import spaces from numpy.random import rand from train import OrderingException, DeepMLP from collections import namedtuple from rl_algorithms.mappo import PPOAgentComputer, PPOGroupTrainer, UpdateException, redistributed_softmax, central_critic_network import rl_algorithms.maddpg.maddpg.common.tf_util as U from rl_algorithms.baselines.baselines.common import explained_variance # from particle_environments.mager.world import MortalAgent _DEBUG = False if _DEBUG: import matplotlib.pyplot as plt class TestPPOAgentComputer1(unittest.TestCase): ''' test PPOAgentComputer class from mappo.py ''' def setUp(self): pass def test_process_individual_agent_episode_returns_and_advantages_1(self): ''' one-step return and advantage calculation with float rewards''' Model = namedtuple('Model', ['value']) Args = namedtuple('Args', ['max_episode_len', 'gamma']) value_func = lambda obs, M: sum(obs) model = Model(value_func) gamma = 1.0 args = Args(1, gamma) ppo_agent = PPOAgentComputer(name="ppo_agent_0", model=model, obs_shape_n=None, act_space_n=None, agent_index=0, args=args, local_q_func=None, lam=1.0) ppo_agent.mbi_observations = [np.array([ 0.52141883, -0.66102998]), np.array([-0.39118867, -0.08772333])] ppo_agent.mbi_rewards = [0.0] ppo_agent.mbi_obs_values = [-0.13961115000000002] ppo_agent.mbi_dones = [False, True] ppo_agent.mbi_actions = [np.random.uniform(-1,1,2)] ppo_agent.mbi_neglogp_actions = [np.random.uniform(0,1)] ppo_agent.mbi_healths = [1.0] ppo_agent.process_individual_agent_episode_returns_and_advantages(factual_values=None, counterfactual_values=None) # check return and advantage self.assertAlmostEqual(ppo_agent.mbi_returns[0], 0.0) self.assertAlmostEqual(ppo_agent.mbi_factual_advantages[0], 0.13961115000000002) def test_process_individual_agent_episode_returns_and_advantages_3(self): '''mappo: two-step return and advantage calculation''' Model = namedtuple('Model', ['value']) Args = namedtuple('Args', ['max_episode_len', 'gamma']) value_func = lambda obs, M: np.mean(obs) model = Model(value_func) gamma = 0.9627477525841408 lam = 0.9447698026141256 args = Args(2, gamma) ppo_agent = PPOAgentComputer(name="ppo_agent_0", model=model, obs_shape_n=None, act_space_n=None, agent_index=0, args=args, local_q_func=None, lam=lam) ppo_agent.mbi_observations = [np.array([ 0.4660721 , -3.39177499]), np.array([-4.13104788, -4.52925146]), np.array([ 3.16713255, -2.30391816])] ppo_agent.mbi_rewards = [-0.71486004, -1.92588795] ppo_agent.mbi_obs_values = [value_func(ppo_agent.mbi_observations[0], M=None), value_func(ppo_agent.mbi_observations[1], M=None)] ppo_agent.mbi_dones = [False, False, True] ppo_agent.mbi_actions = [np.random.uniform(-1,1,2), np.random.uniform(-1,1,2)] ppo_agent.mbi_neglogp_actions = [np.random.uniform(0,1), np.random.uniform(0,1)] ppo_agent.mbi_healths = [1.0, 1.0] # calculate expected values delta_1 = -1.92588795 - np.mean([-4.13104788, -4.52925146]) exp_returns_1 = -1.92588795 exp_advantages_1 = delta_1 delta_0 = -0.71486004 + gamma*np.mean([-4.13104788, -4.52925146]) - np.mean([ 0.4660721 , -3.39177499]) exp_advantages_0 = delta_0 + gamma*lam*delta_1 exp_returns_0 = exp_advantages_0 + np.mean([ 0.4660721 , -3.39177499]) # check return and advantage ppo_agent.process_individual_agent_episode_returns_and_advantages(factual_values=None, counterfactual_values=None) self.assertAlmostEqual(ppo_agent.mbi_returns[1], exp_returns_1,places=5) self.assertAlmostEqual(ppo_agent.mbi_factual_advantages[1], exp_advantages_1,places=5) self.assertAlmostEqual(ppo_agent.mbi_returns[0], exp_returns_0,places=5) self.assertAlmostEqual(ppo_agent.mbi_factual_advantages[0], exp_advantages_0,places=5) def test_process_individual_agent_episode_returns_and_advantages_4(self): '''mappo: error handling for multi-step batch with inconsistent dones''' Model = namedtuple('Model', ['value']) Args = namedtuple('Args', ['max_episode_len', 'gamma']) value_func = lambda obs, M: np.mean(obs) model = Model(value_func) gamma = 0.9627477525841408 lam = 0.9447698026141256 args = Args(2, gamma) ppo_agent = PPOAgentComputer(name="ppo_agent_0", model=model, obs_shape_n=None, act_space_n=None, agent_index=0, args=args, local_q_func=None, lam=lam) ppo_agent.mbi_observations = [np.array([ 0.4660721 , -3.39177499]), np.array([-4.13104788, -4.52925146]), np.array([ 3.16713255, -2.30391816])] ppo_agent.mbi_rewards = [-0.71486004, -1.92588795] ppo_agent.mbi_obs_values = [value_func(ppo_agent.mbi_observations[0], M=None), value_func(ppo_agent.mbi_observations[1], M=None)] ppo_agent.mbi_dones = [False, True, False] ppo_agent.mbi_actions = [np.random.uniform(-1,1,2), np.random.uniform(-1,1,2)] ppo_agent.mbi_neglogp_actions = [np.random.uniform(0,1), np.random.uniform(0,1)] ppo_agent.mbi_healths = [1.0, 1.0] # check error is raised with self.assertRaises(UpdateException): ppo_agent.process_individual_agent_episode_returns_and_advantages(factual_values=None, counterfactual_values=None) def test_process_individual_agent_episode_returns_and_advantages_5(self): '''mappo: extended sequence returns don't depend on value func''' Model = namedtuple('Model', ['value']) Args = namedtuple('Args', ['max_episode_len', 'gamma']) value_func = lambda obs, M: np.mean(obs) reward_func = lambda obs: np.sum(obs) model = Model(value_func) gamma = 1.0 lam = 1.0 args = Args(10, gamma) ppo_agent = PPOAgentComputer(name="ppo_agent_0", model=model, obs_shape_n=None, act_space_n=None, agent_index=0, args=args, local_q_func=None, lam=lam) ppo_agent.mbi_observations = [np.array([-0.61322181, 0.60141474]), np.array([-0.68131643, -0.46429067]), np.array([-0.32310118, -0.21411603]), np.array([ 0.59954657, -0.09719427]), np.array([0.20816313, 0.15251241]), np.array([0.14608069, 0.69522925]), np.array([-0.03096035, 0.10213929]), np.array([ 0.66119021, -0.69454451]), np.array([-0.69480874, 0.09734647]), np.array([0.74504277, 0.20447294]), np.array([0.16639411, 0.67739031])] ppo_agent.mbi_dones = 11*[False] ppo_agent.mbi_dones[-1] = True ppo_agent.mbi_actions = [np.random.uniform(-1,1,2), np.random.uniform(-1,1,2), np.random.uniform(-1,1,2), np.random.uniform(-1,1,2), np.random.uniform(-1,1,2), np.random.uniform(-1,1,2), np.random.uniform(-1,1,2), np.random.uniform(-1,1,2), np.random.uniform(-1,1,2), np.random.uniform(-1,1,2)] ppo_agent.mbi_neglogp_actions = list(np.random.uniform(0,1,10)) ppo_agent.mbi_healths = list(np.ones(10)) for obs in ppo_agent.mbi_observations[:-1]: ppo_agent.mbi_rewards.append(reward_func(obs)) ppo_agent.mbi_obs_values.append(value_func(obs, M=None)) # check returns ppo_agent.process_individual_agent_episode_returns_and_advantages(factual_values=None, counterfactual_values=None) for i, ret in enumerate(ppo_agent.mbi_returns): self.assertAlmostEqual(ret, np.sum([reward_func(obs) for obs in ppo_agent.mbi_observations[i:-1]]), places=5) class TestPPOGroupTrainer1(unittest.TestCase): ''' test PPOGroupTrainer class from mappo.py ''' def setUp(self): ''' the with tf.Graph.as_default()... command allows for multiple calls to setUp without causing variable scopes to "clash". See baselines/common/tests/util.py for examples ''' with tf.Graph().as_default(), tf.Session(config=tf.ConfigProto(allow_soft_placement=True)).as_default(): # create trainer that would live in a simple 1D environment # with 1D continuous observations and actions # and single step episodes self.group_trainer = PPOGroupTrainer( n_agents=2, obs_space=spaces.Box(low=-1.0, high=1.0, shape=(1,), dtype=np.float32), act_space=spaces.Box(low=-1.0, high=1.0, shape=(1,), dtype=np.float32), n_steps_per_episode=1, ent_coef=0.0, local_actor_learning_rate=3e-4, vf_coef=0.5, num_layers=2, num_units=64, activation='tanh', cliprange=0.2, shared_reward=False, critic_type='distributed_local_observations', central_critic_model=None, central_critic_learning_rate=None, central_critic_num_units=None, joint_state_space_len=3*4, max_grad_norm = 0.5, n_opt_epochs = 4, n_episodes_per_batch=1, n_minibatches=1) # overwrite model value estimator with simple pass-through function # to simplify testing self.group_trainer.local_actor_critic_model.value = lambda obs, M: obs # Populate the group with stripped out versions of agents Args = namedtuple('Args', ['max_episode_len', 'gamma']) args = Args(1, 0.99) self.agent_0 = PPOAgentComputer( name="agent_0", model=self.group_trainer.local_actor_critic_model, obs_shape_n=None, act_space_n=None, agent_index=0, args=args, local_q_func=None) self.agent_1 = PPOAgentComputer( name="agent_1", model=self.group_trainer.local_actor_critic_model, obs_shape_n=None, act_space_n=None, agent_index=1, args=args, local_q_func=None) self.group_trainer.update_agent_trainer_group([self.agent_0, self.agent_1]) # give agents artificially, randomly generated experience self.agent_0.mbi_observations = [np.array([-0.78438007]), np.array([-0.62432])] self.agent_0.mbi_rewards = [-0.78438007] self.agent_0.mbi_obs_values = [-0.78438007] # value func just passes through input (ie observations) self.agent_0.mbi_actions = [np.array([-0.90892982])] self.agent_0.mbi_dones = [False, True] self.agent_0.mbi_neglogp_actions = [0.0] self.agent_0.mbi_healths = [0.0] self.agent_1.mbi_observations = [np.array([0.03254343]), np.array([0.24190804])] self.agent_1.mbi_rewards = [0.03254343] self.agent_1.mbi_obs_values = [0.03254343] # value func just passes through input (ie observations) self.agent_1.mbi_actions = [np.array([-0.61390828])] self.agent_1.mbi_dones = [False, True] self.agent_1.mbi_neglogp_actions = [0.0] self.agent_1.mbi_healths = [0.0] def tearDown(self): '''Don't actually tearDown the tf graph Note: it may seem tempting to use tf.reset_default_graph(), but this causes an error in subsequent setUp calls with something to do with op: NoOp ... is not an element of this graph Instead use the with tf.Graph.as_default()... in setUp ''' pass def test_process_individual_agent_episode_returns_and_advantages_1(self): '''mappo: one-step with zero advantage ''' self.agent_0.process_individual_agent_episode_returns_and_advantages(factual_values=None, counterfactual_values=None) self.assertAlmostEqual(self.agent_0.mbi_returns[0], -0.78438007, places=5) self.assertAlmostEqual(self.agent_0.mbi_factual_advantages[0], 0.0, places=5) self.agent_1.process_individual_agent_episode_returns_and_advantages(factual_values=None, counterfactual_values=None) self.assertAlmostEqual(self.agent_1.mbi_returns[0], 0.03254343, places=5) self.assertAlmostEqual(self.agent_0.mbi_factual_advantages[0], 0.0, places=5) def test_update_group_policy_1(self): '''mappo: smoke test - update_group_policy without throwing an error''' self.assertEqual(len(self.group_trainer.agent_trainer_group[0].mbi_rewards), 1) self.assertEqual(len(self.group_trainer.agent_trainer_group[1].mbi_rewards), 1) self.group_trainer.update_group_policy(terminal=1) self.assertEqual(len(self.group_trainer.agent_trainer_group[0].mbi_rewards), 0) self.assertEqual(len(self.group_trainer.agent_trainer_group[1].mbi_rewards), 0) class TestPPOGroupTrainer2(unittest.TestCase): ''' test PPOGroupTrainer class from mappo.py ''' def setUp(self): ''' the with tf.Graph.as_default()... command allows for multiple calls to setUp without causing variable scopes to "clash". See baselines/common/tests/util.py for examples ''' with tf.Graph().as_default(), tf.Session(config=tf.ConfigProto(allow_soft_placement=True)).as_default(): # create trainer that would live in a simple 1D environment # with 1D continuous observations and actions # and single step episodes self.episode_len = 5 self.group_trainer = PPOGroupTrainer( n_agents=3, obs_space=spaces.Box(low=-1.0, high=1.0, shape=(1,), dtype=np.float32), act_space=spaces.Box(low=-1.0, high=1.0, shape=(1,), dtype=np.float32), n_steps_per_episode=self.episode_len, ent_coef=0.0, local_actor_learning_rate=3e-4, vf_coef=0.5, num_layers=2, num_units=64, activation='tanh', cliprange=0.2, n_episodes_per_batch=10, shared_reward=False, critic_type='distributed_local_observations', central_critic_model=None, central_critic_learning_rate=None, central_critic_num_units=None, joint_state_space_len=3*4, max_grad_norm = 0.5, n_opt_epochs = 4, n_minibatches=4) # overwrite model value estimator with simple pass-through function # to simplify testing self.group_trainer.local_actor_critic_model.value = lambda obs, M: obs # Populate the group with stripped out versions of agents Args = namedtuple('Args', ['max_episode_len', 'gamma']) args = Args(self.episode_len, 0.99) self.agent_0 = PPOAgentComputer( name="agent_0", model=self.group_trainer.local_actor_critic_model, obs_shape_n=None, act_space_n=None, agent_index=0, args=args, local_q_func=None, lam=1.0) self.agent_1 = PPOAgentComputer( name="agent_1", model=self.group_trainer.local_actor_critic_model, obs_shape_n=None, act_space_n=None, agent_index=1, args=args, local_q_func=None, lam=1.0) self.agent_2 = PPOAgentComputer( name="agent_1", model=self.group_trainer.local_actor_critic_model, obs_shape_n=None, act_space_n=None, agent_index=2, args=args, local_q_func=None, lam=1.0) self.group_trainer.update_agent_trainer_group([self.agent_0, self.agent_1, self.agent_2]) def tearDown(self): '''Don't actually tearDown the tf graph Note: it may seem tempting to use tf.reset_default_graph(), but this causes an error in subsequent setUp calls with something to do with op: NoOp ... is not an element of this graph Instead use the with tf.Graph.as_default()... in setUp ''' pass def test_iterative_update_group_policy_1(self): '''mappo: run several iterations of update_group_policy calls and check minibatch sizes''' for ep in range(10): # for each episode, the group batch data should grow by number of agents self.assertEqual(len(self.group_trainer.batch_observations), self.group_trainer.n_agents*self.group_trainer.n_steps_per_episode*ep) self.assertEqual(len(self.group_trainer.batch_observations), len(self.group_trainer.batch_factual_values)) self.assertEqual(len(self.group_trainer.batch_observations), len(self.group_trainer.batch_counterfactual_values)) self.assertEqual(len(self.group_trainer.batch_observations), len(self.group_trainer.batch_returns)) self.assertEqual(len(self.group_trainer.batch_observations), len(self.group_trainer.batch_actions)) self.assertEqual(len(self.group_trainer.batch_observations), len(self.group_trainer.batch_neglogp_actions)) self.assertEqual(len(self.group_trainer.batch_observations), len(self.group_trainer.batch_dones)) for ag in self.group_trainer.agent_trainer_group: for step in range(5): ag.mbi_observations.append(np.random.uniform(-1., +1., 1)) ag.mbi_actions.append(np.random.uniform(-1., +1., 1)) ag.mbi_rewards.append(np.random.uniform(0, +1.)) ag.mbi_obs_values.append(np.random.uniform(0, +1.)) ag.mbi_dones.append(False) ag.mbi_neglogp_actions.append(-np.log(np.random.uniform(0,1))) ag.mbi_healths.append(1.0) ag.mbi_observations.append(np.random.uniform(-1., +1., 1)) ag.mbi_dones.append(True) self.group_trainer.update_group_policy(terminal=1) self.assertEqual(len(self.group_trainer.agent_trainer_group[0].mbi_rewards), 0) self.assertEqual(len(self.group_trainer.agent_trainer_group[1].mbi_rewards), 0) self.assertEqual(len(self.group_trainer.agent_trainer_group[2].mbi_rewards), 0) # after 10 episode, a policy update should have occurred and cleared the group # minibatch self.assertEqual(len(self.group_trainer.batch_observations), 0) self.assertEqual(len(self.group_trainer.batch_factual_values), 0) self.assertEqual(len(self.group_trainer.batch_counterfactual_values), 0) self.assertEqual(len(self.group_trainer.batch_actions), 0) self.assertEqual(len(self.group_trainer.batch_returns), 0) self.assertEqual(len(self.group_trainer.batch_dones), 0) self.assertEqual(len(self.group_trainer.batch_neglogp_actions), 0) self.assertEqual(len(self.group_trainer.batch_effective_returns), 0) def test_multi_agent_returns_1(self): '''mappo: equal returns when shared rewards and lamba=1, regardless of individual value estimates''' n_episodes = 10 for ep in range(n_episodes): # Generate true global state of system state = np.zeros(len(self.group_trainer.agent_trainer_group)) for step in range(self.episode_len): for ag_ind, ag in enumerate(self.group_trainer.agent_trainer_group): ag.mbi_observations.append(np.random.normal(state[ag_ind], 0.1, 1)) ag.mbi_actions.append(np.random.normal(1.0, 0.1, 1)) ag.mbi_obs_values.append(np.random.normal(ag.mbi_observations[-1][0], 10.0)) ag.mbi_dones.append(False) ag.mbi_neglogp_actions.append(-np.log(np.random.uniform(0,1))) ag.mbi_healths.append(1.0) # update state state[ag_ind] += ag.mbi_actions[-1][0] # calculate reward: reward = np.mean(state) for ag_ind, ag in enumerate(self.group_trainer.agent_trainer_group): ag.mbi_rewards.append(reward) if step == self.episode_len-1: for ag_ind, ag in enumerate(self.group_trainer.agent_trainer_group): ag.mbi_observations.append(np.random.normal(state[ag_ind], 0.1, 1)) ag.mbi_dones.append(True) # test that returns are same for all agents self.agent_0.process_individual_agent_episode_returns_and_advantages(factual_values=None, counterfactual_values=None) self.agent_1.process_individual_agent_episode_returns_and_advantages(factual_values=None, counterfactual_values=None) self.agent_2.process_individual_agent_episode_returns_and_advantages(factual_values=None, counterfactual_values=None) for step in range(self.episode_len): # rewards self.assertAlmostEqual(self.agent_0.mbi_rewards[step], self.agent_1.mbi_rewards[step], places=5) self.assertAlmostEqual(self.agent_0.mbi_rewards[step], self.agent_2.mbi_rewards[step], places=5) # returns self.assertAlmostEqual(self.agent_0.mbi_returns[step], self.agent_1.mbi_returns[step], places=5) self.assertAlmostEqual(self.agent_0.mbi_returns[step], self.agent_2.mbi_returns[step], places=5) # reset for next episode (not actually calling training) self.agent_0.clear_individual_agent_episode_data() self.agent_1.clear_individual_agent_episode_data() self.agent_2.clear_individual_agent_episode_data() def test_multi_agent_returns_2(self): '''mappo: equal returns, rewards, and advantages when values centralized''' n_episodes = 10 for ep in range(n_episodes): # Generate true global state of system state = np.zeros(len(self.group_trainer.agent_trainer_group)) central_values = np.zeros(self.episode_len+1) for step in range(self.episode_len): for ag_ind, ag in enumerate(self.group_trainer.agent_trainer_group): ag.mbi_observations.append(np.random.normal(state[ag_ind], 0.1, 1)) ag.mbi_actions.append(np.random.normal(1.0, 0.1, 1)) ag.mbi_obs_values.append(np.random.normal(ag.mbi_observations[-1][0], 10.0)) ag.mbi_dones.append(False) ag.mbi_neglogp_actions.append(-np.log(np.random.uniform(0,1))) ag.mbi_healths.append(1.0) if step ==self.episode_len-1: ag.mbi_observations.append(np.random.normal(state[ag_ind], 0.1, 1)) ag.mbi_dones.append(True) # update state state[ag_ind] += ag.mbi_actions[-1][0] # calculate reward: reward = np.mean(state) for ag_ind, ag in enumerate(self.group_trainer.agent_trainer_group): ag.mbi_rewards.append(reward) # calculate centralized values central_values[step] = np.mean([ag.mbi_obs_values[step] for ag in self.group_trainer.agent_trainer_group]) # if step == self.episode_len-1: # for ag_ind, ag in enumerate(self.group_trainer.agent_trainer_group): # ag.mbi_observations.append(np.random.normal(state[ag_ind], 0.1, 1)) # ag.mbi_dones.append(True) # central_values[step+1] = np.mean([np.random.normal(ag.mbi_observations[-1][0], 10.0) for ag in self.group_trainer.agent_trainer_group]) # test that returns, advantages are same for all agents with centralized values self.agent_0.process_individual_agent_episode_returns_and_advantages(factual_values=central_values, counterfactual_values=None) self.agent_1.process_individual_agent_episode_returns_and_advantages(factual_values=central_values, counterfactual_values=None) self.agent_2.process_individual_agent_episode_returns_and_advantages(factual_values=central_values, counterfactual_values=None) for step in range(self.episode_len): # rewards self.assertAlmostEqual(self.agent_0.mbi_rewards[step], self.agent_1.mbi_rewards[step], places=5) self.assertAlmostEqual(self.agent_0.mbi_rewards[step], self.agent_2.mbi_rewards[step], places=5) # returns self.assertAlmostEqual(self.agent_0.mbi_returns[step], self.agent_1.mbi_returns[step], places=5) self.assertAlmostEqual(self.agent_0.mbi_returns[step], self.agent_2.mbi_returns[step], places=5) # advantages self.assertAlmostEqual(self.agent_0.mbi_factual_advantages[step], self.agent_1.mbi_factual_advantages[step], places=5) self.assertAlmostEqual(self.agent_0.mbi_factual_advantages[step], self.agent_2.mbi_factual_advantages[step], places=5) # values self.assertAlmostEqual(self.agent_0.mbi_factual_values[step], self.agent_1.mbi_factual_values[step], places=5) self.assertAlmostEqual(self.agent_0.mbi_factual_values[step], self.agent_2.mbi_factual_values[step], places=5) # reset for next episode (not actually calling training) self.agent_0.clear_individual_agent_episode_data() self.agent_1.clear_individual_agent_episode_data() self.agent_2.clear_individual_agent_episode_data() def test_multi_agent_heuristic_credit_assignment_1(self): '''mappo: heuristic credits: all agents receive equal credit if return equals return mean and all actions same probability''' # change shared_reward to true for this test self.group_trainer.shared_reward = True self.group_trainer.crediting_algorithm = 'batch_mean_deviation_heuristic' for trial in range(10): # generate random reward history that each agent will have for every episode common_reward_history = np.random.normal(0,10, self.group_trainer.n_steps_per_episode) # generate random action probability that all agent use for given step common_neglogp_actions = -np.log(np.random.uniform(0,1, (self.group_trainer.n_episodes_per_batch, self.group_trainer.n_steps_per_episode))) for ep in range(self.group_trainer.n_episodes_per_batch): # check size of batch is growing appropriately self.assertEqual(len(self.group_trainer.batch_observations), self.group_trainer.n_agents*self.group_trainer.n_steps_per_episode*ep) self.assertEqual(len(self.group_trainer.batch_observations), len(self.group_trainer.batch_factual_values)) self.assertEqual(len(self.group_trainer.batch_observations), len(self.group_trainer.batch_counterfactual_values)) self.assertEqual(len(self.group_trainer.batch_observations), len(self.group_trainer.batch_returns)) self.assertEqual(len(self.group_trainer.batch_observations), len(self.group_trainer.batch_actions)) self.assertEqual(len(self.group_trainer.batch_observations), len(self.group_trainer.batch_neglogp_actions)) self.assertEqual(len(self.group_trainer.batch_observations), len(self.group_trainer.batch_dones)) for ag in self.group_trainer.agent_trainer_group: for step in range(self.group_trainer.n_steps_per_episode): ag.mbi_observations.append(np.random.uniform(-1., +1., 1)) ag.mbi_actions.append(np.random.uniform(-1., +1., 1)) ag.mbi_rewards.append(common_reward_history[step]) ag.mbi_obs_values.append(np.random.uniform(0, +1.)) ag.mbi_dones.append(False) ag.mbi_neglogp_actions.append(common_neglogp_actions[ep][step]) ag.mbi_healths.append(1.0) ag.mbi_observations.append(np.random.uniform(-1., +1., 1)) ag.mbi_dones.append(True) if ep < self.group_trainer.n_episodes_per_batch - 1: self.group_trainer.update_group_policy(terminal=1) self.assertEqual(len(self.group_trainer.agent_trainer_group[0].mbi_rewards), 0) self.assertEqual(len(self.group_trainer.agent_trainer_group[1].mbi_rewards), 0) self.assertEqual(len(self.group_trainer.agent_trainer_group[2].mbi_rewards), 0) else: # don't actually run final update call, call batch_credit_assignment instead break # format batch and run credit assignment episode_factual_values, episode_counterfactual_values = self.group_trainer.process_episode_value_centralization_and_credit_assignment() self.group_trainer.process_episode_returns_and_store_group_training_batch(episode_factual_values, episode_counterfactual_values) self.group_trainer.process_episode_clear_data() crediting_info = self.group_trainer.batch_credit_assignment() return_stds = crediting_info[1] credit_scale = crediting_info[2] # check that every agent is receiving the same credit self.assertEqual(len(self.group_trainer.batch_effective_returns), self.group_trainer.n_data_per_batch) for ep in range(self.group_trainer.n_episodes_per_batch): for step in range(self.group_trainer.n_steps_per_episode): self.assertAlmostEqual(return_stds[step], 0.0, places=5) self.assertAlmostEqual(credit_scale[ep][step], 0.0, places=5) for ag in range(self.group_trainer.n_agents): batch_index = (ep*self.group_trainer.n_agents + ag) * self.group_trainer.n_steps_per_episode + step self.assertAlmostEqual(self.group_trainer.batch_neglogp_actions[batch_index], common_neglogp_actions[ep][step], places=5) self.assertAlmostEqual(self.group_trainer.batch_effective_returns[batch_index], self.group_trainer.batch_returns[batch_index]/float(self.group_trainer.n_agents), places=5) # execute training to refresh batch data self.group_trainer.execute_group_training() def test_multi_agent_heurisitic_credit_assignment_2(self): '''mappo: heurisitc credits: one agent receives all the credit when action prob much larger and returns=mean''' # change shared_reward to true for this test self.group_trainer.shared_reward = True self.group_trainer.crediting_algorithm = 'batch_mean_deviation_heuristic' for trial in range(10): # generate random reward history that each agent will have for every episode common_reward_history = np.random.normal(0,10, self.group_trainer.n_steps_per_episode) # generate random action probabilities with one agent recieving high prob and others low high_neglogp_actions = -np.log(np.random.uniform(0.999,1, (self.group_trainer.n_episodes_per_batch, self.group_trainer.n_steps_per_episode))) low_neglogp_actions = -np.log(np.random.uniform(0,0.001, (self.group_trainer.n_episodes_per_batch, self.group_trainer.n_steps_per_episode))) # pick random agent to recieve high probility actions lucky_agent = np.random.randint(self.group_trainer.n_agents, size=(self.group_trainer.n_episodes_per_batch,)) for ep in range(self.group_trainer.n_episodes_per_batch): # check size of batch is growing appropriately self.assertEqual(len(self.group_trainer.batch_observations), self.group_trainer.n_agents*self.group_trainer.n_steps_per_episode*ep) self.assertEqual(len(self.group_trainer.batch_observations), len(self.group_trainer.batch_factual_values)) self.assertEqual(len(self.group_trainer.batch_observations), len(self.group_trainer.batch_counterfactual_values)) self.assertEqual(len(self.group_trainer.batch_observations), len(self.group_trainer.batch_returns)) self.assertEqual(len(self.group_trainer.batch_observations), len(self.group_trainer.batch_actions)) self.assertEqual(len(self.group_trainer.batch_observations), len(self.group_trainer.batch_neglogp_actions)) self.assertEqual(len(self.group_trainer.batch_observations), len(self.group_trainer.batch_dones)) for ag_ind, ag in enumerate(self.group_trainer.agent_trainer_group): for step in range(self.group_trainer.n_steps_per_episode): ag.mbi_observations.append(np.random.uniform(-1., +1., 1)) ag.mbi_actions.append(np.random.uniform(-1., +1., 1)) ag.mbi_rewards.append(common_reward_history[step]) ag.mbi_obs_values.append(np.random.uniform(0, +1.)) ag.mbi_dones.append(False) ag.mbi_healths.append(1.0) if ag_ind == lucky_agent[ep]: ag.mbi_neglogp_actions.append(high_neglogp_actions[ep][step]) else: ag.mbi_neglogp_actions.append(low_neglogp_actions[ep][step]) ag.mbi_observations.append(np.random.uniform(-1., +1., 1)) ag.mbi_dones.append(True) if ep < self.group_trainer.n_episodes_per_batch - 1: self.group_trainer.update_group_policy(terminal=1) self.assertEqual(len(self.group_trainer.agent_trainer_group[0].mbi_rewards), 0) self.assertEqual(len(self.group_trainer.agent_trainer_group[1].mbi_rewards), 0) self.assertEqual(len(self.group_trainer.agent_trainer_group[2].mbi_rewards), 0) else: # don't actually run final update call, call batch_credit_assignment instead break # format batch and run credit assignment episode_factual_values, episode_counterfactual_values = self.group_trainer.process_episode_value_centralization_and_credit_assignment() self.group_trainer.process_episode_returns_and_store_group_training_batch(episode_factual_values, episode_counterfactual_values) self.group_trainer.process_episode_clear_data() crediting_info = self.group_trainer.batch_credit_assignment() return_stds = crediting_info[1] credit_scale = crediting_info[2] # check that one agent recieves almost all the credit self.assertEqual(len(self.group_trainer.batch_effective_returns), self.group_trainer.n_data_per_batch) for ep in range(self.group_trainer.n_episodes_per_batch): for step in range(self.group_trainer.n_steps_per_episode): self.assertAlmostEqual(return_stds[step], 0.0, places=5) self.assertAlmostEqual(credit_scale[ep][step], 0.0, places=5) for ag in range(self.group_trainer.n_agents): batch_index = (ep*self.group_trainer.n_agents + ag) * self.group_trainer.n_steps_per_episode + step tol = abs(self.group_trainer.batch_returns[batch_index])/10.0 if ag == lucky_agent[ep]: self.assertAlmostEqual(self.group_trainer.batch_effective_returns[batch_index], self.group_trainer.batch_returns[batch_index], delta=tol) else: self.assertAlmostEqual(self.group_trainer.batch_effective_returns[batch_index], 0.0, delta=tol) # execute training to refresh batch data self.group_trainer.execute_group_training() def test_multi_agent_heuristic_credit_assignment_3(self): '''mappo: No crediting: check that returns equal credits when no crediting applied''' # change shared_reward to true for this test self.group_trainer.crediting_algorithm = None for trial in range(10): # generate random reward history that each agent will have for every episode common_reward_history = np.random.normal(0,10, self.group_trainer.n_steps_per_episode) for ep in range(10): # for each episode, the group batch data should grow by number of agents self.assertEqual(len(self.group_trainer.batch_observations), self.group_trainer.n_agents*self.group_trainer.n_steps_per_episode*ep) self.assertEqual(len(self.group_trainer.batch_observations), len(self.group_trainer.batch_factual_values)) self.assertEqual(len(self.group_trainer.batch_observations), len(self.group_trainer.batch_counterfactual_values)) self.assertEqual(len(self.group_trainer.batch_observations), len(self.group_trainer.batch_returns)) self.assertEqual(len(self.group_trainer.batch_observations), len(self.group_trainer.batch_actions)) self.assertEqual(len(self.group_trainer.batch_observations), len(self.group_trainer.batch_neglogp_actions)) self.assertEqual(len(self.group_trainer.batch_observations), len(self.group_trainer.batch_dones)) for ag in self.group_trainer.agent_trainer_group: for step in range(5): ag.mbi_observations.append(np.random.uniform(-1., +1., 1)) ag.mbi_actions.append(np.random.uniform(-1., +1., 1)) ag.mbi_rewards.append(common_reward_history[step]) ag.mbi_obs_values.append(np.random.uniform(0, +1.)) ag.mbi_dones.append(False) ag.mbi_neglogp_actions.append(-np.log(np.random.uniform(0,1))) ag.mbi_healths.append(1.0) ag.mbi_observations.append(np.random.uniform(-1., +1., 1)) ag.mbi_dones.append(True) if ep < self.group_trainer.n_episodes_per_batch - 1: self.group_trainer.update_group_policy(terminal=1) self.assertEqual(len(self.group_trainer.agent_trainer_group[0].mbi_rewards), 0) self.assertEqual(len(self.group_trainer.agent_trainer_group[1].mbi_rewards), 0) self.assertEqual(len(self.group_trainer.agent_trainer_group[2].mbi_rewards), 0) else: # don't actually run final update call, call batch_credit_assignment instead break # format batch and run credit assignment episode_factual_values, episode_counterfactual_values = self.group_trainer.process_episode_value_centralization_and_credit_assignment() self.group_trainer.process_episode_returns_and_store_group_training_batch(episode_factual_values, episode_counterfactual_values) self.group_trainer.process_episode_clear_data() self.group_trainer.batch_credit_assignment() # credit_scale = crediting_info[2] # check that one agent recieves almost all the credit self.assertEqual(len(self.group_trainer.batch_effective_returns), self.group_trainer.n_data_per_batch) for ep in range(self.group_trainer.n_episodes_per_batch): for step in range(self.group_trainer.n_steps_per_episode): # self.assertAlmostEqual(credit_scale[ep][step], 0.0, places=5) expected_credit = self.group_trainer.batch_effective_returns[ep*self.group_trainer.n_agents*self.group_trainer.n_steps_per_episode+step] for ag in range(self.group_trainer.n_agents): batch_index = (ep*self.group_trainer.n_agents + ag) * self.group_trainer.n_steps_per_episode + step # tol = abs(self.group_trainer.batch_returns[batch_index])/10.0 self.assertAlmostEqual( self.group_trainer.batch_effective_returns[batch_index], self.group_trainer.batch_returns[batch_index], places=4) self.assertAlmostEqual( self.group_trainer.batch_effective_returns[batch_index], expected_credit, places=4) # execute training to refresh batch data self.group_trainer.execute_group_training() class TestCentralCriticNetwork1(unittest.TestCase): ''' test central_critic_network class from mappo.py ''' def setUp(self): ''' the with tf.Graph.as_default()... command allows for multiple calls to setUp without causing variable scopes to "clash". See baselines/common/tests/util.py for examples ''' with tf.Graph().as_default() as self.setup_graph, tf.Session(config=tf.ConfigProto(allow_soft_placement=True)).as_default() as self.setup_sess: self.test_n_training_iterations = 1000 self.test_n_data_per_batch = 100 self.test_num_layers = 2 self.test_num_units = 8 self.test_activation = 'tanh' self.test_learning_rate = 1e-2 self.test_input_size = 1 self.test_cliprange = 0.2 joint_state_stamped_ph = [U.BatchInput((self.test_input_size, ), name="joint_state").get()] deep_mlp = DeepMLP(num_layers=self.test_num_layers, activation=self.test_activation) self.central_vf_value, self.central_vf_train, self.central_vf_debug = central_critic_network( inputs_placeholder_n=joint_state_stamped_ph, v_func=deep_mlp.deep_mlp_model, optimizer=tf.train.AdamOptimizer(learning_rate=self.test_learning_rate), scope = "joint_state_critic", num_units=self.test_num_units, grad_norm_clipping=0.5 ) def tearDown(self): '''Don't actually tearDown the tf graph Note: it may seem tempting to use tf.reset_default_graph(), but this causes an error in subsequent setUp calls with something to do with op: NoOp ... is not an element of this graph Instead use the with tf.Graph.as_default()... in setUp ''' pass def test_central_critic_network_constant_target(self): '''mappo: central critic learning constant target value''' # randomly generated but fixed constant target, regardless of input const_target = 8.245529015329097 # in order to make calls to the central value function, we need to operate within the tf session # and initialize variables with self.setup_sess: self.setup_sess.run(tf.global_variables_initializer()) for train_iter in range(self.test_n_training_iterations): # create individual training batch of random input but fixed target training_feed = [[], [], [], []] for i in range(self.test_n_data_per_batch): rand_input = np.random.uniform(-1., +1., self.test_input_size) training_feed[0].append(rand_input) training_feed[1].append(const_target) training_feed[2].append(self.central_vf_value(np.expand_dims(rand_input, axis=0))[0]) training_feed[3] = self.test_cliprange # call train and update target network central_vf_loss = self.central_vf_train(*training_feed) # check that value estimate has converged to const_target test_vals = [] for test_iter in range(1000): test_vals.append(self.central_vf_value(np.expand_dims(np.random.uniform(-1., +1., self.test_input_size),axis=0))) # print("test mean = {} | test std = {}".format(np.mean(test_vals), np.std(test_vals))) self.assertAlmostEqual(np.mean(test_vals), const_target, places=3) class TestCentralCriticNetwork2(unittest.TestCase): ''' test central_critic_network class from mappo.py ''' def setUp(self): ''' the with tf.Graph.as_default()... command allows for multiple calls to setUp without causing variable scopes to "clash". See baselines/common/tests/util.py for examples ''' with tf.Graph().as_default() as self.setup_graph, tf.Session(config=tf.ConfigProto(allow_soft_placement=True)).as_default() as self.setup_sess: self.test_n_training_iterations = 1000 self.test_n_data_per_batch = 128 self.test_num_layers = 4 self.test_num_units = 64 self.test_activation = 'elu' self.test_learning_rate = 1e-3 self.test_input_size = 1 self.test_test_size = 10000 self.test_cliprange = 0.2 joint_state_stamped_ph = [U.BatchInput((self.test_input_size, ), name="joint_state").get()] deep_mlp = DeepMLP(num_layers=self.test_num_layers, activation=self.test_activation) self.central_vf_value, self.central_vf_train, self.central_vf_debug = central_critic_network( inputs_placeholder_n=joint_state_stamped_ph, v_func=deep_mlp.deep_mlp_model, optimizer=tf.train.AdamOptimizer(learning_rate=self.test_learning_rate), scope = "joint_state_critic", num_units=self.test_num_units, grad_norm_clipping=0.5 ) def tearDown(self): '''Don't actually tearDown the tf graph Note: it may seem tempting to use tf.reset_default_graph(), but this causes an error in subsequent setUp calls with something to do with op: NoOp ... is not an element of this graph Instead use the with tf.Graph.as_default()... in setUp ''' pass def test_central_critic_network_periodic_target(self): '''mappo: central critic learning periodic function (this may take a while)''' # sinusoidal target function periodic_target = lambda x: np.sin(x) # in order to make calls to the central value function, we need to operate within the tf session # and