hexsha string | size int64 | ext string | lang string | max_stars_repo_path string | max_stars_repo_name string | max_stars_repo_head_hexsha string | max_stars_repo_licenses list | max_stars_count int64 | max_stars_repo_stars_event_min_datetime string | max_stars_repo_stars_event_max_datetime string | max_issues_repo_path string | max_issues_repo_name string | max_issues_repo_head_hexsha string | max_issues_repo_licenses list | max_issues_count int64 | max_issues_repo_issues_event_min_datetime string | max_issues_repo_issues_event_max_datetime string | max_forks_repo_path string | max_forks_repo_name string | max_forks_repo_head_hexsha string | max_forks_repo_licenses list | max_forks_count int64 | max_forks_repo_forks_event_min_datetime string | max_forks_repo_forks_event_max_datetime string | content string | avg_line_length float64 | max_line_length int64 | alphanum_fraction float64 | qsc_code_num_words_quality_signal int64 | qsc_code_num_chars_quality_signal float64 | qsc_code_mean_word_length_quality_signal float64 | qsc_code_frac_words_unique_quality_signal float64 | qsc_code_frac_chars_top_2grams_quality_signal float64 | qsc_code_frac_chars_top_3grams_quality_signal float64 | qsc_code_frac_chars_top_4grams_quality_signal float64 | qsc_code_frac_chars_dupe_5grams_quality_signal float64 | qsc_code_frac_chars_dupe_6grams_quality_signal float64 | qsc_code_frac_chars_dupe_7grams_quality_signal float64 | qsc_code_frac_chars_dupe_8grams_quality_signal float64 | qsc_code_frac_chars_dupe_9grams_quality_signal float64 | qsc_code_frac_chars_dupe_10grams_quality_signal float64 | qsc_code_frac_chars_replacement_symbols_quality_signal float64 | qsc_code_frac_chars_digital_quality_signal float64 | qsc_code_frac_chars_whitespace_quality_signal float64 | qsc_code_size_file_byte_quality_signal float64 | qsc_code_num_lines_quality_signal float64 | qsc_code_num_chars_line_max_quality_signal float64 | qsc_code_num_chars_line_mean_quality_signal float64 | qsc_code_frac_chars_alphabet_quality_signal float64 | qsc_code_frac_chars_comments_quality_signal float64 | qsc_code_cate_xml_start_quality_signal float64 | qsc_code_frac_lines_dupe_lines_quality_signal float64 | qsc_code_cate_autogen_quality_signal float64 | qsc_code_frac_lines_long_string_quality_signal float64 | qsc_code_frac_chars_string_length_quality_signal float64 | qsc_code_frac_chars_long_word_length_quality_signal float64 | qsc_code_frac_lines_string_concat_quality_signal float64 | qsc_code_cate_encoded_data_quality_signal float64 | qsc_code_frac_chars_hex_words_quality_signal float64 | qsc_code_frac_lines_prompt_comments_quality_signal float64 | qsc_code_frac_lines_assert_quality_signal float64 | qsc_codepython_cate_ast_quality_signal float64 | qsc_codepython_frac_lines_func_ratio_quality_signal float64 | qsc_codepython_cate_var_zero_quality_signal bool | qsc_codepython_frac_lines_pass_quality_signal float64 | qsc_codepython_frac_lines_import_quality_signal float64 | qsc_codepython_frac_lines_simplefunc_quality_signal float64 | qsc_codepython_score_lines_no_logic_quality_signal float64 | qsc_codepython_frac_lines_print_quality_signal float64 | qsc_code_num_words int64 | qsc_code_num_chars int64 | qsc_code_mean_word_length int64 | qsc_code_frac_words_unique null | qsc_code_frac_chars_top_2grams int64 | qsc_code_frac_chars_top_3grams int64 | qsc_code_frac_chars_top_4grams int64 | qsc_code_frac_chars_dupe_5grams int64 | qsc_code_frac_chars_dupe_6grams int64 | qsc_code_frac_chars_dupe_7grams int64 | qsc_code_frac_chars_dupe_8grams int64 | qsc_code_frac_chars_dupe_9grams int64 | qsc_code_frac_chars_dupe_10grams int64 | qsc_code_frac_chars_replacement_symbols int64 | qsc_code_frac_chars_digital int64 | qsc_code_frac_chars_whitespace int64 | qsc_code_size_file_byte int64 | qsc_code_num_lines int64 | qsc_code_num_chars_line_max int64 | qsc_code_num_chars_line_mean int64 | qsc_code_frac_chars_alphabet int64 | qsc_code_frac_chars_comments int64 | qsc_code_cate_xml_start int64 | qsc_code_frac_lines_dupe_lines int64 | qsc_code_cate_autogen int64 | qsc_code_frac_lines_long_string int64 | qsc_code_frac_chars_string_length int64 | qsc_code_frac_chars_long_word_length int64 | qsc_code_frac_lines_string_concat null | qsc_code_cate_encoded_data int64 | qsc_code_frac_chars_hex_words int64 | qsc_code_frac_lines_prompt_comments int64 | qsc_code_frac_lines_assert int64 | qsc_codepython_cate_ast int64 | qsc_codepython_frac_lines_func_ratio int64 | qsc_codepython_cate_var_zero int64 | qsc_codepython_frac_lines_pass int64 | qsc_codepython_frac_lines_import int64 | qsc_codepython_frac_lines_simplefunc int64 | qsc_codepython_score_lines_no_logic int64 | qsc_codepython_frac_lines_print int64 | effective string | hits int64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
12f9bf6b178620d43a7f13e6380549a5f972896e | 67 | py | Python | drop/multilabel_classification/mymodel.py | MRD-Git/Huggingface-course | 7c0440584e630cb8885c2a237bc6e8213cfd5572 | [
"MIT"
] | null | null | null | drop/multilabel_classification/mymodel.py | MRD-Git/Huggingface-course | 7c0440584e630cb8885c2a237bc6e8213cfd5572 | [
"MIT"
] | null | null | null | drop/multilabel_classification/mymodel.py | MRD-Git/Huggingface-course | 7c0440584e630cb8885c2a237bc6e8213cfd5572 | [
"MIT"
] | null | null | null | from transformers import BertForSequenceClassification
import torch | 33.5 | 54 | 0.925373 | 6 | 67 | 10.333333 | 0.833333 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.074627 | 67 | 2 | 55 | 33.5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
429292bf78f0494230b3604ef21f5b7ab89cc543 | 34 | py | Python | A1_PS10_IPL_GROUP070/__init__.py | narendranss/A1_PS10_IPL_GROUP070 | 57bbd724003e19f0f484f5683b1503eefda2f40e | [
"MIT"
] | null | null | null | A1_PS10_IPL_GROUP070/__init__.py | narendranss/A1_PS10_IPL_GROUP070 | 57bbd724003e19f0f484f5683b1503eefda2f40e | [
"MIT"
] | null | null | null | A1_PS10_IPL_GROUP070/__init__.py | narendranss/A1_PS10_IPL_GROUP070 | 57bbd724003e19f0f484f5683b1503eefda2f40e | [
"MIT"
] | null | null | null | from A1_PS10_IPL_GROUP070 import * | 34 | 34 | 0.882353 | 6 | 34 | 4.5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.193548 | 0.088235 | 34 | 1 | 34 | 34 | 0.677419 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
42aadc99fa9634e705e7ec7648dbcc2993452319 | 136 | py | Python | scripts/npc/autogen_face_henesys2.py | doriyan13/doristory | 438caf3b123922da3f5f3b16fcc98a26a8ab85ce | [
"MIT"
] | null | null | null | scripts/npc/autogen_face_henesys2.py | doriyan13/doristory | 438caf3b123922da3f5f3b16fcc98a26a8ab85ce | [
"MIT"
] | null | null | null | scripts/npc/autogen_face_henesys2.py | doriyan13/doristory | 438caf3b123922da3f5f3b16fcc98a26a8ab85ce | [
"MIT"
] | null | null | null | # Character field ID when accessed: 100000103
# ObjectID: 1000001
# ParentID: 1052005
# Object Position X: 102
# Object Position Y: 133
| 22.666667 | 45 | 0.757353 | 18 | 136 | 5.722222 | 0.888889 | 0.271845 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.256637 | 0.169118 | 136 | 5 | 46 | 27.2 | 0.654867 | 0.919118 | 0 | null | 0 | null | 0 | 0 | null | 0 | 0 | 0 | null | 1 | null | true | 0 | 0 | null | null | null | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
c4373f8e7104583a6ef77eb6de3c04bf5a14d1d5 | 12,221 | py | Python | site-packages/freezerclient/tests/unit/v2/test_client_jobs.py | hariza17/freezer_libraries | e0bd890eba5e7438976fb3b4d66c41c128bab790 | [
"PSF-2.0"
] | null | null | null | site-packages/freezerclient/tests/unit/v2/test_client_jobs.py | hariza17/freezer_libraries | e0bd890eba5e7438976fb3b4d66c41c128bab790 | [
"PSF-2.0"
] | null | null | null | site-packages/freezerclient/tests/unit/v2/test_client_jobs.py | hariza17/freezer_libraries | e0bd890eba5e7438976fb3b4d66c41c128bab790 | [
"PSF-2.0"
] | null | null | null | # (c) Copyright 2018 ZTE 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.
import json
import unittest
import mock
from freezerclient import exceptions
from freezerclient.v2.managers import jobs
class TestJobManager(unittest.TestCase):
def setUp(self):
self.mock_client = mock.Mock()
self.mock_response = mock.Mock()
self.mock_client.endpoint = 'http://testendpoint:9999'
self.mock_client.project_id = 'tecs'
self.mock_client.auth_token = 'testtoken'
self.mock_client.client_id = 'test_client_id_78900987'
self.job_manager = jobs.JobManager(self.mock_client)
self.headers = {
'X-Auth-Token': 'testtoken',
'Content-Type': 'application/json',
'Accept': 'application/json'
}
@mock.patch('freezerclient.v2.managers.jobs.requests')
def test_create(self, mock_requests):
self.assertEqual('http://testendpoint:9999/v2/tecs/jobs/',
self.job_manager.endpoint)
self.assertEqual({'X-Auth-Token': 'testtoken',
'Content-Type': 'application/json',
'Accept': 'application/json'},
self.job_manager.headers)
@mock.patch('freezerclient.v2.managers.jobs.requests')
def test_create_ok(self, mock_requests):
self.mock_response.status_code = 201
self.mock_response.json.return_value = {'job_id': 'qwerqwer'}
mock_requests.post.return_value = self.mock_response
retval = self.job_manager.create({'job': 'metadata'})
self.assertEqual('qwerqwer', retval)
@mock.patch('freezerclient.v2.managers.jobs.json')
@mock.patch('freezerclient.v2.managers.jobs.requests')
def test_create_adds_client_id_if_not_provided(self, mock_requests,
mock_json):
self.mock_response.status_code = 201
self.mock_response.json.return_value = {'job_id': 'qwerqwer'}
mock_json.dumps.return_value = {'job': 'mocked'}
mock_requests.post.return_value = self.mock_response
retval = self.job_manager.create({'job': 'metadata'})
mock_json.dumps.assert_called_with({
'job': 'metadata', 'client_id': 'test_client_id_78900987'
})
self.assertEqual('qwerqwer', retval)
@mock.patch('freezerclient.v2.managers.jobs.json')
@mock.patch('freezerclient.v2.managers.jobs.requests')
def test_create_leaves_provided_client_id(self, mock_requests, mock_json):
self.mock_response.status_code = 201
self.mock_response.json.return_value = {'job_id': 'qwerqwer'}
mock_json.dumps.return_value = {'job': 'mocked'}
mock_requests.post.return_value = self.mock_response
retval = self.job_manager.create(
{'job': 'metadata', 'client_id': 'parmenide'})
mock_json.dumps.assert_called_with({'job': 'metadata',
'client_id': 'parmenide'})
self.assertEqual('qwerqwer', retval)
@mock.patch('freezerclient.v2.managers.jobs.requests')
def test_create_fail_when_api_return_error_code(self, mock_requests):
self.mock_response.status_code = 500
mock_requests.post.return_value = self.mock_response
self.assertRaises(exceptions.ApiClientException,
self.job_manager.create, {'job': 'metadata'})
@mock.patch('freezerclient.v2.managers.jobs.requests')
def test_delete_ok(self, mock_requests):
self.mock_response.status_code = 204
mock_requests.delete.return_value = self.mock_response
retval = self.job_manager.delete('test_job_id')
self.assertIsNone(retval)
@mock.patch('freezerclient.v2.managers.jobs.requests')
def test_delete_fail(self, mock_requests):
self.mock_response.status_code = 500
mock_requests.delete.return_value = self.mock_response
self.assertRaises(exceptions.ApiClientException,
self.job_manager.delete, 'test_job_id')
@mock.patch('freezerclient.v2.managers.jobs.requests')
def test_get_ok(self, mock_requests):
self.mock_response.status_code = 200
self.mock_response.json.return_value = {'job_id': 'qwerqwer'}
mock_requests.get.return_value = self.mock_response
retval = self.job_manager.get('test_job_id')
self.assertEqual({'job_id': 'qwerqwer'}, retval)
@mock.patch('freezerclient.v2.managers.jobs.requests')
def test_get_fails_on_error_different_from_404(self, mock_requests):
self.mock_response.status_code = 500
mock_requests.get.return_value = self.mock_response
self.assertRaises(exceptions.ApiClientException, self.job_manager.get,
'test_job_id')
@mock.patch('freezerclient.v2.managers.jobs.requests')
def test_get_none(self, mock_requests):
self.mock_response.status_code = 404
mock_requests.get.return_value = self.mock_response
retval = self.job_manager.get('test_job_id')
self.assertIsNone(retval)
@mock.patch('freezerclient.v2.managers.jobs.requests')
def test_list_ok(self, mock_requests):
self.mock_response.status_code = 200
job_list = [{'job_id_0': 'bomboloid'}, {'job_id_1': 'asdfasdf'}]
self.mock_response.json.return_value = {'jobs': job_list}
mock_requests.get.return_value = self.mock_response
retval = self.job_manager.list()
self.assertEqual(job_list, retval)
@mock.patch('freezerclient.v2.managers.jobs.requests')
def test_list_error(self, mock_requests):
self.mock_response.status_code = 404
job_list = [{'job_id_0': 'bomboloid'}, {'job_id_1': 'asdfasdf'}]
self.mock_response.json.return_value = {'clients': job_list}
mock_requests.get.return_value = self.mock_response
self.assertRaises(exceptions.ApiClientException, self.job_manager.list)
@mock.patch('freezerclient.v2.managers.jobs.requests')
def test_update_ok(self, mock_requests):
self.mock_response.status_code = 200
self.mock_response.json.return_value = {
"patch": {"status": "bamboozled"},
"version": 12,
"job_id": "d454beec-1f3c-4d11-aa1a-404116a40502"
}
mock_requests.patch.return_value = self.mock_response
retval = self.job_manager.update(
'd454beec-1f3c-4d11-aa1a-404116a40502', {'status': 'bamboozled'})
self.assertEqual(12, retval)
@mock.patch('freezerclient.v2.managers.jobs.requests')
def test_update_raise_MetadataUpdateFailure_when_api_return_error_code(
self, mock_requests):
self.mock_response.json.return_value = {
"patch": {"status": "bamboozled"},
"version": 12,
"job_id": "d454beec-1f3c-4d11-aa1a-404116a40502"
}
self.mock_response.status_code = 404
self.mock_response.text = (
'{"title": "Not Found","description":"No document found with ID '
'd454beec-1f3c-4d11-aa1a-404116a40502x"}'
)
mock_requests.patch.return_value = self.mock_response
self.assertRaises(exceptions.ApiClientException,
self.job_manager.update,
'd454beec-1f3c-4d11-aa1a-404116a40502',
{'status': 'bamboozled'})
@mock.patch('freezerclient.v2.managers.jobs.requests')
def test_start_job_posts_proper_data(self, mock_requests):
job_id = 'jobdfsfnqwerty1234'
self.mock_response.status_code = 202
self.mock_response.json.return_value = {'result': 'success'}
mock_requests.post.return_value = self.mock_response
# /v2/{project_id}/jobs/{job_id}/event
endpoint = '{0}/v2/{1}/jobs/{2}/event'.format(
self.mock_client.endpoint,
self.mock_client.project_id,
job_id)
data = {"start": None}
retval = self.job_manager.start_job(job_id)
self.assertEqual({'result': 'success'}, retval)
args = mock_requests.post.call_args[0]
kwargs = mock_requests.post.call_args[1]
self.assertEqual(endpoint, args[0])
self.assertEqual(data, json.loads(kwargs['data']))
self.assertEqual(self.headers, kwargs['headers'])
@mock.patch('freezerclient.v2.managers.jobs.requests')
def test_start_job_raise_ApiClientException_when_api_return_error_code(
self, mock_requests):
job_id = 'jobdfsfnqwerty1234'
self.mock_response.status_code = 500
self.mock_response.json.return_value = {'result': 'success'}
mock_requests.post.return_value = self.mock_response
self.assertRaises(exceptions.ApiClientException,
self.job_manager.start_job, job_id)
@mock.patch('freezerclient.v2.managers.jobs.requests')
def test_stop_job_posts_proper_data(self, mock_requests):
job_id = 'jobdfsfnqwerty1234'
self.mock_response.status_code = 202
self.mock_response.json.return_value = {'result': 'success'}
mock_requests.post.return_value = self.mock_response
# /v2/{project_id}/jobs/{job_id}/event
endpoint = '{0}/v2/{1}/jobs/{2}/event'.format(
self.mock_client.endpoint,
self.mock_client.project_id,
job_id)
data = {"stop": None}
retval = self.job_manager.stop_job(job_id)
self.assertEqual({'result': 'success'}, retval)
args = mock_requests.post.call_args[0]
kwargs = mock_requests.post.call_args[1]
self.assertEqual(endpoint, args[0])
self.assertEqual(data, json.loads(kwargs['data']))
self.assertEqual(self.headers, kwargs['headers'])
@mock.patch('freezerclient.v2.managers.jobs.requests')
def test_stop_job_raise_ApiClientException_when_api_return_error_code(
self, mock_requests):
job_id = 'jobdfsfnqwerty1234'
self.mock_response.status_code = 500
self.mock_response.json.return_value = {'result': 'success'}
mock_requests.post.return_value = self.mock_response
self.assertRaises(exceptions.ApiClientException,
self.job_manager.start_job, job_id)
@mock.patch('freezerclient.v2.managers.jobs.requests')
def test_abort_job_posts_proper_data(self, mock_requests):
job_id = 'jobdfsfnqwerty1234'
self.mock_response.status_code = 202
self.mock_response.json.return_value = {'result': 'success'}
mock_requests.post.return_value = self.mock_response
# /v2/{project_id}/jobs/{job_id}/event
endpoint = '{0}/v2/{1}/jobs/{2}/event'.format(
self.mock_client.endpoint,
self.mock_client.project_id,
job_id)
data = {"abort": None}
retval = self.job_manager.abort_job(job_id)
self.assertEqual({'result': 'success'}, retval)
args = mock_requests.post.call_args[0]
kwargs = mock_requests.post.call_args[1]
self.assertEqual(endpoint, args[0])
self.assertEqual(data, json.loads(kwargs['data']))
self.assertEqual(self.headers, kwargs['headers'])
@mock.patch('freezerclient.v2.managers.jobs.requests')
def test_abort_job_raise_ApiClientException_when_api_return_error_code(
self, mock_requests):
job_id = 'jobdfsfnqwerty1234'
self.mock_response.status_code = 500
self.mock_response.json.return_value = {'result': 'success'}
mock_requests.post.return_value = self.mock_response
self.assertRaises(exceptions.ApiClientException,
self.job_manager.abort_job, job_id)
| 44.60219 | 79 | 0.664757 | 1,460 | 12,221 | 5.312329 | 0.122603 | 0.088706 | 0.111398 | 0.068076 | 0.847473 | 0.832646 | 0.828907 | 0.811501 | 0.804023 | 0.77411 | 0 | 0.028742 | 0.219949 | 12,221 | 273 | 80 | 44.765568 | 0.784853 | 0.054578 | 0 | 0.630631 | 0 | 0 | 0.18731 | 0.102106 | 0 | 0 | 0 | 0 | 0.144144 | 1 | 0.094595 | false | 0 | 0.022523 | 0 | 0.121622 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
c44110cbc171f46ec5b9816d6b6fc98f95c3e4c6 | 39,581 | py | Python | FSJ_django20_project/FSJ/tests/test_browser_coordinator.py | CMPUT401FSJ/FSJAwards | 01f630e5060d70eecf7cb8f35b576f8799e2d7af | [
"MIT"
] | 6 | 2018-02-03T21:37:14.000Z | 2020-11-20T19:07:20.000Z | FSJ_django20_project/FSJ/tests/test_browser_coordinator.py | CMPUT401FSJ/FSJAwards | 01f630e5060d70eecf7cb8f35b576f8799e2d7af | [
"MIT"
] | 145 | 2018-02-01T02:38:17.000Z | 2018-06-06T16:22:05.000Z | FSJ_django20_project/FSJ/tests/test_browser_coordinator.py | CMPUT401FSJ/FSJAwards | 01f630e5060d70eecf7cb8f35b576f8799e2d7af | [
"MIT"
] | 4 | 2018-05-04T22:04:29.000Z | 2020-10-01T11:45:15.000Z | from ..models import *
from django.contrib.auth.models import User
from selenium.webdriver.support.wait import WebDriverWait
timeout = 15
import time
import pytz
import datetime
from .selenium_test import SeleniumTest
from django.conf import settings
from selenium.webdriver.firefox.webdriver import WebDriver
class CoordinatorSeleniumTest(SeleniumTest):
@classmethod
def setUpClass(cls):
super(CoordinatorSeleniumTest, cls).setUpClass()
def setUp(self):
self.password = "coord_password"
self.ccid = "coordinator"
self.first_name = "A"
self.last_name = "Coordinator"
self.email = "coordinator@csjawards.ca"
self.lang_pref = "en"
self.user = User.objects.create_user(username=self.ccid,
password=self.password)
self.coordinator = Coordinator.objects.create(ccid=self.ccid, first_name=self.first_name,
last_name=self.last_name, email=self.email,
user=self.user, lang_pref=self.lang_pref)
self.user = User.objects.get(username=self.ccid)
self.selenium.get('%s%s' % (self.live_server_url, '/login/'))
WebDriverWait(self.selenium, timeout).until(
lambda driver: driver.find_element_by_id("id_username"))
username = self.selenium.find_element_by_id("id_username")
username.send_keys(self.ccid)
WebDriverWait(self.selenium, timeout).until(
lambda driver: driver.find_element_by_id("id_password"))
password =self.selenium.find_element_by_id("id_password")
password.send_keys(self.password)
save = self.selenium.find_element_by_css_selector("button.btn:nth-child(5)")
save.click()
WebDriverWait(self.selenium, timeout).until(
lambda driver: driver.find_element_by_tag_name('body'))
def tearDown(self):
self.selenium.get('%s%s' % (self.live_server_url, '/logout/'))
def test_coordinator_awards(self):
self.award_name = "An Award Name"
self.award_description = "An Award Description"
self.new_award_description = "This is a new description"
self.award_value = "An Award Value"
self.program_name = "A Program Name"
self.program_code = "A Code"
self.year_name = "Year 1"
self.award_program = Program.objects.create(name=self.program_name, code=self.program_code)
self.award_year = YearOfStudy.objects.create(year=self.year_name)
self.start_date = "2018-01-01"
self.end_date = "2018-12-31"
self.new_start_date = "2019-01-01"
self.new_end_date = "2019-12-31"
self.selenium.get('%s%s' % (self.live_server_url, '/awards/'))
self.selenium.find_element_by_link_text("Add award").click()
WebDriverWait(self.selenium, timeout).until(
lambda driver: driver.find_element_by_tag_name('body'))
self.assertEquals(self.selenium.current_url, ("%s%s" % (self.live_server_url, '/awards/add/')))
self.selenium.find_element_by_id("id_name").send_keys(self.award_name)
self.selenium.find_element_by_id("id_description").send_keys(self.award_description)
self.selenium.find_element_by_id("id_value").send_keys(self.award_value)
self.selenium.find_element_by_css_selector("#id_programs_0").click()
self.selenium.find_element_by_css_selector("#id_years_of_study_0").click()
self.selenium.find_element_by_id("id_start_date").send_keys(self.start_date)
self.selenium.find_element_by_id("id_end_date").send_keys(self.end_date)
self.selenium.find_element_by_id("id_documents_needed").click()
self.selenium.find_element_by_id("id_is_active").click()
self.selenium.find_element_by_css_selector("button.btn.btn-success").click()
WebDriverWait(self.selenium, timeout).until(
lambda driver: driver.current_url == ("%s%s" % (self.live_server_url, '/awards/')))
award = Award.objects.get(name = self.award_name)
self.assertEquals(award.description, self.award_description)
self.assertEquals(award.value, self.award_value)
self.assertEquals(award.programs.get(code=self.program_code), self.award_program)
self.assertEquals(award.years_of_study.get(year=self.year_name), self.award_year)
self.assertEquals(award.start_date.astimezone(pytz.timezone('America/Edmonton')).strftime('%Y-%m-%d'), self.start_date)
self.assertEquals(award.end_date.astimezone(pytz.timezone('America/Edmonton')).strftime('%Y-%m-%d'), self.end_date)
self.assertEquals(award.documents_needed, True)
self.assertEquals(award.is_active, True)
WebDriverWait(self.selenium, timeout).until(
lambda driver: driver.find_element_by_name("awardaction"))
self.selenium.find_element_by_name("awardaction").click()
self.selenium.find_element_by_name("_deactivate").click()
WebDriverWait(self.selenium, timeout).until(
lambda driver: driver.find_element_by_name("awardaction"))
award.refresh_from_db()
self.assertEquals(award.is_active, False)
self.selenium.find_element_by_name("awardaction").click()
self.selenium.find_element_by_name("_activate").click()
WebDriverWait(self.selenium, timeout).until(
lambda driver: driver.find_element_by_name("awardaction"))
award.refresh_from_db()
self.assertEquals(award.is_active, True)
self.selenium.find_element_by_name("awardaction").click()
self.selenium.find_element_by_css_selector("body > div > div:nth-child(3) > div > div:nth-child(2) > form:nth-child(2) > div:nth-child(9) > div:nth-child(2) > #id_start_date").send_keys(self.new_start_date)
self.selenium.find_element_by_css_selector("body > div > div:nth-child(3) > div > div:nth-child(2) > form:nth-child(2) > div:nth-child(10) > div:nth-child(2) > #id_end_date").send_keys(self.new_end_date)
self.selenium.find_element_by_name("_changeDate").click()
WebDriverWait(self.selenium, timeout).until(
lambda driver: driver.find_element_by_name("awardaction"))
award.refresh_from_db()
self.assertEquals(award.start_date.astimezone(pytz.timezone('America/Edmonton')).strftime('%Y-%m-%d'), self.new_start_date)
self.assertEquals(award.end_date.astimezone(pytz.timezone('America/Edmonton')).strftime('%Y-%m-%d'), self.new_end_date)
self.selenium.find_element_by_link_text("Edit").click()
WebDriverWait(self.selenium, timeout).until(
lambda driver: driver.find_element_by_id("id_description"))
description = self.selenium.find_element_by_id("id_description")
description.clear()
description.send_keys(self.new_award_description)
self.selenium.find_element_by_css_selector("button.btn.btn-success").click()
WebDriverWait(self.selenium, timeout).until(
lambda driver: driver.find_element_by_name("awardaction"))
award.refresh_from_db()
self.assertEquals(award.description, self.new_award_description)
self.selenium.find_element_by_name("awardaction").click()
self.selenium.find_element_by_css_selector("button.btn.btn-danger.pull-right").click()
self.selenium.switch_to_alert().accept()
time.sleep(5)
with self.assertRaises(Award.DoesNotExist):
award = Award.objects.get(name=self.award_name)
self.selenium.find_element_by_link_text("Add award").click()
WebDriverWait(self.selenium, timeout).until(
lambda driver: driver.find_element_by_tag_name('body'))
self.assertEquals(self.selenium.current_url, ("%s%s" % (self.live_server_url, '/awards/add/')))
self.selenium.find_element_by_id("id_name").send_keys(self.award_name)
self.selenium.find_element_by_id("id_description").send_keys(self.award_description)
self.selenium.find_element_by_id("id_value").send_keys(self.award_value)
self.selenium.find_element_by_css_selector("#id_programs_0").click()
self.selenium.find_element_by_css_selector("#id_years_of_study_0").click()
self.selenium.find_element_by_id("id_start_date").send_keys(self.start_date)
self.selenium.find_element_by_id("id_end_date").send_keys(self.end_date)
self.selenium.find_element_by_id("id_documents_needed").click()
self.selenium.find_element_by_id("id_is_active").click()
self.selenium.find_element_by_link_text("Cancel").click()
WebDriverWait(self.selenium, timeout).until(
lambda driver: driver.find_element_by_tag_name('body'))
with self.assertRaises(Award.DoesNotExist):
award = Award.objects.get(name=self.award_name)
def test_coordinator_students(self):
self.student_ccid = "student"
self.student_first_name = "A"
self.student_middle_name = "Normal"
self.student_last_name = "Student"
self.student_ualberta_id = "123456789"
self.program_name = "A Program Name"
self.program_code = "A Code"
self.year_name = "Year 1"
self.student_program = Program.objects.create(name=self.program_name, code=self.program_code)
self.student_year = YearOfStudy.objects.create(year=self.year_name)
self.student_email = "student@csjawards.ca"
self.student_gpa = "4.0"
self.student_credits = "24"
self.new_student_first_name = "New"
self.new_student_middle_name = "Student"
self.new_student_last_name = "Name"
self.selenium.get('%s%s' % (self.live_server_url, '/students/'))
self.selenium.find_element_by_link_text("Add student").click()
self.selenium.find_element_by_id("id_ccid").send_keys(self.student_ccid)
self.selenium.find_element_by_id("id_first_name").send_keys(self.student_first_name)
self.selenium.find_element_by_id("id_middle_name").send_keys(self.student_middle_name)
self.selenium.find_element_by_id("id_last_name").send_keys(self.student_last_name)
self.selenium.find_element_by_id("id_email").send_keys(self.student_email)
self.selenium.find_element_by_css_selector("#id_program > option:nth-child(2)").click()
self.selenium.find_element_by_css_selector("#id_year > option:nth-child(2)").click()
self.selenium.find_element_by_id("id_student_id").send_keys(self.student_ualberta_id)
self.selenium.find_element_by_id("id_gpa").send_keys(self.student_gpa)
self.selenium.find_element_by_css_selector("button.btn.btn-success").click()
WebDriverWait(self.selenium, timeout).until(
lambda driver: driver.find_element_by_name("instance"))
student = Student.objects.get(ccid = self.student_ccid)
self.assertEquals(student.first_name, self.student_first_name)
self.assertEquals(student.middle_name, self.student_middle_name)
self.assertEquals(student.last_name, self.student_last_name)
self.assertEquals(student.email, self.student_email)
self.assertEquals(student.program, self.student_program)
self.assertEquals(student.year, self.student_year)
self.assertEquals(student.student_id, self.student_ualberta_id)
self.assertEquals(student.gpa, self.student_gpa)
user = User.objects.get(username = self.student_ccid)
self.assertEquals(student.user, user)
self.assertEquals(student.email, user.email, self.student_email)
self.selenium.find_element_by_link_text("Edit").click()
WebDriverWait(self.selenium, timeout).until(
lambda driver: driver.find_element_by_id("id_first_name"))
first_name = self.selenium.find_element_by_id("id_first_name")
first_name.clear()
first_name.send_keys(self.new_student_first_name)
middle_name = self.selenium.find_element_by_id("id_middle_name")
middle_name.clear()
middle_name.send_keys(self.new_student_middle_name)
last_name = self.selenium.find_element_by_id("id_last_name")
last_name.clear()
last_name.send_keys(self.new_student_last_name)
self.selenium.find_element_by_css_selector("button.btn.btn-success").click()
WebDriverWait(self.selenium, timeout).until(
lambda driver: driver.find_element_by_name("instance"))
student.refresh_from_db()
self.assertEquals(student.first_name, self.new_student_first_name)
self.assertEquals(student.middle_name, self.new_student_middle_name)
self.assertEquals(student.last_name, self.new_student_last_name)
self.selenium.find_element_by_name("instance").click()
self.selenium.find_element_by_name("delete").click()
WebDriverWait(self.selenium, timeout).until(
lambda driver: driver.find_element_by_link_text("Upload student files"))
with self.assertRaises(Student.DoesNotExist):
student = Student.objects.get(ccid = self.student_ccid)
with self.assertRaises(User.DoesNotExist):
user = User.objects.get(username = self.student_ccid)
self.selenium.find_element_by_link_text("Upload student files").click()
WebDriverWait(self.selenium, timeout).until(
lambda driver: driver.find_element_by_id("id_student_file"))
self.selenium.find_element_by_id("id_encoding_0").click()
self.selenium.find_element_by_id("id_student_file").send_keys(settings.TEST_FILE_ROOT+'\selenium_test_student.csv')
self.selenium.find_element_by_id("id_gpa_file").send_keys(settings.TEST_FILE_ROOT+'\selenium_test_gpa.csv')
self.selenium.find_element_by_css_selector("button.btn.btn-success").click()
WebDriverWait(self.selenium, timeout).until(
lambda driver: driver.find_element_by_tag_name("body"))
self.selenium.get('%s%s' % (self.live_server_url, '/students/'))
WebDriverWait(self.selenium, timeout).until(
lambda driver: driver.find_element_by_tag_name("body"))
student = Student.objects.get(ccid=self.student_ccid)
self.assertEquals(student.first_name, self.student_first_name)
self.assertEquals(student.middle_name, self.student_middle_name)
self.assertEquals(student.last_name, self.student_last_name)
self.assertEquals(student.email, self.student_email)
self.assertEquals(student.program, self.student_program)
self.assertEquals(student.year, self.student_year)
self.assertEquals(student.student_id, self.student_ualberta_id)
self.assertEquals(student.gpa, self.student_gpa)
self.assertEquals(student.credits, self.student_credits)
def test_coordinator_adjudicators(self):
self.adjudicator_ccid = "adjudicator"
self.adjudicator_first_name = "An"
self.adjudicator_last_name = "Adjudicator"
self.adjudicator_email = "adjudicator@csjawards.ca"
self.new_adjudicator_email = "newadjudicatoremail@csjawards.ca"
self.selenium.get('%s%s' % (self.live_server_url, '/adjudicators/'))
WebDriverWait(self.selenium, timeout).until(
lambda driver: driver.find_element_by_tag_name("body"))
self.selenium.find_element_by_link_text("Add adjudicator").click()
WebDriverWait(self.selenium, timeout).until(
lambda driver: driver.find_element_by_tag_name("body"))
self.selenium.find_element_by_id("id_ccid").send_keys(self.adjudicator_ccid)
self.selenium.find_element_by_id("id_first_name").send_keys(self.adjudicator_first_name)
self.selenium.find_element_by_id("id_last_name").send_keys(self.adjudicator_last_name)
self.selenium.find_element_by_id("id_email").send_keys(self.adjudicator_email)
self.selenium.find_element_by_css_selector("button.btn.btn-success").click()
WebDriverWait(self.selenium, timeout).until(
lambda driver: driver.current_url == ("%s%s" % (self.live_server_url, '/adjudicators/')))
adjudicator = Adjudicator.objects.get(ccid=self.adjudicator_ccid)
self.assertEquals(adjudicator.first_name, self.adjudicator_first_name)
self.assertEquals(adjudicator.last_name, self.adjudicator_last_name)
self.assertEquals(adjudicator.email, self.adjudicator_email)
user = User.objects.get(username=self.adjudicator_ccid)
self.assertEquals(adjudicator.user, user)
self.assertEquals(adjudicator.user.email, user.email, self.adjudicator_email)
self.selenium.find_element_by_link_text("Edit").click()
WebDriverWait(self.selenium, timeout).until(
lambda driver: driver.find_element_by_tag_name("body"))
email = self.selenium.find_element_by_id("id_email")
email.clear()
email.send_keys(self.new_adjudicator_email)
self.selenium.find_element_by_css_selector("button.btn.btn-success").click()
WebDriverWait(self.selenium, timeout).until(
lambda driver: driver.find_element_by_tag_name("body"))
adjudicator.refresh_from_db()
user.refresh_from_db()
self.assertEquals(adjudicator.email, user.email, self.new_adjudicator_email)
self.selenium.find_element_by_name("instance").click()
self.selenium.find_element_by_css_selector("button.btn.btn-danger").click()
WebDriverWait(self.selenium, timeout).until(
lambda driver: driver.find_element_by_tag_name("body"))
with self.assertRaises(Adjudicator.DoesNotExist):
adjudicator = Adjudicator.objects.get(ccid = self.adjudicator_ccid)
with self.assertRaises(User.DoesNotExist):
user = User.objects.get(username = self.adjudicator_ccid)
def test_coordinator_committees(self):
self.committee_name = "A Committee"
self.adjudicator_ccid = "adjudicator"
self.adjudicator_first_name = "An"
self.adjudicator_last_name = "Adjudicator"
self.adjudicator_email = "adjudicator@csjawards.ca"
self.adjudicator = Adjudicator.objects.create(ccid = self.adjudicator_ccid, first_name = self.adjudicator_first_name,
last_name = self.adjudicator_last_name, email = self.adjudicator_email)
self.year = "First"
self.year_of_study = YearOfStudy.objects.create(year=self.year)
self.program_code = "PRFG"
self.program_name = "Science"
self.program = Program.objects.create(code=self.program_code, name=self.program_name)
self.award_name = "This award"
self.award_description = "For students"
self.award_value = "One gold pen"
self.award_start_date = str(datetime.datetime.now(pytz.timezone('America/Vancouver')))
self.award_end_date = str(datetime.datetime.now(pytz.timezone('America/Edmonton')))
self.award_documents_needed = False
self.award_is_active = True
self.award = Award.objects.create(name=self.award_name, description=self.award_description, value=self.award_value,
start_date=self.award_start_date, end_date=self.award_end_date,
documents_needed=self.award_documents_needed, is_active=self.award_is_active)
self.award.programs.add(self.program)
self.award.years_of_study.add(self.year_of_study)
self.selenium.get('%s%s' % (self.live_server_url, '/committees/'))
WebDriverWait(self.selenium, timeout).until(
lambda driver: driver.find_element_by_tag_name("body"))
self.selenium.find_element_by_link_text("Add Committee").click()
WebDriverWait(self.selenium, timeout).until(
lambda driver: driver.find_element_by_tag_name("body"))
self.selenium.find_element_by_id("id_committee_name").send_keys(self.committee_name)
self.selenium.find_element_by_id("id_adjudicators_0").click()
self.selenium.find_element_by_id("id_awards_0").click()
self.selenium.find_element_by_css_selector("button.btn.btn-success").click()
WebDriverWait(self.selenium, timeout).until(
lambda driver: driver.find_element_by_tag_name("body"))
committee = Committee.objects.get(committee_name = self.committee_name)
self.assertEquals(committee.adjudicators.get(ccid = self.adjudicator_ccid), self.adjudicator)
self.assertEquals(committee.awards.get(name = self.award_name), self.award)
self.second_award_name = "A different award"
self.second_award = Award.objects.create(name=self.second_award_name, description=self.award_description,
value=self.award_value,
start_date=self.award_start_date, end_date=self.award_end_date,
documents_needed=self.award_documents_needed,
is_active=self.award_is_active)
self.selenium.find_element_by_link_text("Edit").click()
WebDriverWait(self.selenium, timeout).until(
lambda driver: driver.find_element_by_tag_name("body"))
self.selenium.find_element_by_id("id_awards_1").click()
self.selenium.find_element_by_css_selector("button.btn.btn-success").click()
WebDriverWait(self.selenium, timeout).until(
lambda driver: driver.find_element_by_tag_name("body"))
committee.refresh_from_db()
self.assertEquals(committee.awards.get(name = self.second_award_name), self.second_award)
self.selenium.find_element_by_name("instance").click()
self.selenium.find_element_by_name("delete").click()
WebDriverWait(self.selenium, timeout).until(
lambda driver: driver.find_element_by_tag_name("body"))
with self.assertRaises(Committee.DoesNotExist):
committee = Committee.objects.get(committee_name = self.committee_name)
def test_coordinator_programs(self):
self.program_code = "CODE123"
self.program_name = "Program 123"
self.new_program_code = "CODE124"
self.new_program_name = "Program 124"
self.selenium.get('%s%s' % (self.live_server_url, '/programs/'))
WebDriverWait(self.selenium, timeout).until(
lambda driver: driver.find_element_by_tag_name("body"))
self.selenium.find_element_by_link_text("Add program").click()
WebDriverWait(self.selenium, timeout).until(
lambda driver: driver.find_element_by_tag_name("body"))
self.selenium.find_element_by_id("id_code").send_keys(self.program_code)
self.selenium.find_element_by_id("id_name").send_keys(self.program_name)
self.selenium.find_element_by_css_selector("button.btn.btn-success").click()
WebDriverWait(self.selenium, timeout).until(
lambda driver: driver.find_element_by_tag_name("body"))
program = Program.objects.get(code = self.program_code)
self.assertEquals(program.name, self.program_name)
self.selenium.find_element_by_link_text("Edit").click()
WebDriverWait(self.selenium, timeout).until(
lambda driver: driver.find_element_by_tag_name("body"))
code = self.selenium.find_element_by_id("id_code")
code.clear()
code.send_keys(self.new_program_code)
name = self.selenium.find_element_by_id("id_name")
name.clear()
name.send_keys(self.new_program_name)
self.selenium.find_element_by_css_selector("button.btn.btn-success").click()
WebDriverWait(self.selenium, timeout).until(
lambda driver: driver.find_element_by_tag_name("body"))
program.refresh_from_db()
self.assertEquals(program.code, self.new_program_code)
self.assertEquals(program.name, self.new_program_name)
self.selenium.find_element_by_name("todelete").click()
self.selenium.find_element_by_name("delete").click()
WebDriverWait(self.selenium, timeout).until(
lambda driver: driver.find_element_by_tag_name("body"))
with self.assertRaises(Program.DoesNotExist):
program = Program.objects.get(code = self.new_program_code)
def test_coordinator_years(self):
self.year_name = "Year 1"
self.new_year_name = "Y2"
self.selenium.get('%s%s' % (self.live_server_url, '/years/'))
WebDriverWait(self.selenium, timeout).until(
lambda driver: driver.find_element_by_tag_name("body"))
self.selenium.find_element_by_link_text("Add year").click()
WebDriverWait(self.selenium, timeout).until(
lambda driver: driver.find_element_by_tag_name("body"))
self.selenium.find_element_by_id("id_year").send_keys(self.year_name)
self.selenium.find_element_by_css_selector("button.btn.btn-success").click()
WebDriverWait(self.selenium, timeout).until(
lambda driver: driver.find_element_by_tag_name("body"))
year = YearOfStudy.objects.get(year = self.year_name)
self.selenium.find_element_by_link_text("Edit").click()
WebDriverWait(self.selenium, timeout).until(
lambda driver: driver.find_element_by_tag_name("body"))
year_name = self.selenium.find_element_by_id("id_year")
year_name.clear()
year_name.send_keys(self.new_year_name)
self.selenium.find_element_by_css_selector("button.btn.btn-success").click()
WebDriverWait(self.selenium, timeout).until(
lambda driver: driver.find_element_by_tag_name("body"))
year.refresh_from_db()
self.assertEquals(year.year, self.new_year_name)
self.selenium.find_element_by_name("todelete").click()
self.selenium.find_element_by_name("delete").click()
WebDriverWait(self.selenium, timeout).until(
lambda driver: driver.find_element_by_tag_name("body"))
with self.assertRaises(YearOfStudy.DoesNotExist):
year = YearOfStudy.objects.get(year = self.new_year_name)
def test_coordinator_applications_tab(self):
self.award_name = "An Award Name"
self.award_description = "An Award Description"
self.award_value = "An Award Value"
self.program_name = "A Program Name"
self.program_code = "A Code"
self.year_name = "Year 1"
self.program = Program.objects.create(name=self.program_name, code=self.program_code)
self.year = YearOfStudy.objects.create(year=self.year_name)
self.award_start_date = str(datetime.datetime.now(pytz.timezone('America/Vancouver')))
self.award_end_date = str(datetime.datetime.now(pytz.timezone('America/Edmonton')))
self.award_documents_needed = False
self.award_is_active = True
self.award = Award.objects.create(name=self.award_name, description=self.award_description,
value=self.award_value,
start_date=self.award_start_date, end_date=self.award_end_date,
documents_needed=self.award_documents_needed, is_active=self.award_is_active)
self.award.programs.add(self.program)
self.award.years_of_study.add(self.year)
self.adjudicator_ccid = "adjudicator"
self.adjudicator_first_name = "An"
self.adjudicator_last_name = "Adjudicator"
self.adjudicator_email = "adjudicator@csjawards.ca"
self.adjudicator = Adjudicator.objects.create(ccid=self.adjudicator_ccid,
first_name=self.adjudicator_first_name,
last_name=self.adjudicator_last_name,
email=self.adjudicator_email)
self.student_ccid = "student"
self.student_first_name = "A"
self.student_middle_name = "Normal"
self.student_last_name = "Student"
self.student_ualberta_id = "123456789"
self.student_email = "student@csjawards.ca"
self.student_gpa = "4.0"
self.student = Student.objects.create(ccid = self.student_ccid, first_name = self.student_first_name,
middle_name = self.student_middle_name, last_name = self.student_last_name,
student_id = self.student_ualberta_id, email = self.student_email,
gpa = self.student_gpa, year = self.year, program = self.program)
self.application = Application.objects.create(student = self.student, award = self.award, is_submitted=True)
self.assertFalse(self.application.is_reviewed)
self.selenium.get('%s%s' % (self.live_server_url, '/applications/'))
self.selenium.find_element_by_link_text("View Application").click()
WebDriverWait(self.selenium, timeout).until(
lambda driver: driver.find_element_by_tag_name("body"))
self.selenium.find_element_by_name("_review").click()
WebDriverWait(self.selenium, timeout).until(
lambda driver: driver.find_element_by_tag_name("body"))
self.application.refresh_from_db()
self.assertTrue(self.application.is_reviewed)
self.selenium.find_element_by_link_text("Review Completed").click()
WebDriverWait(self.selenium, timeout).until(
lambda driver: driver.find_element_by_tag_name("body"))
self.selenium.find_element_by_name("_unreview").click()
WebDriverWait(self.selenium, timeout).until(
lambda driver: driver.find_element_by_tag_name("body"))
self.application.refresh_from_db()
self.assertFalse(self.application.is_reviewed)
self.selenium.find_element_by_link_text("Review Pending").click()
WebDriverWait(self.selenium, timeout).until(
lambda driver: driver.find_element_by_tag_name("body"))
self.selenium.find_element_by_link_text("Return to Applications").click()
WebDriverWait(self.selenium, timeout).until(
lambda driver: driver.find_element_by_tag_name("body"))
self.assertEquals(self.selenium.current_url, '%s%s' % (self.live_server_url, '/applications/'))
self.selenium.find_element_by_name("applicationaction").click()
self.selenium.find_element_by_name("_review").click()
WebDriverWait(self.selenium, timeout).until(
lambda driver: driver.find_element_by_tag_name("body"))
self.application.refresh_from_db()
self.assertTrue(self.application.is_reviewed)
self.assertFalse(self.application.is_archived)
self.selenium.find_element_by_name("applicationaction").click()
self.selenium.find_element_by_name("_archive").click()
WebDriverWait(self.selenium, timeout).until(
lambda driver: driver.find_element_by_tag_name("body"))
self.application.refresh_from_db()
self.assertTrue(self.application.is_archived)
self.selenium.find_element_by_name("applicationaction").click()
self.selenium.find_element_by_name("_removeFromArchive").click()
WebDriverWait(self.selenium, timeout).until(
lambda driver: driver.find_element_by_tag_name("body"))
self.application.refresh_from_db()
self.assertFalse(self.application.is_archived)
self.selenium.find_element_by_name("applicationaction").click()
self.selenium.find_element_by_name("_delete").click()
self.selenium.switch_to_alert().accept()
WebDriverWait(self.selenium, timeout).until(
lambda driver: driver.find_element_by_tag_name("body"))
time.sleep(5)
with self.assertRaises(Application.DoesNotExist):
application = Application.objects.get(student = self.student, award = self.award)
def test_coordinator_application_list(self):
self.award_name = "An Award Name"
self.award_description = "An Award Description"
self.award_value = "An Award Value"
self.program_name = "A Program Name"
self.program_code = "A Code"
self.year_name = "Year 1"
self.program = Program.objects.create(name=self.program_name, code=self.program_code)
self.year = YearOfStudy.objects.create(year=self.year_name)
self.award_start_date = str(datetime.datetime.now(pytz.timezone('America/Vancouver')))
self.award_end_date = str(datetime.datetime.now(pytz.timezone('America/Edmonton')))
self.award_documents_needed = False
self.award_is_active = True
self.award = Award.objects.create(name=self.award_name, description=self.award_description,
value=self.award_value,
start_date=self.award_start_date, end_date=self.award_end_date,
documents_needed=self.award_documents_needed, is_active=self.award_is_active)
self.award.programs.add(self.program)
self.award.years_of_study.add(self.year)
self.adjudicator_ccid = "adjudicator"
self.adjudicator_first_name = "An"
self.adjudicator_last_name = "Adjudicator"
self.adjudicator_email = "adjudicator@csjawards.ca"
self.adjudicator = Adjudicator.objects.create(ccid=self.adjudicator_ccid,
first_name=self.adjudicator_first_name,
last_name=self.adjudicator_last_name,
email=self.adjudicator_email)
self.student_ccid = "student"
self.student_first_name = "A"
self.student_middle_name = "Normal"
self.student_last_name = "Student"
self.student_ualberta_id = "123456789"
self.student_email = "student@csjawards.ca"
self.student_gpa = "4.0"
self.student = Student.objects.create(ccid=self.student_ccid, first_name=self.student_first_name,
middle_name=self.student_middle_name, last_name=self.student_last_name,
student_id=self.student_ualberta_id, email=self.student_email,
gpa=self.student_gpa, year=self.year, program=self.program)
Application.objects.create(student=self.student, award=self.award, is_submitted=True, is_archived=False,
is_reviewed=False)
application = Application.objects.get(student = self.student, award = self.award)
self.selenium.get('%s%s' % (self.live_server_url, '/awards/'))
WebDriverWait(self.selenium, timeout).until(
lambda driver: driver.find_element_by_tag_name("body"))
self.selenium.find_element_by_link_text("Review Required").click()
WebDriverWait(self.selenium, timeout).until(
lambda driver: driver.find_element_by_tag_name("body"))
self.selenium.find_element_by_link_text("View Application").click()
WebDriverWait(self.selenium, timeout).until(
lambda driver: driver.find_element_by_tag_name("body"))
self.selenium.find_element_by_name("_review").click()
WebDriverWait(self.selenium, timeout).until(
lambda driver: driver.find_element_by_tag_name("body"))
application.refresh_from_db()
self.assertTrue(application.is_reviewed)
self.selenium.find_element_by_link_text("Review Completed").click()
WebDriverWait(self.selenium, timeout).until(
lambda driver: driver.find_element_by_tag_name("body"))
self.selenium.find_element_by_name("_unreview").click()
WebDriverWait(self.selenium, timeout).until(
lambda driver: driver.find_element_by_tag_name("body"))
application.refresh_from_db()
self.assertFalse(application.is_reviewed)
self.selenium.find_element_by_link_text("Review Pending").click()
WebDriverWait(self.selenium, timeout).until(
lambda driver: driver.find_element_by_tag_name("body"))
self.selenium.find_element_by_link_text("Return to Applications").click()
WebDriverWait(self.selenium, timeout).until(
lambda driver: driver.find_element_by_tag_name("body"))
self.assertEquals(self.selenium.current_url, '%s%s%s' % (self.live_server_url, '/awards/applications/?award_id=', self.award.awardid))
application.refresh_from_db()
WebDriverWait(self.selenium, timeout).until(
lambda driver: driver.find_element_by_name("_review"))
self.selenium.find_element_by_name("applicationaction").click()
self.selenium.find_element_by_name("_review").click()
WebDriverWait(self.selenium, timeout).until(
lambda driver: driver.find_element_by_tag_name("body"))
application.refresh_from_db()
self.assertTrue(application.is_reviewed)
self.assertFalse(application.is_archived)
self.selenium.find_element_by_name("applicationaction").click()
self.selenium.find_element_by_name("_archive").click()
WebDriverWait(self.selenium, timeout).until(
lambda driver: driver.find_element_by_tag_name("body"))
application.refresh_from_db()
self.assertTrue(application.is_archived)
self.selenium.get('%s%s%s' % (self.live_server_url, '/awards/applications/?award_id=', self.award.awardid))
WebDriverWait(self.selenium, timeout).until(
lambda driver: driver.find_element_by_link_text("View Archive"))
self.selenium.find_element_by_link_text("View Archive").click()
WebDriverWait(self.selenium, timeout).until(
lambda driver: driver.find_element_by_tag_name("body"))
self.selenium.find_element_by_link_text("View").click()
WebDriverWait(self.selenium, timeout).until(
lambda driver: driver.find_element_by_tag_name("body"))
self.selenium.find_element_by_link_text("Return to Applications").click()
WebDriverWait(self.selenium, timeout).until(
lambda driver: driver.find_element_by_tag_name("body"))
self.selenium.find_element_by_name("archiveaction").click()
self.selenium.find_element_by_name("_removeFromArchive").click()
WebDriverWait(self.selenium, timeout).until(
lambda driver: driver.find_element_by_tag_name("body"))
application.refresh_from_db()
self.assertFalse(application.is_archived)
self.selenium.find_element_by_link_text("Return to Applications").click()
WebDriverWait(self.selenium, timeout).until(
lambda driver: driver.find_element_by_tag_name("body"))
self.selenium.find_element_by_name("applicationaction").click()
self.selenium.find_element_by_name("_delete").click()
WebDriverWait(self.selenium, timeout).until(
lambda driver: driver.find_element_by_tag_name("body"))
time.sleep(5)
with self.assertRaises(Application.DoesNotExist):
application = Application.objects.get(student=self.student, award=self.award)
| 44.42312 | 214 | 0.685581 | 4,855 | 39,581 | 5.285479 | 0.043048 | 0.104283 | 0.102841 | 0.119208 | 0.896263 | 0.87444 | 0.84607 | 0.828027 | 0.795916 | 0.752543 | 0 | 0.003554 | 0.203886 | 39,581 | 890 | 215 | 44.473034 | 0.810796 | 0 | 0 | 0.651466 | 0 | 0.003257 | 0.096222 | 0.015768 | 0 | 0 | 0 | 0 | 0.123779 | 1 | 0.017915 | false | 0.008143 | 0.014658 | 0 | 0.034202 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
c467e8f77d2dfdb1bffe1e3f59975e9960925291 | 182 | py | Python | questioneer/users/models.py | markdentoom/Questioneer | 79cd65168a4ff2e0f7cf7c52c51e6d1cbffb1318 | [
"MIT"
] | null | null | null | questioneer/users/models.py | markdentoom/Questioneer | 79cd65168a4ff2e0f7cf7c52c51e6d1cbffb1318 | [
"MIT"
] | null | null | null | questioneer/users/models.py | markdentoom/Questioneer | 79cd65168a4ff2e0f7cf7c52c51e6d1cbffb1318 | [
"MIT"
] | null | null | null | from django.contrib.auth.models import AbstractUser
class CustomUser(AbstractUser):
# Use default AbstractUser, but inherit it so we can change it as we see fit later
pass
| 26 | 86 | 0.769231 | 27 | 182 | 5.185185 | 0.851852 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.186813 | 182 | 6 | 87 | 30.333333 | 0.945946 | 0.43956 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0.333333 | 0.333333 | 0 | 0.666667 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 6 |
679758b2818ca7ea5e4f5803a514ce4ca458b200 | 34 | py | Python | xformer/__init__.py | apoorvnandan/xformer | 4309a07c152e9c3579f63d0d86ac563730bf39e8 | [
"Apache-2.0"
] | null | null | null | xformer/__init__.py | apoorvnandan/xformer | 4309a07c152e9c3579f63d0d86ac563730bf39e8 | [
"Apache-2.0"
] | null | null | null | xformer/__init__.py | apoorvnandan/xformer | 4309a07c152e9c3579f63d0d86ac563730bf39e8 | [
"Apache-2.0"
] | 1 | 2021-04-02T08:40:30.000Z | 2021-04-02T08:40:30.000Z | from xformer.transformer import *
| 17 | 33 | 0.823529 | 4 | 34 | 7 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.117647 | 34 | 1 | 34 | 34 | 0.933333 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
679a79128c62117d3c69b52f22e3dfcc2393c2e3 | 84 | py | Python | pdcli/__init__.py | koyeung/python-pdcli | e7328bf647cbdaa91573615cbfa6d5f585314a58 | [
"Apache-2.0"
] | null | null | null | pdcli/__init__.py | koyeung/python-pdcli | e7328bf647cbdaa91573615cbfa6d5f585314a58 | [
"Apache-2.0"
] | null | null | null | pdcli/__init__.py | koyeung/python-pdcli | e7328bf647cbdaa91573615cbfa6d5f585314a58 | [
"Apache-2.0"
] | null | null | null | import importlib.metadata
__version__ = importlib.metadata.version("python-pdcli")
| 21 | 56 | 0.821429 | 9 | 84 | 7.222222 | 0.666667 | 0.523077 | 0.738462 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.071429 | 84 | 3 | 57 | 28 | 0.833333 | 0 | 0 | 0 | 0 | 0 | 0.142857 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
679c6a61741128e7574981911584d76f313738df | 34 | py | Python | funicorn/__init__.py | juanbenitopr/funicorn | 4f21ccee1e56d74975d032bb6139644c3d331416 | [
"MIT"
] | 3 | 2020-06-10T07:47:17.000Z | 2022-01-09T11:32:50.000Z | funicorn/__init__.py | juanbenitopr/funicorn | 4f21ccee1e56d74975d032bb6139644c3d331416 | [
"MIT"
] | null | null | null | funicorn/__init__.py | juanbenitopr/funicorn | 4f21ccee1e56d74975d032bb6139644c3d331416 | [
"MIT"
] | null | null | null | from funicorn.main import Funicorn | 34 | 34 | 0.882353 | 5 | 34 | 6 | 0.8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.088235 | 34 | 1 | 34 | 34 | 0.967742 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
db0261ebea1c5925e000bdae74629f16c1e15feb | 27 | py | Python | repository/__init__.py | kex5n/Vehicles-Dispatch-Simulator | d0cca03fbf56e4b0ceeef8dafc59de105c1d4507 | [
"MIT"
] | 7 | 2021-05-12T14:59:14.000Z | 2021-07-02T04:22:27.000Z | repository/__init__.py | kex5n/Vehicles-Dispatch-Simulator | d0cca03fbf56e4b0ceeef8dafc59de105c1d4507 | [
"MIT"
] | 5 | 2021-11-14T10:47:01.000Z | 2021-11-14T11:30:54.000Z | repository/__init__.py | kex5n/Vehicles-Dispatch-Simulator | d0cca03fbf56e4b0ceeef8dafc59de105c1d4507 | [
"MIT"
] | 1 | 2016-10-11T19:18:53.000Z | 2016-10-11T19:18:53.000Z | from .memory import Memory
| 13.5 | 26 | 0.814815 | 4 | 27 | 5.5 | 0.75 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.148148 | 27 | 1 | 27 | 27 | 0.956522 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
db0791dd4e56aa013d60bf158280e552a752ad19 | 178 | py | Python | scraper/utils.py | ArthurPBressan/steam-wallpaper-scraper | 10fbb2202b19a19570c15ee6d4777c18fa8b55ab | [
"MIT"
] | null | null | null | scraper/utils.py | ArthurPBressan/steam-wallpaper-scraper | 10fbb2202b19a19570c15ee6d4777c18fa8b55ab | [
"MIT"
] | 6 | 2018-01-26T19:05:19.000Z | 2018-01-28T13:50:24.000Z | scraper/utils.py | ArthurPBressan/steam-wallpaper-scraper | 10fbb2202b19a19570c15ee6d4777c18fa8b55ab | [
"MIT"
] | null | null | null | def clean_game_title(game_title):
return game_title.replace('- Foil Badge', '').strip()
def clean_card_title(card_title):
return card_title.replace('\\/', '-').strip()
| 25.428571 | 57 | 0.691011 | 24 | 178 | 4.791667 | 0.416667 | 0.234783 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.123596 | 178 | 6 | 58 | 29.666667 | 0.737179 | 0 | 0 | 0 | 0 | 0 | 0.089888 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.5 | false | 0 | 0 | 0.5 | 1 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 6 |
db136e143a9ddc49e293fecea64baa3194f514e4 | 8,513 | py | Python | tests/utils/postprocess/test_waterfall.py | vdestraitt/toucan-data-sdk | 09327592daef6bc94d0d6f79a041b581226c2b7b | [
"BSD-3-Clause"
] | null | null | null | tests/utils/postprocess/test_waterfall.py | vdestraitt/toucan-data-sdk | 09327592daef6bc94d0d6f79a041b581226c2b7b | [
"BSD-3-Clause"
] | null | null | null | tests/utils/postprocess/test_waterfall.py | vdestraitt/toucan-data-sdk | 09327592daef6bc94d0d6f79a041b581226c2b7b | [
"BSD-3-Clause"
] | null | null | null | import pandas as pd
import pytest
from numpy import inf, nan, testing
from toucan_data_sdk.utils.postprocess import waterfall
@pytest.fixture
def sample_data():
return [
{'ord': 1, 'category_name': 'Clap', 'category_id': 'clap', 'product_id': 'super clap',
'date': 't1', 'played': 12},
{'ord': 10, 'category_name': 'Clap', 'category_id': 'clap', 'product_id': 'clap clap',
'date': 't1', 'played': 1},
{'ord': 1, 'category_name': 'Snare', 'category_id': 'snare', 'product_id': 'tac',
'date': 't1', 'played': 1},
{'ord': 1, 'category_name': 'Clap', 'category_id': 'clap', 'product_id': 'super clap',
'date': 't2', 'played': 10},
{'ord': 1, 'category_name': 'Snare', 'category_id': 'snare', 'product_id': 'tac',
'date': 't2', 'played': 100},
{'ord': 1, 'category_name': 'Tom', 'category_id': 'tom', 'product_id': 'bom',
'date': 't2', 'played': 1}
]
def test_waterfall(sample_data):
""" It should return value for waterfall """
kwargs = {
'upperGroup': {'id': 'category_id', 'label': 'category_name'},
'insideGroup': {'id': 'product_id', 'groupsOrder': 'ord'},
'date': 'date',
'value': 'played',
'start': {'label': 'Trimestre 1', 'id': 't1'},
'end': {'label': 'Trimester 2', 'id': 't2'},
}
expected = [
{'variation': nan, 'label': 'Trimestre 1', 'value': 14.0,
'groups': nan, 'type': nan, 'order': nan},
{'variation': -0.23076923076923078, 'label': 'Clap', 'value': -3.0,
'groups': 'clap', 'type': 'parent', 'order': nan},
{'variation': -0.16666666666666666, 'label': 'super clap', 'value': -2.0,
'groups': 'clap', 'type': 'child', 'order': 1.0},
{'variation': -1.000000, 'label': 'clap clap', 'value': -1.0,
'groups': 'clap', 'type': 'child', 'order': 10.0},
{'variation': 99.0, 'label': 'Snare', 'value': 99.0,
'groups': 'snare', 'type': 'parent', 'order': nan},
{'variation': 99.0, 'label': 'tac', 'value': 99.0,
'groups': 'snare', 'type': 'child', 'order': 1.0},
{'variation': inf, 'label': 'Tom', 'value': 1.0,
'groups': 'tom', 'type': 'parent', 'order': nan},
{'variation': inf, 'label': 'bom', 'value': 1.0,
'groups': 'tom', 'type': 'child', 'order': 1.0},
{'variation': nan, 'label': 'Trimester 2', 'value': 111.0,
'groups': nan, 'type': nan, 'order': nan}
]
df = pd.DataFrame(sample_data)
df = waterfall(df, **kwargs)
wa = [{k: v for k, v in zip(df.columns, row)} for row in df.values]
assert wa[0].keys() == expected[0].keys()
for i in range(len(expected)):
testing.assert_equal(wa[i], expected[i])
def test_waterfall_upperGroup_groupsOrder(sample_data):
for line in sample_data:
line['category_order'] = len(line['category_name'])
del line['ord']
kwargs = {
'upperGroup': {
'id': 'category_id',
'label': 'category_name',
'groupsOrder': 'category_order'
},
'insideGroup': {
'id': 'product_id'
},
'date': 'date',
'value': 'played',
'start': {'label': 'Trimestre 1', 'id': 't1'},
'end': {'label': 'Trimester 2', 'id': 't2'},
}
expected = [
{'variation': nan, 'label': 'Trimestre 1', 'value': 14.0, 'groups': nan, 'type': nan,
'order': nan},
{'variation': inf, 'label': 'Tom', 'value': 1.0, 'groups': 'tom', 'type': 'parent',
'order': 3.0},
{'variation': inf, 'label': 'bom', 'value': 1.0, 'groups': 'tom', 'type': 'child',
'order': nan},
{'variation': -0.23076923076923078, 'label': 'Clap', 'value': -3.0, 'groups': 'clap',
'type': 'parent', 'order': 4.0},
{'variation': -1.0, 'label': 'clap clap', 'value': -1.0, 'groups': 'clap',
'type': 'child', 'order': nan},
{'variation': -0.16666666666666666, 'label': 'super clap', 'value': -2.0,
'groups': 'clap', 'type': 'child', 'order': nan},
{'variation': 99.0, 'label': 'Snare', 'value': 99.0, 'groups': 'snare',
'type': 'parent', 'order': 5.0},
{'variation': 99.0, 'label': 'tac', 'value': 99.0, 'groups': 'snare', 'type': 'child',
'order': nan},
{'variation': nan, 'label': 'Trimester 2', 'value': 111.0, 'groups': nan, 'type': nan,
'order': nan}
]
df = pd.DataFrame(sample_data)
df = waterfall(df, **kwargs)
wa = [{k: v for k, v in zip(df.columns, row)} for row in df.values]
assert wa[0].keys() == expected[0].keys()
for i in range(len(expected)):
testing.assert_equal(wa[i], expected[i])
def test_waterfall_no_value_start():
kwargs = {
'upperGroup': {'id': 'category_id', 'label': 'category_name'},
'insideGroup': {'id': 'product_id', 'groupsOrder': 'ord'},
'date': 'date',
'value': 'played',
'start': {'label': 'Trimestre 1', 'id': 't1'},
'end': {'label': 'Trimester 2', 'id': 't2'},
}
data = [
{'ord': 1, 'category_name': 'Clap', 'category_id': 'clap',
'product_id': 'super clap', 'date': 't2', 'played': 10},
{'ord': 1, 'category_name': 'Snare', 'category_id': 'snare',
'product_id': 'tac', 'date': 't2', 'played': 100},
{'ord': 1, 'category_name': 'Tom', 'category_id': 'tom',
'product_id': 'bom', 'date': 't2', 'played': 1}
]
expected = [
{'variation': nan, 'label': 'Trimestre 1', 'value': 0, 'groups': nan, 'type': nan,
'order': nan},
{'variation': inf, 'label': 'Clap', 'value': 10, 'groups': 'clap', 'type': 'parent',
'order': nan},
{'variation': inf, 'label': 'super clap', 'value': 10, 'groups': 'clap',
'type': 'child', 'order': 1.0},
{'variation': inf, 'label': 'Snare', 'value': 100.0, 'groups': 'snare',
'type': 'parent', 'order': nan},
{'variation': inf, 'label': 'tac', 'value': 100.0, 'groups': 'snare', 'type': 'child',
'order': 1.0},
{'variation': inf, 'label': 'Tom', 'value': 1.0, 'groups': 'tom', 'type': 'parent',
'order': nan},
{'variation': inf, 'label': 'bom', 'value': 1.0, 'groups': 'tom', 'type': 'child',
'order': 1.0},
{'variation': nan, 'label': 'Trimester 2', 'value': 111.0, 'groups': nan, 'type': nan,
'order': nan}
]
df = pd.DataFrame(data)
df = waterfall(df, **kwargs)
wa = [{k: v for k, v in zip(df.columns, row)} for row in df.values]
assert wa[0].keys() == expected[0].keys()
for i in range(len(expected)):
testing.assert_equal(wa[i], expected[i])
def test_waterfall_null():
kwargs = {
'upperGroup': {'id': 'category_id', 'label': 'category_name'},
'insideGroup': {'id': 'product_id', 'groupsOrder': 'ord'},
'date': 'date',
'value': 'played',
'start': {'label': 'Trimestre 1', 'id': 't1'},
'end': {'label': 'Trimester 2', 'id': 't2'},
}
data = None
df = pd.DataFrame(data)
df = waterfall(df, **kwargs)
wa = [{k: v for k, v in zip(df.columns, row)} for row in df.values]
assert wa == []
def test_waterfall_not_implemented(sample_data):
""" It should raise Error for not implemented features """
kwargs = {
'upperGroup': {'id': 'category_id', 'label': 'category_name'},
'insideGroup': {'id': 'product_id', 'groupsOrder': 'ord'},
'date': 'date',
'value': 'played',
'start': {'label': 'Trimestre 1', 'id': 't1'},
'end': {'label': 'Trimester 2', 'id': 't2'},
'breakdown': ['id']
}
df = pd.DataFrame(sample_data)
with pytest.raises(NotImplementedError) as exc_info:
waterfall(df, **kwargs)
assert str(exc_info.value) == 'We will add breakdown support ' \
'on your request, please contact the devs'
kwargs = {
'upperGroup': {'id': 'category_id', 'label': 'category_name'},
'date': 'date',
'value': 'played',
'start': {'label': 'Trimestre 1', 'id': 't1'},
'end': {'label': 'Trimester 2', 'id': 't2'},
}
df = pd.DataFrame(sample_data)
with pytest.raises(NotImplementedError) as exc_info:
waterfall(df, **kwargs)
assert str(exc_info.value) == 'We will add support for upperGroup only ' \
'on you request, please contact the devs'
| 41.526829 | 94 | 0.518854 | 998 | 8,513 | 4.347695 | 0.117234 | 0.038719 | 0.050933 | 0.0295 | 0.870708 | 0.85711 | 0.848352 | 0.826919 | 0.769302 | 0.769302 | 0 | 0.039679 | 0.253965 | 8,513 | 204 | 95 | 41.730392 | 0.643521 | 0.010337 | 0 | 0.513812 | 0 | 0 | 0.338129 | 0 | 0 | 0 | 0 | 0 | 0.049724 | 1 | 0.033149 | false | 0 | 0.022099 | 0.005525 | 0.060773 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
fbfa5d568b0d334d019cd465efc30bbc9993b042 | 240 | py | Python | test_account.py | vv31415926/lesson_08 | cef647e124fd099e3e681e291733f718220579a2 | [
"MIT"
] | null | null | null | test_account.py | vv31415926/lesson_08 | cef647e124fd099e3e681e291733f718220579a2 | [
"MIT"
] | null | null | null | test_account.py | vv31415926/lesson_08 | cef647e124fd099e3e681e291733f718220579a2 | [
"MIT"
] | null | null | null | import myLibAcc
def test_setAccount():
assert myLibAcc.setAccount( 0, 10, input=True ) == (10,True )
assert myLibAcc.setAccount( 10, 5, input=False) == ( 5,True )
assert myLibAcc.setAccount( 5, 10, input=False) == ( 5,False) | 40 | 66 | 0.6625 | 32 | 240 | 4.9375 | 0.375 | 0.265823 | 0.455696 | 0.35443 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.066667 | 0.1875 | 240 | 6 | 67 | 40 | 0.74359 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.6 | 1 | 0.2 | true | 0 | 0.2 | 0 | 0.4 | 0 | 0 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
2209fe10197583446ceaa1b383cb1ef2111fd256 | 403 | py | Python | tests/test_helpers.py | maximz/kdict | bec84c5b27b644de752bbbbd003c6bb35fc493b5 | [
"MIT"
] | null | null | null | tests/test_helpers.py | maximz/kdict | bec84c5b27b644de752bbbbd003c6bb35fc493b5 | [
"MIT"
] | 14 | 2022-01-13T16:38:40.000Z | 2022-03-31T16:26:59.000Z | tests/test_helpers.py | maximz/kdict | bec84c5b27b644de752bbbbd003c6bb35fc493b5 | [
"MIT"
] | null | null | null | from kdict.helpers import _is_iterable_but_not_string
def test_is_iterable_but_not_string():
assert _is_iterable_but_not_string([1, 2, 3])
assert _is_iterable_but_not_string({1, 2, 3, "a"})
assert _is_iterable_but_not_string({"a": 5, "b": 6})
assert _is_iterable_but_not_string((1, 2, 3))
assert not _is_iterable_but_not_string("str")
assert not _is_iterable_but_not_string(5)
| 36.636364 | 56 | 0.756824 | 70 | 403 | 3.785714 | 0.285714 | 0.301887 | 0.392453 | 0.483019 | 0.856604 | 0.690566 | 0.584906 | 0.373585 | 0.373585 | 0.279245 | 0 | 0.034582 | 0.138958 | 403 | 10 | 57 | 40.3 | 0.729107 | 0 | 0 | 0 | 0 | 0 | 0.014888 | 0 | 0 | 0 | 0 | 0 | 0.75 | 1 | 0.125 | true | 0 | 0.125 | 0 | 0.25 | 0 | 0 | 0 | 0 | null | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
2245ad09fff9814c787db9d31b09b59918de0363 | 84 | py | Python | aas_timeseries/screenshot/setup_package.py | astrofrog/aas-time-series-affiliated | 7fe352c126bc2dbe63116dde88281b931e8fab13 | [
"BSD-3-Clause"
] | 3 | 2019-01-22T14:25:26.000Z | 2019-07-31T15:41:39.000Z | aas_timeseries/screenshot/setup_package.py | astrofrog/aas-time-series-affiliated | 7fe352c126bc2dbe63116dde88281b931e8fab13 | [
"BSD-3-Clause"
] | 41 | 2019-01-18T17:30:47.000Z | 2021-05-06T13:46:39.000Z | aas_timeseries/screenshot/setup_package.py | astrofrog/aas-time-series-affiliated | 7fe352c126bc2dbe63116dde88281b931e8fab13 | [
"BSD-3-Clause"
] | 1 | 2019-09-19T14:01:26.000Z | 2019-09-19T14:01:26.000Z | def get_package_data():
return {'aas_timeseries.screenshot': ['template.html']}
| 28 | 59 | 0.72619 | 10 | 84 | 5.8 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.107143 | 84 | 2 | 60 | 42 | 0.773333 | 0 | 0 | 0 | 0 | 0 | 0.452381 | 0.297619 | 0 | 0 | 0 | 0 | 0 | 1 | 0.5 | true | 0 | 0 | 0.5 | 1 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 6 |
97d8b476d68681feb02943608a3687fe1f27ef51 | 346 | py | Python | uri/iniciante/1041.py | AllefLobo/AlgorithmsProblemsSolution | 7e89d43d0811c45944b1729385b2c36fa07a847f | [
"MIT"
] | 2 | 2016-07-24T17:46:35.000Z | 2017-04-16T03:01:54.000Z | uri/iniciante/1041.py | AllefLobo/AlgorithmsProblemsSolution | 7e89d43d0811c45944b1729385b2c36fa07a847f | [
"MIT"
] | null | null | null | uri/iniciante/1041.py | AllefLobo/AlgorithmsProblemsSolution | 7e89d43d0811c45944b1729385b2c36fa07a847f | [
"MIT"
] | null | null | null |
x, y = map( float, raw_input().split() )
if x == 0.0 and y == 0.0:
print "Origem"
elif x == 0.0 and y != 0.0:
print "Eixo Y"
elif x != 0.0 and y == 0.0:
print "Eixo X"
elif x > 0.0 and y > 0.0:
print "Q1"
elif x < 0.0 and y > 0.0:
print "Q2"
elif x < 0.0 and y < 0.0:
print "Q3"
elif x > 0.0 and y < 0.0:
print "Q4"
| 19.222222 | 40 | 0.49711 | 79 | 346 | 2.164557 | 0.227848 | 0.163743 | 0.122807 | 0.245614 | 0.760234 | 0.760234 | 0.760234 | 0.760234 | 0.678363 | 0.25731 | 0 | 0.135021 | 0.315029 | 346 | 17 | 41 | 20.352941 | 0.586498 | 0 | 0 | 0 | 0 | 0 | 0.075362 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | null | 0 | 0 | null | null | 0.466667 | 0 | 0 | 0 | null | 0 | 0 | 1 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 6 |
3f1b50800579d9946abc5704dfc5b967cd267383 | 24,096 | py | Python | code/semantic_segmentation/pspnet/pspnet_isic/utils_arma/networks.py | mazeiomli/ARMA-Networks | a7932abad7c4022311c0ec5263a302ab1cc6a354 | [
"MIT"
] | null | null | null | code/semantic_segmentation/pspnet/pspnet_isic/utils_arma/networks.py | mazeiomli/ARMA-Networks | a7932abad7c4022311c0ec5263a302ab1cc6a354 | [
"MIT"
] | null | null | null | code/semantic_segmentation/pspnet/pspnet_isic/utils_arma/networks.py | mazeiomli/ARMA-Networks | a7932abad7c4022311c0ec5263a302ab1cc6a354 | [
"MIT"
] | 1 | 2021-12-17T23:08:12.000Z | 2021-12-17T23:08:12.000Z | import torch
import torch.nn as nn
import torch.nn.functional as F
from utils.layers import ARMA2d
def init_weights(net, init_type = 'normal', gain = 0.02):
def init_func(m):
classname = m.__class__.__name__
if hasattr(m, 'weight') and (classname.find('Conv') != -1 or classname.find('Linear') != -1):
if init_type == 'normal':
torch.nn.init.normal_(m.weight.data, 0.0, gain)
elif init_type == 'xavier':
torch.nn.init.xavier_normal_(m.weight.data, gain=gain)
elif init_type == 'kaiming':
torch.nn.init.kaiming_normal_(m.weight.data, a=0, mode='fan_in')
elif init_type == 'orthogonal':
torch.nn.init.orthogonal_(m.weight.data, gain=gain)
else:
raise NotImplementedError('initialization method [%s] is not implemented' % init_type)
if hasattr(m, 'bias') and m.bias is not None:
torch.nn.init.zeros_(m.bias.data)
elif classname.find('BatchNorm2d') != -1:
torch.init.normal_(m.weight.data, 1.0, gain)
torch.init.constant_(m.bias.data, 0.0)
print('initialize network with %s' % init_type)
net.apply(init_func)
class Attention_block(nn.Module):
def __init__(self, gate_channels, input_channels, inter_channels):
"""
Construction of attention block.