initialize variables with self.setup_sess: self.setup_sess.run(tf.global_variables_initializer()) central_vf_loss = [] central_vf_expvar = [] for train_iter in range(self.test_n_training_iterations): # create individual training batch of random input but fixed target training_feed = [[], [], [], []] for i in range(self.test_n_data_per_batch): rand_input = np.random.uniform(-10., +10., self.test_input_size) training_feed[0].append(rand_input) training_feed[1].append(periodic_target(rand_input)[0]) training_feed[2].append(self.central_vf_value(np.expand_dims(rand_input, axis=0))[0]) training_feed[3] = self.test_cliprange # call train and update target network central_vf_loss.append(self.central_vf_train(*training_feed)) central_vf_expvar.append(explained_variance(self.central_vf_value(training_feed[0]), np.asarray(training_feed[1]))) if _DEBUG: rand_in = np.random.uniform(-10., +10., self.test_input_size) val_est = self.central_vf_value(np.expand_dims(rand_in,axis=0)) val_tar = periodic_target(rand_in[0]) example_diff = val_est - val_tar print("iter {} | in={:5.2f} | tar={:5.2f} | est={:7.3f} | diff={:7.3f} | loss={:7.3E} | expln var={:7.3E}".format( train_iter, rand_in[0], val_tar, val_est[0], example_diff[0], central_vf_loss[-1], central_vf_expvar[-1] )) if _DEBUG: ti = np.arange(self.test_n_training_iterations) plt.plot(ti, central_vf_loss, ti, central_vf_expvar) plt.xlabel('training iteration') plt.ylabel('value loss & explained variance') plt.legend(['value loss', 'explained variance']) plt.show() # check value loss has converged to expected level (based on emperical testing) self.assertLessEqual(np.mean(central_vf_loss[-int(self.test_n_training_iterations*.005):]), 5e-3) self.assertGreaterEqual(np.mean(central_vf_expvar[-int(self.test_n_training_iterations*.005):]), 0.975) # check that value estimate has converged test_vals = [[],[],[],[]] for test_iter in range(self.test_test_size): test_vals[0].append(np.random.uniform(-10., +10., self.test_input_size)) test_vals[1].append(self.central_vf_value(np.expand_dims(test_vals[0][-1],axis=0))) test_vals[2].append(periodic_target(test_vals[0][-1])) test_vals[3].append(test_vals[1][-1] - test_vals[2][-1]) # print("test mean = {} | test std = {}".format(np.mean(test_vals), np.std(test_vals))) self.assertAlmostEqual(np.mean(test_vals[3]), 0.0, places=1) self.assertLessEqual(np.std(test_vals[3]), 0.1) class TestCentralCriticNetwork3(unittest.TestCase): ''' test central_critic_network class from mappo.py ''' def setUp(self): ''' the with tf.Graph.as_default()... command allows for multiple calls to setUp without causing variable scopes to "clash". See baselines/common/tests/util.py for examples ''' with tf.Graph().as_default() as self.setup_graph, tf.Session(config=tf.ConfigProto(allow_soft_placement=True)).as_default() as self.setup_sess: self.test_n_training_iterations = 1000 self.test_n_data_per_batch = 128 self.test_num_layers = 4 self.test_num_units = 64 self.test_activation = 'elu' self.test_learning_rate = 1e-3 self.test_agent_state_len = 5 self.test_n_agents = 4 self.test_input_size = 1 + self.test_agent_state_len*self.test_n_agents self.test_test_size = 10000 self.test_cliprange = 0.2 joint_state_stamped_ph = [U.BatchInput((self.test_input_size, ), name="joint_state").get()] deep_mlp = DeepMLP(num_layers=self.test_num_layers, activation=self.test_activation) self.central_vf_value, self.central_vf_train, self.central_vf_debug = central_critic_network( inputs_placeholder_n=joint_state_stamped_ph, v_func=deep_mlp.deep_mlp_model, optimizer=tf.train.AdamOptimizer(learning_rate=self.test_learning_rate), scope = "joint_state_critic", num_units=self.test_num_units, grad_norm_clipping=0.5 ) def tearDown(self): '''Don't actually tearDown the tf graph Note: it may seem tempting to use tf.reset_default_graph(), but this causes an error in subsequent setUp calls with something to do with op: NoOp ... is not an element of this graph Instead use the with tf.Graph.as_default()... in setUp ''' pass def test_central_critic_network_terminated_target(self): '''mappo: central critic learning nonlinear terminated target similar to XOR (this may take a while)''' # randomly generated but fixed constant target, regardless of input def terminated_target(s): # reward if only one agent is terminated n_term = sum(s[self.test_agent_state_len::self.test_agent_state_len]) if np.isclose(n_term, 1.0): return s[0] else: return 0.0 def gen_rand_input(): rand_input = [np.random.randint(50)+1] for agsi in range(1, self.test_input_size, self.test_agent_state_len): rand_input.extend(np.random.uniform(-10., +10., self.test_agent_state_len-1)) rand_input.extend([np.random.randint(2)]) return rand_input # in order to make calls to the central value function, we need to operate within the tf session # and initialize variables with self.setup_sess: self.setup_sess.run(tf.global_variables_initializer()) central_vf_loss = [] central_vf_expvar = [] for train_iter in range(self.test_n_training_iterations): # create individual training batch of random input but fixed target training_feed = [[], [], [], []] for i in range(self.test_n_data_per_batch): rand_input = gen_rand_input() training_feed[0].append(rand_input) training_feed[1].append(terminated_target(rand_input)) training_feed[2].append(self.central_vf_value(np.expand_dims(rand_input, axis=0))[0]) training_feed[3] = self.test_cliprange # call train and update target network central_vf_loss.append(self.central_vf_train(*training_feed)) central_vf_expvar.append(explained_variance(self.central_vf_value(training_feed[0]), np.asarray(training_feed[1]))) if _DEBUG: rand_in = gen_rand_input() val_est = self.central_vf_value(np.expand_dims(rand_in,axis=0)) val_tar = terminated_target(rand_in) example_diff = val_est - val_tar print("iter {} | in={:5.2f} | tar={:5.2f} | est={:7.3f} | diff={:7.3f} | loss={:7.3E} | expln var={:7.3E}".format( train_iter, rand_in[0], val_tar, val_est[0], example_diff[0], central_vf_loss[-1], central_vf_expvar[-1] )) if _DEBUG: ti = np.arange(self.test_n_training_iterations) plt.plot(ti, central_vf_loss, ti, central_vf_expvar) plt.xlabel('training iteration') plt.ylabel('value loss & explained variance') plt.legend(['value loss', 'explained variance']) plt.show() # check value loss and explained variance has converged to expected level (based on emperical testing) self.assertLessEqual(np.mean(central_vf_loss[-int(self.test_n_training_iterations*.005):]), 2.0) self.assertGreaterEqual(np.mean(central_vf_expvar[-int(self.test_n_training_iterations*.005):]), 0.975) # # check that value estimate has converged # test_vals = [[],[],[],[]] # for test_iter in range(self.test_test_size): # test_vals[0].append(np.random.uniform(-10., +10., self.test_input_size)) # test_vals[1].append(self.central_vf_value(np.expand_dims(test_vals[0][-1],axis=0))) # test_vals[2].append(periodic_target(test_vals[0][-1])) # test_vals[3].append(test_vals[1][-1] - test_vals[2][-1]) # # print("test mean = {} | test std = {}".format(np.mean(test_vals), np.std(test_vals))) # self.assertAlmostEqual(np.mean(test_vals[3]), 0.0, places=2) # self.assertLessEqual(np.std(test_vals[3]), 0.1) class TestPPOGroupTrainer3(unittest.TestCase): ''' test PPOGroupTrainer class from mappo.py ''' def setUp(self): ''' the with tf.Graph.as_default()... command allows for multiple calls to setUp without causing variable scopes to "clash". See baselines/common/tests/util.py for examples ''' with tf.Graph().as_default(), tf.Session(config=tf.ConfigProto(allow_soft_placement=True)).as_default(): self.test_n_training_iterations = 1000 self.test_episode_len = 5 self.test_n_episodes_per_batch = 10 self.test_num_layers = 2 self.test_activation = 'tanh' self.test_n_opt_epochs = 4 self.test_n_minibatches = 4 self.test_gamma = 0.99 self.test_joint_state_space_len = 1 deep_mlp = DeepMLP(num_layers=self.test_num_layers, activation=self.test_activation) # create trainer that would live in a simple 1D environment # with 1D continuous observations and actions # and single step episodes self.group_trainer = PPOGroupTrainer( n_agents=3, obs_space=spaces.Box(low=-1.0, high=1.0, shape=(1,), dtype=np.float32), act_space=spaces.Box(low=-1.0, high=1.0, shape=(1,), dtype=np.float32), n_steps_per_episode=self.test_episode_len, ent_coef=0.0, local_actor_learning_rate=3e-4, vf_coef=0.5, num_layers=2, num_units=4, activation=self.test_activation, cliprange=0.2, n_episodes_per_batch=self.test_n_episodes_per_batch, shared_reward=True, critic_type='central_joint_state', central_critic_model=deep_mlp.deep_mlp_model, central_critic_learning_rate=3e-4, central_critic_num_units=4, joint_state_space_len=self.test_joint_state_space_len, max_grad_norm = 0.5, n_opt_epochs=self.test_n_opt_epochs, n_minibatches=self.test_n_minibatches) # Populate the group with stripped out versions of agents Args = namedtuple('Args', ['max_episode_len', 'gamma']) args = Args(self.test_episode_len, self.test_gamma) self.agent_0 = PPOAgentComputer( name="agent_0", model=self.group_trainer.local_actor_critic_model, obs_shape_n=None, act_space_n=None, agent_index=0, args=args, local_q_func=None, lam=1.0) self.agent_1 = PPOAgentComputer( name="agent_1", model=self.group_trainer.local_actor_critic_model, obs_shape_n=None, act_space_n=None, agent_index=1, args=args, local_q_func=None, lam=1.0) self.agent_2 = PPOAgentComputer( name="agent_1", model=self.group_trainer.local_actor_critic_model, obs_shape_n=None, act_space_n=None, agent_index=2, args=args, local_q_func=None, lam=1.0) self.group_trainer.update_agent_trainer_group([self.agent_0, self.agent_1, self.agent_2]) def tearDown(self): '''Don't actually tearDown the tf graph Note: it may seem tempting to use tf.reset_default_graph(), but this causes an error in subsequent setUp calls with something to do with op: NoOp ... is not an element of this graph Instead use the with tf.Graph.as_default()... in setUp ''' pass def nontest_execute_group_training_central_joint_state_critic_1(self): '''mappo: (this test currently deprecated but not removed yet because using some of the code as a guide) integration test of many functions to ensure central joint state critic converges given constant input ''' self.assertTrue(False) # this test currently deprecated but not removed yet because using some of the code as a guide const_reward = 8.245529015329097 # in order to make calls to the central value function, we need to operate within the tf session # and initialize variables # with self.group_trainer.sess: # tf.global_variables_initializer() with self.group_trainer.sess as sess: sess.run(tf.global_variables_initializer()) training_loss_stats = [] for train_iter in range(self.test_n_training_iterations): for ep in range(self.test_n_episodes_per_batch): # for each episode, the group batch data should grow by number of agents*time steps self.assertEqual(len(self.group_trainer.batch_observations), self.group_trainer.n_agents*self.group_trainer.n_steps_per_episode*ep) self.assertEqual(len(self.group_trainer.batch_observations), len(self.group_trainer.batch_factual_values)) self.assertEqual(len(self.group_trainer.batch_observations), len(self.group_trainer.batch_counterfactual_values)) self.assertEqual(len(self.group_trainer.batch_observations), len(self.group_trainer.batch_returns)) self.assertEqual(len(self.group_trainer.batch_observations), len(self.group_trainer.batch_actions)) self.assertEqual(len(self.group_trainer.batch_observations), len(self.group_trainer.batch_neglogp_actions)) self.assertEqual(len(self.group_trainer.batch_observations), len(self.group_trainer.batch_dones)) # for each episode, joint data should grow by number of time steps self.assertEqual(len(self.group_trainer.batch_joint_state_stamped), (self.group_trainer.n_steps_per_episode+1)*ep) # populate episode with random data, except rewards, those are constant for ag in self.group_trainer.agent_trainer_group: for step in range(self.test_episode_len): ag.mbi_observations.append(np.random.uniform(-1., +1., 1)) ag.mbi_actions.append(np.random.uniform(-1., +1., 1)) ag.mbi_rewards.append(const_reward) # only element that is constant, not randomly varying ag.mbi_obs_values.append(np.random.uniform(0, +1.)) ag.mbi_dones.append(False) ag.mbi_neglogp_actions.append(-np.log(np.random.uniform(0,1))) ag.mbi_healths.append(1.0) ag.mbi_observations.append(np.random.uniform(-1., +1., 1)) ag.mbi_dones.append(True) for step in range(self.test_episode_len+1): # self.group_trainer.record_joint_state(np.array([ # np.random.uniform(-1., +1., 4), np.random.uniform(-1., +1., 4), np.random.uniform(-1., +1., 4)])) self.group_trainer.record_joint_state(np.array([np.random.uniform(-1., +1., self.test_joint_state_space_len)])) # self.group_trainer.update_group_policy(terminal=1) episode_factual_values, episode_counterfactual_values = self.group_trainer.process_episode_value_centralization_and_credit_assignment() self.group_trainer.process_episode_returns_and_store_group_training_batch(episode_factual_values, episode_counterfactual_values) self.group_trainer.process_episode_clear_data() # check that returns are always the same sequence, given the constant reward cur_return = const_reward for ep_step in range(self.test_episode_len): self.assertAlmostEqual(self.group_trainer.batch_joint_state_stamped[ep_step][0], self.test_episode_len-ep_step) self.assertAlmostEqual(self.group_trainer.batch_joint_returns[-ep_step-1], cur_return, places=5) cur_return = const_reward + self.test_gamma*cur_return # check that individuals' memories are properly cleared out self.assertEqual(len(self.group_trainer.agent_trainer_group[0].mbi_rewards), 0) self.assertEqual(len(self.group_trainer.agent_trainer_group[1].mbi_rewards), 0) self.assertEqual(len(self.group_trainer.agent_trainer_group[2].mbi_rewards), 0) # after episodes per batch, update policy self.group_trainer.batch_credit_assignment() batch_loss_stats = self.group_trainer.execute_group_training() training_loss_stats += [[self.test_episode_len*self.test_n_episodes_per_batch*(train_iter+1)] + L for L in batch_loss_stats] self.assertEqual(len(self.group_trainer.batch_observations), 0) self.assertEqual(len(self.group_trainer.batch_joint_state_stamped), 0) self.assertEqual(len(self.group_trainer.batch_joint_returns), 0) self.assertEqual(len(self.group_trainer.batch_factual_values), 0) self.assertEqual(len(self.group_trainer.batch_counterfactual_values), 0) self.assertEqual(len(self.group_trainer.batch_actions), 0) self.assertEqual(len(self.group_trainer.batch_returns), 0) self.assertEqual(len(self.group_trainer.batch_dones), 0) self.assertEqual(len(self.group_trainer.batch_neglogp_actions), 0) self.assertEqual(len(self.group_trainer.batch_effective_returns), 0) print("training iter {}: value at t = {}: {} | value at t = {}: {}".format( train_iter, self.test_episode_len, self.group_trainer.central_vf_value(np.expand_dims(np.concatenate(([0], np.random.uniform(-1., +1., self.test_joint_state_space_len))),axis=0)), 0, self.group_trainer.central_vf_value(np.expand_dims(np.concatenate(([self.test_episode_len], np.random.uniform(-1., +1., self.test_joint_state_space_len))),axis=0)))) print(self.group_trainer.central_vf_value(np.expand_dims(np.concatenate(([self.test_episode_len], np.random.uniform(-1., +1., self.test_joint_state_space_len))),axis=0))) self.assertTrue(False) class TestPPOGroupTrainer_LocalCritic_NoCrediting_1(unittest.TestCase): '''Unit tests for individual subroutines in PPOGroupTrainer''' def setUp(self): ''' the with tf.Graph.as_default()... command allows for multiple calls to setUp without causing variable scopes to "clash". See baselines/common/tests/util.py for examples ''' with tf.Graph().as_default(), tf.Session(config=tf.ConfigProto(allow_soft_placement=True)).as_default(): self.group_trainer = PPOGroupTrainer( n_agents=3, obs_space=spaces.Box(low=-1.0, high=1.0, shape=(1,), dtype=np.float32), act_space=spaces.Box(low=-1.0, high=1.0, shape=(1,), dtype=np.float32), n_steps_per_episode=50, ent_coef=0.0, local_actor_learning_rate=3e-4, vf_coef=0.5, num_layers=2, num_units=4, activation='tanh', cliprange=0.2, n_episodes_per_batch=16, shared_reward=True, critic_type='distributed_local_observations', central_critic_model=None, central_critic_learning_rate=None, central_critic_num_units=None, joint_state_space_len=None, max_grad_norm = 0.5, n_opt_epochs=4, n_minibatches=4) def tearDown(self): '''Don't actually tearDown the tf graph Note: it may seem tempting to use tf.reset_default_graph(), but this causes an error in subsequent setUp calls with something to do with op: NoOp ... is not an element of this graph Instead use the with tf.Graph.as_default()... in setUp ''' pass def test_process_episode_value_centralization_and_credit_assignment_1(self): '''mappo:process_episode_value_centralization_and_credit_assignment: local critic, no crediting''' # create trainer that would live in a simple 1D environment # with 1D continuous observations and actions # and single step episodes # Populate the group with generic objects self.group_trainer.update_agent_trainer_group([object, object, object]) # call the centralization and crediting function episode_factual_values, episode_counterfactual_values = self.group_trainer.process_episode_value_centralization_and_credit_assignment() # check outputs self.assertTrue(episode_factual_values is None) self.assertTrue(episode_counterfactual_values is None) self.assertEqual(len(self.group_trainer.batch_joint_observations_stamped), 51) self.assertEqual(len(self.group_trainer.batch_joint_state_stamped), 51) for i,_ in enumerate(self.group_trainer.batch_joint_observations_stamped): self.assertTrue(self.group_trainer.batch_joint_observations_stamped[i] is None) self.assertTrue(self.group_trainer.batch_joint_state_stamped[i] is None) class TestPPOGroupTrainer_JointObserveCritic_NoCrediting_1(unittest.TestCase): '''Unit tests for individual subroutines in PPOGroupTrainer''' def setUp(self): ''' the with tf.Graph.as_default()... command allows for multiple calls to setUp without causing variable scopes to "clash". See baselines/common/tests/util.py for examples ''' with tf.Graph().as_default(), tf.Session(config=tf.ConfigProto(allow_soft_placement=True)).as_default(): # create trainer that would live in a simple 1D environment # with 1D continuous observations and actions # with randomized parameterized when they are not important for this test self.group_trainer = PPOGroupTrainer( n_agents=np.random.randint(9)+2, obs_space=spaces.Box(low=-1.0, high=1.0, shape=(1,), dtype=np.float32), act_space=spaces.Box(low=-1.0, high=1.0, shape=(1,), dtype=np.float32), n_steps_per_episode=50, ent_coef=np.random.rand(), local_actor_learning_rate=np.random.rand(), vf_coef=np.random.rand(), num_layers=np.random.randint(15)+2, num_units=np.random.randint(255)+2, activation='tanh', cliprange=np.random.rand(), n_episodes_per_batch=np.random.randint(1024)+1, shared_reward=True, critic_type='central_joint_observations', central_critic_model=DeepMLP(num_layers=np.random.randint(16)+1, activation='tanh').deep_mlp_model, central_critic_learning_rate=np.random.rand(), joint_state_space_len=np.random.randint(256)+1, central_critic_num_units=np.random.randint(255)+2, max_grad_norm = np.random.rand(), n_opt_epochs=np.random.randint(16)+1, n_minibatches=np.random.randint(16)+1) def tearDown(self): '''Don't actually tearDown the tf graph Note: it may seem tempting to use tf.reset_default_graph(), but this causes an error in subsequent setUp calls with something to do with op: NoOp ... is not an element of this graph Instead use the with tf.Graph.as_default()... in setUp ''' pass def test_process_episode_value_centralization_and_credit_assignment_1(self): '''mappo:process_episode_value_centralization_and_credit_assignment: joint observations critic, no crediting''' n_steps = self.group_trainer.n_steps_per_episode n_agents = self.group_trainer.n_agents # Overwrite central value function with simple, dummy value function self.group_trainer.central_vf_value = lambda jnt_obs: [sum(sum(jnt_obs))] # Populate the group with stripped out versions of agents with random observation class DummyAgent(object): def __init__(self, nsteps): # self.mbi_observations = list(np.random.uniform(-1,1,group_trainer.n_steps_per_episode+1)) self.mbi_observations = [[np.random.uniform(-1,1)] for i in range(nsteps+1)] agent_group = [] for i in range(self.group_trainer.n_agents): agent_group.append(DummyAgent(n_steps)) self.group_trainer.update_agent_trainer_group(agent_group) # call the centralization and crediting function episode_factual_values, episode_counterfactual_values = self.group_trainer.process_episode_value_centralization_and_credit_assignment() # check outputs self.assertEqual(n_agents, self.group_trainer.n_agents) self.assertEqual(len(episode_factual_values), n_steps+1) self.assertEqual(len(episode_counterfactual_values), n_agents) self.assertEqual(len(self.group_trainer.batch_joint_observations_stamped), n_steps+1) self.assertEqual(len(self.group_trainer.batch_joint_state_stamped), n_steps+1) for i in range(n_steps+1): self.assertEqual(len(self.group_trainer.batch_joint_observations_stamped[i]), n_agents+1) self.assertAlmostEqual(self.group_trainer.batch_joint_observations_stamped[i][0], n_steps+1-i) # check time stamp expect_value = n_steps+1 - i + sum([ag.mbi_observations[i][0] for ag in agent_group]) if i == n_steps: expect_value = 0.0 self.assertAlmostEqual(episode_factual_values[i], expect_value) # all equal without crediting for agi in range(n_agents): self.assertTrue(episode_counterfactual_values[agi][i] is None) # No crediting class TestPPOGroupTrainer_JointStateCritic_NoCrediting_1(unittest.TestCase): '''Unit tests for individual subroutines in PPOGroupTrainer''' def setUp(self): ''' the with tf.Graph.as_default()... command allows for multiple calls to setUp without causing variable scopes to "clash". See baselines/common/tests/util.py for examples ''' with tf.Graph().as_default(), tf.Session(config=tf.ConfigProto(allow_soft_placement=True)).as_default(): # create trainer that would live in a simple 1D environment # with 1D continuous observations and actions # with randomized parameterized when they are not important for this test n_agents=np.random.randint(9)+2 self.group_trainer = PPOGroupTrainer( n_agents=n_agents, obs_space=spaces.Box(low=-1.0, high=1.0, shape=(1,), dtype=np.float32), act_space=spaces.Box(low=-1.0, high=1.0, shape=(1,), dtype=np.float32), n_steps_per_episode=50, ent_coef=np.random.rand(), local_actor_learning_rate=np.random.rand(), vf_coef=np.random.rand(), num_layers=np.random.randint(8)+1, num_units=np.random.randint(63)+2, activation='tanh', cliprange=np.random.rand(), n_episodes_per_batch=np.random.randint(63)+2, shared_reward=True, critic_type='central_joint_state', central_critic_model=DeepMLP(num_layers=np.random.randint(8)+1, activation='tanh').deep_mlp_model, central_critic_learning_rate=np.random.rand(), central_critic_num_units=np.random.randint(63)+2, joint_state_space_len=2*n_agents, max_grad_norm = np.random.rand(), n_opt_epochs=np.random.randint(16)+1, n_minibatches=np.random.randint(16)+1, joint_state_entity_len=2) def tearDown(self): '''Don't actually tearDown the tf graph Note: it may seem tempting to use tf.reset_default_graph(), but this causes an error in subsequent setUp calls with something to do with op: NoOp ... is not an element of this graph Instead use the with tf.Graph.as_default()... in setUp ''' pass def test_process_episode_value_centralization_and_credit_assignment_1(self): '''mappo:process_episode_value_centralization_and_credit_assignment: joint state critic, no crediting''' n_steps = self.group_trainer.n_steps_per_episode n_agents = self.group_trainer.n_agents # Overwrite central value function with simple, dummy value function self.group_trainer.central_vf_value = lambda jnt_obs: [sum(sum(jnt_obs))] # Populate the group with stripped out versions of agents class DummyAgent(object): def __init__(self): pass agent_group = [] for i in range(self.group_trainer.n_agents): agent_group.append(DummyAgent()) self.group_trainer.update_agent_trainer_group(agent_group) # create randomized central state generator self.group_trainer.episode_joint_state = [np.random.uniform(-1,1,n_agents) for i in range(n_steps+1)] # call the centralization and crediting function episode_factual_values, episode_counterfactual_values = self.group_trainer.process_episode_value_centralization_and_credit_assignment() # check outputs self.assertEqual(n_agents, self.group_trainer.n_agents) self.assertEqual(len(episode_factual_values), n_steps+1) self.assertEqual(len(episode_counterfactual_values), n_agents) self.assertEqual(len(self.group_trainer.batch_joint_observations_stamped), n_steps+1) self.assertEqual(len(self.group_trainer.batch_joint_state_stamped), n_steps+1) for i in range(n_steps+1): self.assertEqual(len(self.group_trainer.batch_joint_state_stamped[i]), n_agents+1) self.assertAlmostEqual(self.group_trainer.batch_joint_state_stamped[i][0], n_steps+1-i) # check time stamp expect_value = n_steps+1 - i + sum(self.group_trainer.episode_joint_state[i]) if i == n_steps: expect_value = 0.0 self.assertAlmostEqual(episode_factual_values[i], expect_value) # all equal without crediting for agi in range(n_agents): self.assertTrue(episode_counterfactual_values[agi][i] is None) # No crediting def test_process_episode_subroutines_1(self): '''mappo:process_episode_[subroutine]: joint state critic, no crediting''' n_steps = self.group_trainer.n_steps_per_episode n_agents = self.group_trainer.n_agents n_episodes = self.group_trainer.n_episodes_per_batch n_trials = 10 gamma = 0.99 lam = 1.0 # Overwrite central value function with simple, simple value function self.group_trainer.central_vf_value = lambda jnt_obs: [sum(sum(jnt_obs))] # Establish args for stripped out versions of agents Args = namedtuple('Args', ['max_episode_len', 'gamma']) args = Args(n_steps, gamma) for trial in range(n_trials): # generate random reward history that each agent will have for every episode common_reward_history = np.random.normal(0,10, n_steps) # generate new group of agents agent_group = [] for agi in range(n_agents): # agent_group.append(DummyAgent(n_steps, gamma, lam)) agent_group.append(PPOAgentComputer( name="agent_{}".format(agi), model=self.group_trainer.local_actor_critic_model, obs_shape_n=None, act_space_n=None, agent_index=agi, args=args, local_q_func=None, lam=lam)) self.group_trainer.update_agent_trainer_group(agent_group) for ep in range(n_episodes): # for each episode, the group batch data should grow by number of agents expect_len = n_agents*n_steps*ep self.assertEqual(len(self.group_trainer.batch_observations), expect_len) self.assertEqual(len(self.group_trainer.batch_factual_values), expect_len) self.assertEqual(len(self.group_trainer.batch_counterfactual_values), expect_len) self.assertEqual(len(self.group_trainer.batch_actions), expect_len) self.assertEqual(len(self.group_trainer.batch_neglogp_actions), expect_len) self.assertEqual(len(self.group_trainer.batch_dones), expect_len) self.assertEqual(len(self.group_trainer.batch_returns), expect_len) self.assertEqual(len(self.group_trainer.batch_joint_observations_stamped), ep*(n_steps+1)) self.assertEqual(len(self.group_trainer.batch_joint_state_stamped), ep*(n_steps+1)) # fill agent history with random input, except rewards the same for ag in self.group_trainer.agent_trainer_group: for step in range(n_steps): ag.mbi_observations.append(np.random.uniform(-1., +1., 1)) ag.mbi_actions.append(np.random.uniform(-1., +1., 1)) ag.mbi_rewards.append(common_reward_history[step]) ag.mbi_obs_values.append(np.random.uniform(0, +1.)) ag.mbi_dones.append(False) ag.mbi_neglogp_actions.append(-np.log(np.random.uniform(0,1))) ag.mbi_healths.append(1.0) ag.mbi_observations.append(np.random.uniform(-1., +1., 1)) ag.mbi_dones.append(True) # create randomized central state generator self.group_trainer.episode_joint_state = [np.random.uniform(-1,1,n_agents) for i in range(n_steps+1)] # get episode baseline values episode_factual_values, episode_counterfactual_values = self.group_trainer.process_episode_value_centralization_and_credit_assignment() # check baseline values self.assertEqual(len(episode_factual_values), self.group_trainer.n_steps_per_episode+1) self.assertEqual(len(episode_counterfactual_values), self.group_trainer.n_agents) for i in range(n_steps+1): self.assertEqual(len(self.group_trainer.batch_joint_state_stamped[i]), n_agents+1) self.assertAlmostEqual(self.group_trainer.batch_joint_state_stamped[i][0], n_steps+1-i) # check time stamp expect_value = n_steps+1 - i + sum(self.group_trainer.episode_joint_state[i]) if i == n_steps: expect_value = 0.0 self.assertAlmostEqual(episode_factual_values[i], expect_value) # all equal without crediting for agi in range(n_agents): self.assertTrue(episode_counterfactual_values[agi][i] is None) # No crediting # calculate returns, advantages and store in batch self.group_trainer.process_episode_returns_and_store_group_training_batch(episode_factual_values, episode_counterfactual_values) # check episode and batch data for agi, ag in enumerate(self.group_trainer.agent_trainer_group): # with no crediting, returns and values should match batch_joint values self.assertTrue(np.allclose(self.group_trainer.batch_joint_returns[-n_steps-1:], ag.mbi_returns)) s1 = -n_steps*(n_agents-agi) s2 = -n_steps*(n_agents-agi-1) if -n_steps*(n_agents-agi-1) < 0 else None self.assertTrue(np.allclose(self.group_trainer.batch_factual_values[s1:s2], ag.mbi_factual_values[:-1])) self.assertTrue(np.allclose(self.group_trainer.batch_actions[s1:s2], ag.mbi_actions)) self.assertTrue(np.allclose(self.group_trainer.batch_returns[s1:s2], ag.mbi_returns[:-1])) self.assertTrue(np.allclose(self.group_trainer.batch_neglogp_actions[s1:s2], ag.mbi_neglogp_actions)) self.assertTrue(np.allclose(self.group_trainer.batch_healths[s1:s2], ag.mbi_healths)) # clear episode data self.group_trainer.process_episode_clear_data() # check episode data cleared out self.assertEqual(len(self.group_trainer.episode_joint_state), 0) # episode state cleared out for ag in self.group_trainer.agent_trainer_group: # each agent's episode data cleared self.assertEqual(len(ag.mbi_observations), 0) self.assertEqual(len(ag.mbi_actions), 0) self.assertEqual(len(ag.mbi_rewards), 0) self.assertEqual(len(ag.mbi_obs_values), 0) self.assertEqual(len(ag.mbi_dones), 0) self.assertEqual(len(ag.mbi_neglogp_actions), 0) self.assertEqual(len(ag.mbi_healths), 0) if ep == n_episodes - 1: # clear out batch data (don't actually run any of the training functions, # trying to keep this test more trimmed down) # Clear out group batch self.group_trainer.batch_observations = [] self.group_trainer.batch_joint_observations_stamped = [] self.group_trainer.batch_joint_state_stamped = [] self.group_trainer.batch_returns = [] self.group_trainer.batch_joint_returns = [] self.group_trainer.batch_effective_returns = [] self.group_trainer.batch_dones = [] self.group_trainer.batch_actions = [] self.group_trainer.batch_factual_values = [] self.group_trainer.batch_counterfactual_values = [] self.group_trainer.batch_effective_values = [] self.group_trainer.batch_neglogp_actions = [] class TestPPOGroupTrainer_JointStateCritic_TerminatedBaselineCrediting_1(unittest.TestCase): '''Tests for individual subroutines in PPOGroupTrainer with joint-state critic and terminated baseline crediting''' def setUp(self): ''' the with tf.Graph.as_default()... command allows for multiple calls to setUp without causing variable scopes to "clash". See baselines/common/tests/util.py for examples ''' with tf.Graph().as_default(), tf.Session(config=tf.ConfigProto(allow_soft_placement=True)).as_default(): # create trainer that would live in a simple 1D environment # with 1D continuous observations and actions # with randomized parameterized when they are not important for this test n_agents = np.random.randint(9)+2 self.entity_state_len = 5 self.group_trainer = PPOGroupTrainer( n_agents=n_agents, obs_space=spaces.Box(low=-1.0, high=1.0, shape=(1,), dtype=np.float32), act_space=spaces.Box(low=-1.0, high=1.0, shape=(1,), dtype=np.float32), n_steps_per_episode=50, ent_coef=np.random.rand(), local_actor_learning_rate=np.random.rand(), vf_coef=np.random.rand(), num_layers=np.random.randint(8)+1, num_units=np.random.randint(63)+2, activation='tanh', cliprange=np.random.rand(), n_episodes_per_batch=np.random.randint(63)+2, shared_reward=True, critic_type='central_joint_state', central_critic_model=DeepMLP(num_layers=np.random.randint(8)+1, activation='tanh').deep_mlp_model, central_critic_learning_rate=np.random.rand(), joint_state_space_len=self.entity_state_len*n_agents, central_critic_num_units=np.random.randint(63)+2, max_grad_norm = np.random.rand(), n_opt_epochs=np.random.randint(16)+1, n_minibatches=np.random.randint(16)+1, crediting_algorithm = 'terminated_baseline') def tearDown(self): '''Don't actually tearDown the tf graph Note: it may seem tempting to use tf.reset_default_graph(), but this causes an error in subsequent setUp calls with something to do with op: NoOp ... is not an element of this graph Instead use the with tf.Graph.as_default()... in setUp ''' pass def test_process_episode_value_centralization_and_credit_assignment_1(self): '''mappo:process_episode_value_centralization_and_credit_assignment: joint state critic, terminated baseline crediting''' n_steps = self.group_trainer.n_steps_per_episode n_agents = self.group_trainer.n_agents entity_state_len = self.entity_state_len # Overwrite central value function with simple function that sums non-terminated states # self.group_trainer.central_vf_value = lambda s: [sum([s1*(1-s2) for s1,s2 in zip(s[1::entity_state_len], s[5::entity_state_len])])] def value_func(jss): jss = jss[0] # strip off additional layer that is added in mappo return [sum([s1*(1-s2) for s1,s2 in zip(jss[1::entity_state_len], jss[5::entity_state_len])])] self.group_trainer.central_vf_value = value_func # Populate the group with stripped out versions of agents class DummyAgent(object): def __init__(self): pass agent_group = [] jsl = [] for agi in range(self.group_trainer.n_agents): agent_group.append(DummyAgent()) jsl.append("agent_{}".format(agi)) self.group_trainer.update_agent_trainer_group(agent_group) self.group_trainer.joint_state_labels = jsl # create randomized central state generator with all agents at full health # self.group_trainer.episode_joint_state = [[None]*n_agents]*(n_steps+1) self.group_trainer.episode_joint_state = [None]*(n_steps+1) for i in range(n_steps+1): cur_state = [] for agi in range(n_agents): cur_state.extend(np.append(np.random.uniform(-1,1,entity_state_len-1), 0.0)) self.group_trainer.episode_joint_state[i] = cur_state # call the centralization and crediting function episode_factual_values, episode_counterfactual_values = self.group_trainer.process_episode_value_centralization_and_credit_assignment() # check outputs self.assertEqual(n_agents, self.group_trainer.n_agents) self.assertEqual(len(episode_factual_values), n_steps+1) self.assertEqual(len(episode_counterfactual_values), n_agents) self.assertEqual(len(self.group_trainer.batch_joint_observations_stamped), n_steps+1) self.assertEqual(len(self.group_trainer.batch_joint_state_stamped), n_steps+1) for i in range(n_steps+1): self.assertEqual(len(self.group_trainer.batch_joint_state_stamped[i]), entity_state_len*n_agents+1) self.assertAlmostEqual(self.group_trainer.batch_joint_state_stamped[i][0], n_steps+1-i) # check time stamp actual_expect_value = sum(self.group_trainer.batch_joint_state_stamped[i][1::entity_state_len]) # expected true value of state is sum over non-terminated states, ignoring stamp, with no agents terminated self.assertAlmostEqual(self.group_trainer.central_vf_value(np.expand_dims(self.group_trainer.batch_joint_state_stamped[i],axis=0))[0], actual_expect_value) if i == n_steps: self.assertAlmostEqual(episode_factual_values[i], 0.0) else: self.assertAlmostEqual(episode_factual_values[i], actual_expect_value) for agi in range(n_agents): self.assertAlmostEqual(self.group_trainer.batch_joint_state_stamped[i][1+(agi+1)*entity_state_len-1], 0) # actual termination values are false counterfactual_expect_value = actual_expect_value - self.group_trainer.batch_joint_state_stamped[i][1+agi*entity_state_len] self.assertAlmostEqual(episode_counterfactual_values[agi][i], counterfactual_expect_value) # all equal without crediting class TestRedistributedSoftmax(unittest.TestCase): ''' ''' def setUp(self): pass def test_redistributed_softmax_single_value(self): '''redistributed_softmax: random single-value''' for _ in range(100): p_arr = [np.random.normal(0.0, 10.0)] scale = np.random.uniform(0.0, 1.0) p_scaled = redistributed_softmax(p_arr, scale) self.assertAlmostEqual(p_scaled[0], 1.0) def test_redistributed_softmax_two_values(self): '''redistributed_softmax: random two-values''' for _ in range(100): p_arr = np.random.normal(0.0, 10.0, 2) scale = np.random.uniform(0.0, 1.0) p_scaled = redistributed_softmax(p_arr, scale) self.assertAlmostEqual(sum(p_scaled), 1.0) if scale > 0.5: self.assertGreaterEqual(p_scaled[p_arr.argmin()], p_scaled[p_arr.argmax()]) else: self.assertLessEqual(p_scaled[p_arr.argmin()], p_scaled[p_arr.argmax()]) def test_redistributed_softmax_multi_values(self): '''redistributed_softmax: random multi-values''' for _ in range(100): n = np.random.randint(1,20) p_arr = np.random.normal(0.0, 10.0, n) scale = np.random.uniform(0.0, 1.0) p_scaled = redistributed_softmax(p_arr, scale) self.assertAlmostEqual(sum(p_scaled), 1.0) if n > 1 and scale > 1.0 - 1.0/float(n): self.assertFalse(p_scaled.argmax() == p_arr.argmax()) if __name__ == '__main__': unittest.main()

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py

Python

tests/test_maltracx.py

maltra-cx/maltracx_python_client

7cb6f03815f3cdcc86d6d16434b1b31ab6a31948

[ "BSD-3-Clause" ]

null

tests/test_maltracx.py

maltra-cx/maltracx_python_client

7cb6f03815f3cdcc86d6d16434b1b31ab6a31948

[ "BSD-3-Clause" ]

null

tests/test_maltracx.py

maltra-cx/maltracx_python_client

7cb6f03815f3cdcc86d6d16434b1b31ab6a31948

[ "BSD-3-Clause" ]

null

import os import sys rootdir = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) sys.path = [rootdir] + sys.path

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Python

gym_anybullet/envs/anymal_steerable_envs.py

bibbygoodwin/rl-baselines-zoo

5550cc0407fbcc70c1a01eccca06c5a07c9fbe6e

[ "MIT" ]

4

2020-02-24T12:32:21.000Z

2020-02-24T19:11:14.000Z

gym_anybullet/envs/anymal_steerable_envs.py

bibbygoodwin/rl-baselines-zoo

5550cc0407fbcc70c1a01eccca06c5a07c9fbe6e

[ "MIT" ]

null

gym_anybullet/envs/anymal_steerable_envs.py

bibbygoodwin/rl-baselines-zoo

5550cc0407fbcc70c1a01eccca06c5a07c9fbe6e

[ "MIT" ]

null

import gym from gym import spaces from gym.utils import seeding import pybullet as p import numpy as np from common.paths import MODELS_PATH import time def gaussian(x, mu, sig): return np.exp(-np.power(x - mu, 2.) / (2 * np.power(sig, 2.))) class ANYmalHistoryNC(gym.Env): """ An env NC = No Constraints? """ def __init__(self, render=False): self._observation = [] self.observation_space = spaces.Box(-1 * np.array([np.inf] * 66), np.array([np.inf] * 66), dtype=np.float32) self.quadruped_joint_angles = [0.03, 0.4, -0.8, -0.03, 0.4, -0.8, 0.03, -0.4, 0.8, -0.03, -0.4, 0.8] # actions_low = np.asarray([-0.09, -0.2, -1.4, -0.15, -0.2, -1.4, -0.09, -1.0, 0.2, -0.15, -1.0, 0.2]) # actions_high = np.asarray([0.15, 1.0, -0.2, 0.09, 1.0, -0.2, 0.15, 0.2, 1.4, 0.09, 0.2, 1.4]) actions_low = np.asarray([-0.12, -0.873, -0.873, -0.20, -0.873, -0.873, -0.12, -0.873, -0.873, -0.20, -0.873, -0.873]) actions_high = np.asarray([0.20, 0.873, 0.873, 0.12, 0.873, 0.873, 0.20, 0.873, 0.873, 0.12, 0.873, 0.873]) self.action_space = spaces.Box(actions_low, actions_high, dtype=np.float32) self.timestep = 0.01 self.render_mode = render if render: self.physics_client = p.connect(p.GUI) else: self.physics_client = p.connect(p.DIRECT) p.resetSimulation() p.setGravity(0, 0, -9.81) p.setTimeStep(self.timestep) self.plane_id = p.loadURDF(MODELS_PATH + 'plane/plane.urdf') self.quadruped_start_pos = [0, 0, 0.5] self.quadruped_start_orientation = p.getQuaternionFromEuler([0, 0, 0]) self.prev_joint_states = np.zeros((4, 12)) self.prev_joint_states[:-1, :] = self.prev_joint_states[1:, :] self.prev_joint_states[-1, :] = self.quadruped_joint_angles self.prev_action = self.quadruped_joint_angles self.quadruped_id = p.loadURDF(MODELS_PATH + 'anymal_boxy/anymal_boxy.urdf', self.quadruped_start_pos, self.quadruped_start_orientation) p.setPhysicsEngineParameter(numSolverIterations=100) self.quadruped_joint_ids = [] active_joint = 0 for j in range(p.getNumJoints(self.quadruped_id)): p.changeDynamics(self.quadruped_id, j, linearDamping=0, angularDamping=0) info = p.getJointInfo(self.quadruped_id, j) joint_type = info[2] if joint_type == p.JOINT_PRISMATIC or joint_type == p.JOINT_REVOLUTE: self.quadruped_joint_ids.append(j) p.resetJointState(self.quadruped_id, j, self.quadruped_joint_angles[active_joint]) active_joint += 1 self.feet_ids = {'LF':5, 'RF':10, 'LH':15, 'RH':20} self.env_step_counter = 0 self.quadruped_pos = self.quadruped_start_pos self.quadruped_orientation = self.quadruped_start_orientation joint_torques = [] for j in self.quadruped_joint_ids: joint_torques.append(p.getJointState(self.quadruped_id, j)[3]) self.prev_torques = np.asarray(joint_torques) def step(self, action): self.quadruped_pos, self.quadruped_orientation = p.getBasePositionAndOrientation(self.quadruped_id) action = np.clip(np.asarray(action[:]), self.action_space.low, self.action_space.high) self._perform_action(action) p.stepSimulation() self.prev_action = action self._observation = self._compute_observation() reward = self._compute_reward() done = self._compute_done() self.env_step_counter += 1 return np.array(self._observation), reward, done, {} def reset(self): self.env_step_counter = 0 p.resetSimulation() p.setGravity(0, 0, -9.81) p.setTimeStep(self.timestep) p.loadURDF(MODELS_PATH + 'plane/plane.urdf') quadruped_start_pos = [0, 0, 0.5] quadruped_start_orientation = p.getQuaternionFromEuler([0, 0, 0]) self.quadruped_id = p.loadURDF(MODELS_PATH + 'anymal_boxy/anymal_boxy.urdf', quadruped_start_pos, quadruped_start_orientation) active_joint = 0 for j in self.quadruped_joint_ids: p.resetJointState(self.quadruped_id, j, self.quadruped_joint_angles[active_joint]) active_joint += 1 self._observation = self._compute_observation() return np.array(self._observation) def _perform_action(self, action): i = 0 for j in self.quadruped_joint_ids: p.setJointMotorControl2(self.quadruped_id, j, p.POSITION_CONTROL, action[i], force=40) i += 1 def _compute_observation(self): quadruped_pos, quadruped_orientation = p.getBasePositionAndOrientation(self.quadruped_id) quadruped_orientation = p.getEulerFromQuaternion(quadruped_orientation) joint_states = [] for j in self.quadruped_joint_ids: joint_states.append(p.getJointState(self.quadruped_id, j)[0]) self.prev_joint_states[:-1, :] = self.prev_joint_states[1:, :] self.prev_joint_states[-1, :] = joint_states observations = np.concatenate([quadruped_pos, quadruped_orientation, self.prev_joint_states.flatten(), self.prev_action]) return observations def _compute_reward(self): quadruped_pos, quadruped_orientation = p.getBasePositionAndOrientation(self.quadruped_id) quadruped_linear_vel, quadruped_angular_vel = p.getBaseVelocity(self.quadruped_id) joint_torques = [] for j in self.quadruped_joint_ids: joint_torques.append(p.getJointState(self.quadruped_id, j)[3]) vel_x = quadruped_linear_vel[0] vel_y = quadruped_linear_vel[1] vel_yaw = quadruped_angular_vel[2] quadruped_orientation = p.getEulerFromQuaternion(quadruped_orientation) if vel_x < 0.7: rew_vel_x = vel_x else: rew_vel_x = 1.4 - vel_x reward = 1 * rew_vel_x - 0.01 * np.abs(vel_y) \ - 0.01 * np.abs(vel_yaw) \ - 0.01 * np.abs(quadruped_orientation[0]) - 0.01 * np.abs(quadruped_orientation[1]) \ - 0.005 * np.abs(0.5 - quadruped_pos[2]) \ - 0.00001 * np.linalg.norm(np.asarray(joint_torques)) \ - 0.0001 * np.linalg.norm(self.prev_torques - joint_torques) self.prev_torques = np.asarray(joint_torques) return reward def _get_velocity(self): quadruped_linear_vel, quadruped_angular_vel = p.getBaseVelocity(self.quadruped_id) vel_x = quadruped_linear_vel[0] vel_y = quadruped_linear_vel[1] return np.sqrt(vel_x**2+vel_y**2) def _get_foot_contacts(self): LF = 0 if p.getContactPoints(self.quadruped_id, self.plane_id, linkIndexA=self.feet_ids['LF']) == () else 1 RF = 0 if p.getContactPoints(self.quadruped_id, self.plane_id, linkIndexA=self.feet_ids['RF']) == () else 1 LH = 0 if p.getContactPoints(self.quadruped_id, self.plane_id, linkIndexA=self.feet_ids['LH']) == () else 1 RH = 0 if p.getContactPoints(self.quadruped_id, self.plane_id, linkIndexA=self.feet_ids['RH']) == () else 1 return LF, RF, LH, RH def _compute_done(self): quadruped_pos, quadruped_orientation = p.getBasePositionAndOrientation(self.quadruped_id) quadruped_linear_vel, _ = p.getBaseVelocity(self.quadruped_id) vel_x = quadruped_linear_vel[0] quadruped_orientation = p.getEulerFromQuaternion(quadruped_orientation) done = bool(quadruped_pos[2] < 0.3) done = bool(done or np.abs(quadruped_orientation[0]) >= np.pi / 4) done = bool(done or np.abs(quadruped_orientation[1]) >= np.pi / 4) done = bool(done or np.abs(quadruped_orientation[2]) >= np.pi / 4) done = bool(done or vel_x > 1) done = bool(done or self.env_step_counter >= 4096) return done def render(self, mode='human', close=False): if not self.render_mode: p.disconnect() self.render_mode = True self.physics_client = p.connect(p.GUI) p.configureDebugVisualizer(p.COV_ENABLE_GUI, 0) p.resetSimulation() p.setGravity(0, 0, -9.81) p.setTimeStep(0.01) p.loadURDF(MODELS_PATH + 'plane/plane.urdf') self.quadruped_id = p.loadURDF(MODELS_PATH + 'anymal_boxy/anymal_boxy.urdf', self.quadruped_start_pos, self.quadruped_start_orientation) p.setPhysicsEngineParameter(numSolverIterations=100) self.quadruped_joint_ids = [] for j in range(p.getNumJoints(self.quadruped_id)): p.changeDynamics(self.quadruped_id, j, linearDamping=0, angularDamping=0) info = p.getJointInfo(self.quadruped_id, j) joint_type = info[2] if joint_type == p.JOINT_PRISMATIC or joint_type == p.JOINT_REVOLUTE: self.quadruped_joint_ids.append(j) p.setRealTimeSimulation(1) time.sleep(0.01) class ANYmalHistory3(ANYmalHistoryNC): def __init__(self, *args, **kwargs): super(ANYmalHistory3, self).__init__(*args, **kwargs) self.prev_joint_states = np.zeros((3, 12)) self.observation_space = spaces.Box(-1 * np.array([np.inf] * 42), np.array([np.inf] * 42), dtype=np.float32) def _perform_action(self, action): i = 0 for j in self.quadruped_joint_ids: p.setJointMotorControl2(self.quadruped_id, j, p.POSITION_CONTROL, action[i], force=40) i += 1 def _compute_observation(self): quadruped_pos, quadruped_orientation = p.getBasePositionAndOrientation(self.quadruped_id) quadruped_orientation = p.getEulerFromQuaternion(quadruped_orientation) joint_states = [] for j in self.quadruped_joint_ids: joint_states.append(p.getJointState(self.quadruped_id, j)[0]) self.prev_joint_states[:-1, :] = self.prev_joint_states[1:, :] self.prev_joint_states[-1, :] = joint_states observations = np.concatenate([quadruped_pos, quadruped_orientation, self.prev_joint_states.flatten()]) return observations def _compute_reward(self): quadruped_pos, quadruped_orientation = p.getBasePositionAndOrientation(self.quadruped_id) quadruped_linear_vel, quadruped_angular_vel = p.getBaseVelocity(self.quadruped_id) joint_torques = [] for j in self.quadruped_joint_ids: joint_torques.append(p.getJointState(self.quadruped_id, j)[3]) vel_x = quadruped_linear_vel[0] vel_y = quadruped_linear_vel[1] vel_yaw = quadruped_angular_vel[2] quadruped_orientation = p.getEulerFromQuaternion(quadruped_orientation) if vel_x < 0.7: rew_vel_x = vel_x else: rew_vel_x = 1.4 - vel_x reward = 1 * rew_vel_x - 0.01 * np.abs(vel_y) \ - 0.01 * np.abs(vel_yaw) \ - 0.01 * np.abs(quadruped_orientation[0]) - 0.01 * np.abs(quadruped_orientation[1]) \ - 0.0001 * np.linalg.norm(self.prev_torques - joint_torques) self.prev_torques = np.asarray(joint_torques) return reward class ANYmalHistory3Steer(ANYmalHistory3): def __init__(self, *args, **kwargs): super(ANYmalHistory3, self).__init__(*args, **kwargs) self.goal_velocity_low = 0.2 self.goal_velocity_high = 0.9 self.target_velocity = np.random.uniform(self.goal_velocity_low, self.goal_velocity_high) self.observation_space = spaces.Box(-1 * np.array([np.inf] * 55), np.array([np.inf] * 55), dtype=np.float32) self.prev_joint_states = np.zeros((3, 18)) def _compute_observation(self): quadruped_pos, quadruped_orientation = p.getBasePositionAndOrientation(self.quadruped_id) quadruped_orientation = p.getEulerFromQuaternion(quadruped_orientation) joint_states = [] for j in self.quadruped_joint_ids: joint_states.append(p.getJointState(self.quadruped_id, j)[0]) self.prev_joint_states[:-1, :] = self.prev_joint_states[1:, :] self.prev_joint_states[-1, 0:12] = joint_states self.prev_joint_states[-1, 12:15] = quadruped_pos self.prev_joint_states[-1, 15:18] = quadruped_orientation observations = np.concatenate([self.prev_joint_states.flatten(), np.array(self.target_velocity).reshape(1)]) return observations def reset(self): self.env_step_counter = 0 self.target_velocity = np.random.uniform(self.goal_velocity_low, self.goal_velocity_high) p.resetSimulation() p.setGravity(0, 0, -9.81) p.setTimeStep(self.timestep) p.loadURDF(MODELS_PATH + 'plane/plane.urdf') quadruped_start_pos = [0, 0, 0.5] quadruped_start_orientation = p.getQuaternionFromEuler([0, 0, 0]) self.quadruped_id = p.loadURDF(MODELS_PATH + 'anymal_boxy/anymal_boxy.urdf', quadruped_start_pos, quadruped_start_orientation) active_joint = 0 for j in self.quadruped_joint_ids: p.resetJointState(self.quadruped_id, j, self.quadruped_joint_angles[active_joint]) active_joint += 1 self._observation = self._compute_observation() return np.array(self._observation) def _compute_reward(self): quadruped_pos, quadruped_orientation = p.getBasePositionAndOrientation(self.quadruped_id) quadruped_linear_vel, quadruped_angular_vel = p.getBaseVelocity(self.quadruped_id) joint_torques = [] for j in self.quadruped_joint_ids: joint_torques.append(p.getJointState(self.quadruped_id, j)[3]) vel_x = quadruped_linear_vel[0] vel_y = quadruped_linear_vel[1] vel_yaw = quadruped_angular_vel[2] quadruped_orientation = p.getEulerFromQuaternion(quadruped_orientation) if vel_x < self.target_velocity: rew_vel_x = vel_x else: rew_vel_x = (2 * self.target_velocity) - vel_x reward = 1 * rew_vel_x - 0.01 * np.abs(vel_y) \ - 0.01 * np.abs(vel_yaw) \ - 0.01 * np.abs(quadruped_orientation[0]) - 0.01 * np.abs(quadruped_orientation[1]) \ - 0.0001 * np.linalg.norm(self.prev_torques - joint_torques) self.prev_torques = np.asarray(joint_torques) return reward

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148

py

Python

user/models.py

pspyasasvi/webapp

e56c0186271a23c69433ca5e8bc418d8d3069919

[ "MIT" ]

6

2021-02-20T00:56:11.000Z

2022-02-09T00:29:41.000Z

user/models.py

pspyasasvi/webapp

e56c0186271a23c69433ca5e8bc418d8d3069919

[ "MIT" ]

null

user/models.py

pspyasasvi/webapp

e56c0186271a23c69433ca5e8bc418d8d3069919

[ "MIT" ]

1

2021-02-28T15:10:55.000Z

from django.db import models from django.contrib.auth.models import AbstractUser # Create your models here. class UserModel(AbstractUser): pass

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Python

python/testData/refactoring/move/baseClass/before/src/a.py

jnthn/intellij-community

8fa7c8a3ace62400c838e0d5926a7be106aa8557

[ "Apache-2.0" ]

2

2018-12-29T09:53:39.000Z

2018-12-29T09:53:42.000Z

python/testData/refactoring/move/baseClass/before/src/a.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/baseClass/before/src/a.py

jnthn/intellij-community

8fa7c8a3ace62400c838e0d5926a7be106aa8557

[ "Apache-2.0" ]

2

2020-03-15T08:57:37.000Z

2020-04-07T04:48:14.000Z

class B(object): def __init__(self): pass class C(B): def __init__(self): super(C, self).__init__()

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47275d252249cc5c7d45106aa6c7c61cc56cfe3a

266

py

Python

project-5/RL/tasks/__init__.py

linuxbender/Deep_Learning

3df4b26777a71ddbe461ac46dafa36b34be84348

[ "MIT" ]

null

project-5/RL/tasks/__init__.py

linuxbender/Deep_Learning

3df4b26777a71ddbe461ac46dafa36b34be84348

[ "MIT" ]

null

project-5/RL/tasks/__init__.py

linuxbender/Deep_Learning

3df4b26777a71ddbe461ac46dafa36b34be84348

[ "MIT" ]

null

from quad_controller_rl.tasks.base_task import BaseTask from quad_controller_rl.tasks.takeoff import Takeoff from quad_controller_rl.tasks.hover import Hover from quad_controller_rl.tasks.landing import Landing from quad_controller_rl.tasks.combined import Combined

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5b2fdadfbc78f404c65007befa2aa87311144c2f

37

py

Python

cm2.py

896385665/testRebase

6a4478a7c47b250d86dd275040139719900d92b7

[ "MIT" ]

null

cm2.py

896385665/testRebase

6a4478a7c47b250d86dd275040139719900d92b7

[ "MIT" ]

null

cm2.py

896385665/testRebase

6a4478a7c47b250d86dd275040139719900d92b7

[ "MIT" ]

null

'''cm2''' a = 11 d = 14 f = 8 u = 88

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null

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6

5b4283230a742d72dfe76cef967f034e3c8c70fa

93

py

Python

dashboard/admin.py

iDevam/FoodPantry

fe0b64813b895e53ce7675d4316e1dbc96cdf7c9

[ "MIT" ]

null

dashboard/admin.py

iDevam/FoodPantry

fe0b64813b895e53ce7675d4316e1dbc96cdf7c9

[ "MIT" ]

null

dashboard/admin.py

iDevam/FoodPantry

fe0b64813b895e53ce7675d4316e1dbc96cdf7c9

[ "MIT" ]

8

2020-04-21T01:45:14.000Z

2020-09-19T13:10:04.000Z

from django.contrib import admin from dashboard.models import * admin.site.register(profile)

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6

5b431124751f4d49054f199ed986df9eb761b8d5

152

py

Python

0_basic_versions_check.py

codeclassifiers/nnfs

8583c1ccf3d155779057cb5041d52a3002282b04

[ "MIT" ]

1

2021-09-18T05:00:05.000Z

0_basic_versions_check.py

codeclassifiers/nnfs

8583c1ccf3d155779057cb5041d52a3002282b04

[ "MIT" ]

null

0_basic_versions_check.py

codeclassifiers/nnfs

8583c1ccf3d155779057cb5041d52a3002282b04

[ "MIT" ]

1

2021-09-18T05:00:06.000Z

import sys import numpy as np import matplotlib print("Python", sys.version) print("Numpy", np.__version__) print("Matplotlib", matplotlib.__version__)

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6

5b86216cb19c875a3598cf940da62ba23a6c6c22

303

py

Python

three.py/mathutils/__init__.py

lukestanley/three.py

a3fa99cb3553aca8c74ceabb8203edeb55450803

[ "MIT" ]

80

2019-04-04T13:41:32.000Z

2022-01-12T18:40:19.000Z

three.py/mathutils/__init__.py

lukestanley/three.py

a3fa99cb3553aca8c74ceabb8203edeb55450803

[ "MIT" ]

9

2019-04-04T14:43:50.000Z

2020-03-29T04:50:53.000Z

three.py/mathutils/__init__.py

lukestanley/three.py

a3fa99cb3553aca8c74ceabb8203edeb55450803

[ "MIT" ]

17

2019-04-04T14:20:42.000Z

2022-03-03T16:26:29.000Z

from mathutils.MatrixFactory import * from mathutils.Matrix import * from mathutils.Curve import * from mathutils.CurveFactory import * from mathutils.Multicurve import * from mathutils.Surface import * from mathutils.Hilbert3D import * from mathutils.RandomUtils import * from mathutils.Tween import *

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5b92d9ae57f660ed01b9fa9f53311d76fdb91799

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py

Python

crabageprediction/venv/Lib/site-packages/mpl_toolkits/axes_grid/clip_path.py

13rianlucero/CrabAgePrediction

92bc7fbe1040f49e820473e33cc3902a5a7177c7

[ "MIT" ]

603

2020-12-23T13:49:32.000Z

2022-03-31T23:38:03.000Z

venv/lib/python3.7/site-packages/mpl_toolkits/axes_grid/clip_path.py

John1001Song/Big-Data-Robo-Adviser

9444dce96954c546333d5aecc92a06c3bfd19aa5

[ "MIT" ]

387

2020-12-15T14:54:04.000Z

2022-03-31T07:00:21.000Z

venv/lib/python3.7/site-packages/mpl_toolkits/axes_grid/clip_path.py

John1001Song/Big-Data-Robo-Adviser

9444dce96954c546333d5aecc92a06c3bfd19aa5

[ "MIT" ]

64

2018-04-25T08:51:57.000Z

2022-01-29T14:13:57.000Z

from mpl_toolkits.axisartist.clip_path import *

24

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5.571429

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true

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null

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1

0

null

0

1

0

1

0

1

0

6

5ba2a0dbaa39ffbbe22dd3c3ae8bce24ee985f69

34

py

Python

vimeodownload/__init__.py

jamiegyoung/vimeodownload.py

bdbb75491337082a473a258bcc09afd25dba2bdd

[ "MIT" ]

2

2021-04-01T13:45:27.000Z

2021-11-02T04:10:20.000Z

vimeodownload/__init__.py

jamiegyoung/vimeo-download-py

bdbb75491337082a473a258bcc09afd25dba2bdd

[ "MIT" ]

null

vimeodownload/__init__.py

jamiegyoung/vimeo-download-py

bdbb75491337082a473a258bcc09afd25dba2bdd

[ "MIT" ]

null

from .downloader import get_video

17

33

0.852941

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5bb98597d11d68baaf61fcb2d882b5dc895ebd99

26,457

py

Python

wsol/inception.py

umairjavaid/anonymous

84e9a2b2b8ceeed0d0097c3c0489090138985dea

[ "MIT" ]

null

wsol/inception.py

umairjavaid/anonymous

84e9a2b2b8ceeed0d0097c3c0489090138985dea

[ "MIT" ]