Arguments:
----------
input_channels: int
The number of channels of the input features.
gate_channels: int
The number of channels of the gate signal.
inter_channels: int
The number of intermediate channels in the block.
"""
super(Attention_block, self).__init__()
self.W_g = nn.Sequential(
nn.Conv2d(gate_channels, inter_channels, 1),
nn.BatchNorm2d(inter_channels)
)
self.W_x = nn.Sequential(
nn.Conv2d(input_channels, inter_channels, 1),
nn.BatchNorm2d(inter_channels)
)
self.psi = nn.Sequential(
nn.Conv2d(inter_channels, 1, 1),
nn.BatchNorm2d(1),
nn.Sigmoid()
)
self.relu = nn.ReLU(inplace = True)
def forward(self, g, x):
"""
Computation of attention block.
Arguments:
----------
inputs: a 4-th order tensor of size
[batch_size, input_channels, height, width]
gates: a 4-th order tensor of size
[batch_size, gate_channels, height, width]
Returns:
--------
outputs: a 4-th order tensor of size
[batch_size, input_channels, height, width]
"""
g1 = self.W_g(g)
x1 = self.W_x(x)
psi = self.relu(g1 + x1)
psi = self.psi(psi)
return x * psi
class conv_block(nn.Module):
def __init__(self, in_channels, out_channels, arma = False):
"""
Construction of a convolutional block.
Arguments:
----------
in_channels: int
The number of input channels.
out_channels: int
The number of output channels.
arma: bool
Whether to use ARMA layers or standard convolutional layer.
default: False, i.e. use standard convolutioal layer
"""
super(conv_block, self).__init__()
if arma: # 2D ARMA layer
nn_Conv2d = lambda in_channels, out_channels: ARMA2d(
in_channels = in_channels, out_channels = out_channels,
w_kernel_size = 3, w_padding = 1, a_kernel_size = 3)
else: # standard 2D convolutional layer
nn_Conv2d = lambda in_channels, out_channels: nn.Conv2d(
in_channels = in_channels, out_channels = out_channels,
kernel_size = 3, padding = 1)
self.conv_block = nn.Sequential(
nn_Conv2d(in_channels, out_channels),
nn.BatchNorm2d(out_channels),
nn.ReLU(inplace = True),
nn_Conv2d(out_channels, out_channels),
nn.BatchNorm2d(out_channels),
nn.ReLU(inplace = True)
)
def forward(self, inputs):
"""
Computation of the convolutional block.
Arguments:
----------
inputs: a 4-th order tensor of size
[batch_size, in_channels, height, width]
Input to the convolutional block.
Returns:
--------
outputs: another 4-th order tensor of size
[batch_size, out_channels, height, width]
Output of the convolutional block.
"""
outputs = self.conv_block(inputs)
return outputs
class up_conv(nn.Module):
def __init__(self, in_channels, out_channels, arma = False):
"""
Construction of a upsampling convolutional block.
Arguments:
----------
in_channels: int
The number of input channels.
out_channels: int
The number of output channels.
arma: bool
Whether to use ARMA layers or standard convolutional layer.
default: False, i.e. use standard convolutioal layer
"""
super(up_conv,self).__init__()
if arma: # 2D ARMA layer
nn_Conv2d = lambda in_channels, out_channels: ARMA2d(
in_channels = in_channels, out_channels = out_channels,
w_kernel_size = 3, w_padding = 1, a_kernel_size = 3)
else: # standard 2D convolutional layer
nn_Conv2d = lambda in_channels, out_channels: nn.Conv2d(
in_channels = in_channels, out_channels = out_channels,
kernel_size = 3, padding = 1)
self.up_conv = nn.Sequential(
nn.Upsample(scale_factor = 2),
nn_Conv2d(in_channels, out_channels),
nn.BatchNorm2d(out_channels),
nn.ReLU(inplace = True)
)
def forward(self, inputs):
"""
Computation of a upsampling convolutional block.
Arguments:
----------
inputs: a 4-th order tensor of size
[batch_size, in_channels, in_height, in_width]
Input to the upsampling convolutional block.
Returns:
--------
outputs: another 4-th order tensor of size
[batch_size, out_channels, out_height, out_width]
Note: out_height = in_height // 2
out_width = in_width // 2
Output of the upsampling convolutional block.
"""
outputs = self.up_conv(inputs)
return outputs
class Recurrent_block(nn.Module):
def __init__(self, channels, arma = False, steps = 2):
"""
Construction of a recurrent convolutional block.
Arguments:
----------
channels: int
The number of input/output channels.
arma: bool
Whether to use ARMA layers or standard convolutional layers.
default: False, i.e. use standard convolutioal layers
steps: int
The number of recurrent steps of the block.
default: 2
"""
super(Recurrent_block,self).__init__()
if arma: # 2D ARMA layer
nn_Conv2d = lambda in_channels, out_channels: ARMA2d(
in_channels = in_channels, out_channels = out_channels,
w_kernel_size = 3, w_padding = 1, a_kernel_size = 3)
else: # standard 2D convolutional layer
nn_Conv2d = lambda in_channels, out_channels: nn.Conv2d(
in_channels = in_channels, out_channels = out_channels,
kernel_size = 3, padding = 1)
self.conv = nn.Sequential(
nn_Conv2d(channels, channels),
nn.BatchNorm2d(channels),
nn.ReLU(inplace = True)
)
self.steps = steps
def forward(self, inputs):
"""
Computation of the recurrent convolutional block.
Arguments:
----------
inputs: a 4-th order tensor of size
[batch_size, channels, height, width]
Input to the recurrent convolutional block.
Returns:
--------
outputs: another 4-th order tensor of size
[batch_size, channels, height, width]
Output of the recurrent convolutional block.
"""
outputs = self.conv(inputs)
for _ in range(self.steps - 1):
outputs = self.conv(outputs + inputs)
return outputs
class RRCNN_block(nn.Module):
def __init__(self, in_channels, out_channels, arma = False, steps = 2):
"""
Construction of a recurrent residual convolutional block.
Arguments:
----------
in_channels: int
The number of input channels.
out_channels: int
The number of output channels.
arma: bool
Whether to use ARMA layers or standard convolutional layers.
default: False, i.e. use standard convolutioal layers
steps: int
The number of recurrent steps of the block.
default: 2
"""
super(RRCNN_block, self).__init__()
self.Conv_1x1 = nn.Conv2d(in_channels, out_channels, 1)
self.RCNN = nn.Sequential(
Recurrent_block(out_channels, arma = arma, steps = steps),
Recurrent_block(out_channels, arma = arma, steps = steps)
)
def forward(self, inputs):
"""
Computation of the recurrent residual convolutional block.
Arguments:
----------
inputs: a 4-th order tensor of size
[batch_size, in_channels, height, width]
Input to the recurrent residual convolutional block.
Returns:
--------
outputs: a 4-th order tensor of size
[batch_size, out_channels, height, width]
Output of the recurrent residual convolutional block.
"""
inputs = self.Conv_1x1(inputs)
outputs = self.RCNN(inputs)
return inputs + outputs
class single_conv(nn.Module):
def __init__(self, in_channels, out_channels, arma = False):
"""
Construction of a single convolutional block.
Arguments:
----------
in_channels: int
The number of input channels.
out_channels: int
The number of output channels.
arma: bool
Whether to use ARMA layers or standard convolutional layers.
default: False, i.e. use standard convolutioal layers
"""
super(single_conv,self).__init__()
if arma: # 2D ARMA layer
nn_Conv2d = lambda in_channels, out_channels: ARMA2d(
in_channels = in_channels, out_channels = out_channels,
w_kernel_size = 3, w_padding = 1, a_kernel_size = 3)
else: # standard 2D convolutional layer
nn_Conv2d = lambda in_channels, out_channels: nn.Conv2d(
in_channels = in_channels, out_channels = out_channels,
kernel_size = 3, padding = 1)
self.conv = nn.Sequential(
nn_Conv2d(in_channels, out_channels),
nn.BatchNorm2d(out_channels),
nn.ReLU(inplace=True)
)
def forward(self, inputs):
"""
Computation of the single convolutional block.
Arguments:
----------
inputs: a 4-th order tensor of size
[batch_size, input_channels, height, width]
Input to the single convolutional block.
Returns:
--------
outputs: a 4-th order tensor of size
[batch_size, output_channels, height, width]
Output of the single convolutional block.
"""
outputs = self.conv(inputs)
return outputs
class U_Net(nn.Module):
def __init__(self, in_channels = 3, out_channels = 1, factor = 1, arma = False, arma2 =False):
"""
Construction of U-Net.
Arguments:
----------
in_channels: int
The number of input channels.
out_channels: int
The number of output channels.
arma: bool
Whether to use ARMA layers or standard convolutional layers.
default: False, i.e. use standard convolutioal layers
"""
super(U_Net,self).__init__()
self.Maxpool = nn.MaxPool2d(kernel_size = 2)
self.Conv1 = conv_block(in_channels, 64//factor, arma = arma2)
self.Conv2 = conv_block(64//factor, 128//factor, arma = arma)
self.Conv3 = conv_block(128//factor, 256//factor, arma = arma)
self.Conv4 = conv_block(256//factor, 512//factor, arma = arma)
self.Conv5 = conv_block(512//factor, 1024//factor, arma = arma)
self.Up5 = up_conv(1024//factor, 512//factor, arma = arma)
self.Up_conv5 = conv_block(1024//factor, 512//factor, arma = arma)
self.Up4 = up_conv(512//factor, 256//factor, arma = arma)
self.Up_conv4 = conv_block(512//factor, 256//factor, arma = arma)
self.Up3 = up_conv(256//factor, 128//factor, arma = arma)
self.Up_conv3 = conv_block(256//factor, 128//factor, arma = arma)
self.Up2 = up_conv(128//factor, 64//factor, arma = arma)
self.Up_conv2 = conv_block(128//factor, 64//factor, arma = arma2)
self.Conv_1x1 = nn.Conv2d(64//factor, out_channels, 1)
def forward(self, x):
"""
Computation of the U-Net.
Arguments:
----------
inputs: a 4-th order tensor of size
[batch_size, in_channels, height, width]
Input to the U-Net.
Returns:
--------
outputs: a 4-th order tensor of size
[batch_size, out_channels, height, width]
Output of the U-Net.
"""
# encoding path
x1 = self.Conv1(x)
x2 = self.Maxpool(x1)
x2 = self.Conv2(x2)
x3 = self.Maxpool(x2)
x3 = self.Conv3(x3)
x4 = self.Maxpool(x3)
x4 = self.Conv4(x4)
x5 = self.Maxpool(x4)
x5 = self.Conv5(x5)
# decoding + concat path
d5 = self.Up5(x5)
d5 = torch.cat((x4,d5),dim=1)
d5 = self.Up_conv5(d5)
d4 = self.Up4(d5)
d4 = torch.cat((x3,d4),dim=1)
d4 = self.Up_conv4(d4)
d3 = self.Up3(d4)
d3 = torch.cat((x2,d3),dim=1)
d3 = self.Up_conv3(d3)
d2 = self.Up2(d3)
d2 = torch.cat((x1,d2),dim=1)
d2 = self.Up_conv2(d2)
d1 = self.Conv_1x1(d2)
return d1
class R2U_Net(nn.Module):
def __init__(self, in_channels = 3, out_channels = 1, arma = False, steps = 2):
"""
Construction of Recurrent Residual U-Net.
Arguments:
----------
in_channels: int
The number of input channels.
out_channels: int
The number of output channels.
arma: bool
Whether to use ARMA layers or standard convolutional layers.
default: False, i.e. use standard convolutioal layers
steps: int
The number of recurrent steps of each recurrent block.
default: 2
"""
super(R2U_Net,self).__init__()
self.Maxpool = nn.MaxPool2d(2)
self.Upsample = nn.Upsample(2)
self.RRCNN1 = RRCNN_block(in_channels, 64, arma = arma, steps = steps)
self.RRCNN2 = RRCNN_block(64, 128, arma = arma, steps = steps)
self.RRCNN3 = RRCNN_block(128, 256, arma = arma, steps = steps)
self.RRCNN4 = RRCNN_block(256, 512, arma = arma, steps = steps)
self.RRCNN5 = RRCNN_block(512, 1024, arma = arma, steps = steps)
self.Up5 = up_conv(1024, 512, arma = arma)
self.Up_RRCNN5 = RRCNN_block(1024, 512, arma = arma, steps = steps)
self.Up4 = up_conv(512, 256, arma = arma)
self.Up_RRCNN4 = RRCNN_block(512, 256, arma = arma, steps = steps)
self.Up3 = up_conv(256, 128, arma = arma)
self.Up_RRCNN3 = RRCNN_block(256, 128, arma = arma, steps = steps)
self.Up2 = up_conv(128, 64, arma = arma)
self.Up_RRCNN2 = RRCNN_block(128, 64, arma = arma, steps = steps)
self.Conv_1x1 = nn.Conv2d(64, out_channels, 1)
def forward(self, x):
"""
Computation of the Recurrent Residual U-Net.
Arguments:
----------
inputs: a 4-th order tensor of size
[batch_size, in_channels, height, width]
Input to the Recurrent Residual U-Net.
Returns:
--------
outputs: a 4-th order tensor of size
[batch_size, out_channels, height, width]
Output of the Recurrent Residual U-Net.
"""
# encoding path
x1 = self.RRCNN1(x)
x2 = self.Maxpool(x1)
x2 = self.RRCNN2(x2)
x3 = self.Maxpool(x2)
x3 = self.RRCNN3(x3)
x4 = self.Maxpool(x3)
x4 = self.RRCNN4(x4)
x5 = self.Maxpool(x4)
x5 = self.RRCNN5(x5)
# decoding + concat path
d5 = self.Up5(x5)
d5 = torch.cat((x4,d5),dim=1)
d5 = self.Up_RRCNN5(d5)
d4 = self.Up4(d5)
d4 = torch.cat((x3,d4),dim=1)
d4 = self.Up_RRCNN4(d4)
d3 = self.Up3(d4)
d3 = torch.cat((x2,d3),dim=1)
d3 = self.Up_RRCNN3(d3)
d2 = self.Up2(d3)
d2 = torch.cat((x1,d2),dim=1)
d2 = self.Up_RRCNN2(d2)
d1 = self.Conv_1x1(d2)
return d1
class AttU_Net(nn.Module):
def __init__(self, in_channels = 3, out_channels = 1, factor=1, arma = False, arma2=False):
"""
Construction of Attention U-Net.
Arguments:
----------
in_channels: int
The number of input channels.
out_channels: int
The number of output channels.
arma: bool
Whether to use ARMA layers or standard convolutional layer.
default: False, i.e. use standard convolutioal layer
"""
super(AttU_Net,self).__init__()
self.Maxpool = nn.MaxPool2d(2)
self.Conv1 = conv_block(in_channels, 64//factor)
self.Conv2 = conv_block(64//factor, 128//factor, arma = arma2)
self.Conv3 = conv_block(128//factor, 256//factor, arma = arma)
self.Conv4 = conv_block(256//factor, 512//factor, arma = arma)
self.Conv5 = conv_block(512//factor, 1024//factor, arma = arma)
self.Up5 = up_conv(1024//factor, 512//factor, arma = arma)
self.Att5 = Attention_block(512//factor, 512//factor, 256//factor)
self.Up_conv5 = conv_block(1024//factor, 512//factor, arma = arma)
self.Up4 = up_conv(512//factor, 256//factor, arma = arma)
self.Att4 = Attention_block(256//factor, 256//factor, 128//factor)
self.Up_conv4 = conv_block(512//factor, 256//factor, arma = arma)
self.Up3 = up_conv(256//factor, 128//factor, arma = arma)
self.Att3 = Attention_block(128//factor, 128//factor, 64//factor)
self.Up_conv3 = conv_block(256//factor, 128//factor, arma = arma)
self.Up2 = up_conv(128//factor, 64//factor, arma = arma)
self.Att2 = Attention_block(64//factor, 64//factor, 32//factor)
self.Up_conv2 = conv_block(128//factor, 64//factor, arma = arma2)
self.Conv_1x1 = nn.Conv2d(64//factor, out_channels, 1)
def forward(self,x):
"""
Computation of the Attention U-Net.
Arguments:
----------
inputs: a 4-th order tensor of size
[batch_size, in_channels, height, width]
Input to the Attention U-Net.
Returns:
--------
outputs: a 4-th order tensor of size
[batch_size, out_channels, height, width]
Output of the Attention U-Net.
"""
# encoding path
x1 = self.Conv1(x)
x2 = self.Maxpool(x1)
x2 = self.Conv2(x2)
x3 = self.Maxpool(x2)
x3 = self.Conv3(x3)
x4 = self.Maxpool(x3)
x4 = self.Conv4(x4)
x5 = self.Maxpool(x4)
x5 = self.Conv5(x5)
# decoding + concat path
d5 = self.Up5(x5)
x4 = self.Att5(g=d5,x=x4)
d5 = torch.cat((x4,d5),dim=1)
d5 = self.Up_conv5(d5)
d4 = self.Up4(d5)
x3 = self.Att4(g=d4,x=x3)
d4 = torch.cat((x3,d4),dim=1)
d4 = self.Up_conv4(d4)
d3 = self.Up3(d4)
x2 = self.Att3(g=d3,x=x2)
d3 = torch.cat((x2,d3),dim=1)
d3 = self.Up_conv3(d3)
d2 = self.Up2(d3)
x1 = self.Att2(g=d2,x=x1)
d2 = torch.cat((x1,d2),dim=1)
d2 = self.Up_conv2(d2)
d1 = self.Conv_1x1(d2)
return d1
class R2AttU_Net(nn.Module):
def __init__(self, in_channels = 3, out_channels = 1, arma = False, steps = 2):
"""
Construction of Recurrent Residual Attention U-Net.
Arguments:
----------
in_channels: int
The number of input channels.
out_channels: int
The number of output channels.
arma: bool
Whether to use ARMA layers or standard convolutional layers.
default: False, i.e. use standard convolutioal layers
steps: int
The number of recurrent steps of each recurrent block.
default: 2
"""
super(R2AttU_Net,self).__init__()
self.Maxpool = nn.MaxPool2d(2, 2)
self.Upsample = nn.Upsample(2)
self.RRCNN1 = RRCNN_block(in_channels, 64, arma = arma, steps = steps)
self.RRCNN2 = RRCNN_block(64, 128, arma = arma, steps = steps)
self.RRCNN3 = RRCNN_block(128, 256, arma = arma, steps = steps)
self.RRCNN4 = RRCNN_block(256, 512, arma = arma, steps = steps)
self.RRCNN5 = RRCNN_block(512, 1024, arma = arma, steps = steps)
self.Up5 = up_conv(1024, 512, arma = arma)
self.Att5 = Attention_block(512, 512, 256)
self.Up_RRCNN5 = RRCNN_block(1024, 512, arma = arma, steps = steps)
self.Up4 = up_conv(512, 256)
self.Att4 = Attention_block(256, 256, 128)
self.Up_RRCNN4 = RRCNN_block(512, 256, arma = arma, steps = steps)
self.Up3 = up_conv(256, 128, arma = arma)
self.Att3 = Attention_block(128, 128, 64)
self.Up_RRCNN3 = RRCNN_block(256, 128, arma = arma, steps = steps)
self.Up2 = up_conv(128, 64, arma = arma)
self.Att2 = Attention_block(64, 64, 32)
self.Up_RRCNN2 = RRCNN_block(128, 64, arma = arma, steps = steps)
self.Conv_1x1 = nn.Conv2d(64, out_channels, 1)
def forward(self, x):
"""
Computation of the Recurrent Residual Attention U-Net.
Arguments:
----------
inputs: a 4-th order tensor of size
[batch_size, in_channels, height, width]
Input to the Recurrent Residual Attention U-Net.
Returns:
--------
outputs: a 4-th order tensor of size
[batch_size, out_channels, height, width]
Output of the Recurrent Residual Attention U-Net.
"""
# encoding path
x1 = self.RRCNN1(x)
x2 = self.Maxpool(x1)
x2 = self.RRCNN2(x2)
x3 = self.Maxpool(x2)
x3 = self.RRCNN3(x3)
x4 = self.Maxpool(x3)
x4 = self.RRCNN4(x4)
x5 = self.Maxpool(x4)
x5 = self.RRCNN5(x5)
# decoding + concat path
d5 = self.Up5(x5)
x4 = self.Att5(g=d5,x=x4)
d5 = torch.cat((x4,d5),dim=1)
d5 = self.Up_RRCNN5(d5)
d4 = self.Up4(d5)
x3 = self.Att4(g=d4,x=x3)
d4 = torch.cat((x3,d4),dim=1)
d4 = self.Up_RRCNN4(d4)
d3 = self.Up3(d4)
x2 = self.Att3(g=d3,x=x2)
d3 = torch.cat((x2,d3),dim=1)
d3 = self.Up_RRCNN3(d3)
d2 = self.Up2(d3)
x1 = self.Att2(g=d2,x=x1)
d2 = torch.cat((x1,d2),dim=1)
d2 = self.Up_RRCNN2(d2)
d1 = self.Conv_1x1(d2)
return d1
| 29.78492 | 102 | 0.564368 | 3,020 | 24,096 | 4.355629 | 0.062914 | 0.052684 | 0.059222 | 0.025544 | 0.867189 | 0.84499 | 0.824236 | 0.80561 | 0.791926 | 0.758933 | 0 | 0.055384 | 0.327108 | 24,096 | 808 | 103 | 29.821782 | 0.75589 | 0.29507 | 0 | 0.680851 | 0 | 0 | 0.009609 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.066869 | false | 0 | 0.012158 | 0 | 0.139818 | 0.00304 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
3f2752ddd623a33e74de916458d4145124e2ad85 | 133 | py | Python | src/tentaclio/hooks/__init__.py | datavaluepeople/tentaclio | eb6920a0e115c6c08043063a8c1013d812ec34c8 | [
"MIT"
] | 12 | 2019-04-30T16:07:42.000Z | 2021-12-08T08:02:09.000Z | src/tentaclio/hooks/__init__.py | octoenergy/tentaclio | eb6920a0e115c6c08043063a8c1013d812ec34c8 | [
"MIT"
] | 74 | 2019-04-25T11:18:22.000Z | 2022-01-18T11:31:14.000Z | src/tentaclio/hooks/__init__.py | datavaluepeople/tentaclio | eb6920a0e115c6c08043063a8c1013d812ec34c8 | [
"MIT"
] | 4 | 2019-05-05T13:13:21.000Z | 2022-01-14T00:33:07.000Z | """Hooks that use tentaclio clients."""
# TODO maybe move this guy somewhere else out of this repo
from .slack_hook import * # noqa
| 33.25 | 58 | 0.736842 | 21 | 133 | 4.619048 | 0.952381 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.180451 | 133 | 3 | 59 | 44.333333 | 0.889908 | 0.721805 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.333333 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
3f27800384b22aeb3b4e07f93258bc9ec1ec57f5 | 8,367 | py | Python | tests/test_actions.py | gaetanars/alerta | c14a1d8f6fc1aaf1cfb32c53f9f98e5a547dd886 | [
"Apache-2.0"
] | 1,233 | 2017-11-01T00:29:12.000Z | 2022-03-29T04:13:09.000Z | tests/test_actions.py | gaetanars/alerta | c14a1d8f6fc1aaf1cfb32c53f9f98e5a547dd886 | [
"Apache-2.0"
] | 760 | 2017-10-27T20:33:41.000Z | 2022-03-28T17:01:41.000Z | tests/test_actions.py | gaetanars/alerta | c14a1d8f6fc1aaf1cfb32c53f9f98e5a547dd886 | [
"Apache-2.0"
] | 238 | 2017-11-02T14:58:15.000Z | 2022-03-29T03:59:20.000Z | import json
import unittest
from uuid import uuid4
from alerta.app import alarm_model, create_app, db, plugins
class ActionsTestCase(unittest.TestCase):
def setUp(self):
test_config = {
'TESTING': True,
'AUTH_REQUIRED': False,
'ALERT_TIMEOUT': 120,
'HISTORY_LIMIT': 5
}
self.app = create_app(test_config)
self.client = self.app.test_client()
self.resource = str(uuid4()).upper()[:8]
self.fatal_alert = {
'event': 'node_down',
'resource': self.resource,
'environment': 'Production',
'service': ['Network'],
'severity': 'critical',
'correlate': ['node_down', 'node_marginal', 'node_up'],
'tags': ['foo'],
'attributes': {'foo': 'abc def', 'bar': 1234, 'baz': False},
}
self.critical_alert = {
'event': 'node_marginal',
'resource': self.resource,
'environment': 'Production',
'service': ['Network'],
'severity': 'critical',
'correlate': ['node_down', 'node_marginal', 'node_up'],
'timeout': 30
}
self.major_alert = {
'event': 'node_marginal',
'resource': self.resource,
'environment': 'Production',
'service': ['Network'],
'severity': 'major',
'correlate': ['node_down', 'node_marginal', 'node_up'],
'timeout': 40
}
self.warn_alert = {
'event': 'node_marginal',
'resource': self.resource,
'environment': 'Production',
'service': ['Network'],
'severity': 'warning',
'correlate': ['node_down', 'node_marginal', 'node_up'],
'timeout': 50
}
self.normal_alert = {
'event': 'node_up',
'resource': self.resource,
'environment': 'Production',
'service': ['Network'],
'severity': 'normal',
'correlate': ['node_down', 'node_marginal', 'node_up'],
'timeout': 100
}
self.ok_alert = {
'event': 'node_up',
'resource': self.resource,
'environment': 'Production',
'service': ['Network'],
'severity': 'ok',
'correlate': ['node_down', 'node_marginal', 'node_up']
}
self.cleared_alert = {
'event': 'node_up',
'resource': self.resource,
'environment': 'Production',
'service': ['Network'],
'severity': 'cleared',
'correlate': ['node_down', 'node_marginal', 'node_up']
}
self.headers = {
'Content-type': 'application/json',
'X-Forwarded-For': '10.0.0.1'
}
def tearDown(self):
plugins.plugins.clear()
db.destroy()
def test_alert(self):
# create alert
response = self.client.post('/alert', data=json.dumps(self.major_alert), headers=self.headers)
self.assertEqual(response.status_code, 201)
data = json.loads(response.data.decode('utf-8'))
self.assertEqual(data['alert']['resource'], self.resource)
self.assertEqual(data['alert']['status'], 'open')
self.assertEqual(data['alert']['duplicateCount'], 0)
self.assertEqual(data['alert']['trendIndication'], 'moreSevere')
alert_id = data['id']
# ack alert
response = self.client.put('/alert/' + alert_id + '/action',
data=json.dumps({'action': 'ack'}), headers=self.headers)
self.assertEqual(response.status_code, 200)
response = self.client.get('/alert/' + alert_id)
self.assertEqual(response.status_code, 200)
data = json.loads(response.data.decode('utf-8'))
self.assertEqual(data['alert']['severity'], 'major')
self.assertEqual(data['alert']['status'], 'ack')
# un-ack alert
response = self.client.put('/alert/' + alert_id + '/action',
data=json.dumps({'action': 'unack'}), headers=self.headers)
self.assertEqual(response.status_code, 200)
response = self.client.get('/alert/' + alert_id)
self.assertEqual(response.status_code, 200)
data = json.loads(response.data.decode('utf-8'))
self.assertEqual(data['alert']['severity'], 'major')
self.assertEqual(data['alert']['status'], 'open')
# close alert
response = self.client.put('/alert/' + alert_id + '/action',
data=json.dumps({'action': 'close'}), headers=self.headers)
self.assertEqual(response.status_code, 200)
response = self.client.get('/alert/' + alert_id)
self.assertEqual(response.status_code, 200)
data = json.loads(response.data.decode('utf-8'))
self.assertEqual(data['alert']['severity'], 'normal')
self.assertEqual(data['alert']['status'], 'closed')
# open alert
response = self.client.put('/alert/' + alert_id + '/action',
data=json.dumps({'action': 'open'}), headers=self.headers)
self.assertEqual(response.status_code, 200)
response = self.client.get('/alert/' + alert_id)
self.assertEqual(response.status_code, 200)
data = json.loads(response.data.decode('utf-8'))
self.assertEqual(data['alert']['severity'], 'major')
self.assertEqual(data['alert']['status'], alarm_model.DEFAULT_STATUS)
def test_unwind_actions(self):
# new alert => open
# create alert
response = self.client.post('/alert', data=json.dumps(self.warn_alert), headers=self.headers)
self.assertEqual(response.status_code, 201)
data = json.loads(response.data.decode('utf-8'))
self.assertEqual(data['alert']['resource'], self.resource)
self.assertEqual(data['alert']['severity'], 'warning')
self.assertEqual(data['alert']['status'], 'open')
self.assertEqual(data['alert']['duplicateCount'], 0)
self.assertEqual(data['alert']['trendIndication'], 'moreSevere')
alert_id = data['id']
# ack alert
response = self.client.put('/alert/' + alert_id + '/action',
data=json.dumps({'action': 'ack'}), headers=self.headers)
self.assertEqual(response.status_code, 200)
response = self.client.get('/alert/' + alert_id)
self.assertEqual(response.status_code, 200)
data = json.loads(response.data.decode('utf-8'))
self.assertEqual(data['alert']['severity'], 'warning')
self.assertEqual(data['alert']['status'], 'ack')
# shelve alert
response = self.client.put('/alert/' + alert_id + '/action',
data=json.dumps({'action': 'shelve'}), headers=self.headers)
self.assertEqual(response.status_code, 200)
response = self.client.get('/alert/' + alert_id)
self.assertEqual(response.status_code, 200)
data = json.loads(response.data.decode('utf-8'))
self.assertEqual(data['alert']['severity'], 'warning')
self.assertEqual(data['alert']['status'], 'shelved')
# unshelve alert
response = self.client.put('/alert/' + alert_id + '/action',
data=json.dumps({'action': 'unshelve'}), headers=self.headers)
self.assertEqual(response.status_code, 200)
response = self.client.get('/alert/' + alert_id)
self.assertEqual(response.status_code, 200)
data = json.loads(response.data.decode('utf-8'))
self.assertEqual(data['alert']['severity'], 'warning')
self.assertEqual(data['alert']['status'], 'ack')
# unack alert
response = self.client.put('/alert/' + alert_id + '/action',
data=json.dumps({'action': 'unack'}), headers=self.headers)
self.assertEqual(response.status_code, 200)
response = self.client.get('/alert/' + alert_id)
self.assertEqual(response.status_code, 200)
data = json.loads(response.data.decode('utf-8'))
self.assertEqual(data['alert']['severity'], 'warning')
self.assertEqual(data['alert']['status'], 'open')
| 40.814634 | 102 | 0.558384 | 845 | 8,367 | 5.426036 | 0.132544 | 0.140676 | 0.103599 | 0.130862 | 0.849727 | 0.843184 | 0.841658 | 0.841658 | 0.795638 | 0.795638 | 0 | 0.015031 | 0.276443 | 8,367 | 204 | 103 | 41.014706 | 0.742319 | 0.016613 | 0 | 0.640719 | 0 | 0 | 0.206183 | 0 | 0 | 0 | 0 | 0 | 0.257485 | 1 | 0.023952 | false | 0 | 0.023952 | 0 | 0.053892 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
3f2cbe4288a105f0d5ef62128d2a22322194156a | 952 | py | Python | strings contain.py | nimdvir/python | ebb6324535a5787ef6fa6ba0cc938c9dc3beded8 | [
"MIT"
] | 1 | 2020-08-30T20:07:57.000Z | 2020-08-30T20:07:57.000Z | strings contain.py | nimdvir/python | ebb6324535a5787ef6fa6ba0cc938c9dc3beded8 | [
"MIT"
] | null | null | null | strings contain.py | nimdvir/python | ebb6324535a5787ef6fa6ba0cc938c9dc3beded8 | [
"MIT"
] | null | null | null | # How to check if Python string contains another string
# The find method
# string.find(substring)
a_string="PythonProgramming"
substring1="Programming"
substring2="Language"
print("Check if "+a_string+" contains "+substring1+":")
print(a_string.find(substring1))
print("Check if "+a_string+" contains "+substring2+":")
print(a_string.find(substring2))
# The in operator
# substring in string
a_string="PythonProgramming"
substring1="Programming"
substring2="Language"
print("Check if "+a_string+" contains "+substring1+":")
print(substring1 in a_string)
print("Check if "+a_string+" contains "+substring2+":")
print(substring2 in a_string)
# The count method
# string.count(substring)
a_string="PythonProgramming"
substring1="Programming"
substring2="Language"
print("Check if "+a_string+" contains "+substring1+":")
print(a_string.count(substring1))
print("Check if "+a_string+" contains "+substring2+":")
print(a_string.count(substring2)) | 27.2 | 55 | 0.757353 | 120 | 952 | 5.883333 | 0.183333 | 0.148725 | 0.101983 | 0.110482 | 0.71813 | 0.71813 | 0.71813 | 0.71813 | 0.65864 | 0.65864 | 0 | 0.020857 | 0.093487 | 952 | 35 | 56 | 27.2 | 0.797219 | 0.177521 | 0 | 0.714286 | 0 | 0 | 0.293814 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0.571429 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 6 |
3f68bcc7d9313eb0c561d42a22c35192cf2d62e0 | 25 | py | Python | wooey/conf/project_template/urls/__init__.py | fridmundklaus/wooey | 4a2e31c282bfe86edf77b0ff8f58f4177eeab9dd | [
"BSD-3-Clause"
] | 1,572 | 2015-06-19T21:31:41.000Z | 2022-03-30T23:37:13.000Z | wooey/conf/project_template/urls/__init__.py | fridmundklaus/wooey | 4a2e31c282bfe86edf77b0ff8f58f4177eeab9dd | [
"BSD-3-Clause"
] | 309 | 2015-07-08T02:33:08.000Z | 2022-02-08T00:37:11.000Z | wooey/conf/project_template/urls/__init__.py | fridmundklaus/wooey | 4a2e31c282bfe86edf77b0ff8f58f4177eeab9dd | [
"BSD-3-Clause"
] | 220 | 2015-07-01T10:30:27.000Z | 2022-02-05T04:10:54.000Z | from .user_urls import *
| 12.5 | 24 | 0.76 | 4 | 25 | 4.5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.16 | 25 | 1 | 25 | 25 | 0.857143 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
18dd6866fa2b1fb7aaa3d29caa0a2b0241a53986 | 88 | py | Python | tests/test_all.py | gunawanw9/piradar | 4ac239ec7afdc45fa1af16e6ce74b57eaabeb7cd | [
"Apache-2.0"
] | 47 | 2017-03-13T14:25:40.000Z | 2021-03-11T19:10:37.000Z | tests/test_all.py | gunawanw9/piradar | 4ac239ec7afdc45fa1af16e6ce74b57eaabeb7cd | [
"Apache-2.0"
] | 1 | 2017-09-22T09:48:47.000Z | 2017-09-24T05:23:11.000Z | tests/test_all.py | gunawanw9/piradar | 4ac239ec7afdc45fa1af16e6ce74b57eaabeb7cd | [
"Apache-2.0"
] | 13 | 2017-08-08T11:11:02.000Z | 2020-01-26T02:46:55.000Z | #!/usr/bin/env python
import piradar
def test_import():
assert piradar is not None | 14.666667 | 30 | 0.727273 | 14 | 88 | 4.5 | 0.857143 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.181818 | 88 | 6 | 30 | 14.666667 | 0.875 | 0.227273 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.333333 | 1 | 0.333333 | true | 0 | 0.666667 | 0 | 1 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
7a1945a7a6c89d6ba73ed629e0eab049fc968e37 | 21 | py | Python | tf_trees/__init__.py | hazimehh/google-research | 81ff754d88f9ad479448c78d7ab615bef140423d | [
"Apache-2.0"
] | null | null | null | tf_trees/__init__.py | hazimehh/google-research | 81ff754d88f9ad479448c78d7ab615bef140423d | [
"Apache-2.0"
] | null | null | null | tf_trees/__init__.py | hazimehh/google-research | 81ff754d88f9ad479448c78d7ab615bef140423d | [
"Apache-2.0"
] | null | null | null | from .tel import TEL
| 10.5 | 20 | 0.761905 | 4 | 21 | 4 | 0.75 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.190476 | 21 | 1 | 21 | 21 | 0.941176 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
e1c7af8c55ed1671f5a2aeb4cfd1372ce61c6367 | 24,935 | py | Python | api/data/constants/reforges.py | UP929312/CommunityBot | c16294e8ff4f47d9a1e8c18c9cd4011e7ebbd67a | [
"Apache-2.0"
] | 1 | 2021-06-15T07:31:13.000Z | 2021-06-15T07:31:13.000Z | api/data/constants/reforges.py | UP929312/CommunityBot | c16294e8ff4f47d9a1e8c18c9cd4011e7ebbd67a | [
"Apache-2.0"
] | 1 | 2021-06-01T10:14:32.000Z | 2021-06-02T10:54:12.000Z | api/data/constants/reforges.py | UP929312/CommunityBot | c16294e8ff4f47d9a1e8c18c9cd4011e7ebbd67a | [
"Apache-2.0"
] | 2 | 2021-06-01T10:59:15.000Z | 2021-06-03T18:29:36.000Z | REFORGE_DICT = {
"ambered;PICKAXE": {
"INTERNAL_NAME": "AMBER_MATERIAL",
"REFORGE_COST": {
"COMMON": 20000,
"UNCOMMON": 40000,
"RARE": 80000,
"EPIC": 150000,
"LEGENDARY": 300000,
"MYTHIC": 600000
}
},
"bloody;ACCESSORY": {
"INTERNAL_NAME": "BEATING_HEART",
"REFORGE_COST": {
"COMMON": 750,
"UNCOMMON": 1500,
"RARE": 3650,
"EPIC": 7500,
"LEGENDARY": 15000,
"MYTHIC": 30000
}
},
"blessed;AXE": {
"INTERNAL_NAME": "BLESSED_FRUIT",
"REFORGE_COST": {
"COMMON": 10000,
"UNCOMMON": 10000,
"RARE": 10000,
"EPIC": 10000,
"LEGENDARY": 10000,
"MYTHIC": 10000
}
},
"blessed;HOE": {
"INTERNAL_NAME": "BLESSED_FRUIT",
"REFORGE_COST": {
"COMMON": 10000,
"UNCOMMON": 10000,
"RARE": 10000,
"EPIC": 10000,
"LEGENDARY": 10000,
"MYTHIC": 10000
}
},
"bulky;SWORD": {
"INTERNAL_NAME": "BULKY_STONE",
"REFORGE_COST": {
"COMMON": 20000,
"UNCOMMON": 40000,
"RARE": 80000,
"EPIC": 150000,
"LEGENDARY": 300000,
"MYTHIC": 600000
}
},
"bountiful;HOE": {
"INTERNAL_NAME": "GOLDEN_BALL",
"REFORGE_COST": {
"COMMON": 20000,
"UNCOMMON": 40000,
"RARE": 80000,
"EPIC": 150000,
"LEGENDARY": 300000,
"MYTHIC": 600000
}
},
"candied;HELMET": {
"INTERNAL_NAME": "CANDY_CORN",
"REFORGE_COST": {
"COMMON": 20000,
"UNCOMMON": 40000,
"RARE": 80000,
"EPIC": 150000,
"LEGENDARY": 300000,
"MYTHIC": 600000
}
},
"candied;CHESTPLATE": {
"INTERNAL_NAME": "CANDY_CORN",
"REFORGE_COST": {
"COMMON": 20000,
"UNCOMMON": 40000,
"RARE": 80000,
"EPIC": 150000,
"LEGENDARY": 300000,
"MYTHIC": 600000
}
},
"candied;LEGGINGS": {
"INTERNAL_NAME": "CANDY_CORN",
"REFORGE_COST": {
"COMMON": 20000,
"UNCOMMON": 40000,
"RARE": 80000,
"EPIC": 150000,
"LEGENDARY": 300000,
"MYTHIC": 600000
}
},
"candied;BOOTS": {
"INTERNAL_NAME": "CANDY_CORN",
"REFORGE_COST": {
"COMMON": 20000,
"UNCOMMON": 40000,
"RARE": 80000,
"EPIC": 150000,
"LEGENDARY": 300000,
"MYTHIC": 600000
}
},
"shaded;ACCESSORY": {
"INTERNAL_NAME": "DARK_ORB",
"REFORGE_COST": {
"COMMON": 25000,
"UNCOMMON": 50000,
"RARE": 100000,
"EPIC": 200000,
"LEGENDARY": 400000,
"MYTHIC": 800000
}
},
"submerged;HELMET": {
"INTERNAL_NAME": "DEEP_SEA_ORB",
"REFORGE_COST": {
"COMMON": 50000,
"UNCOMMON": 150000,
"RARE": 350000,
"EPIC": 600000,
"LEGENDARY": 750000,
"MYTHIC": 800000
}
},
"submerged;CHESTPLATE": {
"INTERNAL_NAME": "DEEP_SEA_ORB",
"REFORGE_COST": {
"COMMON": 50000,
"UNCOMMON": 150000,
"RARE": 350000,
"EPIC": 600000,
"LEGENDARY": 750000,
"MYTHIC": 800000
}
},
"submerged;LEGGINGS": {
"INTERNAL_NAME": "DEEP_SEA_ORB",
"REFORGE_COST": {
"COMMON": 50000,
"UNCOMMON": 150000,
"RARE": 350000,
"EPIC": 600000,
"LEGENDARY": 750000,
"MYTHIC": 800000
}
},
"submerged;BOOTS": {
"INTERNAL_NAME": "DEEP_SEA_ORB",
"REFORGE_COST": {
"COMMON": 50000,
"UNCOMMON": 150000,
"RARE": 350000,
"EPIC": 600000,
"LEGENDARY": 750000,
"MYTHIC": 800000
}
},
"fleet;PICKAXE": {
"INTERNAL_NAME": "DIAMONITE",
"REFORGE_COST": {
"COMMON": 15000,
"UNCOMMON": 30000,
"RARE": 60000,
"EPIC": 125000,
"LEGENDARY": 250000,
"MYTHIC": 500000
}
},
"dirty;SWORD": {
"INTERNAL_NAME": "DIRT_BOTTLE",
"REFORGE_COST": {
"COMMON": 1000,
"UNCOMMON": 5000,
"RARE": 10000,
"EPIC": 15000,
"LEGENDARY": 50000,
"MYTHIC": 75000
}
},
"dirty;ROD": {
"INTERNAL_NAME": "DIRT_BOTTLE",
"REFORGE_COST": {
"COMMON": 1000,
"UNCOMMON": 5000,
"RARE": 10000,
"EPIC": 15000,
"LEGENDARY": 50000,
"MYTHIC": 75000
}
},
"fabled;SWORD": {
"INTERNAL_NAME": "DRAGON_CLAW",
"REFORGE_COST": {
"COMMON": 60000,
"UNCOMMON": 125000,
"RARE": 250000,
"EPIC": 500000,
"LEGENDARY": 1000000,
"MYTHIC": 2000000
}
},
"renowned;HELMET": {
"INTERNAL_NAME": "DRAGON_HORN",
"REFORGE_COST": {
"EPIC": 500000,
"LEGENDARY": 1000000,
"MYTHIC": 2000000
}
},
"renowned;CHESTPLATE": {
"INTERNAL_NAME": "DRAGON_HORN",
"REFORGE_COST": {
"EPIC": 500000,
"LEGENDARY": 1000000,
"MYTHIC": 2000000
}
},
"renowned;LEGGINGS": {
"INTERNAL_NAME": "DRAGON_HORN",
"REFORGE_COST": {
"EPIC": 500000,
"LEGENDARY": 1000000,
"MYTHIC": 2000000
}
},
"renowned;BOOTS": {
"INTERNAL_NAME": "DRAGON_HORN",
"REFORGE_COST": {
"EPIC": 500000,
"LEGENDARY": 1000000,
"MYTHIC": 2000000
}
},
"spiked;HELMET": {
"INTERNAL_NAME": "DRAGON_SCALE",
"REFORGE_COST": {
"COMMON": 30000,
"UNCOMMON": 75000,
"RARE": 150000,
"EPIC": 300000,
"LEGENDARY": 600000,
"MYTHIC": 2000000
}
},
"spiked;CHESTPLATE": {
"INTERNAL_NAME": "DRAGON_SCALE",
"REFORGE_COST": {
"COMMON": 30000,
"UNCOMMON": 75000,
"RARE": 150000,
"EPIC": 300000,
"LEGENDARY": 600000,
"MYTHIC": 2000000
}
},
"spiked;LEGGINGS": {
"INTERNAL_NAME": "DRAGON_SCALE",
"REFORGE_COST": {
"COMMON": 30000,
"UNCOMMON": 75000,
"RARE": 150000,
"EPIC": 300000,
"LEGENDARY": 600000,
"MYTHIC": 2000000
}
},
"spiked;BOOTS": {
"INTERNAL_NAME": "DRAGON_SCALE",
"REFORGE_COST": {
"COMMON": 30000,
"UNCOMMON": 75000,
"RARE": 150000,
"EPIC": 300000,
"LEGENDARY": 600000,
"MYTHIC": 2000000
}
},
"perfect;HELMET": {
"INTERNAL_NAME": "DIAMOND_ATOM",
"REFORGE_COST": {
"COMMON": 25000,
"UNCOMMON": 50000,
"RARE": 150000,
"EPIC": 300000,
"LEGENDARY": 600000,
"MYTHIC": 800000
}
},
"perfect;CHESTPLATE": {
"INTERNAL_NAME": "DIAMOND_ATOM",
"REFORGE_COST": {
"COMMON": 25000,
"UNCOMMON": 50000,
"RARE": 150000,
"EPIC": 300000,
"LEGENDARY": 600000,
"MYTHIC": 800000
}
},
"perfect;LEGGINGS": {
"INTERNAL_NAME": "DIAMOND_ATOM",
"REFORGE_COST": {
"COMMON": 25000,
"UNCOMMON": 50000,
"RARE": 150000,
"EPIC": 300000,
"LEGENDARY": 600000,
"MYTHIC": 800000
}
},
"perfect;BOOTS": {
"INTERNAL_NAME": "DIAMOND_ATOM",
"REFORGE_COST": {
"COMMON": 25000,
"UNCOMMON": 50000,
"RARE": 150000,
"EPIC": 300000,
"LEGENDARY": 600000,
"MYTHIC": 800000
}
},
"warped;HELMET": {
"INTERNAL_NAME": "ENDSTONE_GEODE",
"REFORGE_COST": {
"COMMON": 5000,
"UNCOMMON": 10000,
"RARE": 20000,
"EPIC": 50000,
"LEGENDARY": 100000,
"MYTHIC": 200000
}
},
"warped;CHESTPLATE": {
"INTERNAL_NAME": "ENDSTONE_GEODE",
"REFORGE_COST": {
"COMMON": 5000,
"UNCOMMON": 10000,
"RARE": 20000,
"EPIC": 50000,
"LEGENDARY": 100000,
"MYTHIC": 200000
}
},
"warped;LEGGINGS": {
"INTERNAL_NAME": "ENDSTONE_GEODE",
"REFORGE_COST": {
"COMMON": 5000,
"UNCOMMON": 10000,
"RARE": 20000,
"EPIC": 50000,
"LEGENDARY": 100000,
"MYTHIC": 200000
}
},
"warped;BOOTS": {
"INTERNAL_NAME": "ENDSTONE_GEODE",
"REFORGE_COST": {
"COMMON": 5000,
"UNCOMMON": 10000,
"RARE": 20000,
"EPIC": 50000,
"LEGENDARY": 100000,
"MYTHIC": 200000
}
},
"giant;HELMET": {
"INTERNAL_NAME": "GIANT_TOOTH",
"REFORGE_COST": {
"COMMON": 60000,
"UNCOMMON": 125000,
"RARE": 250000,
"EPIC": 500000,
"LEGENDARY": 1000000,
"MYTHIC": 2000000
}
},
"giant;CHESTPLATE": {
"INTERNAL_NAME": "GIANT_TOOTH",
"REFORGE_COST": {
"COMMON": 60000,
"UNCOMMON": 125000,
"RARE": 250000,
"EPIC": 500000,
"LEGENDARY": 1000000,
"MYTHIC": 2000000
}
},
"giant;LEGGINGS": {
"INTERNAL_NAME": "GIANT_TOOTH",
"REFORGE_COST": {
"COMMON": 60000,
"UNCOMMON": 125000,
"RARE": 250000,
"EPIC": 500000,
"LEGENDARY": 1000000,
"MYTHIC": 2000000
}
},
"giant;BOOTS": {
"INTERNAL_NAME": "GIANT_TOOTH",
"REFORGE_COST": {
"COMMON": 60000,
"UNCOMMON": 125000,
"RARE": 250000,
"EPIC": 500000,
"LEGENDARY": 1000000,
"MYTHIC": 2000000
}
},
"stiff;ROD": {
"INTERNAL_NAME": "HARDENED_WOOD",
"REFORGE_COST": {
"COMMON": 4000,
"UNCOMMON": 7500,
"RARE": 15000,
"EPIC": 40000,
"LEGENDARY": 75000,
"MYTHIC": 150000
}
},
"heated;PICKAXE": {
"INTERNAL_NAME": "HOT_STUFF",
"REFORGE_COST": {
"COMMON": 20000,
"UNCOMMON": 40000,
"RARE": 80000,
"EPIC": 150000,
"LEGENDARY": 300000,
"MYTHIC": 600000
}
},
"jerry's;SWORD": {
"INTERNAL_NAME": "JERRY_STONE",
"REFORGE_COST": {
"COMMON": 1,
"UNCOMMON": 2,
"RARE": 3
}
},
"jaded;HELMET": {
"INTERNAL_NAME": "JADERALD",
"REFORGE_COST": {
"COMMON": 20000,
"UNCOMMON": 40000,
"RARE": 80000,
"EPIC": 150000,
"LEGENDARY": 300000,
"MYTHIC": 600000
}
},
"jaded;CHESTPLATE": {
"INTERNAL_NAME": "JADERALD",
"REFORGE_COST": {
"COMMON": 20000,
"UNCOMMON": 40000,
"RARE": 80000,
"EPIC": 150000,
"LEGENDARY": 300000,
"MYTHIC": 600000
}
},
"jaded;LEGGINGS": {
"INTERNAL_NAME": "JADERALD",
"REFORGE_COST": {
"COMMON": 20000,
"UNCOMMON": 40000,
"RARE": 80000,
"EPIC": 150000,
"LEGENDARY": 300000,
"MYTHIC": 600000
}
},
"jaded;BOOTS": {
"INTERNAL_NAME": "JADERALD",
"REFORGE_COST": {
"COMMON": 20000,
"UNCOMMON": 40000,
"RARE": 80000,
"EPIC": 150000,
"LEGENDARY": 300000,
"MYTHIC": 600000
}
},
"magnetic;PICKAXE": {
"INTERNAL_NAME": "LAPIS_CRYSTAL",
"REFORGE_COST": {
"COMMON": 250,
"UNCOMMON": 500,
"RARE": 1000,
"EPIC": 2500,
"LEGENDARY": 5000,
"MYTHIC": 10000
}
},
"silky;ACCESSORY": {
"INTERNAL_NAME": "LUXURIOUS_SPOOL",
"REFORGE_COST": {
"COMMON": 250,
"UNCOMMON": 500,
"RARE": 1000,
"EPIC": 2500,
"LEGENDARY": 5000,
"MYTHIC": 10000
}
},
"lucky;ROD": {
"INTERNAL_NAME": "LUCKY_DICE",
"REFORGE_COST": {
"COMMON": 20000,
"UNCOMMON": 40000,
"RARE": 80000,
"EPIC": 150000,
"LEGENDARY": 300000,
"MYTHIC": 600000
}
},
"gilded;SWORD": {
"INTERNAL_NAME": "MIDAS_JEWEL",
"REFORGE_COST": {
"LEGENDARY": 5000000,
"MYTHIC": 10000000
}
},
"moil;AXE": {
"INTERNAL_NAME": "MOIL_LOG",
"REFORGE_COST": {
"COMMON": 1000,
"UNCOMMON": 5000,
"RARE": 10000,
"EPIC": 15000,
"LEGENDARY": 50000,
"MYTHIC": 75000
}
},
"cubic;HELMET": {
"INTERNAL_NAME": "MOLTEN_CUBE",
"REFORGE_COST": {
"COMMON": 4000,
"UNCOMMON": 7500,
"RARE": 15000,
"EPIC": 40000,
"LEGENDARY": 75000,
"MYTHIC": 150000
}
},
"cubic;CHESTPLATE": {
"INTERNAL_NAME": "MOLTEN_CUBE",
"REFORGE_COST": {
"COMMON": 4000,
"UNCOMMON": 7500,
"RARE": 15000,
"EPIC": 40000,
"LEGENDARY": 75000,
"MYTHIC": 150000
}
},
"cubic;LEGGINGS": {
"INTERNAL_NAME": "MOLTEN_CUBE",
"REFORGE_COST": {
"COMMON": 4000,
"UNCOMMON": 7500,
"RARE": 15000,
"EPIC": 40000,
"LEGENDARY": 75000,
"MYTHIC": 150000
}
},
"cubic;BOOTS": {
"INTERNAL_NAME": "MOLTEN_CUBE",
"REFORGE_COST": {
"COMMON": 4000,
"UNCOMMON": 7500,
"RARE": 15000,
"EPIC": 40000,
"LEGENDARY": 75000,
"MYTHIC": 150000
}
},
"necrotic;HELMET": {
"INTERNAL_NAME": "NECROMANCER_BROOCH",
"REFORGE_COST": {
"COMMON": 20000,
"UNCOMMON": 40000,
"RARE": 80000,
"EPIC": 150000,
"LEGENDARY": 300000,
"MYTHIC": 1000000
}
},
"necrotic;CHESTPLATE": {
"INTERNAL_NAME": "NECROMANCER_BROOCH",
"REFORGE_COST": {
"COMMON": 20000,
"UNCOMMON": 40000,
"RARE": 80000,
"EPIC": 150000,
"LEGENDARY": 300000,
"MYTHIC": 1000000
}
},
"necrotic;LEGGINGS": {
"INTERNAL_NAME": "NECROMANCER_BROOCH",
"REFORGE_COST": {
"COMMON": 20000,
"UNCOMMON": 40000,
"RARE": 80000,
"EPIC": 150000,
"LEGENDARY": 300000,
"MYTHIC": 1000000
}
},
"necrotic;BOOTS": {
"INTERNAL_NAME": "NECROMANCER_BROOCH",
"REFORGE_COST": {
"COMMON": 20000,
"UNCOMMON": 40000,
"RARE": 80000,
"EPIC": 150000,
"LEGENDARY": 300000,
"MYTHIC": 1000000
}
},
"fruitful;PICKAXE": {
"INTERNAL_NAME": "ONYX",
"REFORGE_COST": {
"COMMON": 100,
"UNCOMMON": 250,
"RARE": 500,
"EPIC": 1000,
"LEGENDARY": 2500,
"MYTHIC": 15000
}
},
"precise;BOW": {
"INTERNAL_NAME": "OPTICAL_LENS",
"REFORGE_COST": {
"COMMON": 30000,
"UNCOMMON": 75000,
"RARE": 150000,
"EPIC": 300000,
"LEGENDARY": 600000,
"MYTHIC": 2000000
}
},
"stellar;PICKAXE": {
"INTERNAL_NAME": "PETRIFIED_STARFALL",
"REFORGE_COST": {
"COMMON": 25000,
"UNCOMMON": 50000,
"RARE": 100000,
"EPIC": 200000,
"LEGENDARY": 400000
}
},
"ancient;HELMET": {
"INTERNAL_NAME": "PRECURSOR_GEAR",
"REFORGE_COST": {
"COMMON": 10000,
"UNCOMMON": 20000,
"RARE": 30000,
"EPIC": 40000,
"LEGENDARY": 50000,
"MYTHIC": 60000
}
},
"ancient;CHESTPLATE": {
"INTERNAL_NAME": "PRECURSOR_GEAR",
"REFORGE_COST": {
"COMMON": 10000,
"UNCOMMON": 20000,
"RARE": 30000,
"EPIC": 40000,
"LEGENDARY": 50000,
"MYTHIC": 60000
}
},
"ancient;LEGGINGS": {
"INTERNAL_NAME": "PRECURSOR_GEAR",
"REFORGE_COST": {
"COMMON": 10000,
"UNCOMMON": 20000,
"RARE": 30000,
"EPIC": 40000,
"LEGENDARY": 50000,
"MYTHIC": 60000
}
},
"ancient;BOOTS": {
"INTERNAL_NAME": "PRECURSOR_GEAR",
"REFORGE_COST": {
"COMMON": 10000,
"UNCOMMON": 20000,
"RARE": 30000,
"EPIC": 40000,
"LEGENDARY": 50000,
"MYTHIC": 60000
}
},
"undead;HELMET": {
"INTERNAL_NAME": "PREMIUM_FLESH",
"REFORGE_COST": {
"COMMON": 5000,
"UNCOMMON": 15000,
"RARE": 30000,
"EPIC": 75000,
"LEGENDARY": 150000,
"MYTHIC": 300000
}
},
"undead;CHESTPLATE": {
"INTERNAL_NAME": "PREMIUM_FLESH",
"REFORGE_COST": {