1

2021-07-01T07:53:38.000Z

wsol/inception.py

umairjavaid/wsol2

7d258b6b4a99df62b35747656937a58f58bc36b7

[ "MIT" ]

null

""" Original code: https://github.com/pytorch/vision/blob/master/torchvision/models/inception.py """ import os import torch import torch.nn as nn import torch.nn.functional as F from torch.utils.model_zoo import load_url from .method import AcolBase from .method import ADL from .method import normalize_tensor from .method import spg from .method import mymodel2 from .method import MyModel2 from .util import initialize_weights from .util import remove_layer __all__ = ['inception_v3'] model_urls = { 'inception_v3_google': 'https://download.pytorch.org/models/inception_v3_google-1a9a5a14.pth', } class BasicConv2d(nn.Module): def __init__(self, in_channels, out_channels, kernel_size, **kwargs): super(BasicConv2d, self).__init__() self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, bias=False, **kwargs) self.bn = nn.BatchNorm2d(out_channels, eps=0.001) def forward(self, x): x = self.conv(x) x = self.bn(x) return F.relu(x, inplace=True) class InceptionA(nn.Module): def __init__(self, in_channels, pool_features): super(InceptionA, self).__init__() self.branch1x1 = BasicConv2d(in_channels, 64, 1) self.branch5x5_1 = BasicConv2d(in_channels, 48, 1) self.branch5x5_2 = BasicConv2d(48, 64, 5, padding=2) self.branch3x3dbl_1 = BasicConv2d(in_channels, 64, 1) self.branch3x3dbl_2 = BasicConv2d(64, 96, 3, padding=1) self.branch3x3dbl_3 = BasicConv2d(96, 96, 3, padding=1) self.branch_pool = BasicConv2d(in_channels, pool_features, 1) def forward(self, x): branch1x1 = self.branch1x1(x) branch5x5 = self.branch5x5_1(x) branch5x5 = self.branch5x5_2(branch5x5) branch3x3dbl = self.branch3x3dbl_1(x) branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl) branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl) branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1) branch_pool = self.branch_pool(branch_pool) outputs = [branch1x1, branch5x5, branch3x3dbl, branch_pool] return torch.cat(outputs, 1) class InceptionB(nn.Module): def __init__(self, in_channels, kernel_size=3, stride=2, padding=0): super(InceptionB, self).__init__() self.branch3x3 = BasicConv2d(in_channels, 384, kernel_size, stride=stride, padding=padding) self.branch3x3dbl_1 = BasicConv2d(in_channels, 64, 1) self.branch3x3dbl_2 = BasicConv2d(64, 96, 3, padding=1) self.branch3x3dbl_3 = BasicConv2d(96, 96, 3, stride=stride, padding=padding) self.stride = stride def forward(self, x): branch3x3 = self.branch3x3(x) branch3x3dbl = self.branch3x3dbl_1(x) branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl) branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl) branch_pool = F.max_pool2d(x, kernel_size=3, stride=self.stride, padding=1) outputs = [branch3x3, branch3x3dbl, branch_pool] return torch.cat(outputs, 1) class InceptionC(nn.Module): def __init__(self, in_channels, channels_7x7): super(InceptionC, self).__init__() self.branch1x1 = BasicConv2d(in_channels, 192, 1) c7 = channels_7x7 self.branch7x7_1 = BasicConv2d(in_channels, c7, 1) self.branch7x7_2 = BasicConv2d(c7, c7, (1, 7), padding=(0, 3)) self.branch7x7_3 = BasicConv2d(c7, 192, (7, 1), padding=(3, 0)) self.branch7x7dbl_1 = BasicConv2d(in_channels, c7, 1) self.branch7x7dbl_2 = BasicConv2d(c7, c7, (7, 1), padding=(3, 0)) self.branch7x7dbl_3 = BasicConv2d(c7, c7, (1, 7), padding=(0, 3)) self.branch7x7dbl_4 = BasicConv2d(c7, c7, (7, 1), padding=(3, 0)) self.branch7x7dbl_5 = BasicConv2d(c7, 192, (1, 7), padding=(0, 3)) self.branch_pool = BasicConv2d(in_channels, 192, 1) def forward(self, x): branch1x1 = self.branch1x1(x) branch7x7 = self.branch7x7_1(x) branch7x7 = self.branch7x7_2(branch7x7) branch7x7 = self.branch7x7_3(branch7x7) branch7x7dbl = self.branch7x7dbl_1(x) branch7x7dbl = self.branch7x7dbl_2(branch7x7dbl) branch7x7dbl = self.branch7x7dbl_3(branch7x7dbl) branch7x7dbl = self.branch7x7dbl_4(branch7x7dbl) branch7x7dbl = self.branch7x7dbl_5(branch7x7dbl) branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1) branch_pool = self.branch_pool(branch_pool) outputs = [branch1x1, branch7x7, branch7x7dbl, branch_pool] return torch.cat(outputs, 1) class InceptionCam(nn.Module): def __init__(self, num_classes=1000, large_feature_map=False, **kwargs): super(InceptionCam, self).__init__() self.large_feature_map = large_feature_map self.Conv2d_1a_3x3 = BasicConv2d(3, 32, 3, stride=2, padding=1) self.Conv2d_2a_3x3 = BasicConv2d(32, 32, 3, stride=1, padding=0) self.Conv2d_2b_3x3 = BasicConv2d(32, 64, 3, stride=1, padding=1) self.Conv2d_3b_1x1 = BasicConv2d(64, 80, 1, stride=1, padding=0) self.Conv2d_4a_3x3 = BasicConv2d(80, 192, 3, stride=1, padding=0) self.Mixed_5b = InceptionA(192, pool_features=32) self.Mixed_5c = InceptionA(256, pool_features=64) self.Mixed_5d = InceptionA(288, pool_features=64) self.Mixed_6a = InceptionB(288, kernel_size=3, stride=1, padding=1) self.Mixed_6b = InceptionC(768, channels_7x7=128) self.Mixed_6c = InceptionC(768, channels_7x7=160) self.Mixed_6d = InceptionC(768, channels_7x7=160) self.Mixed_6e = InceptionC(768, channels_7x7=192) self.SPG_A3_1b = nn.Sequential( nn.Conv2d(768, 1024, 3, padding=1), nn.ReLU(True), ) self.SPG_A3_2b = nn.Sequential( nn.Conv2d(1024, 1024, 3, padding=1), nn.ReLU(True), ) self.SPG_A4 = nn.Conv2d(1024, num_classes, 1, padding=0) self.avgpool = nn.AdaptiveAvgPool2d(1) initialize_weights(self.modules(), init_mode='xavier') def forward(self, x, labels=None, return_cam=False): batch_size = x.shape[0] x = self.Conv2d_1a_3x3(x) x = self.Conv2d_2a_3x3(x) x = self.Conv2d_2b_3x3(x) x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1, ceil_mode=True) x = self.Conv2d_3b_1x1(x) x = self.Conv2d_4a_3x3(x) x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1, ceil_mode=True) x = self.Mixed_5b(x) x = self.Mixed_5c(x) x = self.Mixed_5d(x) if not self.large_feature_map: x = F.max_pool2d(x, kernel_size=3, stride=2, ceil_mode=True) x = self.Mixed_6a(x) x = self.Mixed_6b(x) x = self.Mixed_6c(x) x = self.Mixed_6d(x) feat = self.Mixed_6e(x) x = F.dropout(feat, 0.5, self.training) x = self.SPG_A3_1b(x) x = F.dropout(x, 0.5, self.training) x = self.SPG_A3_2b(x) x = F.dropout(x, 0.5, self.training) feat_map = self.SPG_A4(x) logits = self.avgpool(feat_map) logits = logits.view(logits.shape[0:2]) if return_cam: feature_map = feat_map.clone().detach() cams = feature_map[range(batch_size), labels] return cams return {'logits': logits} def get_loss(self, logits, target): loss_cls = nn.CrossEntropyLoss()(logits, target.long()) return loss_cls class InceptionAcol(AcolBase): def __init__(self, num_classes=1000, large_feature_map=False, **kwargs): super(InceptionAcol, self).__init__() self.large_feature_map = large_feature_map self.drop_threshold = kwargs['acol_drop_threshold'] self.Conv2d_1a_3x3 = BasicConv2d(3, 32, 3, stride=2, padding=1) self.Conv2d_2a_3x3 = BasicConv2d(32, 32, 3) self.Conv2d_2b_3x3 = BasicConv2d(32, 64, 3, padding=1) self.Conv2d_3b_1x1 = BasicConv2d(64, 80, 1) self.Conv2d_4a_3x3 = BasicConv2d(80, 192, 3) self.Mixed_5b = InceptionA(192, pool_features=32) self.Mixed_5c = InceptionA(256, pool_features=64) self.Mixed_5d = InceptionA(288, pool_features=64) self.Mixed_6a = InceptionB(288, kernel_size=3, stride=1, padding=1) self.Mixed_6b = InceptionC(768, channels_7x7=128) self.Mixed_6c = InceptionC(768, channels_7x7=160) self.Mixed_6d = InceptionC(768, channels_7x7=160) self.Mixed_6e = InceptionC(768, channels_7x7=192) self.classifier_A = nn.Sequential( nn.Conv2d(768, 1024, kernel_size=3, stride=1, padding=1), nn.ReLU(True), nn.Conv2d(1024, 1024, kernel_size=3, stride=1, padding=1), nn.ReLU(True), nn.Conv2d(1024, num_classes, kernel_size=1, padding=0) ) self.classifier_B = nn.Sequential( nn.Conv2d(768, 1024, kernel_size=3, stride=1, padding=1), nn.ReLU(True), nn.Conv2d(1024, 1024, kernel_size=3, stride=1, padding=1), nn.ReLU(True), nn.Conv2d(1024, num_classes, kernel_size=1, padding=0) ) self.avgpool = nn.AdaptiveAvgPool2d(1) initialize_weights(self.modules(), init_mode='xavier') def forward(self, x, labels=None, return_cam=False): batch_size = x.shape[0] x = self.Conv2d_1a_3x3(x) x = self.Conv2d_2a_3x3(x) x = self.Conv2d_2b_3x3(x) x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1, ceil_mode=True) x = self.Conv2d_3b_1x1(x) x = self.Conv2d_4a_3x3(x) x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1, ceil_mode=True) x = self.Mixed_5b(x) x = self.Mixed_5c(x) x = self.Mixed_5d(x) if not self.large_feature_map: x = F.max_pool2d(x, kernel_size=3, stride=2, ceil_mode=True) x = self.Mixed_6a(x) x = self.Mixed_6b(x) x = self.Mixed_6c(x) x = self.Mixed_6d(x) feature = self.Mixed_6e(x) logits_dict = self._acol_logits(feature=feature, labels=labels, drop_threshold=self.drop_threshold) if return_cam: normalized_a = normalize_tensor( logits_dict['feat_map_a'].clone().detach()) normalized_b = normalize_tensor( logits_dict['feat_map_b'].clone().detach()) feature_maps = torch.max(normalized_a, normalized_b) cams = feature_maps[range(batch_size), labels] return cams return logits_dict class InceptionSpg(nn.Module): def __init__(self, num_classes=1000, large_feature_map=False, **kwargs): super(InceptionSpg, self).__init__() self.large_feature_map = large_feature_map self.Conv2d_1a_3x3 = BasicConv2d(3, 32, 3, stride=2, padding=1) self.Conv2d_2a_3x3 = BasicConv2d(32, 32, 3, stride=1, padding=0) self.Conv2d_2b_3x3 = BasicConv2d(32, 64, 3, stride=1, padding=1) self.Conv2d_3b_1x1 = BasicConv2d(64, 80, 1, stride=1, padding=0) self.Conv2d_4a_3x3 = BasicConv2d(80, 192, 3, stride=1, padding=0) self.Mixed_5b = InceptionA(192, pool_features=32) self.Mixed_5c = InceptionA(256, pool_features=64) self.Mixed_5d = InceptionA(288, pool_features=64) self.Mixed_6a = InceptionB(288, kernel_size=3, stride=1, padding=1) self.Mixed_6b = InceptionC(768, channels_7x7=128) self.Mixed_6c = InceptionC(768, channels_7x7=160) self.Mixed_6d = InceptionC(768, channels_7x7=160) self.Mixed_6e = InceptionC(768, channels_7x7=192) self.SPG_A3_1b = nn.Sequential( nn.Conv2d(768, 1024, kernel_size=3, padding=1), nn.ReLU(True), ) self.SPG_A3_2b = nn.Sequential( nn.Conv2d(1024, 1024, kernel_size=3, padding=1), nn.ReLU(True), ) self.SPG_A4 = nn.Conv2d(1024, num_classes, kernel_size=1, padding=0) self.avgpool = nn.AdaptiveAvgPool2d(1) self.SPG_B_1a = nn.Sequential( nn.Conv2d(288, 512, kernel_size=3, padding=1), nn.ReLU(inplace=True), ) self.SPG_B_2a = nn.Sequential( nn.Conv2d(768, 512, kernel_size=3, padding=1), nn.ReLU(inplace=True), ) self.SPG_B_shared = nn.Sequential( nn.Conv2d(512, 512, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.Conv2d(512, 1, kernel_size=1, padding=0), ) self.SPG_C = nn.Sequential( nn.Conv2d(1024, 512, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.Conv2d(512, 1, kernel_size=1), ) initialize_weights(self.modules(), init_mode='xavier') def forward(self, x, labels=None, return_cam=False): batch_size = x.shape[0] x = self.Conv2d_1a_3x3(x) x = self.Conv2d_2a_3x3(x) x = self.Conv2d_2b_3x3(x) x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1, ceil_mode=True) x = self.Conv2d_3b_1x1(x) x = self.Conv2d_4a_3x3(x) x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1, ceil_mode=True) x = self.Mixed_5b(x) x = self.Mixed_5c(x) x = self.Mixed_5d(x) if not self.large_feature_map: x = F.max_pool2d(x, kernel_size=3, stride=2, ceil_mode=True) logits_b1 = self.SPG_B_1a(x) logits_b1 = self.SPG_B_shared(logits_b1) x = self.Mixed_6a(x) x = self.Mixed_6b(x) x = self.Mixed_6c(x) x = self.Mixed_6d(x) feat = self.Mixed_6e(x) logits_b2 = self.SPG_B_2a(x) logits_b2 = self.SPG_B_shared(logits_b2) x = F.dropout(feat, 0.5, self.training) x = self.SPG_A3_1b(x) x = F.dropout(x, 0.5, self.training) x = self.SPG_A3_2b(x) x = F.dropout(x, 0.5, self.training) feat_map = self.SPG_A4(x) logits_c = self.SPG_C(x) logits = self.avgpool(feat_map) logits = logits.view(logits.shape[0:2]) labels = logits.argmax(dim=1).long() if labels is None else labels attention, fused_attention = spg.compute_attention( feat_map=feat_map, labels=labels, logits_b1=logits_b1, logits_b2=logits_b2) if return_cam: feature_map = feat_map.clone().detach() cams = feature_map[range(batch_size), labels] return cams return {'attention': attention, 'fused_attention': fused_attention, 'logits': logits, 'logits_b1': logits_b1, 'logits_b2': logits_b2, 'logits_c': logits_c} class InceptionAdl(nn.Module): def __init__(self, num_classes=1000, large_feature_map=False, **kwargs): super(InceptionAdl, self).__init__() self.large_feature_map = large_feature_map self.adl_drop_rate = kwargs['adl_drop_rate'] self.adl_threshold = kwargs['adl_drop_threshold'] self.ADL_5d = ADL(self.adl_drop_rate, self.adl_threshold) self.ADL_6e = ADL(self.adl_drop_rate, self.adl_threshold) self.ADL_A3_2b = ADL(self.adl_drop_rate, self.adl_threshold) self.Conv2d_1a_3x3 = BasicConv2d(3, 32, 3, stride=2, padding=1) self.Conv2d_2a_3x3 = BasicConv2d(32, 32, 3, stride=1, padding=0) self.Conv2d_2b_3x3 = BasicConv2d(32, 64, 3, stride=1, padding=1) self.Conv2d_3b_1x1 = BasicConv2d(64, 80, 1, stride=1, padding=0) self.Conv2d_4a_3x3 = BasicConv2d(80, 192, 3, stride=1, padding=0) self.Mixed_5b = InceptionA(192, pool_features=32) self.Mixed_5c = InceptionA(256, pool_features=64) self.Mixed_5d = InceptionA(288, pool_features=64) self.Mixed_6a = InceptionB(288, kernel_size=3, stride=1, padding=1) self.Mixed_6b = InceptionC(768, channels_7x7=128) self.Mixed_6c = InceptionC(768, channels_7x7=160) self.Mixed_6d = InceptionC(768, channels_7x7=160) self.Mixed_6e = InceptionC(768, channels_7x7=192) self.SPG_A3_1b = nn.Sequential( nn.Conv2d(768, 1024, 3, padding=1), nn.ReLU(True), ) self.SPG_A3_2b = nn.Sequential( nn.Conv2d(1024, 1024, 3, padding=1), nn.ReLU(True), ) self.SPG_A4 = nn.Conv2d(1024, num_classes, 1, padding=0) self.avgpool = nn.AdaptiveAvgPool2d(1) initialize_weights(self.modules(), init_mode='xavier') def forward(self, x, labels=None, return_cam=False): batch_size = x.shape[0] x = self.Conv2d_1a_3x3(x) x = self.Conv2d_2a_3x3(x) x = self.Conv2d_2b_3x3(x) x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1, ceil_mode=True) x = self.Conv2d_3b_1x1(x) x = self.Conv2d_4a_3x3(x) x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1, ceil_mode=True) x = self.Mixed_5b(x) x = self.Mixed_5c(x) x = self.Mixed_5d(x) x = self.ADL_5d(x) if not self.large_feature_map: x = F.max_pool2d(x, kernel_size=3, stride=2, ceil_mode=True) x = self.Mixed_6a(x) x = self.Mixed_6b(x) x = self.Mixed_6c(x) x = self.Mixed_6d(x) x = self.Mixed_6e(x) x = self.ADL_6e(x) x = self.SPG_A3_1b(x) x = self.SPG_A3_2b(x) x = self.ADL_A3_2b(x) x = self.SPG_A4(x) logits = self.avgpool(x) logits = logits.view(x.shape[0:2]) if return_cam: feature_map = x.clone().detach() cams = feature_map[range(batch_size), labels] return cams return {'logits': logits} class InceptionMyModel46(nn.Module): def __init__(self, num_classes=1000, large_feature_map=False, **kwargs): super(InceptionMyModel46, self).__init__() self.large_feature_map = large_feature_map self.Conv2d_1a_3x3 = BasicConv2d(3, 32, 3, stride=2, padding=1) self.Conv2d_2a_3x3 = BasicConv2d(32, 32, 3) self.Conv2d_2b_3x3 = BasicConv2d(32, 64, 3, padding=1) self.Conv2d_3b_1x1 = BasicConv2d(64, 80, 1) self.Conv2d_4a_3x3 = BasicConv2d(80, 192, 3) self.Mixed_5b = InceptionA(192, pool_features=32) self.Mixed_5c = InceptionA(256, pool_features=64) self.Mixed_5d = InceptionA(288, pool_features=64) self.Mixed_6a = InceptionB(288, kernel_size=3, stride=1, padding=1) self.Mixed_6b = InceptionC(768, channels_7x7=128) self.Mixed_6c = InceptionC(768, channels_7x7=160) self.Mixed_6d = InceptionC(768, channels_7x7=160) self.Mixed_6e = InceptionC(768, channels_7x7=192) self.conv6 = nn.Conv2d(768, 1024, kernel_size=3, padding=1) self.conv7 = nn.Conv2d(1024, num_classes, kernel_size=1) self.conv8 = nn.Conv2d(768, 1024, kernel_size=3, padding=1) self.conv9 = nn.Conv2d(1024, num_classes, kernel_size=1) self.conv10 = nn.Conv2d(768, 1024, kernel_size=3, padding=1) self.conv11 = nn.Conv2d(1024, num_classes, kernel_size=1) #self.conv12 = nn.Conv2d(768, 1024, kernel_size=3, padding=1) #self.conv13 = nn.Conv2d(1024, num_classes, kernel_size=1) #self.conv12 = nn.Conv2d(512, 1024, kernel_size=3, padding=1) #self.conv13 = nn.Conv2d(1024, num_classes, kernel_size=1) self.mymod2 = MyModel2() self.relu = nn.ReLU(inplace=False) self.avgpool = nn.AdaptiveAvgPool2d(1) initialize_weights(self.modules(), init_mode='xavier') def features(self, x): x = self.Conv2d_1a_3x3(x) x = self.Conv2d_2a_3x3(x) x = self.Conv2d_2b_3x3(x) x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1, ceil_mode=True) x = self.Conv2d_3b_1x1(x) x = self.Conv2d_4a_3x3(x) x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1, ceil_mode=True) x = self.Mixed_5b(x) x = self.Mixed_5c(x) x = self.Mixed_5d(x) x = self.mymod2(x) if not self.large_feature_map: x = F.max_pool2d(x, kernel_size=3, stride=2, ceil_mode=True) x = self.Mixed_6a(x) x = self.Mixed_6b(x) x = self.Mixed_6c(x) x = self.Mixed_6d(x) x = self.Mixed_6e(x) x = self.mymod2(x) return x def forward(self, x, labels=None, return_cam=False): batch_size = x.shape[0] x1 = self.features(x) x1 = self.conv6(x1) x1 = self.relu(x1) x1 = self.mymod2(x1) x1 = self.conv7(x1) x1 = self.relu(x1) x2 = self.features(x) x2 = self.conv8(x2) x2 = self.relu(x2) x2 = self.conv9(x2) x2 = self.relu(x2) x3 = self.features(x) x3 = self.conv10(x3) x3 = self.relu(x3) x3 = self.conv11(x3) x3 = self.relu(x3) #x4 = self.features(x) #x4 = self.conv12(x4) #x4 = self.relu(x4) #x4 = self.conv13(x4) #x4 = self.relu(x4) x = torch.max(x1 ,x2) x = torch.max(x ,x3) #x = torch.max(x,x4) if return_cam: x = x1.detach().clone() x = x + x2.detach().clone() x = x + x3.detach().clone() #x = x + x4.detach().clone() x = normalize_tensor(x.detach().clone()) x = x[range(batch_size), labels] return x x = self.avgpool(x) x = x.view(x.size(0), -1) return {'logits': x} class InceptionMyModel47(nn.Module): def __init__(self, num_classes=1000, large_feature_map=False, **kwargs): super(InceptionMyModel47, self).__init__() self.large_feature_map = large_feature_map self.Conv2d_1a_3x3 = BasicConv2d(3, 32, 3, stride=2, padding=1) self.Conv2d_2a_3x3 = BasicConv2d(32, 32, 3) self.Conv2d_2b_3x3 = BasicConv2d(32, 64, 3, padding=1) self.Conv2d_3b_1x1 = BasicConv2d(64, 80, 1) self.Conv2d_4a_3x3 = BasicConv2d(80, 192, 3) self.Mixed_5b = InceptionA(192, pool_features=32) self.Mixed_5c = InceptionA(256, pool_features=64) self.Mixed_5d = InceptionA(288, pool_features=64) self.Mixed_6a = InceptionB(288, kernel_size=3, stride=1, padding=1) self.Mixed_6b = InceptionC(768, channels_7x7=128) self.Mixed_6c = InceptionC(768, channels_7x7=160) self.Mixed_6d = InceptionC(768, channels_7x7=160) self.Mixed_6e = InceptionC(768, channels_7x7=192) self.conv6 = nn.Conv2d(768, 1024, kernel_size=3, padding=1) self.conv7 = nn.Conv2d(1024, num_classes, kernel_size=1) self.conv8 = nn.Conv2d(768, 1024, kernel_size=3, padding=1) self.conv9 = nn.Conv2d(1024, num_classes, kernel_size=1) self.conv10 = nn.Conv2d(768, 1024, kernel_size=3, padding=1) self.conv11 = nn.Conv2d(1024, num_classes, kernel_size=1) self.conv12 = nn.Conv2d(768, 1024, kernel_size=3, padding=1) self.conv13 = nn.Conv2d(1024, num_classes, kernel_size=1) #self.conv12 = nn.Conv2d(512, 1024, kernel_size=3, padding=1) #self.conv13 = nn.Conv2d(1024, num_classes, kernel_size=1) self.mymod2 = MyModel2() self.relu = nn.ReLU(inplace=False) self.avgpool = nn.AdaptiveAvgPool2d(1) initialize_weights(self.modules(), init_mode='xavier') def features(self, x): x = self.Conv2d_1a_3x3(x) x = self.Conv2d_2a_3x3(x) x = self.Conv2d_2b_3x3(x) x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1, ceil_mode=True) x = self.Conv2d_3b_1x1(x) x = self.Conv2d_4a_3x3(x) x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1, ceil_mode=True) x = self.Mixed_5b(x) x = self.Mixed_5c(x) x = self.Mixed_5d(x) x = self.mymod2(x) if not self.large_feature_map: x = F.max_pool2d(x, kernel_size=3, stride=2, ceil_mode=True) x = self.Mixed_6a(x) x = self.Mixed_6b(x) x = self.Mixed_6c(x) x = self.Mixed_6d(x) x = self.Mixed_6e(x) x = self.mymod2(x) return x def forward(self, x, labels=None, return_cam=False): batch_size = x.shape[0] x1 = self.features(x) x1 = self.conv6(x1) x1 = self.relu(x1) x1 = self.mymod2(x1) x1 = self.conv7(x1) x1 = self.relu(x1) x2 = self.features(x) x2 = self.conv8(x2) x2 = self.relu(x2) x2 = self.conv9(x2) x2 = self.relu(x2) x3 = self.features(x) x3 = self.conv10(x3) x3 = self.relu(x3) x3 = self.conv11(x3) x3 = self.relu(x3) x4 = self.features(x) x4 = self.conv12(x4) x4 = self.relu(x4) x4 = self.conv13(x4) x4 = self.relu(x4) x = torch.max(x1 ,x2) x = torch.max(x ,x3) x = torch.max(x,x4) if return_cam: x = x1.detach().clone() x = x + x2.detach().clone() x = x + x3.detach().clone() x = x + x4.detach().clone() x = normalize_tensor(x.detach().clone()) x = x[range(batch_size), labels] return x x = self.avgpool(x) x = x.view(x.size(0), -1) return {'logits': x} def load_pretrained_model(model, path=None): if path: state_dict = torch.load( os.path.join(path, 'inception_v3.pth')) else: state_dict = load_url(model_urls['inception_v3_google'], progress=True) remove_layer(state_dict, 'Mixed_7') remove_layer(state_dict, 'AuxLogits') remove_layer(state_dict, 'fc.') model.load_state_dict(state_dict, strict=False) return model def inception_v3(architecture_type, pretrained=False, pretrained_path=None, **kwargs): model = {'cam': InceptionCam, 'acol': InceptionAcol, 'spg': InceptionSpg, 'adl': InceptionAdl, 'mymodel46':InceptionMyModel46, 'mymodel47':InceptionMyModel47}[architecture_type](**kwargs) if pretrained: model = load_pretrained_model(model, pretrained_path) return model

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6

5be83b45042af88abf73358ddb30e3fbf6b7e18e

117

py

Python

diverse/fields/__init__.py

sakkada/django-diverse

dbd13bb13c3663d6149a28d94daaf06c1e47b0f4

[ "MIT" ]

null

diverse/fields/__init__.py

sakkada/django-diverse

dbd13bb13c3663d6149a28d94daaf06c1e47b0f4

[ "MIT" ]

null

diverse/fields/__init__.py

sakkada/django-diverse

dbd13bb13c3663d6149a28d94daaf06c1e47b0f4

[ "MIT" ]

null

from .fields import DiverseFileField, DiverseImageField from .widgets import DiverseFileInput, DiverseImageFileInput

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6

5bfc7c3a6ecc8e522d9c9e0fbf763a2b52637993

109

py

Python

server/nst/nst/views.py

dilawarm/neural-style-transfer

100ea2d0f05e28542dddc3c22512cf7945c1e39d

[ "MIT" ]

5

2020-03-31T17:26:36.000Z

2021-04-07T14:12:50.000Z

server/nst/nst/views.py

shuhuai007/neural-style-transfer

99babd8d4d899124198710fcc3b2ab5513a67dea

[ "MIT" ]

8

2021-03-30T12:56:58.000Z

2022-02-10T01:48:27.000Z

server/nst/nst/views.py

shuhuai007/neural-style-transfer

99babd8d4d899124198710fcc3b2ab5513a67dea

[ "MIT" ]

1

2020-08-02T14:42:31.000Z

from django.http import HttpResponse def homepage(request): return HttpResponse("<h1>Server :)</h1>")

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1

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6

75062909b1a341801bb388e7c523cd579383304c

34

py

Python

Bot/1_Find/Logic/_Top_Movers.py

ReedGraff/High-Low

c8ba0339d7818e344cacf9a73a83d24dc539c2ca

[ "MIT" ]

1

2022-01-06T05:50:53.000Z

Bot/1_Find/Logic/_Top_Movers.py

ReedGraff/High-Low

c8ba0339d7818e344cacf9a73a83d24dc539c2ca

[ "MIT" ]

null

Bot/1_Find/Logic/_Top_Movers.py

ReedGraff/High-Low

c8ba0339d7818e344cacf9a73a83d24dc539c2ca

[ "MIT" ]

null

def Top_Movers(self): return 0

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1

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1

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6

753d70e20dcfb3dbd3c77cc503fac460ecd86e00

7,413

py

Python

tests/views/test_admin_review.py

priyanshu-kumar02/personfinder

d5390b60709cd0ccaaade9a3b6224a60cd523ed9

[ "Apache-2.0" ]

561

2015-02-16T07:59:42.000Z

2022-03-30T17:31:21.000Z

tests/views/test_admin_review.py

Anthonymcqueen21/personfinder

ee7791fbc434eb4ec5cfad449288a1e884db5b1e

[ "Apache-2.0" ]

591

2015-01-30T05:09:30.000Z

2022-02-26T09:31:25.000Z

tests/views/test_admin_review.py

Anthonymcqueen21/personfinder

ee7791fbc434eb4ec5cfad449288a1e884db5b1e

[ "Apache-2.0" ]

258

2015-01-25T18:35:12.000Z

2021-12-25T01:44:14.000Z

# Copyright 2019 Google Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Tests for the admin review page.""" import django import django.http import django.test import model import view_tests_base class AdminReviewViewTests(view_tests_base.ViewTestsBase): """Tests the admin review view.""" def setUp(self): super(AdminReviewViewTests, self).setUp() self.data_generator.repo() self.person = self.data_generator.person() self.login_as_moderator() def test_get_no_notes(self): """Tests GET requests when there are no notes.""" resp = self.client.get('/haiti/admin/review', secure=True) self.assertEqual(len(resp.context['notes']), 0) self.assertEqual(resp.context['next_url'], None) self.assertEqual(resp.context['source_options_nav'][0][0], 'all') self.assertEqual(resp.context['source_options_nav'][0][1], None) self.assertEqual( resp.context['source_options_nav'][1][0], 'haiti.personfinder.google.org') self.assertEqual( resp.context['source_options_nav'][1][1], '/haiti/admin/review?source=haiti.personfinder.google.org&' 'status=all') self.assertEqual( resp.context['status_options_nav'][1][0], 'unspecified') self.assertEqual( resp.context['status_options_nav'][1][1], '/haiti/admin/review?source=all&status=unspecified') def test_get(self): """Tests GET requests when there are notes.""" for i in range(5): self.data_generator.note(person_id=self.person.record_id) resp = self.client.get('/haiti/admin/review', secure=True) self.assertEqual(len(resp.context['notes']), 5) self.assertEqual(resp.context['next_url'], None) self.assertEqual(resp.context['source_options_nav'][0][0], 'all') self.assertEqual(resp.context['source_options_nav'][0][1], None) self.assertEqual( resp.context['source_options_nav'][1][0], 'haiti.personfinder.google.org') self.assertEqual( resp.context['source_options_nav'][1][1], '/haiti/admin/review?source=haiti.personfinder.google.org&' 'status=all') self.assertEqual( resp.context['status_options_nav'][1][0], 'unspecified') self.assertEqual( resp.context['status_options_nav'][1][1], '/haiti/admin/review?source=all&status=unspecified') def test_get_specified_status(self): for i in range(5): self.data_generator.note(person_id=self.person.record_id) for i in range(5): self.data_generator.note( person_id=self.person.record_id, status='is_note_author') resp = self.client.get( '/haiti/admin/review?status=is_note_author', secure=True) self.assertEqual(len(resp.context['notes']), 5) self.assertEqual(resp.context['next_url'], None) self.assertEqual(resp.context['source_options_nav'][0][0], 'all') self.assertEqual(resp.context['source_options_nav'][0][1], None) self.assertEqual( resp.context['source_options_nav'][1][0], 'haiti.personfinder.google.org') self.assertEqual( resp.context['source_options_nav'][1][1], '/haiti/admin/review?source=haiti.personfinder.google.org&' 'status=is_note_author') self.assertEqual( resp.context['status_options_nav'][1][0], 'unspecified') self.assertEqual( resp.context['status_options_nav'][1][1], '/haiti/admin/review?source=all&status=unspecified') def test_get_specified_source(self): other_source_person = self.data_generator.person( record_id='haiti.example.org/Person.1') for i in range(5): self.data_generator.note(person_id=self.person.record_id) for i in range(5): self.data_generator.note( person_id=other_source_person.record_id) resp = self.client.get( '/haiti/admin/review?source=haiti.example.org', secure=True) self.assertEqual(len(resp.context['notes']), 5) self.assertEqual(resp.context['next_url'], None) self.assertEqual(resp.context['source_options_nav'][0][0], 'all') self.assertEqual( resp.context['source_options_nav'][0][1], '/haiti/admin/review?source=all&status=all') self.assertEqual( resp.context['source_options_nav'][1][0], 'haiti.personfinder.google.org') self.assertEqual( resp.context['source_options_nav'][1][1], '/haiti/admin/review?source=haiti.personfinder.google.org&' 'status=all') self.assertEqual( resp.context['status_options_nav'][1][0], 'unspecified') self.assertEqual( resp.context['status_options_nav'][1][1], '/haiti/admin/review?source=haiti.example.org&status=unspecified') def test_accept_note(self): """Tests POST requests to accept a note.""" note = self.data_generator.note(person_id=self.person.record_id) get_doc = self.to_doc(self.client.get( '/haiti/admin/review/', secure=True)) xsrf_token = get_doc.cssselect_one('input[name="xsrf_token"]').get( 'value') post_resp = self.client.post('/haiti/admin/review/', { 'note.%s' % note.record_id: 'accept', 'xsrf_token': xsrf_token, }, secure=True) # Check that the user's redirected to the repo's main admin page. self.assertIsInstance(post_resp, django.http.HttpResponseRedirect) self.assertEqual(post_resp.url, '/haiti/admin/review/') # Reload the Note from Datastore. note = model.Note.get('haiti', note.record_id) self.assertIs(note.reviewed, True) self.assertIs(note.hidden, False) def test_flag_note(self): """Tests POST requests to flag a note.""" note = self.data_generator.note(person_id=self.person.record_id) get_doc = self.to_doc(self.client.get( '/haiti/admin/review/', secure=True)) xsrf_token = get_doc.cssselect_one('input[name="xsrf_token"]').get( 'value') post_resp = self.client.post('/haiti/admin/review/', { 'note.%s' % note.record_id: 'flag', 'xsrf_token': xsrf_token, }, secure=True) # Check that the user's redirected to the repo's main admin page. self.assertIsInstance(post_resp, django.http.HttpResponseRedirect) self.assertEqual(post_resp.url, '/haiti/admin/review/') # Reload the Note from Datastore. note = model.Note.get('haiti', note.record_id) self.assertIs(note.reviewed, True) self.assertIs(note.hidden, True)