"COMMON": 5000,
"UNCOMMON": 15000,
"RARE": 30000,
"EPIC": 75000,
"LEGENDARY": 150000,
"MYTHIC": 300000
}
},
"undead;LEGGINGS": {
"INTERNAL_NAME": "PREMIUM_FLESH",
"REFORGE_COST": {
"COMMON": 5000,
"UNCOMMON": 15000,
"RARE": 30000,
"EPIC": 75000,
"LEGENDARY": 150000,
"MYTHIC": 300000
}
},
"undead;BOOTS": {
"INTERNAL_NAME": "PREMIUM_FLESH",
"REFORGE_COST": {
"COMMON": 5000,
"UNCOMMON": 15000,
"RARE": 30000,
"EPIC": 75000,
"LEGENDARY": 150000,
"MYTHIC": 300000
}
},
"mithraic;PICKAXE": {
"INTERNAL_NAME": "PURE_MITHRIL",
"REFORGE_COST": {
"COMMON": 15000,
"UNCOMMON": 30000,
"RARE": 60000,
"EPIC": 125000,
"LEGENDARY": 250000
}
},
"reinforced;HELMET": {
"INTERNAL_NAME": "RARE_DIAMOND",
"REFORGE_COST": {
"COMMON": 2500,
"UNCOMMON": 5000,
"RARE": 10000,
"EPIC": 25000,
"LEGENDARY": 50000,
"MYTHIC": 100000
}
},
"reinforced;CHESTPLATE": {
"INTERNAL_NAME": "RARE_DIAMOND",
"REFORGE_COST": {
"COMMON": 2500,
"UNCOMMON": 5000,
"RARE": 10000,
"EPIC": 25000,
"LEGENDARY": 50000,
"MYTHIC": 100000
}
},
"reinforced;LEGGINGS": {
"INTERNAL_NAME": "RARE_DIAMOND",
"REFORGE_COST": {
"COMMON": 2500,
"UNCOMMON": 5000,
"RARE": 10000,
"EPIC": 25000,
"LEGENDARY": 50000,
"MYTHIC": 100000
}
},
"reinforced;BOOTS": {
"INTERNAL_NAME": "RARE_DIAMOND",
"REFORGE_COST": {
"COMMON": 2500,
"UNCOMMON": 5000,
"RARE": 10000,
"EPIC": 25000,
"LEGENDARY": 50000,
"MYTHIC": 100000
}
},
"ridiculous;HELMET": {
"INTERNAL_NAME": "RED_NOSE",
"REFORGE_COST": {
"COMMON": 7500,
"UNCOMMON": 15000,
"RARE": 30000,
"EPIC": 75000,
"LEGENDARY": 150000,
"MYTHIC": 300000
}
},
"loving;CHESTPLATE": {
"INTERNAL_NAME": "RED_SCARF",
"REFORGE_COST": {
"COMMON": 30000,
"UNCOMMON": 75000,
"RARE": 150000,
"EPIC": 300000,
"LEGENDARY": 600000,
"MYTHIC": 1200000
}
},
"refined;PICKAXE": {
"INTERNAL_NAME": "REFINED_AMBER",
"REFORGE_COST": {
"COMMON": 10000,
"UNCOMMON": 10000,
"RARE": 10000,
"EPIC": 10000,
"LEGENDARY": 10000,
"SPECIAL": 10000
}
},
"sweet;ACCESSORY": {
"INTERNAL_NAME": "ROCK_CANDY",
"REFORGE_COST": {
"COMMON": 20000,
"UNCOMMON": 40000,
"RARE": 80000,
"EPIC": 150000,
"LEGENDARY": 300000,
"MYTHIC": 600000
}
},
"auspicious;PICKAXE": {
"INTERNAL_NAME": "ROCK_GEMSTONE",
"REFORGE_COST": {
"COMMON": 20000,
"UNCOMMON": 40000,
"RARE": 80000,
"EPIC": 150000,
"LEGENDARY": 300000,
"MYTHIC": 300000
}
},
"empowered;HELMET": {
"INTERNAL_NAME": "SADAN_BROOCH",
"REFORGE_COST": {
"COMMON": 60000,
"UNCOMMON": 125000,
"RARE": 250000,
"EPIC": 500000,
"LEGENDARY": 1000000,
"MYTHIC": 2000000
}
},
"empowered;CHESTPLATE": {
"INTERNAL_NAME": "SADAN_BROOCH",
"REFORGE_COST": {
"COMMON": 60000,
"UNCOMMON": 125000,
"RARE": 250000,
"EPIC": 500000,
"LEGENDARY": 1000000,
"MYTHIC": 2000000
}
},
"empowered;LEGGINGS": {
"INTERNAL_NAME": "SADAN_BROOCH",
"REFORGE_COST": {
"COMMON": 60000,
"UNCOMMON": 125000,
"RARE": 250000,
"EPIC": 500000,
"LEGENDARY": 1000000,
"MYTHIC": 2000000
}
},
"empowered;BOOTS": {
"INTERNAL_NAME": "SADAN_BROOCH",
"REFORGE_COST": {
"COMMON": 60000,
"UNCOMMON": 125000,
"RARE": 250000,
"EPIC": 500000,
"LEGENDARY": 1000000,
"MYTHIC": 2000000
}
},
"spiritual;BOW": {
"INTERNAL_NAME": "SPIRIT_DECOY",
"REFORGE_COST": {
"COMMON": 60000,
"UNCOMMON": 125000,
"RARE": 250000,
"EPIC": 500000,
"LEGENDARY": 1000000,
"MYTHIC": 2000000
}
},
"suspicious;SWORD": {
"INTERNAL_NAME": "SUSPICIOUS_VIAL",
"REFORGE_COST": {
"COMMON": 60000,
"UNCOMMON": 125000,
"RARE": 250000,
"EPIC": 500000,
"LEGENDARY": 1000000,
"MYTHIC": 2000000
}
},
"toil;AXE": {
"INTERNAL_NAME": "TOIL_LOG",
"REFORGE_COST": {
"COMMON": 10000,
"UNCOMMON": 10000,
"RARE": 10000,
"EPIC": 10000,
"LEGENDARY": 10000,
"MYTHIC": 10000
}
},
"warped;SWORD": {
"INTERNAL_NAME": "AOTE_STONE",
"REFORGE_COST": {
"RARE": 5000000,
"EPIC": 10000000,
"LEGENDARY": 20000000
}
},
"withered;SWORD": {
"INTERNAL_NAME": "WITHER_BLOOD",
"REFORGE_COST": {
"COMMON": 10000,
"UNCOMMON": 20000,
"RARE": 30000,
"EPIC": 40000,
"LEGENDARY": 50000,
"MYTHIC": 60000
}
},
"headstrong;BOW": {
"INTERNAL_NAME": "SALMON_OPAL",
"REFORGE_COST": {
"COMMON": 15000,
"UNCOMMON": 30000,
"RARE": 60000,
"EPIC": 125000,
"LEGENDARY": 250000,
"MYTHIC": 500000
}
}
} | 25.759298 | 46 | 0.421536 | 1,753 | 24,935 | 5.842556 | 0.102111 | 0.105448 | 0.139426 | 0.040812 | 0.813806 | 0.813806 | 0.813806 | 0.813025 | 0.813025 | 0.813025 | 0 | 0.197547 | 0.434209 | 24,935 | 968 | 47 | 25.759298 | 0.528424 | 0 | 0 | 0.661157 | 0 | 0 | 0.312921 | 0.000842 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
e1cbb78360bb456bd975d95903bddf94aac5bd5c | 150 | py | Python | test_template1.py | gregdetre/unit-testing-pres | c28cd3a938436b18f2175bfb43d0c33820dfc7ee | [
"MIT"
] | null | null | null | test_template1.py | gregdetre/unit-testing-pres | c28cd3a938436b18f2175bfb43d0c33820dfc7ee | [
"MIT"
] | null | null | null | test_template1.py | gregdetre/unit-testing-pres | c28cd3a938436b18f2175bfb43d0c33820dfc7ee | [
"MIT"
] | null | null | null | # USAGE:
# $ nose2 test_template1
def test_passes():
assert True
def test_fails():
assert False
def test_equals():
assert 1 == 1
| 10.714286 | 24 | 0.626667 | 20 | 150 | 4.5 | 0.6 | 0.233333 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.036697 | 0.273333 | 150 | 13 | 25 | 11.538462 | 0.788991 | 0.193333 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.5 | 1 | 0.5 | true | 0.166667 | 0 | 0 | 0.5 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 6 |
bee5f294b4d2ca8dddd9c5730e987573c75b4e02 | 47 | py | Python | landmark_detection/__init__.py | Chichichkin/LandmarkDetector | bf06b4846f139ccd9902efdc0aa8f0fd724272cb | [
"MIT"
] | null | null | null | landmark_detection/__init__.py | Chichichkin/LandmarkDetector | bf06b4846f139ccd9902efdc0aa8f0fd724272cb | [
"MIT"
] | null | null | null | landmark_detection/__init__.py | Chichichkin/LandmarkDetector | bf06b4846f139ccd9902efdc0aa8f0fd724272cb | [
"MIT"
] | null | null | null | from .landmark_detector import LandmarkDetector | 47 | 47 | 0.914894 | 5 | 47 | 8.4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.06383 | 47 | 1 | 47 | 47 | 0.954545 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
831496746d52ede2643c60e367e0b125a0eab884 | 58 | py | Python | tests/__init__.py | gouline/outages-sample | d0f0c9403d5cb048d4c29fdb74bf9ab8b921d7ce | [
"MIT"
] | null | null | null | tests/__init__.py | gouline/outages-sample | d0f0c9403d5cb048d4c29fdb74bf9ab8b921d7ce | [
"MIT"
] | null | null | null | tests/__init__.py | gouline/outages-sample | d0f0c9403d5cb048d4c29fdb74bf9ab8b921d7ce | [
"MIT"
] | null | null | null | from .test_calendar import *
from .test_provider import *
| 19.333333 | 28 | 0.793103 | 8 | 58 | 5.5 | 0.625 | 0.363636 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.137931 | 58 | 2 | 29 | 29 | 0.88 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
831ee5e506e83ea2605bfeb36baf1759cbc7844d | 33 | py | Python | test.py | FilaGM/peml-language | 8696967d106303fb89a1b3b0a83a3999c277c934 | [
"MIT"
] | null | null | null | test.py | FilaGM/peml-language | 8696967d106303fb89a1b3b0a83a3999c277c934 | [
"MIT"
] | null | null | null | test.py | FilaGM/peml-language | 8696967d106303fb89a1b3b0a83a3999c277c934 | [
"MIT"
] | null | null | null | def void():
print("clicked")
| 11 | 20 | 0.575758 | 4 | 33 | 4.75 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.212121 | 33 | 2 | 21 | 16.5 | 0.730769 | 0 | 0 | 0 | 0 | 0 | 0.212121 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.5 | true | 0 | 0 | 0 | 0.5 | 0.5 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 6 |
36077285142cc3071e77d320c7cc2c95bf17ec92 | 236 | py | Python | sc_python_harvester/test.py | spatialcurrent/sc-python-harvester | e90d0553d0827969960cbc776df4b9bdb7c0e143 | [
"MIT"
] | null | null | null | sc_python_harvester/test.py | spatialcurrent/sc-python-harvester | e90d0553d0827969960cbc776df4b9bdb7c0e143 | [
"MIT"
] | 1 | 2017-05-16T15:41:01.000Z | 2017-05-16T15:41:01.000Z | sc_python_harvester/test.py | spatialcurrent/sc-python-harvester | e90d0553d0827969960cbc776df4b9bdb7c0e143 | [
"MIT"
] | null | null | null | # -*- coding: utf-8 -*-
#########################################################################
#
# Copyright (C) 2017 Spatial Current, Inc.
#
#########################################################################
import unittest
| 26.222222 | 73 | 0.233051 | 11 | 236 | 5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.023041 | 0.080508 | 236 | 8 | 74 | 29.5 | 0.230415 | 0.262712 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
365d41d82aeabe71099e116a22ce3598e5efd813 | 217 | py | Python | translated_fields/__init__.py | jghyllebert/django-translated-fields | 5a812331b11342faafc99a9e707c16d33edb3fa2 | [
"BSD-3-Clause"
] | null | null | null | translated_fields/__init__.py | jghyllebert/django-translated-fields | 5a812331b11342faafc99a9e707c16d33edb3fa2 | [
"BSD-3-Clause"
] | null | null | null | translated_fields/__init__.py | jghyllebert/django-translated-fields | 5a812331b11342faafc99a9e707c16d33edb3fa2 | [
"BSD-3-Clause"
] | null | null | null | VERSION = (0, 7, 2)
__version__ = ".".join(map(str, VERSION))
try:
from .admin import * # noqa
from .fields import * # noqa
from .utils import * # noqa
except ImportError: # pragma: no cover
pass
| 21.7 | 41 | 0.612903 | 28 | 217 | 4.607143 | 0.714286 | 0.232558 | 0.217054 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.018634 | 0.258065 | 217 | 9 | 42 | 24.111111 | 0.782609 | 0.142857 | 0 | 0 | 0 | 0 | 0.005525 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0.125 | 0.5 | 0 | 0.5 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 6 |
366d488743904f2eeef7a2b66618582fd3d8054a | 93 | py | Python | aartisteel/config/aartisteel.py | venku31/aartisteel | 0a6d5a6e459248fb66f765754c3ecf777f9b9944 | [
"MIT"
] | null | null | null | aartisteel/config/aartisteel.py | venku31/aartisteel | 0a6d5a6e459248fb66f765754c3ecf777f9b9944 | [
"MIT"
] | null | null | null | aartisteel/config/aartisteel.py | venku31/aartisteel | 0a6d5a6e459248fb66f765754c3ecf777f9b9944 | [
"MIT"
] | null | null | null | from __future__ import unicode_literals
from frappe import _
def get_data():
return [
] | 15.5 | 39 | 0.752688 | 12 | 93 | 5.25 | 0.833333 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.193548 | 93 | 6 | 40 | 15.5 | 0.84 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.2 | true | 0 | 0.4 | 0.2 | 0.8 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 1 | 0 | 0 | 6 |
36b4003ccedbfcfb23982b3a95bd3089adbc1c8d | 64 | py | Python | lib/metric/__init__.py | ishine/TextNormSeq2Seq | 585b6a7f17910876c76240ff82ee811c66e23104 | [
"MIT"
] | 36 | 2019-04-20T15:06:45.000Z | 2022-03-03T22:42:57.000Z | lib/metric/__init__.py | ishine/TextNormSeq2Seq | 585b6a7f17910876c76240ff82ee811c66e23104 | [
"MIT"
] | 5 | 2019-06-06T14:48:54.000Z | 2021-06-05T15:40:09.000Z | lib/metric/__init__.py | ishine/TextNormSeq2Seq | 585b6a7f17910876c76240ff82ee811c66e23104 | [
"MIT"
] | 13 | 2019-05-11T02:59:54.000Z | 2022-03-23T18:24:10.000Z | from .metrics import *
from .utils import *
from .loss import *
| 16 | 22 | 0.71875 | 9 | 64 | 5.111111 | 0.555556 | 0.434783 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.1875 | 64 | 3 | 23 | 21.333333 | 0.884615 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
7fd26ad9c7008bd8a0a9dc30a2e9d92b293f3418 | 142 | py | Python | tests/integration/__init__.py | andrew-chang-dewitt/hoops-api | 3530c5127c35742aad84df8d6a5286b9f5ad3608 | [
"MIT"
] | null | null | null | tests/integration/__init__.py | andrew-chang-dewitt/hoops-api | 3530c5127c35742aad84df8d6a5286b9f5ad3608 | [
"MIT"
] | 10 | 2021-11-02T23:31:56.000Z | 2021-12-07T03:41:12.000Z | tests/integration/__init__.py | andrew-chang-dewitt/hoops | 3530c5127c35742aad84df8d6a5286b9f5ad3608 | [
"MIT"
] | null | null | null | """Integration tests."""
from . import test_account
from . import test_error_handling
from . import test_transaction
from . import test_user
| 20.285714 | 33 | 0.788732 | 19 | 142 | 5.631579 | 0.526316 | 0.373832 | 0.523364 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.133803 | 142 | 6 | 34 | 23.666667 | 0.869919 | 0.126761 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 6 |
3d117af7ed29b994c992340764c02d0bcd5fe2aa | 42 | py | Python | zoho_oauth2/__init__.py | sauravkoli31/zoho_oauth2 | a854d9597e48fd46ee59e6f27fe201c16fa2df1c | [
"MIT"
] | null | null | null | zoho_oauth2/__init__.py | sauravkoli31/zoho_oauth2 | a854d9597e48fd46ee59e6f27fe201c16fa2df1c | [
"MIT"
] | null | null | null | zoho_oauth2/__init__.py | sauravkoli31/zoho_oauth2 | a854d9597e48fd46ee59e6f27fe201c16fa2df1c | [
"MIT"
] | null | null | null | from zoho_oauth2.main import ZohoAPITokens | 42 | 42 | 0.904762 | 6 | 42 | 6.166667 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.025641 | 0.071429 | 42 | 1 | 42 | 42 | 0.923077 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
3d26cceb885c64b9699245a4058423bae56c40c0 | 1,413 | py | Python | experiment/test_naive_lia2bv.py | ERATOMMSD/mind_the_gap | 33ca14e3321a4846aa728a00ab32408576ef0433 | [
"BSD-3-Clause"
] | null | null | null | experiment/test_naive_lia2bv.py | ERATOMMSD/mind_the_gap | 33ca14e3321a4846aa728a00ab32408576ef0433 | [
"BSD-3-Clause"
] | null | null | null | experiment/test_naive_lia2bv.py | ERATOMMSD/mind_the_gap | 33ca14e3321a4846aa728a00ab32408576ef0433 | [
"BSD-3-Clause"
] | null | null | null | from unittest import TestCase
import naive_lia2bv
import util
import smtutil
import treeutil
class TestNaive_lia2bv_normalized_ineq(TestCase):
def test_naive_lia2bv_normalized_ineq(self):
x_c = {"var_0_x": 1, "var_0_y": 2}
b = 5
m = 8
orig = ["<=", ["+", ["*", "var_0_x", "1"], ["*", "var_0_y", "2"]], "5"]
gen = naive_lia2bv.naive_lia2bv_normalized_ineq(x_c, b, m)
gen = ["and", gen, smtutil.get_range_constraints(x_c.keys(), m)]
print(util.debug_print_list(gen))
orig = ["and", orig, smtutil.get_range_constraints(x_c.keys(), m)]
print(util.debug_print_list(orig))
assr = ["and", ["=>", gen, orig], ["=>", orig, gen]]
res = smtutil.check_sat_lia(assr)
self.assertTrue(res)
def test_naive_lia2bv_normalized_ineq2(self):
x_c = {"var_0_x": 20, "var_0_y": 2}
b = 5
m = 8
orig = ["<=", ["+", ["*", "var_0_x", "20"], ["*", "var_0_y", "2"]], "5"]
gen = naive_lia2bv.naive_lia2bv_normalized_ineq(x_c, b, m)
gen = ["and", gen, smtutil.get_range_constraints(x_c.keys(), m)]
print(util.debug_print_list(gen))
orig = ["and", orig, smtutil.get_range_constraints(x_c.keys(), m)]
print(util.debug_print_list(orig))
assr = ["and", ["=>", gen, orig], ["=>", orig, gen]]
res = smtutil.check_sat_lia(assr)
self.assertTrue(res)
| 39.25 | 80 | 0.581033 | 201 | 1,413 | 3.766169 | 0.223881 | 0.021136 | 0.10568 | 0.031704 | 0.826948 | 0.752972 | 0.73712 | 0.73712 | 0.715984 | 0.715984 | 0 | 0.030499 | 0.234253 | 1,413 | 35 | 81 | 40.371429 | 0.669131 | 0 | 0 | 0.625 | 0 | 0 | 0.070064 | 0 | 0.0625 | 0 | 0 | 0 | 0.0625 | 1 | 0.0625 | false | 0 | 0.15625 | 0 | 0.25 | 0.125 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
3d5b03084b46562c2d0b47440d7633e698342804 | 1,174 | py | Python | permissions_auditor/defaults.py | jeffgabhart/django-permissions-auditor | bac834da01345bc179a95344729cf680587c3419 | [
"MIT"
] | 11 | 2019-02-25T05:11:53.000Z | 2021-10-05T14:57:48.000Z | permissions_auditor/defaults.py | jeffgabhart/django-permissions-auditor | bac834da01345bc179a95344729cf680587c3419 | [
"MIT"
] | 11 | 2019-10-10T19:18:42.000Z | 2022-01-13T15:50:57.000Z | permissions_auditor/defaults.py | jeffgabhart/django-permissions-auditor | bac834da01345bc179a95344729cf680587c3419 | [
"MIT"
] | 2 | 2020-01-09T16:19:28.000Z | 2021-02-15T08:27:11.000Z | from django.conf import settings
PERMISSIONS_AUDITOR_PROCESSORS = [
'permissions_auditor.processors.auth_mixins.PermissionRequiredMixinProcessor',
'permissions_auditor.processors.auth_mixins.LoginRequiredMixinProcessor',
'permissions_auditor.processors.auth_mixins.UserPassesTestMixinProcessor',
'permissions_auditor.processors.auth_decorators.PermissionRequiredDecoratorProcessor',
'permissions_auditor.processors.auth_decorators.LoginRequiredDecoratorProcessor',
'permissions_auditor.processors.auth_decorators.StaffMemberRequiredDecoratorProcessor',
'permissions_auditor.processors.auth_decorators.SuperUserRequiredDecoratorProcessor',
'permissions_auditor.processors.auth_decorators.UserPassesTestDecoratorProcessor',
]
PERMISSIONS_AUDITOR_BLACKLIST = {
'namespaces': [
'admin',
],
'view_names': [
'django.views.generic.base.RedirectView',
],
'modules': [],
}
PERMISSIONS_AUDITOR_ADMIN = True
PERMISSIONS_AUDITOR_ADMIN_OVERRIDE_GROUPS = True
PERMISSIONS_AUDITOR_ROOT_URLCONF = settings.ROOT_URLCONF
PERMISSIONS_AUDITOR_CACHE_KEY = 'permissions_auditor_views'
PERMISSIONS_AUDITOR_CACHE_TIMEOUT = 900
| 40.482759 | 91 | 0.82368 | 98 | 1,174 | 9.469388 | 0.387755 | 0.310345 | 0.271552 | 0.275862 | 0.349138 | 0 | 0 | 0 | 0 | 0 | 0 | 0.002836 | 0.098808 | 1,174 | 28 | 92 | 41.928571 | 0.874291 | 0 | 0 | 0.08 | 0 | 0 | 0.610733 | 0.583475 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0.08 | 0.04 | 0 | 0.04 | 0 | 0 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 6 |
e9ee9e3c91ae784448408d2b026489380a740316 | 285 | py | Python | CAT/strategy/__init__.py | nnnyt/CAT | 471f573dd51b9cc09339ea73241ad9ac9e5d0d8f | [
"MIT"
] | 6 | 2021-02-15T13:10:45.000Z | 2022-03-08T12:58:49.000Z | CAT/strategy/__init__.py | nnnyt/CAT | 471f573dd51b9cc09339ea73241ad9ac9e5d0d8f | [
"MIT"
] | null | null | null | CAT/strategy/__init__.py | nnnyt/CAT | 471f573dd51b9cc09339ea73241ad9ac9e5d0d8f | [
"MIT"
] | 4 | 2021-01-11T15:19:41.000Z | 2022-03-21T06:02:55.000Z | from .abstract_strategy import AbstractStrategy
from .random_strategy import RandomStrategy
from .MFI_strategy import MFIStrategy
from .MFI_strategy import DoptStrategy
from .KLI_strategy import KLIStrategy
from .KLI_strategy import MKLIStrategy
from .MAAT_strategy import MAATStrategy | 40.714286 | 47 | 0.880702 | 35 | 285 | 6.971429 | 0.428571 | 0.401639 | 0.122951 | 0.172131 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.094737 | 285 | 7 | 48 | 40.714286 | 0.945736 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | null | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
e9fabd71ff8e824a1a99b29be808e0a04892f176 | 209 | py | Python | easy_comment/admin.py | hsyao/django_blog | 3fe8215e627960e933abe9548eda123987e94f13 | [
"MIT"
] | 137 | 2017-05-05T11:57:11.000Z | 2021-01-06T18:56:56.000Z | easy_comment/admin.py | hsyao/django_blog | 3fe8215e627960e933abe9548eda123987e94f13 | [
"MIT"
] | 10 | 2018-05-20T06:36:10.000Z | 2022-03-11T23:19:21.000Z | easy_comment/admin.py | wangchaocc21/django_blog | 3fe8215e627960e933abe9548eda123987e94f13 | [
"MIT"
] | 24 | 2017-06-19T18:08:59.000Z | 2019-02-02T04:15:13.000Z | from django.contrib import admin
from mptt.admin import MPTTModelAdmin
from .models import Comment, Favour
# Register your models here.
admin.site.register(Comment, MPTTModelAdmin)
admin.site.register(Favour) | 29.857143 | 44 | 0.827751 | 28 | 209 | 6.178571 | 0.5 | 0.104046 | 0.196532 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.100478 | 209 | 7 | 45 | 29.857143 | 0.920213 | 0.124402 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.6 | 0 | 0.6 | 0 | 1 | 0 | 0 | null | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
e9ff5105ab67f6c5ecb1763c5c48b0eb6dc16e51 | 120,130 | py | Python | FEV_KEGG/Evolution/Clade.py | ryhaberecht/FEV-KEGG | f55f294aae07b76954ed823f0c2e6d189fb2b1bb | [
"MIT"
] | null | null | null | FEV_KEGG/Evolution/Clade.py | ryhaberecht/FEV-KEGG | f55f294aae07b76954ed823f0c2e6d189fb2b1bb | [
"MIT"
] | 2 | 2019-05-30T06:42:08.000Z | 2021-05-06T10:37:40.000Z | FEV_KEGG/Evolution/Clade.py | ryhaberecht/FEV-KEGG | f55f294aae07b76954ed823f0c2e6d189fb2b1bb | [
"MIT"
] | null | null | null | from FEV_KEGG.Graph.SubstanceGraphs import SubstanceEcGraph, SubstanceEnzymeGraph
from FEV_KEGG.Evolution.Taxonomy import NCBI, Taxonomy
from FEV_KEGG.KEGG.Organism import Group
from FEV_KEGG.Evolution.Events import GeneFunctionAddition, GeneFunctionLoss, GeneFunctionDivergence, GeneFunctionConservation, SimpleGeneDuplication,\
NeofunctionalisedECs, NeofunctionalisedEnzymes, Neofunctionalisation, FunctionChange
from FEV_KEGG import settings
from builtins import str
from FEV_KEGG.Drawing import Export
import math
from typing import Dict, Set, Tuple
from FEV_KEGG.Graph.Elements import Enzyme, GeneID, EcNumber
defaultExcludeUnclassified = True
"""
If *True*, ignore taxons with a path containing the string 'unclassified'.
This can be overridden in each relevant method's `excludeUnclassified` parameter in this module.
"""
defaultExcludeMultifunctionalEnzymes = settings.defaultNoMultifunctional
"""
If *True*, ignore enzymes with more than one EC number.
This can be overridden in each relevant method's `excludeMultifunctionalEnzymes` parameter in this module.
"""
defaultMajorityPercentageCoreMetabolism = 80
"""
Default percentage of organisms in the clade, which have to possess an EC number, for it to be included in the core metabolism of the clade.
See :func:`FEV_KEGG.KEGG.Organism.Group.majorityEcGraph`.
This can be overridden in each relevant method's `majorityPercentageCoreMetabolism` parameter in this module.
"""
defaultMajorityPercentageNeofunctionalisation = 0
"""
Default percentage of organisms in the clade, which have to possess the same "neofunctionalised" EC number, for it to be included in the set of "neofunctionalised" EC numbers of the clade.
See :class:`FEV_KEGG.KEGG.Evolution.Events.NeofunctionalisedECs`.
This can be overridden in each relevant method's `majorityPercentageNeofunctionalisation` parameter in this module.
"""
defaultEValue = settings.defaultEvalue
"""
Default threshold for the statistical expectation value (E-value), below which a sequence alignment is considered significant.
"""
defaultOneOrganismPerSpecies = settings.defaultOneOrganismPerSpecies
"""
Default descision whether to use only the first organism for each species in NCBI taxonomy.
"""
class Clade(object):
def __init__(self, ncbiNames: 'e.g. Enterobacter or Proteobacteria/Gammaproteobacteria. Allows list of names, e.g. ["Gammaproteobacteria", "/Archaea"]', excludeUnclassified = defaultExcludeUnclassified, oneOrganismPerSpecies = defaultOneOrganismPerSpecies):
"""
A clade in NCBI taxonomy, containing all leaf taxon's KEGG organisms.
Parameters
----------
ncbiNames : str or Iterable[str]
String(s) a taxon's path must contain to be included in this clade.
excludeUnclassified : bool, optional
If *True*, ignore taxons with a path containing the string 'unclassified'.
oneOrganismPerSpecies : bool, optional
If *True*, use only the first organism of each species.
Attributes
----------
self.ncbiNames : Iterable[str]
Part of the path of each leaf taxon to be included in this clade. A single string is wrapped in a list.
self.group
The :class:`FEV_KEGG.KEGG.Organism.Group` of KEGG organisms created from the found leaf taxons.
Raises
------
ValueError
If no clade with `ncbiNames` in its path could be found.
Warnings
--------
It is possible to include organisms of several clades in the same Clade object!
For example, if you were to search for `ncbiNames` == 'Donaldus Duckus', you would get every organism within '/Bacteria/Donaldus Duckus' **and** '/Archaea/Order/Donaldus Duckus'.
Use the slash (/) notation to make sure you only get the taxon you want, e.g. 'Proteobacteria/Gammaproteobacteria' or '/Archaea'.
"""
taxonomy = NCBI.getTaxonomy()
if isinstance(ncbiNames, str):
ncbiNames = [ncbiNames]
self.ncbiNames = ncbiNames
allOrganisms = set()
for ncbiName in ncbiNames:
organisms = taxonomy.getOrganismAbbreviationsByPath(ncbiName, exceptPaths=('unclassified' if excludeUnclassified else None), oneOrganismPerSpecies=oneOrganismPerSpecies)
if organisms is None or len(organisms) == 0:
raise ValueError("No clade of this path found: " + ncbiName)
allOrganisms.update(organisms)
self.group = Group( allOrganisms )
self._lastNeofunctionalisedEnzymesCache = None
self._lastGeneDuplicatedEnzymesMatches = None
def collectiveMetabolism(self, excludeMultifunctionalEnzymes = defaultExcludeMultifunctionalEnzymes, addEcDescriptions = False) -> SubstanceEcGraph:
"""
The Substance-EC graph representing the collective metabolic network, occuring in any organism of the clade.
This includes each and every EC number which occurs in any organism of this clade.
Parameters
----------
excludeMultifunctionalEnzymes : bool, optional
If *True*, ignore enzymes with more than one EC number.
Returns
-------
SubstanceEcGraph
Collective metabolic network of EC numbers, including counts of occurence in each of the clade's organisms.
Raises
------
TypeError
If you failed to enable :attr:`FEV_KEGG.settings.automaticallyStartProcessPool` or to provide a :attr:`FEV_KEGG.Util.Parallelism.processPool`. See :func:`FEV_KEGG.KEGG.Organism.Group._getGraphsParallelly`.
HTTPError
If fetching any of the underlying graphs fails.
URLError
If connection to KEGG fails.
"""
graph = self.group.collectiveEcGraph(noMultifunctional = excludeMultifunctionalEnzymes, addCount = True, keepOnHeap = True, addEcDescriptions = addEcDescriptions)
graph.name = 'Collective metabolism ECs ' + ' '.join(self.ncbiNames)
return graph
def collectiveMetabolismEnzymes(self, excludeMultifunctionalEnzymes = defaultExcludeMultifunctionalEnzymes) -> SubstanceEnzymeGraph:
"""
The Substance-Enzyme graph representing the collective metabolic network, occuring in any organism of the clade.
This includes each and every enzyme of every organism of this clade.
Parameters
----------
excludeMultifunctionalEnzymes : bool, optional
If *True*, ignore enzymes with more than one EC number.
Returns
-------
SubstanceEnzymeGraph
Collective metabolic network of enzymes.
Raises
------
TypeError
If you failed to enable :attr:`FEV_KEGG.settings.automaticallyStartProcessPool` or to provide a :attr:`FEV_KEGG.Util.Parallelism.processPool`. See :func:`FEV_KEGG.KEGG.Organism.Group._getGraphsParallelly`.
HTTPError
If fetching any of the underlying graphs fails.
URLError
If connection to KEGG fails.
"""
graph = self.group.collectiveEnzymeGraph(noMultifunctional = excludeMultifunctionalEnzymes, keepOnHeap = True)
graph.name = 'Collective metabolism enzymes ' + ' '.join(self.ncbiNames)
return graph
def coreMetabolism(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, excludeMultifunctionalEnzymes = defaultExcludeMultifunctionalEnzymes) -> SubstanceEcGraph:
"""
The Substance-EC graph representing the common metabolic network, shared among all organisms of the clade.
This includes only EC numbers which occur in at least `majorityPercentageCoreMetabolism` % of all organisms of this clade.
Parameters
----------
majorityPercentageCoreMetabolism : int, optional
A path (substance -> EC -> product) has to occur in `majorityPercentageCoreMetabolism` % of the clade's organisms to be included.
excludeMultifunctionalEnzymes : bool, optional
If *True*, ignore enzymes with more than one EC number.
Returns
-------
SubstanceEcGraph
Core metabolic network of EC numbers.
Raises
------
TypeError
If you failed to enable :attr:`FEV_KEGG.settings.automaticallyStartProcessPool` or to provide a :attr:`FEV_KEGG.Util.Parallelism.processPool`. See :func:`FEV_KEGG.KEGG.Organism.Group._getGraphsParallelly`.
HTTPError
If fetching any of the underlying graphs fails.
URLError
If connection to KEGG fails.
"""
graph = self.group.majorityEcGraph(majorityPercentage = majorityPercentageCoreMetabolism, noMultifunctional = excludeMultifunctionalEnzymes, keepOnHeap = True)
graph.name = 'Core metabolism ECs ' + ' '.join(self.ncbiNames)
return graph
def coreMetabolismEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, excludeMultifunctionalEnzymes = defaultExcludeMultifunctionalEnzymes) -> SubstanceEnzymeGraph:
"""
The Substance-Enzyme graph representing the common metabolic network, shared among all organisms of the clade.
This includes every Enzyme associated with an EC number occuring in core metabolism (see :func:`substanceEcGraph`), no matter from which organism it stems.
Parameters
----------
majorityPercentageCoreMetabolism : int, optional
A path (substance -> EC -> product) has to occur in `majorityPercentageCoreMetabolism` % of the clade's organisms to be included.
excludeMultifunctionalEnzymes : bool, optional
If *True*, ignore enzymes with more than one EC number.
Returns
-------
SubstanceEnzymeGraph
Core metabolic network of enzymes.
Raises
------
TypeError
If you failed to enable :attr:`FEV_KEGG.settings.automaticallyStartProcessPool` or to provide a :attr:`FEV_KEGG.Util.Parallelism.processPool`. See :func:`FEV_KEGG.KEGG.Organism.Group._getGraphsParallelly`.
HTTPError
If fetching any of the underlying graphs fails.
URLError
If connection to KEGG fails.
"""
graph = self.group.collectiveEnzymeGraphByEcMajority(majorityPercentage = majorityPercentageCoreMetabolism, majorityTotal = None, noMultifunctional = excludeMultifunctionalEnzymes)
graph.name = 'Core metabolism Enzymes ' + ' '.join(self.ncbiNames)
return graph
@property
def organismsCount(self) -> int:
"""
The number of organisms (leaf taxons) in this clade.
Returns
-------
int
The number of organisms (leaf taxons) in this clade.
"""
return self.group.organismsCount
# gene duplication
def geneDuplicatedEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, colour = False) -> SubstanceEnzymeGraph:
"""
The substance-Enzyme graph of all gene duplicated enzymes of the core metabolism.
Parameters
----------
majorityPercentageCoreMetabolism : int, optional
Every substance-EC-product edge has to occur in `majorityPercentageCoreMetabolism` % of organisms constituting the clade, to be included in the core metabolism.
colour : bool, optional
If *True*, colours the gene-duplicated enzyme edges in green. The colouring is realised by adding a 'colour' attribute to each edge. Nodes are not coloured.
Alternatively, you can specify a :class:`Export.Colour`.
Returns
-------
SubstanceEnzymeGraph
Substance-Enzyme graph containing all gene-duplicated enzymes, and nothing else.
If `colour` == *True*, returns the full core metabolism enzyme graph, colouring gene-duplicated enzymes green.
Raises
------
TypeError
If you failed to enable :attr:`FEV_KEGG.settings.automaticallyStartProcessPool` or to provide a :attr:`FEV_KEGG.Util.Parallelism.processPool`. See :func:`FEV_KEGG.KEGG.Organism.Group._getGraphsParallelly`.
HTTPError
If fetching any of the underlying graphs fails.
URLError
If connection to KEGG fails.
"""
enzymeGraph = self.coreMetabolismEnzymes(majorityPercentageCoreMetabolism)
geneDuplicationModel = SimpleGeneDuplication
# geneDuplicationModel = SimpleGroupGeneDuplication(sameGroupOrganisms = self.group)
# filter core metabolism enzyme graph
geneDuplicatedEnzymes = geneDuplicationModel.filterEnzymes(enzymeGraph, eValue = defaultEValue, ignoreDuplicatesOutsideSet = True, preCalculatedEnzymes = None)
# colour core metabolism
if colour is not False:
if colour is True:
colourToUse = Export.Colour.GREEN
else:
colourToUse = colour
geneDuplicatedEnzymesOnly = geneDuplicatedEnzymes
geneDuplicatedEnzymes = enzymeGraph
Export.addColourAttribute(geneDuplicatedEnzymes, colourToUse, nodes = False, edges = geneDuplicatedEnzymesOnly.getEdges())
geneDuplicatedEnzymes.name = 'Gene-duplicated core metabolism enzymes ' + ' '.join(self.ncbiNames)
return geneDuplicatedEnzymes
def geneDuplicatedEnzymesDict(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism) -> Dict[Enzyme, Set[GeneID]]:
"""
All gene duplicated enzymes of the core metabolism, pointing to all their duplicates.
Parameters
----------
majorityPercentageCoreMetabolism : int, optional
Every substance-EC-product edge has to occur in `majorityPercentageCoreMetabolism` % of organisms constituting the clade, to be included in the core metabolism.
Returns
-------
Dict[Enzyme, Set[GeneID]]
Each gene ID on the right usually has an entry of its own, as an enzyme object, on the left, because they are each others homologs.
Raises
------
TypeError
If you failed to enable :attr:`FEV_KEGG.settings.automaticallyStartProcessPool` or to provide a :attr:`FEV_KEGG.Util.Parallelism.processPool`. See :func:`FEV_KEGG.KEGG.Organism.Group._getGraphsParallelly`.
HTTPError
If fetching any of the underlying graphs fails.
URLError
If connection to KEGG fails.
"""
enzymeGraph = self.coreMetabolismEnzymes(majorityPercentageCoreMetabolism)
geneDuplicationModel = SimpleGeneDuplication
geneIDsForEnzyme = geneDuplicationModel.getEnzymes(enzymeGraph, returnMatches = True, ignoreDuplicatesOutsideSet = True, preCalculatedEnzymes = None)
# if keepOnHeap is True:
# self._geneDuplicatedEnzymesObject = geneIDsForEnzyme
return geneIDsForEnzyme
def geneDuplicatedEnzymePairs(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism) -> Set[Tuple[Enzyme, Enzyme]]:
"""
All gene duplicated enzymes of the core metabolism, paired with each of their duplicates.
If enzyme A is a duplicate of enzyme B and vice versa, this does not return duplicates, but returns only one pair, with the "smaller" enzyme as the first value. An enzyme is "smaller" if its gene ID string is "smaller".
Parameters
----------
majorityPercentageCoreMetabolism : int, optional
Every substance-EC-product edge has to occur in `majorityPercentageCoreMetabolism` % of organisms constituting the clade, to be included in the core metabolism.
Returns
-------
Set[Tuple[Enzyme, Enzyme]]
Set of gene-duplicated enzymes, broken down into pairs of enzymes.
Can obviously create many duplicates left and right.
Raises
------
TypeError
If you failed to enable :attr:`FEV_KEGG.settings.automaticallyStartProcessPool` or to provide a :attr:`FEV_KEGG.Util.Parallelism.processPool`. See :func:`FEV_KEGG.KEGG.Organism.Group._getGraphsParallelly`.
HTTPError
If fetching any of the underlying graphs fails.
URLError
If connection to KEGG fails.
"""
enzymes = self.coreMetabolismEnzymes(majorityPercentageCoreMetabolism).getEnzymes()
geneDuplicationModel = SimpleGeneDuplication
geneIdToEnzyme = dict()
for enzyme in enzymes:
geneIdToEnzyme[enzyme.geneID] = enzyme
enzymePairs = geneDuplicationModel.getEnzymePairs(enzymes, ignoreDuplicatesOutsideSet = True, geneIdToEnzyme = geneIdToEnzyme, preCalculatedEnzymes = None)
return enzymePairs
# neofunctionalisation
def _neofunctionalisedEnzymes(self, majorityPercentageCoreMetabolism, eValue = defaultEValue, considerOnlyECs = None):
# check if the last calculation can be returned
if hasattr(self, '_lastNeofunctionalisedEnzymesCache') and self._lastNeofunctionalisedEnzymesCache is not None and considerOnlyECs is None:
lastMajorityPercentage, lastNeofunctionalisedEnzymes = self._lastNeofunctionalisedEnzymesCache
if lastMajorityPercentage == majorityPercentageCoreMetabolism:
return lastNeofunctionalisedEnzymes
else:
self._lastNeofunctionalisedEnzymesCache = None
# calculate
enzymes = self.coreMetabolismEnzymes(majorityPercentageCoreMetabolism)
if considerOnlyECs is not None:
enzymes.keepEnzymesByEC(considerOnlyECs)
enzymes = enzymes.getEnzymes()
geneDuplicationModel = SimpleGeneDuplication
# geneDuplicationModel = SimpleGroupGeneDuplication(sameGroupOrganisms = self.group)
neofunctionalisedEnzymes = NeofunctionalisedEnzymes(enzymes, geneDuplicationModel, eValue = eValue)
# Cache calculation
if considerOnlyECs is None:
self._lastNeofunctionalisedEnzymesCache = (majorityPercentageCoreMetabolism, neofunctionalisedEnzymes)
return neofunctionalisedEnzymes
def neofunctionalisedEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, colour = False, eValue = defaultEValue, considerOnlyECs = None) -> SubstanceEnzymeGraph:
"""
The substance-Enzyme graph of all neofunctionalised enzymes of the core metabolism.
Parameters
----------
majorityPercentageCoreMetabolism : int, optional
Every substance-EC-product edge has to occur in `majorityPercentageCoreMetabolism` % of organisms constituting the clade, to be included in the core metabolism.
colour : bool, optional
If *True*, colours the neofunctionalised enzyme edges in green. The colouring is realised by adding a 'colour' attribute to each edge. Nodes are not coloured.
Alternatively, you can specify a :class:`Export.Colour`.
eValue : float, optional
Threshold for the statistical expectation value (E-value), below which a sequence alignment is considered significant.
considerOnlyECs : Iterable[EcNumber], optional
If given, only enzymes with an EC number in `considerOnlyECs` are tested for neofunctionalisation.
Returns
-------
SubstanceEnzymeGraph
Substance-Enzyme graph containing all neofunctionalised enzymes, and nothing else.
If `colour` == *True*, returns the full core metabolism enzyme graph, colouring neofunctionalised enzymes green.
Raises
------
TypeError
If you failed to enable :attr:`FEV_KEGG.settings.automaticallyStartProcessPool` or to provide a :attr:`FEV_KEGG.Util.Parallelism.processPool`. See :func:`FEV_KEGG.KEGG.Organism.Group._getGraphsParallelly`.
HTTPError
If fetching any of the underlying graphs fails.
URLError
If connection to KEGG fails.
"""
# get neofunctionalisations
neofunctionalisedEnzymes = self._neofunctionalisedEnzymes(majorityPercentageCoreMetabolism, eValue, considerOnlyECs)
# filter core metabolism enzyme graph
enzymeGraph = self.coreMetabolismEnzymes(majorityPercentageCoreMetabolism)
neofunctionalisedMetabolism = neofunctionalisedEnzymes.filterGraph(enzymeGraph, minimumEcDifference = None)
# colour core metabolism
if colour is not False:
if colour is True:
colourToUse = Export.Colour.GREEN
else:
colourToUse = colour
neofunctionalisedMetabolismOnly = neofunctionalisedMetabolism
neofunctionalisedMetabolism = enzymeGraph
Export.addColourAttribute(neofunctionalisedMetabolism, colourToUse, nodes = False, edges = neofunctionalisedMetabolismOnly.getEdges())
neofunctionalisedMetabolism.name = 'Neofunctionalised core metabolism enzymes ' + ' '.join(self.ncbiNames)
return neofunctionalisedMetabolism
def neofunctionalisedECs(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation = defaultMajorityPercentageNeofunctionalisation, colour = False, eValue = defaultEValue, considerOnlyECs = None) -> SubstanceEcGraph:
"""
The substance-EC graph of EC numbers belonging to function changes of neofunctionalised enzymes of the core metabolism.
Only EC numbers which could have actually taken part in a function change are reported. This is because enzymes can have multiple EC numbers, while only some might be eligible for a function change.
For example, consider enzyme A (1.2.3.4, 6.5.4.3) and enzyme B (1.2.3.4, 4.5.6.7). 1.2.3.4 can never change its function to itself, which leaves 1.2.3.4 <-> 6.5.4.3, 1.2.3.4 <-> 4.5.6.7, and 4.5.6.7 <-> 6.5.4.3 as possible function changes.
This obviously requires a function to change to a single other function, without splitting or merging, which might be biologically inacurate.
However, this should happen rarely, plus one could exclude all enzymes with multiple functions from the core metabolism in the first place.
The maximum expectation value (e-value) necessary for a sequence alignment to constitute a "similar sequence" can be changed via :attr:`defaultEValue`.
Parameters
----------
majorityPercentageCoreMetabolism : int, optional
Every substance-EC-product edge has to occur in `majorityPercentageCoreMetabolism` % of organisms constituting the clade, to be included in the core metabolism.
majorityPercentageNeofunctionalisation : int, optional
Every EC number considered for neofunctionalisation has to be associated with a function change of neofunctionalisations whose enzymes involve at least `majorityPercentageNeofunctionalisation` % of of the clade's organisms.
A high `majorityPercentageNeofunctionalisation` disallows us to detect neofunctionalisations which happened a long time ago, with their genes having diverged significantly;
or only recently, with not all organisms of the child clade having picked up the new function, yet.
colour : bool, optional
If *True*, colours the neofunctionalised EC edges in green. The colouring is realised by adding a 'colour' attribute to each edge. Nodes are not coloured.
Alternatively, you can specify a :class:`Export.Colour`.
eValue : float, optional
Threshold for the statistical expectation value (E-value), below which a sequence alignment is considered significant.
considerOnlyECs : Iterable[EcNumber], optional
If given, only enzymes with an EC number in `considerOnlyECs` are tested for neofunctionalisation.
Returns
-------
SubstanceEcGraph
The substance-EC graph representing the metabolic network which was probably affected due to neofunctionalisations of the core metabolism of the clade.
If `colour` == *True*, returns the full union of parent and child, colouring neofunctionalised ECs green.
Raises
------
TypeError
If you failed to enable :attr:`FEV_KEGG.settings.automaticallyStartProcessPool` or to provide a :attr:`FEV_KEGG.Util.Parallelism.processPool`. See :func:`FEV_KEGG.KEGG.Organism.Group._getGraphsParallelly`.
HTTPError
If fetching any of the underlying graphs fails.
URLError
If connection to KEGG fails.
"""
# get neofunctionalisations
neofunctionalisedECs = NeofunctionalisedECs(self._neofunctionalisedEnzymes(majorityPercentageCoreMetabolism, eValue, considerOnlyECs))
# filter core metabolism EC graph
coreMetabolism = self.coreMetabolism(majorityPercentageCoreMetabolism)
minimumOrganismsCount = math.ceil(self.organismsCount * (majorityPercentageNeofunctionalisation / 100))
neofunctionalisedMetabolism = neofunctionalisedECs.filterGraph(coreMetabolism, minimumEcDifference = None, minimumOrganismsCount = minimumOrganismsCount)
# colour core metabolism
if colour is not False:
if colour is True:
colourToUse = Export.Colour.GREEN
else:
colourToUse = colour
neofunctionalisedMetabolismOnly = neofunctionalisedMetabolism
neofunctionalisedMetabolism = coreMetabolism
Export.addColourAttribute(neofunctionalisedMetabolism, colourToUse, nodes = False, edges = neofunctionalisedMetabolismOnly.getEdges())
neofunctionalisedMetabolism.name = 'Neofunctionalised core metabolism ' + ' '.join(self.ncbiNames)
return neofunctionalisedMetabolism
def neofunctionalisations(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, eValue = defaultEValue, considerOnlyECs = None) -> Set[Neofunctionalisation]:
"""
Get neofunctionalisation events of all enzymes in the core metabolism.
Parameters
----------
majorityPercentageCoreMetabolism : int, optional
Every substance-EC-product edge has to occur in `majorityPercentageCoreMetabolism` % of organisms constituting the clade, to be included in the core metabolism.
eValue : float, optional
Threshold for the statistical expectation value (E-value), below which a sequence alignment is considered significant.
considerOnlyECs : Iterable[EcNumber], optional
If given, only enzymes with an EC number in `considerOnlyECs` are tested for neofunctionalisation.
Returns
-------
Set[Neofunctionalisation]
Set of possible neofunctionalisation events.
Raises
------
TypeError
If you failed to enable :attr:`FEV_KEGG.settings.automaticallyStartProcessPool` or to provide a :attr:`FEV_KEGG.Util.Parallelism.processPool`. See :func:`FEV_KEGG.KEGG.Organism.Group._getGraphsParallelly`.
HTTPError
If fetching any of the underlying graphs fails.
URLError
If connection to KEGG fails.
"""
# get neofunctionalisations
return self._neofunctionalisedEnzymes(majorityPercentageCoreMetabolism, eValue, considerOnlyECs).getNeofunctionalisations()
def neofunctionalisationsForFunctionChange(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation = defaultMajorityPercentageNeofunctionalisation, eValue = defaultEValue, considerOnlyECs = None) -> Dict[FunctionChange, Set[Neofunctionalisation]]:
"""
Get neofunctionalisation events of all enzymes in the core metabolism, grouped by each possible function change event.
Parameters
----------
majorityPercentageCoreMetabolism : int, optional
Every substance-EC-product edge has to occur in `majorityPercentageCoreMetabolism` % of organisms constituting the clade, to be included in the core metabolism.
majorityPercentageNeofunctionalisation : int, optional
Every EC number considered for neofunctionalisation has to be associated with a function change of neofunctionalisations whose enzymes involve at least `majorityPercentageNeofunctionalisation` % of of the clade's organisms.
A high `majorityPercentageNeofunctionalisation` disallows us to detect neofunctionalisations which happened a long time ago, with their genes having diverged significantly;
or only recently, with not all organisms of the child clade having picked up the new function, yet.
eValue : float, optional
Threshold for the statistical expectation value (E-value), below which a sequence alignment is considered significant.
considerOnlyECs : Iterable[EcNumber], optional
If given, only enzymes with an EC number in `considerOnlyECs` are tested for neofunctionalisation.
Returns
-------
Dict[FunctionChange, Set[Neofunctionalisation]]
Dictionary of function changes, pointing to a set of neofunctionalisations which might have caused them.
Since an enzyme of a neofunctionalisation can have multiple EC numbers, all combinations of the two enzymes' EC numbers are formed and treated as separate possible function changes.
The neofunctionalisation is then saved again for each function change, which obviously leads to duplicated neofunctionalisation objects.
Raises
------
TypeError
If you failed to enable :attr:`FEV_KEGG.settings.automaticallyStartProcessPool` or to provide a :attr:`FEV_KEGG.Util.Parallelism.processPool`. See :func:`FEV_KEGG.KEGG.Organism.Group._getGraphsParallelly`.
HTTPError
If fetching any of the underlying graphs fails.
URLError
If connection to KEGG fails.
"""
# get neofunctionalisations
minimumOrganismsCount = math.ceil(self.organismsCount * (majorityPercentageNeofunctionalisation / 100))
return NeofunctionalisedECs(self._neofunctionalisedEnzymes(majorityPercentageCoreMetabolism, eValue, considerOnlyECs)).getNeofunctionalisationsForFunctionChange(minimumOrganismsCount = minimumOrganismsCount)
# redundancy of neofunctionalisation
def redundantECsForContributingNeofunctionalisation(self,
majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism,
majorityPercentageNeofunctionalisation = defaultMajorityPercentageNeofunctionalisation,
eValue = defaultEValue,
redundancyType: 'RedundancyType' = None,
considerOnlyECs = None) -> Dict[Neofunctionalisation, Set[EcNumber]]:
"""
Get neofunctionalisation events of all enzymes in the core metabolism, which contribute to redundancy, pointing to the EC numbers their function changes' EC numbers provides redundancy for.
Parameters
----------
majorityPercentageCoreMetabolism : int, optional
Every substance-EC-product edge has to occur in `majorityPercentageCoreMetabolism` % of organisms constituting the clade, to be included in the core metabolism.
majorityPercentageNeofunctionalisation : int, optional
Every EC number considered for neofunctionalisation has to be associated with a function change of neofunctionalisations whose enzymes involve at least `majorityPercentageNeofunctionalisation` % of of the clade's organisms.
A high `majorityPercentageNeofunctionalisation` disallows us to detect neofunctionalisations which happened a long time ago, with their genes having diverged significantly;
or only recently, with not all organisms of the child clade having picked up the new function, yet.
eValue : float, optional
Threshold for the statistical expectation value (E-value), below which a sequence alignment is considered significant.
redundancyType : RedundancyType
Definition of redundancy for which to check the neofunctionalisation's contribution. Default to `RedundancyType.default`.
considerOnlyECs : Iterable[EcNumber], optional
If given, only enzymes with an EC number in `considerOnlyECs` are tested for neofunctionalisation.
Returns
-------
Dict[FunctionChange, Set[Neofunctionalisation]]
Dictionary of function changes, pointing to a set of neofunctionalisations which might have caused them.
Since an enzyme of a neofunctionalisation can have multiple EC numbers, all combinations of the two enzymes' EC numbers are formed and treated as separate possible function changes.
The neofunctionalisation is then saved again for each function change, which obviously leads to duplicated neofunctionalisation objects.
Raises
------
TypeError
If you failed to enable :attr:`FEV_KEGG.settings.automaticallyStartProcessPool` or to provide a :attr:`FEV_KEGG.Util.Parallelism.processPool`. See :func:`FEV_KEGG.KEGG.Organism.Group._getGraphsParallelly`.
HTTPError
If fetching any of the underlying graphs fails.
URLError
If connection to KEGG fails.
"""
from FEV_KEGG.Robustness.Topology.Redundancy import Redundancy, RedundancyContribution, RedundancyType
if redundancyType is None:
redundancyType = RedundancyType.default
#- calculate "neofunctionalised" ECs
neofunctionalisedMetabolismSet = self.neofunctionalisedECs(majorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation, eValue, considerOnlyECs).getECs()
neofunctionalisationsForFunctionChange = self.neofunctionalisationsForFunctionChange(majorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation, eValue, considerOnlyECs)
#- calculate redundancy
redundancy = Redundancy( self.coreMetabolism(majorityPercentageCoreMetabolism) )
redundancyContribution = RedundancyContribution(redundancy, neofunctionalisedMetabolismSet)
contributedECsForContributingNeofunctionalisedEC = redundancyContribution.getContributedKeysForSpecial(redundancyType)
contributingNeofunctionalisedECs = set(contributedECsForContributingNeofunctionalisedEC.keys())
#- REPEAT for each function change consisting of "neofunctionalised" ECs, which also contribute to redundancy
contributingNeofunctionalisations = dict()
for functionChange, neofunctionalisations in neofunctionalisationsForFunctionChange.items():
#- report enzyme pairs of neofunctionalisations, which caused the EC to be considered "neofunctionalised", and are in return contributing to redundancy
if functionChange.ecA in contributingNeofunctionalisedECs or functionChange.ecB in contributingNeofunctionalisedECs: # function change contributes to redundancy
for neofunctionalisation in neofunctionalisations:
currentSetOfContributedECs = contributingNeofunctionalisations.get(neofunctionalisation, None)
if currentSetOfContributedECs is None:
currentSetOfContributedECs = set()
contributingNeofunctionalisations[neofunctionalisation] = currentSetOfContributedECs
for ec in functionChange.ecPair:
contributedECs = contributedECsForContributingNeofunctionalisedEC.get(ec, None)
if contributedECs is not None:
currentSetOfContributedECs.update(contributedECs)
return contributingNeofunctionalisations
class CladePair(object):
def __init__(self, parent, child, excludeUnclassified = defaultExcludeUnclassified, oneOrganismPerSpecies = defaultOneOrganismPerSpecies):
"""
Two clades in NCBI taxonomy, 'child' is assumed younger than 'parent'.
Does not check if the child taxon is actually a child of the parent taxon.
Therefore, it would be possible to pass a list of NCBI names to the underlying :class:`Clade` objects by instantiating `parent` = List[str] and/or `child` = List[str].
This is useful when comparing groups of organisms which are, according to NCBI, not related.
Parameters
----------
parent : str or List[str] or Clade
Path(s) of the parent clade's taxon, as defined by NCBI taxonomy, e.g. 'Proteobacteria/Gammaproteobacteria'. Or a ready :class:`Clade` object.
child : str or List[str] or Clade
Path(s) of the child clade's taxon, as defined by NCBI taxonomy, e.g. 'Enterobacter'. Or a ready :class:`Clade` object.
excludeUnclassified : bool, optional
If *True*, ignore taxons with a path containing the string 'unclassified'. Only used if one of `parent` and/or `child` is not already a :class:`Clade`.
oneOrganismPerSpecies : bool, optional
If *True*, use only the first organism of each species.
Attributes
----------
self.childClade : :class:`Clade`
self.parentClade : :class:`Clade`
"""
# read NCBI names from Clade object, if necessary
if isinstance(parent, Clade):
self.parentClade = parent
else:
self.parentClade = Clade(parent, excludeUnclassified, oneOrganismPerSpecies=oneOrganismPerSpecies)
if isinstance(child, Clade):
self.childClade = child
else:
self.childClade = Clade(child, excludeUnclassified, oneOrganismPerSpecies=oneOrganismPerSpecies)
@property
def parentNCBInames(self):
"""
All names/paths in NCBI taxonomy used to create the parent clade.
"""
return self.parentClade.ncbiNames
@property
def childNCBInames(self):
"""
All names/paths in NCBI taxonomy used to create the child clade.
"""
return self.childClade.ncbiNames
# set-operations on core metabolism
## for EC graphs
def conservedMetabolism(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism) -> SubstanceEcGraph:
"""
Substance-EC graph of the conserved core metabolism.
Parameters
----------
majorityPercentageCoreMetabolism : int, optional
Every substance-EC-product edge has to occur in `majorityPercentageCoreMetabolism` % of organisms constituting the clade, to be included in the core metabolism. This is individually true for both parent clade and child clade.
The parent clade fully includes the child clade, therefore, the occurence of a substance-EC-product edge in the child clade's core metabolism counts towards the percentage for the parent clade's core metabolism.
Meaning: if an EC number does not occur in the child clade's core metabolism, it is unlikely that it will occur in the parent clade's core metabolism, unless `majorityPercentageCoreMetabolism` is consecutively lowered towards 0.
Returns
-------
SubstanceEcGraph
The substance-EC graph representing the metabolic network which stayed the same between the core metabolism of the parent (assumed older) and the core metabolism of the child (assumed younger).
Raises
------
TypeError
If you failed to enable :attr:`FEV_KEGG.settings.automaticallyStartProcessPool` or to provide a :attr:`FEV_KEGG.Util.Parallelism.processPool`. See :func:`FEV_KEGG.KEGG.Organism.Group._getGraphsParallelly`.
HTTPError
If fetching any of the underlying graphs fails.
URLError
If connection to KEGG fails.
"""
parentCoreMetabolism = self.parentClade.coreMetabolism(majorityPercentageCoreMetabolism)
childCoreMetabolism = self.childClade.coreMetabolism(majorityPercentageCoreMetabolism)
graph = GeneFunctionConservation.getGraph(parentCoreMetabolism, childCoreMetabolism)
graph.name = 'Conserved metabolism ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)
return graph
def addedMetabolism(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism) -> SubstanceEcGraph:
"""
Substance-EC graph of the added core metabolism.
Parameters
----------
majorityPercentageCoreMetabolism : int, optional
Every substance-EC-product edge has to occur in `majorityPercentageCoreMetabolism` % of organisms constituting the clade, to be included in the core metabolism. This is individually true for both parent clade and child clade.
The parent clade fully includes the child clade, therefore, the occurence of a substance-EC-product edge in the child clade's core metabolism counts towards the percentage for the parent clade's core metabolism.
Meaning: if an EC number does not occur in the child clade's core metabolism, it is unlikely that it will occur in the parent clade's core metabolism, unless `majorityPercentageCoreMetabolism` is consecutively lowered towards 0.
Returns
-------
SubstanceEcGraph
The substance-EC graph representing the metabolic network which was added to the core metabolism of the parent (assumed older) on the way to the core metabolism of the child (assumed younger).
Raises
------
TypeError
If you failed to enable :attr:`FEV_KEGG.settings.automaticallyStartProcessPool` or to provide a :attr:`FEV_KEGG.Util.Parallelism.processPool`. See :func:`FEV_KEGG.KEGG.Organism.Group._getGraphsParallelly`.
HTTPError
If fetching any of the underlying graphs fails.
URLError
If connection to KEGG fails.
"""
parentCoreMetabolism = self.parentClade.coreMetabolism(majorityPercentageCoreMetabolism)
childCoreMetabolism = self.childClade.coreMetabolism(majorityPercentageCoreMetabolism)
graph = GeneFunctionAddition.getGraph(parentCoreMetabolism, childCoreMetabolism)
graph.name = 'Added metabolism ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)
return graph
def lostMetabolism(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism) -> SubstanceEcGraph:
"""
Substance-EC graph of the lost core metabolism.
Parameters
----------
majorityPercentageCoreMetabolism : int, optional
Every substance-EC-product edge has to occur in `majorityPercentageCoreMetabolism` % of organisms constituting the clade, to be included in the core metabolism. This is individually true for both parent clade and child clade.
The parent clade fully includes the child clade, therefore, the occurence of a substance-EC-product edge in the child clade's core metabolism counts towards the percentage for the parent clade's core metabolism.
Meaning: if an EC number does not occur in the child clade's core metabolism, it is unlikely that it will occur in the parent clade's core metabolism, unless `majorityPercentageCoreMetabolism` is consecutively lowered towards 0.
Returns
-------
SubstanceEcGraph
The substance-EC graph representing the metabolic network which got lost from the core metabolism of the parent (assumed older) on the way to the core metabolism of the child (assumed younger).
Raises
------
TypeError
If you failed to enable :attr:`FEV_KEGG.settings.automaticallyStartProcessPool` or to provide a :attr:`FEV_KEGG.Util.Parallelism.processPool`. See :func:`FEV_KEGG.KEGG.Organism.Group._getGraphsParallelly`.
HTTPError
If fetching any of the underlying graphs fails.
URLError
If connection to KEGG fails.
"""
parentCoreMetabolism = self.parentClade.coreMetabolism(majorityPercentageCoreMetabolism)
childCoreMetabolism = self.childClade.coreMetabolism(majorityPercentageCoreMetabolism)
graph = GeneFunctionLoss.getGraph(parentCoreMetabolism, childCoreMetabolism)
graph.name = 'Lost metabolism ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)
return graph
def divergedMetabolism(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, colour = False) -> SubstanceEcGraph:
"""
Substance-EC graph of the diverged core metabolism.
Parameters
----------
majorityPercentageCoreMetabolism : int, optional
Every substance-EC-product edge has to occur in `majorityPercentageCoreMetabolism` % of organisms constituting the clade, to be included in the core metabolism. This is individually true for both parent clade and child clade.
The parent clade fully includes the child clade, therefore, the occurence of a substance-EC-product edge in the child clade's core metabolism counts towards the percentage for the parent clade's core metabolism.
Meaning: if an EC number does not occur in the child clade's core metabolism, it is unlikely that it will occur in the parent clade's core metabolism, unless `majorityPercentageCoreMetabolism` is consecutively lowered towards 0.
colour : bool, optional
If *True*, colours the lost EC edges in blue, and the added EC edges in red. The colouring is realised by adding a 'colour' attribute to each edge. Nodes are not coloured.
Returns
-------
SubstanceEcGraph
The substance-EC graph representing the metabolic network which changed between the core metabolism of the parent (assumed older) and the core metabolism of the child (assumed younger).
Raises
------
TypeError
If you failed to enable :attr:`FEV_KEGG.settings.automaticallyStartProcessPool` or to provide a :attr:`FEV_KEGG.Util.Parallelism.processPool`. See :func:`FEV_KEGG.KEGG.Organism.Group._getGraphsParallelly`.
HTTPError
If fetching any of the underlying graphs fails.
URLError
If connection to KEGG fails.
"""
parentCoreMetabolism = self.parentClade.coreMetabolism(majorityPercentageCoreMetabolism)
childCoreMetabolism = self.childClade.coreMetabolism(majorityPercentageCoreMetabolism)
if colour is True:
lostGraph = GeneFunctionLoss.getGraph(parentCoreMetabolism, childCoreMetabolism)
lostEdges = lostGraph.getEdges()
addedGraph = GeneFunctionAddition.getGraph(parentCoreMetabolism, childCoreMetabolism)
addedEdges = addedGraph.getEdges()
graph = lostGraph.union(addedGraph, addCount = False, updateName = False)
Export.addColourAttribute(graph, colour = Export.Colour.BLUE, nodes = False, edges = lostEdges)
Export.addColourAttribute(graph, colour = Export.Colour.RED, nodes = False, edges = addedEdges)
else:
graph = GeneFunctionDivergence.getGraph(parentCoreMetabolism, childCoreMetabolism)
graph.name = 'Diverged metabolism ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)
return graph
def unifiedMetabolism(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, colour = False) -> SubstanceEcGraph:
"""
Substance-EC graph of the unified core metabolisms.
The lost metabolism of the parent is coloured in blue, the conserved metabolism of both in red, and the added metabolism of the child in pink.
The colouring is realised by adding a 'colour' attribute to each edge. Nodes are not coloured.
Parameters
----------
majorityPercentageCoreMetabolism : int, optional
See :func:`conservedMetabolism`.
colour : bool, optional
If *True*, colours the parent's EC edges in blue, the child's EC edges in red, and the shared EC edges in pink. The colouring is realised by adding a 'colour' attribute to each edge. Nodes are not coloured.
Returns
-------
SubstanceEcGraph
The substance-EC graph representing the combined metabolic networks of both, child and parent. If `colour` == *True*, coloured differently for the lost, conserved, and added edges. Nodes are not coloured.
Raises
------
TypeError
If you failed to enable :attr:`FEV_KEGG.settings.automaticallyStartProcessPool` or to provide a :attr:`FEV_KEGG.Util.Parallelism.processPool`. See :func:`FEV_KEGG.KEGG.Organism.Group._getGraphsParallelly`.
HTTPError
If fetching any of the underlying graphs fails.
URLError
If connection to KEGG fails.
See Also
--------
:mod:`FEV_KEGG.Drawing.Export` : Export the graph into a file, e.g. for visualisation in Cytoscape.
"""
parentCoreMetabolism = self.parentClade.coreMetabolism(majorityPercentageCoreMetabolism)
childCoreMetabolism = self.childClade.coreMetabolism(majorityPercentageCoreMetabolism)
graph = parentCoreMetabolism.union(childCoreMetabolism, addCount = False, updateName = False)
graph.name = 'Unified metabolism ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)
if colour is True:
lostGraph = GeneFunctionLoss.getGraph(parentCoreMetabolism, childCoreMetabolism)
lostEdges = lostGraph.getEdges()
addedGraph = GeneFunctionAddition.getGraph(parentCoreMetabolism, childCoreMetabolism)
addedEdges = addedGraph.getEdges()
conservedGraph = GeneFunctionConservation.getGraph(parentCoreMetabolism, childCoreMetabolism)
conservedEdges = conservedGraph.getEdges()
Export.addColourAttribute(graph, colour = Export.Colour.BLUE, nodes = False, edges = lostEdges)
Export.addColourAttribute(graph, colour = Export.Colour.RED, nodes = False, edges = addedEdges)
Export.addColourAttribute(graph, colour = Export.Colour.PINK, nodes = False, edges = conservedEdges)
return graph
## for enzyme graphs
def conservedMetabolismEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, colour = False):
"""
Two Substance-Enzyme graphs derived from the conserved core metabolism, see :func:`conservedMetabolism`.
First, the conserved core metabolism is calculated. Then, the enzymes associated with the conserved EC numbers are extracted from the collective parent's and child's metabolism individually.
Parameters
----------
majorityPercentageCoreMetabolism : int, optional
See :func:`conservedMetabolism`.
colour : bool, optional
If *True*, colours the enzyme edges from the parent in blue, and from the child in red. When doing so, a single :class:`SubstanceEnzymeGraph` is returned, not a :class:`Tuple`. The colouring is realised by adding a 'colour' attribute to each edge. Nodes are not coloured.
Returns
-------
Tuple[SubstanceEnzymeGraph, SubstanceEnzymeGraph] or SubstanceEnzymeGraph
Tuple of two Substance-Enzyme graphs calculated using the conserved EC numbers found by :func:`conservedMetabolism`. The first graph is from the parent clade, the second graph from the child clade.
If `colour` == *True*, returns a single Substance-Enzyme graph, coloured blue for parent and red for child.
Raises
------
TypeError
If you failed to enable :attr:`FEV_KEGG.settings.automaticallyStartProcessPool` or to provide a :attr:`FEV_KEGG.Util.Parallelism.processPool`. See :func:`FEV_KEGG.KEGG.Organism.Group._getGraphsParallelly`.
HTTPError
If fetching any of the underlying graphs fails.
URLError
If connection to KEGG fails.
"""
parentCoreMetabolism = self.parentClade.coreMetabolism(majorityPercentageCoreMetabolism)
childCoreMetabolism = self.childClade.coreMetabolism(majorityPercentageCoreMetabolism)
conservedECs = GeneFunctionConservation.getECs(parentCoreMetabolism, childCoreMetabolism)
parentGraph = self.parentClade.collectiveMetabolismEnzymes().keepEnzymesByEC(conservedECs)
childGraph = self.childClade.collectiveMetabolismEnzymes().keepEnzymesByEC(conservedECs)
if colour is True:
parentEdges = parentGraph.getEdges()
childEdges = childGraph.getEdges()
graph = parentGraph.union(childGraph, addCount = False, updateName = False)
Export.addColourAttribute(graph, colour = Export.Colour.BLUE, nodes = False, edges = parentEdges)
Export.addColourAttribute(graph, colour = Export.Colour.RED, nodes = False, edges = childEdges)
graph.name = 'Conserved metabolism enzymes ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)
return graph
else:
parentGraph.name = 'Conserved metabolism enzymes *' + ' '.join(self.parentNCBInames) + '* -> ' + ' '.join(self.childNCBInames)
childGraph.name = 'Conserved metabolism enzymes ' + ' '.join(self.parentNCBInames) + ' -> *' + ' '.join(self.childNCBInames) + '*'
return (parentGraph, childGraph)
def addedMetabolismEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism) -> SubstanceEnzymeGraph:
"""
Substance-Enzyme graph derived from the added core metabolism, see :func:`addedMetabolism`.
First, the added core metabolism is calculated. Then, the enzymes associated with the added EC numbers are extracted from the child's enzyme metabolism.
Parameters
----------
majorityPercentageCoreMetabolism : int, optional
See :func:`addedMetabolism`.
Returns
-------
SubstanceEnzymeGraph
Substance-Enzyme graph of enzymes from the child clade. Calculated using the added EC numbers found by :func:`addedMetabolism`.
Raises
------
TypeError
If you failed to enable :attr:`FEV_KEGG.settings.automaticallyStartProcessPool` or to provide a :attr:`FEV_KEGG.Util.Parallelism.processPool`. See :func:`FEV_KEGG.KEGG.Organism.Group._getGraphsParallelly`.
HTTPError
If fetching any of the underlying graphs fails.
URLError
If connection to KEGG fails.
"""
parentCoreMetabolism = self.parentClade.coreMetabolism(majorityPercentageCoreMetabolism)
childCoreMetabolism = self.childClade.coreMetabolism(majorityPercentageCoreMetabolism)
addedECs = GeneFunctionAddition.getECs(parentCoreMetabolism, childCoreMetabolism)
childGraph = self.childClade.collectiveMetabolismEnzymes().keepEnzymesByEC(addedECs)
childGraph.name = 'Added metabolism enzymes ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)
return childGraph
def lostMetabolismEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism) -> SubstanceEnzymeGraph:
"""
Substance-Enzyme graph derived from the lost core metabolism, see :func:`lostMetabolism`.
First, the lost core metabolism is calculated. Then, the enzymes associated with the added EC numbers are extracted from the parent's enzyme metabolism.
Parameters
----------
majorityPercentageCoreMetabolism : int, optional
See :func:`lostMetabolism`.
Returns
-------
SubstanceEnzymeGraph
Substance-Enzyme graph of enzymes from the parent clade. Calculated using the lost EC numbers found by :func:`lostMetabolism`.
Raises
------
TypeError
If you failed to enable :attr:`FEV_KEGG.settings.automaticallyStartProcessPool` or to provide a :attr:`FEV_KEGG.Util.Parallelism.processPool`. See :func:`FEV_KEGG.KEGG.Organism.Group._getGraphsParallelly`.
HTTPError
If fetching any of the underlying graphs fails.
URLError
If connection to KEGG fails.
"""
parentCoreMetabolism = self.parentClade.coreMetabolism(majorityPercentageCoreMetabolism)
childCoreMetabolism = self.childClade.coreMetabolism(majorityPercentageCoreMetabolism)
lostECs = GeneFunctionLoss.getECs(parentCoreMetabolism, childCoreMetabolism)
parentGraph = self.parentClade.collectiveMetabolismEnzymes().keepEnzymesByEC(lostECs)
parentGraph.name = 'Lost metabolism enzymes ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)
return parentGraph
def divergedMetabolismEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, colour = False):
"""
Two Substance-Enzyme graphs derived from the diverged core metabolism, see :func:`divergedMetabolism`.
First, the diverged core metabolism is calculated. Then, the enzymes associated with the added EC numbers are extracted from the collective parent's and child's metabolism individually.
Parameters
----------
majorityPercentageCoreMetabolism : int, optional
See :func:`divergedMetabolism`.
colour : bool, optional
If *True*, colours the lost enzyme edges in blue, and the added enzyme edges in red. When doing so, a single :class:`SubstanceEnzymeGraph` is returned, not a :class:`Tuple`. The colouring is realised by adding a 'colour' attribute to each edge. Nodes are not coloured.
Returns
-------
Tuple[SubstanceEnzymeGraph, SubstanceEnzymeGraph] or SubstanceEnzymeGraph
Tuple of two Substance-Enzyme graphs calculated using the diverged EC numbers found by :func:`divergedMetabolism`. The first graph is from the parent clade, the second graph from the child clade.
If `colour` == *True*, returns a single Substance-Enzyme graph, coloured blue for parent and red for child.
Raises
------
TypeError
If you failed to enable :attr:`FEV_KEGG.settings.automaticallyStartProcessPool` or to provide a :attr:`FEV_KEGG.Util.Parallelism.processPool`. See :func:`FEV_KEGG.KEGG.Organism.Group._getGraphsParallelly`.
HTTPError
If fetching any of the underlying graphs fails.
URLError
If connection to KEGG fails.
"""
parentCoreMetabolism = self.parentClade.coreMetabolism(majorityPercentageCoreMetabolism)
childCoreMetabolism = self.childClade.coreMetabolism(majorityPercentageCoreMetabolism)
divergedECs = GeneFunctionDivergence.getECs(parentCoreMetabolism, childCoreMetabolism)
parentGraph = self.parentClade.collectiveMetabolismEnzymes().keepEnzymesByEC(divergedECs)
childGraph = self.childClade.collectiveMetabolismEnzymes().keepEnzymesByEC(divergedECs)
if colour is True:
parentEdges = parentGraph.getEdges()
childEdges = childGraph.getEdges()
graph = parentGraph.union(childGraph, addCount = False, updateName = False)
Export.addColourAttribute(graph, colour = Export.Colour.BLUE, nodes = False, edges = parentEdges)
Export.addColourAttribute(graph, colour = Export.Colour.RED, nodes = False, edges = childEdges)
graph.name = 'Diverged metabolism enzymes ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)
return graph
else:
parentGraph.name = 'Diverged metabolism enzymes *' + ' '.join(self.parentNCBInames) + '* -> ' + ' '.join(self.childNCBInames)
childGraph.name = 'Diverged metabolism enzymes ' + ' '.join(self.parentNCBInames) + ' -> *' + ' '.join(self.childNCBInames) + '*'
return (parentGraph, childGraph)
def unifiedMetabolismEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, colour = False) -> SubstanceEnzymeGraph:
"""
Substance-Enzyme graph derived from the unified core metabolisms.
The lost metabolism of the parent is coloured in blue, the conserved metabolism of both in red, and the added metabolism of the child in pink.
The colouring is realised by adding a 'colour' attribute to each edge. Nodes are not coloured.
Parameters
----------
majorityPercentageCoreMetabolism : int, optional
See :func:`conservedMetabolism`.
colour : bool, optional
If *True*, colours the parent's enzyme edges in blue, and the child's enzyme edges in red. The colouring is realised by adding a 'colour' attribute to each edge. Nodes are not coloured.
Returns
-------
SubstanceEnzymeGraph
The substance-Enzyme graph representing the combined metabolic networks of both, child and parent. If `colour` == *True*, coloured differently for the lost, conserved, and added edges. Nodes are not coloured.
Raises
------
TypeError
If you failed to enable :attr:`FEV_KEGG.settings.automaticallyStartProcessPool` or to provide a :attr:`FEV_KEGG.Util.Parallelism.processPool`. See :func:`FEV_KEGG.KEGG.Organism.Group._getGraphsParallelly`.
HTTPError
If fetching any of the underlying graphs fails.
URLError
If connection to KEGG fails.
"""
parentGraph = self.parentClade.coreMetabolismEnzymes(majorityPercentageCoreMetabolism)
childGraph = self.childClade.coreMetabolismEnzymes(majorityPercentageCoreMetabolism)
graph = parentGraph.union(childGraph, addCount = False, updateName = False)
graph.name = 'Unified metabolism enzymes ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)
if colour is True:
parentEdges = parentGraph.getEdges()
childEdges = childGraph.getEdges()
Export.addColourAttribute(graph, colour = Export.Colour.BLUE, nodes = False, edges = parentEdges)
Export.addColourAttribute(graph, colour = Export.Colour.RED, nodes = False, edges = childEdges)
return graph
# set-operations on gene-duplicated core metabolism
## for enzymes
### for enzyme graphs
def conservedMetabolismGeneDuplicatedEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, colour = False):
"""
Two Substance-Enzyme graphs of gene-duplicated enzymes, derived from the conserved core metabolism.
First, the conserved core metabolism is calculated. Then, the enzymes associated with the conserved EC numbers are extracted from the collective parent's and child's metabolism individually.
Then, for parent and child, the gene-duplicated enzymes are calculated. Finally, the gene-duplicated enzymes of the conserved core metabolism enzymes are reported.
Parameters
----------
majorityPercentageCoreMetabolism : int, optional
See :func:`conservedMetabolism`.
colour : bool, optional
If *True*, colours the enzyme edges from the parent in blue, and from the child in red. Gene-duplicated enzyme edges of the parent are coloured in green, the ones of the child in yellow.
When doing so, a single :class:`SubstanceEnzymeGraph` is returned, not a :class:`Tuple`. The colouring is realised by adding a 'colour' attribute to each edge. Nodes are not coloured.
Returns
-------
Tuple[SubstanceEnzymeGraph, SubstanceEnzymeGraph] or SubstanceEnzymeGraph
Tuple of two Substance-Enzyme graphs calculated using the conserved EC numbers found by :func:`conservedMetabolism`. The first graph is from the parent clade, the second graph from the child clade.
If `colour` == *True*, returns a single Substance-Enzyme graph.
Raises
------
TypeError
If you failed to enable :attr:`FEV_KEGG.settings.automaticallyStartProcessPool` or to provide a :attr:`FEV_KEGG.Util.Parallelism.processPool`. See :func:`FEV_KEGG.KEGG.Organism.Group._getGraphsParallelly`.
HTTPError
If fetching any of the underlying graphs fails.
URLError
If connection to KEGG fails.
"""
conservedMetabolismEnzymes = self.conservedMetabolismEnzymes(majorityPercentageCoreMetabolism, colour = colour)
parentGeneDuplicated = self.parentClade.geneDuplicatedEnzymes(majorityPercentageCoreMetabolism, colour = False)
childGeneDuplicated = self.childClade.geneDuplicatedEnzymes(majorityPercentageCoreMetabolism, colour = False)
if colour is True:
parentEdges = parentGeneDuplicated.getEdges()
childEdges = childGeneDuplicated.getEdges()
graph = conservedMetabolismEnzymes
Export.addColourAttribute(graph, colour = Export.Colour.GREEN, nodes = False, edges = parentEdges)
Export.addColourAttribute(graph, colour = Export.Colour.YELLOW, nodes = False, edges = childEdges)
graph.name = 'Conserved metabolism gene-duplicated enzymes ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)
return graph
else:
parentGraph = conservedMetabolismEnzymes[0].removeAllEnzymesExcept(parentGeneDuplicated.getEnzymes())
childGraph = conservedMetabolismEnzymes[1].removeAllEnzymesExcept(childGeneDuplicated.getEnzymes())
parentGraph.name = 'Conserved metabolism gene-duplicated enzymes *' + ' '.join(self.parentNCBInames) + '* -> ' + ' '.join(self.childNCBInames)
childGraph.name = 'Conserved metabolism gene-duplicated enzymes ' + ' '.join(self.parentNCBInames) + ' -> *' + ' '.join(self.childNCBInames) + '*'
return (parentGraph, childGraph)
def addedMetabolismGeneDuplicatedEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism) -> SubstanceEnzymeGraph:
"""
Substance-Enzyme graph of gene-duplicated enzymes, derived from the added core metabolism.
First, the added core metabolism is calculated. Then, the enzymes associated with the added EC numbers are extracted from the child's enzyme metabolism.
Parameters
----------
majorityPercentageCoreMetabolism : int, optional
See :func:`addedMetabolism`.
Returns
-------
SubstanceEnzymeGraph
Substance-Enzyme graph of enzymes from the child clade. Calculated using the added EC numbers found by :func:`addedMetabolism`.
Raises
------
TypeError
If you failed to enable :attr:`FEV_KEGG.settings.automaticallyStartProcessPool` or to provide a :attr:`FEV_KEGG.Util.Parallelism.processPool`. See :func:`FEV_KEGG.KEGG.Organism.Group._getGraphsParallelly`.
HTTPError
If fetching any of the underlying graphs fails.
URLError
If connection to KEGG fails.
"""
parentCoreMetabolism = self.parentClade.coreMetabolism(majorityPercentageCoreMetabolism)
childCoreMetabolism = self.childClade.coreMetabolism(majorityPercentageCoreMetabolism)
addedECs = GeneFunctionAddition.getECs(parentCoreMetabolism, childCoreMetabolism)
childGraph = self.childClade.geneDuplicatedEnzymes(majorityPercentageCoreMetabolism, colour = False).keepEnzymesByEC(addedECs)
childGraph.name = 'Added metabolism gene-duplicated enzymes ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)
return childGraph
def lostMetabolismGeneDuplicatedEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism) -> SubstanceEnzymeGraph:
"""
Substance-Enzyme graph of gene-duplicated enzymes, derived from the lost core metabolism.
First, the lost core metabolism is calculated. Then, the enzymes associated with the added EC numbers are extracted from the parent's enzyme metabolism.
Parameters
----------
majorityPercentageCoreMetabolism : int, optional
See :func:`lostMetabolism`.
Returns
-------
SubstanceEnzymeGraph
Substance-Enzyme graph of enzymes from the parent clade. Calculated using the lost EC numbers found by :func:`lostMetabolism`.
Raises
------
TypeError
If you failed to enable :attr:`FEV_KEGG.settings.automaticallyStartProcessPool` or to provide a :attr:`FEV_KEGG.Util.Parallelism.processPool`. See :func:`FEV_KEGG.KEGG.Organism.Group._getGraphsParallelly`.
HTTPError
If fetching any of the underlying graphs fails.
URLError
If connection to KEGG fails.
"""
parentCoreMetabolism = self.parentClade.coreMetabolism(majorityPercentageCoreMetabolism)
childCoreMetabolism = self.childClade.coreMetabolism(majorityPercentageCoreMetabolism)
lostECs = GeneFunctionLoss.getECs(parentCoreMetabolism, childCoreMetabolism)
parentGraph = self.parentClade.geneDuplicatedEnzymes(majorityPercentageCoreMetabolism, colour = False).keepEnzymesByEC(lostECs)
parentGraph.name = 'Lost metabolism gene-duplicated enzymes ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)
return parentGraph
def divergedMetabolismGeneDuplicatedEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, colour = False):
"""
Two Substance-Enzyme graphs of gene-duplicated enzymes, derived from the diverged core metabolism.
First, the diverged core metabolism is calculated. Then, the enzymes associated with the added EC numbers are extracted from the collective parent's and child's metabolism individually.
Parameters
----------
majorityPercentageCoreMetabolism : int, optional
See :func:`divergedMetabolism`.
colour : bool, optional
If *True*, colours the lost enzyme edges in blue, and the added enzyme edges in red. Gene-duplicated enzyme edges of the parent are coloured in green, the ones of the child in yellow.
When doing so, a single :class:`SubstanceEnzymeGraph` is returned, not a :class:`Tuple`. The colouring is realised by adding a 'colour' attribute to each edge. Nodes are not coloured.
Returns
-------
Tuple[SubstanceEnzymeGraph, SubstanceEnzymeGraph] or SubstanceEnzymeGraph
Tuple of two Substance-Enzyme graphs calculated using the diverged EC numbers found by :func:`divergedMetabolism`. The first graph is from the parent clade, the second graph from the child clade.
If `colour` == *True*, returns a single Substance-Enzyme graph, coloured blue for parent and red for child.
Raises
------
TypeError
If you failed to enable :attr:`FEV_KEGG.settings.automaticallyStartProcessPool` or to provide a :attr:`FEV_KEGG.Util.Parallelism.processPool`. See :func:`FEV_KEGG.KEGG.Organism.Group._getGraphsParallelly`.
HTTPError
If fetching any of the underlying graphs fails.
URLError
If connection to KEGG fails.
"""
divergedMetabolismEnzymes = self.divergedMetabolismEnzymes(majorityPercentageCoreMetabolism, colour = colour)
parentGeneDuplicated = self.parentClade.geneDuplicatedEnzymes(majorityPercentageCoreMetabolism, colour = False)
childGeneDuplicated = self.childClade.geneDuplicatedEnzymes(majorityPercentageCoreMetabolism, colour = False)
if colour is True:
parentEdges = parentGeneDuplicated.getEdges()
childEdges = childGeneDuplicated.getEdges()
graph = divergedMetabolismEnzymes
Export.addColourAttribute(graph, colour = Export.Colour.GREEN, nodes = False, edges = parentEdges)
Export.addColourAttribute(graph, colour = Export.Colour.YELLOW, nodes = False, edges = childEdges)
graph.name = 'Diverged metabolism gene-duplicated enzymes ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)
return graph
else:
parentGraph = divergedMetabolismEnzymes[0].removeAllEnzymesExcept(parentGeneDuplicated.getEnzymes())
childGraph = divergedMetabolismEnzymes[1].removeAllEnzymesExcept(childGeneDuplicated.getEnzymes())
parentGraph.name = 'Diverged metabolism gene-duplicated enzymes *' + ' '.join(self.parentNCBInames) + '* -> ' + ' '.join(self.childNCBInames)
childGraph.name = 'Diverged metabolism gene-duplicated enzymes ' + ' '.join(self.parentNCBInames) + ' -> *' + ' '.join(self.childNCBInames) + '*'
return (parentGraph, childGraph)
def unifiedMetabolismGeneDuplicatedEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, colour = False) -> SubstanceEnzymeGraph:
"""
Substance-Enzyme graph of gene-duplicated enzymes, derived from the unified core metabolisms.
The lost metabolism of the parent is coloured in blue, the conserved metabolism of both in red, and the added metabolism of the child in pink.
The colouring is realised by adding a 'colour' attribute to each edge. Nodes are not coloured.
Parameters
----------
majorityPercentageCoreMetabolism : int, optional
See :func:`conservedMetabolism`.
colour : bool, optional
If *True*, colours the parent's enzyme edges in blue, and the child's enzyme edges in red. Gene-duplicated enzyme edges of the parent are coloured in green, the ones of the child in yellow.
The colouring is realised by adding a 'colour' attribute to each edge. Nodes are not coloured.
Returns
-------
SubstanceEcGraph
The substance-Enzyme graph representing the combined metabolic networks of both, child and parent. If `colour` == *True*, coloured differently for the lost, conserved, and added edges. Nodes are not coloured.
Raises
------
TypeError
If you failed to enable :attr:`FEV_KEGG.settings.automaticallyStartProcessPool` or to provide a :attr:`FEV_KEGG.Util.Parallelism.processPool`. See :func:`FEV_KEGG.KEGG.Organism.Group._getGraphsParallelly`.
HTTPError
If fetching any of the underlying graphs fails.
URLError
If connection to KEGG fails.
"""
parentGeneDuplicated = self.parentClade.geneDuplicatedEnzymes(majorityPercentageCoreMetabolism, colour = False)
childGeneDuplicated = self.childClade.geneDuplicatedEnzymes(majorityPercentageCoreMetabolism, colour = False)
if colour is False:
graph = parentGeneDuplicated.union(childGeneDuplicated, addCount = False, updateName = False)
else:
unifiedMetabolismEnzymes = self.unifiedMetabolismEnzymes(majorityPercentageCoreMetabolism, colour = True)
parentEdges = parentGeneDuplicated.getEdges()
childEdges = childGeneDuplicated.getEdges()
graph = unifiedMetabolismEnzymes
Export.addColourAttribute(graph, colour = Export.Colour.GREEN, nodes = False, edges = parentEdges)
Export.addColourAttribute(graph, colour = Export.Colour.YELLOW, nodes = False, edges = childEdges)
return graph
### for enzyme pairs
def conservedMetabolismGeneDuplicatedEnzymePairs(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism) -> Tuple[Set[Tuple[Enzyme, Enzyme]]]:
"""
Pairs of gene-duplicated enzymes, derived from the conserved core metabolism.
First, the conserved core metabolism is calculated. Then, the enzymes associated with the conserved EC numbers are extracted from the collective parent's and child's metabolism individually.
Then, for parent and child, the gene-duplicated enzyme pairs are calculated. Finally, the gene-duplicated enzymes where both enzymes are in the conserved core metabolism are reported.
Parameters
----------
majorityPercentageCoreMetabolism : int, optional
See :func:`conservedMetabolism`.
Returns
-------
Tuple[Set[Tuple[Enzyme, Enzyme]]]
Tuple of two sets of tuples of gene-duplicated enzyme pairs calculated using the conserved EC numbers found by :func:`conservedMetabolism`. The first set is from the parent clade, the second set from the child clade.
Raises
------
TypeError
If you failed to enable :attr:`FEV_KEGG.settings.automaticallyStartProcessPool` or to provide a :attr:`FEV_KEGG.Util.Parallelism.processPool`. See :func:`FEV_KEGG.KEGG.Organism.Group._getGraphsParallelly`.
HTTPError
If fetching any of the underlying graphs fails.
URLError
If connection to KEGG fails.
"""
# get conserved metabolism
conservedMetabolismEnzymes = self.conservedMetabolismEnzymes(majorityPercentageCoreMetabolism).getEnzymes()
# get gene-duplicate enzyme pairs
parentGeneDuplicated = self.parentClade.geneDuplicatedEnzymePairs(majorityPercentageCoreMetabolism)
childGeneDuplicated = self.childClade.geneDuplicatedEnzymePairs(majorityPercentageCoreMetabolism)
# filter gene-duplicated enzyme pairs for the ones with both enzymes in the conserved metabolism
parentGeneDuplicatedConserved = set()
childGeneDuplicatedConserved = set()
for enzymeTuple in parentGeneDuplicated:
if enzymeTuple[0] in conservedMetabolismEnzymes and enzymeTuple[1] in conservedMetabolismEnzymes:
parentGeneDuplicatedConserved.add(enzymeTuple)
for enzymeTuple in childGeneDuplicated:
if enzymeTuple[0] in conservedMetabolismEnzymes and enzymeTuple[1] in conservedMetabolismEnzymes:
childGeneDuplicatedConserved.add(enzymeTuple)
return (parentGeneDuplicatedConserved, childGeneDuplicatedConserved)
def addedMetabolismGeneDuplicatedEnzymePairs(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism) -> Set[Tuple[Enzyme, Enzyme]]:
"""
Pairs of gene-duplicated enzymes, derived from the added core metabolism.
First, the added core metabolism is calculated. Then, the enzymes associated with the added EC numbers are extracted from the child's enzyme metabolism.
Then the gene-duplicated enzymes are calculated. Finally, the gene-duplicated enzyme pairs of the conserved core metabolism enzymes are reported.
Parameters
----------
majorityPercentageCoreMetabolism : int, optional
See :func:`addedMetabolism`.
Returns
-------
Set[Tuple[Enzyme, Enzyme]]
Pairs of enzymes from the child clade. Calculated using the added EC numbers found by :func:`addedMetabolism`.
Raises
------
TypeError
If you failed to enable :attr:`FEV_KEGG.settings.automaticallyStartProcessPool` or to provide a :attr:`FEV_KEGG.Util.Parallelism.processPool`. See :func:`FEV_KEGG.KEGG.Organism.Group._getGraphsParallelly`.
HTTPError
If fetching any of the underlying graphs fails.
URLError
If connection to KEGG fails.
"""
# get added metabolism
addedMetabolismEnzymes = self.addedMetabolismEnzymes(majorityPercentageCoreMetabolism).getEnzymes()
# get gene-duplicated enzyme pairs
geneDuplicated = self.childClade.geneDuplicatedEnzymePairs(majorityPercentageCoreMetabolism)
# filter gene-duplicated enzyme pairs for the ones with both enzymes in the added metabolism
geneDuplicatedAdded = set()
for enzymeTuple in geneDuplicated:
if enzymeTuple[0] in addedMetabolismEnzymes and enzymeTuple[1] in addedMetabolismEnzymes:
geneDuplicatedAdded.add(enzymeTuple)
return geneDuplicatedAdded
def lostMetabolismGeneDuplicatedEnzymePairs(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism) -> Set[Tuple[Enzyme, Enzyme]]:
"""
Pairs of gene-duplicated enzymes, derived from the lost core metabolism.
First, the lost core metabolism is calculated. Then, the enzymes associated with the added EC numbers are extracted from the parent's enzyme metabolism.
Then the gene-duplicated enzymes are calculated. Finally, the gene-duplicated enzyme pairs of the conserved core metabolism enzymes are reported.
Parameters
----------
majorityPercentageCoreMetabolism : int, optional
See :func:`lostMetabolism`.
Returns
-------
Set[Tuple[Enzyme, Enzyme]]
Pairs of enzymes from the parent clade. Calculated using the lost EC numbers found by :func:`lostMetabolism`.
Raises
------
TypeError
If you failed to enable :attr:`FEV_KEGG.settings.automaticallyStartProcessPool` or to provide a :attr:`FEV_KEGG.Util.Parallelism.processPool`. See :func:`FEV_KEGG.KEGG.Organism.Group._getGraphsParallelly`.
HTTPError
If fetching any of the underlying graphs fails.
URLError
If connection to KEGG fails.
"""
# get added metabolism
lostMetabolismEnzymes = self.lostMetabolismEnzymes(majorityPercentageCoreMetabolism).getEnzymes()
# get gene-duplicated enzyme pairs
geneDuplicated = self.childClade.geneDuplicatedEnzymePairs(majorityPercentageCoreMetabolism)
# filter gene-duplicated enzyme pairs for the ones with both enzymes in the lost metabolism
geneDuplicatedLost = set()
for enzymeTuple in geneDuplicated:
if enzymeTuple[0] in lostMetabolismEnzymes and enzymeTuple[1] in lostMetabolismEnzymes:
geneDuplicatedLost.add(enzymeTuple)
return geneDuplicatedLost
def divergedMetabolismGeneDuplicatedEnzymePairs(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism) -> Set[Tuple[Enzyme, Enzyme]]:
"""
Pairs of gene-duplicated enzymes, derived from the diverged core metabolism.
First, the diverged core metabolism is calculated. Then, the enzymes associated with the added EC numbers are extracted from the collective parent's and child's metabolism individually.
Then, for parent and child, the gene-duplicated enzyme pairs are calculated. Finally, the gene-duplicated enzymes where both enzymes are in the conserved core metabolism are reported.
Parameters
----------
majorityPercentageCoreMetabolism : int, optional
See :func:`divergedMetabolism`.
colour : bool, optional
If *True*, colours the lost enzyme edges in blue, and the added enzyme edges in red. Gene-duplicated enzyme edges of the parent are coloured in green, the ones of the child in yellow.
When doing so, a single :class:`SubstanceEnzymeGraph` is returned, not a :class:`Tuple`. The colouring is realised by adding a 'colour' attribute to each edge. Nodes are not coloured.
Returns
-------
Set[Tuple[Enzyme, Enzyme]
Sets of tuples of gene-duplicated enzyme pairs calculated using the diverged EC numbers found by :func:`divergedMetabolism`.
Raises
------
TypeError
If you failed to enable :attr:`FEV_KEGG.settings.automaticallyStartProcessPool` or to provide a :attr:`FEV_KEGG.Util.Parallelism.processPool`. See :func:`FEV_KEGG.KEGG.Organism.Group._getGraphsParallelly`.
HTTPError
If fetching any of the underlying graphs fails.
URLError
If connection to KEGG fails.
"""
# get diverged metabolism
divergedMetabolismEnzymes = self.divergedMetabolismEnzymes(majorityPercentageCoreMetabolism).getEnzymes()
# get gene-duplicate enzyme pairs
parentGeneDuplicated = self.parentClade.geneDuplicatedEnzymePairs(majorityPercentageCoreMetabolism)
childGeneDuplicated = self.childClade.geneDuplicatedEnzymePairs(majorityPercentageCoreMetabolism)
# filter gene-duplicated enzyme pairs for the ones with both enzymes in the diverged metabolism
parentGeneDuplicatedDiverged = set()
childGeneDuplicatedDiverged = set()
for enzymeTuple in parentGeneDuplicated:
if enzymeTuple[0] in divergedMetabolismEnzymes and enzymeTuple[1] in divergedMetabolismEnzymes:
parentGeneDuplicatedDiverged.add(enzymeTuple)
for enzymeTuple in childGeneDuplicated:
if enzymeTuple[0] in divergedMetabolismEnzymes and enzymeTuple[1] in divergedMetabolismEnzymes:
childGeneDuplicatedDiverged.add(enzymeTuple)
return parentGeneDuplicatedDiverged.union(childGeneDuplicatedDiverged)
def unifiedMetabolismGeneDuplicatedEnzymePairs(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism) -> Set[Tuple[Enzyme, Enzyme]]:
"""
Pairs of gene-duplicated enzymes, derived from the unified core metabolisms.
Parameters
----------
majorityPercentageCoreMetabolism : int, optional
See :func:`conservedMetabolism`.
Returns
-------
Set[Tuple[Enzyme, Enzyme]
Set of enzyme pairs representing the gene-duplicated enzymes of the combined metabolic networks of both, child and parent.
Raises
------
TypeError
If you failed to enable :attr:`FEV_KEGG.settings.automaticallyStartProcessPool` or to provide a :attr:`FEV_KEGG.Util.Parallelism.processPool`. See :func:`FEV_KEGG.KEGG.Organism.Group._getGraphsParallelly`.
HTTPError
If fetching any of the underlying graphs fails.
URLError
If connection to KEGG fails.
"""
parentGeneDuplicated = self.parentClade.geneDuplicatedEnzymePairs(majorityPercentageCoreMetabolism)
childGeneDuplicated = self.childClade.geneDuplicatedEnzymePairs(majorityPercentageCoreMetabolism)
return parentGeneDuplicated.union(childGeneDuplicated)
# set-operations on neofunctionalised core metabolism
## for enzyme graphs
def conservedMetabolismNeofunctionalisedEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, colour = False):
"""
Two Substance-Enzyme graphs of neofunctionalised enzymes, derived from the conserved core metabolism.
First, the conserved core metabolism is calculated. Then, the enzymes associated with the conserved EC numbers are extracted from the collective parent's and child's metabolism individually.
Then, for parent and child, the gene-duplicated enzymes are calculated. Then, the gene-duplicated enzymes of the conserved core metabolism enzymes are identified.
Finally, the pairs of enzymes in which EC numbers differ are reported.
Parameters
----------
majorityPercentageCoreMetabolism : int, optional
See :func:`conservedMetabolism`.
colour : bool, optional
If *True*, colours the enzyme edges from the parent in blue, and from the child in red. Neofunctionalised enzyme edges of the parent are coloured in green, the ones of the child in yellow.
When doing so, a single :class:`SubstanceEnzymeGraph` is returned, not a :class:`Tuple`. The colouring is realised by adding a 'colour' attribute to each edge. Nodes are not coloured.
Returns
-------
Tuple[SubstanceEnzymeGraph, SubstanceEnzymeGraph] or SubstanceEnzymeGraph
Tuple of two Substance-Enzyme graphs calculated using the conserved EC numbers found by :func:`conservedMetabolism`. The first graph is from the parent clade, the second graph from the child clade.
If `colour` == *True*, returns a single Substance-Enzyme graph.
Raises
------
TypeError
If you failed to enable :attr:`FEV_KEGG.settings.automaticallyStartProcessPool` or to provide a :attr:`FEV_KEGG.Util.Parallelism.processPool`. See :func:`FEV_KEGG.KEGG.Organism.Group._getGraphsParallelly`.
HTTPError
If fetching any of the underlying graphs fails.
URLError
If connection to KEGG fails.
"""
conservedMetabolismEnzymes = self.conservedMetabolismEnzymes(majorityPercentageCoreMetabolism, colour = colour)
parentNeofunctionalised= self.parentClade.neofunctionalisedEnzymes(majorityPercentageCoreMetabolism, colour = False)
childNeofunctionalised = self.childClade.neofunctionalisedEnzymes(majorityPercentageCoreMetabolism, colour = False)
if colour is True:
parentEdges = parentNeofunctionalised.getEdges()
childEdges = childNeofunctionalised.getEdges()
graph = conservedMetabolismEnzymes
Export.addColourAttribute(graph, colour = Export.Colour.GREEN, nodes = False, edges = parentEdges)
Export.addColourAttribute(graph, colour = Export.Colour.YELLOW, nodes = False, edges = childEdges)
graph.name = 'Conserved metabolism neofunctionalised enzymes ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)
return graph
else:
parentGraph = conservedMetabolismEnzymes[0].removeAllEnzymesExcept(parentNeofunctionalised.getEnzymes())
childGraph = conservedMetabolismEnzymes[1].removeAllEnzymesExcept(childNeofunctionalised.getEnzymes())
parentGraph.name = 'Conserved metabolism neofunctionalised enzymes *' + ' '.join(self.parentNCBInames) + '* -> ' + ' '.join(self.childNCBInames)
childGraph.name = 'Conserved metabolism neofunctionalised enzymes ' + ' '.join(self.parentNCBInames) + ' -> *' + ' '.join(self.childNCBInames) + '*'
return (parentGraph, childGraph)
def addedMetabolismNeofunctionalisedEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism) -> SubstanceEnzymeGraph:
"""
Substance-Enzyme graph of neofunctionalised enzymes, derived from the added core metabolism.
Parameters
----------
majorityPercentageCoreMetabolism : int, optional
See :func:`addedMetabolism`.
Returns
-------
SubstanceEnzymeGraph
Substance-Enzyme graph of enzymes from the child clade. Calculated using the added EC numbers found by :func:`addedMetabolism`.
Raises
------
TypeError
If you failed to enable :attr:`FEV_KEGG.settings.automaticallyStartProcessPool` or to provide a :attr:`FEV_KEGG.Util.Parallelism.processPool`. See :func:`FEV_KEGG.KEGG.Organism.Group._getGraphsParallelly`.
HTTPError
If fetching any of the underlying graphs fails.
URLError
If connection to KEGG fails.
"""
parentCoreMetabolism = self.parentClade.coreMetabolism(majorityPercentageCoreMetabolism)
childCoreMetabolism = self.childClade.coreMetabolism(majorityPercentageCoreMetabolism)
addedECs = GeneFunctionAddition.getECs(parentCoreMetabolism, childCoreMetabolism)
childGraph = self.childClade.neofunctionalisedEnzymes(majorityPercentageCoreMetabolism, colour = False).keepEnzymesByEC(addedECs)
childGraph.name = 'Added metabolism neofunctionalised enzymes ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)
return childGraph
def lostMetabolismNeofunctionalisedEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism) -> SubstanceEnzymeGraph:
"""
Substance-Enzyme graph of neofunctionalised enzymes, derived from the lost core metabolism.
Parameters
----------
majorityPercentageCoreMetabolism : int, optional
See :func:`lostMetabolism`.
Returns
-------
SubstanceEnzymeGraph
Substance-Enzyme graph of enzymes from the parent clade. Calculated using the lost EC numbers found by :func:`lostMetabolism`.
Raises
------
TypeError
If you failed to enable :attr:`FEV_KEGG.settings.automaticallyStartProcessPool` or to provide a :attr:`FEV_KEGG.Util.Parallelism.processPool`. See :func:`FEV_KEGG.KEGG.Organism.Group._getGraphsParallelly`.
HTTPError
If fetching any of the underlying graphs fails.
URLError
If connection to KEGG fails.
"""
parentCoreMetabolism = self.parentClade.coreMetabolism(majorityPercentageCoreMetabolism)
childCoreMetabolism = self.childClade.coreMetabolism(majorityPercentageCoreMetabolism)
lostECs = GeneFunctionLoss.getECs(parentCoreMetabolism, childCoreMetabolism)
parentGraph = self.parentClade.neofunctionalisedEnzymes(majorityPercentageCoreMetabolism, colour = False).keepEnzymesByEC(lostECs)
parentGraph.name = 'Lost metabolism neofunctionalised enzymes ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)
return parentGraph
def divergedMetabolismNeofunctionalisedEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, colour = False):
"""
Two Substance-Enzyme graphs of neofunctionalised enzymes, derived from the diverged core metabolism.
Parameters
----------
majorityPercentageCoreMetabolism : int, optional
See :func:`divergedMetabolism`.
colour : bool, optional
If *True*, colours the lost enzyme edges in blue, and the added enzyme edges in red. Neofunctionalised enzyme edges of the parent are coloured in green, the ones of the child in yellow.
When doing so, a single :class:`SubstanceEnzymeGraph` is returned, not a :class:`Tuple`. The colouring is realised by adding a 'colour' attribute to each edge. Nodes are not coloured.
Returns
-------
Tuple[SubstanceEnzymeGraph, SubstanceEnzymeGraph] or SubstanceEnzymeGraph
Tuple of two Substance-Enzyme graphs calculated using the diverged EC numbers found by :func:`divergedMetabolism`. The first graph is from the parent clade, the second graph from the child clade.
If `colour` == *True*, returns a single Substance-Enzyme graph, coloured blue for parent and red for child.
Raises
------
TypeError
If you failed to enable :attr:`FEV_KEGG.settings.automaticallyStartProcessPool` or to provide a :attr:`FEV_KEGG.Util.Parallelism.processPool`. See :func:`FEV_KEGG.KEGG.Organism.Group._getGraphsParallelly`.
HTTPError
If fetching any of the underlying graphs fails.
URLError
If connection to KEGG fails.
"""
divergedMetabolismEnzymes = self.divergedMetabolismEnzymes(majorityPercentageCoreMetabolism, colour = colour)
parentNeofunctionalised = self.parentClade.neofunctionalisedEnzymes(majorityPercentageCoreMetabolism, colour = False)
childNeofunctionalised = self.childClade.neofunctionalisedEnzymes(majorityPercentageCoreMetabolism, colour = False)
if colour is True:
parentEdges = parentNeofunctionalised.getEdges()
childEdges = childNeofunctionalised.getEdges()
graph = divergedMetabolismEnzymes
Export.addColourAttribute(graph, colour = Export.Colour.GREEN, nodes = False, edges = parentEdges)
Export.addColourAttribute(graph, colour = Export.Colour.YELLOW, nodes = False, edges = childEdges)
graph.name = 'Diverged metabolism neofunctionalised enzymes ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)
return graph
else:
parentGraph = divergedMetabolismEnzymes[0].removeAllEnzymesExcept(parentNeofunctionalised.getEnzymes())
childGraph = divergedMetabolismEnzymes[1].removeAllEnzymesExcept(childNeofunctionalised.getEnzymes())
parentGraph.name = 'Diverged metabolism neofunctionalised enzymes *' + ' '.join(self.parentNCBInames) + '* -> ' + ' '.join(self.childNCBInames)
childGraph.name = 'Diverged metabolism neofunctionalised enzymes ' + ' '.join(self.parentNCBInames) + ' -> *' + ' '.join(self.childNCBInames) + '*'
return (parentGraph, childGraph)
def unifiedMetabolismNeofunctionalisedEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, colour = False) -> SubstanceEnzymeGraph:
"""
Substance-Enzyme graph of neofunctionalised enzymes, derived from the unified core metabolisms.
Parameters
----------
majorityPercentageCoreMetabolism : int, optional
See :func:`conservedMetabolism`.
colour : bool, optional
If *True*, colours the parent's enzyme edges in blue, and the child's enzyme edges in red. Neofunctionalised enzyme edges of the parent are coloured in green, the ones of the child in yellow.
The colouring is realised by adding a 'colour' attribute to each edge. Nodes are not coloured.
Returns
-------
SubstanceEcGraph
The substance-Enzyme graph representing the combined metabolic networks of both, child and parent. If `colour` == *True*, coloured differently for the lost, conserved, and added edges. Nodes are not coloured.
Raises
------
TypeError
If you failed to enable :attr:`FEV_KEGG.settings.automaticallyStartProcessPool` or to provide a :attr:`FEV_KEGG.Util.Parallelism.processPool`. See :func:`FEV_KEGG.KEGG.Organism.Group._getGraphsParallelly`.
HTTPError
If fetching any of the underlying graphs fails.
URLError
If connection to KEGG fails.
"""
parentNeofunctionalised = self.parentClade.neofunctionalisedEnzymes(majorityPercentageCoreMetabolism, colour = False)
childNeofunctionalised = self.childClade.neofunctionalisedEnzymes(majorityPercentageCoreMetabolism, colour = False)
if colour is False:
graph = parentNeofunctionalised.union(childNeofunctionalised, addCount = False, updateName = False)
else:
unifiedMetabolismEnzymes = self.unifiedMetabolismEnzymes(majorityPercentageCoreMetabolism, colour = True)
parentEdges = parentNeofunctionalised.getEdges()
childEdges = childNeofunctionalised.getEdges()
graph = unifiedMetabolismEnzymes
Export.addColourAttribute(graph, colour = Export.Colour.GREEN, nodes = False, edges = parentEdges)
Export.addColourAttribute(graph, colour = Export.Colour.YELLOW, nodes = False, edges = childEdges)
graph.name = 'Diverged metabolism neofunctionalised enzymes ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)
return graph
## for EC graphs
def conservedMetabolismNeofunctionalisedECs(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation = defaultMajorityPercentageNeofunctionalisation, colour = False):
"""
Two Substance-EC graphs of "neofunctionalised" EC numbers, derived from the conserved core metabolism.