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0

null

0

1

0

1

0

null

0

6

753fdc4692e52d5eeea5721f588354c78fd3adcc

3,187

py

Python

test/pithy/parse/precedence.py

gwk/glossy

6976ca4fd1efc09d9cd670b1fe37817c05b4b529

[ "CC0-1.0" ]

7

2019-05-04T00:51:38.000Z

2021-12-10T15:36:31.000Z

test/pithy/parse/precedence.py

gwk/glossy

6976ca4fd1efc09d9cd670b1fe37817c05b4b529

[ "CC0-1.0" ]

null

test/pithy/parse/precedence.py

gwk/glossy

6976ca4fd1efc09d9cd670b1fe37817c05b4b529

[ "CC0-1.0" ]

1

2016-07-30T22:38:08.000Z

#!/usr/bin/env python3 from pithy.parse import Adjacency, Atom, Infix, Left, Parser, Precedence, Right, Suffix, token_extract_text from pithy.py.lex import lexer from tolkien import Source from utest import * left = Parser(lexer, dict( name=Atom('name', transform=token_extract_text), expr=Precedence( ('name',), Left(Infix('plus')), Left(Infix('star')), )), drop=('spaces',)) utest(('+', ('+', 'a', ('*', 'b', 'c')), 'd'), left.parse, 'expr', Source('', 'a + b * c + d')) utest(('+', ('*', 'a', 'b'), ('*', ('*', 'c', 'd'), 'e')), left.parse, 'expr', Source('', 'a * b + c * d * e')) right = Parser(lexer, dict( name=Atom('name', transform=token_extract_text), expr=Precedence( ('name',), Right(Infix('plus')), Right(Infix('star')), )), drop=('spaces',)) utest(('+', 'a', ('+', ('*', 'b', 'c'), 'd')), right.parse, 'expr', Source('', 'a + b * c + d')) utest(('+', ('*', 'a', 'b'), ('*', 'c', ('*', 'd', 'e'))), right.parse, 'expr', Source('', 'a * b + c * d * e')) left_adj_dot = Parser(lexer, dict( name=Atom('name', transform=token_extract_text), expr=Precedence( ('name',), Left(Adjacency()), Left(Infix('dot')), )), drop=('spaces',)) utest(((('.', 'a', 'b'), 'c'), 'd'), left_adj_dot.parse, 'expr', Source('', 'a.b c d')) utest((('a', ('.', 'b', 'c')), 'd'), left_adj_dot.parse, 'expr', Source('', 'a b.c d')) left_dot_adj = Parser(lexer, dict( name=Atom('name', transform=token_extract_text), expr=Precedence( ('name',), Left(Infix('dot')), Left(Adjacency()), )), drop=('spaces',)) utest(('.', 'a', (('b', 'c'), 'd')), left_dot_adj.parse, 'expr', Source('', 'a . b c d')) utest(('.', (('a', 'b'), 'c'), 'd'), left_dot_adj.parse, 'expr', Source('', 'a b c . d')) right_adj_dot = Parser(lexer, dict( name=Atom('name', transform=token_extract_text), expr=Precedence( ('name',), Right(Adjacency()), Right(Infix('dot')), )), drop=('spaces',)) utest((('.', 'a', 'b'), ('c', 'd')), right_adj_dot.parse, 'expr', Source('', 'a.b c d')) utest(('a', (('.', 'b', 'c'), 'd')), right_adj_dot.parse, 'expr', Source('', 'a b.c d')) right_dot_adj = Parser(lexer, dict( name=Atom('name', transform=token_extract_text), expr=Precedence( ('name',), Right(Infix('dot')), Right(Adjacency()), )), drop=('spaces',)) utest(('.', 'a', ('b', ('c', 'd'))), right_dot_adj.parse, 'expr', Source('', 'a . b c d')) utest(('.', ('a', ('b', 'c')), 'd'), right_dot_adj.parse, 'expr', Source('', 'a b c . d')) right_adj_qmark = Parser(lexer, dict( name=Atom('name', transform=token_extract_text), expr=Precedence( ('name',), Right(Adjacency()), Right(Suffix('qmark')), )), drop=('spaces',)) utest(('a', (('?', 'b'), 'c')), right_adj_qmark.parse, 'expr', Source('', 'a b? c')) right_qmark_adj = Parser(lexer, dict( name=Atom('name', transform=token_extract_text), expr=Precedence( ('name',), Right(Suffix('qmark')), Right(Adjacency()), )), drop=('spaces',)) utest(('?', ('a', 'b')), right_qmark_adj.parse, 'expr', Source('', 'a b ?'))

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754278cd4b446f300682dc7017ee73862d7674a5

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py

Python

venv/lib/python3.8/site-packages/pip/_internal/network/utils.py

Retraces/UkraineBot

3d5d7f8aaa58fa0cb8b98733b8808e5dfbdb8b71

[ "MIT" ]

2

2022-03-13T01:58:52.000Z

2022-03-31T06:07:54.000Z

venv/lib/python3.8/site-packages/pip/_internal/network/utils.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

19

2021-11-20T04:09:18.000Z

2022-03-23T15:05:55.000Z

venv/lib/python3.8/site-packages/pip/_internal/network/utils.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

null

/home/runner/.cache/pip/pool/ba/a4/fa/4243bd347530a93c3780705631015d698a9869b078db741466e8900f77

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null

1

0

1

0

6

f3c5b1235edc3b63bd485c2fd27ca71f9d597023

113

py

Python

akusherstvo_parser/utils.py

ilkoretskiy/Parsers

46528f88b3784c9cc26b05b8b8ae9ac7d974de45

[ "MIT" ]

null

akusherstvo_parser/utils.py

ilkoretskiy/Parsers

46528f88b3784c9cc26b05b8b8ae9ac7d974de45

[ "MIT" ]

null

akusherstvo_parser/utils.py

ilkoretskiy/Parsers

46528f88b3784c9cc26b05b8b8ae9ac7d974de45

[ "MIT" ]

null

def download_test_data(): pass def main(): download_test_data() if __name__ == "__main__": main()

11.3

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3418023681025e47e5dd30a619dc86023bd68c73

91

py

Python

src/xsd_training/models/__init__.py

minyiky/xSACdb

8c407e9a9da196750a66ad53613ad67c8c56e1c3

[ "MIT" ]

2

2017-08-14T14:40:17.000Z

2019-02-07T13:10:23.000Z

src/xsd_training/models/__init__.py

minyiky/xSACdb

8c407e9a9da196750a66ad53613ad67c8c56e1c3

[ "MIT" ]

19

2016-02-07T18:02:53.000Z

2019-11-03T17:48:13.000Z

src/xsd_training/models/__init__.py

minyiky/xSACdb

8c407e9a9da196750a66ad53613ad67c8c56e1c3

[ "MIT" ]

4

2015-10-19T17:24:35.000Z

2021-05-12T07:30:32.000Z

from .group import * from .lesson import * from .qualification import * from .sdc import *

18.2

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6

3419e3e8768cf1943603c6af8a514b6c5ab33e76

1,755

py

Python

tests/test_events.py

modera-manyrepo-packages/mcloud

8ce3b1cc7bac01682a41c7b9d8d82f13a853d223

[ "Apache-2.0" ]

null

tests/test_events.py

modera-manyrepo-packages/mcloud

8ce3b1cc7bac01682a41c7b9d8d82f13a853d223

[ "Apache-2.0" ]

null

tests/test_events.py

modera-manyrepo-packages/mcloud

8ce3b1cc7bac01682a41c7b9d8d82f13a853d223

[ "Apache-2.0" ]

null

import inject from mcloud.events import EventBus import pytest from twisted.internet import reactor import txredisapi as redis @pytest.inlineCallbacks def test_events(): inject.clear() rc = yield redis.Connection(dbid=2) yield rc.flushdb() eb = EventBus(rc) yield eb.connect() test_events.test = None def boo(pattern, message): assert message == 'hoho' assert pattern == 'foo' test_events.test = message eb.on('foo', boo) yield eb.fire_event('foo', 'hoho') def check_results(): assert test_events.test == 'hoho' reactor.callLater(50, check_results) @pytest.inlineCallbacks def test_events_pattern(): inject.clear() rc = yield redis.Connection(dbid=2) yield rc.flushdb() eb = EventBus(rc) yield eb.connect() test_events_pattern.test = None def boo(pattern, message): assert message == 'hoho' assert pattern == 'foo.baz' test_events_pattern.test = message eb.on('foo.*', boo) yield eb.fire_event('foo.baz', 'hoho') def check_results(): assert test_events_pattern.test == 'hoho' reactor.callLater(50, check_results) @pytest.inlineCallbacks def test_events_pattern_wrong(): inject.clear() rc = yield redis.Connection(dbid=2) yield rc.flushdb() eb = EventBus(rc) yield eb.connect() test_events_pattern_wrong.test = None def boo(pattern, message): assert message == 'hoho' assert pattern == 'foo.baz' test_events_pattern_wrong.test = message eb.on('bar.*', boo) yield eb.fire_event('foo.baz', 'hoho') def check_results(): assert test_events_pattern_wrong.test is None reactor.callLater(50, check_results)

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0.25

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null

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1

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null

0

6

341ac9111e4252a2edb8c631f738fc2e728b34d3

646

py

Python

vrchatapi/__init__.py

vrchatapi/vrchatapi-python

996b7ddf2914059f1fd4e5def5e3555e678634c0

[ "MIT" ]

8

2021-08-25T02:35:30.000Z

2022-03-28T18:11:58.000Z

vrchatapi/__init__.py

vrchatapi/vrchatapi-python

996b7ddf2914059f1fd4e5def5e3555e678634c0

[ "MIT" ]

1

2022-03-18T20:29:30.000Z

2022-03-18T20:35:05.000Z

vrchatapi/__init__.py

vrchatapi/vrchatapi-python

996b7ddf2914059f1fd4e5def5e3555e678634c0

[ "MIT" ]

1

2022-01-11T10:49:12.000Z

# flake8: noqa """ VRChat API Documentation The version of the OpenAPI document: 1.6.7 Contact: me@ruby.js.org Generated by: https://openapi-generator.tech """ __version__ = "1.0.0" # import ApiClient from vrchatapi.api_client import ApiClient # import Configuration from vrchatapi.configuration import Configuration # import exceptions from vrchatapi.exceptions import OpenApiException from vrchatapi.exceptions import ApiAttributeError from vrchatapi.exceptions import ApiTypeError from vrchatapi.exceptions import ApiValueError from vrchatapi.exceptions import ApiKeyError from vrchatapi.exceptions import ApiException

23.071429

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null

0

1

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1

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6

34331c49853bdf9fa3d94e3fad65c7d744f2990d

194

py

Python

network/__init__.py

SebOh/arp_spoof

9c4493c9bc7b80f70710d7e4a644b102f0bd8c4d

[ "MIT" ]

null

network/__init__.py

SebOh/arp_spoof

9c4493c9bc7b80f70710d7e4a644b102f0bd8c4d

[ "MIT" ]

null

network/__init__.py

SebOh/arp_spoof

9c4493c9bc7b80f70710d7e4a644b102f0bd8c4d

[ "MIT" ]

null

from sys import platform from .network_commands import NetworkCommands def is_windows(): return platform == "win32" def is_linux(): return platform == "linux" or platform == "linux2"

19.4

54

0.721649

24

194

5.708333

0.625

0.072993

0

0.018868

0.180412

194

10

54

19.4

0.842767

0

0.082051

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0.333333

true

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1

0

null

0

null

0

1

0

1

0

6

346f16408e765715546e1b15a3b253ffb5128287

5,500

py

Python

dnd/nodes.py

tvarney/dndtools

80a36db85d704d7d7b632b365156504f676841a0

[ "MIT" ]

null

dnd/nodes.py

tvarney/dndtools

80a36db85d704d7d7b632b365156504f676841a0

[ "MIT" ]

null

dnd/nodes.py

tvarney/dndtools

80a36db85d704d7d7b632b365156504f676841a0

[ "MIT" ]

null

from typing import TYPE_CHECKING if TYPE_CHECKING: from typing import Union import dnd.roll PrecedenceValue = 100 PrecedencePower = 30 PrecedenceMulDiv = 20 PrecedenceAddSub = 10 def nodestr(node, parent_precedence: "int") -> "str": if node.precedence() < parent_precedence: return "({})".format(str(node)) return str(node) class Value(object): __slots__ = ("value",) def __init__(self, value: "Union[float, int]") -> None: self.value = value def precedence(self) -> int: return PrecedenceValue def __call__(self) -> "Union[float, int]": return self.value def __repr__(self) -> "str": return "Value({})".format(self.value) def __str__(self) -> "str": return str(self.value) class Dice(object): __slots__ = ("dice",) def __init__(self, value: "dnd.roll.Dice") -> None: self.dice = value def precedence(self) -> int: return PrecedenceValue def __call__(self) -> "int": return self.dice.roll().result def __repr__(self) -> "str": return repr(self.dice) def __str__(self) -> "str": return str(self.dice) class Add(object): __slots__ = ("lhs", "rhs") def __init__(self, lhs, rhs) -> None: self.lhs = lhs self.rhs = rhs def precedence(self) -> int: return PrecedenceAddSub def __call__(self) -> "Union[float, int]": return self.lhs() + self.rhs() def __repr__(self) -> "str": return "Add({}, {})".format(repr(self.lhs), repr(self.rhs)) def __str__(self) -> "str": return "{} + {}".format( nodestr(self.lhs, PrecedenceAddSub), nodestr(self.rhs, PrecedenceAddSub) ) class Subtract(object): __slots__ = ("lhs", "rhs") def __init__(self, lhs, rhs) -> None: self.lhs = lhs self.rhs = rhs def __call__(self) -> "Union[float, int]": return self.lhs() - self.rhs() def __repr__(self) -> "str": return "Subtract({}, {})".format(repr(self.lhs), repr(self.rhs)) def __str__(self) -> "str": return "{} - {}".format( nodestr(self.lhs, PrecedenceAddSub), nodestr(self.rhs, PrecedenceAddSub) ) class Negative(object): __slots__ = ("value",) def __init__(self, value) -> None: self.value = value def precedence(self) -> int: return PrecedenceValue def __call__(self) -> "Union[float, int]": return -(self.value()) def __repr__(self) -> "str": return "Negative({})".format(repr(self.value)) def __str__(self) -> "str": if self.value.precedence == PrecedenceValue: if type(self.value) is Dice: return "-({})".format(self.value) return "-{}".format(self.value) return "-({})".format(self.value) class Multiply(object): __slots__ = ("lhs", "rhs") def __init__(self, lhs, rhs) -> None: self.lhs = lhs self.rhs = rhs def precedence(self) -> "int": return PrecedenceMulDiv def __call__(self) -> "Union[float, int]": return self.lhs() * self.rhs() def __repr__(self) -> "str": return "Multiply({}, {})".format(repr(self.lhs), repr(self.rhs)) def __str__(self) -> "str": return "{} * {}".format( nodestr(self.lhs, PrecedenceMulDiv), nodestr(self.rhs, PrecedenceMulDiv) ) class Divide(object): __slots__ = ("lhs", "rhs") def __init__(self, lhs, rhs) -> None: self.lhs = lhs self.rhs = rhs def precedence(self) -> "int": return PrecedenceMulDiv def __call__(self) -> "Union[float, int]": return self.lhs() / self.rhs() def __repr__(self) -> "str": return "Divide({}, {})".format(repr(self.lhs), repr(self.rhs)) def __str__(self) -> "str": return "{} / {}".format( nodestr(self.lhs, PrecedenceMulDiv), nodestr(self.rhs, PrecedenceMulDiv) ) class FloorDiv(object): __slots__ = ("lhs", "rhs") def __init__(self, lhs, rhs) -> None: self.lhs = lhs self.rhs = rhs def precedence(self) -> "int": return PrecedenceMulDiv def __call__(self) -> "int": return self.lhs() // self.rhs() def __repr__(self) -> "str": return "FloorDiv({}, {})".format(repr(self.lhs), repr(self.rhs)) def __str__(self) -> "str": return "{} // {}".format( nodestr(self.lhs, PrecedenceMulDiv), nodestr(self.rhs, PrecedenceMulDiv) ) class Power(object): __slots__ = ("lhs", "rhs") def __init__(self, lhs, rhs) -> None: self.lhs = lhs self.rhs = rhs def precedence(self) -> "int": return PrecedencePower def __call__(self) -> "Union[int, float]": return self.lhs() ** self.rhs() def __repr__(self) -> "str": return "Power({}, {})".format(repr(self.lhs), repr(self.rhs)) def __str__(self) -> "str": return "{}**{}".format( nodestr(self.lhs, PrecedencePower), nodestr(self.rhs, PrecedencePower) ) class Modulo(object): __slots__ = ("lhs", "rhs") def __init__(self, lhs, rhs) -> None: self.lhs = lhs self.rhs = rhs def precedence(self) -> "int": return PrecedenceMulDiv def __call__(self) -> "Union[int, float]": return self.lhs() % self.rhs() def __repr__(self) -> "str": return "Modulo({}, {})".format(repr(self.lhs), repr(self.rhs))

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5,500

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5,500

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null

0

1

0

1

0

6

3480da2b26d77eea7faebd9c8a08816d793a29b6

330

py

Python

appannie/__init__.py

Julian-O/appannie

b5e053c43a9fbabda2d84a8992c2efb2e76c8aef

[ "MIT" ]

21

2017-07-08T06:07:52.000Z

2022-02-14T07:58:11.000Z

appannie/__init__.py

Julian-O/appannie

b5e053c43a9fbabda2d84a8992c2efb2e76c8aef

[ "MIT" ]

2

2018-03-17T16:32:43.000Z

2018-03-20T14:02:26.000Z

appannie/__init__.py

Julian-O/appannie

b5e053c43a9fbabda2d84a8992c2efb2e76c8aef

[ "MIT" ]

22

2017-10-13T04:00:34.000Z

2022-02-05T11:00:40.000Z

from __future__ import absolute_import from .version import __version__ from .exception import (AppAnnieException, AppAnnieBadRequestException, AppAnnieNotFoundException, AppAnnieUnauthorizedException, AppAnnieRateLimitException) from .api import AppAnnie

33

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21

330

10.380952

0.619048

0

0.284848

330

9

72

36.666667

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true

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1

0

null

0

1

0

1

0

1

0

6

caabd8ee309ff2f118ff034e7da475a708545c96

91

py

Python

server/settings.py

computmaxer/marantz-rest

6467c930dd909da784ddd5c72a47f75c5724c23c

[ "Apache-2.0" ]

2

2020-06-05T06:18:01.000Z

2020-06-05T14:17:15.000Z

server/settings.py

computmaxer/marantz-rest

6467c930dd909da784ddd5c72a47f75c5724c23c

[ "Apache-2.0" ]

null

server/settings.py

computmaxer/marantz-rest

6467c930dd909da784ddd5c72a47f75c5724c23c

[ "Apache-2.0" ]

null

BASE_API = '/api%s' MARANTZ_URL = 'http://172.16.2.4%s' XBOX_URL = 'http://172.16.2.11%s'

18.2

35

0.615385

20

91

2.65

0.6

0.264151

0.377358

0.45283

0.490566

0

0.185185

0.10989

91

4

36

22.75

0.469136

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0

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false

0

1

0

null

1

0

1

0

1

0

null

0

6

caddd8abf36896224e4c96180d09516d9f401804

185

py

Python

pysce/__init__.py

dchary/pysce

183f43ef24a80d4a3c10afe8ee553ae58087dd9a

[ "MIT" ]

null

pysce/__init__.py

dchary/pysce

183f43ef24a80d4a3c10afe8ee553ae58087dd9a

[ "MIT" ]

null

pysce/__init__.py

dchary/pysce

183f43ef24a80d4a3c10afe8ee553ae58087dd9a

[ "MIT" ]

null

# Load metadata for package from ._metadata import __version__, __author__, __email__ from ._metadata import __date__, __institution__, __laboratory__ from ._pysce import score_entropy

37

64

0.843243

21

185

6.095238

0.714286

0.1875

0.28125

0

0.113514

185

5

65

37

0.780488

0.135135

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

1b06d39349113581abaf8221be5fdf4eba7edf98

156

py

Python

python/ML/Core/__init__.py

valiro21/ML

33475c4800a38ffba6c15eac3db49763de3400e5

[ "MIT" ]

1

2017-08-18T12:22:15.000Z

python/ML/Core/__init__.py

valiro21/ML

33475c4800a38ffba6c15eac3db49763de3400e5

[ "MIT" ]

2

2017-08-17T22:12:03.000Z

2017-08-19T17:22:56.000Z

python/ML/Core/__init__.py

valiro21/ML

33475c4800a38ffba6c15eac3db49763de3400e5

[ "MIT" ]

null

from ML.Core.Functions import Functions, FunctionsDerivative from ML.Core.FeedforwardNeuralNetwork.FeedforwardNeuralNetwork import FeedforwardNeuralNetwork

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6

1b2ca36d0a05baf7fe12d0ef5c1ec44957d3fa7d

25

py

Python

app/rooms/examples/eg002_create_room_with_template/__init__.py

olegliubimov/code-examples-python

7af8c58138a9dd0f3b0be12eff1768ae23e449d3

[ "MIT" ]

21

2020-05-13T21:08:44.000Z

2022-02-18T01:32:16.000Z

app/rooms/examples/eg002_create_room_with_template/__init__.py

olegliubimov/code-examples-python

7af8c58138a9dd0f3b0be12eff1768ae23e449d3

[ "MIT" ]

8

2020-11-23T09:28:04.000Z

2022-02-02T12:04:08.000Z

app/rooms/examples/eg002_create_room_with_template/__init__.py

olegliubimov/code-examples-python

7af8c58138a9dd0f3b0be12eff1768ae23e449d3

[ "MIT" ]

26

2020-05-12T22:20:01.000Z

2022-03-09T10:57:27.000Z

from .views import eg002

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6

1b311d41f50c0516e938defa02b363d0a114f23a

18,904

py

Python

crichtonweb/prodmgmt/migrations/0001_initial.py

bpluly/crichton

a2fa09c181ba1e44ee1aae7a57769e1778de7f3a

[ "Apache-2.0" ]

null

crichtonweb/prodmgmt/migrations/0001_initial.py

bpluly/crichton

a2fa09c181ba1e44ee1aae7a57769e1778de7f3a

[ "Apache-2.0" ]

null

crichtonweb/prodmgmt/migrations/0001_initial.py

bpluly/crichton

a2fa09c181ba1e44ee1aae7a57769e1778de7f3a

[ "Apache-2.0" ]

null

# Crichton, Admirable Source Configuration Management # Copyright 2012 British Broadcasting Corporation # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # # # encoding: utf-8 import datetime from south.db import db from south.v2 import SchemaMigration from django.db import models class Migration(SchemaMigration): def forwards(self, orm): # Adding model 'ApplicationAuditLogEntry' db.create_table('prodmgmt_applicationauditlogentry', ( ('id', self.gf('django.db.models.fields.IntegerField')(db_index=True, blank=True)), ('name', self.gf('django.db.models.fields.CharField')(max_length=128, db_index=True)), ('display_name', self.gf('django.db.models.fields.CharField')(max_length=200, blank=True)), ('product', self.gf('django.db.models.fields.related.ForeignKey')(related_name='_auditlog_applications', to=orm['prodmgmt.Product'])), ('deleted', self.gf('django.db.models.fields.BooleanField')(default=False)), ('action_id', self.gf('django.db.models.fields.AutoField')(primary_key=True)), ('action_date', self.gf('django.db.models.fields.DateTimeField')(default=datetime.datetime.now)), ('action_user', self.gf('audit_log.models.fields.LastUserField')(related_name='_application_audit_log_entry')), ('action_type', self.gf('django.db.models.fields.CharField')(max_length=1)), )) db.send_create_signal('prodmgmt', ['ApplicationAuditLogEntry']) # Adding model 'Application' db.create_table('prodmgmt_application', ( ('id', self.gf('django.db.models.fields.AutoField')(primary_key=True)), ('name', self.gf('django.db.models.fields.CharField')(unique=True, max_length=128)), ('display_name', self.gf('django.db.models.fields.CharField')(max_length=200, blank=True)), ('product', self.gf('django.db.models.fields.related.ForeignKey')(related_name='applications', to=orm['prodmgmt.Product'])), ('deleted', self.gf('django.db.models.fields.BooleanField')(default=False)), )) db.send_create_signal('prodmgmt', ['Application']) # Adding model 'PersonAuditLogEntry' db.create_table('prodmgmt_personauditlogentry', ( ('id', self.gf('django.db.models.fields.IntegerField')(db_index=True, blank=True)), ('username', self.gf('django.db.models.fields.CharField')(max_length=30, db_index=True)), ('first_name', self.gf('django.db.models.fields.CharField')(max_length=30, blank=True)), ('last_name', self.gf('django.db.models.fields.CharField')(max_length=30, blank=True)), ('email', self.gf('django.db.models.fields.EmailField')(max_length=75, blank=True)), ('distinguished_name', self.gf('django.db.models.fields.CharField')(max_length=1024, blank=True)), ('deleted', self.gf('django.db.models.fields.BooleanField')(default=False)), ('action_id', self.gf('django.db.models.fields.AutoField')(primary_key=True)), ('action_date', self.gf('django.db.models.fields.DateTimeField')(default=datetime.datetime.now)), ('action_user', self.gf('audit_log.models.fields.LastUserField')(related_name='_person_audit_log_entry')), ('action_type', self.gf('django.db.models.fields.CharField')(max_length=1)), )) db.send_create_signal('prodmgmt', ['PersonAuditLogEntry']) # Adding model 'Person' db.create_table('prodmgmt_person', ( ('id', self.gf('django.db.models.fields.AutoField')(primary_key=True)), ('username', self.gf('django.db.models.fields.CharField')(unique=True, max_length=30)), ('first_name', self.gf('django.db.models.fields.CharField')(max_length=30, blank=True)), ('last_name', self.gf('django.db.models.fields.CharField')(max_length=30, blank=True)), ('email', self.gf('django.db.models.fields.EmailField')(max_length=75, blank=True)), ('distinguished_name', self.gf('django.db.models.fields.CharField')(max_length=1024, blank=True)), ('deleted', self.gf('django.db.models.fields.BooleanField')(default=False)), )) db.send_create_signal('prodmgmt', ['Person']) # Adding model 'ProductAuditLogEntry' db.create_table('prodmgmt_productauditlogentry', ( ('id', self.gf('django.db.models.fields.IntegerField')(db_index=True, blank=True)), ('name', self.gf('django.db.models.fields.SlugField')(max_length=128, db_index=True)), ('display_name', self.gf('django.db.models.fields.CharField')(max_length=200, blank=True)), ('owner', self.gf('django.db.models.fields.related.ForeignKey')(related_name='_auditlog_owned_products', to=orm['prodmgmt.Person'])), ('pipeline_issue', self.gf('django.db.models.fields.related.ForeignKey')(blank=True, related_name='_auditlog_+', null=True, to=orm['issue.Issue'])), ('deleted', self.gf('django.db.models.fields.BooleanField')(default=False)), ('action_id', self.gf('django.db.models.fields.AutoField')(primary_key=True)), ('action_date', self.gf('django.db.models.fields.DateTimeField')(default=datetime.datetime.now)), ('action_user', self.gf('audit_log.models.fields.LastUserField')(related_name='_product_audit_log_entry')), ('action_type', self.gf('django.db.models.fields.CharField')(max_length=1)), )) db.send_create_signal('prodmgmt', ['ProductAuditLogEntry']) # Adding model 'Product' db.create_table('prodmgmt_product', ( ('id', self.gf('django.db.models.fields.AutoField')(primary_key=True)), ('name', self.gf('django.db.models.fields.SlugField')(unique=True, max_length=128, db_index=True)), ('display_name', self.gf('django.db.models.fields.CharField')(max_length=200, blank=True)), ('owner', self.gf('django.db.models.fields.related.ForeignKey')(related_name='owned_products', to=orm['prodmgmt.Person'])), ('pipeline_issue', self.gf('django.db.models.fields.related.ForeignKey')(blank=True, related_name='+', null=True, to=orm['issue.Issue'])), ('deleted', self.gf('django.db.models.fields.BooleanField')(default=False)), )) db.send_create_signal('prodmgmt', ['Product']) def backwards(self, orm): # Deleting model 'ApplicationAuditLogEntry' db.delete_table('prodmgmt_applicationauditlogentry') # Deleting model 'Application' db.delete_table('prodmgmt_application') # Deleting model 'PersonAuditLogEntry' db.delete_table('prodmgmt_personauditlogentry') # Deleting model 'Person' db.delete_table('prodmgmt_person') # Deleting model 'ProductAuditLogEntry' db.delete_table('prodmgmt_productauditlogentry') # Deleting model 'Product' db.delete_table('prodmgmt_product') models = { 'auth.group': { 'Meta': {'object_name': 'Group'}, 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '80'}), 'permissions': ('django.db.models.fields.related.ManyToManyField', [], {'to': "orm['auth.Permission']", 'symmetrical': 'False', 'blank': 'True'}) }, 'auth.permission': { 'Meta': {'ordering': "('content_type__app_label', 'content_type__model', 'codename')", 'unique_together': "(('content_type', 'codename'),)", 'object_name': 'Permission'}, 'codename': ('django.db.models.fields.CharField', [], {'max_length': '100'}), 'content_type': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['contenttypes.ContentType']"}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '50'}) }, 'auth.user': { 'Meta': {'object_name': 'User'}, 'date_joined': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}), 'email': ('django.db.models.fields.EmailField', [], {'max_length': '75', 'blank': 'True'}), 'first_name': ('django.db.models.fields.CharField', [], {'max_length': '30', 'blank': 'True'}), 'groups': ('django.db.models.fields.related.ManyToManyField', [], {'to': "orm['auth.Group']", 'symmetrical': 'False', 'blank': 'True'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'is_active': ('django.db.models.fields.BooleanField', [], {'default': 'True'}), 'is_staff': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'is_superuser': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'last_login': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}), 'last_name': ('django.db.models.fields.CharField', [], {'max_length': '30', 'blank': 'True'}), 'password': ('django.db.models.fields.CharField', [], {'max_length': '128'}), 'user_permissions': ('django.db.models.fields.related.ManyToManyField', [], {'to': "orm['auth.Permission']", 'symmetrical': 'False', 'blank': 'True'}), 'username': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '30'}) }, 'contenttypes.contenttype': { 'Meta': {'ordering': "('name',)", 'unique_together': "(('app_label', 'model'),)", 'object_name': 'ContentType', 'db_table': "'django_content_type'"}, 'app_label': ('django.db.models.fields.CharField', [], {'max_length': '100'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'model': ('django.db.models.fields.CharField', [], {'max_length': '100'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '100'}) }, 'issue.issue': { 'Meta': {'ordering': "('name',)", 'unique_together': "(('name', 'project'),)", 'object_name': 'Issue'}, 'deleted': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.SlugField', [], {'max_length': '128', 'db_index': 'True'}), 'project': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'issues'", 'to': "orm['issue.IssueTrackerProject']"}) }, 'issue.issuetracker': { 'Meta': {'ordering': "('name',)", 'object_name': 'IssueTracker'}, 'deleted': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'display_name': ('django.db.models.fields.CharField', [], {'max_length': '200', 'blank': 'True'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'issue_url_pattern': ('django.db.models.fields.URLField', [], {'max_length': '255', 'blank': 'True'}), 'name': ('django.db.models.fields.SlugField', [], {'unique': 'True', 'max_length': '128', 'db_index': 'True'}), 'tracker_type': ('django.db.models.fields.CharField', [], {'default': "'jira'", 'max_length': '12'}), 'url': ('django.db.models.fields.URLField', [], {'max_length': '255', 'blank': 'True'}) }, 'issue.issuetrackerproject': { 'Meta': {'ordering': "('name',)", 'unique_together': "(('name', 'issue_tracker'),)", 'object_name': 'IssueTrackerProject'}, 'deleted': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'display_name': ('django.db.models.fields.CharField', [], {'max_length': '200', 'blank': 'True'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'issue_tracker': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'projects'", 'to': "orm['issue.IssueTracker']"}), 'name': ('django.db.models.fields.SlugField', [], {'max_length': '128', 'db_index': 'True'}) }, 'prodmgmt.application': { 'Meta': {'ordering': "('name',)", 'object_name': 'Application'}, 'deleted': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'display_name': ('django.db.models.fields.CharField', [], {'max_length': '200', 'blank': 'True'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '128'}), 'product': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'applications'", 'to': "orm['prodmgmt.Product']"}) }, 'prodmgmt.applicationauditlogentry': { 'Meta': {'ordering': "('-action_date',)", 'object_name': 'ApplicationAuditLogEntry'}, 'action_date': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}), 'action_id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'action_type': ('django.db.models.fields.CharField', [], {'max_length': '1'}), 'action_user': ('audit_log.models.fields.LastUserField', [], {'related_name': "'_application_audit_log_entry'"}), 'deleted': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'display_name': ('django.db.models.fields.CharField', [], {'max_length': '200', 'blank': 'True'}), 'id': ('django.db.models.fields.IntegerField', [], {'db_index': 'True', 'blank': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '128', 'db_index': 'True'}), 'product': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'_auditlog_applications'", 'to': "orm['prodmgmt.Product']"}) }, 'prodmgmt.person': { 'Meta': {'ordering': "('username',)", 'object_name': 'Person'}, 'deleted': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'distinguished_name': ('django.db.models.fields.CharField', [], {'max_length': '1024', 'blank': 'True'}), 'email': ('django.db.models.fields.EmailField', [], {'max_length': '75', 'blank': 'True'}), 'first_name': ('django.db.models.fields.CharField', [], {'max_length': '30', 'blank': 'True'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'last_name': ('django.db.models.fields.CharField', [], {'max_length': '30', 'blank': 'True'}), 'username': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '30'}) }, 'prodmgmt.personauditlogentry': { 'Meta': {'ordering': "('-action_date',)", 'object_name': 'PersonAuditLogEntry'}, 'action_date': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}), 'action_id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'action_type': ('django.db.models.fields.CharField', [], {'max_length': '1'}), 'action_user': ('audit_log.models.fields.LastUserField', [], {'related_name': "'_person_audit_log_entry'"}), 'deleted': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'distinguished_name': ('django.db.models.fields.CharField', [], {'max_length': '1024', 'blank': 'True'}), 'email': ('django.db.models.fields.EmailField', [], {'max_length': '75', 'blank': 'True'}), 'first_name': ('django.db.models.fields.CharField', [], {'max_length': '30', 'blank': 'True'}), 'id': ('django.db.models.fields.IntegerField', [], {'db_index': 'True', 'blank': 'True'}), 'last_name': ('django.db.models.fields.CharField', [], {'max_length': '30', 'blank': 'True'}), 'username': ('django.db.models.fields.CharField', [], {'max_length': '30', 'db_index': 'True'}) }, 'prodmgmt.product': { 'Meta': {'ordering': "('name',)", 'object_name': 'Product'}, 'deleted': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'display_name': ('django.db.models.fields.CharField', [], {'max_length': '200', 'blank': 'True'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.SlugField', [], {'unique': 'True', 'max_length': '128', 'db_index': 'True'}), 'owner': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'owned_products'", 'to': "orm['prodmgmt.Person']"}), 'pipeline_issue': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "'+'", 'null': 'True', 'to': "orm['issue.Issue']"}) }, 'prodmgmt.productauditlogentry': { 'Meta': {'ordering': "('-action_date',)", 'object_name': 'ProductAuditLogEntry'}, 'action_date': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}), 'action_id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'action_type': ('django.db.models.fields.CharField', [], {'max_length': '1'}), 'action_user': ('audit_log.models.fields.LastUserField', [], {'related_name': "'_product_audit_log_entry'"}), 'deleted': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'display_name': ('django.db.models.fields.CharField', [], {'max_length': '200', 'blank': 'True'}), 'id': ('django.db.models.fields.IntegerField', [], {'db_index': 'True', 'blank': 'True'}), 'name': ('django.db.models.fields.SlugField', [], {'max_length': '128', 'db_index': 'True'}), 'owner': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'_auditlog_owned_products'", 'to': "orm['prodmgmt.Person']"}), 'pipeline_issue': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "'_auditlog_+'", 'null': 'True', 'to': "orm['issue.Issue']"}) } } complete_apps = ['prodmgmt']