First, the conserved core metabolism is calculated. Then, the enzymes associated with the conserved EC numbers are extracted from the collective parent's and child's metabolism individually.
Then, for parent and child, the gene-duplicated enzymes are calculated. Then, the gene-duplicated enzymes of the conserved core metabolism enzymes are identified.
Then, the pairs of enzymes in which EC numbers differ are identified. Finally, the EC numbers which are part of these function changes are reported.
Parameters
----------
majorityPercentageCoreMetabolism : int, optional
See :func:`conservedMetabolism`.
colour : bool, optional
If *True*, colours the EC edges from the parent in blue, and from the child in red. "Neofunctionalised" EC edges of the parent are coloured in green, the ones of the child in yellow.
When doing so, a single :class:`SubstanceEcGraph` is returned, not a :class:`Tuple`. The colouring is realised by adding a 'colour' attribute to each edge. Nodes are not coloured.
Returns
-------
Tuple[SubstanceEcGraph, SubstanceEcGraph] or SubstanceEcGraph
Tuple of two Substance-EC graphs calculated using the conserved EC numbers found by :func:`conservedMetabolism`. The first graph is from the parent clade, the second graph from the child clade.
If `colour` == *True*, returns a single Substance-EC graph.
Raises
------
TypeError
If you failed to enable :attr:`FEV_KEGG.settings.automaticallyStartProcessPool` or to provide a :attr:`FEV_KEGG.Util.Parallelism.processPool`. See :func:`FEV_KEGG.KEGG.Organism.Group._getGraphsParallelly`.
HTTPError
If fetching any of the underlying graphs fails.
URLError
If connection to KEGG fails.
"""
conservedMetabolism = self.conservedMetabolism(majorityPercentageCoreMetabolism)
parentNeofunctionalised= self.parentClade.neofunctionalisedECs(majorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation, colour = False)
childNeofunctionalised = self.childClade.neofunctionalisedECs(majorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation, colour = False)
if colour is True:
parentEdges = parentNeofunctionalised.getEdges()
childEdges = childNeofunctionalised.getEdges()
graph = conservedMetabolism
Export.addColourAttribute(graph, colour = Export.Colour.GREEN, nodes = False, edges = parentEdges)
Export.addColourAttribute(graph, colour = Export.Colour.YELLOW, nodes = False, edges = childEdges)
graph.name = 'Conserved metabolism neofunctionalised ECs ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)
return graph
else:
parentGraph = conservedMetabolism[0].removeAllECsExcept(parentNeofunctionalised.getECs())
childGraph = conservedMetabolism[1].removeAllECsExcept(childNeofunctionalised.getECs())
parentGraph.name = 'Conserved metabolism neofunctionalised ECs *' + ' '.join(self.parentNCBInames) + '* -> ' + ' '.join(self.childNCBInames)
childGraph.name = 'Conserved metabolism neofunctionalised ECs ' + ' '.join(self.parentNCBInames) + ' -> *' + ' '.join(self.childNCBInames) + '*'
return (parentGraph, childGraph)
def addedMetabolismNeofunctionalisedECs(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation = defaultMajorityPercentageNeofunctionalisation) -> SubstanceEcGraph:
"""
Substance-EC graph of "neofunctionalised" EC numbers, derived from the added core metabolism.
Parameters
----------
majorityPercentageCoreMetabolism : int, optional
See :func:`addedMetabolism`.
Returns
-------
SubstanceEcGraph
Substance-EC graph of ECs from the child clade. Calculated using the added EC numbers found by :func:`addedMetabolism`.
Raises
------
TypeError
If you failed to enable :attr:`FEV_KEGG.settings.automaticallyStartProcessPool` or to provide a :attr:`FEV_KEGG.Util.Parallelism.processPool`. See :func:`FEV_KEGG.KEGG.Organism.Group._getGraphsParallelly`.
HTTPError
If fetching any of the underlying graphs fails.
URLError
If connection to KEGG fails.
"""
parentCoreMetabolism = self.parentClade.coreMetabolism(majorityPercentageCoreMetabolism)
childCoreMetabolism = self.childClade.coreMetabolism(majorityPercentageCoreMetabolism)
addedECs = GeneFunctionAddition.getECs(parentCoreMetabolism, childCoreMetabolism)
childGraph = self.childClade.neofunctionalisedECs(majorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation, colour = False).removeAllECsExcept(addedECs)
childGraph.name = 'Added metabolism neofunctionalised ECs ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)
return childGraph
def lostMetabolismNeofunctionalisedECs(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation = defaultMajorityPercentageNeofunctionalisation) -> SubstanceEcGraph:
"""
Substance-EC graph of "neofunctionalised" EC numbers, derived from the lost core metabolism.
Parameters
----------
majorityPercentageCoreMetabolism : int, optional
See :func:`lostMetabolism`.
Returns
-------
SubstanceEcGraph
Substance-EC graph of ECs from the parent clade. Calculated using the lost EC numbers found by :func:`lostMetabolism`.
Raises
------
TypeError
If you failed to enable :attr:`FEV_KEGG.settings.automaticallyStartProcessPool` or to provide a :attr:`FEV_KEGG.Util.Parallelism.processPool`. See :func:`FEV_KEGG.KEGG.Organism.Group._getGraphsParallelly`.
HTTPError
If fetching any of the underlying graphs fails.
URLError
If connection to KEGG fails.
"""
parentCoreMetabolism = self.parentClade.coreMetabolism(majorityPercentageCoreMetabolism)
childCoreMetabolism = self.childClade.coreMetabolism(majorityPercentageCoreMetabolism)
lostECs = GeneFunctionLoss.getECs(parentCoreMetabolism, childCoreMetabolism)
parentGraph = self.parentClade.neofunctionalisedECs(majorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation, colour = False).removeAllECsExcept(lostECs)
parentGraph.name = 'Lost metabolism neofunctionalised ECs ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)
return parentGraph
def divergedMetabolismNeofunctionalisedECs(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation = defaultMajorityPercentageNeofunctionalisation, colour = False):
"""
Two Substance-EC graphs of "neofunctionalised" EC numbers, derived from the diverged core metabolism.
Parameters
----------
majorityPercentageCoreMetabolism : int, optional
See :func:`divergedMetabolism`.
colour : bool, optional
If *True*, colours the lost EC edges in blue, and the added EC edges in red. "Neofunctionalised" EC edges of the parent are coloured in green, the ones of the child in yellow.
When doing so, a single :class:`SubstanceEcGraph` is returned, not a :class:`Tuple`. The colouring is realised by adding a 'colour' attribute to each edge. Nodes are not coloured.
Returns
-------
Tuple[SubstanceEcGraph, SubstanceEcGraph] or SubstanceEcGraph
Tuple of two Substance-EC graphs calculated using the diverged EC numbers found by :func:`divergedMetabolism`. The first graph is from the parent clade, the second graph from the child clade.
If `colour` == *True*, returns a single Substance-EC graph, coloured blue for parent and red for child.
Raises
------
TypeError
If you failed to enable :attr:`FEV_KEGG.settings.automaticallyStartProcessPool` or to provide a :attr:`FEV_KEGG.Util.Parallelism.processPool`. See :func:`FEV_KEGG.KEGG.Organism.Group._getGraphsParallelly`.
HTTPError
If fetching any of the underlying graphs fails.
URLError
If connection to KEGG fails.
"""
divergedMetabolism = self.divergedMetabolism(majorityPercentageCoreMetabolism, colour = colour)
parentNeofunctionalised = self.parentClade.neofunctionalisedECs(majorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation, colour = False)
childNeofunctionalised = self.childClade.neofunctionalisedECs(majorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation, colour = False)
if colour is True:
parentEdges = parentNeofunctionalised.getEdges()
childEdges = childNeofunctionalised.getEdges()
graph = divergedMetabolism
Export.addColourAttribute(graph, colour = Export.Colour.GREEN, nodes = False, edges = parentEdges)
Export.addColourAttribute(graph, colour = Export.Colour.YELLOW, nodes = False, edges = childEdges)
graph.name = 'Diverged metabolism neofunctionalised ECs ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)
return graph
else:
parentGraph = divergedMetabolism[0].removeAllECsExcept(parentNeofunctionalised.getECs())
childGraph = divergedMetabolism[1].removeAllECsExcept(childNeofunctionalised.getECs())
parentGraph.name = 'Diverged metabolism neofunctionalised ECs *' + ' '.join(self.parentNCBInames) + '* -> ' + ' '.join(self.childNCBInames)
childGraph.name = 'Diverged metabolism neofunctionalised ECs ' + ' '.join(self.parentNCBInames) + ' -> *' + ' '.join(self.childNCBInames) + '*'
return (parentGraph, childGraph)
def unifiedMetabolismNeofunctionalisedECs(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation = defaultMajorityPercentageNeofunctionalisation, colour = False) -> SubstanceEcGraph:
"""
Substance-EC graph of "neofunctionalised" EC numbers, derived from the unified core metabolisms.
Parameters
----------
majorityPercentageCoreMetabolism : int, optional
See :func:`conservedMetabolism`.
colour : bool, optional
If *True*, colours the parent's EC edges in blue, and the child's EC edges in red. "Neofunctionalised" EC edges of the parent are coloured in green, the ones of the child in yellow.
The colouring is realised by adding a 'colour' attribute to each edge. Nodes are not coloured.
Returns
-------
SubstanceEcGraph
The substance-EC graph representing the combined metabolic networks of both, child and parent. If `colour` == *True*, coloured differently for the lost, conserved, and added edges. Nodes are not coloured.
Raises
------
TypeError
If you failed to enable :attr:`FEV_KEGG.settings.automaticallyStartProcessPool` or to provide a :attr:`FEV_KEGG.Util.Parallelism.processPool`. See :func:`FEV_KEGG.KEGG.Organism.Group._getGraphsParallelly`.
HTTPError
If fetching any of the underlying graphs fails.
URLError
If connection to KEGG fails.
"""
parentNeofunctionalised = self.parentClade.neofunctionalisedECs(majorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation, colour = False)
childNeofunctionalised = self.childClade.neofunctionalisedECs(majorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation, colour = False)
if colour is False:
graph = parentNeofunctionalised.union(childNeofunctionalised, addCount = False, updateName = False)
else:
unifiedMetabolism = self.unifiedMetabolism(majorityPercentageCoreMetabolism, colour = True)
parentEdges = parentNeofunctionalised.getEdges()
childEdges = childNeofunctionalised.getEdges()
graph = unifiedMetabolism
Export.addColourAttribute(graph, colour = Export.Colour.GREEN, nodes = False, edges = parentEdges)
Export.addColourAttribute(graph, colour = Export.Colour.YELLOW, nodes = False, edges = childEdges)
graph.name = 'Diverged metabolism neofunctionalised ECs ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)
return graph
class NestedCladePair(CladePair):
def __init__(self, parent, child, excludeUnclassified = defaultExcludeUnclassified):
"""
Two clades in NCBI taxonomy, 'child' is assumed younger and must be nested somewhere inside 'parent'.
This only checks nestedness for the first node found in taxonomy, by the first parent's/child's NCBI name, respectively. The latter being relevant if you pass a :class:`Clade`, which has a list of NCBI names, or a list of NCBI names itself.
Parameters
----------
parent : str or List[str] or Clade
Path(s) of the parent clade's taxon, as defined by NCBI taxonomy, e.g. 'Proteobacteria/Gammaproteobacteria'. Or a ready :class:`Clade` object.
child : str or List[str] or Clade
Path(s) of the child clade's taxon, as defined by NCBI taxonomy, e.g. 'Enterobacter'. Or a ready :class:`Clade` object.
excludeUnclassified : bool, optional
If *True*, ignore taxons with a path containing the string 'unclassified'.
Attributes
----------
self.childClade : :class:`Clade`
self.parentClade : :class:`Clade`
Raises
------
ValueError
If parent or child are unknown taxons. Or if the child taxon is not actually a child of the parent taxon.
"""
# read first NCBI name from Clade object, if necessary
if isinstance(parent, Clade):
parentNCBIname = parent.ncbiNames[0]
elif not isinstance(parent, str):
# must be iterable, else fail
parentNCBIname = parent[0]
if isinstance(child, Clade):
childNCBIname = child.ncbiNames[0]
elif not isinstance(child, str):
# must be iterable, else fail
childNCBIname = child[0]
# check if child is really a child of parent
taxonomy = NCBI.getTaxonomy()
parentNode = taxonomy.searchNodesByPath(parentNCBIname, exceptPaths=('unclassified' if excludeUnclassified else None))
if parentNode is None or len(parentNode) == 0:
raise ValueError("No clade of this path found: " + parentNCBIname)
else: # only consider first element
parentNode = parentNode[0]
childNode = taxonomy.searchNodesByPath(childNCBIname, exceptPaths=('unclassified' if excludeUnclassified else None))
if childNode is None or len(childNode) == 0:
raise ValueError("No clade of this path found: " + childNCBIname)
else: # only consider first element
childNode = childNode[0]
foundParent = False
for ancestor in childNode.ancestors:
if Taxonomy.nodePath2String(ancestor) == Taxonomy.nodePath2String(parentNode):
foundParent = True
break
if foundParent == False:
raise ValueError("Child is not a descendant of parent.")
super().__init__(parent, child, excludeUnclassified)
| 55.25759 | 316 | 0.681262 | 11,178 | 120,130 | 7.302827 | 0.056003 | 0.011834 | 0.011319 | 0.010474 | 0.815818 | 0.795629 | 0.775624 | 0.747878 | 0.735211 | 0.711041 | 0 | 0.001023 | 0.251311 | 120,130 | 2,173 | 317 | 55.283019 | 0.906593 | 0.48152 | 0 | 0.481752 | 0 | 0.001825 | 0.044581 | 0.001789 | 0.085766 | 0 | 0 | 0 | 0 | 1 | 0.089416 | false | 0 | 0.020073 | 0 | 0.215328 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
183d37b9eace3b9230286a82c171e2f3f47b5e35 | 162 | py | Python | python/axi_soc_ultra_plus_core/__init__.py | slaclab/axi-soc-ultra-plus-core | e2b207bf88505724ad088c99756907beaab7db98 | [
"BSD-3-Clause-LBNL"
] | 1 | 2021-09-07T03:12:22.000Z | 2021-09-07T03:12:22.000Z | python/axi_soc_ultra_plus_core/__init__.py | slaclab/axi-soc-ultra-plus-core | e2b207bf88505724ad088c99756907beaab7db98 | [
"BSD-3-Clause-LBNL"
] | null | null | null | python/axi_soc_ultra_plus_core/__init__.py | slaclab/axi-soc-ultra-plus-core | e2b207bf88505724ad088c99756907beaab7db98 | [
"BSD-3-Clause-LBNL"
] | null | null | null | from axi_soc_ultra_plus_core._AxiVersion import *
from axi_soc_ultra_plus_core._SysMonLvAuxDet import *
from axi_soc_ultra_plus_core._AxiSocCore import *
| 40.5 | 53 | 0.839506 | 24 | 162 | 5.041667 | 0.416667 | 0.173554 | 0.247934 | 0.371901 | 0.669421 | 0.669421 | 0.479339 | 0 | 0 | 0 | 0 | 0 | 0.123457 | 162 | 3 | 54 | 54 | 0.852113 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 6 |
a17508823f0afee3f4c52b8c7caabe5fcddc530c | 170 | py | Python | tools/test_freezing_lib/__main__.py | reffs123/PyQtDarkTheme | f12ba581648f5d374b9467d3fea0de59ddd3371c | [
"MIT"
] | 48 | 2021-08-18T07:37:29.000Z | 2022-03-30T11:39:21.000Z | tools/test_freezing_lib/__main__.py | reffs123/PyQtDarkTheme | f12ba581648f5d374b9467d3fea0de59ddd3371c | [
"MIT"
] | 89 | 2021-10-31T19:13:18.000Z | 2022-03-28T18:35:18.000Z | tools/test_freezing_lib/__main__.py | reffs123/PyQtDarkTheme | f12ba581648f5d374b9467d3fea0de59ddd3371c | [
"MIT"
] | 5 | 2021-08-18T07:37:30.000Z | 2022-03-29T08:06:03.000Z | """Module allowing for `python -m tools.test_freezing_lib`."""
import sys
from tools.test_freezing_lib.main import main
if __name__ == "__main__":
sys.exit(main())
| 21.25 | 62 | 0.729412 | 25 | 170 | 4.48 | 0.64 | 0.160714 | 0.303571 | 0.357143 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.135294 | 170 | 7 | 63 | 24.285714 | 0.761905 | 0.329412 | 0 | 0 | 0 | 0 | 0.074074 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.5 | 0 | 0.5 | 0 | 1 | 0 | 0 | null | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 6 |
a19dfe63c41b773002e97c5bdd6faeee51c736fe | 11,720 | py | Python | tests/packagedcode/test_plugin.py | jimjag/scancode-toolk | c100d815ca2f49d165c322762549612df601bea7 | [
"Apache-2.0",
"CC-BY-4.0"
] | 1,511 | 2015-07-01T15:29:03.000Z | 2022-03-30T13:40:05.000Z | tests/packagedcode/test_plugin.py | sthagen/scancode-toolkit | dcf593e524773afcc9f5c19429403775a6c49d2e | [
"Apache-2.0",
"CC-BY-4.0"
] | 2,695 | 2015-07-01T16:01:35.000Z | 2022-03-31T19:17:44.000Z | tests/packagedcode/test_plugin.py | sthagen/scancode-toolkit | dcf593e524773afcc9f5c19429403775a6c49d2e | [
"Apache-2.0",
"CC-BY-4.0"
] | 540 | 2015-07-01T15:08:19.000Z | 2022-03-31T12:13:11.000Z | #
# Copyright (c) nexB Inc. and others. All rights reserved.
# ScanCode is a trademark of nexB Inc.
# SPDX-License-Identifier: Apache-2.0
# See http://www.apache.org/licenses/LICENSE-2.0 for the license text.
# See https://github.com/nexB/scancode-toolkit for support or download.
# See https://aboutcode.org for more information about nexB OSS projects.
#
import os
from unittest.case import skipIf
from commoncode.system import on_windows
from packages_test_utils import PackageTester
from scancode.cli_test_utils import check_json_scan
from scancode.cli_test_utils import run_scan_click
class TestPlugins(PackageTester):
test_data_dir = os.path.join(os.path.dirname(__file__), 'data')
def test_package_list_command(self, regen=False):
expected_file = self.get_test_loc('plugin/help.txt')
result = run_scan_click(['--list-packages'])
if regen:
with open(expected_file, 'w') as ef:
ef.write(result.output)
assert result.output == open(expected_file).read()
@skipIf(on_windows, 'somehow this fails on Windows')
def test_package_command_scan_python(self):
test_dir = self.get_test_loc('recon/pypi')
result_file = self.get_temp_file('json')
expected_file = self.get_test_loc('plugin/python-package-expected.json')
run_scan_click(['--package', '--strip-root', '--processes', '-1', test_dir, '--json', result_file])
check_json_scan(expected_file, result_file, regen=False)
def test_package_command_scan_maven(self):
test_dir = self.get_test_loc('maven2')
result_file = self.get_temp_file('json')
expected_file = self.get_test_loc('plugin/maven-package-expected.json')
run_scan_click(['--package', '--strip-root', '--processes', '-1', test_dir, '--json', result_file])
check_json_scan(expected_file, result_file, regen=False)
def test_package_command_scan_about(self):
test_dir = self.get_test_loc('about/aboutfiles/')
result_file = self.get_temp_file('json')
expected_file = self.get_test_loc('plugin/about-package-expected.json')
run_scan_click(['--package', '--strip-root', '--processes', '-1', test_dir, '--json', result_file])
check_json_scan(expected_file, result_file, regen=False)
def test_package_command_scan_bower(self):
test_dir = self.get_test_loc('bower/package')
result_file = self.get_temp_file('json')
expected_file = self.get_test_loc('plugin/bower-package-expected.json')
run_scan_click(['--package', '--strip-root', '--processes', '-1', test_dir, '--json', result_file])
check_json_scan(expected_file, result_file, regen=False)
def test_package_command_scan_cargo(self):
test_dir = self.get_test_loc('cargo/package')
result_file = self.get_temp_file('json')
expected_file = self.get_test_loc('plugin/cargo-package-expected.json')
run_scan_click(['--package', '--strip-root', '--processes', '-1', test_dir, '--json', result_file])
check_json_scan(expected_file, result_file, regen=False)
def test_package_command_scan_chef(self):
test_dir = self.get_test_loc('chef/package')
result_file = self.get_temp_file('json')
expected_file = self.get_test_loc('plugin/chef-package-expected.json')
run_scan_click(['--package', '--strip-root', '--processes', '-1', test_dir, '--json', result_file])
check_json_scan(expected_file, result_file, regen=False)
def test_package_command_scan_conda(self):
test_dir = self.get_test_loc('conda/package')
result_file = self.get_temp_file('json')
expected_file = self.get_test_loc('plugin/conda-package-expected.json')
run_scan_click(['--package', '--strip-root', '--processes', '-1', test_dir, '--json', result_file])
check_json_scan(expected_file, result_file, regen=False)
def test_package_command_scan_freebsd(self):
test_dir = self.get_test_loc('freebsd/package')
result_file = self.get_temp_file('json')
expected_file = self.get_test_loc('plugin/freebsd-package-expected.json')
run_scan_click(['--package', '--strip-root', '--processes', '-1', test_dir, '--json', result_file])
check_json_scan(expected_file, result_file, regen=False)
def test_package_command_scan_haxe(self):
test_dir = self.get_test_loc('haxe/package')
result_file = self.get_temp_file('json')
expected_file = self.get_test_loc('plugin/haxe-package-expected.json')
run_scan_click(['--package', '--strip-root', '--processes', '-1', test_dir, '--json', result_file])
check_json_scan(expected_file, result_file, regen=False)
def test_package_command_scan_npm(self):
test_dir = self.get_test_loc('npm/package')
result_file = self.get_temp_file('json')
expected_file = self.get_test_loc('plugin/npm-package-expected.json')
run_scan_click(['--package', '--strip-root', '--processes', '-1', test_dir, '--json', result_file])
check_json_scan(expected_file, result_file, regen=False)
def test_package_command_scan_nuget(self):
test_dir = self.get_test_loc('nuget/package')
result_file = self.get_temp_file('json')
expected_file = self.get_test_loc('plugin/nuget-package-expected.json')
run_scan_click(['--package', '--strip-root', '--processes', '-1', test_dir, '--json', result_file])
check_json_scan(expected_file, result_file, regen=False)
def test_package_command_scan_opam(self):
test_dir = self.get_test_loc('opam/package')
result_file = self.get_temp_file('json')
expected_file = self.get_test_loc('plugin/opam-package-expected.json')
run_scan_click(['--package', '--strip-root', '--processes', '-1', test_dir, '--json', result_file])
check_json_scan(expected_file, result_file, regen=False)
def test_package_command_scan_phpcomposer(self):
test_dir = self.get_test_loc('phpcomposer/package')
result_file = self.get_temp_file('json')
expected_file = self.get_test_loc('plugin/phpcomposer-package-expected.json')
run_scan_click(['--package', '--strip-root', '--processes', '-1', test_dir, '--json', result_file])
check_json_scan(expected_file, result_file, regen=False)
def test_package_command_scan_rubygems(self):
test_dir = self.get_test_loc('rubygems/package')
result_file = self.get_temp_file('json')
expected_file = self.get_test_loc('plugin/rubygems-package-expected.json')
run_scan_click(['--package', '--strip-root', '--processes', '-1', test_dir, '--json', result_file])
check_json_scan(expected_file, result_file, regen=False)
def test_package_command_scan_rpm(self):
test_dir = self.get_test_loc('rpm/package')
result_file = self.get_temp_file('json')
expected_file = self.get_test_loc('plugin/rpm-package-expected.json')
run_scan_click(['--package', '--strip-root', '--processes', '-1', test_dir, '--json', result_file])
check_json_scan(expected_file, result_file, regen=False)
def test_package_command_scan_cran_r_package(self):
test_dir = self.get_test_loc('cran/package')
result_file = self.get_temp_file('json')
expected_file = self.get_test_loc('plugin/cran-package-expected.json')
run_scan_click(['--package', '--strip-root', '--processes', '-1', test_dir, '--json', result_file])
check_json_scan(expected_file, result_file, regen=False)
def test_package_command_scan_win_pe(self):
test_dir = self.get_test_loc('win_pe/file.exe')
result_file = self.get_temp_file('json')
expected_file = self.get_test_loc('plugin/win_pe-package-expected.json')
run_scan_click(['--package', '--strip-root', '--processes', '-1', test_dir, '--json', result_file])
check_json_scan(expected_file, result_file, regen=False)
def test_package_command_scan_mum(self):
test_dir = self.get_test_loc('windows/mum/test.mum')
result_file = self.get_temp_file('json')
expected_file = self.get_test_loc('plugin/mum-package-expected.json')
run_scan_click(['--package', '--strip-root', '--processes', '-1', test_dir, '--json', result_file])
check_json_scan(expected_file, result_file, regen=False)
def test_package_command_scan_pubspec_package(self):
test_dir = self.get_test_loc('pubspec/specs/authors-pubspec.yaml')
result_file = self.get_temp_file('json')
expected_file = self.get_test_loc('plugin/pubspec-expected.json', must_exist=False)
run_scan_click(['--package', '--strip-root', '--processes', '-1', test_dir, '--json', result_file])
check_json_scan(expected_file, result_file, regen=False)
def test_package_command_scan_pubspec_lock_package(self):
test_dir = self.get_test_loc('pubspec/locks/dart-pubspec.lock')
result_file = self.get_temp_file('json')
expected_file = self.get_test_loc('plugin/pubspec-lock-expected.json', must_exist=False)
run_scan_click(['--package', '--strip-root', '--processes', '-1', test_dir, '--json', result_file])
check_json_scan(expected_file, result_file, regen=False)
def test_package_command_scan_mui(self):
test_dir = self.get_test_loc('win_pe/clfs.sys.mui')
result_file = self.get_temp_file('json')
expected_file = self.get_test_loc('plugin/mui-package-expected.json')
run_scan_click(['--package', '--strip-root', '--processes', '-1', test_dir, '--json', result_file])
check_json_scan(expected_file, result_file, regen=False)
def test_package_command_scan_mun(self):
test_dir = self.get_test_loc('win_pe/crypt32.dll.mun')
result_file = self.get_temp_file('json')
expected_file = self.get_test_loc('plugin/mun-package-expected.json')
run_scan_click(['--package', '--strip-root', '--processes', '-1', test_dir, '--json', result_file])
check_json_scan(expected_file, result_file, regen=False)
def test_package_command_scan_com(self):
test_dir = self.get_test_loc('win_pe/chcp.com')
result_file = self.get_temp_file('json')
expected_file = self.get_test_loc('plugin/com-package-expected.json')
run_scan_click(['--package', '--strip-root', '--processes', '-1', test_dir, '--json', result_file])
check_json_scan(expected_file, result_file, regen=False)
def test_package_command_scan_tlb(self):
test_dir = self.get_test_loc('win_pe/stdole2.tlb')
result_file = self.get_temp_file('json')
expected_file = self.get_test_loc('plugin/tlb-package-expected.json')
run_scan_click(['--package', '--strip-root', '--processes', '-1', test_dir, '--json', result_file])
check_json_scan(expected_file, result_file, regen=False)
def test_package_command_scan_sys(self):
test_dir = self.get_test_loc('win_pe/tbs.sys')
result_file = self.get_temp_file('json')
expected_file = self.get_test_loc('plugin/sys-package-expected.json')
run_scan_click(['--package', '--strip-root', '--processes', '-1', test_dir, '--json', result_file])
check_json_scan(expected_file, result_file, regen=False)
def test_package_command_scan_winmd(self):
test_dir = self.get_test_loc('win_pe/Windows.AI.winmd')
result_file = self.get_temp_file('json')
expected_file = self.get_test_loc('plugin/winmd-package-expected.json')
run_scan_click(['--package', '--strip-root', '--processes', '-1', test_dir, '--json', result_file])
check_json_scan(expected_file, result_file, regen=False)
| 55.283019 | 107 | 0.691724 | 1,625 | 11,720 | 4.625846 | 0.089846 | 0.073567 | 0.077558 | 0.09871 | 0.84861 | 0.845284 | 0.837302 | 0.779832 | 0.775575 | 0.737262 | 0 | 0.003461 | 0.16186 | 11,720 | 211 | 108 | 55.545024 | 0.761784 | 0.02901 | 0 | 0.453488 | 0 | 0 | 0.21933 | 0.086184 | 0 | 0 | 0 | 0 | 0.005814 | 1 | 0.156977 | false | 0 | 0.034884 | 0 | 0.203488 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
b808bc87a68996d999e8dfc31009c26b6d155ca6 | 10,146 | py | Python | podcast-backend/tests/test_recommendations.py | cuappdev/archives | 061d0f9cccf278363ffaeb27fc655743b1052ae5 | [
"MIT"
] | null | null | null | podcast-backend/tests/test_recommendations.py | cuappdev/archives | 061d0f9cccf278363ffaeb27fc655743b1052ae5 | [
"MIT"
] | null | null | null | podcast-backend/tests/test_recommendations.py | cuappdev/archives | 061d0f9cccf278363ffaeb27fc655743b1052ae5 | [
"MIT"
] | null | null | null | import sys
from flask import json
from tests.test_case import *
from app.pcasts.dao import episodes_dao, users_dao, recommendations_dao
from app import constants
class RecommendationsTestCase(TestCase):
def setUp(self):
super(RecommendationsTestCase, self).setUp()
Recommendation.query.delete()
episodes_dao.clear_all_recommendations_counts()
db_session_commit()
def tearDown(self):
super(RecommendationsTestCase, self).tearDown()
Recommendation.query.delete()
episodes_dao.clear_all_recommendations_counts()
db_session_commit()
def test_create_recommendations(self):
episode_title1 = 'Colombians to deliver their verdict on peace accord'
episode_id1 = episodes_dao.\
get_episode_by_title(episode_title1, self.user1.uid).id
episode_title2 = 'Battle of the camera drones'
episode_id2 = episodes_dao.\
get_episode_by_title(episode_title2, self.user1.uid).id
recommended = Recommendation.query.\
filter(Recommendation.episode_id == episode_id1).first()
self.assertIsNone(recommended)
response = self.user1.post('api/v1/recommendations/{}/'.format(episode_id1))
recommended = Recommendation.query.\
filter(Recommendation.episode_id == episode_id1).first()
self.assertEquals(recommended.episode_id, int(episode_id1))
self.assertIsNone(recommended.blurb)
data = json.loads(response.data)['data']
self.assertEquals(data['recommendation']['episode']['title'],
episode_title1)
self.assertTrue(data['recommendation']['episode']['is_recommended'])
bdata = json.dumps({'blurb': 'update'})
response = self.user1.post('api/v1/recommendations/{}/'
.format(episode_id1), data=bdata)
recommended = Recommendation.query.\
filter(Recommendation.episode_id == episode_id1).first()
self.assertEquals(recommended.blurb, "update")
data = json.loads(response.data)['data']
self.assertEquals(data['recommendation']['episode']['title'],
episode_title1)
self.assertIsNotNone(data['recommendation']['episode']['is_recommended'])
bdata = json.dumps({'blurb': None})
self.user1.post('api/v1/recommendations/{}/'
.format(episode_id1), data=bdata)
recommended = Recommendation.query.\
filter(Recommendation.episode_id == episode_id1).first()
self.assertIsNone(recommended.blurb)
bdata = json.dumps({'blurb': 'test blurb'})
self.user1.post('api/v1/recommendations/{}/'
.format(episode_id2), data=bdata)
recommended = Recommendation.query.\
filter(Recommendation.episode_id == episode_id2).first()
self.assertEquals(recommended.episode_id, int(episode_id2))
self.assertEquals(recommended.blurb, "test blurb")
self.user1.post('api/v1/recommendations/{}/'
.format(episode_id2))
recommended = Recommendation.query.\
filter(Recommendation.episode_id == episode_id2).first()
self.assertEquals(recommended.episode_id, int(episode_id2))
self.assertIsNone(recommended.blurb)
recommendations = Recommendation.query.all()
self.assertEquals(len(recommendations), 2)
def test_get_user_recommendations(self):
episode_title1 = 'Colombians to deliver their verdict on peace accord'
episode_id1 = episodes_dao.\
get_episode_by_title(episode_title1, self.user1.uid).id
episode_title2 = 'Battle of the camera drones'
episode_id2 = episodes_dao.\
get_episode_by_title(episode_title2, self.user1.uid).id
test_user_id = users_dao.\
get_user_by_google_id(constants.TEST_USER_GOOGLE_ID1).id
response = self.user1.get('api/v1/recommendations/users/{}/'.
format(test_user_id))
data = json.loads(response.data)
self.assertEquals(len(data['data']['recommendations']), 0)
bdata = json.dumps({'blurb': 'test'})
self.user1.post('api/v1/recommendations/{}/'.format(episode_id1))
self.user1.post('api/v1/recommendations/{}/'
.format(episode_id2), data=bdata)
response = self.user1.get('api/v1/recommendations/users/{}/'
.format(test_user_id))
data = json.loads(response.data)
self.assertEquals(len(data['data']['recommendations']), 2)
self.assertEquals(data['data']['recommendations'][1]['blurb'], 'test')
self.assertEquals(len(data['data']['recommendations']), 2)
self.assertTrue(
data['data']['recommendations'][0]['episode']['id'] == int(episode_id1)
or
data['data']['recommendations'][1]['episode']['id'] == int(episode_id1)
)
self.assertTrue(
data['data']['recommendations'][0]['episode']['id'] == int(episode_id2)
or
data['data']['recommendations'][1]['episode']['id'] == int(episode_id2)
)
def test_get_user_recommendations2(self):
user = User.query \
.filter(User.google_id == constants.TEST_USER_GOOGLE_ID1).first()
user_2 = users_dao.\
get_user_by_google_id(constants.TEST_USER_GOOGLE_ID2)
episode_title1 = 'Colombians to deliver their verdict on peace accord'
episode_title2 = 'Battle of the camera drones'
episode_id1 = episodes_dao.\
get_episode_by_title(episode_title1, self.user1.uid).id
episode_id2 = episodes_dao.\
get_episode_by_title(episode_title2, self.user1.uid).id
recommendations_dao.create_or_update_recommendation(episode_id1, user_2)
recommendations_dao.create_or_update_recommendation(episode_id2, user_2)
recommendations_dao.create_or_update_recommendation(episode_id1, user)
response = self.user1.get('api/v1/recommendations/users/{}/'
.format(user_2.id))
data = json.loads(response.data)
self.assertEquals(len(data['data']['recommendations']), 2)
self.assertEquals(data['data']['recommendations'][0]['episode']\
['is_recommended'], True)
self.assertEquals(data['data']['recommendations'][0]['episode']\
['recommendations_count'], 2)
self.assertEquals(data['data']['recommendations'][1]['episode']\
['is_recommended'], False)
self.assertEquals(data['data']['recommendations'][1]['episode']\
['recommendations_count'], 1)
def test_get_recommendations(self):
episode_title1 = 'Colombians to deliver their verdict on peace accord'
episode_id1 = episodes_dao.\
get_episode_by_title(episode_title1, self.user1.uid).id
episode_title2 = 'Battle of the camera drones'
episode_id2 = episodes_dao.\
get_episode_by_title(episode_title2, self.user1.uid).id
test_user_id = users_dao.\
get_user_by_google_id(constants.TEST_USER_GOOGLE_ID1).id
self.user1.post('api/v1/recommendations/{}/'.format(episode_id1))
self.user1.post('api/v1/recommendations/{}/'.format(episode_id2))
response = self.user1.get('api/v1/recommendations/{}/?offset=0&max=5'
.format(episode_id1))
data = json.loads(response.data)
self.assertEquals(len(data['data']['recommendations']), 1)
self.assertEquals(data['data']['recommendations'][0]['user']['id'],
test_user_id)
response = self.user1.get('api/v1/recommendations/{}/?offset=0&max=5'
.format(episode_id2))
data = json.loads(response.data)
self.assertEquals(len(data['data']['recommendations']), 1)
self.assertEquals(data['data']['recommendations'][0]['user']['id'],
test_user_id)
def test_delete_recommendations(self):
test_user_id = self.user1.uid
episode_title1 = 'Colombians to deliver their verdict on peace accord'
episode_id1 = episodes_dao.\
get_episode_by_title(episode_title1, self.user1.uid).id
episode_title2 = 'Battle of the camera drones'
episode_id2 = episodes_dao.\
get_episode_by_title(episode_title2, self.user1.uid).id
self.user1.post('api/v1/recommendations/{}/'.format(episode_id1))
self.user1.post('api/v1/recommendations/{}/'.format(episode_id2))
response = self.user1.get('api/v1/recommendations/users/{}/'
.format(test_user_id))
data = json.loads(response.data)
self.assertEquals(len(data['data']['recommendations']), 2)
response = self.user1.delete('api/v1/recommendations/{}/'
.format(episode_id1))
data = json.loads(response.data)['data']
self.assertEquals(data['recommendation']['episode']['title'],
episode_title1)
self.assertIsNotNone(
data['recommendation']['episode']['is_recommended'])
self.user1.delete('api/v1/recommendations/{}/'.format(episode_id2))
response = self.user1.get('api/v1/recommendations/users/{}/'
.format(test_user_id))
data = json.loads(response.data)['data']
self.assertEquals(len(data['recommendations']), 0)
self.assertRaises(
Exception,
self.user1.delete('api/v1/recommendations/{}/'.format(episode_id1)),
)
self.assertRaises(
Exception,
self.user1.delete('api/v1/recommendations/{}/'.format(episode_id2)),
)
def test_is_recommendations(self):
episode_title = 'Colombians to deliver their verdict on peace accord'
episode_id = episodes_dao.\
get_episode_by_title(episode_title, self.user1.uid).id
episode = episodes_dao.get_episode(episode_id, self.user1.uid)
self.assertFalse(episode.is_recommended)
self.user1.post('api/v1/recommendations/{}/'.format(episode_id))
episode = episodes_dao.get_episode(episode_id, self.user1.uid)
self.assertTrue(episode.is_recommended)
response = self.user1.get('api/v1/recommendations/{}/?offset=0&max=5'
.format(episode_id))
data = json.loads(response.data)
self.assertTrue(
data['data']['recommendations'][0]['episode']['is_recommended']
)
self.user1.delete('api/v1/recommendations/{}/'.format(episode_id))
episode = episodes_dao.get_episode(episode_id, self.user1.uid)
self.assertFalse(episode.is_recommended)
| 41.753086 | 80 | 0.678198 | 1,179 | 10,146 | 5.635284 | 0.080577 | 0.054184 | 0.075256 | 0.066526 | 0.879741 | 0.869958 | 0.868453 | 0.8236 | 0.804485 | 0.728627 | 0 | 0.020369 | 0.182239 | 10,146 | 242 | 81 | 41.92562 | 0.780403 | 0 | 0 | 0.645 | 0 | 0 | 0.194461 | 0.075596 | 0 | 0 | 0 | 0 | 0.195 | 1 | 0.04 | false | 0 | 0.025 | 0 | 0.07 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
62a1fdeff077d46be9668a148572b857ce26c42f | 96 | py | Python | flask_reqval/__init__.py | ttt-fifo/flask-reqval | eebf5be9a98f26c294036e3f6e434aed97c02e88 | [
"BSD-2-Clause"
] | null | null | null | flask_reqval/__init__.py | ttt-fifo/flask-reqval | eebf5be9a98f26c294036e3f6e434aed97c02e88 | [
"BSD-2-Clause"
] | null | null | null | flask_reqval/__init__.py | ttt-fifo/flask-reqval | eebf5be9a98f26c294036e3f6e434aed97c02e88 | [
"BSD-2-Clause"
] | null | null | null | from .validate_request_decorator import validate_request
from .exceptions import InvalidRequest
| 32 | 56 | 0.895833 | 11 | 96 | 7.545455 | 0.636364 | 0.361446 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.083333 | 96 | 2 | 57 | 48 | 0.943182 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
62abedc2dc82e36f54b4f63dadd145b2b3e728cd | 80 | py | Python | src/semantics/__init__.py | CoolCows/cool-compiler-2021 | 1411566675707d33afdfddfc6b9d6d8a5a4c32bf | [
"MIT"
] | null | null | null | src/semantics/__init__.py | CoolCows/cool-compiler-2021 | 1411566675707d33afdfddfc6b9d6d8a5a4c32bf | [
"MIT"
] | 1 | 2021-02-27T00:15:05.000Z | 2021-02-27T00:15:05.000Z | src/semantics/__init__.py | CoolCows/cool-compiler-2021 | 1411566675707d33afdfddfc6b9d6d8a5a4c32bf | [
"MIT"
] | 1 | 2021-02-24T14:39:38.000Z | 2021-02-24T14:39:38.000Z | from .type_builder import TypeBuilder
from .type_collector import TypeCollector
| 26.666667 | 41 | 0.875 | 10 | 80 | 6.8 | 0.7 | 0.235294 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.1 | 80 | 2 | 42 | 40 | 0.944444 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
62d89a20994a79bd99bf629d5e44453e9490826c | 154 | py | Python | py_tdlib/constructors/chat_event_description_changed.py | Mr-TelegramBot/python-tdlib | 2e2d21a742ebcd439971a32357f2d0abd0ce61eb | [
"MIT"
] | 24 | 2018-10-05T13:04:30.000Z | 2020-05-12T08:45:34.000Z | py_tdlib/constructors/chat_event_description_changed.py | MrMahdi313/python-tdlib | 2e2d21a742ebcd439971a32357f2d0abd0ce61eb | [
"MIT"
] | 3 | 2019-06-26T07:20:20.000Z | 2021-05-24T13:06:56.000Z | py_tdlib/constructors/chat_event_description_changed.py | MrMahdi313/python-tdlib | 2e2d21a742ebcd439971a32357f2d0abd0ce61eb | [
"MIT"
] | 5 | 2018-10-05T14:29:28.000Z | 2020-08-11T15:04:10.000Z | from ..factory import Type
class chatEventDescriptionChanged(Type):
old_description = None # type: "string"
new_description = None # type: "string"
| 22 | 41 | 0.746753 | 17 | 154 | 6.647059 | 0.647059 | 0.265487 | 0.336283 | 0.442478 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.155844 | 154 | 6 | 42 | 25.666667 | 0.869231 | 0.188312 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.25 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 6 |
1a0438d4404d6cb0d50e343b38a2ede908bb4f33 | 3,861 | py | Python | test_project/unit/supporting/test_prefix.py | TimNapierVST/dbtvault-dev | 479645b229cbc28d5244164efb92e4b503395243 | [
"Apache-2.0"
] | null | null | null | test_project/unit/supporting/test_prefix.py | TimNapierVST/dbtvault-dev | 479645b229cbc28d5244164efb92e4b503395243 | [
"Apache-2.0"
] | null | null | null | test_project/unit/supporting/test_prefix.py | TimNapierVST/dbtvault-dev | 479645b229cbc28d5244164efb92e4b503395243 | [
"Apache-2.0"
] | null | null | null | import pytest
from unittest import TestCase
@pytest.mark.usefixtures('dbt_test_utils', 'clean_database')
class TestPrefixMacro(TestCase):
maxDiff = None
def test_prefix_column_in_single_item_list_is_successful(self):
var_dict = {'columns': ["CUSTOMER_HASHDIFF"], 'prefix': 'c'}
process_logs = self.dbt_test_utils.run_dbt_model(model_name=self.current_test_name, args=var_dict)
actual_sql = self.dbt_test_utils.retrieve_compiled_model(self.current_test_name)
expected_sql = self.dbt_test_utils.retrieve_expected_sql(self.current_test_name)
assert 'Done' in process_logs
self.assertEqual(expected_sql, actual_sql)
def test_prefix_multiple_columns_is_successful(self):
var_dict = {'columns': ["CUSTOMER_HASHDIFF", 'CUSTOMER_PK', 'LOADDATE', 'SOURCE'], 'prefix': 'c'}
process_logs = self.dbt_test_utils.run_dbt_model(model_name=self.current_test_name, args=var_dict)
actual_sql = self.dbt_test_utils.retrieve_compiled_model(self.current_test_name)
expected_sql = self.dbt_test_utils.retrieve_expected_sql(self.current_test_name)
assert 'Done' in process_logs
self.assertEqual(expected_sql, actual_sql)
def test_prefix_aliased_column_is_successful(self):
columns = [{"source_column": "CUSTOMER_HASHDIFF", "alias": "HASHDIFF"}, "CUSTOMER_PK", "LOADDATE"]
var_dict = {'columns': columns, 'prefix': 'c'}
process_logs = self.dbt_test_utils.run_dbt_model(model_name=self.current_test_name, args=var_dict)
actual_sql = self.dbt_test_utils.retrieve_compiled_model(self.current_test_name)
expected_sql = self.dbt_test_utils.retrieve_expected_sql(self.current_test_name)
assert 'Done' in process_logs
self.assertEqual(expected_sql, actual_sql)
def test_prefix_aliased_column_with_alias_target_as_source_is_successful(self):
columns = [{"source_column": "CUSTOMER_HASHDIFF", "alias": "HASHDIFF"}, "CUSTOMER_PK", "LOADDATE"]
var_dict = {'columns': columns, 'prefix': 'c', 'alias_target': 'source'}
process_logs = self.dbt_test_utils.run_dbt_model(model_name=self.current_test_name, args=var_dict)
actual_sql = self.dbt_test_utils.retrieve_compiled_model(self.current_test_name)
expected_sql = self.dbt_test_utils.retrieve_expected_sql(self.current_test_name)
assert 'Done' in process_logs
self.assertEqual(expected_sql, actual_sql)
def test_prefix_aliased_column_with_alias_target_as_target_is_successful(self):
columns = [{"source_column": "CUSTOMER_HASHDIFF", "alias": "HASHDIFF"}, "CUSTOMER_PK", "LOADDATE"]
var_dict = {'columns': columns, 'prefix': 'c', 'alias_target': 'target'}
process_logs = self.dbt_test_utils.run_dbt_model(model_name=self.current_test_name, args=var_dict)
actual_sql = self.dbt_test_utils.retrieve_compiled_model(self.current_test_name)
expected_sql = self.dbt_test_utils.retrieve_expected_sql(self.current_test_name)
assert 'Done' in process_logs
self.assertEqual(expected_sql, actual_sql)
def test_prefix_with_no_columns_raises_error(self):
var_dict = {'prefix': 'c'}
process_logs = self.dbt_test_utils.run_dbt_model(model_name=self.current_test_name, args=var_dict)
assert "Invalid parameters provided to prefix macro. Expected: " \
"(columns [list/string], prefix_str [string]) got: (None, c)" in process_logs
def test_prefix_with_empty_column_list_raises_error(self):
var_dict = {'columns': [], 'prefix': 'c'}
process_logs = self.dbt_test_utils.run_dbt_model(model_name=self.current_test_name, args=var_dict)
assert "Invalid parameters provided to prefix macro. Expected: " \
"(columns [list/string], prefix_str [string]) got: ([], c)" in process_logs
| 52.890411 | 106 | 0.735302 | 522 | 3,861 | 5.003831 | 0.130268 | 0.048239 | 0.082695 | 0.104135 | 0.885911 | 0.869066 | 0.869066 | 0.869066 | 0.833844 | 0.833844 | 0 | 0 | 0.159803 | 3,861 | 72 | 107 | 53.625 | 0.805179 | 0 | 0 | 0.603774 | 0 | 0 | 0.167314 | 0 | 0 | 0 | 0 | 0 | 0.226415 | 1 | 0.132075 | false | 0 | 0.037736 | 0 | 0.207547 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
c569a979dfb0e1aca3810aec521568ea29084df3 | 86 | py | Python | cyk/cyk/__init__.py | azoimide/cyk | 0dd06fc70136246ae59b783c566889802e50b06c | [
"MIT"
] | null | null | null | cyk/cyk/__init__.py | azoimide/cyk | 0dd06fc70136246ae59b783c566889802e50b06c | [
"MIT"
] | null | null | null | cyk/cyk/__init__.py | azoimide/cyk | 0dd06fc70136246ae59b783c566889802e50b06c | [
"MIT"
] | null | null | null | from linear_cyk import linear_cyk
from cyk_td import cyk_td
from cyk_bu import cyk_bu
| 21.5 | 33 | 0.860465 | 18 | 86 | 3.777778 | 0.333333 | 0.264706 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.139535 | 86 | 3 | 34 | 28.666667 | 0.918919 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
3d72855346f921aae44a13c8db6d6c7f58d91041 | 42 | py | Python | cloudbutton_geospatial/geoprocesses/__init__.py | berkevaroll/geospatial-usecase | d3db18607be0976badde073b3ee7c8b9613372e1 | [
"Apache-2.0"
] | null | null | null | cloudbutton_geospatial/geoprocesses/__init__.py | berkevaroll/geospatial-usecase | d3db18607be0976badde073b3ee7c8b9613372e1 | [
"Apache-2.0"
] | null | null | null | cloudbutton_geospatial/geoprocesses/__init__.py | berkevaroll/geospatial-usecase | d3db18607be0976badde073b3ee7c8b9613372e1 | [
"Apache-2.0"
] | 4 | 2021-03-29T09:03:52.000Z | 2021-09-21T18:27:01.000Z | """
AUTHOR: Juanjo
DATE: 25/02/2019
""" | 6 | 16 | 0.571429 | 6 | 42 | 4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.235294 | 0.190476 | 42 | 7 | 17 | 6 | 0.470588 | 0.761905 | 0 | null | 0 | null | 0 | 0 | null | 0 | 0 | 0 | null | 1 | null | true | 0 | 0 | null | null | null | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
3d82cd63e244cca1db62f0b8fa9c04dab41bcb1f | 107 | py | Python | api_crawler/endpoint/__init__.py | pawnhearts/aio_api_crawler | 543c4edb8be77bbe9e2ac017fca9a9d6020b51b8 | [
"BSD-3-Clause"
] | 2 | 2020-10-31T07:53:20.000Z | 2020-11-11T22:32:44.000Z | api_crawler/endpoint/__init__.py | pawnhearts/aio_api_crawler | 543c4edb8be77bbe9e2ac017fca9a9d6020b51b8 | [
"BSD-3-Clause"
] | null | null | null | api_crawler/endpoint/__init__.py | pawnhearts/aio_api_crawler | 543c4edb8be77bbe9e2ac017fca9a9d6020b51b8 | [
"BSD-3-Clause"
] | null | null | null | from .base import Endpoint
from .json_endpoint import JsonEndpoint
from .html_endpoint import HtmlEndpoint
| 26.75 | 39 | 0.859813 | 14 | 107 | 6.428571 | 0.571429 | 0.311111 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.11215 | 107 | 3 | 40 | 35.666667 | 0.947368 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
3df43c3a94928e25c306647709a40992f97d6787 | 25,733 | py | Python | gui/spinner.py | Mendes11/lotery_helper | bb9bbe4ea0df918a489eff0886a6b081f4b31d03 | [
"MIT"
] | null | null | null | gui/spinner.py | Mendes11/lotery_helper | bb9bbe4ea0df918a489eff0886a6b081f4b31d03 | [
"MIT"
] | null | null | null | gui/spinner.py | Mendes11/lotery_helper | bb9bbe4ea0df918a489eff0886a6b081f4b31d03 | [
"MIT"
] | null | null | null | try:
from Tkinter import Canvas
from Tkconstants import NW
except ImportError:
from tkinter import Canvas
from tkinter.constants import NW
import base64
from PIL import ImageTk, Image
from io import BytesIO
class RotatingIcon(Canvas):
_bind_tag_ID = 0
def __init__(self, master, filename=None, data=None, start_animation=True,
time=40, **kwargs):
if data is not None:
self._image = Image.open(BytesIO(base64.b64decode(data)))
elif filename is not None:
self._image = Image.open(filename)
elif hasattr(self, "data"):
self._image = Image.open(BytesIO(base64.b64decode(self.data)))
else:
raise Exception("No image data or file")
if self._image.format == "XBM":
self._imagetk_class = ImageTk.BitmapImage
else:
self._imagetk_class = ImageTk.PhotoImage
width, height = self._image.size
self._time = time
kwargs.setdefault("width", width)
kwargs.setdefault("height", height)
kwargs.setdefault("highlightthickness", 0)
if "borderwidth" not in kwargs and "bd" not in kwargs:
kwargs["borderwidth"] = 0
Canvas.__init__(self, master, **kwargs)
self._bind_tag = "icon_rotating%s" % RotatingIcon._bind_tag_ID
RotatingIcon._bind_tag_ID += 1
self.bind_class(self._bind_tag, "<Unmap>", self._on_unmap)
self.bind_class(self._bind_tag, "<Map>", self._on_map)
self._running = False
self._is_mapped = False
if start_animation:
self.start_animation()
def _on_unmap(self, event):
self._is_mapped = False
if self._ID_of_delayed_callback is not None:
self.after_cancel(self._ID_of_delayed_callback)
def _on_map(self, event):
self._is_mapped = True
self._animate()
def start_animation(self):
if self._running: return
new_tags = (self._bind_tag,) + self.bindtags()
self.bindtags(new_tags)
generator = self._animate_generator()
if hasattr(generator, "next"):
# Python2
self._animate = generator.next
else:
# Python3
self._animate = generator.__next__
if self._is_mapped: self._animate()
def stop_animation(self):
if not self._running: return
self._running = False
if self._ID_of_delayed_callback is not None:
self.after_cancel(self._ID_of_delayed_callback)
self._ID_of_delayed_callback = None
new_tags = self.bindtags()[1:]
self.bindtags(new_tags)
def _animate_generator(self):
angle = 0
while True:
tkimage = self._imagetk_class(self._image.rotate(angle))
canvas_obj = self.create_image(0, 0, image=tkimage, anchor=NW)
self._ID_of_delayed_callback = self.after(self._time,
self._update_animation)
yield
self.delete(canvas_obj)
angle = (angle + 10) % 360
def _update_animation(self):
self._ID_of_delayed_callback = self.after_idle(self._animate)
class MultiSizeRotatingIcon(RotatingIcon):
def __init__(self, master, size, start_animation=True, time=40, **kwargs):
data = self.image_per_size[size]
RotatingIcon.__init__(self, master, data=data,
start_animation=start_animation, time=time,
**kwargs)
class Spinner(MultiSizeRotatingIcon):
image_per_size = {
16: 'I2RlZmluZSBpbWFnZV93aWR0aCAxNgojZGVmaW5lIGltYWdlX2hlaWdodCAxNgpzdGF0aWMgY2hhciBpbWFnZV9iaXRzW10gPSB7CjB4ODAsMHgwMSwweGMwLDB4MDMsMHhjMCwweDNiLDB4ZGMsMHgzZiwweDFjLDB4M2MsMHgwMCwweDM4LDB4MDAsMHgwMCwweDA2LAoweDYwLDB4MDYsMHg2MCwweDAwLDB4MDAsMHgwMCwweDAwLDB4MDgsMHgxMCwweDE4LDB4MTgsMHg4MCwweDAxLDB4ODAsMHgwMSwKMHgwMCwweDAwCn07',
22: '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',
24: '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',
32: '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',
48: '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',
64: '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',
128: '#define image_width 128
#define image_height 128
static char image_bits[] = {
0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xf0,0x0f,0x00,0x00,
0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xfe,0x7f,0x00,
0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x80,0xff,0xff,
0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xc0,0xff,
0xff,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xe0,
0xff,0xff,0x07,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
0xf0,0xff,0xff,0x0f,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
0x00,0xf8,0xff,0xff,0x1f,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
0x00,0x00,0xfc,0xff,0xff,0x3f,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
0x00,0x00,0x00,0xfc,0xff,0xff,0x3f,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
0x00,0x00,0x00,0x00,0xfe,0xff,0xff,0x7f,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
0x00,0x00,0x00,0x00,0x00,0xfe,0xff,0xff,0x7f,0x00,0x00,0x00,0x00,0x00,0x00,
0x00,0x00,0x00,0x00,0x00,0x00,0xfe,0xff,0xff,0x7f,0x00,0x00,0x00,0x00,0x00,
0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xff,0xff,0xff,0xff,0x00,0x00,0x00,0x00,
0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xff,0xff,0xff,0xff,0x00,0x00,0x00,
0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xff,0xff,0xff,0xff,0x00,0xc0,
0x3f,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xff,0xff,0xff,0xff,0x00,
0xf8,0xff,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xff,0xff,0xff,0xff,
0x00,0xfe,0xff,0x07,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xff,0xff,0xff,
0xff,0x00,0xff,0xff,0x0f,0x00,0x00,0x00,0x00,0x00,0xf8,0x01,0x00,0xff,0xff,
0xff,0xff,0x80,0xff,0xff,0x1f,0x00,0x00,0x00,0x00,0x00,0xff,0x0f,0x00,0xff,
0xff,0xff,0xff,0xc0,0xff,0xff,0x3f,0x00,0x00,0x00,0x00,0x80,0xff,0x1f,0x00,
0xfe,0xff,0xff,0x7f,0xe0,0xff,0xff,0x7f,0x00,0x00,0x00,0x00,0xc0,0xff,0x3f,
0x00,0xfe,0xff,0xff,0x7f,0xf0,0xff,0xff,0xff,0x00,0x00,0x00,0x00,0xe0,0xff,
0x7f,0x00,0xfe,0xff,0xff,0x7f,0xf0,0xff,0xff,0xff,0x00,0x00,0x00,0x00,0xf0,
0xff,0xff,0x00,0xfc,0xff,0xff,0x3f,0xf8,0xff,0xff,0xff,0x01,0x00,0x00,0x00,
0xf8,0xff,0xff,0x01,0xfc,0xff,0xff,0x3f,0xf8,0xff,0xff,0xff,0x01,0x00,0x00,
0x00,0xf8,0xff,0xff,0x01,0xf8,0xff,0xff,0x1f,0xf8,0xff,0xff,0xff,0x01,0x00,
0x00,0x00,0xfc,0xff,0xff,0x01,0xf0,0xff,0xff,0x0f,0xfc,0xff,0xff,0xff,0x03,
0x00,0x00,0x00,0xfc,0xff,0xff,0x03,0xe0,0xff,0xff,0x07,0xfc,0xff,0xff,0xff,
0x03,0x00,0x00,0x00,0xfc,0xff,0xff,0x03,0xc0,0xff,0xff,0x03,0xfc,0xff,0xff,
0xff,0x03,0x00,0x00,0x00,0xfc,0xff,0xff,0x03,0x80,0xff,0xff,0x01,0xfc,0xff,
0xff,0xff,0x03,0x00,0x00,0x00,0xfc,0xff,0xff,0x03,0x00,0xfe,0x7f,0x00,0xfc,
0xff,0xff,0xff,0x03,0x00,0x00,0x00,0xfc,0xff,0xff,0x03,0x00,0xf0,0x0f,0x00,
0xfc,0xff,0xff,0xff,0x03,0x00,0x00,0x00,0xfc,0xff,0xff,0x03,0x00,0x00,0x00,
0x00,0xfc,0xff,0xff,0xff,0x03,0x00,0x00,0x00,0xfc,0xff,0xff,0x03,0x00,0x00,
0x00,0x00,0xfc,0xff,0xff,0xff,0x03,0x00,0x00,0x00,0xf8,0xff,0xff,0x01,0x00,
0x00,0x00,0x00,0xf8,0xff,0xff,0xff,0x01,0x00,0x00,0x00,0xf8,0xff,0xff,0x01,
0x00,0x00,0x00,0x00,0xf8,0xff,0xff,0xff,0x01,0x00,0x00,0x00,0xf0,0xff,0xff,
0x00,0x00,0x00,0x00,0x00,0xf8,0xff,0xff,0xff,0x01,0x00,0x00,0x00,0xe0,0xff,
0x7f,0x00,0x00,0x00,0x00,0x00,0xf0,0xff,0xff,0xff,0x00,0x00,0x00,0x00,0xc0,
0xff,0x7f,0x00,0x00,0x00,0x00,0x00,0xf0,0xff,0xff,0xff,0x00,0x00,0x00,0x00,
0x80,0xff,0x1f,0x00,0x00,0x00,0x00,0x00,0xe0,0xff,0xff,0x7f,0x00,0x00,0x00,
0x00,0x00,0xff,0x0f,0x00,0x00,0x00,0x00,0x00,0xc0,0xff,0xff,0x3f,0x00,0x00,
0x00,0x00,0x00,0xfc,0x03,0x00,0x00,0x00,0x00,0x00,0x80,0xff,0xff,0x1f,0x00,
0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xff,0xff,0x0f,
0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xfe,0xff,
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};'
} | 204.230159 | 13,959 | 0.941709 | 438 | 25,733 | 55.013699 | 0.267123 | 0.002034 | 0.002324 | 0.004358 | 0.02407 | 0.018509 | 0.013861 | 0.006391 | 0.006391 | 0.006391 | 0 | 0.052037 | 0.044107 | 25,733 | 126 | 13,960 | 204.230159 | 0.927555 | 0.000583 | 0 | 0.138298 | 0 | 0 | 0.859543 | 0.855187 | 0 | 1 | 0 | 0 | 0 | 1 | 0.085106 | false | 0 | 0.085106 | 0 | 0.223404 | 0 | 0 | 0 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
9a838385f3e614bedb5fbc70e007582df93c3898 | 74 | py | Python | covid19_id/data/sembuh/jenis_kelamin.py | hexatester/covid19-id | 8d8aa3f9092a40461a308f4db054ab4f95374849 | [
"MIT"
] | null | null | null | covid19_id/data/sembuh/jenis_kelamin.py | hexatester/covid19-id | 8d8aa3f9092a40461a308f4db054ab4f95374849 | [
"MIT"
] | null | null | null | covid19_id/data/sembuh/jenis_kelamin.py | hexatester/covid19-id | 8d8aa3f9092a40461a308f4db054ab4f95374849 | [
"MIT"
] | null | null | null | from . import BaseSembuh
class SembuhJenisKelamin(BaseSembuh):
pass
| 12.333333 | 37 | 0.77027 | 7 | 74 | 8.142857 | 0.857143 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.175676 | 74 | 5 | 38 | 14.8 | 0.934426 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0.333333 | 0.333333 | 0 | 0.666667 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 6 |
9af1359ee361d603a4a180819c795f041a52b737 | 28 | py | Python | step_2.py | NaiveSunset/201128 | 9874a9b260336e2e1eb5295ac3aca1e9d655c8d9 | [
"Apache-2.0"
] | null | null | null | step_2.py | NaiveSunset/201128 | 9874a9b260336e2e1eb5295ac3aca1e9d655c8d9 | [
"Apache-2.0"
] | null | null | null | step_2.py | NaiveSunset/201128 | 9874a9b260336e2e1eb5295ac3aca1e9d655c8d9 | [
"Apache-2.0"
] | null | null | null | print('What is your name?')
| 14 | 27 | 0.678571 | 5 | 28 | 3.8 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.142857 | 28 | 1 | 28 | 28 | 0.791667 | 0 | 0 | 0 | 0 | 0 | 0.642857 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 6 |
9affb979d25f27dc0a60002f5b41ee2f4a066a2c | 15,219 | py | Python | tests/models/fulltext/extract_test.py | elifesciences/sciencebeam-parser | 66964f283612b8d6fa8a23ad8790292c1ec07651 | [
"MIT"
] | 13 | 2021-08-04T12:11:17.000Z | 2022-03-28T20:41:20.000Z | tests/models/fulltext/extract_test.py | elifesciences/sciencebeam-parser | 66964f283612b8d6fa8a23ad8790292c1ec07651 | [
"MIT"
] | 33 | 2021-08-05T08:37:59.000Z | 2022-03-29T18:42:09.000Z | tests/models/fulltext/extract_test.py | elifesciences/sciencebeam-parser | 66964f283612b8d6fa8a23ad8790292c1ec07651 | [
"MIT"
] | 1 | 2022-01-05T14:53:06.000Z | 2022-01-05T14:53:06.000Z | import logging
from typing import Optional, Tuple
from sciencebeam_parser.document.layout_document import LayoutBlock
from sciencebeam_parser.document.semantic_document import (
SemanticFigureCitation,
SemanticHeading,
SemanticLabel,
SemanticRawEquation,
SemanticRawEquationContent,
SemanticRawFigure,
SemanticRawTable,
SemanticReferenceCitation,
SemanticSection,
SemanticTableCitation,
SemanticTitle
)
from sciencebeam_parser.models.fulltext.extract import (
get_section_label_and_title_from_layout_block,
FullTextSemanticExtractor
)
LOGGER = logging.getLogger(__name__)
SECTION_TITLE_1 = 'the title 1'
SECTION_PARAGRAPH_1 = 'the paragraph 1'
SECTION_PARAGRAPH_2 = 'the paragraph 2'
SECTION_PARAGRAPH_3 = 'the paragraph 3'
def _get_layout_block_text(layout_block: Optional[LayoutBlock]) -> Optional[str]:
if layout_block is None:
return None
return layout_block.text
def _get_section_label_and_title_text_from_layout_block(
layout_block: LayoutBlock
) -> Tuple[Optional[str], str]:
(
section_label_layout_block, section_title_layout_block
) = get_section_label_and_title_from_layout_block(layout_block)
return _get_layout_block_text(section_label_layout_block), section_title_layout_block.text
class TestGetSectionLabelAndTitleFromLayoutBlock:
def test_should_not_return_section_label_if_block_is_empty(self):
section_heading_layout_block = LayoutBlock(lines=[])
(
section_label_layout_block, section_title_layout_block
) = get_section_label_and_title_from_layout_block(section_heading_layout_block)
assert section_label_layout_block is None
assert section_title_layout_block == section_heading_layout_block
def test_should_not_return_section_label_if_section_title_does_not_contain_any(self):
section_heading_layout_block = LayoutBlock.for_text(SECTION_TITLE_1)
(
section_label_layout_block, section_title_layout_block
) = get_section_label_and_title_from_layout_block(section_heading_layout_block)
assert section_label_layout_block is None
assert section_title_layout_block == section_heading_layout_block
def test_should_parse_single_number_without_dot_section_label(self):
section_heading_layout_block = LayoutBlock.for_text('1 ' + SECTION_TITLE_1)
(
section_label_text, section_title_text
) = _get_section_label_and_title_text_from_layout_block(section_heading_layout_block)
assert section_label_text == '1'
assert section_title_text == SECTION_TITLE_1
def test_should_parse_single_number_with_dot_section_label(self):
section_heading_layout_block = LayoutBlock.for_text('1. ' + SECTION_TITLE_1)
(
section_label_text, section_title_text
) = _get_section_label_and_title_text_from_layout_block(section_heading_layout_block)
assert section_label_text == '1.'
assert section_title_text == SECTION_TITLE_1
def test_should_parse_sub_section_number_with_dot_section_label(self):
section_heading_layout_block = LayoutBlock.for_text('1.2. ' + SECTION_TITLE_1)
(
section_label_text, section_title_text
) = _get_section_label_and_title_text_from_layout_block(section_heading_layout_block)
assert section_label_text == '1.2.'
assert section_title_text == SECTION_TITLE_1
def test_should_parse_sub_sub_section_number_with_dot_section_label(self):
section_heading_layout_block = LayoutBlock.for_text('1.2.3. ' + SECTION_TITLE_1)
(
section_label_text, section_title_text
) = _get_section_label_and_title_text_from_layout_block(section_heading_layout_block)
assert section_label_text == '1.2.3.'
assert section_title_text == SECTION_TITLE_1
class TestFullTextSemanticExtractor:
def test_should_add_section_title_and_paragraph(self):
semantic_content_list = list(
FullTextSemanticExtractor().iter_semantic_content_for_entity_blocks([
('<section>', LayoutBlock.for_text(SECTION_TITLE_1)),
('<paragraph>', LayoutBlock.for_text(SECTION_PARAGRAPH_1))
])
)
assert len(semantic_content_list) == 1
section = semantic_content_list[0]
assert isinstance(section, SemanticSection)
semantic_headings = list(section.iter_by_type(SemanticHeading))
assert len(semantic_headings) == 1
assert semantic_headings[0].get_text_by_type(SemanticLabel) == ''
assert semantic_headings[0].get_text_by_type(SemanticTitle) == SECTION_TITLE_1
assert section.get_paragraph_text_list() == [SECTION_PARAGRAPH_1]
def test_should_add_separate_section_label(self):
semantic_content_list = list(
FullTextSemanticExtractor().iter_semantic_content_for_entity_blocks([
('<section>', LayoutBlock.for_text('1 ' + SECTION_TITLE_1)),
('<paragraph>', LayoutBlock.for_text(SECTION_PARAGRAPH_1))
])
)
assert len(semantic_content_list) == 1
section = semantic_content_list[0]
assert isinstance(section, SemanticSection)
semantic_headings = list(section.iter_by_type(SemanticHeading))
assert len(semantic_headings) == 1
assert semantic_headings[0].get_text_by_type(SemanticLabel) == '1'
assert semantic_headings[0].get_text_by_type(SemanticTitle) == SECTION_TITLE_1
def test_should_add_paragraphs_without_title(self):
semantic_content_list = list(
FullTextSemanticExtractor().iter_semantic_content_for_entity_blocks([
('<paragraph>', LayoutBlock.for_text(SECTION_PARAGRAPH_1)),
('<paragraph>', LayoutBlock.for_text(SECTION_PARAGRAPH_2))
])
)
assert len(semantic_content_list) == 1
section = semantic_content_list[0]
assert isinstance(section, SemanticSection)
assert section.get_paragraph_text_list() == [
SECTION_PARAGRAPH_1,
SECTION_PARAGRAPH_2
]
def test_should_include_citation_in_paragraph(self):
semantic_content_list = list(
FullTextSemanticExtractor().iter_semantic_content_for_entity_blocks([
('<paragraph>', LayoutBlock.for_text(SECTION_PARAGRAPH_1)),
('<citation_marker>', LayoutBlock.for_text(SECTION_PARAGRAPH_2)),
('<paragraph>', LayoutBlock.for_text(SECTION_PARAGRAPH_3)),
])
)
LOGGER.debug('semantic_content_list: %s', semantic_content_list)
assert len(semantic_content_list) == 1
section = semantic_content_list[0]
assert isinstance(section, SemanticSection)
assert section.get_paragraph_text_list() == [
' '.join([SECTION_PARAGRAPH_1, SECTION_PARAGRAPH_2, SECTION_PARAGRAPH_3])
]
def test_should_include_figure_citation_in_paragraph(self):
semantic_content_list = list(
FullTextSemanticExtractor().iter_semantic_content_for_entity_blocks([
('<paragraph>', LayoutBlock.for_text(SECTION_PARAGRAPH_1)),
('<figure_marker>', LayoutBlock.for_text('Figure 1')),
('<paragraph>', LayoutBlock.for_text(SECTION_PARAGRAPH_2)),
])
)
LOGGER.debug('semantic_content_list: %s', semantic_content_list)
assert len(semantic_content_list) == 1
section = semantic_content_list[0]
assert isinstance(section, SemanticSection)
figure_citations = list(section.iter_by_type_recursively(SemanticFigureCitation))
assert len(figure_citations) == 1
assert figure_citations[0].get_text() == 'Figure 1'
def test_should_include_table_citation_in_paragraph(self):
semantic_content_list = list(
FullTextSemanticExtractor().iter_semantic_content_for_entity_blocks([
('<paragraph>', LayoutBlock.for_text(SECTION_PARAGRAPH_1)),
('<table_marker>', LayoutBlock.for_text('Table 1')),
('<paragraph>', LayoutBlock.for_text(SECTION_PARAGRAPH_2)),
])
)
LOGGER.debug('semantic_content_list: %s', semantic_content_list)
assert len(semantic_content_list) == 1
section = semantic_content_list[0]
assert isinstance(section, SemanticSection)
table_citations = list(section.iter_by_type_recursively(SemanticTableCitation))
assert len(table_citations) == 1
assert table_citations[0].get_text() == 'Table 1'
def test_should_include_reference_citation_in_paragraph(self):
semantic_content_list = list(
FullTextSemanticExtractor().iter_semantic_content_for_entity_blocks([
('<paragraph>', LayoutBlock.for_text(SECTION_PARAGRAPH_1)),
('<citation_marker>', LayoutBlock.for_text('Ref 1')),
('<paragraph>', LayoutBlock.for_text(SECTION_PARAGRAPH_2)),
])
)
LOGGER.debug('semantic_content_list: %s', semantic_content_list)
assert len(semantic_content_list) == 1
section = semantic_content_list[0]
assert isinstance(section, SemanticSection)
reference_citations = list(section.iter_by_type_recursively(SemanticReferenceCitation))
assert len(reference_citations) == 1
assert reference_citations[0].get_text() == 'Ref 1'
def test_should_include_raw_equation_with_label_after_in_paragraph(self):
semantic_content_list = list(
FullTextSemanticExtractor().iter_semantic_content_for_entity_blocks([
('<paragraph>', LayoutBlock.for_text(SECTION_PARAGRAPH_1)),
('<equation>', LayoutBlock.for_text('Equation 1')),
('<equation_label>', LayoutBlock.for_text('(1)')),
('<paragraph>', LayoutBlock.for_text(SECTION_PARAGRAPH_2)),
])
)
LOGGER.debug('semantic_content_list: %s', semantic_content_list)
assert len(semantic_content_list) == 1
section = semantic_content_list[0]
assert isinstance(section, SemanticSection)
raw_equations = list(section.iter_by_type_recursively(SemanticRawEquation))
assert len(raw_equations) == 1
assert raw_equations[0].get_text() == 'Equation 1 (1)'
assert raw_equations[0].get_text_by_type(SemanticLabel) == '(1)'
assert raw_equations[0].get_text_by_type(SemanticRawEquationContent) == 'Equation 1'
def test_should_include_raw_equation_with_label_before_in_paragraph(self):
semantic_content_list = list(
FullTextSemanticExtractor().iter_semantic_content_for_entity_blocks([
('<paragraph>', LayoutBlock.for_text(SECTION_PARAGRAPH_1)),
('<equation_label>', LayoutBlock.for_text('(1)')),
('<equation>', LayoutBlock.for_text('Equation 1')),
('<paragraph>', LayoutBlock.for_text(SECTION_PARAGRAPH_2)),
])
)
LOGGER.debug('semantic_content_list: %s', semantic_content_list)
assert len(semantic_content_list) == 1
section = semantic_content_list[0]
assert isinstance(section, SemanticSection)
raw_equations = list(section.iter_by_type_recursively(SemanticRawEquation))
assert len(raw_equations) == 1
assert raw_equations[0].get_text() == '(1) Equation 1'
assert raw_equations[0].get_text_by_type(SemanticLabel) == '(1)'
assert raw_equations[0].get_text_by_type(SemanticRawEquationContent) == 'Equation 1'
def test_should_include_multiple_raw_equations_without_label(self):
semantic_content_list = list(
FullTextSemanticExtractor().iter_semantic_content_for_entity_blocks([
('<paragraph>', LayoutBlock.for_text(SECTION_PARAGRAPH_1)),
('<equation>', LayoutBlock.for_text('Equation 1')),
('<equation>', LayoutBlock.for_text('Equation 2')),
('<paragraph>', LayoutBlock.for_text(SECTION_PARAGRAPH_2)),
])
)
LOGGER.debug('semantic_content_list: %s', semantic_content_list)
assert len(semantic_content_list) == 1
section = semantic_content_list[0]
assert isinstance(section, SemanticSection)
raw_equations = list(section.iter_by_type_recursively(SemanticRawEquation))
assert len(raw_equations) == 2
assert raw_equations[0].get_text() == 'Equation 1'
assert raw_equations[1].get_text() == 'Equation 2'
def test_should_add_note_for_other_text_to_section(self):
semantic_content_list = list(
FullTextSemanticExtractor().iter_semantic_content_for_entity_blocks([
('<paragraph>', LayoutBlock.for_text(SECTION_PARAGRAPH_1)),
('O', LayoutBlock.for_text(SECTION_PARAGRAPH_2))
])
)
assert len(semantic_content_list) == 1
section = semantic_content_list[0]
assert isinstance(section, SemanticSection)
assert section.get_paragraph_text_list() == [SECTION_PARAGRAPH_1]
assert section.get_notes_text_list('fulltext:other') == [SECTION_PARAGRAPH_2]
def test_should_add_note_for_other_text_to_body(self):
semantic_content_list = list(
FullTextSemanticExtractor().iter_semantic_content_for_entity_blocks([
('O', LayoutBlock.for_text(SECTION_PARAGRAPH_1)),
('<paragraph>', LayoutBlock.for_text(SECTION_PARAGRAPH_2))
])
)
parent_section = SemanticSection(semantic_content_list)
assert parent_section.get_notes_text_list('fulltext:other') == [SECTION_PARAGRAPH_1]
sections = parent_section.sections
assert len(sections) == 1
assert sections[0].get_paragraph_text_list() == [SECTION_PARAGRAPH_2]
def test_should_add_raw_figure_for_figure_text_to_section(self):
semantic_content_list = list(
FullTextSemanticExtractor().iter_semantic_content_for_entity_blocks([
('<paragraph>', LayoutBlock.for_text(SECTION_PARAGRAPH_1)),
('<figure>', LayoutBlock.for_text(SECTION_PARAGRAPH_2))
])
)
assert len(semantic_content_list) == 1
section = semantic_content_list[0]
assert isinstance(section, SemanticSection)
assert section.get_paragraph_text_list() == [SECTION_PARAGRAPH_1]
assert section.get_text_by_type(SemanticRawFigure) == SECTION_PARAGRAPH_2
def test_should_raw_table_for_table_text_to_section(self):
semantic_content_list = list(
FullTextSemanticExtractor().iter_semantic_content_for_entity_blocks([
('<paragraph>', LayoutBlock.for_text(SECTION_PARAGRAPH_1)),
('<table>', LayoutBlock.for_text(SECTION_PARAGRAPH_2))
])
)
assert len(semantic_content_list) == 1
section = semantic_content_list[0]
assert isinstance(section, SemanticSection)
assert section.get_paragraph_text_list() == [SECTION_PARAGRAPH_1]
assert section.get_text_by_type(SemanticRawTable) == SECTION_PARAGRAPH_2
| 48.009464 | 95 | 0.701426 | 1,685 | 15,219 | 5.846884 | 0.065875 | 0.105055 | 0.10607 | 0.073589 | 0.843991 | 0.825721 | 0.80065 | 0.768372 | 0.74462 | 0.719448 | 0 | 0.012583 | 0.211512 | 15,219 | 316 | 96 | 48.161392 | 0.808417 | 0 | 0 | 0.549296 | 0 | 0 | 0.056837 | 0.010119 | 0 | 0 | 0 | 0 | 0.257042 | 1 | 0.077465 | false | 0 | 0.017606 | 0 | 0.112676 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
b1476d1576c39918990e0a2e2b35e28f163bad33 | 928 | py | Python | sdk/python/pulumi_azure_nextgen/labservices/v20181015/__init__.py | test-wiz-sec/pulumi-azure-nextgen | 20a695af0d020b34b0f1c336e1b69702755174cc | [
"Apache-2.0"
] | null | null | null | sdk/python/pulumi_azure_nextgen/labservices/v20181015/__init__.py | test-wiz-sec/pulumi-azure-nextgen | 20a695af0d020b34b0f1c336e1b69702755174cc | [
"Apache-2.0"
] | null | null | null | sdk/python/pulumi_azure_nextgen/labservices/v20181015/__init__.py | test-wiz-sec/pulumi-azure-nextgen | 20a695af0d020b34b0f1c336e1b69702755174cc | [
"Apache-2.0"
] | null | null | null | # coding=utf-8
# *** WARNING: this file was generated by the Pulumi SDK Generator. ***
# *** Do not edit by hand unless you're certain you know what you are doing! ***
# Export this package's modules as members:
from .environment import *
from .environment_setting import *
from .gallery_image import *
from .get_environment import *
from .get_environment_setting import *
from .get_gallery_image import *
from .get_global_user_environment import *
from .get_global_user_operation_batch_status import *
from .get_global_user_operation_status import *
from .get_global_user_personal_preferences import *
from .get_lab import *
from .get_lab_account import *
from .get_lab_account_regional_availability import *
from .get_user import *
from .lab import *
from .lab_account import *
from .list_global_user_environments import *
from .list_global_user_labs import *
from .user import *
from ._inputs import *
from . import outputs
| 34.37037 | 80 | 0.796336 | 136 | 928 | 5.147059 | 0.397059 | 0.285714 | 0.204286 | 0.108571 | 0.361429 | 0.141429 | 0 | 0 | 0 | 0 | 0 | 0.001244 | 0.133621 | 928 | 26 | 81 | 35.692308 | 0.869403 | 0.21875 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
b17cf5a8c1f24bb7c29c320c892ce42403852220 | 96 | py | Python | salt-get-api/src/resources/__init__.py | mdm373/salt-get | 72fd08f1c26e8812f2b3838085b6a7b935c4cc0d | [
"MIT"
] | null | null | null | salt-get-api/src/resources/__init__.py | mdm373/salt-get | 72fd08f1c26e8812f2b3838085b6a7b935c4cc0d | [
"MIT"
] | 4 | 2022-01-03T22:16:13.000Z | 2022-02-15T10:17:30.000Z | salt-get-api/src/resources/__init__.py | mdm373/salt-get | 72fd08f1c26e8812f2b3838085b6a7b935c4cc0d | [
"MIT"
] | null | null | null | from .distance_resource import DistanceResource
from .settings_resource import SettingsResource
| 32 | 47 | 0.895833 | 10 | 96 | 8.4 | 0.7 | 0.333333 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.083333 | 96 | 2 | 48 | 48 | 0.954545 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
b18733d35055bb1ac851623651add54db931c655 | 980 | py | Python | gaqqie_door/rest/__init__.py | gaqqie/gaqqie-door | eae3c2a3ccf901f9853ea0ab2ddea9a1b851e52d | [
"Apache-2.0"
] | 1 | 2021-08-31T05:17:39.000Z | 2021-08-31T05:17:39.000Z | gaqqie_door/rest/__init__.py | gaqqie/gaqqie-door | eae3c2a3ccf901f9853ea0ab2ddea9a1b851e52d | [
"Apache-2.0"
] | null | null | null | gaqqie_door/rest/__init__.py | gaqqie/gaqqie-door | eae3c2a3ccf901f9853ea0ab2ddea9a1b851e52d | [
"Apache-2.0"
] | null | null | null | # coding: utf-8
# flake8: noqa
"""
gaqqie user API
No description provided (generated by Swagger Codegen https://github.com/swagger-api/swagger-codegen) # noqa: E501
OpenAPI spec version: 0.2.0
Contact: tknstyk@gmail.com
Generated by: https://github.com/swagger-api/swagger-codegen.git
"""
from __future__ import absolute_import
# import apis into sdk package
from gaqqie_door.rest.api.device_api import DeviceApi
from gaqqie_door.rest.api.job_api import JobApi
from gaqqie_door.rest.api.provider_api import ProviderApi
# import ApiClient
from gaqqie_door.rest.api_client import ApiClient
from gaqqie_door.rest.configuration import Configuration
# import models into sdk package
from gaqqie_door.rest.models.device import Device
from gaqqie_door.rest.models.job import Job
from gaqqie_door.rest.models.jobbeforesubmission import Jobbeforesubmission
from gaqqie_door.rest.models.provider import Provider
from gaqqie_door.rest.models.result import Result
| 32.666667 | 119 | 0.809184 | 143 | 980 | 5.412587 | 0.34965 | 0.129199 | 0.180879 | 0.232558 | 0.459948 | 0.26615 | 0.180879 | 0 | 0 | 0 | 0 | 0.00927 | 0.119388 | 980 | 29 | 120 | 33.793103 | 0.887601 | 0.365306 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
49139fdb177dfccec1bb3145c3316d8485d3a40b | 17,245 | py | Python | humann2/tests/functional_tests_tools.py | bmpbos/humann | 4a8fee5596d89d805af6568d3260844f80c8f9a2 | [
"MIT"
] | null | null | null | humann2/tests/functional_tests_tools.py | bmpbos/humann | 4a8fee5596d89d805af6568d3260844f80c8f9a2 | [
"MIT"
] | null | null | null | humann2/tests/functional_tests_tools.py | bmpbos/humann | 4a8fee5596d89d805af6568d3260844f80c8f9a2 | [
"MIT"
] | null | null | null | import unittest
import tempfile
import os
import cfg
import utils
class TestFunctionalHumann2Tools(unittest.TestCase):
"""
Test humann2.tools
"""
def test_humann2_join_tables_tsv(self):
"""
Test joining tsv files with humann2_join_tables
"""
# create a temp file
file_out, new_file=tempfile.mkstemp(prefix="humann2_temp")
# join the files
utils.run_command(["humann2_join_tables","--input",
cfg.data_folder,"--output",new_file,"--file_name",
cfg.multi_sample_genefamilies_split_basename,"--verbose"])
# check the joined file is as expected
self.assertTrue(utils.files_almost_equal(new_file, cfg.multi_sample_genefamilies))
# remove the temp file
utils.remove_temp_file(new_file)
def test_humann2_split_tables_tsv(self):
"""
Test splitting a tsv file with humann2_split_tables
"""
input_file=cfg.multi_sample_genefamilies
# create a temp directory
temp_directory=utils.create_temp_folder("split_tables_tsv")
# split the file
utils.run_command(["humann2_split_table","--input", input_file,
"--output",temp_directory,"--verbose"])
# test the split files are as expected
output_files=os.listdir(temp_directory)
# sort the output files
file_pairs=[]
for file in output_files:
filebasename=os.path.basename(file)
# get the sample number for the file
file=os.path.join(temp_directory,file)
if filebasename[-1] == 1:
file_pairs.append([file,cfg.multi_sample_genefamilies_split1])
elif filebasename[-1] == 2:
file_pairs.append([file,cfg.multi_sample_genefamilies_split2])
for temp_file, file in file_pairs:
self.assertTrue(utils.files_almost_equal(temp_file, file))
# remove the temp folder
utils.remove_temp_folder(temp_directory)
def test_humann2_regroup_table_uniref50_rxn_tsv(self):
"""
Test regrouping the tsv file with humann2_regroup_table
Test with uniref50 to reactions mappings
"""
# create a temp file
file_out, new_file=tempfile.mkstemp(prefix="humann2_temp")
# run the command
utils.run_command(["humann2_regroup_table","--input",cfg.regroup_input,"--output",
new_file,"--groups","uniref50_rxn"])
# check the output is as expected
self.assertTrue(utils.files_almost_equal(new_file, cfg.regroup_rxn_output))
# remove the temp file
utils.remove_temp_file(new_file)
def test_humann2_regroup_table_uniref50_rxn_tsv_mean(self):
"""
Test regrouping the tsv file with humann2_regroup_table
Test with uniref50 to reactions mappings
Test with the mean instead of sum output
"""
# create a temp file
file_out, new_file=tempfile.mkstemp(prefix="humann2_temp")
# run the command
utils.run_command(["humann2_regroup_table","--input",cfg.regroup_input,"--output",
new_file,"--groups","uniref50_rxn","--function","mean"])
# check the output is as expected
self.assertTrue(utils.files_almost_equal(new_file, cfg.regroup_rxn_mean_output))
# remove the temp file
utils.remove_temp_file(new_file)
def test_humann2_regroup_table_custom_grouping_tsv(self):
"""
Test regrouping the tsv file with humann2_regroup_table
Test with custom mappings
"""
# create a temp file
file_out, new_file=tempfile.mkstemp(prefix="humann2_temp")
# run the command
utils.run_command(["humann2_regroup_table","--input",cfg.regroup_custom_input,"--output",
new_file,"--custom",cfg.regroup_custom_groups])
# check the output is as expected
self.assertTrue(utils.files_almost_equal(new_file, cfg.regroup_custom_groups_output))
# remove the temp file
utils.remove_temp_file(new_file)
def test_humann2_rename_table_uniref50_tsv(self):
"""
Test renaming the tsv file entries with humann2_rename_table
Test with uniref50 names
"""
# create a temp file
file_out, new_file=tempfile.mkstemp(prefix="humann2_temp")
# run the command
utils.run_command(["humann2_rename_table","--input",cfg.rename_input,"--output",
new_file,"--names","uniref50"])
# check the output is as expected
self.assertTrue(utils.files_almost_equal(new_file, cfg.rename_uniref50_output))
# remove the temp file
utils.remove_temp_file(new_file)
def test_humann2_rename_table_ko_tsv(self):
"""
Test renaming the tsv file entries with humann2_rename_table
Test with ko names
"""
# create a temp file
file_out, new_file=tempfile.mkstemp(prefix="humann2_temp")
# run the command
utils.run_command(["humann2_rename_table","--input",cfg.rename_ko_input,"--output",
new_file,"--names","kegg-orthology"])
# check the output is as expected
self.assertTrue(utils.files_almost_equal(new_file, cfg.rename_ko_output))
# remove the temp file
utils.remove_temp_file(new_file)
def test_humann2_rename_table_ec_tsv(self):
"""
Test renaming the tsv file entries with humann2_rename_table
Test with ec names
"""
# create a temp file
file_out, new_file=tempfile.mkstemp(prefix="humann2_temp")
# run the command
utils.run_command(["humann2_rename_table","--input",cfg.rename_ec_input,"--output",
new_file,"--names","ec"])
# check the output is as expected
self.assertTrue(utils.files_almost_equal(new_file, cfg.rename_ec_output))
# remove the temp file
utils.remove_temp_file(new_file)
def test_humann2_rename_table_rxn_tsv(self):
"""
Test renaming the tsv file entries with humann2_rename_table
Test with rxn names
"""
# create a temp file
file_out, new_file=tempfile.mkstemp(prefix="humann2_temp")
# run the command
utils.run_command(["humann2_rename_table","--input",cfg.rename_rxn_input,"--output",
new_file,"--names","metacyc-rxn"])
# check the output is as expected
self.assertTrue(utils.files_almost_equal(new_file, cfg.rename_rxn_output))
# remove the temp file
utils.remove_temp_file(new_file)
def test_humann2_rename_table_pathways_tsv(self):
"""
Test renaming the tsv file entries with humann2_rename_table
Test with pathways names
"""
# create a temp file
file_out, new_file=tempfile.mkstemp(prefix="humann2_temp")
# run the command
utils.run_command(["humann2_rename_table","--input",cfg.rename_pathway_input,"--output",
new_file,"--names","metacyc-pwy"])
# check the output is as expected
self.assertTrue(utils.files_almost_equal(new_file, cfg.rename_pathway_output))
# remove the temp file
utils.remove_temp_file(new_file)
def test_humann2_rename_table_custom_tsv(self):
"""
Test renaming the tsv file entries with humann2_rename_table
Test with custom names file
"""
# create a temp file
file_out, new_file=tempfile.mkstemp(prefix="humann2_temp")
# run the command
utils.run_command(["humann2_rename_table","--input",cfg.rename_input,"--output",
new_file,"--custom",cfg.rename_custom_mapping])
# check the output is as expected
self.assertTrue(utils.files_almost_equal(new_file, cfg.rename_custom_output))
# remove the temp file
utils.remove_temp_file(new_file)
def test_humann2_renorm_table_cpm_tsv(self):
"""
Test renorm the tsv file entries with humann2_renorm_table
Test with cpm
"""
# create a temp file
file_out, new_file=tempfile.mkstemp(prefix="humann2_temp")
# run the command
utils.run_command(["humann2_renorm_table","--input",cfg.renorm_input,"--output",
new_file,"--units","cpm"])
# check the output is as expected
self.assertTrue(utils.files_almost_equal(new_file, cfg.renorm_cpm_output))
# remove the temp file
utils.remove_temp_file(new_file)
def test_humann2_renorm_table_relab_tsv(self):
"""
Test renorm the tsv file entries with humann2_renorm_table
Test with relab
"""
# create a temp file
file_out, new_file=tempfile.mkstemp(prefix="humann2_temp")
# run the command
utils.run_command(["humann2_renorm_table","--input",cfg.renorm_input,"--output",
new_file,"--units","relab"])
# check the output is as expected
self.assertTrue(utils.files_almost_equal(new_file, cfg.renorm_relab_output))
# remove the temp file
utils.remove_temp_file(new_file)
def test_humann2_rna_dna_norm_laplace_tsv(self):
"""
Test norm the tsv file entries from dna and rna input files with humann2_rna_dna_norm_table
Test with laplace
"""
# create a temp folder
tempdir=utils.create_temp_folder("rna_dna_norm_laplace")
output_basename=os.path.join(tempdir,"rna_dna_norm")
# run the command
utils.run_command(["humann2_rna_dna_norm","--input_dna",cfg.rna_dna_norm_dna_input,
"--input_rna",cfg.rna_dna_norm_rna_input,"--output_basename",
output_basename,"--method","laplace"])
# check the output files are as expected
for file_extension, expected_output_file in zip(cfg.rna_dna_norm_file_names, cfg.rna_dna_norm_laplace_output_files):
self.assertTrue(utils.files_almost_equal(output_basename+file_extension, expected_output_file))
# remove the temp file
utils.remove_temp_folder(tempdir)
def test_humann2_rna_dna_norm_witten_bell_tsv(self):
"""
Test norm the tsv file entries from dna and rna input files with humann2_rna_dna_norm_table
Test with witten bell
"""
# create a temp folder
tempdir=utils.create_temp_folder("rna_dna_norm_witten_bell")
output_basename=os.path.join(tempdir,"rna_dna_norm")
# run the command
utils.run_command(["humann2_rna_dna_norm","--input_dna",cfg.rna_dna_norm_dna_input,
"--input_rna",cfg.rna_dna_norm_rna_input,"--output_basename",
output_basename,"--method","witten_bell"])
# check the output files are as expected
for file_extension, expected_output_file in zip(cfg.rna_dna_norm_file_names, cfg.rna_dna_norm_witten_bell_output_files):
self.assertTrue(utils.files_almost_equal(output_basename+file_extension, expected_output_file))
# remove the temp file
utils.remove_temp_folder(tempdir)
def test_humann2_rna_dna_norm_log_tsv(self):
"""
Test norm the tsv file entries from dna and rna input files with humann2_rna_dna_norm_table
Test with log transform
"""
# create a temp folder
tempdir=utils.create_temp_folder("rna_dna_norm_log")
output_basename=os.path.join(tempdir,"rna_dna_norm")
# run the command
utils.run_command(["humann2_rna_dna_norm","--input_dna",cfg.rna_dna_norm_dna_input,
"--input_rna",cfg.rna_dna_norm_rna_input,"--output_basename",
output_basename,"--log_transform"])
# check the output files are as expected
for file_extension, expected_output_file in zip(cfg.rna_dna_norm_file_names, cfg.rna_dna_norm_log_output_files):
self.assertTrue(utils.files_almost_equal(output_basename+file_extension, expected_output_file))
# remove the temp file
utils.remove_temp_folder(tempdir)
def test_humann2_rna_dna_norm_log_10_tsv(self):
"""
Test norm the tsv file entries from dna and rna input files with humann2_rna_dna_norm_table
Test with log transform with base 10
"""
# create a temp folder
tempdir=utils.create_temp_folder("rna_dna_norm_log_10")
output_basename=os.path.join(tempdir,"rna_dna_norm")
# run the command
utils.run_command(["humann2_rna_dna_norm","--input_dna",cfg.rna_dna_norm_dna_input,
"--input_rna",cfg.rna_dna_norm_rna_input,"--output_basename",
output_basename,"--log_transform", "--log_base","10"])
# check the output files are as expected
# allow for varying precision in the calculations with almost equal
for file_extension, expected_output_file in zip(cfg.rna_dna_norm_file_names, cfg.rna_dna_norm_log_10_output_files):
self.assertTrue(utils.files_almost_equal(output_basename+file_extension, expected_output_file))
# remove the temp file
utils.remove_temp_folder(tempdir)
def test_humann2_strain_profile_tsv(self):
"""
Test the tsv file entries running humann2_strain_profile
Test with critical mean and critical count values
"""
# create a temp folder
tempdir=utils.create_temp_folder("strain_profile")
# move to this folder as the output files will be created in the current working folder
current_working_directory=os.getcwd()
try:
os.chdir(tempdir)
except EnvironmentError:
print("Warning: Unable to move to temp directory: " + tempdir)
# run the command
utils.run_command(["humann2_strain_profiler","--input",cfg.strain_profile_input,
"--critical_mean","1","--critical_count","2"])
# check the output files are as expected
# allow for varying precision in the calculations with almost equal
for file, expected_output_file in zip(cfg.strain_profile_file_names, cfg.strain_profile_m1_n2_output_files):
self.assertTrue(utils.files_almost_equal(os.path.join(tempdir,file), expected_output_file))
# return to original working directory
os.chdir(current_working_directory)
# remove the temp file
utils.remove_temp_folder(tempdir)
def test_humann2_merge_abundance_tsv(self):
"""
Test the tsv gene families and pathway abundance file entries with humann2_merge_abundance_tables
"""
# create a temp file
file_out, new_file=tempfile.mkstemp(prefix="humann2_temp")
# run the command
utils.run_command(["humann2_merge_abundance_tables","--input-genes",cfg.merge_abundance_genefamilies_input,
"--input-pathways",cfg.merge_abundance_pathways_input,"--output",
new_file])
# check the output file is as expected
# allow for varying precision in the calculations with almost equal
self.assertTrue(utils.files_almost_equal(new_file, cfg.merge_abundance_output))
# remove the temp file
utils.remove_temp_file(new_file)
def test_humann2_merge_abundance_remove_taxonomy_tsv(self):
"""
Test the tsv gene families and pathway abundance file entries with humann2_merge_abundance_tables
Test with the remove taxonomy option which stratifies by pathway then gene instead of
stratifying by pathway, taxonomy, then gene
"""
# create a temp file
file_out, new_file=tempfile.mkstemp(prefix="humann2_temp")
# run the command
utils.run_command(["humann2_merge_abundance_tables","--input-genes",cfg.merge_abundance_genefamilies_input,
"--input-pathways",cfg.merge_abundance_pathways_input,"--output",
new_file,"--remove-taxonomy"])
# check the output file is as expected
# allow for varying precision in the calculations with almost equal
self.assertTrue(utils.files_almost_equal(new_file, cfg.merge_abundance_remove_taxonomy_output))
# remove the temp file
utils.remove_temp_file(new_file)
| 39.28246 | 128 | 0.634097 | 2,118 | 17,245 | 4.850803 | 0.076015 | 0.038155 | 0.03504 | 0.042827 | 0.81604 | 0.799007 | 0.780417 | 0.77701 | 0.758809 | 0.754429 | 0 | 0.009407 | 0.284952 | 17,245 | 438 | 129 | 39.372146 | 0.823777 | 0.247144 | 0 | 0.371069 | 0 | 0 | 0.126508 | 0.013857 | 0 | 0 | 0 | 0 | 0.125786 | 1 | 0.125786 | false | 0 | 0.031447 | 0 | 0.163522 | 0.006289 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
4934edba26facfd9df06fb59c5a70d4b70d91cff | 22 | py | Python | gen/__init__.py | seen-idc/image-gen | 025257cde07579a634aaefca1e17482f3c02ad45 | [
"MIT"
] | null | null | null | gen/__init__.py | seen-idc/image-gen | 025257cde07579a634aaefca1e17482f3c02ad45 | [
"MIT"
] | null | null | null | gen/__init__.py | seen-idc/image-gen | 025257cde07579a634aaefca1e17482f3c02ad45 | [
"MIT"
] | null | null | null | from .welcome import * | 22 | 22 | 0.772727 | 3 | 22 | 5.666667 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.136364 | 22 | 1 | 22 | 22 | 0.894737 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
499b8237a1f401e2cc0f87897b8e9e71d043e298 | 36 | py | Python | extraction/trainer/__init__.py | nutalk/Gabor-Filter | f1ffb6efbda66645e704160d49ea5b32afb5f70b | [
"Apache-2.0"
] | null | null | null | extraction/trainer/__init__.py | nutalk/Gabor-Filter | f1ffb6efbda66645e704160d49ea5b32afb5f70b | [
"Apache-2.0"
] | null | null | null | extraction/trainer/__init__.py | nutalk/Gabor-Filter | f1ffb6efbda66645e704160d49ea5b32afb5f70b | [
"Apache-2.0"
] | null | null | null | from .fcn_trainer import FCNTrainer
| 18 | 35 | 0.861111 | 5 | 36 | 6 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.111111 | 36 | 1 | 36 | 36 | 0.9375 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
7737fa6e8350089620df6859e9a742afea0d95c4 | 123 | py | Python | library/particle_set.py | Paul-St-Young/qmcpack_objects | 14d614978222165e3f08a5db8fca9cc6024d0e66 | [
"MIT"
] | null | null | null | library/particle_set.py | Paul-St-Young/qmcpack_objects | 14d614978222165e3f08a5db8fca9cc6024d0e66 | [
"MIT"
] | null | null | null | library/particle_set.py | Paul-St-Young/qmcpack_objects | 14d614978222165e3f08a5db8fca9cc6024d0e66 | [
"MIT"
] | null | null | null | class ParticleSet:
def __init__(self):
pass
# end def
def init_from_xml_node(self,node):
pass
| 15.375 | 38 | 0.601626 | 16 | 123 | 4.1875 | 0.625 | 0.208955 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.325203 | 123 | 7 | 39 | 17.571429 | 0.807229 | 0.056911 | 0 | 0.4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.4 | false | 0.4 | 0 | 0 | 0.6 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 6 |
774ee3167bfb39a34800085361a19a92905df47b | 119,117 | py | Python | tests/scripts/thread-cert/test_lowpan.py | mostafanfs/openthread | 28c049f2ce0b664abf2f85562cfd9b79b4680389 | [
"BSD-3-Clause"
] | null | null | null | tests/scripts/thread-cert/test_lowpan.py | mostafanfs/openthread | 28c049f2ce0b664abf2f85562cfd9b79b4680389 | [
"BSD-3-Clause"
] | null | null | null | tests/scripts/thread-cert/test_lowpan.py | mostafanfs/openthread | 28c049f2ce0b664abf2f85562cfd9b79b4680389 | [
"BSD-3-Clause"
] | null | null | null | #!/usr/bin/python
#
# Copyright (c) 2016, The OpenThread Authors.
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
# 1. Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# 2. Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
# 3. Neither the name of the copyright holder nor the
# names of its contributors may be used to endorse or promote products
# derived from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
#
import io
import random
import struct
import unittest
import common
import config
import ipv6
import lowpan
def create_default_lowpan_parser(context_manager):
return lowpan.LowpanParser(
lowpan_mesh_header_factory=lowpan.LowpanMeshHeaderFactory(),
lowpan_decompressor=config.create_default_lowpan_decompressor(context_manager),
lowpan_fragements_buffers_manager=lowpan.LowpanFragmentsBuffersManager(),
ipv6_packet_factory=ipv6.IPv6PacketFactory(
ehf=config.create_default_ipv6_extension_headers_factories(),
ulpf={
17: ipv6.UDPDatagramFactory(
udp_header_factory=ipv6.UDPHeaderFactory(),
udp_payload_factory=ipv6.UDPBytesPayloadFactory()),
58: ipv6.ICMPv6Factory(
body_factories=config.create_default_ipv6_icmp_body_factories()
)
}
)
)
def any_tf():
return random.getrandbits(2)
def any_nh():
return random.getrandbits(1)
def any_hlim():
return random.getrandbits(2)
def any_cid():
return random.getrandbits(1)
def any_sac():
return random.getrandbits(1)
def any_sam():
return random.getrandbits(2)
def any_m():
return random.getrandbits(1)
def any_dac():
return random.getrandbits(1)
def any_dam():
return random.getrandbits(2)
def any_ecn():
return random.getrandbits(2)
def any_dscp():
return random.getrandbits(6)
def any_flow_label():
return random.getrandbits(6)
def any_hop_limit():
return random.getrandbits(8)
def any_src_addr():
return bytearray([random.getrandbits(8) for _ in range(16)])
def any_dst_addr():
return bytearray([random.getrandbits(8) for _ in range(16)])
def any_eui64():
return bytearray([random.getrandbits(8) for _ in range(8)])
def any_rloc16():
return bytearray([random.getrandbits(8) for _ in range(2)])
def any_48bits_addr():
return bytearray([random.getrandbits(8) for _ in range(6)])
def any_32bits_addr():
return bytearray([random.getrandbits(8) for _ in range(4)])
def any_8bits_addr():
return bytearray([random.getrandbits(8)])
def any_c():
return random.getrandbits(1)
def any_p():
return random.getrandbits(2)
def any_src_port():
return random.getrandbits(16)
def any_dst_port():
return random.getrandbits(16)
def any_compressable_src_port():
return 0xf000 + random.getrandbits(8)
def any_compressable_dst_port():
return 0xf000 + random.getrandbits(8)
def any_nibble_src_port():
return 0xf0b0 + random.getrandbits(4)
def any_nibble_dst_port():
return 0xf0b0 + random.getrandbits(4)
def any_checksum():
return random.getrandbits(16)
def any_next_header():
return random.getrandbits(8)
def any_sci():
return random.getrandbits(4)
def any_dci():
return random.getrandbits(4)
def any_src_mac_addr():
return bytearray([random.getrandbits(8) for _ in range(8)])
def any_dst_mac_addr():
return bytearray([random.getrandbits(8) for _ in range(8)])
def any_context():
prefix = bytearray([random.getrandbits(8) for _ in range(random.randint(2, 15))])
prefix_length = len(prefix)
return lowpan.Context(prefix, prefix_length * 8)
def any_mac_address():
length = random.choice([2, 8])
if length == 2:
return common.MacAddress.from_rloc16(bytearray([random.getrandbits(8) for _ in range(length)]))
elif length == 8:
return common.MacAddress.from_eui64(bytearray([random.getrandbits(8) for _ in range(length)]))
def any_hops_left():
return random.getrandbits(4)
def any_data(length=None):
length = length if length is not None else random.randint(1, 64)
return bytearray([random.getrandbits(8) for _ in range(length)])
def any_datagram_size():
return random.getrandbits(11)
def any_datagram_tag():
return random.getrandbits(16)
def any_datagram_offset():
return random.getrandbits(8)
class TestLowpanIPHC(unittest.TestCase):
def test_should_create_LowpanIPHC_object_when_from_bytes_classmethod_is_called(self):
# GIVEN
tf = any_tf()
nh = any_nh()
hlim = any_hlim()
cid = any_cid()
sac = any_sac()
sam = any_sam()
m = any_m()
dac = any_dac()
dam = any_dam()
byte0 = (3 << 5) | (tf << 3) | (nh << 2) | hlim
byte1 = (cid << 7) | (sac << 6) | (sam << 4) | (m << 3) | (dac << 2) | dam
data_bytes = bytearray([byte0, byte1])
# WHEN
actual = lowpan.LowpanIPHC.from_bytes(data_bytes)
# THEN
self.assertEqual(tf, actual.tf)
self.assertEqual(nh, actual.nh)
self.assertEqual(hlim, actual.hlim)
self.assertEqual(cid, actual.cid)
self.assertEqual(sac, actual.sac)
self.assertEqual(sam, actual.sam)
self.assertEqual(m, actual.m)
self.assertEqual(dac, actual.dac)
self.assertEqual(dam, actual.dam)
class TestLowpanParser(unittest.TestCase):
def test_should_parse_6lowpan_packet_with_not_compressed_udp_and_without_hop_by_hop_extension_header_when_decompress_method_is_called(self):
# GIVEN
lowpan_packet = bytearray([0x7a, 0x33, 0x11, 0x16, 0x33, 0x16, 0x34, 0x00,
0x14, 0xcf, 0x63, 0x80, 0x00, 0xfa, 0xa5, 0x0b,
0xc0, 0x00, 0x04, 0x4e, 0x92, 0xbb, 0x53])
ipv6_packet = bytearray([0x60, 0x00, 0x00, 0x00, 0x00, 0x14, 0x11, 0x40,
0xfe, 0x80, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x02, 0x99, 0x99, 0xff, 0xfe, 0x22, 0x11, 0x00,
0xfe, 0x80, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x36, 0x29, 0x96, 0xff, 0xfe, 0xac, 0xff, 0x17,
0x16, 0x33, 0x16, 0x34, 0x00, 0x14, 0xcf, 0x63,
0x80, 0x00, 0xfa, 0xa5, 0x0b, 0xc0, 0x00, 0x04,
0x4e, 0x92, 0xbb, 0x53])
message_info = common.MessageInfo()
message_info.source_mac_address = common.MacAddress.from_eui64(
bytearray([0x00, 0x99, 0x99, 0xff, 0xfe, 0x22, 0x11, 0x00]))
message_info.destination_mac_address = common.MacAddress.from_eui64(
bytearray([0x34, 0x29, 0x96, 0xff, 0xfe, 0xac, 0xff, 0x17]))
parser = create_default_lowpan_parser(context_manager=None)
# WHEN
actual_ipv6_packet = parser.parse(io.BytesIO(lowpan_packet), message_info)
# THEN
self.assertEqual(ipv6_packet, actual_ipv6_packet.to_bytes())
def test_should_parse_6lowpan_packet_with_compressed_udp_and_without_hop_by_hop_extension_header_when_decompress_method_is_called(self):
# GIVEN
lowpan_packet = bytearray([0x7e, 0x33, 0xf0, 0x16, 0x33, 0x16, 0x34, 0x04,
0xd2, 0x80, 0x00, 0xfa, 0xa5, 0x0b, 0xc0, 0x00,
0x04, 0x4e, 0x92, 0xbb, 0x53])
ipv6_packet = bytearray([0x60, 0x00, 0x00, 0x00, 0x00, 0x14, 0x11, 0x40,
0xfe, 0x80, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x02, 0x99, 0x99, 0xff, 0xfe, 0x22, 0x11, 0x00,
0xfe, 0x80, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x36, 0x29, 0x96, 0xff, 0xfe, 0xac, 0xff, 0x17,
0x16, 0x33, 0x16, 0x34, 0x00, 0x14, 0xcf, 0x63,
0x80, 0x00, 0xfa, 0xa5, 0x0b, 0xc0, 0x00, 0x04,
0x4e, 0x92, 0xbb, 0x53])
message_info = common.MessageInfo()
message_info.source_mac_address = common.MacAddress.from_eui64(
bytearray([0x00, 0x99, 0x99, 0xff, 0xfe, 0x22, 0x11, 0x00]))
message_info.destination_mac_address = common.MacAddress.from_eui64(
bytearray([0x34, 0x29, 0x96, 0xff, 0xfe, 0xac, 0xff, 0x17]))
parser = create_default_lowpan_parser(context_manager=None)
# WHEN
actual_ipv6_packet = parser.parse(io.BytesIO(lowpan_packet), message_info)
# THEN
self.assertEqual(ipv6_packet, actual_ipv6_packet.to_bytes())
def test_should_parse_6lowpan_packet_with_not_compressed_udp_and_with_not_compressed_hop_by_hop_extension_header_when_decompress_method_is_called(self):
# GIVEN
lowpan_packet = bytearray([0x7a, 0x33, 0x00, 0x11, 0x00, 0x6d, 0x04, 0x40,
0x02, 0x00, 0x18, 0x16, 0x33, 0x16, 0x34, 0x00,
0x0c, 0x04, 0xd2, 0x80, 0x00, 0xfa, 0xa5, 0x0b,
0xc0, 0x00, 0x04, 0x4e, 0x92, 0xbb, 0x53])
ipv6_packet = bytearray([0x60, 0x00, 0x00, 0x00, 0x00, 0x1c, 0x00, 0x40,
0xfe, 0x80, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x02, 0x99, 0x99, 0xff, 0xfe, 0x22, 0x11, 0x00,
0xfe, 0x80, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x36, 0x29, 0x96, 0xff, 0xfe, 0xac, 0xff, 0x17,
0x11, 0x00, 0x6d, 0x04, 0x40, 0x02, 0x00, 0x18,
0x16, 0x33, 0x16, 0x34, 0x00, 0x14, 0xcf, 0x63,
0x80, 0x00, 0xfa, 0xa5, 0x0b, 0xc0, 0x00, 0x04,
0x4e, 0x92, 0xbb, 0x53])
message_info = common.MessageInfo()
message_info.source_mac_address = common.MacAddress.from_eui64(
bytearray([0x00, 0x99, 0x99, 0xff, 0xfe, 0x22, 0x11, 0x00]))
message_info.destination_mac_address = common.MacAddress.from_eui64(
bytearray([0x34, 0x29, 0x96, 0xff, 0xfe, 0xac, 0xff, 0x17]))
parser = create_default_lowpan_parser(context_manager=None)
# WHEN
actual_ipv6_packet = parser.parse(io.BytesIO(lowpan_packet), message_info)
# THEN
self.assertEqual(ipv6_packet, actual_ipv6_packet.to_bytes())
def test_should_parse_6lowpan_packet_with_not_compressed_udp_and_with_compressed_hop_by_hop_extension_header_when_decompress_method_is_called(self):
# GIVEN
lowpan_packet = bytearray([0x7e, 0x33, 0xe0, 0x11, 0x06, 0x6d, 0x04, 0x40,
0x02, 0x00, 0x18, 0x16, 0x33, 0x16, 0x34, 0x00,
0x0c, 0x04, 0xd2, 0x80, 0x00, 0xfa, 0xa5, 0x0b,
0xc0, 0x00, 0x04, 0x4e, 0x92, 0xbb, 0x53])
ipv6_packet = bytearray([0x60, 0x00, 0x00, 0x00, 0x00, 0x1c, 0x00, 0x40,
0xfe, 0x80, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x02, 0x99, 0x99, 0xff, 0xfe, 0x22, 0x11, 0x00,
0xfe, 0x80, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x36, 0x29, 0x96, 0xff, 0xfe, 0xac, 0xff, 0x17,
0x11, 0x00, 0x6d, 0x04, 0x40, 0x02, 0x00, 0x18,
0x16, 0x33, 0x16, 0x34, 0x00, 0x14, 0xcf, 0x63,
0x80, 0x00, 0xfa, 0xa5, 0x0b, 0xc0, 0x00, 0x04,
0x4e, 0x92, 0xbb, 0x53])
message_info = common.MessageInfo()
message_info.source_mac_address = common.MacAddress.from_eui64(
bytearray([0x00, 0x99, 0x99, 0xff, 0xfe, 0x22, 0x11, 0x00]))
message_info.destination_mac_address = common.MacAddress.from_eui64(
bytearray([0x34, 0x29, 0x96, 0xff, 0xfe, 0xac, 0xff, 0x17]))
parser = create_default_lowpan_parser(context_manager=None)
# WHEN
actual_ipv6_packet = parser.parse(io.BytesIO(lowpan_packet), message_info)
# THEN
self.assertEqual(ipv6_packet, actual_ipv6_packet.to_bytes())