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1b7ec836af633bfaa2a21b98da42344ec352d840

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Python

Bioinformatics I/Week II/ApproximatePatternMatching.py

egeulgen/Bioinformatics_Specialization

38581b471a54c41d780d9eeb26a7033eb57f3a01

[ "MIT" ]

3

2021-04-03T23:46:42.000Z

2021-08-08T01:19:32.000Z

Bioinformatics I/Week II/ApproximatePatternMatching.py

egeulgen/Bioinformatics_Specialization

38581b471a54c41d780d9eeb26a7033eb57f3a01

[ "MIT" ]

null

Bioinformatics I/Week II/ApproximatePatternMatching.py

egeulgen/Bioinformatics_Specialization

38581b471a54c41d780d9eeb26a7033eb57f3a01

[ "MIT" ]

null

def ApproximatePatternMatching(Text, Pattern, d): k = len(Pattern) L = len(Text) start_idx = [] for i in range(L - k + 1): if HammingDistance(Text[i:i+k], Pattern) <= d: start_idx.append(i) return start_idx def HammingDistance(p, q): mm = [p[i] != q[i] for i in range(len(p))] return sum(mm) Text = 'TGCGAGCGGTGGGTAGGCTCTACTTACAGTCGGGAGCAGTCAAGTTTCGATCACTGCTGGCGGCTGCAGGGGCGCCCAGGCAAGCGTCTTGGTCCGGGCCCGCTCCAATGGCATAACGGGAATGAAACCACCTTCTTAGGATGGAGCGCCTAGAACCAAAACAAGAAAGGCGGATCTCAGTCCTAGGACCCCCCGGAAAATGGACACCCTCCAGTACCCATTCATACGGATTAGTTGGAAAGTTAAAGCTCCGTGTGACACCGCTAGCCGATACCAATAATTACTGTAAGCGTCACAAATCCATCCGTCAAATGAGGTATGTTTGAAGGGGCGCGTTGTTGTGATGGAGGCCAACGGAAGAGCGCGTTTACTAGTGATTCGCAAAGCGCTGCGCATCAGAGGCGCGCGTTGAATTATCTATACCGGCAGTGGTGACAGGAGAAATCCGGCAACCGAGCCCCTACCATCAAAAAGTTAGTATAATCGGTCTTACCATTCCCTCCTGTTGGGAACGTCTCGCAGCAAATTCTATAGACTGTTAAGATAGCGCCGAGCTGACCGTAATCGTCTATGGAACGGAATGGTATCAAGCGCCGTATCGGCAGCCGAGTCGTTTGTGGTAACTAGCCCATGGCTATTTATTTAAGGTGATTCCTATCAGTATGACCTATGTCTATAAACCAAGGGACGTCCCGTAAGGGCGCAAAACAGACCACGGTGGCCTATGAGACATGGGGAATACACAATGTATAATAATCAGACTCAATGTGGAATGGCTCATTAGCAAGGTTCTTCCCGGATCGTAATTAGGATGCAACGTCTCTATCTCGCTGGCGAATACAAATCCGTCAGATACACCTCCAGCGAACTTCTAAACGGCATTCAGTGGACCGCACAGATGGAGGTTGGGATGCTAACGTATGCCATATTCCTATTAGTTCGCAGGGAGTACATAGATTTCCAGACTGCGCGTGCTCTATCCAGTTTAGTAATCCTTATGATGGACCTTAGGTACTCTGTAGGAGAAACGAGGCCATTCCAGGTATTCTATCTTAGGGAGCTGCTCTGACAGCGGTGTGTCTTCATGCCCCAGGGTTAGAGCTATAACTCGGGGAACAAGGAAGGTCGTCGTGAGCCCCTAGTCTTTGGATGCGTTTAAGAGCCCTGAGGAATACTGTTAAGGCATCGTCTTAACGCTCACATCCGCGTCTATTAAGGTGGTTACCACGTTGTCCGAGAATCCATTCGGCGTCTTTATCTGGATACATCCCCGTTTACCCTTTAGTAGCGCGTGGCAATTGCCCTATAGCCAGGTATTTCCGAGCTTCCGCGGCGATTAAGTACGTCTAACAGTATAAAGTAACTACTATGCAGCTATGCCGGCTGTCCCCTCCTGGAGCCGGTCAGGTGACTGGGGACCGACCCGAAAGGTCTCATAGGAAAGCTACGAGGCTGTACCCTGCGCGATTAAATTGGCCCTAGCGAAGGCCGCGCTGGTAGAAAGACACGTGTGTCCTGGGTCATAGTGGGGGAACGCTTTTTCCCAAGTTCTAGCCTCGCGGCGGGCCCCACCGAACTCTAGATATACGAAAATTACATAGATGTTGAGCGAAAGCGATTGCGACATGCGTTGTTAGGAGGGGTGGGTATAATCTCGACCTTAGGGTGGCAAGTCAAGATCGTACACGGTGAGCAGATTAGCTGTCCAGGTTGTCATCTCAACTATCGGGTCTTTCTGTCTCCGCACGTTCTATACCATGTGTACATAGGTAGGATATTGCGGGAAAAGACATGCTACGAAGTACGGGGGAAACCCGTACGCTGAAGCCACACACACTAGTTTTGAGATTGCCTAGAAGTAGAAAACAGTAAAAGGCCCCAATTTAAGTGGGTTATTGTAGCCTCTCGGTAAAGCTGTTCAAGATAGACAAAGGTTATGGGTAAACTCACGGCACGGGGCGTCGCCGTACCGGTTGCCTGCCAGTATGTCTGTAGCTAGCGGGCAAGAATAAGTAAGCCCAATACTCTATTTTATCCACCCGGATATCCGGCTTCTGCCAAGCGCTTAGTGGGAGGGTCTTACCCCGCAGGGCCCTCCTAGCTTCAAACTGTTGGGATACCGACTTGACTGTACCCCTGGTCTTGCGAGAGATAGAGCATACCGTACCGTGCGTTTTTGCCGAGGCCTCGACTATAGCAGCGCGTTAGCTTAGCCCCAGAGCGTATCGTCAGTGCAGTTGAACGGTGTTGTGACGTGGACTTCGAGGGAATTCATAATCTCTCCGTGGCTACGTTGTCGACCACGGGGACTGGGTCCCGTCTACATCCACCCTTCTTGGACTACCCGATGGGTTTCTTCTATAAAATGATACCGCGTCCTAGACAGTATAAAAGTCTCGAGCGTGGATGTACCTCTGGATGTGTCGTGAGTGCTGGCCCCGTACAGTACCAATCATTGAACTACTACGGCCAATGTTCCTCCATTGAGAGTGTATAACACATGGGAAACGTGGATGTCGGACTCTACTGCTCGGATCGGACATGCTCGTCCGGAACACAAACCGGTTCCAGGAGTACCGTCGCACAACTTGCTCTGGTTGAACCATACTAGGCTCCGCAACTTTCGGGACTTAGTGTACTTTCCGCTCTACCGCTTCGCTGACGACGATTGTTAAAATACAGAGTATTCGAAGTAATAGTTTAGTGATTACATGGGCTTCCCTAGACACCAAGTGGCACAGATGTGACACTGGGATACACAGACTCAGACCAACACGGCTGAGCACAAGCAAGGGCAATCCGGAGATAGCGGATCGCGAGCTCTCCTCCAGGCGCTACACCAGCTGCGCCACACCTACCGCTCCTTGTCGACCTGACCTCGCTTAATACCGGCTGTCTGAAGATGCTAAAGCACGTCACTGAGCTTGTGTCGACACAATACTGTGGCATAGCCGCTATACGTCCCCTTAGAGCATGCTAGCATCCTGGTCAACGCGCACGAGGATCTAAGCAAGTCGCCGCATAAGAGGCGCATCCAGCTTACAAATGTATCGTGTGACCTGGTTCCACCTCGGCTATACCTTTTCTATCTCAGATCGTCATGACCTCTCCGTTCCACCTATTGCTAGAGATTCACTCGTCGCGGGCGCGTCCGACTTCAGCGGGCTGGACCCTGTACAGACGATGCCTACGAGTTAGGGCGTTCAGATCTACCGACAAAAGTACCAACTCCCATGTACAGACCTTGAGACGGGCGGGAGCGTTCAGTTCCAGACGTTAATGATACGTCGATCCTCCCAGGCCAGGGCGCATGTACGAGATGTCCGCACGTGTTGTGAAAACGGCAACGGCATCGAACGATCTCCAGTCAAGCTTCGGGGAAATGCACTCGATAGATTACGCTACAGAGAAACGTGCCAAACTTGGCCCCTCTAACGTGAACCGATGGTTGTGCTCCAGCCAAGAACCTGCACGGATCTATGCAAACCACCCCGCTAGCTAACCACTTGTTAGGTCAAGGCGTGGTATCATAAGCTTGGTGGACACACTTTTATATCTAGAAGTTAAGGTCTTCTGGGCGGGGTAGGCTGGAGTTAAGGCTGGGCTGTACACCCTGTGCAATGGACGTTAGTCGGCACCTGGCTCCGCCATCGATCGCGTGACATAGCTAATTGGGAGGGCAGCGCTATCATAAAAATTAGCCGCACAAGACATGACTCTATCTTAAATTGTTGTTCATCTGGGGGACGGTTATTATCGGCTGGTAACGGAGTCACCATCATAGCTTGTCCCAAGCTTTCTGATGTGCATCCAGGACGAGCTAACCCGTAAGTCGCATCCTACTAGGTCGTCTGCATAACAACGCTCATGGTGTAATTGTAGCCGACCGGTACTTTTCTAACCGAGATTTACGAGATACTGCCGTTGACTAAGAACCCGTACAAAACTAAACGTTGTTTCTACCGGCAACGGTCCTAATGCTAATGACAAGGCCAACCCAATCTGTGCGACCTATCGGCGAGCCTTCACGTGCCTCTAAGGACCTAAGTTCGCCTCTACGTATAACCCCAAATGCTGCTAAAAACATCAGGTGCGAGGAGGAGGAGCCGATCTTGATAGAAGGATGCTTGCGGGCGTTGCCCTTGTGGATGTGGGTGATTGCAGAGTCCATACTCAGCGATCGATAGTTTTCAATGCGTCGATGCAACCACCCGAAAGAATGCACTGCCTCGAATGTCCATCTTGATCTTATACCTCCGCATCGACGGAAGTCGCAATGTAAAGAAAATCTACAGTCAGATTTTCAACCTGACGCCGCGCGTGGTCAGCTGGAAGGGGGGGGCATGTCCTCGGAATGCGTAGACCGGGTTGGCTAGCTTCTACTCCGTTGCAGGAGGGAGCTTCGTCAGCTAATGCCCGCCTTGCGGAAAACCATAAGACAGGTATGCGCCTTTGGATTAACGCTAATCCAAGTTCTTTGTATCCCAATTTTTTGCCGAGCGACTAGCGTAGATGTTGCTAATAGTTATGAACAACACACCACGCGCAGATCTTCTGGGCAAAGGCGTGTGACATACGTCTGCAATGATGGAAAGAGGGCTTTATCGCCGTCTGTCAAGCAAACACACGATGCGGGAGGAGAACTGGACACGGGGGTGAAACTATAGACCTTATAAAGTGTTTGTTTTGGCCTTACTTCTGTACACGTTGGACACGCCGGCACACGGTTGCCCTATCACGGGATAGGTGCACTTAGGTCTGGTGTCTGTGGCTCAGAATGTTCTTATATGTTGACAGTGGATCCTTCGTGCGACATGACGGTTTCGCTTCAGTGTCCTCACAGAGTACGTGCTACGAATCATACCTAGACTCCAGGGGCAGATGTCCGAGCATAATTCCTAAAAGTACACTGTCTTTGCGTTGGGGCTCCTTGGAGCAGGAGAACGATACGAGAAGCGGGGGAGAGTACGGCCTCGTCGCATGTCACGTGTTCGATGTCTGGTGAACCGGCCCGGAAACCTACCGAGTGCATAGTATTCTGCCCAATTAAATGAGTGGCGGATGATCATCTGAGACCATTACAATCCGTTTGCGTGTGCCTGCTTACAACAACTTAAAGTAGGTGGCGTAATGATCCACCTTTGCGACTACGCCACGAGTCGGGAGTTGGCTGTCCGCAATCGCGTCGCTCGATTATCTGTGTCTTGGAGAATCTAATTTATAGCGGCGGGCCTGTGTTGGTTTTAGTATCTGGGTTAGAACAAACAAATGGAATCGTAACAAGACCGTCAATCTAAGATGAGCGCCTTGGTTTGCCGAGGATTTACGTTTGTCTCTTGAACATTAGACTGTTCCTAAGGGCCGGAATTTTCTTTGCTTAATCCACTTACGCAGTCAGCACTTTACCTATTATCAGGCTCTCACTGACACGGTGTAGAAATCAACGAAACGACGTAGTGGTAACATAGTCCAGGACTCCTTCCGGCAATTCATACGCTATATCGCGCTCCTCGCTACAGGTTCGTGTGGGGGTCGCGTGTCGTGGGTCGCTTTAGCGAGTGGCTACGGTGTCGTGCGCAGGTGCTACCGATAGTTTTCACCATCATAATCGCGCTTAAGGCATCGTTCCTTGGTAGAGCCTCCTACAGGATAATCGCAGGAGTTCCAATTTACTATGATGGCTAATTGTTTTATATTCTCTAACCGAGTGCAAACCTAAGGCCTCGGACCTTCGAATGCAAATGCATTGCGATTTTGAACGGTACGATGTTTTCGGTCAGGAGATCCTGACCCGGTACCGGCTTGATGAGCCTCAACGTCCGCACTGGGGATGGTCTAGGTGCCTTATTGGGATGGACGTAAGAAAGCTGTATCGGCACCTATCTTCGATGCCTCTGTAGTGCCGAAGGTTAATCGGTTAATATAGACCCATGCCCAATAAAGAGAACAAACTTATGATTTGACTCCCGATAAAGGAAGAGCCAGATGCGGTAAAGAGTCAGTGTCCTAGACTTTGGTAAGGCGTGATTCAGTCATGATAGCTATATAAAGATTCACCAGCCAGAGACCCACGGTAGTAACACCGGTCAAAAGGATCCGCTGGGGGACTGAATCTTTGGCTTAAGGATCCCGCTACTAAACGTTGTGTTTGAACCCCGTGTTTACTGAAATGCGGCCCCGAGGATGTGACATAAGACATAACCATATATGCTTGCCCTATCTAAATTCGTTTGTGCCTGGCGCTATAAAGTCGGCATTAGAATCACACGGCAATAAGTATTGAATAACGTCGGCTTTTCCTCTCTAGAGCGACGGGGGTACTGGAGTCGCTGGCTTATTCTCTCCCCGATAAATCAGCTGGCCCACGGTTCTCAACTAGGGACCGTCGAGGCCGGGTAATATCTTAATGCGATGCAACGCCCAGAAGATGAGCTCGTGTGCCCTAATGATTGTGAGCACCTCTCACCCACCAATATAATCTTCGCCTACCCATCCGTCAGCTGCTATCATGTGGGGCGACACATCAACTATGCTCCTGATCTACGTTTGAACTAGAACTGATTATAGGGGCAGACGCTATGAGATGCACAACCTATGGAGACCCGACTCAAGGCAGATCTGGCAAACGGTAGCTGTCGGCCCGAGCGTCGTAGTGCTAGGCGCGCCGTAAATGATGGGTGCACATTAAGTCCGCAAAAATTCTAACAATGATCAAAGTACAATTATGAGTGAGCCTTAAGTGGCCTAAGAGCGTCTTGCTTTTCTAAGTCTCCCGTGACGTAGCCGGAATCCCGTGACATCACGTCGGAATAGTCAGCAGATAGTAACCCTATGCCAAACCTGGAGAAACGACTAGTGCATCAATAAGCATCGGTCGTATTCCGAAGCAAGGGCGGCTTCTAACGAATCTACCTAAGCAACCCCAACAACCGCTAGGTGGAAGGTAGACCTCCACGGCAATGTGTGGGGGGGCAGGCGTGCTTGAATTAAGACTGGCCAACTACTACATCCGAGTTAGTACCCTCATCTCGACGACGCAGCTACACCCTCCGTGTGTCCACCTTTGATTATCGACTGATAAGGCTTATCACACCGACAACGCCTCATCTGTCCTGATGTATGCCTAATCTCGGCGCTACGGTAGCATAGACCCGGAACCGGACCTGATAGTGTATTCTTTGTTGTGCTCGCAACTCAATGGCAGGTACTTTGATTCACCCGAAGGATAGTCTTACTCGCCGTCGGAGCTCTTGACAAGCCGGGTCTGCTTTTGCGGAGTAGGTCGAAGCGCGCATATGGTATCACATCTAGTGAAGCATCGCAGTACCCTCGCCCTTCGATCATTTAATATAAGAGTGGGAACGAGGCAAAGAAGAGTGCCATCTCCACGTTCGAGTCCAAAGGCGCCAGGGTACACTGAACAGGGGACTCCCCGGCACAAGATTGCAACAGTGAGTAGCTTTACCCGGATATGTCGTACAAAAACCGCCTGCCCGAAGTCGGGGTACCCGAAGATCGCCCAGAAACAGGATCAGTTGCCCAGGCAGTATTCTAGGGAGCTCCGGGTATCGTCAATGAATAGAGTTGTGTGAGGAACCGGAAATTTGCTAAGGCATCCTCCAATTTGGTATATGTATTGTGAGTACACCAGCAACTGCAGGCAAGACAGACCGTACACAAGGACATCGTGCACCTCCGGAATCGCCAGTAGATGGGCGCACGGTGGAGTAGAGCTTTCACTTGGCCTTGTTGCAGTCACTTGAATCGCTTTTATTCTTGGGTTAGTCCAGCGTCAAGGACGGTTTTAAAGACTAAGGTATGTGATGATGATAGGTAATCTCTATGATTGGAAGCGCCGTCTACCACTTGAAACAACGGTGGACTTGGTCTCAAGCACGTACCTATTGGAATTAATGTGATACAAGACATTAAAACAGTGCGGGCCTTTCAATGGATGGCGCAATTGGAACCGTTTACCGAGCTAACTAAATTTTGAGACGCCTGATTTCCCACAATAGGTCTCGCTGTAGTCGAGGATAACCACTACCGGCAATACGCAGTTTTGCTATGGTCAAATCTGAAATGGTTGCCGACGTTATCAGGCTCTGGTCCCATTCACTTAGTTGGATTGTGACTCTCCCCCTGCCCAGCTTGCGTATACATGAGTTACCGCTTACCATTTCTGGACGGACGTTCACTTTGTAACGTGGCCGTGAAGTGCCCTATCGTCAAAGTAGTTGCAATGAGCGCGTCAATTAGCCGGTTCGTTATTATAAGGACGCGGCGGCATCATAATACTTATCCGTGGCACCGACGCCGCGCCTTGACTACATCCTTCTGGGATTATGGGGTGTTACTAGTAAGTTCTTAACCGCCACGCTCTGACGACGGCGCTACATGCAAGGTCCGTGCACTTGCCATACGACACCACAGTCCAGTAGTCACGTGACTGTATTCCAGCAGCTTAGTCGCAGGAGGTTTTGTTATGGTACGCAGACCACACGAATGTCATATTCAGGGGTTTCCACCGGCTTTAACTGTCCCCGATCCCACGCTAGAAATCGCTGCCGAACATCGATTCGGTAGTACCTATGGTAATGGCCGGATAGACGAGAGCATCCCAACACGGCACTGGCTTACACGCAAGTGGTACATCGGGAAGTCCACGGGGAGGAAGCTTGAAAGTTCTTGCCCAAGGGGGTCCTTGAAGAAGGCGTACGTAGTCCTACGTCGTCTCTTGGGGTGTAGGAGGAAAGGGCTATTAGCGATTGAAATTCAGTCTCGGGCAAAGCGTCGTTTCTTGCAGCGTGTGTCATCCGGAGGGGGCGTACCTTCGCTTACGCTTGGTCCGCGTGTATACGCCTCCGAAATGATCCTTCTAGTGTATGTTGCGATCGGGGGGTGCACCAATTCGGTGTAGGCATCTGAATAGGGTGAAAGGTAACAGAGCATAAAGCCGTAATCCGCCCTGGCCGGAGGCATTCACCGGACGGGGGCGGAGTTCCCACATGCTACAGCTAACTAACCGGTAAACCCTATACCATGAGCCGGAGGACGCTGTACCACGCACTAAGGATCTGGCCGGTCCCGCTGCTTGCTAACGGCACCCCTCCACCATAGCCTCGTACACCATGTATTTTCAATCCAGTCGCGTCGAGACAACCTCCCAAAACGCCGCCCGTGGGAAGGCTCTATCTTTGGTGAGCTTGTATGATGTATCAGGGAACGCAGAAACAAAGGTGAGAAACTTAAGGGGAGAATCCCCAATCAGTCTGGTTCGATGCCCAAGGATGGCGAAGGGCTGGTCTATACGAGCAATGTTATGCATACTACGTTTCAGACTTGATTATCGTCCTAGTAACAAGCCACGTGCATCCATAAAACAAGCGCCGTGGAGGGCTTCAATCCTTCTCGTACAAACTGAGGGTGCGACAGGATGTGACTTGGGTGCTATCGGGCTATCTTGCTATTTGATCTTAGAAACAAGACTCACCGTGAGAAGTGATTGTACGCAAGGCCAGAGATCCATCATTTACATGTCCACGCGAACTTCAGCGCGTACACAGTGTGGCTGCTCCTTCAGCACGTTATACGAGTAGGAGCGGTGTCCTGCTTACTCTGTGTTCCAAGACGGGCAGCTTAGTACGAAAGAGATCAATGCGAATAAAGCCCTTAGAATAGAGCCCCCGTACAAGCTCTGCCGCCCAAGCGTGTACAATTGGGACTTTATGTTCTCCTGCGAAAGGTGCGTCACGTAAGCGAGTTACATTTTCGTAAAACTCTCTCAGTGGCGGTGTTGACCCTTTATTGGATATAAGGTGCATTGCGACGGTGAACGTTACAAACGCCATTGTCTACGTAAAGGGCATGATTGTGGGCTACTAAGGGCAAATTTCTGATCACCCCTCTTACCCAGTAAACCAATAGCAAGGTAGAACAGCACACATAGAAAGCTTACTACTACGAGCCGGAAACATAGGATATCCAATGTTCCTAATCTCTGAGCGCCAGAGGCGGTCGCCCACAGCGGAATACGCGGAAGTAAAAGATAGACCCGACCGTCGGAAACGGCAAAACGAAGAGGTGAGGGAGTACTATTCCTAGCTTTTAAGTGACCATGACGCCCCTGGTGGTAACAACCCGAATAGTAGTATCACCCATCGGAAAGCCAGTTACCTTGCAAAATTAAAGCGGACTTCCTGAGCAACATGAAGGTATAATGACCGGGGTTATACTATCCACAAGGGAGGGAAGTTACTCATTGTTGATTTTGATATCAAGAGGTTGAGAAATTCGAGTCAGCCATTTGTGGACCTTTAAACCACCCCCGAGACTGGTATAAATGTGGAAGGCTGCCACTCAGCTTCTCATCAACGCTCGCTTGCGCGTTAGTACGTGGCCTCCTGAAGCCGACCCTCGTAAATGACGTGTGCTAGCCGACTTTGCATATAAGTCATACATTGGGGAAGTTGGTCTCTCAGCTCGTTTCCAAACCGGCGACCTGATCATGCCCTTACAATGAAATGATCTGTAAAGATGACTGTGAGCCAATCGCCTTCCTGGCTAAAGCTATTAACCCTAAAGTGATCTCGCGCTAAGCAGGAACGCATGGTACTCGCTTATAACGTAAACCGAATAGGCATTATTGCGCCTGTACGTTCCACCTTCGCAGCGTTAAGTGGGTGTCTTTAACAACTGCTTAATCCTTCAGGAGTTCAACCAGCGGGGTCTGGAGGGAACTGCTCACACTTCGCACTCGACCCTAGGAACTAGTCATTAACAGACTATCCCAAAGGAAGGCCACTCCATAGAACTTTCTAGTTGATGATCTGACTAAAACAAGTCCACCCTCGATTGCAACACGTGTAAAGCGGACTAGCCATCTTCAAAGGACTTGGGTCGCTTTGCCACAATTACTCATGAGGATATATGCGATCTCATTTTAGTTTTTAAGCGTGGCGGCGCCAGGAGCATCTCTGGATCATATACAAACGCTAAGTACCAGCACATCTCTCCCCTTGTCCAAGGGTGTCATTGTCTCCTCTGTGCATAGTGGGTGAATATACCACTATAATCCCGCTTTGAGGCCAAGGAGTGTTCGTCTCGACATCCCCTTAACATTTTATGGACCCACAGTAACCTGGAGTAGCTCTCACCTTGCATTATGAATGCAACTTATGTTACCTGAGTGCCTCCGCCTCGGTGCCCTCTAGAGCTGGTTAAGTATTTTTTAGGGTCAAAACCTGCCTCCGTTCGCTACCCAACGGCTGCTGAAAGCCTTTTGGGCCTACCGGATGCATTAAGTATCAGCAAGTACAGCTGGTAACCGCACCAGCATTACGTACGTTCGTGATAAAATCTGAATTTCTACTCTACGCCAGCGCGGGAAACAAATAGTCTCGTCGTGATATTTCGAACTCCATACGGTAATCATATTCCGGTTCGGCAAGTGCTGCATGGACCTACCTGTGTGTAAGCACTACGGGGCCCCTACCACGTTCTAAACAAGCTCGAAGGCTCTTAGTTCGATTTCTTTCGCAGCAGTGCATGGGTCAGGACGCCTCGATAGTGGTTTTTAGATTTTTTAAGCCCAGTAGCACAAGCACATCGCCGTCGACGATAGCCCCAGACAATGACAGCATAACACAGGGCAGCGTATAAGCGAAAAGAGTTGCTTGTTGAACACGGTGAACCGATTTTGGACCGGTTACCGATATGTTCCAAGCAGAGATCGTCATTTTATCCACATGCTGCACAAGTCGCCCAGGGTACTCATGTTGACTGACAGGTCGCAACACGATGGCCTATCGGGTTTGTAACAATATCCCCTGAAGGCATTCCAAGCCCGAAGGTTGGAGTTAGCTTATTATAGCAATGTGGGAACGGCCAATTCTGCCGACATCAATAGGCGTCTCGGACCTAGCGACGCTGGCGTTGTAAAACTCATCCACAAGTGCTTCGATCGCGATTCTAAGCAGGTAGGACGTACGCTGGACCCCTGTGTCTGCTACTCTTGATCAAACTTGGTGAGTGTGGGTAAAAAGGCGTTTTCGGGAGCCCTCAGCTTGACCTTGAGGAGTTTACCATATACATAAACTCCGGGGGAATCCTAACATAGCCACTGACCAGGCCTTACTTGATTGCAACGGGTTAATGAATAGATTGTTTCTGGAGTAGCTAAGGGACCCCTCGTAGAGTACTCTGCGTCTCTGTAACCGCATACGTGGAAGGGCTCAATGAAACGTCACAACCAGATGCCCCAGGAGCGCTTTTACGTATAGAAAAATATAGGGTGGAGAATAACCGGGTAATTACCATTGTAGTTCGTATTTACCATGGAATGCTAATCTCTCAAACATGCCGTGGTGCCGCCGGGCCGCATTTTCGCCATGCACTCATAGCTAATCAGGGACGCCTAAAGTGCTCGAGTATACCTAGACCAGCCTCAAAGAGGATCTAGTGATGGCACTCGTACCGGGATCTAGTCTATTTTCCCCTCACAGAGCCATTGCAGTCCGTGTGGGGCTCGGGTATCTAGTAGAAGACCTCGTCTGGTATTCGCGGTCAAATCTCTTTCACATCGCTCGCATAAGGAACCTCATACACCCCAACAAATCACGCGAGGTATTTCTTCGCCAATTCTAAGGGAGGCGGAAGATTATTTCACGGAATTTCATTTAACCATGGAGATGATAACAGCGCGGTATACGCGATCGTCATAACTCTGCCATAAAGCCATTGTGCACTTTCAGAGATTTGCTGCGAGGCAGCATATCGGAGAAGGAGAATTGAACTTGTTCTAGGACTATAGGCTCTCCCATATCTATAAGCACTGGGAGCTCCAGAAGGCCACCGAACCAACAACTTTAGCTGTCGCTGCGGTAACTCTTGAGGTTAGGCGCGCGAACACGACAGGGCGCTCTGGCTCGTCACGGTTTTGGGGGTACCGGCCGTTGAATAGAAATGTAGCTTTAGCAACCTCATAGGCTGCGGTAAGGTCTCAGATCTAGTGAGCGTATGACTGGCTTAAGGCTGTGGACAAGAGTGCAAAACACTTTAATACTGTAAGTAATTAGCCCGGCCGTCGACTATAGCTACAGACAGTGTACACGATGATTACAAAATTGTTATTTGGTACGCACTTCTGTGTGTCGCGATAATAGCAAAGACGTCGGATAATACTCATCTTAACAATAGCAAATCAGACAATCGTTAGGCCTGCGTTTGTTGTATCATACTCAGTCGACTCCGCCCTTACAACGTTGGGTCTTTAATTATCTGGGTCGGACTGGCGAAGGGGAAGGAATCCGGAGGGGGGATGCTCGCACAGTGTGAGGTCTGCGGAAATTCGATAGATTTAGCCTAACTAGAAGGCCGTATAACATAAAAACACACTCTCTCTGCGACGAACAAGGGGCTTCAAATGGCTCTGAGGCGCTTGGCGGCATTTGTAGTCCTTTATGATCAACTGTGATACTGCATTTTGCATATTGAAAGCCCGCGGTTTTGAATTGCCGGGACGCTTTTTTACCGTTAAGTATGGGACAGTCGCTGCTTTACATGACCGCAGTATTTATCCCAGTTAATCATTCTCTGGGTTGTTGTGCTTGCCTTCTACCCGATCCTCGTGGCACGCTCCGCGAAGCAACCTCCTACTCCAGCATTGATCAACGTTCCATGGATTCTGATGTGAGTCCAGGTGGGGGCATGTCGTACAATCTGCTAACAACCGAGGGACAGCTGGTATCCTCTACGGTACACCTAGCTATCTAGAACAGATATTTGAAACCTCAATCTGGCAATGGTTTCACCCTCAATAATGTCTTCACAGCAATTTTAAAAGGACTTTTTGGGGAGTGGCGCGGATAGGCCTCCTTCACCCCCCAATAATAGTGAACATGCTGTTCGGGGAAGCTAACCAACGATTTCACTAGTGTCTTGGCCCGTCTCTAAGGATTGTGGGGTTTTATTGGGACCACACGGGTTAACCGTACCTCGTTTACGGGCATTATGAACCCGTGAGGGCATTCCCGGCTTATTTCTTTATATGTAGTCGGGTATCAGGGGTATGCTCGATTGTTCCAGCTGTAACGGTACGCACCCTTGTGCGATCGCTTGACCCCGATCCGTTAGACACGAAGGCACCACTTAAATTCCTGCTGCCGAAGAGCATAAAGGCCAGTCATATACCCTTATTACTGCCCCGCCCCACGACTTTTCGGCTTAGGAACTGCAACTCGATAGCGTGGCGACAAAGTCAACCCACCCTCTGAACTTTGTGCTTGTTGCGGGTTGTGGGCATCGCGACCCTAAGCTAATGCGAGGCTTCAACCTCAACGTGCGGACGTCACCTGATTATCTTCACTGCACTCACTATCCAGAGACCCGAAGAGGAGATCAGCATCTACGTTTGCATCTAGCGCGTTACGCGAGTTCGAAAGGAAATTAGATGGTGTGGTGAGGGGGTTTACTGACTCCACCTCGCCGAAAGTACATCTCTTAACCGTGGTAGTTATACGTCTCTGTGGTGTAGTCGTAGGACAGTTGTACTAATTCCAGAAGGTTGGCCGGCATTCGTCCGCCCGCGCTAAGGGGATCGTCCAACCTGAAGGGTTCCGTACGGGATCCGTCGGTCATGCAGTGGCTTTTAGTGTGAGTCCTCTTCCACGTGAACCCGATAAGAGGATGTCTCGCGCTCCAATGCGGTAGGCAACAAAGAACTGTCTCTGCTTCCCCGGAGCGCAATGATCTGTAGTAACTAGCCCTGGGCAAGCACCCTACGTTCGTATTGGCCTAGTGTAGCACGCACCCCGCTGTCGTAAGATATTAAGGAAGATGCTCCTTTTTATATCGCTTTGAGCCGTAGAAAGCTAGTCGTCTTGCCCCACATTAAAGCCTCAAGCTGGACGATTCCGAGTCCTAATTCCCTACCTTTATACTTCGGTTCAGTGCAATGCATAGTTAACCACTTAGCCCACAATGCGGAAGCTTAAGATTCGTCCCCCCAAGTAGAATCTAGAAGCTGTACCCGGGCGATTCAATGGTGAGCACTTGAGTATGTCAGGGATTTCTTTGTATAGCGCCTACAATGCTCTAAATGAATTTATTGGTAGCATACAGCAACATGCGAAGTACGATATAGTTCTCGTAGTACGTTATGGGGGGGCCGCTAGGACTCACCCAAACGATTGCATCAATCTTCTACCGATATGTGGGGTGGCGACTAGAGCGAGGTACGCCACGCGAGACGCGTAGTCTTGTAAACCTCACGCCGCGGTAGGTACGGTCCGGGATGGGCTGATACTGAAGCGAACTGTGGTCTCGTCTCCACCCAGACTAGAGGCATTACCGGGGCATAGCCAGAGCATTCGTATATAGCGATTGACCACTGGCTAAGCGCGTAATTGTAGACGGCGGTTAGGACGTGCAAGTACGACCTACTGTGTATCGGGGTGTAACGATATCGAACCGCTGAATACTTTTCGTATCTGCCTATTCATGCGTGTCCGCTCGCTATGCAGCATTTCTGGTCTGCTTGGACCTAGACGGAACAGATCCGAGTACGCAGTGACATTTGGCGACGTCCAAGGAGGCCCTAGACAGATAGCATCAGTATCAGTGCGAGCTCTCGTATGGATACACCTCGAATACGGATAGGGGTCCCAACACCTACCGAACATAAAGCGGAGACGAGCACACTAAATCGTTACACGGGGGCGCTTATATCTGTATAGGTCTAACCAAGGACGTCACCATATTGTAACACATAAACGCTGGAGGTCTCAGGCCTCGGAGGACAGGACTATAACCCAATCTTGATCTGTGTGTCTAGCGTGGATCTCGCAAGAGACCACCTGTTGCCATCCGTTTAGTGAAACTACGAAGAACGACCTTTCTGTGATTCCTCTCACGCAGTCTAGTAGGAAAAGCTAAGGGGTGAGGGGGACCTATCTCGTACTGCGCGCGGAACAACAGGTTCACATTTATAGCGTCTGCTAGGGCGCCCCACCGTCGTGGGGTGCCGACAGCCGCATCTATTCACTCTAGAGCCTGGGCTTAGAAACAAGTCAGAGGAGCCTCTTTCTAATACAACATAAATTGGGCCAACTTATCTGGCGCGCCCTCGACCGCAGCTGACAAAATGTAAAACGGGGCTAGAATCCGGAGCCACGCCTTGCGGTTGTAGGCACAGATATTACGTTGTGAACGATACGGGCTGGCGATAGTATACGTCTTACGCATTCCCGCGCTCGCCTGGGCGTTTAGGCTATATTGAATCTCGATCGATAGGGGCGGCAACCGAGGCCAGTAGGCGCGTAACTCACGCGATTTTACTTGATTATGTGCCGTATTAAGTAAACGTGTTCCGTGCGGGTAACGAATCACCACGTACTCCCTTGCGGTTTCGGAGCACGATTAACTTAACGTTGTACGTAAACGACCCTATTGATCTGTTCACTGTACGAGGTCTTATGCACGTACCCTAACTAAGAGAGAATGATGCGGTTAGGGCTTAAATTGGGTGAGAGAAGCCAACGGCAATTGCCCAGCCCCCCACCGATGGTTCTGAGGCAGCAAGGGCATCGACCGTACACCTATTCCTCTCTTAACGGGGTTACGCTCATTTCTGCAGCGCACACCGAAACTGAGCAGAGGTACACATCATAAGAATACATTGAGCGATTGACTTCAAATGACACCTTTCCGAGGTACCTCAACTTTCTCTCGTGCAGCACAGGCTGGTTGAGGTCGCGTTATTGTACTTTGACATTCTGTAAAAGAATGTCTAGATCGTAGCTGTAACGGACTTTGGGCCCTATTGTCATAAGCTGCGGAGATCCTGCCATGTCGAGATTCCATTTACCTTTTGCGTCCGTTCCACAAACGCCTGGTTGAATGAATTAGGAACAATATCGCGGCTCCCCCTAAAATTGAGTGCGATCTTTGTACCTTATGCAGTCATAACCACGCAATAATCAAAGAGCTGTAGCATTGGGCTACATGAGTGGGCTAAAATGTAGATTCAGTACACGATGTCGGTGCCTCCGAAACCCGGGTAAAATAGTGTCTGCTAGCTGAGAACACTCCTACGATGCTTATAACTCAGAGAACGCTCAAAGGGCAACTTGGTTTTGAAATAAAGGCCGTTGAAAATTTTGAACATTACATCGGCCTGCTGTCATTTCGTGTTTGACATACTCATCGGTCAATGTCACGCAATCGCGGTTTCCTTACCTTTGGATAAGGTGTTATGCTAGGTGCCCCATGATGTGTGAGTTTGACTGCCGAGCACGTAAGGACGAGTTAGTTCGCGTGTCTGGCGTCAGTATACTTAGCGAGCGGTAGTCTAGGCCCAGCAACGCTTGTTTCTGCGTGATGGCGTATTCAGCGGAGCTGGTGAGCCGGTAGAAGCATTAAAAGAACCTCCATACTGTAGAACGTAAAATCGGCACTAGTCAGAGGTACTAATATTAATACCATGCCTTATAAGGCGGACATCGTGAACAACTCAGACGGCGGACGCTAGAACGAGTGGTCAGCTCATTGTTGCCGTTGTGGATTTCAGAGAGGATATCGTATCGGGGGCGCACTTAAGTATGACTGGTGTCTCGAAAGGACGCAAGCATTGATAACTCCCATTGACTATAAGGCACACTGGAATTCATACAGCAGATTAGCCCAGCCGGCACAGTCGCTAGCAAAGCATGAGGTCGACCTAGGAAGAAATCTCGGGCACCTTTAACTGTCATTGGTGAGTGCCTTTCAGTATGCGGCCCTGAGGATAGATCGTTTGTAATTGGAGATCGGATAATTAACTGTACTAGCAAGATTTAGTGGGTCGGAAATTCCTACTCCCCTGTCTTGCACATTCGCGTTTCGGGCCTAGCATTTCCGCACGACTATTGTGGTGCCCACCGACCCTCCATAGTGCGGTTTAAAGCGGTTCTTAAGAATGTCAGGCCCTATTAATTGCTTAGGATGAAACACCGACGGTCAGGACCTCATCTTCTTGGCGGGACGTCCTTAATGCCGTATCACATCGCACATCCTATACGTCTAAGAATCTCAGGCCTTGATACGTACTGCCCCCGTTTCTATGACCGAGGAATCGTACTGTTGCTCATCTAGCATATGCGTAAAGTGTTCACGGCCTTGCTAACGTATTCGTCTTGACCGGTGCACAATTTGATGTACATGATAAAGGGGTAATGACGCGTGGTTGAATCTTTATAGTCCGAACTGAAATGCCCCTACAGGCCCAGCATGCCGCTGTCTAGGACCTCACAAGTAGGCGCCTACTAGTTAGGAGTGGCGTAACGGGACATATCGGCGCGTAGGGGACAAGTTTAAGCGTGTTTTACTATGCTTTGAGTCCAGTAAACAGATGGCCGCACAGGGCCGTGGTATGGTACGGCAATGATCTTGCGTTGCCTGCACAGATATGTGGACATGTTACATCGGGGCGGACGTTTCGTTGGGATTATTGATACGGTCGTTCGTTCCGGCCTCGAGCTCATGTGCAAGACTTGCACCGATTATCACCATCCACGTGATGCTTCGACCTGTGAAGCCAGCTATGCAACATCAGATGCTCGATTCAAACACAAAAACTACAGTATGACCTGTGTTGAAGGCTATTTCTTCTAATATAACCAGACACTGCATTCTCTCGGCGGCTATTATTTCTTGCGATCTAGAACTACGCCGGGCACAGGTCGTATGTAGAGAGTACACTCCCGCTTTGGATACGGACGACTAGCCCTGATGCGATCCTTCGCCACTGCTTCCGTGTGGTCGTCAAACCAGGATCCAGGGTCCGCATTAGACACGATCGGCTTGATACCGTCACTCACGTAGTAACACGCGCGTATTATTCAATACGACGAGAACCGGGACTCTGTAGAGAGCTAGTTGATCCGCGTCGGACGGAAGAGTCCAGGATCCTCCGATTGTGCTTTGGAAACCCACTCCTGATTAAGGCCTTGGCCTATGC' Pattern = 'CTTGGCCTATGC' d = 5 res = ApproximatePatternMatching(Text, Pattern, d) ' '.join(map(str, res)) count = len(ApproximatePatternMatching('AACAAGCTGATAAACATTTAAAGAG', 'AAAAA', 2)) def ApproximatePatternCount(Text, Pattern, d): count = 0 # initialize count variable k = len(Pattern) L = len(Text) for i in range(L - k + 1): if HammingDistance(Text[i:i+k], Pattern) <= d: count += 1 return count file = open('dataset_9_6.txt', 'r') for i, line in enumerate(file): temp = line.rstrip() if i == 0: Pattern = temp elif i == 1: Text = temp else: d = int(temp)