def test_should_parse_6lowpan_packet_with_compressed_udp_and_with_compressed_hop_by_hop_extension_header_when_decompress_method_is_called(self):
# GIVEN
lowpan_packet = bytearray([0x7e, 0x33, 0xe1, 0x06, 0x6d, 0x04, 0x40, 0x02,
0x00, 0x18, 0xf0, 0x16, 0x33, 0x16, 0x34, 0x04,
0xd2, 0x80, 0x00, 0xfa, 0xa5, 0x0b, 0xc0, 0x00,
0x04, 0x4e, 0x92, 0xbb, 0x53])
ipv6_packet = bytearray([0x60, 0x00, 0x00, 0x00, 0x00, 0x1c, 0x00, 0x40,
0xfe, 0x80, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x02, 0x99, 0x99, 0xff, 0xfe, 0x22, 0x11, 0x00,
0xfe, 0x80, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x36, 0x29, 0x96, 0xff, 0xfe, 0xac, 0xff, 0x17,
0x11, 0x00, 0x6d, 0x04, 0x40, 0x02, 0x00, 0x18,
0x16, 0x33, 0x16, 0x34, 0x00, 0x14, 0xcf, 0x63,
0x80, 0x00, 0xfa, 0xa5, 0x0b, 0xc0, 0x00, 0x04,
0x4e, 0x92, 0xbb, 0x53])
message_info = common.MessageInfo()
message_info.source_mac_address = common.MacAddress.from_eui64(
bytearray([0x00, 0x99, 0x99, 0xff, 0xfe, 0x22, 0x11, 0x00]))
message_info.destination_mac_address = common.MacAddress.from_eui64(
bytearray([0x34, 0x29, 0x96, 0xff, 0xfe, 0xac, 0xff, 0x17]))
parser = create_default_lowpan_parser(context_manager=None)
# WHEN
actual_ipv6_packet = parser.parse(io.BytesIO(lowpan_packet), message_info)
# THEN
self.assertEqual(ipv6_packet, actual_ipv6_packet.to_bytes())
def test_should_parse_6lowpan_packet_with_compressed_icmp_and_without_compressed_hop_by_hop_extension_header_when_decompress_method_is_called(self):
# GIVEN
lowpan_packet = bytearray([0x7a, 0xd5, 0xaa, 0x3a, 0x02, 0x99, 0x99, 0xff,
0xfe, 0x22, 0x11, 0x01, 0x36, 0x29, 0x96, 0xff,
0xfe, 0xac, 0xff, 0x18, 0x80, 0x00, 0xfa, 0xa5,
0x0b, 0xc0, 0x00, 0x04, 0x4e, 0x92, 0xbb, 0x53])
ipv6_packet = bytearray([0x60, 0x00, 0x00, 0x00, 0x00, 0x0c, 0x3a, 0x40,
0x20, 0x00, 0x0d, 0xb8, 0x00, 0x00, 0x00, 0x00,
0x02, 0x99, 0x99, 0xff, 0xfe, 0x22, 0x11, 0x01,
0x20, 0x00, 0x0d, 0xb8, 0x00, 0x00, 0x00, 0x00,
0x36, 0x29, 0x96, 0xff, 0xfe, 0xac, 0xff, 0x18,
0x80, 0x00, 0x97, 0xf3, 0x0b, 0xc0, 0x00, 0x04,
0x4e, 0x92, 0xbb, 0x53])
message_info = common.MessageInfo()
message_info.source_mac_address = common.MacAddress.from_eui64(
bytearray([0x00, 0x99, 0x99, 0xff, 0xfe, 0x22, 0x11, 0x00]))
message_info.destination_mac_address = common.MacAddress.from_eui64(
bytearray([0x34, 0x29, 0x96, 0xff, 0xfe, 0xac, 0xff, 0x17]))
context_manager = lowpan.ContextManager()
context_manager[10] = lowpan.Context(prefix="2000:0db8::/64")
parser = create_default_lowpan_parser(context_manager)
# WHEN
actual_ipv6_packet = parser.parse(io.BytesIO(lowpan_packet), message_info)
# THEN
self.assertEqual(ipv6_packet, actual_ipv6_packet.to_bytes())
def test_should_parse_6lowpan_packet_with_compressed_icmp_and_without_compressed_hop_by_hop_extension_header_when_decompress_method_is_called_1(self):
# GIVEN
lowpan_packet = bytearray([0x7a, 0xd5, 0xaa, 0x3a, 0x02, 0x99, 0x99, 0xff,
0xfe, 0x22, 0x11, 0x01, 0x36, 0x29, 0x96, 0xff,
0xfe, 0xac, 0xff, 0x18, 0x80, 0x00, 0xfa, 0xa5,
0x0b, 0xc0, 0x00, 0x04, 0x4e, 0x92, 0xbb, 0x53])
ipv6_packet = bytearray([0x60, 0x00, 0x00, 0x00, 0x00, 0x0c, 0x3a, 0x40,
0x20, 0x00, 0x0d, 0xb8, 0x00, 0x00, 0x00, 0x00,
0x02, 0x99, 0x99, 0xff, 0xfe, 0x22, 0x11, 0x01,
0x20, 0x00, 0x0d, 0xb8, 0x00, 0x00, 0x00, 0x00,
0x36, 0x29, 0x96, 0xff, 0xfe, 0xac, 0xff, 0x18,
0x80, 0x00, 0x97, 0xf3, 0x0b, 0xc0, 0x00, 0x04,
0x4e, 0x92, 0xbb, 0x53])
message_info = common.MessageInfo()
message_info.source_mac_address = common.MacAddress.from_eui64(
bytearray([0x00, 0x99, 0x99, 0xff, 0xfe, 0x22, 0x11, 0x00]))
message_info.destination_mac_address = common.MacAddress.from_eui64(
bytearray([0x34, 0x29, 0x96, 0xff, 0xfe, 0xac, 0xff, 0x17]))
context_manager = lowpan.ContextManager()
context_manager[10] = lowpan.Context(prefix="2000:0db8::/64")
parser = create_default_lowpan_parser(context_manager)
# WHEN
actual_ipv6_packet = parser.parse(io.BytesIO(lowpan_packet), message_info)
# THEN
self.assertEqual(ipv6_packet, actual_ipv6_packet.to_bytes())
def test_should_parse_6lowpan_packet_with_compressed_icmp_and_without_compressed_hop_by_hop_extension_header_when_decompress_method_is_called_2(self):
# GIVEN
lowpan_packet = bytearray([0x7a, 0xf0, 0xa0, 0x3a, 0x20, 0x0d, 0x14, 0x56,
0x12, 0x55, 0x00, 0x00, 0x25, 0x14, 0x46, 0xff,
0xfe, 0xdd, 0x2a, 0xfe, 0x80, 0x00, 0xfa, 0xa5,
0x0b, 0xc0, 0x00, 0x04, 0x4e, 0x92, 0xbb, 0x53])
ipv6_packet = bytearray([0x60, 0x00, 0x00, 0x00, 0x00, 0x0c, 0x3a, 0x40,
0x20, 0x00, 0x0d, 0xb8, 0x00, 0x00, 0x00, 0x00,
0x02, 0x99, 0x99, 0xff, 0xfe, 0x22, 0x11, 0x00,
0x20, 0x0d, 0x14, 0x56, 0x12, 0x55, 0x00, 0x00,
0x25, 0x14, 0x46, 0xff, 0xfe, 0xdd, 0x2a, 0xfe,
0x80, 0x00, 0xb3, 0xf3, 0x0b, 0xc0, 0x00, 0x04,
0x4e, 0x92, 0xbb, 0x53])
message_info = common.MessageInfo()
message_info.source_mac_address = common.MacAddress.from_eui64(
bytearray([0x00, 0x99, 0x99, 0xff, 0xfe, 0x22, 0x11, 0x00]))
message_info.destination_mac_address = common.MacAddress.from_eui64(
bytearray([0x34, 0x29, 0x96, 0xff, 0xfe, 0xac, 0xff, 0x17]))
context_manager = lowpan.ContextManager()
context_manager[10] = lowpan.Context(prefix="2000:0db8::/64")
parser = create_default_lowpan_parser(context_manager)
# WHEN
actual_ipv6_packet = parser.parse(io.BytesIO(lowpan_packet), message_info)
# THEN
self.assertEqual(ipv6_packet, actual_ipv6_packet.to_bytes())
def test_should_parse_6lowpan_packet_with_compressed_icmp_and_without_compressed_hop_by_hop_extension_header_when_decompress_method_is_called_3(self):
# GIVEN
lowpan_packet = bytearray([0x7a, 0xd5, 0xaa, 0x3a, 0x02, 0x99, 0x99, 0xff,
0xfe, 0x22, 0x11, 0x01, 0x36, 0x29, 0x96, 0xff,
0xfe, 0xac, 0xff, 0x18, 0x80, 0x00, 0xfa, 0xa5,
0x0b, 0xc0, 0x00, 0x04, 0x4e, 0x92, 0xbb, 0x53])
ipv6_packet = bytearray([0x60, 0x00, 0x00, 0x00, 0x00, 0x0c, 0x3a, 0x40,
0x20, 0x00, 0x0d, 0xb8, 0x00, 0x00, 0x00, 0x00,
0x02, 0x99, 0x99, 0xff, 0xfe, 0x22, 0x11, 0x01,
0x20, 0x00, 0x0d, 0xb8, 0x00, 0x00, 0x00, 0x00,
0x36, 0x29, 0x96, 0xff, 0xfe, 0xac, 0xff, 0x18,
0x80, 0x00, 0x97, 0xf3, 0x0b, 0xc0, 0x00, 0x04,
0x4e, 0x92, 0xbb, 0x53])
message_info = common.MessageInfo()
message_info.source_mac_address = common.MacAddress.from_eui64(
bytearray([0x00, 0x99, 0x99, 0xff, 0xfe, 0x22, 0x11, 0x00]))
message_info.destination_mac_address = common.MacAddress.from_eui64(
bytearray([0x34, 0x29, 0x96, 0xff, 0xfe, 0xac, 0xff, 0x17]))
context_manager = lowpan.ContextManager()
context_manager[10] = lowpan.Context(prefix="2000:0db8::/64")
parser = create_default_lowpan_parser(context_manager)
# WHEN
actual_ipv6_packet = parser.parse(io.BytesIO(lowpan_packet), message_info)
# THEN
self.assertEqual(ipv6_packet, actual_ipv6_packet.to_bytes())
def test_should_parse_6lowpan_packet_with_compressed_icmp_and_without_compressed_hop_by_hop_extension_header_when_decompress_method_is_called_4(self):
# GIVEN
lowpan_packet = bytearray([0x7a, 0xf5, 0xaa, 0x3a, 0x36, 0x29, 0x96, 0xff,
0xfe, 0xac, 0xff, 0x18, 0x80, 0x00, 0xfa, 0xa5,
0x0b, 0xc0, 0x00, 0x04, 0x4e, 0x92, 0xbb, 0x53])
ipv6_packet = bytearray([0x60, 0x00, 0x00, 0x00, 0x00, 0x0c, 0x3a, 0x40,
0x20, 0x00, 0x0d, 0xb8, 0x00, 0x00, 0x00, 0x00,
0x02, 0x99, 0x99, 0xff, 0xfe, 0x22, 0x11, 0x00,
0x20, 0x00, 0x0d, 0xb8, 0x00, 0x00, 0x00, 0x00,
0x36, 0x29, 0x96, 0xff, 0xfe, 0xac, 0xff, 0x18,
0x80, 0x00, 0x97, 0xf4, 0x0b, 0xc0, 0x00, 0x04,
0x4e, 0x92, 0xbb, 0x53])
message_info = common.MessageInfo()
message_info.source_mac_address = common.MacAddress.from_eui64(
bytearray([0x00, 0x99, 0x99, 0xff, 0xfe, 0x22, 0x11, 0x00]))
message_info.destination_mac_address = common.MacAddress.from_eui64(
bytearray([0x34, 0x29, 0x96, 0xff, 0xfe, 0xac, 0xff, 0x17]))
context_manager = lowpan.ContextManager()
context_manager[10] = lowpan.Context(prefix="2000:0db8::/64")
parser = create_default_lowpan_parser(context_manager)
# WHEN
actual_ipv6_packet = parser.parse(io.BytesIO(lowpan_packet), message_info)
# THEN
self.assertEqual(ipv6_packet, actual_ipv6_packet.to_bytes())
def test_should_parse_6lowpan_packet_with_compressed_icmp_and_without_compressed_hop_by_hop_extension_header_when_decompress_method_is_called_5(self):
# GIVEN
lowpan_packet = bytearray([0x7a, 0xf7, 0xac, 0x3a, 0x80, 0x00, 0xfa, 0xa5,
0x0b, 0xc0, 0x00, 0x04, 0x4e, 0x92, 0xbb, 0x53])
ipv6_packet = bytearray([0x60, 0x00, 0x00, 0x00, 0x00, 0x0c, 0x3a, 0x40,
0x20, 0x00, 0x0d, 0xb8, 0x00, 0x00, 0x00, 0x00,
0x02, 0x99, 0x99, 0xff, 0xfe, 0x22, 0x11, 0x00,
0x20, 0x0d, 0x14, 0x56, 0x12, 0x55, 0x00, 0x00,
0x25, 0x14, 0x46, 0xff, 0xfe, 0xdd, 0x2a, 0xfe,
0x80, 0x00, 0xb3, 0xf3, 0x0b, 0xc0, 0x00, 0x04,
0x4e, 0x92, 0xbb, 0x53])
message_info = common.MessageInfo()
message_info.source_mac_address = common.MacAddress.from_eui64(
bytearray([0x00, 0x99, 0x99, 0xff, 0xfe, 0x22, 0x11, 0x00]))
message_info.destination_mac_address = common.MacAddress.from_eui64(
bytearray([0x34, 0x29, 0x96, 0xff, 0xfe, 0xac, 0xff, 0x17]))
context_manager = lowpan.ContextManager()
context_manager[10] = lowpan.Context(prefix="2000:0db8::/64")
context_manager[12] = lowpan.Context(prefix="200d:1456:1255:0000:2514:46ff:fedd:2afe/128")
parser = create_default_lowpan_parser(context_manager)
# WHEN
actual_ipv6_packet = parser.parse(io.BytesIO(lowpan_packet), message_info)
# THEN
self.assertEqual(ipv6_packet, actual_ipv6_packet.to_bytes())
def test_should_parse_6lowpan_packet_with_compressed_icmp_and_without_compressed_hop_by_hop_extension_header_when_decompress_method_is_called_6(self):
# GIVEN
lowpan_packet = bytearray([0x7a, 0xf0, 0xc0, 0x3a, 0x20, 0x0d, 0x14, 0x56,
0x12, 0x54, 0x00, 0x00, 0x12, 0x54, 0x11, 0xff,
0xfe, 0x1c, 0x7e, 0xff, 0x80, 0x00, 0xfa, 0xa5,
0x0b, 0xc0, 0x00, 0x04, 0x4e, 0x92, 0xbb, 0x53])
ipv6_packet = bytearray([0x60, 0x00, 0x00, 0x00, 0x00, 0x0c, 0x3a, 0x40,
0x20, 0x0d, 0x14, 0x56, 0x12, 0x55, 0x00, 0x00,
0x25, 0x14, 0x46, 0xff, 0xfe, 0xdd, 0x2a, 0xfe,
0x20, 0x0d, 0x14, 0x56, 0x12, 0x54, 0x00, 0x00,
0x12, 0x54, 0x11, 0xff, 0xfe, 0x1c, 0x7e, 0xff,
0x80, 0x00, 0xa5, 0x40, 0x0b, 0xc0, 0x00, 0x04,
0x4e, 0x92, 0xbb, 0x53])
message_info = common.MessageInfo()
message_info.source_mac_address = common.MacAddress.from_eui64(
bytearray([0x00, 0x99, 0x99, 0xff, 0xfe, 0x22, 0x11, 0x00]))
message_info.destination_mac_address = common.MacAddress.from_eui64(
bytearray([0x34, 0x29, 0x96, 0xff, 0xfe, 0xac, 0xff, 0x17]))
context_manager = lowpan.ContextManager()
context_manager[12] = lowpan.Context(prefix="200d:1456:1255:0000:2514:46ff:fedd:2afe/128")
parser = create_default_lowpan_parser(context_manager)
# WHEN
actual_ipv6_packet = parser.parse(io.BytesIO(lowpan_packet), message_info)
# THEN
self.assertEqual(ipv6_packet, actual_ipv6_packet.to_bytes())
def test_should_parse_6lowpan_packet_with_compressed_icmp_and_without_compressed_hop_by_hop_extension_header_when_decompress_method_is_called_7(self):
# GIVEN
lowpan_packet = bytearray([0x7a, 0xd0, 0xd0, 0x3a, 0x00, 0x02, 0x98, 0xff,
0xfe, 0x22, 0x12, 0x00, 0x20, 0x0d, 0x14, 0x56,
0x12, 0x55, 0x00, 0x00, 0x25, 0x14, 0x46, 0xff,
0xfe, 0xdd, 0x2a, 0xfe, 0x80, 0x00, 0xfa, 0xa5,
0x0b, 0xc0, 0x00, 0x04, 0x4e, 0x92, 0xbb, 0x53])
ipv6_packet = bytearray([0x60, 0x00, 0x00, 0x00, 0x00, 0x0c, 0x3a, 0x40,
0xaa, 0xbb, 0xcc, 0xdd, 0x00, 0x00, 0x00, 0x00,
0x77, 0x82, 0x98, 0xff, 0xfe, 0x22, 0x12, 0x00,
0x20, 0x0d, 0x14, 0x56, 0x12, 0x55, 0x00, 0x00,
0x25, 0x14, 0x46, 0xff, 0xfe, 0xdd, 0x2a, 0xfe,
0x80, 0x00, 0xf5, 0x28, 0x0b, 0xc0, 0x00, 0x04,
0x4e, 0x92, 0xbb, 0x53])
message_info = common.MessageInfo()
message_info.source_mac_address = common.MacAddress.from_eui64(
bytearray([0x00, 0x99, 0x99, 0xff, 0xfe, 0x22, 0x11, 0x00]))
message_info.destination_mac_address = common.MacAddress.from_eui64(
bytearray([0x34, 0x29, 0x96, 0xff, 0xfe, 0xac, 0xff, 0x17]))
context_manager = lowpan.ContextManager()
context_manager[13] = lowpan.Context(prefix="AABB:CCDD:0000:0000:7796::/75")
parser = create_default_lowpan_parser(context_manager)
# WHEN
actual_ipv6_packet = parser.parse(io.BytesIO(lowpan_packet), message_info)
# THEN
self.assertEqual(ipv6_packet, actual_ipv6_packet.to_bytes())
def test_should_parse_6lowpan_packet_with_compressed_icmp_and_without_compressed_hop_by_hop_extension_header_when_decompress_method_is_called_8(self):
# GIVEN
lowpan_packet = bytearray([0x7a, 0xf0, 0xd0, 0x3a, 0x20, 0x0d, 0x14, 0x56,
0x12, 0x55, 0x00, 0x00, 0x25, 0x14, 0x46, 0xff,
0xfe, 0xdd, 0x2a, 0xfe, 0x80, 0x00, 0xfa, 0xa5,
0x0b, 0xc0, 0x00, 0x04, 0x4e, 0x92, 0xbb, 0x53])
ipv6_packet = bytearray([0x60, 0x00, 0x00, 0x00, 0x00, 0x0c, 0x3a, 0x40,
0xaa, 0xbb, 0xcc, 0xdd, 0x00, 0x00, 0x00, 0x00,
0x77, 0x99, 0x99, 0xff, 0xfe, 0x22, 0x11, 0x00,
0x20, 0x0d, 0x14, 0x56, 0x12, 0x55, 0x00, 0x00,
0x25, 0x14, 0x46, 0xff, 0xfe, 0xdd, 0x2a, 0xfe,
0x80, 0x00, 0xf5, 0x11, 0x0b, 0xc0, 0x00, 0x04,
0x4e, 0x92, 0xbb, 0x53])
message_info = common.MessageInfo()
message_info.source_mac_address = common.MacAddress.from_eui64(
bytearray([0x00, 0x99, 0x99, 0xff, 0xfe, 0x22, 0x11, 0x00]))
message_info.destination_mac_address = common.MacAddress.from_eui64(
bytearray([0x34, 0x29, 0x96, 0xff, 0xfe, 0xac, 0xff, 0x17]))
context_manager = lowpan.ContextManager()
context_manager[13] = lowpan.Context(prefix="AABB:CCDD:0000:0000:7796::/75")
parser = create_default_lowpan_parser(context_manager)
# WHEN
actual_ipv6_packet = parser.parse(io.BytesIO(lowpan_packet), message_info)
# THEN
self.assertEqual(ipv6_packet, actual_ipv6_packet.to_bytes())
def test_should_parse_6lowpan_packet_with_compressed_icmp_and_without_compressed_hop_by_hop_extension_header_when_decompress_method_is_called_9(self):
# GIVEN
lowpan_packet = bytearray([0x7a, 0xf0, 0xd0, 0x3a, 0x20, 0x0d, 0x14, 0x56,
0x12, 0x55, 0x00, 0x00, 0x25, 0x14, 0x46, 0xff,
0xfe, 0xdd, 0x2a, 0xfe, 0x80, 0x00, 0xfa, 0xa5,
0x0b, 0xc0, 0x00, 0x04, 0x4e, 0x92, 0xbb, 0x53])
ipv6_packet = bytearray([0x60, 0x00, 0x00, 0x00, 0x00, 0x0c, 0x3a, 0x40,
0xaa, 0xbb, 0xcc, 0xdd, 0x00, 0x00, 0x00, 0x00,
0x77, 0x99, 0x99, 0xff, 0xfe, 0x22, 0x11, 0x00,
0x20, 0x0d, 0x14, 0x56, 0x12, 0x55, 0x00, 0x00,
0x25, 0x14, 0x46, 0xff, 0xfe, 0xdd, 0x2a, 0xfe,
0x80, 0x00, 0xf5, 0x11, 0x0b, 0xc0, 0x00, 0x04,
0x4e, 0x92, 0xbb, 0x53])
message_info = common.MessageInfo()
message_info.source_mac_address = common.MacAddress.from_eui64(
bytearray([0x00, 0x99, 0x99, 0xff, 0xfe, 0x22, 0x11, 0x00]))
message_info.destination_mac_address = common.MacAddress.from_eui64(
bytearray([0x34, 0x29, 0x96, 0xff, 0xfe, 0xac, 0xff, 0x17]))
context_manager = lowpan.ContextManager()
context_manager[13] = lowpan.Context(prefix="AABB:CCDD:0000:0000:7796::/75")
parser = create_default_lowpan_parser(context_manager)
# WHEN
actual_ipv6_packet = parser.parse(io.BytesIO(lowpan_packet), message_info)
# THEN
self.assertEqual(ipv6_packet, actual_ipv6_packet.to_bytes())
def test_should_defragment_big_IPv6_packet_when_parse_method_is_called_with_fragments_in_random_order(self):
# GIVEN
fragment_1 = bytearray([0xC5, 0x00, 0x31, 0x9F, 0x7A, 0x33, 0x3A,
0x80, 0x00, 0xFA, 0xA5, 0x0B, 0xC0, 0x00, 0x04,
0x4E, 0x92, 0xBB, 0x53, 0x11, 0x44, 0x66, 0x4E,
0x92, 0xBB, 0x53, 0x1A, 0x44, 0x66, 0x77, 0x99,
0x15, 0xB3, 0x00, 0x54, 0xCC, 0x54, 0x01, 0xAA,
0x44, 0x54, 0x12, 0x43, 0x53, 0x11, 0x44, 0x66,
0x4E, 0x92, 0xBB, 0x53, 0x1A, 0x44, 0x66, 0x77,
0x99, 0x1A, 0x92, 0xBB, 0x53, 0x11, 0x44, 0x66,
0x92, 0xBB, 0x53, 0x1A, 0x44, 0x66, 0x77, 0x99,
0x15, 0xB3, 0x00, 0x54, 0xCC, 0x54, 0x01, 0xAA,
0x11, 0x44, 0x66, 0x4E, 0x92, 0xBB, 0x53, 0x1A,
0x80, 0x00, 0xFA, 0xA5, 0x0B, 0xC0, 0x00, 0x04,
0x4E, 0x92, 0xBB, 0x53, 0x11, 0x4C, 0x66, 0x4E])
fragment_2 = bytearray([0xE5, 0x00, 0x31, 0x9F, 0x11,
0x92, 0xBB, 0x53, 0x1A, 0x44, 0x66, 0x77, 0x99,
0x15, 0xB3, 0x00, 0x54, 0xCC, 0x54, 0x01, 0xAA,
0x44, 0x54, 0x12, 0xa3, 0x53, 0x11, 0x44, 0x66,
0xFE, 0x92, 0xBB, 0x53, 0x1A, 0x44, 0x66, 0x77,
0x99, 0x1A, 0x92, 0xBB, 0x53, 0x11, 0x44, 0x66,
0x92, 0xBB, 0x53, 0x1A, 0x44, 0x66, 0x77, 0x99,
0x15, 0xB3, 0x00, 0x54, 0xCC, 0x54, 0x01, 0xAA,
0x11, 0x44, 0x66, 0x4E, 0x92, 0xBB, 0x53, 0x1A,
0x80, 0x00, 0xFA, 0xA5, 0x0B, 0xC0, 0x00, 0x04,
0x4E, 0x92, 0x1B, 0x53, 0x11, 0x44, 0x66, 0x4E,
0x22, 0xBB, 0x53, 0x1A, 0x44, 0x66, 0x77, 0x99,
0x15, 0xB3, 0x00, 0x54, 0xCC, 0x54, 0x01, 0xAA])
fragment_3 = bytearray([0xE5, 0x00, 0x31, 0x9F, 0x1D,
0x44, 0x54, 0x12, 0xD3, 0x53, 0x11, 0x44, 0x66,
0x4E, 0x92, 0xBB, 0x53, 0x1A, 0x44, 0x66, 0x77,
0x99, 0x1A, 0x92, 0xBB, 0x53, 0x11, 0x44, 0x66,
0x92, 0xBB, 0x53, 0x1A, 0x44, 0x66, 0x77, 0x99,
0x15, 0xB3, 0x00, 0x54, 0xCC, 0x54, 0x01, 0xAA,
0x11, 0x44, 0x66, 0x4E, 0x92, 0xBB, 0x53, 0x1A,
0xC0, 0x00, 0xFA, 0xA5, 0x0B, 0xC0, 0x00, 0x04,
0x4E, 0x92, 0xBB, 0x53, 0x11, 0x44, 0xCC, 0x4E,
0x92, 0xBB, 0x53, 0x1A, 0x44, 0x66, 0x77, 0x99,
0x15, 0xB3, 0x00, 0x54, 0xCC, 0x54, 0x01, 0xAA,
0x44, 0x54, 0x12, 0x43, 0x53, 0x11, 0x44, 0x66,
0x4E, 0x92, 0xBC, 0x53, 0x1A, 0x44, 0x66, 0x77])
fragment_4 = bytearray([0xE5, 0x00, 0x31, 0x9F, 0x29,
0x99, 0x1A, 0x92, 0xBB, 0x53, 0x11, 0x44, 0x66,
0x92, 0xBB, 0x53, 0x1A, 0x44, 0x66, 0x77, 0x99,
0x15, 0xB3, 0x00, 0x54, 0xCC, 0x54, 0x01, 0xAA,
0x11, 0x44, 0x66, 0x4E, 0x92, 0xBB, 0x53, 0x1A,
0x80, 0x00, 0xFA, 0xA5, 0x0B, 0xC0, 0x00, 0x04,
0x4E, 0x92, 0xBB, 0x53, 0x11, 0x44, 0x66, 0x4E,
0x92, 0xBB, 0x53, 0x1A, 0x44, 0x66, 0x77, 0x99,
0x15, 0xB3, 0x00, 0x54, 0xCC, 0x54, 0x01, 0xAA,
0x44, 0x54, 0x12, 0x43, 0x53, 0x11, 0x44, 0x66,
0x4E, 0x92, 0xBB, 0x53, 0x1A, 0x44, 0x66, 0x77,
0x99, 0x1A, 0x92, 0xBB, 0x53, 0x11, 0x44, 0x66,
0x92, 0xBB, 0x53, 0x1A, 0x44, 0x66, 0x77, 0x99])
fragment_5 = bytearray([0xE5, 0x00, 0x31, 0x9F, 0x35,
0x15, 0xB3, 0x00, 0x54, 0xCC, 0x54, 0x01, 0xAA,
0x11, 0x44, 0x66, 0x4E, 0x92, 0xBB, 0x53, 0x1A,
0x80, 0x00, 0xFA, 0xA5, 0x0B, 0xC0, 0x00, 0x04,
0x4E, 0x92, 0xBB, 0x53, 0x11, 0x4C, 0x66, 0x4E,
0x92, 0xBB, 0x53, 0x1A, 0x44, 0x66, 0x77, 0x99,
0x15, 0xB3, 0x00, 0x54, 0xCC, 0x54, 0x01, 0xAA,
0x44, 0x54, 0x12, 0xa3, 0x53, 0x11, 0x44, 0x66,
0xFE, 0x92, 0xBB, 0x53, 0x1A, 0x44, 0x66, 0x77,
0x99, 0x1A, 0x92, 0xBB, 0x53, 0x11, 0x44, 0x66,
0x92, 0xBB, 0x53, 0x1A, 0x44, 0x66, 0x77, 0x99,
0x15, 0xB3, 0x00, 0x54, 0xCC, 0x54, 0x01, 0xAA,
0x11, 0x44, 0x66, 0x4E, 0x92, 0xBB, 0x53, 0x1A])
fragment_6 = bytearray([0xE5, 0x00, 0x31, 0x9F, 0x41,
0x80, 0x00, 0xFA, 0xA5, 0x0B, 0xC0, 0x00, 0x04,
0x4E, 0x92, 0x1B, 0x53, 0x11, 0x44, 0x66, 0x4E,
0x22, 0xBB, 0x53, 0x1A, 0x44, 0x67, 0x77, 0x99,
0x15, 0xB3, 0x00, 0x54, 0xCC, 0x54, 0x01, 0xAA,
0x44, 0x54, 0x12, 0xD3, 0x53, 0x11, 0x44, 0x66,
0x4E, 0x92, 0xBB, 0x53, 0x1A, 0x44, 0x66, 0x77,
0x99, 0x1A, 0x92, 0xBB, 0x53, 0x11, 0x44, 0x66,
0x92, 0xBB, 0x53, 0x1A, 0x44, 0x66, 0x77, 0x99,
0x15, 0xB3, 0x00, 0x54, 0xCC, 0x54, 0x01, 0xAA,
0x11, 0x44, 0x66, 0x4E, 0x92, 0xBB, 0x53, 0x1A,
0xC0, 0x00, 0xFA, 0x15, 0x0B, 0xC0, 0x00, 0x04,
0x4E, 0x92, 0xBB, 0x53, 0x11, 0x44, 0xCC, 0x4E])
fragment_7 = bytearray([0xE5, 0x00, 0x31, 0x9F, 0x4D,
0x92, 0xBB, 0x53, 0x1A, 0x44, 0x66, 0x77, 0x99,
0x15, 0xB3, 0x00, 0x54, 0xCC, 0x54, 0x01, 0xAA,
0x44, 0x54, 0x12, 0x43, 0x53, 0x11, 0x44, 0x66,
0x4E, 0x92, 0xBC, 0x53, 0x1A, 0x44, 0x66, 0x77,
0x99, 0x1A, 0x92, 0xBB, 0x53, 0x11, 0x44, 0x66,
0x92, 0xBB, 0x53, 0x1A, 0x44, 0x66, 0x77, 0x99,
0x15, 0xB3, 0x00, 0x54, 0xCC, 0x54, 0x01, 0xAA,
0x11, 0x44, 0x66, 0x4E, 0x92, 0xBA, 0x53, 0x1A,
0x60, 0x00, 0x00, 0x00, 0x00, 0x10, 0x3A, 0x64,
0xfe, 0x80, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x02, 0x00, 0x00, 0x11, 0x12, 0x13, 0x14, 0x15,
0xfe, 0x80, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00])
fragment_8 = bytearray([0xE5, 0x00, 0x31, 0x9F, 0x59,
0x02, 0x00, 0x1A, 0x2A, 0x3F, 0x09, 0xAB, 0x43,
0x60, 0x00, 0xF0, 0x00, 0x00, 0x10, 0x3A, 0x64,
0xfe, 0x80, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x02, 0x00, 0x00, 0x11, 0x12, 0x13, 0x14, 0x15,
0xfe, 0x80, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x02, 0x00, 0x1A, 0x2A, 0x3F, 0x09, 0xAB, 0x43,
0x80, 0x00, 0xFA, 0xA5, 0x0B, 0xC0, 0x00, 0x04,
0x4E, 0x92, 0xBB, 0x53, 0x11, 0x44, 0x66, 0x4E,
0x92, 0xBB, 0x53, 0x1A, 0x44, 0x66, 0x77, 0x99,
0x15, 0xB3, 0x00, 0x54, 0xCC, 0x54, 0x01, 0xAA,
0x44, 0x54, 0x12, 0x43, 0x53, 0x11, 0x44, 0x66,
0x4E, 0x92, 0xBC, 0x53, 0x1A, 0x44, 0x66, 0x77])
fragment_9 = bytearray([0xE5, 0x00, 0x31, 0x9F, 0x65,
0x99, 0x1A, 0x92, 0xBB, 0x53, 0x11, 0x44, 0x66,
0x92, 0xBB, 0x53, 0x1A, 0x44, 0x66, 0x77, 0x99,
0x15, 0xB3, 0x00, 0x54, 0xCC, 0x54, 0x01, 0xAA,
0x11, 0x44, 0x66, 0x4E, 0x92, 0xBB, 0x53, 0x1A,
0x80, 0x00, 0xFA, 0xA5, 0x0B, 0xC0, 0x00, 0x04,
0x4E, 0x92, 0xBB, 0x53, 0x11, 0x4C, 0x66, 0x4E,
0x92, 0xBB, 0x53, 0x1A, 0x44, 0x66, 0x77, 0x99,
0x15, 0xB3, 0x00, 0x54, 0xCC, 0x54, 0x01, 0xAA,
0x44, 0x54, 0x12, 0xa3, 0x53, 0x11, 0x44, 0x66,
0xFE, 0x92, 0xBB, 0x53, 0x1A, 0x44, 0x66, 0x77,
0x99, 0x1A, 0x92, 0xBB, 0x53, 0x11, 0x44, 0x66,
0x92, 0xBB, 0x53, 0x1A, 0x4D, 0x66, 0x77, 0x99])
fragment_10 = bytearray([0xE5, 0x00, 0x31, 0x9F, 0x71,
0x15, 0xB3, 0x00, 0x54, 0xCC, 0x54, 0x01, 0xAA,
0x11, 0x44, 0x66, 0x4E, 0x92, 0xBB, 0x53, 0x1A,
0x80, 0x00, 0xFA, 0xA5, 0x0B, 0xC0, 0x00, 0x04,
0x4E, 0x92, 0x1B, 0x53, 0x11, 0x44, 0x66, 0x4E,
0x22, 0xBB, 0x51, 0x1A, 0x44, 0x66, 0x77, 0x99,
0x15, 0xB3, 0x00, 0x54, 0xCC, 0x54, 0x01, 0xAA,
0x44, 0x54, 0x12, 0xD3, 0x53, 0x11, 0x44, 0x66,
0x4E, 0x92, 0xBB, 0x53, 0x1A, 0x44, 0x66, 0x77,
0x99, 0x1A, 0x92, 0xBB, 0x53, 0x11, 0x44, 0x66,
0x92, 0xBB, 0x53, 0x1A, 0x44, 0x66, 0x77, 0x99,
0x15, 0xB3, 0x00, 0x54, 0xCC, 0x54, 0x01, 0xAA,
0x11, 0x44, 0x66, 0x4E, 0x92, 0xBB, 0x53, 0x1A])
fragment_11 = bytearray([0xE5, 0x00, 0x31, 0x9F, 0x7D,
0xC0, 0x00, 0xFA, 0xA5, 0x0B, 0xC0, 0x00, 0x04,
0x4E, 0x92, 0xBB, 0x53, 0x11, 0x44, 0xCC, 0x4E,
0x92, 0xBB, 0x53, 0x1A, 0x44, 0x66, 0x77, 0x99,
0x15, 0xB3, 0x00, 0x54, 0xCC, 0x54, 0x01, 0xAA,
0x44, 0x54, 0x12, 0x4A, 0x53, 0x11, 0x44, 0x66,
0x4E, 0x92, 0xBC, 0x53, 0x1A, 0x44, 0x66, 0x77,
0x99, 0x1A, 0x92, 0xBB, 0x53, 0x11, 0x44, 0x66,
0x92, 0xBB, 0x53, 0x1A, 0x44, 0x66, 0x77, 0x99,
0x15, 0xB3, 0x00, 0x54, 0xCC, 0x54, 0x01, 0xAA,
0x11, 0x44, 0x66, 0x4E, 0x92, 0xBB, 0x53, 0x1A,
0x80, 0x00, 0xFA, 0xA5, 0x0B, 0xC0, 0x00, 0x04,
0x4E, 0x92, 0xBB, 0x53, 0x11, 0x44, 0x66, 0x4E])
fragment_12 = bytearray([0xE5, 0x00, 0x31, 0x9F, 0x89,
0x92, 0xBB, 0x53, 0x1A, 0x44, 0x66, 0x77, 0x99,
0x15, 0xB3, 0x00, 0x54, 0xCC, 0x54, 0x01, 0xAA,
0x44, 0x54, 0x12, 0x43, 0x53, 0x11, 0x44, 0x66,
0x4E, 0x92, 0xBB, 0x53, 0x3A, 0x44, 0x66, 0x77,
0x99, 0x1A, 0x92, 0xBB, 0x53, 0x11, 0x44, 0x66,
0x92, 0xBB, 0x53, 0x1A, 0x44, 0x66, 0x77, 0x99,
0x15, 0xB3, 0x00, 0x54, 0xCC, 0x54, 0x01, 0xAA,
0x11, 0x44, 0x66, 0x4E, 0x92, 0xBB, 0x53, 0x1A,
0x80, 0x00, 0xFA, 0xA5, 0x0B, 0xC0, 0x00, 0x04,
0x4E, 0x92, 0xBB, 0x53, 0x11, 0x4C, 0x66, 0x4E,
0x92, 0xBB, 0x53, 0x1A, 0x44, 0x66, 0x77, 0x99,
0x15, 0xB3, 0x00, 0x54, 0xCC, 0x54, 0x01, 0xAA])
fragment_13 = bytearray([0xE5, 0x00, 0x31, 0x9F, 0x95,
0x44, 0x54, 0x12, 0xa3, 0x53, 0x11, 0x44, 0x66,
0xFE, 0x92, 0xBB, 0x53, 0x1A, 0x44, 0x66, 0x77,
0x99, 0x1A, 0x92, 0xBB, 0x53, 0x11, 0x44, 0x66,
0x92, 0xBB, 0x53, 0x1A, 0x44, 0x66, 0x77, 0x99,
0x15, 0xB3, 0x00, 0x54, 0xCC, 0x54, 0x01, 0xAA,
0x11, 0x44, 0x66, 0x4E, 0x92, 0xBB, 0x53, 0x1A,
0x80, 0x00, 0xFA, 0xA5, 0x1B, 0xC0, 0x00, 0x04,
0x4E, 0x92, 0x1B, 0x53, 0x11, 0x44, 0x66, 0x4E,
0x22, 0xBB, 0x53, 0x1A, 0x44, 0x66, 0x77, 0x99,
0x15, 0xB3, 0x00, 0x54, 0xCC, 0x54, 0x01, 0xAA,
0x44, 0x54, 0x12, 0xD3, 0x53, 0x11, 0x44, 0x66])
message_info = common.MessageInfo()
message_info.source_mac_address = common.MacAddress.from_eui64(
bytearray([0x00, 0x00, 0x00, 0x11, 0x12, 0x13, 0x14, 0x15]))
message_info.destination_mac_address = common.MacAddress.from_eui64(
bytearray([0x00, 0x00, 0x1A, 0x2A, 0x3F, 0x09, 0xAB, 0x43]))
parser = create_default_lowpan_parser(context_manager=None)
# WHEN
self.assertIsNone(parser.parse(io.BytesIO(fragment_4), message_info))
self.assertIsNone(parser.parse(io.BytesIO(fragment_2), message_info))
self.assertIsNone(parser.parse(io.BytesIO(fragment_3), message_info))
self.assertIsNone(parser.parse(io.BytesIO(fragment_13), message_info))
self.assertIsNone(parser.parse(io.BytesIO(fragment_5), message_info))
self.assertIsNone(parser.parse(io.BytesIO(fragment_6), message_info))
self.assertIsNone(parser.parse(io.BytesIO(fragment_7), message_info))
self.assertIsNone(parser.parse(io.BytesIO(fragment_8), message_info))
self.assertIsNone(parser.parse(io.BytesIO(fragment_9), message_info))
self.assertIsNone(parser.parse(io.BytesIO(fragment_10), message_info))
self.assertIsNone(parser.parse(io.BytesIO(fragment_11), message_info))
self.assertIsNone(parser.parse(io.BytesIO(fragment_12), message_info))
actual_ipv6_packet = parser.parse(io.BytesIO(fragment_1), message_info)
# THEN
ipv6_packet = bytearray([0x60, 0x00, 0x00, 0x00, 0x04, 0xD8, 0x3A, 0x40,
0xfe, 0x80, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x02, 0x00, 0x00, 0x11, 0x12, 0x13, 0x14, 0x15,
0xfe, 0x80, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x02, 0x00, 0x1A, 0x2A, 0x3F, 0x09, 0xAB, 0x43, # / * 40 * /
0x80, 0x00, 0xAB, 0x64, 0x0B, 0xC0, 0x00, 0x04,
0x4E, 0x92, 0xBB, 0x53, 0x11, 0x44, 0x66, 0x4E,
0x92, 0xBB, 0x53, 0x1A, 0x44, 0x66, 0x77, 0x99,
0x15, 0xB3, 0x00, 0x54, 0xCC, 0x54, 0x01, 0xAA,
0x44, 0x54, 0x12, 0x43, 0x53, 0x11, 0x44, 0x66,
0x4E, 0x92, 0xBB, 0x53, 0x1A, 0x44, 0x66, 0x77,
0x99, 0x1A, 0x92, 0xBB, 0x53, 0x11, 0x44, 0x66,
0x92, 0xBB, 0x53, 0x1A, 0x44, 0x66, 0x77, 0x99,
0x15, 0xB3, 0x00, 0x54, 0xCC, 0x54, 0x01, 0xAA,
0x11, 0x44, 0x66, 0x4E, 0x92, 0xBB, 0x53, 0x1A, # / * 120 * /
0x80, 0x00, 0xFA, 0xA5, 0x0B, 0xC0, 0x00, 0x04,
0x4E, 0x92, 0xBB, 0x53, 0x11, 0x4C, 0x66, 0x4E,
0x92, 0xBB, 0x53, 0x1A, 0x44, 0x66, 0x77, 0x99,
0x15, 0xB3, 0x00, 0x54, 0xCC, 0x54, 0x01, 0xAA,
0x44, 0x54, 0x12, 0xa3, 0x53, 0x11, 0x44, 0x66,
0xFE, 0x92, 0xBB, 0x53, 0x1A, 0x44, 0x66, 0x77,
0x99, 0x1A, 0x92, 0xBB, 0x53, 0x11, 0x44, 0x66,
0x92, 0xBB, 0x53, 0x1A, 0x44, 0x66, 0x77, 0x99,
0x15, 0xB3, 0x00, 0x54, 0xCC, 0x54, 0x01, 0xAA,
0x11, 0x44, 0x66, 0x4E, 0x92, 0xBB, 0x53, 0x1A, # / * 200 * /
0x80, 0x00, 0xFA, 0xA5, 0x0B, 0xC0, 0x00, 0x04,
0x4E, 0x92, 0x1B, 0x53, 0x11, 0x44, 0x66, 0x4E,
0x22, 0xBB, 0x53, 0x1A, 0x44, 0x66, 0x77, 0x99,
0x15, 0xB3, 0x00, 0x54, 0xCC, 0x54, 0x01, 0xAA,
0x44, 0x54, 0x12, 0xD3, 0x53, 0x11, 0x44, 0x66,
0x4E, 0x92, 0xBB, 0x53, 0x1A, 0x44, 0x66, 0x77,
0x99, 0x1A, 0x92, 0xBB, 0x53, 0x11, 0x44, 0x66,
0x92, 0xBB, 0x53, 0x1A, 0x44, 0x66, 0x77, 0x99,
0x15, 0xB3, 0x00, 0x54, 0xCC, 0x54, 0x01, 0xAA,
0x11, 0x44, 0x66, 0x4E, 0x92, 0xBB, 0x53, 0x1A, # / * 280 * /
0xC0, 0x00, 0xFA, 0xA5, 0x0B, 0xC0, 0x00, 0x04,
0x4E, 0x92, 0xBB, 0x53, 0x11, 0x44, 0xCC, 0x4E,
0x92, 0xBB, 0x53, 0x1A, 0x44, 0x66, 0x77, 0x99,
0x15, 0xB3, 0x00, 0x54, 0xCC, 0x54, 0x01, 0xAA,
0x44, 0x54, 0x12, 0x43, 0x53, 0x11, 0x44, 0x66,
0x4E, 0x92, 0xBC, 0x53, 0x1A, 0x44, 0x66, 0x77,
0x99, 0x1A, 0x92, 0xBB, 0x53, 0x11, 0x44, 0x66,
0x92, 0xBB, 0x53, 0x1A, 0x44, 0x66, 0x77, 0x99,
0x15, 0xB3, 0x00, 0x54, 0xCC, 0x54, 0x01, 0xAA,
0x11, 0x44, 0x66, 0x4E, 0x92, 0xBB, 0x53, 0x1A, # / * 360 * /
0x80, 0x00, 0xFA, 0xA5, 0x0B, 0xC0, 0x00, 0x04,
0x4E, 0x92, 0xBB, 0x53, 0x11, 0x44, 0x66, 0x4E,
0x92, 0xBB, 0x53, 0x1A, 0x44, 0x66, 0x77, 0x99,
0x15, 0xB3, 0x00, 0x54, 0xCC, 0x54, 0x01, 0xAA,
0x44, 0x54, 0x12, 0x43, 0x53, 0x11, 0x44, 0x66,
0x4E, 0x92, 0xBB, 0x53, 0x1A, 0x44, 0x66, 0x77,
0x99, 0x1A, 0x92, 0xBB, 0x53, 0x11, 0x44, 0x66,
0x92, 0xBB, 0x53, 0x1A, 0x44, 0x66, 0x77, 0x99,
0x15, 0xB3, 0x00, 0x54, 0xCC, 0x54, 0x01, 0xAA,
0x11, 0x44, 0x66, 0x4E, 0x92, 0xBB, 0x53, 0x1A,
0x80, 0x00, 0xFA, 0xA5, 0x0B, 0xC0, 0x00, 0x04,
0x4E, 0x92, 0xBB, 0x53, 0x11, 0x4C, 0x66, 0x4E,
0x92, 0xBB, 0x53, 0x1A, 0x44, 0x66, 0x77, 0x99,
0x15, 0xB3, 0x00, 0x54, 0xCC, 0x54, 0x01, 0xAA,
0x44, 0x54, 0x12, 0xa3, 0x53, 0x11, 0x44, 0x66,
0xFE, 0x92, 0xBB, 0x53, 0x1A, 0x44, 0x66, 0x77,
0x99, 0x1A, 0x92, 0xBB, 0x53, 0x11, 0x44, 0x66,
0x92, 0xBB, 0x53, 0x1A, 0x44, 0x66, 0x77, 0x99,
0x15, 0xB3, 0x00, 0x54, 0xCC, 0x54, 0x01, 0xAA,
0x11, 0x44, 0x66, 0x4E, 0x92, 0xBB, 0x53, 0x1A,
0x80, 0x00, 0xFA, 0xA5, 0x0B, 0xC0, 0x00, 0x04,
0x4E, 0x92, 0x1B, 0x53, 0x11, 0x44, 0x66, 0x4E,
0x22, 0xBB, 0x53, 0x1A, 0x44, 0x67, 0x77, 0x99,
0x15, 0xB3, 0x00, 0x54, 0xCC, 0x54, 0x01, 0xAA,
0x44, 0x54, 0x12, 0xD3, 0x53, 0x11, 0x44, 0x66,
0x4E, 0x92, 0xBB, 0x53, 0x1A, 0x44, 0x66, 0x77,
0x99, 0x1A, 0x92, 0xBB, 0x53, 0x11, 0x44, 0x66,
0x92, 0xBB, 0x53, 0x1A, 0x44, 0x66, 0x77, 0x99,
0x15, 0xB3, 0x00, 0x54, 0xCC, 0x54, 0x01, 0xAA,
0x11, 0x44, 0x66, 0x4E, 0x92, 0xBB, 0x53, 0x1A,
0xC0, 0x00, 0xFA, 0x15, 0x0B, 0xC0, 0x00, 0x04,
0x4E, 0x92, 0xBB, 0x53, 0x11, 0x44, 0xCC, 0x4E,
0x92, 0xBB, 0x53, 0x1A, 0x44, 0x66, 0x77, 0x99,
0x15, 0xB3, 0x00, 0x54, 0xCC, 0x54, 0x01, 0xAA,
0x44, 0x54, 0x12, 0x43, 0x53, 0x11, 0x44, 0x66,
0x4E, 0x92, 0xBC, 0x53, 0x1A, 0x44, 0x66, 0x77,
0x99, 0x1A, 0x92, 0xBB, 0x53, 0x11, 0x44, 0x66,
0x92, 0xBB, 0x53, 0x1A, 0x44, 0x66, 0x77, 0x99,
0x15, 0xB3, 0x00, 0x54, 0xCC, 0x54, 0x01, 0xAA,
0x11, 0x44, 0x66, 0x4E, 0x92, 0xBA, 0x53, 0x1A,
0x60, 0x00, 0x00, 0x00, 0x00, 0x10, 0x3A, 0x64,
0xfe, 0x80, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x02, 0x00, 0x00, 0x11, 0x12, 0x13, 0x14, 0x15,
0xfe, 0x80, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x02, 0x00, 0x1A, 0x2A, 0x3F, 0x09, 0xAB, 0x43, # / * 720 * /
0x60, 0x00, 0xF0, 0x00, 0x00, 0x10, 0x3A, 0x64,
0xfe, 0x80, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x02, 0x00, 0x00, 0x11, 0x12, 0x13, 0x14, 0x15,
0xfe, 0x80, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x02, 0x00, 0x1A, 0x2A, 0x3F, 0x09, 0xAB, 0x43,
0x80, 0x00, 0xFA, 0xA5, 0x0B, 0xC0, 0x00, 0x04,
0x4E, 0x92, 0xBB, 0x53, 0x11, 0x44, 0x66, 0x4E,
0x92, 0xBB, 0x53, 0x1A, 0x44, 0x66, 0x77, 0x99,
0x15, 0xB3, 0x00, 0x54, 0xCC, 0x54, 0x01, 0xAA,
0x44, 0x54, 0x12, 0x43, 0x53, 0x11, 0x44, 0x66,
0x4E, 0x92, 0xBC, 0x53, 0x1A, 0x44, 0x66, 0x77,
0x99, 0x1A, 0x92, 0xBB, 0x53, 0x11, 0x44, 0x66,
0x92, 0xBB, 0x53, 0x1A, 0x44, 0x66, 0x77, 0x99,
0x15, 0xB3, 0x00, 0x54, 0xCC, 0x54, 0x01, 0xAA,
0x11, 0x44, 0x66, 0x4E, 0x92, 0xBB, 0x53, 0x1A,
0x80, 0x00, 0xFA, 0xA5, 0x0B, 0xC0, 0x00, 0x04,
0x4E, 0x92, 0xBB, 0x53, 0x11, 0x4C, 0x66, 0x4E,
0x92, 0xBB, 0x53, 0x1A, 0x44, 0x66, 0x77, 0x99,
0x15, 0xB3, 0x00, 0x54, 0xCC, 0x54, 0x01, 0xAA,
0x44, 0x54, 0x12, 0xa3, 0x53, 0x11, 0x44, 0x66,
0xFE, 0x92, 0xBB, 0x53, 0x1A, 0x44, 0x66, 0x77,
0x99, 0x1A, 0x92, 0xBB, 0x53, 0x11, 0x44, 0x66,
0x92, 0xBB, 0x53, 0x1A, 0x4D, 0x66, 0x77, 0x99,
0x15, 0xB3, 0x00, 0x54, 0xCC, 0x54, 0x01, 0xAA,
0x11, 0x44, 0x66, 0x4E, 0x92, 0xBB, 0x53, 0x1A,
0x80, 0x00, 0xFA, 0xA5, 0x0B, 0xC0, 0x00, 0x04,
0x4E, 0x92, 0x1B, 0x53, 0x11, 0x44, 0x66, 0x4E,
0x22, 0xBB, 0x51, 0x1A, 0x44, 0x66, 0x77, 0x99,
0x15, 0xB3, 0x00, 0x54, 0xCC, 0x54, 0x01, 0xAA,
0x44, 0x54, 0x12, 0xD3, 0x53, 0x11, 0x44, 0x66,
0x4E, 0x92, 0xBB, 0x53, 0x1A, 0x44, 0x66, 0x77,
0x99, 0x1A, 0x92, 0xBB, 0x53, 0x11, 0x44, 0x66,
0x92, 0xBB, 0x53, 0x1A, 0x44, 0x66, 0x77, 0x99,
0x15, 0xB3, 0x00, 0x54, 0xCC, 0x54, 0x01, 0xAA,
0x11, 0x44, 0x66, 0x4E, 0x92, 0xBB, 0x53, 0x1A,
0xC0, 0x00, 0xFA, 0xA5, 0x0B, 0xC0, 0x00, 0x04,
0x4E, 0x92, 0xBB, 0x53, 0x11, 0x44, 0xCC, 0x4E,
0x92, 0xBB, 0x53, 0x1A, 0x44, 0x66, 0x77, 0x99,
0x15, 0xB3, 0x00, 0x54, 0xCC, 0x54, 0x01, 0xAA,
0x44, 0x54, 0x12, 0x4A, 0x53, 0x11, 0x44, 0x66,
0x4E, 0x92, 0xBC, 0x53, 0x1A, 0x44, 0x66, 0x77,
0x99, 0x1A, 0x92, 0xBB, 0x53, 0x11, 0x44, 0x66,
0x92, 0xBB, 0x53, 0x1A, 0x44, 0x66, 0x77, 0x99,
0x15, 0xB3, 0x00, 0x54, 0xCC, 0x54, 0x01, 0xAA,
0x11, 0x44, 0x66, 0x4E, 0x92, 0xBB, 0x53, 0x1A, # / * 1080 * /
0x80, 0x00, 0xFA, 0xA5, 0x0B, 0xC0, 0x00, 0x04,
0x4E, 0x92, 0xBB, 0x53, 0x11, 0x44, 0x66, 0x4E,
0x92, 0xBB, 0x53, 0x1A, 0x44, 0x66, 0x77, 0x99,
0x15, 0xB3, 0x00, 0x54, 0xCC, 0x54, 0x01, 0xAA,
0x44, 0x54, 0x12, 0x43, 0x53, 0x11, 0x44, 0x66,
0x4E, 0x92, 0xBB, 0x53, 0x3A, 0x44, 0x66, 0x77,
0x99, 0x1A, 0x92, 0xBB, 0x53, 0x11, 0x44, 0x66,
0x92, 0xBB, 0x53, 0x1A, 0x44, 0x66, 0x77, 0x99,
0x15, 0xB3, 0x00, 0x54, 0xCC, 0x54, 0x01, 0xAA,
0x11, 0x44, 0x66, 0x4E, 0x92, 0xBB, 0x53, 0x1A,
0x80, 0x00, 0xFA, 0xA5, 0x0B, 0xC0, 0x00, 0x04,
0x4E, 0x92, 0xBB, 0x53, 0x11, 0x4C, 0x66, 0x4E,
0x92, 0xBB, 0x53, 0x1A, 0x44, 0x66, 0x77, 0x99,
0x15, 0xB3, 0x00, 0x54, 0xCC, 0x54, 0x01, 0xAA,
0x44, 0x54, 0x12, 0xa3, 0x53, 0x11, 0x44, 0x66,
0xFE, 0x92, 0xBB, 0x53, 0x1A, 0x44, 0x66, 0x77,
0x99, 0x1A, 0x92, 0xBB, 0x53, 0x11, 0x44, 0x66,
0x92, 0xBB, 0x53, 0x1A, 0x44, 0x66, 0x77, 0x99,
0x15, 0xB3, 0x00, 0x54, 0xCC, 0x54, 0x01, 0xAA,
0x11, 0x44, 0x66, 0x4E, 0x92, 0xBB, 0x53, 0x1A,
0x80, 0x00, 0xFA, 0xA5, 0x1B, 0xC0, 0x00, 0x04,
0x4E, 0x92, 0x1B, 0x53, 0x11, 0x44, 0x66, 0x4E,
0x22, 0xBB, 0x53, 0x1A, 0x44, 0x66, 0x77, 0x99,
0x15, 0xB3, 0x00, 0x54, 0xCC, 0x54, 0x01, 0xAA,
0x44, 0x54, 0x12, 0xD3, 0x53, 0x11, 0x44, 0x66])
self.assertEqual(ipv6_packet, actual_ipv6_packet.to_bytes())
def test_should_defragment_IPv6_packet_when_parse_method_is_called_with_fragments(self):
# GIVEN
message_info = common.MessageInfo()
message_info.source_mac_address = common.MacAddress.from_eui64(
bytearray([0x00, 0x00, 0x00, 0x11, 0x12, 0x13, 0x14, 0x15]))
message_info.destination_mac_address = common.MacAddress.from_eui64(
bytearray([0x00, 0x00, 0x1A, 0x2A, 0x3F, 0x09, 0xAB, 0x43]))
fragment_1 = bytearray([0xC0, 0x38, 0x12, 0x34, 0x7A, 0x33, 0x3A, 0x80,
0x00, 0x1A, 0x33, 0x0B, 0xC0, 0x00, 0x04])
fragment_2 = bytearray([0xE0, 0x38, 0x12, 0x34, 0x06, 0x4E, 0x92, 0xBB,
0x53, 0x11, 0x12, 0x13, 0x14])
parser = create_default_lowpan_parser(None)
# WHEN
self.assertIsNone(parser.parse(io.BytesIO(fragment_1), message_info=message_info))
actual_ipv6_packet = parser.parse(io.BytesIO(fragment_2), message_info=message_info)
# THEN
ipv6_packet = bytearray([0x60, 0x00, 0x00, 0x00, 0x00, 0x10, 0x3A, 0x40,
0xfe, 0x80, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x02, 0x00, 0x00, 0x11, 0x12, 0x13, 0x14, 0x15,
0xfe, 0x80, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x02, 0x00, 0x1A, 0x2A, 0x3F, 0x09, 0xAB, 0x43,
0x80, 0x00, 0x1A, 0x33, 0x0B, 0xC0, 0x00, 0x04,
0x4E, 0x92, 0xBB, 0x53, 0x11, 0x12, 0x13, 0x14])
self.assertEqual(ipv6_packet, actual_ipv6_packet.to_bytes())
class TestLowpanUdpHeaderFactory(unittest.TestCase):
def test_should_parse_udp_datagram_ports_when_decompress_udp_ports_method_is_called_with_udphc_p_equal_0(self):
# GIVEN
factory = lowpan.LowpanUdpHeaderFactory()
p = factory.UDP_HC_P_BOTH_FULL
udphc = lowpan.LowpanUDPHC(any_c(), p)
src_port = any_src_port()
dst_port = any_dst_port()
data_bytes = struct.pack(">H", src_port) + struct.pack(">H", dst_port)
# WHEN
actual_src_port, actual_dst_port = factory._decompress_udp_ports(udphc, io.BytesIO(data_bytes))
# THEN
self.assertEqual(src_port, actual_src_port)
self.assertEqual(dst_port, actual_dst_port)
self.assertEqual(0, p)
def test_should_parse_udp_datagram_ports_when_decompress_udp_ports_method_is_called_with_udphc_p_equal_1(self):
# GIVEN
factory = lowpan.LowpanUdpHeaderFactory()
p = factory.UDP_HC_P_DST_COMPR
udphc = lowpan.LowpanUDPHC(any_c(), p)
src_port = any_src_port()
dst_port = any_compressable_dst_port()
data_bytes = struct.pack(">H", src_port) + bytearray([struct.pack(">H", dst_port)[1]])
# WHEN
actual_src_port, actual_dst_port = factory._decompress_udp_ports(udphc, io.BytesIO(data_bytes))
# THEN
self.assertEqual(1, p)
self.assertEqual(src_port, actual_src_port)
self.assertEqual(dst_port, actual_dst_port)
def test_should_parse_udp_datagram_ports_when_decompress_udp_ports_method_is_called_with_udphc_p_equal_2(self):
# GIVEN
factory = lowpan.LowpanUdpHeaderFactory()
p = factory.UDP_HC_P_SRC_COMPR
udphc = lowpan.LowpanUDPHC(any_c(), p)
src_port = any_compressable_src_port()
dst_port = any_dst_port()
data_bytes = bytearray([struct.pack(">H", src_port)[1]]) + struct.pack(">H", dst_port)
# WHEN
actual_src_port, actual_dst_port = factory._decompress_udp_ports(udphc, io.BytesIO(data_bytes))
# THEN
self.assertEqual(2, p)
self.assertEqual(src_port, actual_src_port)
self.assertEqual(dst_port, actual_dst_port)
def test_should_parse_udp_datagram_ports_when_decompress_udp_ports_method_is_called_with_udphc_p_equal_3(self):
# GIVEN
factory = lowpan.LowpanUdpHeaderFactory()
p = factory.UDP_HC_P_BOTH_COMPR
udphc = lowpan.LowpanUDPHC(any_c(), p)
src_port = any_nibble_src_port()
dst_port = any_nibble_dst_port()
data_bytes = bytearray([((src_port & 0x0F) << 4) | (dst_port & 0x0F)])
# WHEN
actual_src_port, actual_dst_port = factory._decompress_udp_ports(udphc, io.BytesIO(data_bytes))
# THEN
self.assertEqual(3, p)
self.assertEqual(src_port, actual_src_port)
self.assertEqual(dst_port, actual_dst_port)
def test_should_parse_udp_datagram_checksum_when_decompress_udp_checksum_is_called_with_udphc_c_equal_0(self):
# GIVEN
factory = lowpan.LowpanUdpHeaderFactory()
c = factory.UDP_HC_C_INLINE
udphc = lowpan.LowpanUDPHC(c, any_p())
checksum = any_checksum()
data_bytes = struct.pack(">H", checksum)
# WHEN
actual_checksum = factory._decompress_udp_checksum(udphc, io.BytesIO(data_bytes))
# THEN
self.assertEqual(0, c)
self.assertEqual(checksum, actual_checksum)
def test_should_parse_udp_datagram_checksum_when_decompress_udp_checksum_is_called_with_udphc_c_equal_1(self):
# GIVEN
factory = lowpan.LowpanUdpHeaderFactory()
c = factory.UDP_HC_C_ELIDED
udphc = lowpan.LowpanUDPHC(c, any_p())
data_bytes = bytearray()
# WHEN
actual_checksum = factory._decompress_udp_checksum(udphc, io.BytesIO(data_bytes))
# THEN
self.assertEqual(1, c)
self.assertEqual(0, actual_checksum)
class TestLowpanIpv6HeaderFactory(unittest.TestCase):
IPV6_LINKLOCAL_PREFIX = bytearray([0xfe, 0x80, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00])
def test_should_parse_traffic_class_and_flow_label_when_decompress_tf_method_is_called_with_iphc_tf_equal_0(self):
# GIVEN
ecn = any_ecn()
dscp = any_dscp()
flow_label = any_flow_label()
data_bytes = bytearray()
data_bytes.append((ecn << 6) | dscp)
data_bytes.append((flow_label >> 16) & 0x0F)
data_bytes.append((flow_label >> 8) & 0xFF)
data_bytes.append(flow_label & 0xFF)
factory = lowpan.LowpanIpv6HeaderFactory()
tf = factory.IPHC_TF_4B
iphc = lowpan.LowpanIPHC(tf, any_nh(), any_hlim(), any_cid(), any_sac(),
any_sam(), any_m(), any_dac(), any_dam())
# WHEN
actual_traffic_class, actual_flow_label = factory._decompress_tf(iphc, io.BytesIO(data_bytes))
# THEN
self.assertEqual(0, tf)
self.assertEqual((dscp << 2) | ecn, actual_traffic_class)
self.assertEqual(flow_label, actual_flow_label)
def test_should_parse_traffic_class_and_flow_label_when_decompress_tf_method_is_called_with_iphc_tf_equal_1(self):
# GIVEN
ecn = any_ecn()
flow_label = any_flow_label()
data_bytes = bytearray()
data_bytes.append((ecn << 6) | (flow_label >> 16) & 0x0F)
data_bytes.append((flow_label >> 8) & 0xFF)
data_bytes.append(flow_label & 0xFF)
factory = lowpan.LowpanIpv6HeaderFactory()
tf = factory.IPHC_TF_3B
iphc = lowpan.LowpanIPHC(tf, any_nh(), any_hlim(), any_cid(), any_sac(),
any_sam(), any_m(), any_dac(), any_dam())
# WHEN
actual_traffic_class, actual_flow_label = factory._decompress_tf(iphc, io.BytesIO(data_bytes))
# THEN
self.assertEqual(1, tf)
self.assertEqual(ecn, actual_traffic_class)
self.assertEqual(flow_label, actual_flow_label)
def test_should_parse_traffic_class_and_flow_label_when_decompress_tf_method_is_called_with_iphc_tf_equal_2(self):
# GIVEN
ecn = any_ecn()
dscp = any_dscp()
flow_label = any_flow_label()
data_bytes = bytearray([(ecn << 6) | dscp])
factory = lowpan.LowpanIpv6HeaderFactory()
tf = factory.IPHC_TF_1B
iphc = lowpan.LowpanIPHC(tf, any_nh(), any_hlim(), any_cid(), any_sac(),
any_sam(), any_m(), any_dac(), any_dam())
# WHEN
actual_traffic_class, actual_flow_label = factory._decompress_tf(iphc, io.BytesIO(data_bytes))
# THEN
self.assertEqual(2, tf)
self.assertEqual((dscp << 2) | ecn, actual_traffic_class)
self.assertEqual(0, actual_flow_label)
def test_should_parse_traffic_class_and_flow_label_when_decompress_tf_method_is_called_with_iphc_tf_equal_3(self):
# GIVEN
flow_label = any_flow_label()
data_bytes = bytearray()
factory = lowpan.LowpanIpv6HeaderFactory()
tf = factory.IPHC_TF_ELIDED
iphc = lowpan.LowpanIPHC(tf, any_nh(), any_hlim(), any_cid(), any_sac(),
any_sam(), any_m(), any_dac(), any_dam())
# WHEN
actual_traffic_class, actual_flow_label = factory._decompress_tf(iphc, io.BytesIO(data_bytes))
# THEN
self.assertEqual(3, tf)
self.assertEqual(0, actual_traffic_class)
self.assertEqual(0, actual_flow_label)
def test_should_parse_traffic_class_and_flow_label_when_decompress_nh_method_is_called_with_iphc_nh_equal_0(self):
# GIVEN
factory = lowpan.LowpanIpv6HeaderFactory()
next_header = any_next_header()
nh = factory.IPHC_NH_INLINE
iphc = lowpan.LowpanIPHC(any_tf(), nh, any_hlim(), any_cid(), any_sac(),
any_sam(), any_m(), any_dac(), any_dam())
data_bytes = bytearray([next_header])
# WHEN
actual_next_header = factory._decompress_nh(iphc, io.BytesIO(data_bytes))
# THEN
self.assertEqual(0, nh)
self.assertEqual(next_header, actual_next_header)
def test_should_parse_traffic_class_and_flow_label_when_decompress_nh_method_is_called_with_iphc_nh_equal_1(self):
# GIVEN
factory = lowpan.LowpanIpv6HeaderFactory()
nh = factory.IPHC_NH_COMPRESSED
iphc = lowpan.LowpanIPHC(any_tf(), nh, any_hlim(), any_cid(), any_sac(),
any_sam(), any_m(), any_dac(), any_dam())
data_bytes = bytearray()
# WHEN
actual_next_header = factory._decompress_nh(iphc, io.BytesIO(data_bytes))
# THEN
self.assertEqual(1, nh)
self.assertEqual(None, actual_next_header)
def test_should_parse_hop_limit_when_decompress_hlim_is_called_with_iphc_hlim_equal_0(self):
# GIVEN
hop_limit = any_hop_limit()
factory = lowpan.LowpanIpv6HeaderFactory()
hlim = factory.IPHC_HLIM_INLINE
iphc = lowpan.LowpanIPHC(any_tf(), any_nh(), hlim, any_cid(), any_sac(),
any_sam(), any_m(), any_dac(), any_dam())
data_bytes = bytearray([hop_limit])
# WHEN
actual_hop_limit = factory._decompress_hlim(iphc, io.BytesIO(data_bytes))
# THEN
self.assertEqual(0, hlim)
self.assertEqual(hop_limit, actual_hop_limit)
def test_should_parse_hop_limit_when_decompress_hlim_is_called_with_iphc_hlim_equal_1(self):
# GIVEN
factory = lowpan.LowpanIpv6HeaderFactory()
hlim = factory.IPHC_HLIM_1
iphc = lowpan.LowpanIPHC(any_tf(), any_nh(), hlim, any_cid(), any_sac(),
any_sam(), any_m(), any_dac(), any_dam())
data_bytes = bytearray()
# WHEN
actual_hop_limit = factory._decompress_hlim(iphc, io.BytesIO(data_bytes))
# THEN
self.assertEqual(1, hlim)
self.assertEqual(1, actual_hop_limit)
def test_should_parse_hop_limit_when_decompress_hlim_is_called_with_iphc_hlim_equal_2(self):
# GIVEN
factory = lowpan.LowpanIpv6HeaderFactory()
hlim = factory.IPHC_HLIM_64
iphc = lowpan.LowpanIPHC(any_tf(), any_nh(), hlim, any_cid(), any_sac(),
any_sam(), any_m(), any_dac(), any_dam())
data_bytes = bytearray()
# WHEN
actual_hop_limit = factory._decompress_hlim(iphc, io.BytesIO(data_bytes))
# THEN
self.assertEqual(2, hlim)
self.assertEqual(64, actual_hop_limit)
def test_should_parse_hop_limit_when_decompress_hlim_is_called_with_iphc_hlim_equal_3(self):
# GIVEN
factory = lowpan.LowpanIpv6HeaderFactory()
hlim = factory.IPHC_HLIM_255
iphc = lowpan.LowpanIPHC(any_tf(), any_nh(), hlim, any_cid(), any_sac(),
any_sam(), any_m(), any_dac(), any_dam())
data_bytes = bytearray()
# WHEN
actual_hop_limit = factory._decompress_hlim(iphc, io.BytesIO(data_bytes))
# THEN
self.assertEqual(3, hlim)
self.assertEqual(255, actual_hop_limit)
def test_should_parse_source_address_when_decompress_src_addr_is_called_with_sac_equal_0_and_sam_equal_0(self):
# GIVEN
factory = lowpan.LowpanIpv6HeaderFactory()
src_addr = any_src_addr()
sac = factory.IPHC_SAC_STATELESS
sam = factory.IPHC_SAM_128B
iphc = lowpan.LowpanIPHC(any_tf(), any_nh(), any_hlim(), any_cid(), sac,
sam, any_m(), any_dac(), any_dam())
# WHEN
actual_src_addr = factory._decompress_src_addr(iphc, any_src_mac_addr(), any_sci(), io.BytesIO(src_addr))
# THEN
self.assertEqual(0, sac)
self.assertEqual(0, sam)
self.assertEqual(bytes(src_addr), actual_src_addr)
def test_should_parse_source_address_when_decompress_src_addr_is_called_with_sac_equal_0_and_sam_equal_1(self):
# GIVEN
factory = lowpan.LowpanIpv6HeaderFactory()
eui64 = any_eui64()
sac = factory.IPHC_SAC_STATELESS
sam = factory.IPHC_SAM_64B
iphc = lowpan.LowpanIPHC(any_tf(), any_nh(), any_hlim(), any_cid(), sac,
sam, any_m(), any_dac(), any_dam())
# WHEN
actual_src_addr = factory._decompress_src_addr(iphc, any_src_mac_addr(), any_sci(), io.BytesIO(eui64))
# THEN
self.assertEqual(0, sac)
self.assertEqual(1, sam)
self.assertEqual(self.IPV6_LINKLOCAL_PREFIX + eui64, actual_src_addr)
def test_should_parse_source_address_when_decompress_src_addr_is_called_with_sac_equal_0_and_sam_equal_2(self):
# GIVEN
factory = lowpan.LowpanIpv6HeaderFactory()
rloc16 = any_rloc16()
sac = factory.IPHC_SAC_STATELESS
sam = factory.IPHC_SAM_16B
iphc = lowpan.LowpanIPHC(any_tf(), any_nh(), any_hlim(), any_cid(), sac,
sam, any_m(), any_dac(), any_dam())
# WHEN
actual_src_addr = factory._decompress_src_addr(iphc, any_src_mac_addr(), any_sci(), io.BytesIO(rloc16))
# THEN
self.assertEqual(0, sac)
self.assertEqual(2, sam)
self.assertEqual(self.IPV6_LINKLOCAL_PREFIX +
bytearray([0x00, 0x00, 0x00, 0xff, 0xfe, 0x00]) + rloc16, actual_src_addr)
def test_should_parse_source_address_when_decompress_src_addr_is_called_with_sac_equal_0_and_sam_equal_3(self):
# GIVEN
factory = lowpan.LowpanIpv6HeaderFactory()
src_mac_addr = common.MacAddress.from_eui64(any_src_mac_addr())
sac = factory.IPHC_SAC_STATELESS
sam = factory.IPHC_SAM_ELIDED
iphc = lowpan.LowpanIPHC(any_tf(), any_nh(), any_hlim(), any_cid(), sac,
sam, any_m(), any_dac(), any_dam())
data_bytes = bytearray([])
# WHEN
actual_src_addr = factory._decompress_src_addr(iphc, src_mac_addr, any_sci(), io.BytesIO(data_bytes))
# THEN
self.assertEqual(0, sac)
self.assertEqual(3, sam)
self.assertEqual(self.IPV6_LINKLOCAL_PREFIX +
bytearray([src_mac_addr.mac_address[0] ^ 0x02]) +
src_mac_addr.mac_address[1:], actual_src_addr)
def _merge_prefix_and_address(self, prefix, prefix_length, address):
total_bytes = 16
prefix_length_in_bytes = int(prefix_length / 8)