473.714286

19,003

0.984419

139

19,896

140.870504

0.374101

0.002043

0.001839

0.001685

0.006639

0.004698

0

0.000507

0.008494

19,896

42

19,004

473.714286

0.992092

0.001257

0

0.235294

0

0.958883

0.957172

0

1

0

1

0.088235

false

0

0.176471

0

1

null

0

1

0

1

null

1

0

6

1bceb82e1e7119923bb0324dca3a53a2247e8109

276

py

Python

epymetheus/__init__.py

shishaboy/epymetheus

d8916b20c6b79e86e5aadb39c7c01a582659f03b

[ "BSD-3-Clause" ]

null

epymetheus/__init__.py

shishaboy/epymetheus

d8916b20c6b79e86e5aadb39c7c01a582659f03b

[ "BSD-3-Clause" ]

null

epymetheus/__init__.py

shishaboy/epymetheus

d8916b20c6b79e86e5aadb39c7c01a582659f03b

[ "BSD-3-Clause" ]

null

# flake8: noqa from epymetheus.history import History from epymetheus.strategy import Strategy from epymetheus.strategy import TradeStrategy from epymetheus.trade import Trade from epymetheus.universe import Universe from epymetheus.wealth import Wealth from . import utils

25.090909

45

0.847826

35

276

6.685714

0.342857

0.358974

0.188034

0.239316

0

0.004115

0.119565

276

10

46

27.6

0.958848

0.043478

0

1

0

true

0

1

0

1

0

null

1

0

null

0

1

0

1

0

1

0

6

1bcf26abc265e38d664a03d4f570802ab15ea137

46

py

Python

srcs/python/kungfu/torch/optimizers/__init__.py

Pandinosaurus/KungFu

80dfa463450330e920b413f65cc49d8e013b84a9

[ "Apache-2.0" ]

291

2019-10-25T16:37:59.000Z

2022-03-17T21:47:09.000Z

srcs/python/kungfu/torch/optimizers/__init__.py

Pandinosaurus/KungFu

80dfa463450330e920b413f65cc49d8e013b84a9

[ "Apache-2.0" ]

56

2019-10-26T08:25:33.000Z

2021-09-07T11:11:51.000Z

srcs/python/kungfu/torch/optimizers/__init__.py

Pandinosaurus/KungFu

80dfa463450330e920b413f65cc49d8e013b84a9

[ "Apache-2.0" ]

53

2019-10-25T17:45:40.000Z

2022-02-08T13:09:39.000Z

from .sync_sgd import SynchronousSGDOptimizer

23

45

0.891304

5

46

8

1

0

0.086957

46

1

46

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

1bd374084e19c4b98447f622d6387d179f68be72

87

py

Python

src/sentry/search/snuba/__init__.py

AlexWayfer/sentry

ef935cda2b2e960bd602fda590540882d1b0712d

[ "BSD-3-Clause" ]

4

2019-05-27T13:55:07.000Z

2021-03-30T07:05:09.000Z

src/sentry/search/snuba/__init__.py

AlexWayfer/sentry

ef935cda2b2e960bd602fda590540882d1b0712d

[ "BSD-3-Clause" ]

196

2019-06-10T08:34:10.000Z

2022-02-22T01:26:13.000Z

src/sentry/search/snuba/__init__.py

AlexWayfer/sentry

ef935cda2b2e960bd602fda590540882d1b0712d

[ "BSD-3-Clause" ]

1

2020-08-10T07:55:40.000Z

from __future__ import absolute_import, print_function from .backend import * # NOQA

21.75

54

0.804598

11

87

5.818182

0.727273

0

0.149425

87

3

55

29

0.864865

0.045977

0

1

0

true

0

1

0

1

0.5

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null

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0

1

0

1

0

6

1bd3a996200246b2959bcab7aec57742ddadf21f

199

py

Python

tests/app/factories/feature_flag.py

department-of-veterans-affairs/notification-api

698bc98d8e78a13a0b2cfc432cfc718ff1016b06

[ "MIT" ]

10

2020-05-04T14:11:06.000Z

2022-02-22T19:06:36.000Z

tests/app/factories/feature_flag.py

department-of-veterans-affairs/notification-api

698bc98d8e78a13a0b2cfc432cfc718ff1016b06

[ "MIT" ]

554

2020-05-07T21:56:24.000Z

2022-03-31T23:04:51.000Z

tests/app/factories/feature_flag.py

department-of-veterans-affairs/notification-api

698bc98d8e78a13a0b2cfc432cfc718ff1016b06

[ "MIT" ]

4

2020-08-27T16:43:29.000Z

2021-02-17T22:17:27.000Z

import os from app.feature_flags import FeatureFlag def mock_feature_flag(mocker, feature_flag: FeatureFlag, enabled: str) -> None: mocker.patch.dict(os.environ, {feature_flag.value: enabled})

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6

940fc8d0d015d7caf6b62ce0a736d8a4e75c8cb2

27

py

Python

src/euler_python_package/euler_python/medium/p435.py

wilsonify/euler

5214b776175e6d76a7c6d8915d0e062d189d9b79

[ "MIT" ]

null

src/euler_python_package/euler_python/medium/p435.py

wilsonify/euler

5214b776175e6d76a7c6d8915d0e062d189d9b79

[ "MIT" ]

null

src/euler_python_package/euler_python/medium/p435.py

wilsonify/euler

5214b776175e6d76a7c6d8915d0e062d189d9b79

[ "MIT" ]

null

def problem435(): pass

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0

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27

2

18

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true

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0

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0

1

0

null

0

1

0

null

0

1

0

6

943542ca488ab3e97674cc0337d53bd3c572eeea

23

py

Python

server/server/contest/__init__.py

aweijx/MMW_YNU

0f4aa38c9b359cb7282a322eb3f258f9b7b7eb47

[ "Apache-2.0" ]

2

2020-11-16T06:15:09.000Z

2021-09-07T09:32:55.000Z

server/server/contest/__init__.py

aweijx/MMW_YNU

0f4aa38c9b359cb7282a322eb3f258f9b7b7eb47

[ "Apache-2.0" ]

null

server/server/contest/__init__.py

aweijx/MMW_YNU

0f4aa38c9b359cb7282a322eb3f258f9b7b7eb47

[ "Apache-2.0" ]

null

from .contest import *

11.5

22

0.73913

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23

5.666667

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0.173913

23

1

23

0.894737

0

1

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true

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1

0

1

0

1

0

null

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1

0

null

0

1

0

1

0

1

0

6

9460f3f47dcafb904d0577fc08db71062eff898d

1,746

py

Python

apps/people/validators/people.py

bergran/people

a2639b238005bd37b7a08f220b57c4b5ad5c031d

[ "MIT" ]

null

apps/people/validators/people.py

bergran/people

a2639b238005bd37b7a08f220b57c4b5ad5c031d

[ "MIT" ]

null

apps/people/validators/people.py

bergran/people

a2639b238005bd37b7a08f220b57c4b5ad5c031d

[ "MIT" ]

null

# -*- coding: utf-8 -*- from fastapi import HTTPException from sqlalchemy.sql.functions import count from starlette import status from apps.people.models import People def validate_place_kings(obj, people, session): count_people = session.query(count(People.id)).filter( People.is_king.is_(True), People.is_alive.is_(True), People.place_id == people.place_id ).scalar() if people.is_king and people.is_alive and count_people != 0: detail = 'It can not be 2 kings alive in the same place' raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail=detail) def validate_place_kings_updated(obj, people, session): count_people = session.query(count(People.id)).filter( People.is_king.is_(True), People.is_alive.is_(True), People.place_id == people.place_id, People.id != obj.id ).scalar() if people.is_king and people.is_alive and count_people != 0: detail = 'It can not be 2 kings alive in the same place' raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail=detail) def validate_first_name(obj, people, session): count_people = session.query(count(People.id)).filter( People.first_name == people.first_name ).scalar() if count_people > 0: detail = 'It can not be 2 people with the same first name' raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail=detail) def validate_people(obj, people, session): validate_first_name(obj, people, session) validate_place_kings(obj, people, session) def validate_people_update(obj, people, session): validate_first_name(obj, people, session) validate_place_kings_updated(obj, people, session)

33.576923

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0.715922

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1,746

4.799197

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0.120502

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0

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1,746

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0.012027

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false

0

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0.25

0

null

0

1

0

null

0

6

946db5dc78cd7c3e0a9ec0c544a89ac0c37efa67

5,863

py

Python

common.py

unvercanunlu/pytorch-activation-functions-comparison

e57860845e1a2c572b37e8c83f1b8721ffd0fbc8

[ "MIT" ]

1

2021-12-06T13:19:18.000Z

common.py

unvercanunlu/loss-function-comparison-pytorch

6dfbb2c0935898774452044129c3e22a50d4ec19

[ "MIT" ]

null

common.py

unvercanunlu/loss-function-comparison-pytorch

6dfbb2c0935898774452044129c3e22a50d4ec19

[ "MIT" ]

null

import os import matplotlib.pyplot as graph import numpy as np import torch def train(model, device, loader, optimizer, loss, one_hot_encoded=False, info_per_batch=10): model.train() number_of_batches = len(loader) batch_losses = [] batch_accuracies = [] for batch_index, (batch_input, batch_target) in enumerate(loader): batch_input, batch_target = batch_input.to(device), batch_target.to(device) optimizer.zero_grad() batch_output = model(batch_input) if one_hot_encoded: batch_target_one_hot_encoded = torch.nn.functional.one_hot(batch_target, 10).float() loss_calculation = loss(batch_output, batch_target_one_hot_encoded) else: loss_calculation = loss(batch_output, batch_target) loss_calculation.backward() optimizer.step() batch_loss = loss_calculation.item() batch_losses.append(batch_loss) batch_prediction = batch_output.max(dim=1, keepdim=True)[1] batch_correct = batch_prediction.eq(batch_target.view_as(batch_prediction)).sum().item() batch_size = len(batch_input) batch_accuracy = batch_correct / batch_size batch_accuracies.append(batch_accuracy) if (batch_index + 1) % info_per_batch == 0: info = 'Train: Batch {current_batch}/{number_of_batches}, Loss: {batch_loss:.5f}, Accuracy: % {batch_accuracy:.2f}' print(info.format(current_batch=(batch_index + 1), number_of_batches=number_of_batches, batch_loss=batch_loss, batch_accuracy=(100 * batch_accuracy))) average_loss = sum(batch_losses) / number_of_batches accuracy = sum(batch_accuracies) / number_of_batches return average_loss, accuracy def test(model, device, loader, loss, one_hot_encoded=False, info_name='Test', info_per_batch=10): model.eval() number_of_batches = len(loader) batch_loses = [] batch_accuracies = [] with torch.no_grad(): for batch_index, (batch_input, batch_target) in enumerate(loader): batch_input, batch_target = batch_input.to(device), batch_target.to(device) batch_output = model(batch_input) if one_hot_encoded: batch_target_one_hot_encoded = torch.nn.functional.one_hot(batch_target, 10).float() loss_calculation = loss(batch_output, batch_target_one_hot_encoded) else: loss_calculation = loss(batch_output, batch_target) batch_loss = loss_calculation.item() batch_loses.append(batch_loss) batch_prediction = batch_output.max(dim=1, keepdim=True)[1] batch_correct = batch_prediction.eq(batch_target.view_as(batch_prediction)).sum().item() batch_size = len(batch_input) batch_accuracy = batch_correct / batch_size batch_accuracies.append(batch_accuracy) if (batch_index + 1) % info_per_batch == 0: info = '{info_name}: Batch {current_batch}/{number_of_batches}, Loss: {batch_loss:.5f}, Accuracy: % {batch_accuracy:.2f}' print(info.format(current_batch=(batch_index + 1), number_of_batches=number_of_batches, batch_loss=batch_loss, batch_accuracy=(100 * batch_accuracy), info_name=info_name)) average_loss = sum(batch_loses) / number_of_batches accuracy = sum(batch_accuracies) / number_of_batches return average_loss, accuracy def save_state(model, directory, file_name): file_path = os.path.join(directory, file_name) state = model.state_dict() torch.save(obj=state, f=file_path) info = 'File: {file_name} is saved.' print(info.format(file_name=file_name)) def save_data(array, directory, file_name): file_path = os.path.join(directory, file_name) np.save(file=file_path, arr=array) info = 'File: {file_name} is saved.' print(info.format(file_name=file_name)) def load_data(directory, file_name): file_path = os.path.join(directory, file_name) array = [] if os.path.exists(file_path): array = np.load(file_path) info = 'File: {file_name} is saved.' print(info.format(file_name=file_name)) else: info = 'File: {file_name} does not exist.' print(info.format(file_name=file_name)) return array def draw_multi_lines_graph(lines, x_label, y_label, title, directory=None, file_name=None): graph.clf() labels = [] for line in lines: label = line['label'] labels.append(label) x = line['data']['x'] y = line['data']['y'] graph.xticks(x) graph.plot(x, y) graph.xlabel(xlabel=x_label) graph.ylabel(ylabel=y_label) graph.title(label=title) graph.legend(labels) if directory is not None: if file_name is None: file_name = '_'.join([word.lower() for word in title.split()]) + '.png' file_path = os.path.join(directory, file_name) graph.savefig(file_path) info = 'File: {file_name} is saved.' print(info.format(file_name=file_name)) else: graph.show() def draw_line_graph(x, y, x_label, y_label, title, directory=None, file_name=None): graph.clf() graph.xticks(x) graph.plot(x, y) graph.xlabel(xlabel=x_label) graph.ylabel(ylabel=y_label) graph.title(label=title) if directory is not None: if file_name is None: file_name = '_'.join([word.lower() for word in title.split()]) + '.png' file_path = os.path.join(directory, file_name) graph.savefig(file_path) info = 'File: {file_name} is saved.' print(info.format(file_name=file_name)) else: graph.show()

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0

null

0

1

0

null

0

6

848a7b1382e0f574d86b77ba5360aa12d3d3af0b

61

py

Python

gui/__init__.py

alexsmith2910/Strat_UN

57f79beb923cebed9ced940ccaea9df9172541fe

[ "MIT", "Unlicense" ]

null

gui/__init__.py

alexsmith2910/Strat_UN

57f79beb923cebed9ced940ccaea9df9172541fe

[ "MIT", "Unlicense" ]

3

2020-10-10T11:10:55.000Z

2021-03-30T13:16:52.000Z

gui/__init__.py

alexsmith2910/Strat_UN

57f79beb923cebed9ced940ccaea9df9172541fe

[ "MIT", "Unlicense" ]

null

from .research_elements import elements as research_elements

30.5

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61

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null

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1

0

1

0

1

0

6

84c9c83b61be158da40649176d2623a19c0d3e00

25

py

Python

ClickReaction/__init__.py

Gillingham-Lab/Click

66a742d3fe035e611ef891023a390a030bfd0729

[ "MIT" ]

1

2020-05-23T06:25:14.000Z

ClickReaction/__init__.py

Gillingham-Lab/Click

66a742d3fe035e611ef891023a390a030bfd0729

[ "MIT" ]

null

ClickReaction/__init__.py

Gillingham-Lab/Click

66a742d3fe035e611ef891023a390a030bfd0729

[ "MIT" ]

1

2021-02-22T06:02:50.000Z

from .Reactions import *

12.5

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6.333333

1

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1

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0.904762

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true

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null

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1

0

1

0

1

0

6

ca2b21fbab5765b773051c683b71f47ac7e4c02d

3,556

py

Python

Country_CovidTracker.py

harshagl2002/COVID_CLI

f6f86e48e9477a021a63651a29c4cdfb616a0b99

[ "Apache-2.0" ]

1

2021-06-11T19:54:56.000Z

Country_CovidTracker.py

harshagl2002/COVID_CLI

f6f86e48e9477a021a63651a29c4cdfb616a0b99

[ "Apache-2.0" ]

null

Country_CovidTracker.py

harshagl2002/COVID_CLI

f6f86e48e9477a021a63651a29c4cdfb616a0b99

[ "Apache-2.0" ]

null

import requests import json import datetime def country(): url = "https://covid-193.p.rapidapi.com/history" country = input("Enter the country you would like to search for: ") date = input("Enter the date (yyyy-mm-dd) you would like to search for: ") year,month,day = date.split('-') isValidDate = True try : datetime.datetime(int(year),int(month),int(day)) except ValueError : isValidDate = False if(isValidDate) : querystring = {"country":country,"day":date} headers = { 'x-rapidapi-key': "574d25f133msh5c58c65e8a4c944p1e6b8fjsnee1da55d91cd", 'x-rapidapi-host': "covid-193.p.rapidapi.com" } response = requests.request("GET", url, headers=headers, params=querystring) data = response.text parsed = json.loads(data) if parsed["results"] == 0: print("The requested data is currently not availible. Sorry") else: response_dict = parsed["response"][0] cases_dict = response_dict["cases"] print() print("NEW CASES in", parsed["parameters"]["country"], "on", response_dict["day"], "is", cases_dict["new"]) print("TOTAL number of cases in", parsed["parameters"]["country"], "till", response_dict["day"], "is", cases_dict["total"]) print("ACTIVE CASES in", parsed["parameters"]["country"], "on", response_dict["day"], "is", cases_dict["active"]) print("Number of DEATHS recorded in", parsed["parameters"]["country"], "on", response_dict["day"], "is", response_dict["deaths"]["new"]) print("Number of RECOVERIES recorded in", parsed["parameters"]["country"], "till", response_dict["day"], "is", cases_dict["recovered"]) print("Total number of TESTS conducted in", parsed["parameters"]["country"], "till", response_dict["day"], "is", response_dict["tests"]["total"]) else: print("You have entered an invalid date. Kindly enter a valid date") date_new = input("Enter the date (yyyy-mm-dd) you would like to search for: ") querystring = {"country":country,"day":date_new} headers = { 'x-rapidapi-key': "574d25f133msh5c58c65e8a4c944p1e6b8fjsnee1da55d91cd", 'x-rapidapi-host': "covid-193.p.rapidapi.com" } response = requests.request("GET", url, headers=headers, params=querystring) data = response.text parsed = json.loads(data) if parsed["results"] == 0: print("The requested data is currently not availible. Sorry") else: response_dict = parsed["response"][0] cases_dict = response_dict["cases"] print() print("NEW CASES in", parsed["parameters"]["country"], "on", response_dict["day"], "is", cases_dict["new"]) print("TOTAL number of cases in", parsed["parameters"]["country"], "till", response_dict["day"], "is", cases_dict["total"]) print("ACTIVE CASES in", parsed["parameters"]["country"], "on", response_dict["day"], "is", cases_dict["active"]) print("Number of DEATHS recorded in", parsed["parameters"]["country"], "on", response_dict["day"], "is", response_dict["deaths"]["new"]) print("Number of RECOVERIES recorded in", parsed["parameters"]["country"], "till", response_dict["day"], "is", cases_dict["recovered"]) print("Total number of TESTS conducted in", parsed["parameters"]["country"], "till", response_dict["day"], "is", response_dict["tests"]["total"])

50.8

157

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3,556

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0.099954

0.138825

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3,556

69

158

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0

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0

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1

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false

0

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0

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0.298246

0

null

0

1

0

null

0

6

ca5a992a2c87aeb651726b530b0f6d74463d4055

25

py

Python

v3/as_drivers/htu21d/__init__.py

Dilepa/micropython-async

3c8817d9ead33bcd8399d0935ffb24dd7bcd6e71

[ "MIT" ]

443

2017-01-01T20:54:46.000Z

2022-03-28T06:17:30.000Z

v3/as_drivers/htu21d/__init__.py

Dilepa/micropython-async

3c8817d9ead33bcd8399d0935ffb24dd7bcd6e71

[ "MIT" ]

79

2017-01-28T17:53:32.000Z

2022-02-08T10:05:04.000Z

v3/as_drivers/htu21d/__init__.py

Dilepa/micropython-async

3c8817d9ead33bcd8399d0935ffb24dd7bcd6e71

[ "MIT" ]

126

2017-02-17T13:06:01.000Z

2022-03-07T03:50:50.000Z

from .htu21d_mc import *

12.5

24

0.76

4

25

4.5

1

0

0.095238

0.16

25

1

25

0.761905

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true

0

1

0

1

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0

null

0

1

0

1

0

1

0

6

ca6dfebdb588d759e235c1d3467133a084b992fe

3,767

py

Python

Examples/AdvancedUsage/AddAnnotations/AddPolylineAnnotation.py

groupdocs-annotation-cloud/groupdocs-annotation-cloud-python-samples

5fb6c88d0e173198753d8483ea0a75606479fa41

[ "MIT" ]

null

Examples/AdvancedUsage/AddAnnotations/AddPolylineAnnotation.py

groupdocs-annotation-cloud/groupdocs-annotation-cloud-python-samples

5fb6c88d0e173198753d8483ea0a75606479fa41

[ "MIT" ]

null

Examples/AdvancedUsage/AddAnnotations/AddPolylineAnnotation.py

groupdocs-annotation-cloud/groupdocs-annotation-cloud-python-samples

5fb6c88d0e173198753d8483ea0a75606479fa41

[ "MIT" ]

2

2019-07-08T12:50:55.000Z

2019-07-08T13:21:54.000Z

# Import modules from groupdocs_annotation_cloud import * import groupdocs_annotation_cloud from Common import Common class AddPolylineAnnotation: @classmethod def Run(cls): # Create instance of the API api = groupdocs_annotation_cloud.AnnotateApi.from_config(Common.GetConfig()) try: a1 = groupdocs_annotation_cloud.AnnotationInfo() a1.box = groupdocs_annotation_cloud.Rectangle() a1.box.x = 100 a1.box.y = 100 a1.box.width = 200 a1.box.height = 100 a1.page_number = 0 a1.pen_color = 1201033 a1.pen_style = "Solid" a1.pen_width = 1 a1.opacity = 0.7 a1.type = "Polyline" a1.text = "This is polyline annotation" a1.creator_name = "Anonym A." a1.svgPath = "M250.8280751173709,48.209295774647885l0.6986854460093896,0l0.6986854460093896,-1.3973708920187793l0.6986854460093896,0l0.6986854460093896,-1.3973708920187793l1.3973708920187793,-0.6986854460093896l0.6986854460093896,-0.6986854460093896l0.6986854460093896,0l2.096056338028169,-1.3973708920187793l3.493427230046948,-1.3973708920187793l0.6986854460093896,-0.6986854460093896l1.3973708920187793,-1.3973708920187793l0.6986854460093896,0l1.3973708920187793,-0.6986854460093896l0.6986854460093896,0l0.6986854460093896,-0.6986854460093896l0.6986854460093896,0l0.6986854460093896,0l0,-0.6986854460093896l0.6986854460093896,0l0.6986854460093896,0l1.3973708920187793,0l0,-0.6986854460093896l0.6986854460093896,0l1.3973708920187793,0l0.6986854460093896,0l1.3973708920187793,0l0.6986854460093896,0l2.096056338028169,-0.6986854460093896l1.3973708920187793,0l0.6986854460093896,0l0.6986854460093896,0l1.3973708920187793,0l1.3973708920187793,0l1.3973708920187793,0l2.096056338028169,0l5.589483568075117,0l1.3973708920187793,0l2.096056338028169,0l0.6986854460093896,0l1.3973708920187793,0l0.6986854460093896,0l1.3973708920187793,0l1.3973708920187793,0l0.6986854460093896,0.6986854460093896l1.3973708920187793,0l2.096056338028169,1.3973708920187793l0.6986854460093896,0l0.6986854460093896,0l0,0.6986854460093896l1.3973708920187793,0l0.6986854460093896,0.6986854460093896l1.3973708920187793,0.6986854460093896l0,0.6986854460093896l0.6986854460093896,0l1.3973708920187793,0.6986854460093896l1.3973708920187793,0.6986854460093896l3.493427230046948,0.6986854460093896l1.3973708920187793,0.6986854460093896l2.096056338028169,0.6986854460093896l1.3973708920187793,0.6986854460093896l1.3973708920187793,0l1.3973708920187793,0.6986854460093896l0.6986854460093896,0l0.6986854460093896,0.6986854460093896l1.3973708920187793,0l0.6986854460093896,0l0.6986854460093896,0l2.7947417840375586,0l1.3973708920187793,0l0.6986854460093896,0l1.3973708920187793,0l0.6986854460093896,0l0.6986854460093896,0l1.3973708920187793,0l0.6986854460093896,0l2.7947417840375586,0l0.6986854460093896,0l2.7947417840375586,0l1.3973708920187793,0l0.6986854460093896,0l0.6986854460093896,0l0.6986854460093896,0l0.6986854460093896,0l0.6986854460093896,0l0.6986854460093896,0l0.6986854460093896,-0.6986854460093896l0.6986854460093896,0" file_info = FileInfo() file_info.file_path = "annotationdocs\\one-page.docx" options = AnnotateOptions() options.file_info = file_info options.annotations = [a1] options.output_path = "Output\\output.docx" request = AnnotateRequest(options) result = api.annotate(request) print("AddPolylineAnnotation: Polyline Annotation added: " + result['href']) except ApiException as e: print("Exception when calling AnnotateAPI: {0}".format(e.message))

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ca9bc15f5ad721478b272f0e09caa257710adfc8

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py

Python

src/enum/token_error.py

quadrixm/ya

621f7c12f0bfdcca49068177cfa6e0025f3a3bae

[ "MIT" ]

22

2019-01-26T15:52:24.000Z

2021-11-11T22:24:21.000Z

src/enum/token_error.py

quadrixm/ya

621f7c12f0bfdcca49068177cfa6e0025f3a3bae

[ "MIT" ]

1

2018-07-31T05:39:19.000Z

src/enum/token_error.py

quadrixm/ya

621f7c12f0bfdcca49068177cfa6e0025f3a3bae

[ "MIT" ]

1

2018-07-31T05:30:02.000Z

from enum import Enum class TokenError(Enum): INCOMPLETE_STRING = "INCOMPLETE_STRING هناك مشكلة" INVALID_TOKEN = "هناك مشكلة INVALID_TOKEN" DEFAULT = "هناك مشكلة DEFAULT"

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047fe1384b12faefc1fba734a8dd7672fd7f61b1

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py

Python

pspdfkit/__init__.py

r-kells/py-pspdfkit

f32582f5907c8c5f59d294abc6de68523b4ba1da

[ "MIT" ]

null

pspdfkit/__init__.py

r-kells/py-pspdfkit

f32582f5907c8c5f59d294abc6de68523b4ba1da

[ "MIT" ]

4

2018-05-24T12:54:01.000Z

2020-07-24T16:26:30.000Z

pspdfkit/__init__.py

r-kells/py-pspdfkit

f32582f5907c8c5f59d294abc6de68523b4ba1da

[ "MIT" ]

1

2020-07-23T14:19:49.000Z

# flake8: noqa from .api import API

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6

0486d0f3fd00a3d9009afda4648c4c5729613344

190

py

Python

pantaucovid/pantau_covid/doctype/pasien/pasien.py

iboen/frappe-pantaucovid

38f5272c438dff58d5a98c817cb3869a568a67dc

[ "MIT" ]

null

pantaucovid/pantau_covid/doctype/pasien/pasien.py

iboen/frappe-pantaucovid

38f5272c438dff58d5a98c817cb3869a568a67dc

[ "MIT" ]

null

pantaucovid/pantau_covid/doctype/pasien/pasien.py

iboen/frappe-pantaucovid

38f5272c438dff58d5a98c817cb3869a568a67dc

[ "MIT" ]

null

21.111111

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0.789474

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null

0

1

0

1

0

6

049c1c9632ef95bab381373fb4a901acefd9d2ef

1,325

py

Python

terrascript/teamcity/r.py

mjuenema/python-terrascript

6d8bb0273a14bfeb8ff8e950fe36f97f7c6e7b1d

[ "BSD-2-Clause" ]

507

2017-07-26T02:58:38.000Z

2022-01-21T12:35:13.000Z

terrascript/teamcity/r.py

mjuenema/python-terrascript

6d8bb0273a14bfeb8ff8e950fe36f97f7c6e7b1d

[ "BSD-2-Clause" ]

135

2017-07-20T12:01:59.000Z

2021-10-04T22:25:40.000Z

terrascript/teamcity/r.py

mjuenema/python-terrascript

6d8bb0273a14bfeb8ff8e950fe36f97f7c6e7b1d

[ "BSD-2-Clause" ]

81

2018-02-20T17:55:28.000Z

2022-01-31T07:08:40.000Z

# terrascript/teamcity/r.py # Automatically generated by tools/makecode.py () import warnings warnings.warn( "using the 'legacy layout' is deprecated", DeprecationWarning, stacklevel=2 ) import terrascript class teamcity_agent_pool(terrascript.Resource): pass class teamcity_agent_pool_project_assignment(terrascript.Resource): pass class teamcity_agent_requirement(terrascript.Resource): pass class teamcity_artifact_dependency(terrascript.Resource): pass class teamcity_build_config(terrascript.Resource): pass class teamcity_build_trigger_build_finish(terrascript.Resource): pass class teamcity_build_trigger_schedule(terrascript.Resource): pass class teamcity_build_trigger_vcs(terrascript.Resource): pass class teamcity_feature_commit_status_publisher(terrascript.Resource): pass class teamcity_feature_golang(terrascript.Resource): pass class teamcity_group(terrascript.Resource): pass class teamcity_project(terrascript.Resource): pass class teamcity_project_feature_oauth_provider_settings(terrascript.Resource): pass class teamcity_project_feature_versioned_settings(terrascript.Resource): pass class teamcity_snapshot_dependency(terrascript.Resource): pass class teamcity_vcs_root_git(terrascript.Resource): pass

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null

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6

b6c40ad2df639079a5f8321237ccc7e542c8c343

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py

Python

qleet/examples/__init__.py

AnimeshSinha1309/qaoa-optimizer

2a93a46bacc99f22f49e7b5121eb3aa9f12c0163

[ "Apache-2.0" ]

9

2021-09-26T18:43:43.000Z

2022-03-30T12:34:01.000Z

qleet/examples/__init__.py

QLemma/qLEET

2a93a46bacc99f22f49e7b5121eb3aa9f12c0163

[ "Apache-2.0" ]

12

2021-09-19T13:29:33.000Z

2022-01-09T15:22:49.000Z

qleet/examples/__init__.py

QLemma/qLEET

2a93a46bacc99f22f49e7b5121eb3aa9f12c0163

[ "Apache-2.0" ]

1

2022-03-14T03:02:24.000Z

import qleet.examples.qaoa_maxcut

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b6d4005295625e516d262c7ebcc35a8cd0698e98

174

py

Python

pdover2t/__init__.py

qwilka/PDover2t

4387d153228f1af20a8f5f3f368aa49c42cda2cd

[ "MIT" ]

null

pdover2t/__init__.py

qwilka/PDover2t

4387d153228f1af20a8f5f3f368aa49c42cda2cd

[ "MIT" ]

null

pdover2t/__init__.py

qwilka/PDover2t

4387d153228f1af20a8f5f3f368aa49c42cda2cd

[ "MIT" ]

1

2019-11-24T09:32:12.000Z

"""`pdover2t` computational subsea pipeline engineering. """ from . import utilities from . import pipe from .utilities.helpers import symbol, greek from . import dnvstf101

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6

b6df9eecf06f632003c5c0dbf4d593fceff47300

159

py

Python

stepboard/__init__.py

Stepujacy/stepboard

ffa079792fc4b133bb44f33e0408159da5692f6e

[ "MIT" ]

null

stepboard/__init__.py

Stepujacy/stepboard

ffa079792fc4b133bb44f33e0408159da5692f6e

[ "MIT" ]

null

stepboard/__init__.py

Stepujacy/stepboard

ffa079792fc4b133bb44f33e0408159da5692f6e

[ "MIT" ]

null

from .user import * from .guilds import * from .config import * from .applications import * from .message import * from .webhooks import * from .roles import *

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1

0

1

0

6

8e0bedaa0875e2b9b04b63ccf9cd63d69486742b

240

py

Python

food_delivery/services.py

clemencegoh/machine_learning_service

49ccb65dd8cca544bed801559b920cd7bea2d120

[ "MIT" ]

null

food_delivery/services.py

clemencegoh/machine_learning_service

49ccb65dd8cca544bed801559b920cd7bea2d120

[ "MIT" ]

null

food_delivery/services.py

clemencegoh/machine_learning_service

49ccb65dd8cca544bed801559b920cd7bea2d120

[ "MIT" ]

null

from .models import Restaurant from django.db import models def get_restaurants() -> models.QuerySet: return Restaurant.objects.all() def get_restaurant_by_id(_id: int) -> models.QuerySet: return Restaurant.objects.get(id=_id)

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8e458c6de01d549a9b6d764c8868b01145f69958

846

py

Python

modules/lib/webpage/__init__.py

yansinan/pycameresp

e239b4db110bffeb6bbdae6930d2b78562d21e35

[ "MIT" ]

28

2021-01-19T10:53:20.000Z

2022-03-24T13:57:09.000Z

modules/lib/webpage/__init__.py

yansinan/pycameresp

e239b4db110bffeb6bbdae6930d2b78562d21e35

[ "MIT" ]

5

2021-02-28T23:00:23.000Z

2022-03-30T07:36:21.000Z

modules/lib/webpage/__init__.py

yansinan/pycameresp

e239b4db110bffeb6bbdae6930d2b78562d21e35

[ "MIT" ]

9

2021-02-28T23:01:37.000Z

2022-03-24T13:57:18.000Z

# Distributed under MIT License # Copyright (c) 2021 Remi BERTHOLET """ All web pages defined here """ from webpage.passwordpage import * from webpage.mainpage import * from webpage.changepasswordpage import * from webpage.infopage import * from webpage.pushoverpage import * from webpage.serverpage import * from webpage.wifipage import * from webpage.regionpage import * from webpage.presencepage import * from webpage.batterypage import * from webpage.awakepage import * from webpage.systempage import * from tools.useful import iscamera if iscamera(): # pylint:disable=ungrouped-imports from webpage.streamingpage import * from webpage.camerapage import * from webpage.historicpage import * from webpage.motionpage import *

36.782609

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6

6d2d259dcb73dd3abf099afbfc9cd9827793fc1d

14,437

py

Python

octavia_f5/tests/unit/api/drivers/f5_provider_driver/test_f5_driver.py

sungwon-ahn/octavia-f5-provider-driver

ab99ed806b5249c1f774aa6f807f778dfb2051fa

[ "Apache-2.0" ]

15

2020-01-23T16:06:52.000Z

2022-02-16T08:44:35.000Z

octavia_f5/tests/unit/api/drivers/f5_provider_driver/test_f5_driver.py

sungwon-ahn/octavia-f5-provider-driver

ab99ed806b5249c1f774aa6f807f778dfb2051fa

[ "Apache-2.0" ]

88

2019-12-09T11:14:40.000Z

2022-02-28T11:51:58.000Z

octavia_f5/tests/unit/api/drivers/f5_provider_driver/test_f5_driver.py

sungwon-ahn/octavia-f5-provider-driver

ab99ed806b5249c1f774aa6f807f778dfb2051fa

[ "Apache-2.0" ]

2

2020-03-23T16:21:54.000Z

2022-02-24T15:13:32.000Z

# Copyright 2020 SAP SE # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. import mock from oslo_config import cfg from oslo_config import fixture as oslo_fixture from octavia.common import constants as consts from octavia.tests.unit import base from octavia.tests.common import sample_data_models from octavia_f5.api.drivers.f5_driver import driver from octavia_lib.api.drivers import data_models as driver_dm class TestF5Driver(base.TestRpc): def setUp(self): super(TestF5Driver, self).setUp() conf = self.useFixture(oslo_fixture.Config(cfg.CONF)) self.patches = [ mock.patch('octavia.db.repositories.AmphoraRepository.get'), mock.patch('octavia.db.api.get_session') ] conf.config(group="oslo_messaging", topic='foo_topic') conf.config(group="controller_worker", network_driver='network_noop_driver_f5') self.amp_driver = driver.F5ProviderDriver() self.sample_data = sample_data_models.SampleDriverDataModels() for patch in self.patches: patch.start() def tearDown(self): super(TestF5Driver, self).tearDown() for patch in self.patches: patch.stop() # Load Balancer @mock.patch('oslo_messaging.rpc.client._BaseCallContext.cast') def test_loadbalancer_create(self, mock_cast): provider_lb = driver_dm.LoadBalancer( loadbalancer_id=self.sample_data.lb_id) self.amp_driver.loadbalancer_create(provider_lb) payload = {consts.LOAD_BALANCER_ID: self.sample_data.lb_id, consts.FLAVOR: None} mock_cast.assert_called_with({}, 'create_load_balancer', **payload) @mock.patch('oslo_messaging.rpc.client._BaseCallContext.cast') def test_loadbalancer_delete(self, mock_cast): provider_lb = driver_dm.LoadBalancer( loadbalancer_id=self.sample_data.lb_id) self.amp_driver.loadbalancer_delete(provider_lb) payload = {consts.LOAD_BALANCER_ID: self.sample_data.lb_id, 'cascade': False} mock_cast.assert_called_with({}, 'delete_load_balancer', **payload) # Listener @mock.patch('oslo_messaging.rpc.client._BaseCallContext.cast') def test_listener_create(self, mock_cast): provider_listener = driver_dm.Listener( listener_id=self.sample_data.listener1_id) self.amp_driver.listener_create(provider_listener) payload = {consts.LISTENER_ID: self.sample_data.listener1_id} mock_cast.assert_called_with({}, 'create_listener', **payload) @mock.patch('oslo_messaging.rpc.client._BaseCallContext.cast') def test_listener_delete(self, mock_cast): provider_listener = driver_dm.Listener( listener_id=self.sample_data.listener1_id) self.amp_driver.listener_delete(provider_listener) payload = {consts.LISTENER_ID: self.sample_data.listener1_id} mock_cast.assert_called_with({}, 'delete_listener', **payload) @mock.patch('oslo_messaging.rpc.client._BaseCallContext.cast') def test_listener_update(self, mock_cast): old_provider_listener = driver_dm.Listener( listener_id=self.sample_data.listener1_id) provider_listener = driver_dm.Listener( listener_id=self.sample_data.listener1_id, admin_state_up=False) self.amp_driver.listener_update(old_provider_listener, provider_listener) payload = {consts.LISTENER_ID: self.sample_data.listener1_id, consts.LISTENER_UPDATES: {}} mock_cast.assert_called_with({}, 'update_listener', **payload) @mock.patch('oslo_messaging.rpc.client._BaseCallContext.cast') def test_listener_update_name(self, mock_cast): old_provider_listener = driver_dm.Listener( listener_id=self.sample_data.listener1_id) provider_listener = driver_dm.Listener( listener_id=self.sample_data.listener1_id, name='Great Listener') self.amp_driver.listener_update(old_provider_listener, provider_listener) payload = {consts.LISTENER_ID: self.sample_data.listener1_id, consts.LISTENER_UPDATES: {}} mock_cast.assert_called_with({}, 'update_listener', **payload) # Pool @mock.patch('oslo_messaging.rpc.client._BaseCallContext.cast') def test_pool_create(self, mock_cast): provider_pool = driver_dm.Pool( pool_id=self.sample_data.pool1_id) self.amp_driver.pool_create(provider_pool) payload = {consts.POOL_ID: self.sample_data.pool1_id} mock_cast.assert_called_with({}, 'create_pool', **payload) @mock.patch('oslo_messaging.rpc.client._BaseCallContext.cast') def test_pool_delete(self, mock_cast): provider_pool = driver_dm.Pool( pool_id=self.sample_data.pool1_id) self.amp_driver.pool_delete(provider_pool) payload = {consts.POOL_ID: self.sample_data.pool1_id} mock_cast.assert_called_with({}, 'delete_pool', **payload) @mock.patch('oslo_messaging.rpc.client._BaseCallContext.cast') def test_pool_update(self, mock_cast): old_provider_pool = driver_dm.Pool( pool_id=self.sample_data.pool1_id) provider_pool = driver_dm.Pool( pool_id=self.sample_data.pool1_id, admin_state_up=True) self.amp_driver.pool_update(old_provider_pool, provider_pool) payload = {consts.POOL_ID: self.sample_data.pool1_id, consts.POOL_UPDATES: {}} mock_cast.assert_called_with({}, 'update_pool', **payload) # Member @mock.patch('octavia.db.repositories.PoolRepository.get') @mock.patch('oslo_messaging.rpc.client._BaseCallContext.cast') def test_member_create(self, mock_cast, mock_pool_get): provider_member = driver_dm.Member( member_id=self.sample_data.member1_id) self.amp_driver.member_create(provider_member) payload = {consts.MEMBER_ID: self.sample_data.member1_id} mock_cast.assert_called_with({}, 'create_member', **payload) @mock.patch('octavia.db.repositories.PoolRepository.get') @mock.patch('oslo_messaging.rpc.client._BaseCallContext.cast') def test_member_create_udp_ipv4(self, mock_cast, mock_pool_get): mock_lb = mock.MagicMock() mock_lb.vip = mock.MagicMock() mock_lb.vip.ip_address = "192.0.1.1" mock_listener = mock.MagicMock() mock_listener.load_balancer = mock_lb mock_pool = mock.MagicMock() mock_pool.protocol = consts.PROTOCOL_UDP mock_pool.listeners = [mock_listener] mock_pool_get.return_value = mock_pool provider_member = driver_dm.Member( member_id=self.sample_data.member1_id, address="192.0.2.1") self.amp_driver.member_create(provider_member) payload = {consts.MEMBER_ID: self.sample_data.member1_id} mock_cast.assert_called_with({}, 'create_member', **payload) @mock.patch('octavia.db.repositories.PoolRepository.get') @mock.patch('oslo_messaging.rpc.client._BaseCallContext.cast') def test_member_create_udp_ipv4_ipv6(self, mock_cast, mock_pool_get): mock_lb = mock.MagicMock() mock_lb.vip = mock.MagicMock() mock_lb.vip.ip_address = "fe80::1" mock_listener = mock.MagicMock() mock_listener.load_balancer = mock_lb mock_pool = mock.MagicMock() mock_pool.protocol = consts.PROTOCOL_UDP mock_pool.listeners = [mock_listener] mock_pool_get.return_value = mock_pool provider_member = driver_dm.Member( member_id=self.sample_data.member1_id, address="192.0.2.1") self.amp_driver.member_create(provider_member) payload = {consts.MEMBER_ID: self.sample_data.member1_id} mock_cast.assert_called_with({}, 'create_member', **payload) @mock.patch('oslo_messaging.rpc.client._BaseCallContext.cast') def test_member_delete(self, mock_cast): provider_member = driver_dm.Member( member_id=self.sample_data.member1_id) self.amp_driver.member_delete(provider_member) payload = {consts.MEMBER_ID: self.sample_data.member1_id} mock_cast.assert_called_with({}, 'delete_member', **payload) @mock.patch('oslo_messaging.rpc.client._BaseCallContext.cast') def test_member_update(self, mock_cast): old_provider_member = driver_dm.Member( member_id=self.sample_data.member1_id) provider_member = driver_dm.Member( member_id=self.sample_data.member1_id, admin_state_up=True) self.amp_driver.member_update(old_provider_member, provider_member) payload = {consts.MEMBER_ID: self.sample_data.member1_id, consts.MEMBER_UPDATES: {}} mock_cast.assert_called_with({}, 'update_member', **payload) # L7 Policy @mock.patch('oslo_messaging.rpc.client._BaseCallContext.cast') def test_l7policy_create(self, mock_cast): provider_l7policy = driver_dm.L7Policy( l7policy_id=self.sample_data.l7policy1_id) self.amp_driver.l7policy_create(provider_l7policy) payload = {consts.L7POLICY_ID: self.sample_data.l7policy1_id} mock_cast.assert_called_with({}, 'create_l7policy', **payload) @mock.patch('oslo_messaging.rpc.client._BaseCallContext.cast') def test_l7policy_delete(self, mock_cast): provider_l7policy = driver_dm.L7Policy( l7policy_id=self.sample_data.l7policy1_id) self.amp_driver.l7policy_delete(provider_l7policy) payload = {consts.L7POLICY_ID: self.sample_data.l7policy1_id} mock_cast.assert_called_with({}, 'delete_l7policy', **payload) @mock.patch('oslo_messaging.rpc.client._BaseCallContext.cast') def test_l7policy_update(self, mock_cast): old_provider_l7policy = driver_dm.L7Policy( l7policy_id=self.sample_data.l7policy1_id) provider_l7policy = driver_dm.L7Policy( l7policy_id=self.sample_data.l7policy1_id, admin_state_up=True) self.amp_driver.l7policy_update(old_provider_l7policy, provider_l7policy) payload = {consts.L7POLICY_ID: self.sample_data.l7policy1_id, consts.L7POLICY_UPDATES: {}} mock_cast.assert_called_with({}, 'update_l7policy', **payload) # Health Monitor @mock.patch('oslo_messaging.rpc.client._BaseCallContext.cast') def test_health_monitor_create(self, mock_cast): provider_HM = driver_dm.HealthMonitor( healthmonitor_id=self.sample_data.hm1_id) self.amp_driver.health_monitor_create(provider_HM) payload = {consts.HEALTH_MONITOR_ID: self.sample_data.hm1_id} mock_cast.assert_called_with({}, 'create_health_monitor', **payload) @mock.patch('oslo_messaging.rpc.client._BaseCallContext.cast') def test_health_monitor_delete(self, mock_cast): provider_HM = driver_dm.HealthMonitor( healthmonitor_id=self.sample_data.hm1_id) self.amp_driver.health_monitor_delete(provider_HM) payload = {consts.HEALTH_MONITOR_ID: self.sample_data.hm1_id} mock_cast.assert_called_with({}, 'delete_health_monitor', **payload) @mock.patch('oslo_messaging.rpc.client._BaseCallContext.cast') def test_health_monitor_update(self, mock_cast): old_provider_hm = driver_dm.HealthMonitor( healthmonitor_id=self.sample_data.hm1_id) provider_hm = driver_dm.HealthMonitor( healthmonitor_id=self.sample_data.hm1_id, admin_state_up=True, max_retries=1, max_retries_down=2) self.amp_driver.health_monitor_update(old_provider_hm, provider_hm) payload = {consts.HEALTH_MONITOR_ID: self.sample_data.hm1_id, consts.HEALTH_MONITOR_UPDATES: {}} mock_cast.assert_called_with({}, 'update_health_monitor', **payload) # L7 Rules @mock.patch('oslo_messaging.rpc.client._BaseCallContext.cast') def test_l7rule_create(self, mock_cast): provider_l7rule = driver_dm.L7Rule( l7rule_id=self.sample_data.l7rule1_id) self.amp_driver.l7rule_create(provider_l7rule) payload = {consts.L7RULE_ID: self.sample_data.l7rule1_id} mock_cast.assert_called_with({}, 'create_l7rule', **payload) @mock.patch('oslo_messaging.rpc.client._BaseCallContext.cast') def test_l7rule_delete(self, mock_cast): provider_l7rule = driver_dm.L7Rule( l7rule_id=self.sample_data.l7rule1_id) self.amp_driver.l7rule_delete(provider_l7rule) payload = {consts.L7RULE_ID: self.sample_data.l7rule1_id} mock_cast.assert_called_with({}, 'delete_l7rule', **payload) @mock.patch('oslo_messaging.rpc.client._BaseCallContext.cast') def test_l7rule_update(self, mock_cast): old_provider_l7rule = driver_dm.L7Rule( l7rule_id=self.sample_data.l7rule1_id) provider_l7rule = driver_dm.L7Rule( l7rule_id=self.sample_data.l7rule1_id, admin_state_up=True) self.amp_driver.l7rule_update(old_provider_l7rule, provider_l7rule) payload = {consts.L7RULE_ID: self.sample_data.l7rule1_id, consts.L7RULE_UPDATES: {}} mock_cast.assert_called_with({}, 'update_l7rule', **payload) @mock.patch('oslo_messaging.rpc.client._BaseCallContext.cast') def test_l7rule_update_invert(self, mock_cast): old_provider_l7rule = driver_dm.L7Rule( l7rule_id=self.sample_data.l7rule1_id) provider_l7rule = driver_dm.L7Rule( l7rule_id=self.sample_data.l7rule1_id, invert=True) self.amp_driver.l7rule_update(old_provider_l7rule, provider_l7rule) payload = {consts.L7RULE_ID: self.sample_data.l7rule1_id, consts.L7RULE_UPDATES: {}} mock_cast.assert_called_with({}, 'update_l7rule', **payload)

47.963455

87

0.705687

1,823

14,437

5.229841

0.099835

0.044053

0.0837

0.093979

0.828823

0.811831

0.788861

0.766415

0.756031

0

0.015753

0.195331

14,437

300

88

48.123333

0.804941

0.042114

0

0.558704

0

0.130359

0.102115

0

0.097166

1

0.105263

false

0

0.032389

0

0.1417

0

null

0

1

0

null

0

6

6d3d5cf565a83d8261e89093260221783fd6b910

107

py

Python

onelya_sdk/aeroexpress/__init__.py

tmconsulting/onelya-sdk

eb21398afed916021d74594d094b66e49fdb019c

[ "MIT" ]

6

2017-12-16T13:55:51.000Z

2020-01-28T01:46:23.000Z

onelya_sdk/aeroexpress/__init__.py

tmconsulting/onelya-sdk

eb21398afed916021d74594d094b66e49fdb019c

[ "MIT" ]

null

onelya_sdk/aeroexpress/__init__.py

tmconsulting/onelya-sdk

eb21398afed916021d74594d094b66e49fdb019c

[ "MIT" ]

6

2017-12-08T13:57:58.000Z

2017-12-12T03:16:42.000Z

from .reservation.requests import (OrderFullCustomerRequest, AeroexpressReservationRequest, ProductRequest)

107

0.897196

7

107

13.714286

1

0

0.046729

107

1

107

0.941176

0

1

0

true

0

1

0

1

0

1

0

1

null

0

1

0

1

0

null

0

1

0

1

0

6

edae56d63c517fc52225766c98bdf05b0881bcfc

40

py

Python

api/bybit/__init__.py

sheungon/fx-connectors

1eef5d6617a6a9403ddd1903ec56e826e2126832

[ "Apache-2.0" ]

1

2021-12-04T18:44:37.000Z

api/bybit/__init__.py

sheungon/fx-connectors

1eef5d6617a6a9403ddd1903ec56e826e2126832

[ "Apache-2.0" ]

null

api/bybit/__init__.py

sheungon/fx-connectors

1eef5d6617a6a9403ddd1903ec56e826e2126832

[ "Apache-2.0" ]

1

2022-03-18T07:51:49.000Z

from .bybit_service import BybitService

20

39

0.875

5

40

6.8

1

0

0.1

40

1

40

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

edd936b12a8af287a52efd89fcf998c49d425579

85

py

Python

scripts/__init__.py

HaraldWilhelmi/Baltica

02ea6388f6917db028d26435fea295c58f19fe0d

[ "MIT" ]

null

scripts/__init__.py

HaraldWilhelmi/Baltica

02ea6388f6917db028d26435fea295c58f19fe0d

[ "MIT" ]

null

scripts/__init__.py

HaraldWilhelmi/Baltica

02ea6388f6917db028d26435fea295c58f19fe0d

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: utf-8 """ Created on 11:12 2019-03-14 2019 """

12.142857

33

0.588235

15

85

3.333333

0.933333

0

0.242857

0.176471

85

6

34

14.166667

0.471429

0.847059

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

1

0

1

0

null

0

1

0

6

edf1acdf71d21bac9a7ca4236cfa021aa646122c

67

py

Python

src/model/__init__.py

RobertMcCarter/animal-finder

5ac839a65df62ab312e440ce43416727492e84d8

[ "MIT" ]

null

src/model/__init__.py

RobertMcCarter/animal-finder

5ac839a65df62ab312e440ce43416727492e84d8

[ "MIT" ]

null

src/model/__init__.py

RobertMcCarter/animal-finder

5ac839a65df62ab312e440ce43416727492e84d8

[ "MIT" ]

null

from .image import * from .region2d import * from .size2d import *

16.75

23

0.731343

9

67

5.444444

0.555556

0.408163

0

0.036364

0.179104

67

3

24

22.333333

0.854545

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

edf1d8dd4e788d99ba3567930976c0a2073c520c

28

py

Python

scripts/train_helper/__init__.py

AndAgio/Shallow2Deep

e42e9b3b11fdd2ec035144890a88e93a5154276f

[ "Apache-2.0" ]

null

scripts/train_helper/__init__.py

AndAgio/Shallow2Deep

e42e9b3b11fdd2ec035144890a88e93a5154276f

[ "Apache-2.0" ]

2

2021-02-17T12:07:45.000Z

2021-02-17T12:16:21.000Z

scripts/train_helper/__init__.py

AndAgio/Shallow2Deep

e42e9b3b11fdd2ec035144890a88e93a5154276f

[ "Apache-2.0" ]

null

from .train_helper import *

14

27

0.785714

4

28

5.25

1

0

0.142857

28

1

28

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

6100e3975f9f2924d443c531b0eec0d5a6ff2613

174

py

Python

examples/example_3_class_stub.py

CristianSifuentes/OOPPython

be6fe4d4761eabd06d0548bfa6edd67cbe437bf5

[ "MIT" ]

null

examples/example_3_class_stub.py

CristianSifuentes/OOPPython

be6fe4d4761eabd06d0548bfa6edd67cbe437bf5

[ "MIT" ]

null

examples/example_3_class_stub.py

CristianSifuentes/OOPPython

be6fe4d4761eabd06d0548bfa6edd67cbe437bf5

[ "MIT" ]

null

class Bike(object): def __init__(self): pass def update_sale_price(self): pass def sell(self): pass def service(self): pass

14.5

32

0.545977

21

174

4.238095

0.571429

0.359551

0.370787

0

0.367816

174

12

33

14.5

0.809091

0

0.444444

0

1

0.444444

false

0.444444

0

0.555556

0

1

0

null

1

0

null

0

1

0

1

0

1

0

6

6109276f68ce769b163feb97380ca421d11e2a59

35

py

Python

Netra_1.py

YogendraBhati/HacktoberaaFest

cf1e2e36ac0ec2772fe43a4f6f183a9bf4cd9d33

[ "Apache-2.0" ]

null

Netra_1.py

YogendraBhati/HacktoberaaFest

cf1e2e36ac0ec2772fe43a4f6f183a9bf4cd9d33

[ "Apache-2.0" ]

null

Netra_1.py

YogendraBhati/HacktoberaaFest

cf1e2e36ac0ec2772fe43a4f6f183a9bf4cd9d33

[ "Apache-2.0" ]

1

2021-10-08T22:18:52.000Z

print("File 1 for Hacktober 2021")

17.5

34

0.742857

6

35

4.333333

1

0

0.166667

0.142857

35

1

35

0.7

0

0.714286

0

1

0

true

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

b67998c075f084046012572318f78aa8f5d48372

42

py

Python

autox/autox_recommend/recall_and_rank/__init__.py

OneToolsCollection/4paradigm-AutoX

f8e838021354de17f5bb9bc44e9d68d12dda6427

[ "Apache-2.0" ]

null

autox/autox_recommend/recall_and_rank/__init__.py

OneToolsCollection/4paradigm-AutoX

f8e838021354de17f5bb9bc44e9d68d12dda6427

[ "Apache-2.0" ]

null

autox/autox_recommend/recall_and_rank/__init__.py

OneToolsCollection/4paradigm-AutoX

f8e838021354de17f5bb9bc44e9d68d12dda6427

[ "Apache-2.0" ]

null

from .recall_and_rank import RecallAndRank

42

0.904762

6

42

6

1

0

0.071429

42

1

42

0.923077

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

b6a1d8b9325ae7e6af7dca79406bdf05c0260e78

31

py

Python

codeqaapi/tests/__init__.py

solnsubuga/codeqa-api

e126e4d6bf9a9d588ddcf6b85bf925348a14b66e

[ "MIT" ]

null

codeqaapi/tests/__init__.py

solnsubuga/codeqa-api

e126e4d6bf9a9d588ddcf6b85bf925348a14b66e

[ "MIT" ]

9

2020-02-11T23:38:52.000Z

2022-02-10T09:03:33.000Z

codeqaapi/tests/__init__.py

solnsubuga/codeqa-api

e126e4d6bf9a9d588ddcf6b85bf925348a14b66e

[ "MIT" ]

null

from .base import BaseTestCase

15.5

30

0.83871

4

31

6.5

1

0

0.129032

31

1

31

0.962963

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

b6b262dfc0da2bcba4d00d61c4a3c84a901d7012

134

py

Python

test_dask_lthops.py

cloudbutton/lithops-dataframe

e8f2259dfd663b7fd84f2fc31548839d695a275f

[ "Apache-2.0" ]

null

test_dask_lthops.py

cloudbutton/lithops-dataframe

e8f2259dfd663b7fd84f2fc31548839d695a275f

[ "Apache-2.0" ]

null

test_dask_lthops.py

cloudbutton/lithops-dataframe

e8f2259dfd663b7fd84f2fc31548839d695a275f

[ "Apache-2.0" ]

1

2021-09-18T01:21:31.000Z

import dask as d import dask.array as da a = da.arange(10, chunks=2).sum() #b = da.arange(10, chunks=2).mean() a.compute() print(a)

14.888889

35

0.664179

27

134

3.296296

0.592593

0.224719

0.359551

0.382022

0

0.052632

0.149254

134

8

36

16.75

0.72807

0.253731

0

1

0

false

0

0.4

0

0.4

0.2

1

0

null

1

0

1

0

null

0

1

0

6