if (prefix_length_in_bytes + len(address)) > total_bytes:
total_bytes -= prefix_length_in_bytes
return prefix[:prefix_length_in_bytes] + address[-total_bytes:]
else:
total_bytes -= prefix_length_in_bytes
total_bytes -= len(address)
return prefix[:prefix_length_in_bytes] + bytearray([0x00] * total_bytes) + address
def test_should_parse_source_address_when_decompress_src_addr_is_called_with_sac_equal_1_and_sam_equal_0(self):
# GIVEN
factory = lowpan.LowpanIpv6HeaderFactory(None)
src_addr = any_src_addr()
sac = factory.IPHC_SAC_STATEFUL
sam = factory.IPHC_SAM_UNSPECIFIED
iphc = lowpan.LowpanIPHC(any_tf(), any_nh(), any_hlim(), any_cid(), sac,
sam, any_m(), any_dac(), any_dam())
# WHEN
actual_src_addr = factory._decompress_src_addr(iphc, any_src_mac_addr(), any_sci(), io.BytesIO(src_addr))
# THEN
self.assertEqual(1, sac)
self.assertEqual(0, sam)
self.assertEqual(bytearray([0x00] * 16), actual_src_addr)
def test_should_parse_source_address_when_decompress_src_addr_is_called_with_sac_equal_1_and_sam_equal_1(self):
# GIVEN
sci = any_sci()
context = any_context()
context_manager = lowpan.ContextManager()
context_manager[sci] = context
factory = lowpan.LowpanIpv6HeaderFactory(context_manager)
eui64 = any_eui64()
sac = factory.IPHC_SAC_STATEFUL
sam = factory.IPHC_SAM_64B
iphc = lowpan.LowpanIPHC(any_tf(), any_nh(), any_hlim(), any_cid(), sac,
sam, any_m(), any_dac(), any_dam())
src_addr = self._merge_prefix_and_address(context.prefix, context.prefix_length, eui64)
# WHEN
actual_src_addr = factory._decompress_src_addr(iphc, any_src_mac_addr(), sci, io.BytesIO(eui64))
# THEN
self.assertEqual(1, sac)
self.assertEqual(1, sam)
self.assertEqual(src_addr, actual_src_addr)
def test_should_parse_source_address_when_decompress_src_addr_is_called_with_sac_equal_1_and_sam_equal_2(self):
# GIVEN
sci = any_sci()
context = any_context()
context_manager = lowpan.ContextManager()
context_manager[sci] = context
factory = lowpan.LowpanIpv6HeaderFactory(context_manager)
rloc16 = any_rloc16()
sac = factory.IPHC_SAC_STATEFUL
sam = factory.IPHC_SAM_16B
iphc = lowpan.LowpanIPHC(any_tf(), any_nh(), any_hlim(), any_cid(), sac,
sam, any_m(), any_dac(), any_dam())
iid = bytearray([0x00, 0x00, 0x00, 0xff, 0xfe, 0x00]) + rloc16
src_addr = self._merge_prefix_and_address(context.prefix, context.prefix_length, iid)
# WHEN
actual_src_addr = factory._decompress_src_addr(iphc, any_src_mac_addr(), sci, io.BytesIO(rloc16))
# THEN
self.assertEqual(1, sac)
self.assertEqual(2, sam)
self.assertEqual(src_addr, actual_src_addr)
def test_should_parse_source_address_when_decompress_src_addr_is_called_with_sac_equal_1_and_sam_equal_3(self):
# GIVEN
sci = any_sci()
context = any_context()
context_manager = lowpan.ContextManager()
context_manager[sci] = context
factory = lowpan.LowpanIpv6HeaderFactory(context_manager)
src_mac_addr = common.MacAddress.from_eui64(any_src_mac_addr())
sac = factory.IPHC_SAC_STATEFUL
sam = factory.IPHC_SAM_0B
iphc = lowpan.LowpanIPHC(any_tf(), any_nh(), any_hlim(), any_cid(), sac,
sam, any_m(), any_dac(), any_dam())
iid = bytearray([src_mac_addr.mac_address[0] ^ 0x02]) + src_mac_addr.mac_address[1:]
src_addr = self._merge_prefix_and_address(context.prefix, context.prefix_length, iid)
data_bytes = bytearray([])
# WHEN
actual_src_addr = factory._decompress_src_addr(iphc, src_mac_addr, sci, io.BytesIO(data_bytes))
# THEN
self.assertEqual(1, sac)
self.assertEqual(3, sam)
self.assertEqual(src_addr, actual_src_addr)
def test_should_parse_destination_address_when_decompress_dst_addr_is_called_with_m_equal_0_and_dac_equal_0_and_dam_equal_0(self):
# GIVEN
factory = lowpan.LowpanIpv6HeaderFactory()
ipv6_addr = any_dst_addr()
m = factory.IPHC_M_NO
dac = factory.IPHC_DAC_STATELESS
dam = factory.IPHC_DAM_128B
iphc = lowpan.LowpanIPHC(any_tf(), any_nh(), any_hlim(), any_cid(), any_sac(),
any_sam(), m, dac, dam)
dst_mac_addr = bytearray([0x00] * 8)
# WHEN
actual_dst_addr = factory._decompress_dst_addr(iphc, dst_mac_addr, any_dci(), io.BytesIO(ipv6_addr))
# THEN
self.assertEqual(0, m)
self.assertEqual(0, dac)
self.assertEqual(0, dam)
self.assertEqual(ipv6_addr, actual_dst_addr)
def test_should_parse_destination_address_when_decompress_dst_addr_is_called_with_m_equal_0_and_dac_equal_0_and_dam_equal_1(self):
# GIVEN
factory = lowpan.LowpanIpv6HeaderFactory()
eui64 = any_eui64()
m = factory.IPHC_M_NO
dac = factory.IPHC_DAC_STATELESS
dam = factory.IPHC_DAM_64B
iphc = lowpan.LowpanIPHC(any_tf(), any_nh(), any_hlim(), any_cid(), any_sac(),
any_sam(), m, dac, dam)
# WHEN
actual_dst_addr = factory._decompress_dst_addr(iphc, any_dst_mac_addr(), any_dci(), io.BytesIO(eui64))
# THEN
self.assertEqual(0, m)
self.assertEqual(0, dac)
self.assertEqual(1, dam)
self.assertEqual(self.IPV6_LINKLOCAL_PREFIX + eui64, actual_dst_addr)
def test_should_parse_destination_address_when_decompress_dst_addr_is_called_with_m_equal_0_and_dac_equal_0_and_dam_equal_2(self):
# GIVEN
factory = lowpan.LowpanIpv6HeaderFactory()
rloc16 = any_rloc16()
m = factory.IPHC_M_NO
dac = factory.IPHC_DAC_STATELESS
dam = factory.IPHC_DAM_16B
iphc = lowpan.LowpanIPHC(any_tf(), any_nh(), any_hlim(), any_cid(), any_sac(),
any_sam(), m, dac, dam)
# WHEN
actual_dst_addr = factory._decompress_dst_addr(iphc, any_dst_mac_addr(), any_dci(), io.BytesIO(rloc16))
# THEN
self.assertEqual(0, m)
self.assertEqual(0, dac)
self.assertEqual(2, dam)
self.assertEqual(self.IPV6_LINKLOCAL_PREFIX +
bytearray([0x00, 0x00, 0x00, 0xff, 0xfe, 0x00]) + rloc16, actual_dst_addr)
def test_should_parse_destination_address_when_decompress_dst_addr_is_called_with_m_equal_0_and_dac_equal_0_and_dam_equal_3(self):
# GIVEN
factory = lowpan.LowpanIpv6HeaderFactory()
dst_mac_addr = common.MacAddress.from_eui64(any_dst_mac_addr())
m = factory.IPHC_M_NO
dac = factory.IPHC_DAC_STATELESS
dam = factory.IPHC_DAM_ELIDED
iphc = lowpan.LowpanIPHC(any_tf(), any_nh(), any_hlim(), any_cid(), any_sac(),
any_sam(), m, dac, dam)
data_bytes = bytearray([])
# WHEN
actual_dst_addr = factory._decompress_dst_addr(iphc, dst_mac_addr, any_dci(), io.BytesIO(data_bytes))
# THEN
self.assertEqual(0, m)
self.assertEqual(0, dac)
self.assertEqual(3, dam)
self.assertEqual(self.IPV6_LINKLOCAL_PREFIX +
bytearray([dst_mac_addr.mac_address[0] ^ 0x02]) +
dst_mac_addr.mac_address[1:], actual_dst_addr)
def test_should_raise_RuntimeError_when_decompress_dst_addr_is_called_with_m_equal_0_and_dac_equal_1_and_dam_equal_0(self):
# GIVEN
factory = lowpan.LowpanIpv6HeaderFactory()
ipv6_addr = any_dst_addr()
m = factory.IPHC_M_NO
dac = factory.IPHC_DAC_STATEFUL
dam = factory.IPHC_DAM_128B
iphc = lowpan.LowpanIPHC(any_tf(), any_nh(), any_hlim(), any_cid(), any_sac(),
any_sam(), m, dac, dam)
# WHEN
self.assertRaises(RuntimeError, factory._decompress_dst_addr, iphc,
any_dst_mac_addr(), any_dci(), io.BytesIO(ipv6_addr))
def test_should_parse_destination_address_when_decompress_dst_addr_is_called_with_m_equal_0_and_dac_equal_1_and_dam_equal_1(self):
# GIVEN
dci = any_dci()
context = any_context()
context_manager = lowpan.ContextManager()
context_manager[dci] = context
factory = lowpan.LowpanIpv6HeaderFactory(context_manager)
eui64 = any_eui64()
m = factory.IPHC_M_NO
dac = factory.IPHC_DAC_STATEFUL
dam = factory.IPHC_DAM_64B
iphc = lowpan.LowpanIPHC(any_tf(), any_nh(), any_hlim(), any_cid(), any_sac(),
any_sam(), m, dac, dam)
dst_addr = self._merge_prefix_and_address(context.prefix, context.prefix_length, eui64)
# WHEN
actual_dst_addr = factory._decompress_dst_addr(iphc, any_dst_mac_addr(), dci, io.BytesIO(eui64))
# THEN
self.assertEqual(0, m)
self.assertEqual(1, dac)
self.assertEqual(1, dam)
self.assertEqual(dst_addr, actual_dst_addr)
def test_should_parse_destination_address_when_decompress_dst_addr_is_called_with_m_equal_0_and_dac_equal_1_and_dam_equal_2(self):
# GIVEN
dci = any_dci()
context = any_context()
context_manager = lowpan.ContextManager()
context_manager[dci] = context
factory = lowpan.LowpanIpv6HeaderFactory(context_manager)
rloc16 = any_rloc16()
m = factory.IPHC_M_NO
dac = factory.IPHC_DAC_STATEFUL
dam = factory.IPHC_DAM_16B
iphc = lowpan.LowpanIPHC(any_tf(), any_nh(), any_hlim(), any_cid(), any_sac(),
any_sam(), m, dac, dam)
iid = bytearray([0x00, 0x00, 0x00, 0xff, 0xfe, 0x00]) + rloc16
dst_addr = self._merge_prefix_and_address(context.prefix, context.prefix_length, iid)
# WHEN
actual_dst_addr = factory._decompress_dst_addr(iphc, any_dst_mac_addr(), dci, io.BytesIO(rloc16))
# THEN
self.assertEqual(0, m)
self.assertEqual(1, dac)
self.assertEqual(2, dam)
self.assertEqual(dst_addr, actual_dst_addr)
def test_should_parse_destination_address_when_decompress_dst_addr_is_called_with_m_equal_0_and_dac_equal_1_and_dam_equal_3(self):
# GIVEN
dci = any_dci()
context = any_context()
context_manager = lowpan.ContextManager()
context_manager[dci] = context
factory = lowpan.LowpanIpv6HeaderFactory(context_manager)
dst_mac_addr = common.MacAddress.from_eui64(any_dst_mac_addr())
m = factory.IPHC_M_NO
dac = factory.IPHC_DAC_STATEFUL
dam = factory.IPHC_DAM_0B
iphc = lowpan.LowpanIPHC(any_tf(), any_nh(), any_hlim(), any_cid(), any_sac(),
any_sam(), m, dac, dam)
iid = bytearray([dst_mac_addr.mac_address[0] ^ 0x02]) + dst_mac_addr.mac_address[1:]
dst_addr = self._merge_prefix_and_address(context.prefix, context.prefix_length, iid)
data_bytes = bytearray([])
# WHEN
actual_dst_addr = factory._decompress_dst_addr(iphc, dst_mac_addr, dci, io.BytesIO(data_bytes))
# THEN
self.assertEqual(0, m)
self.assertEqual(1, dac)
self.assertEqual(3, dam)
self.assertEqual(dst_addr, actual_dst_addr)
def test_should_parse_destination_address_when_decompress_dst_addr_is_called_with_m_equal_1_and_dac_equal_0_and_dam_equal_0(self):
# GIVEN
factory = lowpan.LowpanIpv6HeaderFactory()
ipv6_addr = any_dst_addr()
m = factory.IPHC_M_YES
dac = factory.IPHC_DAC_STATELESS
dam = factory.IPHC_DAM_128B
iphc = lowpan.LowpanIPHC(any_tf(), any_nh(), any_hlim(), any_cid(), any_sac(),
any_sam(), m, dac, dam)
# WHEN
actual_dst_addr = factory._decompress_dst_addr(iphc, any_dst_mac_addr(), any_dci(), io.BytesIO(ipv6_addr))
# THEN
self.assertEqual(1, m)
self.assertEqual(0, dac)
self.assertEqual(0, dam)
self.assertEqual(ipv6_addr, actual_dst_addr)
def test_should_parse_destination_address_when_decompress_dst_addr_is_called_with_m_equal_1_and_dac_equal_0_and_dam_equal_1(self):
# GIVEN
factory = lowpan.LowpanIpv6HeaderFactory()
addr48b = any_48bits_addr()
m = factory.IPHC_M_YES
dac = factory.IPHC_DAC_STATELESS
dam = factory.IPHC_DAM_48B
iphc = lowpan.LowpanIPHC(any_tf(), any_nh(), any_hlim(), any_cid(), any_sac(),
any_sam(), m, dac, dam)
expected_dst_addr = bytearray([0xff, addr48b[0], 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, addr48b[1], addr48b[2], addr48b[3], addr48b[4], addr48b[5]])
# WHEN
actual_dst_addr = factory._decompress_dst_addr(iphc, any_dst_mac_addr(), any_dci(), io.BytesIO(addr48b))
# THEN
self.assertEqual(1, m)
self.assertEqual(0, dac)
self.assertEqual(1, dam)
self.assertEqual(expected_dst_addr, actual_dst_addr)
def test_should_parse_destination_address_when_decompress_dst_addr_is_called_with_m_equal_1_and_dac_equal_0_and_dam_equal_2(self):
# GIVEN
factory = lowpan.LowpanIpv6HeaderFactory()
addr32b = any_32bits_addr()
m = factory.IPHC_M_YES
dac = factory.IPHC_DAC_STATELESS
dam = factory.IPHC_DAM_32B
iphc = lowpan.LowpanIPHC(any_tf(), any_nh(), any_hlim(), any_cid(), any_sac(),
any_sam(), m, dac, dam)
expected_dst_addr = bytearray([0xff, addr32b[0], 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, addr32b[1], addr32b[2], addr32b[3]])
# WHEN
actual_dst_addr = factory._decompress_dst_addr(iphc, any_dst_mac_addr(), any_dci(), io.BytesIO(addr32b))
# THEN
self.assertEqual(1, m)
self.assertEqual(0, dac)
self.assertEqual(2, dam)
self.assertEqual(expected_dst_addr, actual_dst_addr)
def test_should_parse_destination_address_when_decompress_dst_addr_is_called_with_m_equal_1_and_dac_equal_0_and_dam_equal_3(self):
# GIVEN
factory = lowpan.LowpanIpv6HeaderFactory()
addr8b = any_8bits_addr()
m = factory.IPHC_M_YES
dac = factory.IPHC_DAC_STATELESS
dam = factory.IPHC_DAM_8B
iphc = lowpan.LowpanIPHC(any_tf(), any_nh(), any_hlim(), any_cid(), any_sac(),
any_sam(), m, dac, dam)
expected_dst_addr = bytearray([0xff, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, addr8b[0]])
# WHEN
actual_dst_addr = factory._decompress_dst_addr(iphc, any_dst_mac_addr(), any_dci(), io.BytesIO(addr8b))
# THEN
self.assertEqual(1, m)
self.assertEqual(0, dac)
self.assertEqual(3, dam)
self.assertEqual(expected_dst_addr, actual_dst_addr)
def test_should_raise_RuntimeError_when_decompress_dst_addr_is_called_with_m_equal_1_and_dac_equal_1_and_dam_equal_0(self):
# GIVEN
dci = any_dci()
context = any_context()
context_manager = lowpan.ContextManager()
context_manager[dci] = context
factory = lowpan.LowpanIpv6HeaderFactory(context_manager)
addr48b = any_48bits_addr()
m = factory.IPHC_M_YES
dac = factory.IPHC_DAC_STATEFUL
dam = factory.IPHC_DAM_128B
iphc = lowpan.LowpanIPHC(any_tf(), any_nh(), any_hlim(), any_cid(), any_sac(),
any_sam(), m, dac, dam)
prefix = context.prefix[:8]
if len(prefix) < 8:
missing_bytes_count = 8 - len(prefix)
prefix += bytearray([0x00] * missing_bytes_count)
prefix_length = context.prefix_length
dst_addr = bytearray([0xff]) + addr48b[:2] + bytearray([prefix_length]) + prefix + addr48b[2:]
# WHEN
actual_dst_addr = factory._decompress_dst_addr(iphc, any_dst_mac_addr(), dci, io.BytesIO(addr48b))
# THEN
self.assertEqual(1, m)
self.assertEqual(1, dac)
self.assertEqual(0, dam)
self.assertEqual(dst_addr, actual_dst_addr)
def test_should_raise_RuntimeError_when_decompress_dst_addr_is_called_with_m_equal_1_and_dac_equal_1_and_dam_equal_1(self):
# GIVEN
factory = lowpan.LowpanIpv6HeaderFactory()
addr48b = any_48bits_addr()
m = factory.IPHC_M_YES
dac = factory.IPHC_DAC_STATEFUL
dam = factory.IPHC_DAM_48B
iphc = lowpan.LowpanIPHC(any_tf(), any_nh(), any_hlim(), any_cid(), any_sac(),
any_sam(), m, dac, dam)
expected_dst_addr = bytearray([0xff, addr48b[0], 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, addr48b[1], addr48b[2], addr48b[3], addr48b[4], addr48b[5]])
# WHEN
self.assertRaises(RuntimeError, factory._decompress_dst_addr, iphc,
any_dst_mac_addr(), any_dci(), io.BytesIO(addr48b))
def test_should_raise_RuntimeError_when_decompress_dst_addr_is_called_with_m_equal_1_and_dac_equal_1_and_dam_equal_2(self):
# GIVEN
factory = lowpan.LowpanIpv6HeaderFactory()
addr32b = any_32bits_addr()
m = factory.IPHC_M_YES
dac = factory.IPHC_DAC_STATEFUL
dam = factory.IPHC_DAM_32B
iphc = lowpan.LowpanIPHC(any_tf(), any_nh(), any_hlim(), any_cid(), any_sac(),
any_sam(), m, dac, dam)
expected_dst_addr = bytearray([0xff, addr32b[0], 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, addr32b[1], addr32b[2], addr32b[3]])
# WHEN
self.assertRaises(RuntimeError, factory._decompress_dst_addr, iphc,
any_dst_mac_addr(), any_dci(), io.BytesIO(addr32b))
def test_should_parse_destination_address_when_decompress_dst_addr_is_called_with_m_equal_1_and_dac_equal_1_and_dam_equal_3(self):
# GIVEN
factory = lowpan.LowpanIpv6HeaderFactory()
addr8b = any_8bits_addr()
m = factory.IPHC_M_YES
dac = factory.IPHC_DAC_STATEFUL
dam = factory.IPHC_DAM_8B
iphc = lowpan.LowpanIPHC(any_tf(), any_nh(), any_hlim(), any_cid(), any_sac(),
any_sam(), m, dac, dam)
expected_dst_addr = bytearray([0xff, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, addr8b[0]])
# WHEN
self.assertRaises(RuntimeError, factory._decompress_dst_addr, iphc,
any_dst_mac_addr(), any_dci(), io.BytesIO(addr8b))
def test_should_merge_prefix_with_address_bytes_when_merge_method_is_called_with_prefix_shorter_than_missing_bits(self):
# GIVEN
factory = lowpan.LowpanIpv6HeaderFactory()
prefix = bytearray([0x20, 0x00, 0x0d, 0xb8])
prefix_length = 32
address_bytes = bytearray([0x1a, 0x2b, 0x3c, 0x4d, 0x5e, 0x6f, 0x70, 0x81])
addr = prefix + bytearray([0x00] * 4) + address_bytes
# WHEN
actual_addr = factory._merge_prefix_with_address(prefix, prefix_length, address_bytes)
# THEN
self.assertEqual(addr, actual_addr)
def test_should_merge_prefix_with_address_bytes_when_merge_method_is_called_with_prefix_longer_than_missing_bits_overlapping(self):
# GIVEN
factory = lowpan.LowpanIpv6HeaderFactory()
prefix = bytearray([0x20, 0x00, 0x0d, 0xb8, 0x00, 0x00, 0x00, 0x00, 0x22])
prefix_length = 68
address_bytes = bytearray([0x1a, 0x2b, 0x3c, 0x4d, 0x5e, 0x6f, 0x70, 0x81])
addr = prefix[:-1] + bytearray([0x2a]) + address_bytes[1:]
# WHEN
actual_addr = factory._merge_prefix_with_address(prefix, prefix_length, address_bytes)
# THEN
self.assertEqual(addr, actual_addr)
def test_should_merge_prefix_with_address_bytes_when_merge_method_is_called_with_prefix_longer_than_missing_bits(self):
# GIVEN
factory = lowpan.LowpanIpv6HeaderFactory()
prefix = bytearray([0x20, 0x00, 0x0d, 0xb8, 0x00, 0x00, 0x00, 0x00,
0x22, 0x00, 0x00, 0x11, 0x01, 0x11, 0x01, 0x22])
prefix_length = 128
address_bytes = bytearray([0x1a, 0x2b, 0x3c, 0x4d, 0x5e, 0x6f, 0x70, 0x81])
addr = prefix
# WHEN
actual_addr = factory._merge_prefix_with_address(prefix, prefix_length, address_bytes)
# THEN
self.assertEqual(addr, actual_addr)
class TestContext(unittest.TestCase):
def test_should_extract_context_from_str_representation_when_constructor_is_called(self):
# GIVEN
prefix = "2000:db8::/64"
# WHEN
c = lowpan.Context(prefix)
# THEN
self.assertEqual(bytearray([0x20, 0x00, 0x0d, 0xb8, 0x00, 0x00, 0x00, 0x00]), c.prefix)
self.assertEqual(64, c.prefix_length)
self.assertEqual(8, c.prefix_length_full_bytes)
def test_should_extract_context_from_bytearray_when_construct_is_called(self):
# GIVEN
prefix = bytearray([0x20, 0x00, 0x0d, 0xb8, 0x00, 0x00, 0x00, 0x00])
# WHEN
c = lowpan.Context(prefix)
# THEN
self.assertEqual(bytearray([0x20, 0x00, 0x0d, 0xb8, 0x00, 0x00, 0x00, 0x00]), c.prefix)
self.assertEqual(8, c.prefix_length_full_bytes)
self.assertEqual(64, c.prefix_length)
class TestContextManager(unittest.TestCase):
def test_should_raise_IndexError_when_index_is_larger_than_15(self):
# GIVEN
context_manager = lowpan.ContextManager()
index = random.randint(16, 255)
# WHEN
with self.assertRaises(IndexError):
context_manager[index] = any_context()
def test_should_raise_IndexError_when_index_is_smaller_than_0(self):
# GIVEN
context_manager = lowpan.ContextManager()
index = random.randint(-255, -1)
# WHEN
with self.assertRaises(IndexError):
context_manager[index] = any_context()
def test_should_raise_TypeError_when_set_value_is_not_Context(self):
# GIVEN
context_manager = lowpan.ContextManager()
# WHEN
with self.assertRaises(TypeError):
context_manager[0] = int
class TestLowpanMeshHeader(unittest.TestCase):
def test_should_return_hops_left_value_when_hops_left_property_is_called(self):
# GIVEN
hops_left = any_hops_left()
mesh_header = lowpan.LowpanMeshHeader(hops_left, any_mac_address(), any_mac_address())
# WHEN
actual_hops_left = mesh_header.hops_left
# THEN
self.assertEqual(hops_left, actual_hops_left)
def test_should_return_originator_address_value_when_originator_address_property_is_called(self):
# GIVEN
originator_address = any_mac_address()
mesh_header = lowpan.LowpanMeshHeader(any_hops_left(), originator_address, any_mac_address())
# WHEN
actual_originator_address = mesh_header.originator_address
# THEN
self.assertEqual(originator_address, actual_originator_address)
def test_should_return_final_destination_address_value_when_final_destination_address_property_is_called(self):
# GIVEN
final_destination_address = any_mac_address()
mesh_header = lowpan.LowpanMeshHeader(any_hops_left(), any_mac_address(), final_destination_address)
# WHEN
actual_final_destination_address = mesh_header.final_destination_address
# THEN
self.assertEqual(final_destination_address, actual_final_destination_address)
class TestLowpanMeshHeaderFactory(unittest.TestCase):
def test_should_create_LowpanMeshHeader_when_parse_method_is_called(self):
# GIVEN
hops_left = any_hops_left()
originator_address = any_mac_address()
final_destination_address = any_mac_address()
v = int(originator_address.type == common.MacAddressType.SHORT)
f = int(final_destination_address.type == common.MacAddressType.SHORT)
mesh_header_data = bytearray([(2 << 6) | (v << 5) | (f << 4) | hops_left]) + \
originator_address.mac_address + final_destination_address.mac_address
mesh_header_factory = lowpan.LowpanMeshHeaderFactory()
# WHEN
mesh_header = mesh_header_factory.parse(io.BytesIO(mesh_header_data), None)
# THEN
self.assertEqual(hops_left, mesh_header.hops_left)
self.assertEqual(originator_address, mesh_header.originator_address)
self.assertEqual(final_destination_address, mesh_header.final_destination_address)
class TestLowpanFragmentationHeader(unittest.TestCase):
def test_should_return_datagram_size_value_when_datagram_size_property_is_called(self):
# GIVEN
datagram_size = any_datagram_size()
fragmentation_header = lowpan.LowpanFragmentationHeader(
datagram_size, any_datagram_tag(), any_datagram_offset())
# WHEN
actual_datagram_size = fragmentation_header.datagram_size
# THEN
self.assertEqual(datagram_size, actual_datagram_size)
def test_should_return_datagram_tag_value_when_datagram_tag_property_is_called(self):
# GIVEN
datagram_tag = any_datagram_tag()
fragmentation_header = lowpan.LowpanFragmentationHeader(
any_datagram_size(), datagram_tag, any_datagram_offset())
# WHEN
actual_datagram_tag = fragmentation_header.datagram_tag
# THEN
self.assertEqual(datagram_tag, actual_datagram_tag)
def test_should_return_datagram_offset_value_when_datagram_offset_property_is_called(self):
# GIVEN
datagram_offset = any_datagram_offset()
fragmentation_header = lowpan.LowpanFragmentationHeader(
any_datagram_size(), any_datagram_tag(), datagram_offset)
# WHEN
actual_datagram_offset = fragmentation_header.datagram_offset
# THEN
self.assertEqual(datagram_offset, actual_datagram_offset)
def test_should_return_False_when_is_first_property_is_called_and_datagram_offset_is_not_equal_0(self):
# GIVEN
datagram_offset = random.randint(1, (1 << 8) - 1)
fragmentation_header = lowpan.LowpanFragmentationHeader(
any_datagram_size(), any_datagram_tag(), datagram_offset)
# WHEN
is_first = fragmentation_header.is_first
# THEN
self.assertFalse(is_first)
def test_should_to_bytes_LowpanFragmentationHeader_from_bytes_when_from_bytes_class_method_is_called(self):
# GIVEN
datagram_size = any_datagram_size()
datagram_tag = any_datagram_tag()
datagram_offset = any_datagram_offset()
data = struct.pack(">HHB", ((3 << 14) | (int(datagram_offset != 0) << 13) | datagram_size),
datagram_tag, datagram_offset)
# WHEN
fragmentation_header = lowpan.LowpanFragmentationHeader.from_bytes(io.BytesIO(data))
# THEN
self.assertEqual(datagram_size, fragmentation_header.datagram_size)
self.assertEqual(datagram_tag, fragmentation_header.datagram_tag)
self.assertEqual(datagram_offset, fragmentation_header.datagram_offset)
class TestLowpanDecompressor(unittest.TestCase):
def test_should_parse_parent_request_when_decompress_method_is_called(self):
# GIVEN
data = bytearray([0x7f, 0x3b, 0x02, 0xf0, 0x4d, 0x4c, 0x4d, 0x4c,
0x5e, 0xaf, 0x00, 0x15, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x01, 0x3b, 0xfb, 0x0e,
0x3b, 0x15, 0xa1, 0xf9, 0xf5, 0x64, 0xf4, 0x99,
0xef, 0x70, 0x78, 0x6c, 0x3c, 0x0f, 0x54, 0x4e,
0x95, 0xe8, 0xf5, 0x27, 0x4c, 0xfc])
message_info = common.MessageInfo()
message_info.source_mac_address = common.MacAddress.from_eui64(
bytearray([0x12, 0xcf, 0xd3, 0x8b, 0x3b, 0x61, 0x55, 0x58]))
decompressor = config.create_default_lowpan_decompressor(context_manager=None)
# WHEN
ipv6_header, extension_headers, udp_header = decompressor.decompress(io.BytesIO(data), message_info)
# THEN
self.assertEqual("fe80::10cf:d38b:3b61:5558", ipv6_header.source_address.compressed)
self.assertEqual("ff02::2", ipv6_header.destination_address.compressed)
self.assertEqual(17, ipv6_header.next_header)
self.assertEqual(255, ipv6_header.hop_limit)
self.assertEqual([], extension_headers)
def test_should_parse_parent_response_when_decompress_method_is_called(self):
# GIVEN
data = bytearray([0x7f, 0x33, 0xf0, 0x4d, 0x4c, 0x4d, 0x4c, 0x0f,
0xe8, 0x00, 0x15, 0x04, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x01, 0x31, 0xb8, 0x16, 0x02,
0x61, 0xcc, 0x98, 0x90, 0xd6, 0xfd, 0x69, 0xd3,
0x89, 0xa0, 0x30, 0x49, 0x83, 0x7c, 0xf7, 0xb5,
0x7f, 0x83, 0x2a, 0x04, 0xf6, 0x3b, 0x8c, 0xe8,
0xb6, 0x37, 0x51, 0x5b, 0x28, 0x9a, 0x3b, 0xbe,
0x0d, 0xb3, 0x4e, 0x9f, 0xd8, 0x14, 0xc8, 0xc9,
0xf4, 0x28, 0xf6, 0x8d, 0xb7, 0xf0, 0x7d, 0x46,
0x13, 0xc2, 0xb1, 0x69, 0x4d, 0xae, 0xc1, 0x23,
0x16, 0x62, 0x90, 0xea, 0xff, 0x1b, 0xb7, 0xd7,
0x1e, 0x5c])
message_info = common.MessageInfo()
message_info.source_mac_address = common.MacAddress.from_eui64(
bytearray([0x3a, 0x3e, 0x9e, 0xed, 0x7a, 0x01, 0x36, 0xa5]))
message_info.destination_mac_address = common.MacAddress.from_eui64(
bytearray([0x12, 0xcf, 0xd3, 0x8b, 0x3b, 0x61, 0x55, 0x58]))
decompressor = config.create_default_lowpan_decompressor(context_manager=None)
# WHEN
ipv6_header, extension_headers, udp_header = decompressor.decompress(io.BytesIO(data), message_info)
# THEN
self.assertEqual("fe80::383e:9eed:7a01:36a5", ipv6_header.source_address.compressed)
self.assertEqual("fe80::10cf:d38b:3b61:5558", ipv6_header.destination_address.compressed)
self.assertEqual(17, ipv6_header.next_header)
self.assertEqual(255, ipv6_header.hop_limit)
self.assertEqual([], extension_headers)
self.assertEqual(19788, udp_header.src_port)
self.assertEqual(19788, udp_header.dst_port)
def test_should_parse_child_id_request_when_decompress_method_is_called(self):
# GIVEN
data = bytearray([0x7f, 0x33, 0xf0, 0x4d, 0x4c, 0x4d, 0x4c, 0x9a,
0x62, 0x00, 0x15, 0x01, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x01, 0x14, 0x03, 0xe3, 0x72,
0x50, 0x4f, 0x8c, 0x5c, 0x42, 0x81, 0x68, 0xe2,
0x11, 0xfc, 0xf5, 0x8c, 0x62, 0x8e, 0x83, 0x99,
0xe7, 0x26, 0x86, 0x34, 0x3b, 0xa7, 0x68, 0xc7,
0x93, 0xfb, 0x72, 0xd9, 0xcc, 0x13, 0x5e, 0x5b,
0x96, 0x0e, 0xf1, 0x80, 0x03, 0x55, 0x4f, 0x27,
0xc2, 0x96, 0xf4, 0x9c, 0x65, 0x82, 0x97, 0xcf,
0x97, 0x35, 0x89, 0xc2])
message_info = common.MessageInfo()
message_info.source_mac_address = common.MacAddress.from_eui64(
bytearray([0x12, 0xcf, 0xd3, 0x8b, 0x3b, 0x61, 0x55, 0x58]))
message_info.destination_mac_address = common.MacAddress.from_eui64(
bytearray([0x3a, 0x3e, 0x9e, 0xed, 0x7a, 0x01, 0x36, 0xa5]))
decompressor = config.create_default_lowpan_decompressor(context_manager=None)
# WHEN
ipv6_header, extension_headers, udp_header = decompressor.decompress(io.BytesIO(data), message_info)
# THEN
self.assertEqual("fe80::10cf:d38b:3b61:5558", ipv6_header.source_address.compressed)
self.assertEqual("fe80::383e:9eed:7a01:36a5", ipv6_header.destination_address.compressed)
self.assertEqual(17, ipv6_header.next_header)
self.assertEqual(255, ipv6_header.hop_limit)
self.assertEqual([], extension_headers)
self.assertEqual(19788, udp_header.src_port)
self.assertEqual(19788, udp_header.dst_port)
def test_should_parse_child_id_response_when_decompress_method_is_called(self):
# GIVEN
data = bytearray([0x7f, 0x33, 0xf0, 0x4d, 0x4c, 0x4d, 0x4c, 0x7b,
0xe3, 0x00, 0x15, 0x05, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x01, 0xe0, 0x57, 0xbf, 0x2f,
0xc0, 0x4b, 0x1d, 0xac, 0x3c, 0x24, 0x16, 0xdf,
0xeb, 0x96, 0xeb, 0xda, 0x42, 0xeb, 0x00, 0x89,
0x5f, 0x39, 0xc9, 0x2b, 0x7d, 0x31, 0xd5, 0x83,
0x9d, 0xdb, 0xb7, 0xc8, 0xe6, 0x25, 0xd3, 0x7a,
0x1e, 0x5f, 0x66, 0x9e, 0x63, 0x2d, 0x42, 0x27,
0x19, 0x41, 0xdc, 0xc4, 0xc4, 0xc0, 0x8c, 0x07])
message_info = common.MessageInfo()
message_info.source_mac_address = common.MacAddress.from_eui64(
bytearray([0x3a, 0x3e, 0x9e, 0xed, 0x7a, 0x01, 0x36, 0xa5]))
message_info.destination_mac_address = common.MacAddress.from_eui64(
bytearray([0x12, 0xcf, 0xd3, 0x8b, 0x3b, 0x61, 0x55, 0x58]))
decompressor = config.create_default_lowpan_decompressor(context_manager=None)
# WHEN
ipv6_header, extension_headers, udp_header = decompressor.decompress(io.BytesIO(data), message_info)
# THEN
self.assertEqual("fe80::383e:9eed:7a01:36a5", ipv6_header.source_address.compressed)
self.assertEqual("fe80::10cf:d38b:3b61:5558", ipv6_header.destination_address.compressed)
self.assertEqual(17, ipv6_header.next_header)
self.assertEqual(255, ipv6_header.hop_limit)
self.assertEqual([], extension_headers)
self.assertEqual(19788, udp_header.src_port)
self.assertEqual(19788, udp_header.dst_port)
def test_should_parse_advertisement_when_decompress_method_is_called(self):
# GIVEN
data = bytearray([0x7f, 0x3b, 0x01, 0xf0, 0x4d, 0x4c, 0x4d, 0x4c,
0x35, 0x9f, 0x00, 0x15, 0x07, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x01, 0x9e, 0xb8, 0xd0,
0x2f, 0x2a, 0xe0, 0x00, 0x5d, 0x66, 0x63, 0x05,
0xa0, 0x59, 0xb0, 0xd4, 0x95, 0x7f, 0xe6, 0x79,
0x17, 0x87, 0x2c, 0x1d, 0x83, 0xad, 0xc2, 0x64,
0x47, 0x20, 0x7a, 0xe2])
message_info = common.MessageInfo()
message_info.source_mac_address = common.MacAddress.from_eui64(
bytearray([0x3a, 0x3e, 0x9e, 0xed, 0x7a, 0x01, 0x36, 0xa5]))
decompressor = config.create_default_lowpan_decompressor(context_manager=None)
# WHEN
ipv6_header, extension_headers, udp_header = decompressor.decompress(io.BytesIO(data), message_info)
# THEN
self.assertEqual("fe80::383e:9eed:7a01:36a5", ipv6_header.source_address.compressed)
self.assertEqual("ff02::1", ipv6_header.destination_address.compressed)
self.assertEqual(17, ipv6_header.next_header)
self.assertEqual(255, ipv6_header.hop_limit)
self.assertEqual([], extension_headers)
self.assertEqual(19788, udp_header.src_port)
self.assertEqual(19788, udp_header.dst_port)
class TestLowpanFragmentsBuffer(unittest.TestCase):
def test_should_raise_ValueError_when_write_method_is_called_with_data_length_bigger_than_buffer_length(self):
# GIVEN
length = random.randint(1, 1280)
fragments_buffer = lowpan.LowpanFragmentsBuffer(buffer_size=(length - 1))
# THEN
self.assertRaises(ValueError, fragments_buffer.write, any_data(length))
def test_should_move_write_position_by_the_data_length_when_write_method_is_called(self):
# GIVEN
length = random.randint(1, 1280)
fragments_buffer = lowpan.LowpanFragmentsBuffer(buffer_size=length)
start_position = fragments_buffer.tell()
data = any_data(length=random.randint(1, length))
# WHEN
fragments_buffer.write(data)
# THEN
self.assertEqual(fragments_buffer.tell() - start_position, len(data))
def test_should_raise_ValueError_when_read_method_is_called_but_not_whole_packet_has_been_stored_in_buffer(self):
# GIVEN
data = any_data(length=3)
fragments_buffer = lowpan.LowpanFragmentsBuffer(buffer_size=random.randint(4, 1280))
fragments_buffer.write(data)
# WHEN
self.assertRaises(ValueError, fragments_buffer.read)
def test_should_raise_ValueError_when_seek_method_is_called_with_offset_bigger_than_buffer_length(self):
# GIVEN
offset = random.randint(1281, 2500)
fragments_buffer = lowpan.LowpanFragmentsBuffer(buffer_size=1280)
# THEN
self.assertRaises(ValueError, fragments_buffer.seek, offset)
def test_should_set_write_position_when_seek_method_is_called(self):
# GIVEN
length = random.randint(1, 1280)
offset = random.randint(0, length - 1)
fragments_buffer = lowpan.LowpanFragmentsBuffer(buffer_size=length)
# WHEN
fragments_buffer.seek(offset)
# THEN
self.assertEqual(offset, fragments_buffer.tell())
def test_should_write_whole_packet_to_buffer_when_write_method_is_called(self):
# GIVEN
data = any_data(length=random.randint(1, 1280))
fragments_buffer = lowpan.LowpanFragmentsBuffer(buffer_size=len(data))
# WHEN
fragments_buffer.write(data)
# THEN
self.assertEqual(data, fragments_buffer.read())
def test_should_write_many_fragments_to_the_buffer_and_return_whole_message_when_write_method_is_called_many_times(self):
# GIVEN
buffer_size = 42
fragments_buffer = lowpan.LowpanFragmentsBuffer(buffer_size=buffer_size)
offset_1 = 0
fragment_1 = bytearray([0x01, 0x02, 0x03, 0x04, 0x05, 0x06, 0x07, 0x08])
offset_2 = 8
fragment_2 = bytearray([0x09, 0x0a, 0x0b, 0x0c, 0x0d, 0x0e, 0x0f, 0x10])
offset_3 = 16
fragment_3 = bytearray([0x11, 0x12, 0x13, 0x14, 0x15, 0x16, 0x17, 0x18])
offset_4 = 24
fragment_4 = bytearray([0x19, 0x1a, 0x1b, 0x1c, 0x1d, 0x1e, 0x1f, 0x20])
offset_5 = 32
fragment_5 = bytearray([0x21, 0x22, 0x23, 0x24, 0x25, 0x26, 0x27, 0x28])
offset_6 = 40
fragment_6 = bytearray([0x29, 0x2a])
# WHEN
fragments_buffer.seek(offset_1)
fragments_buffer.write(fragment_1)
fragments_buffer.seek(offset_2)
fragments_buffer.write(fragment_2)
fragments_buffer.seek(offset_3)
fragments_buffer.write(fragment_3)
fragments_buffer.seek(offset_4)
fragments_buffer.write(fragment_4)
fragments_buffer.seek(offset_5)
fragments_buffer.write(fragment_5)
fragments_buffer.seek(offset_6)
fragments_buffer.write(fragment_6)
# THEN
self.assertEqual(bytearray([0x01, 0x02, 0x03, 0x04, 0x05, 0x06, 0x07, 0x08,
0x09, 0x0a, 0x0b, 0x0c, 0x0d, 0x0e, 0x0f, 0x10,
0x11, 0x12, 0x13, 0x14, 0x15, 0x16, 0x17, 0x18,
0x19, 0x1a, 0x1b, 0x1c, 0x1d, 0x1e, 0x1f, 0x20,
0x21, 0x22, 0x23, 0x24, 0x25, 0x26, 0x27, 0x28,
0x29, 0x2a]),
fragments_buffer.read())
class TestLowpanFragmentsBuffersManager(unittest.TestCase):
def test_should_raise_ValueError_when_get_fragments_buffer_method_is_called_with_invalid_datagram_size(self):
# GIVEN
message_info = common.MessageInfo()
message_info.source_mac_address = any_mac_address()
message_info.destination_mac_address = any_mac_address()
negative_int = -random.randint(1, 1280)
manager = lowpan.LowpanFragmentsBuffersManager()
# THEN
self.assertRaises(ValueError, manager.get_fragments_buffer, message_info, any_datagram_tag(), None)
self.assertRaises(ValueError, manager.get_fragments_buffer, message_info, any_datagram_tag(), negative_int)
def test_should_return_LowpanFragmentsBuffer_when_get_fragments_buffer_method_is_called_with_valid_datagram_size(self):
# GIVEN
message_info = common.MessageInfo()
message_info.source_mac_address = any_mac_address()
message_info.destination_mac_address = any_mac_address()
datagram_size = any_datagram_size()
manager = lowpan.LowpanFragmentsBuffersManager()
# WHEN
fragments_buffer = manager.get_fragments_buffer(message_info, any_datagram_tag(), datagram_size)
# THEN
self.assertIsInstance(fragments_buffer, lowpan.LowpanFragmentsBuffer)
self.assertEqual(datagram_size, len(fragments_buffer))
if __name__ == "__main__":
unittest.main(verbosity=1)
| 44.898982 | 156 | 0.582461 | 13,771 | 119,117 | 4.770314 | 0.046838 | 0.042745 | 0.04585 | 0.04238 | 0.875419 | 0.852082 | 0.814407 | 0.797418 | 0.7695 | 0.738888 | 0 | 0.168842 | 0.321348 | 119,117 | 2,652 | 157 | 44.915913 | 0.643786 | 0.025152 | 0 | 0.677324 | 0 | 0 | 0.004403 | 0.00322 | 0 | 0 | 0.169835 | 0 | 0.132623 | 1 | 0.078153 | false | 0 | 0.004737 | 0.023091 | 0.117229 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
774f1fa9902b69985428eaa80b39fb8e77930858 | 4,072 | py | Python | train/hypotheses.py | smurve/DeepGomoku | ee679e7ec9caacc9c8f2b10c3a403d3f7d2625d8 | [
"Apache-2.0"
] | 1 | 2019-06-26T13:14:50.000Z | 2019-06-26T13:14:50.000Z | other_stuff/DeepGomoku/new/train/hypotheses.py | Project-Ellie/tutorials | 9090cc7669d3e59889b15139724e662ce11be1ee | [
"Apache-2.0"
] | 5 | 2020-01-28T22:33:04.000Z | 2021-11-10T19:45:24.000Z | other_stuff/DeepGomoku/new/train/hypotheses.py | Project-Ellie/tutorials | 9090cc7669d3e59889b15139724e662ce11be1ee | [
"Apache-2.0"
] | null | null | null | import tensorflow as tf
import numpy as np
from tensorflow.feature_column import numeric_column as num
def conv_gomoku(board_size, features, feature_columns, options):
N = board_size
layout = options['layout']
feature_columns = [num('state', shape=((N+2)*(N+2)*2))]
input_layer = tf.feature_column.input_layer(
features, feature_columns=feature_columns)
layer = tf.reshape(input_layer, [-1, N+2, N+2, 2], name='reshape_input')
for filters, kernel in np.reshape(layout, [-1,2]):
layer = tf.layers.conv2d(inputs=layer, filters=filters,
kernel_size=[kernel, kernel], strides=[1,1],
padding='SAME')
beta_l = tf.Variable(-0.5),
beta_r = tf.Variable(0.5)
exotic = layer * (layer - beta_l) * (layer - beta_r) * tf.exp(-layer*layer)
layer = tf.nn.relu(layer)+exotic
layer = tf.layers.conv2d(inputs=layer, filters=1,
kernel_size=[kernel, kernel], strides=[1,1],
padding='SAME')
return layer
def conv_2x1024_5(board_size, features, feature_columns, options):
N = board_size
feature_columns = [num('state', shape=((N+2)*(N+2)*2))]
input_layer = tf.feature_column.input_layer(
features, feature_columns=feature_columns)
reshaped = tf.reshape(input_layer, [-1, N+2, N+2, 2], name='reshape_input')
conva = tf.layers.conv2d(inputs=reshaped, filters=1024, kernel_size=[9,9],
strides=[1,1], padding='SAME', activation=tf.nn.relu)
conv0 = tf.layers.conv2d(inputs=conva, filters=1024, kernel_size=[9,9],
strides=[1,1], padding='SAME', activation=tf.nn.relu)
conv1 = tf.layers.conv2d(inputs=conv0, filters=512, kernel_size=[7,7],
strides=[1,1], padding='SAME', activation=tf.nn.relu)
conv2 = tf.layers.conv2d(inputs=conv1, filters=128, kernel_size=[7,7],
strides=[1,1], padding='SAME', activation=tf.nn.relu)
conv3 = tf.layers.conv2d(inputs=conv2, filters=1, kernel_size=[5,5],
strides=[1,1], padding='SAME')
return conv3
def conv_1024_4(board_size, features, feature_columns, options):
N = board_size
feature_columns = [num('state', shape=((N+2)*(N+2)*2))]
input_layer = tf.feature_column.input_layer(
features, feature_columns=feature_columns)
reshaped = tf.reshape(input_layer, [-1, N+2, N+2, 2], name='reshape_input')
conv0 = tf.layers.conv2d(inputs=reshaped, filters=1024, kernel_size=[9,9],
strides=[1,1], padding='SAME', activation=tf.nn.relu)
conv1 = tf.layers.conv2d(inputs=conv0, filters=512, kernel_size=[9,9],
strides=[1,1], padding='SAME', activation=tf.nn.relu)
conv2 = tf.layers.conv2d(inputs=conv1, filters=128, kernel_size=[7,7],
strides=[1,1], padding='SAME', activation=tf.nn.relu)
conv3 = tf.layers.conv2d(inputs=conv2, filters=1, kernel_size=[5,5],
strides=[1,1], padding='SAME')
return conv3
def conv_512_3(board_size, features, feature_columns, options):
N = board_size
feature_columns = [num('state', shape=((N+2)*(N+2)*2))]
input_layer = tf.feature_column.input_layer(
features, feature_columns=feature_columns)
reshaped = tf.reshape(input_layer, [-1, N+2, N+2, 2], name='reshape_input')
conv1 = tf.layers.conv2d(inputs=reshaped, filters=512, kernel_size=[9,9],
strides=[1,1], padding='SAME', activation=tf.nn.relu)
conv2 = tf.layers.conv2d(inputs=conv1, filters=128, kernel_size=[7,7],
strides=[1,1], padding='SAME', activation=tf.nn.relu)
conv3 = tf.layers.conv2d(inputs=conv2, filters=1, kernel_size=[5,5],
strides=[1,1], padding='SAME')
return conv3
| 34.803419 | 83 | 0.590373 | 536 | 4,072 | 4.352612 | 0.121269 | 0.096014 | 0.084012 | 0.120017 | 0.867981 | 0.852979 | 0.838405 | 0.806687 | 0.806687 | 0.750107 | 0 | 0.057057 | 0.263998 | 4,072 | 116 | 84 | 35.103448 | 0.721388 | 0 | 0 | 0.656716 | 0 | 0 | 0.032924 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.059701 | false | 0 | 0.044776 | 0 | 0.164179 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
91f8da9af2a892495824aafe1087a0b0232f483b | 112 | py | Python | preprocess/__init__.py | doojin88/embedding | dc780bbc0e45c6d156b8406bda716623567a79ce | [
"MIT"
] | 386 | 2019-06-13T02:28:04.000Z | 2022-03-28T05:34:32.000Z | preprocess/__init__.py | doojin88/embedding | dc780bbc0e45c6d156b8406bda716623567a79ce | [
"MIT"
] | 136 | 2019-05-12T05:04:41.000Z | 2022-03-01T09:20:51.000Z | preprocess/__init__.py | doojin88/embedding | dc780bbc0e45c6d156b8406bda716623567a79ce | [
"MIT"
] | 133 | 2019-06-13T04:19:35.000Z | 2022-03-17T01:36:42.000Z | from .supervised_nlputils import get_tokenizer, post_processing
from .unsupervised_nlputils import jamo_sentence | 56 | 63 | 0.901786 | 14 | 112 | 6.857143 | 0.785714 | 0.291667 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.071429 | 112 | 2 | 64 | 56 | 0.923077 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
6228c1300dcdc06e4b01f82ec25452c47f94f2e0 | 187 | py | Python | runemoji.py | sundayliu/flask-tutorial | 8621441b429020ea884b4bf090efa8dd15133af8 | [
"MIT"
] | null | null | null | runemoji.py | sundayliu/flask-tutorial | 8621441b429020ea884b4bf090efa8dd15133af8 | [
"MIT"
] | null | null | null | runemoji.py | sundayliu/flask-tutorial | 8621441b429020ea884b4bf090efa8dd15133af8 | [
"MIT"
] | null | null | null | # -*- coding:utf-8 -*-
from emoji import app
from werkzeug.contrib.fixers import ProxyFix
app.wsgi_app = ProxyFix(app.wsgi_app)
if __name__ == '__main__':
app.run(debug=True) | 23.375 | 45 | 0.695187 | 27 | 187 | 4.444444 | 0.666667 | 0.183333 | 0.25 | 0.3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.006452 | 0.171123 | 187 | 8 | 46 | 23.375 | 0.767742 | 0.106952 | 0 | 0 | 0 | 0 | 0.050314 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.4 | 0 | 0.4 | 0 | 1 | 0 | 0 | null | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 6 |
62315182b1d874519e85958e9e15a9dee6c055c4 | 118 | py | Python | yo_fluq_ds/_fluq/pandas/__init__.py | okulovsky/yo_ds | 9e1fa2e7a1b9746c3982afc152c024169fec45ca | [
"MIT"
] | 16 | 2019-09-26T09:05:42.000Z | 2021-02-04T01:39:09.000Z | yo_fluq_ds/_fluq/pandas/__init__.py | okulovsky/yo_ds | 9e1fa2e7a1b9746c3982afc152c024169fec45ca | [
"MIT"
] | 2 | 2019-10-23T19:01:23.000Z | 2020-06-11T09:08:45.000Z | yo_fluq_ds/_fluq/pandas/__init__.py | okulovsky/yo_ds | 9e1fa2e7a1b9746c3982afc152c024169fec45ca | [
"MIT"
] | 2 | 2019-09-26T09:05:50.000Z | 2019-10-23T18:46:11.000Z | from ._fractions import *
from ._add_ordering_column import *
from ._stratify import *
from ._trimmer import trimmer
| 19.666667 | 35 | 0.79661 | 15 | 118 | 5.866667 | 0.533333 | 0.340909 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.144068 | 118 | 5 | 36 | 23.6 | 0.871287 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
623d669dd9a662028fa23c07bf722f0d472cba4f | 2,395 | py | Python | tests/test_cli.py | arunura/python_api | 2b17d105e9fe1d51365a7abc59a904c2f2702169 | [
"MIT"
] | null | null | null | tests/test_cli.py | arunura/python_api | 2b17d105e9fe1d51365a7abc59a904c2f2702169 | [
"MIT"
] | null | null | null | tests/test_cli.py | arunura/python_api | 2b17d105e9fe1d51365a7abc59a904c2f2702169 | [
"MIT"
] | null | null | null | """Tests the CLI interface for DomainTools' APIs"""
import pytest
from domaintools import __version__, cli
def test_domain_search():
(out_file, out_format, arguments) = cli.parse(['domain_search', 'google', '--anchor-right', 'true'])
assert out_format == 'json'
assert arguments['api_call'] == 'domain_search'
assert arguments['query'] == 'google'
assert arguments['anchor_right'] == 'true'
def test_iris_investigate():
(out_file, out_format, arguments) = cli.parse(['iris_investigate', '--domains', 'domaintools.com'])
assert out_format == 'json'
assert arguments['api_call'] == 'iris_investigate'
assert arguments['domains'] == 'domaintools.com'
def test_iris_investigate_search_hash():
(out_file, out_format, arguments) = cli.parse(['iris_investigate', '--search-hash', 'U2FsdGVkX19G/DWIwCQN8p2e/pbIvv5yRwbZs1vas8BrvEaohzKi5FbgAXPB+souItygalew9jxEpeNvmDNfVD0IuKPknPO5zQA9Eic38zpSpRVPQ9P2jhBpZJkMfseS5VVoM4BSL2lmGAhX0RPpZ8PMXSUtRP8IJUDo8n4HIi0r/+/vD5yIUSdRujA4sXIPpLujjW80PKJkyrFWmT35Y6aYxdlw6U05tBcc1k9ThnVNWL8K/R41OeSrFuTSrmTpCrOTF5YvCcZakbRp+BZUH76k8yTY+mU1HhCsT54fgPY0YsCcvXt2x8y89HXlCAio8Gz+nxLU2YeWaxAsvnNpyqm2WQZPrlXzFTxtbymN8QzVRBwGHxJcqixcW43FlsjA1FIAu6dJ/zS3ibxf9aFqspibOngLc2dufcHRclMXg1i2AmTF6fTM23oLT3GVSc7JwYycRwn94xbC4eQDzkzVQiU/60mVMEIKegTPByoYBJU='])
assert out_format == 'json'
assert arguments['api_call'] == 'iris_investigate'
assert arguments['search_hash'] == 'U2FsdGVkX19G/DWIwCQN8p2e/pbIvv5yRwbZs1vas8BrvEaohzKi5FbgAXPB+souItygalew9jxEpeNvmDNfVD0IuKPknPO5zQA9Eic38zpSpRVPQ9P2jhBpZJkMfseS5VVoM4BSL2lmGAhX0RPpZ8PMXSUtRP8IJUDo8n4HIi0r/+/vD5yIUSdRujA4sXIPpLujjW80PKJkyrFWmT35Y6aYxdlw6U05tBcc1k9ThnVNWL8K/R41OeSrFuTSrmTpCrOTF5YvCcZakbRp+BZUH76k8yTY+mU1HhCsT54fgPY0YsCcvXt2x8y89HXlCAio8Gz+nxLU2YeWaxAsvnNpyqm2WQZPrlXzFTxtbymN8QzVRBwGHxJcqixcW43FlsjA1FIAu6dJ/zS3ibxf9aFqspibOngLc2dufcHRclMXg1i2AmTF6fTM23oLT3GVSc7JwYycRwn94xbC4eQDzkzVQiU/60mVMEIKegTPByoYBJU='
def test_not_authenticated():
(out_file, out_format, arguments) = cli.parse(args=['-c', 'non-existent', 'domain_search', 'google',
'--max-length', '100'])
assert out_format == 'json'
assert not arguments.get('user')
assert not arguments.get('key')
def test_stream_in():
with pytest.raises((OSError, IOError)):
cli.parse(['domain_search', 'google', '--max-length', '-'])
| 61.410256 | 584 | 0.784134 | 185 | 2,395 | 9.92973 | 0.340541 | 0.039194 | 0.021775 | 0.034839 | 0.777354 | 0.720196 | 0.720196 | 0.684268 | 0.661949 | 0.60969 | 0 | 0.072566 | 0.108142 | 2,395 | 38 | 585 | 63.026316 | 0.787453 | 0.018789 | 0 | 0.222222 | 0 | 0.074074 | 0.563993 | 0.419795 | 0 | 0 | 0 | 0 | 0.481481 | 1 | 0.185185 | true | 0 | 0.074074 | 0 | 0.259259 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | null | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
627c60962408d790267b7a23f2cf83b6f4134010 | 495 | py | Python | third_party/WebKit/Tools/Scripts/webkitpy/tool/commands/__init__.py | wenfeifei/miniblink49 | 2ed562ff70130485148d94b0e5f4c343da0c2ba4 | [
"Apache-2.0"
] | 5,964 | 2016-09-27T03:46:29.000Z | 2022-03-31T16:25:27.000Z | third_party/WebKit/Tools/Scripts/webkitpy/tool/commands/__init__.py | w4454962/miniblink49 | b294b6eacb3333659bf7b94d670d96edeeba14c0 | [
"Apache-2.0"
] | 459 | 2016-09-29T00:51:38.000Z | 2022-03-07T14:37:46.000Z | third_party/WebKit/Tools/Scripts/webkitpy/tool/commands/__init__.py | w4454962/miniblink49 | b294b6eacb3333659bf7b94d670d96edeeba14c0 | [
"Apache-2.0"
] | 1,006 | 2016-09-27T05:17:27.000Z | 2022-03-30T02:46:51.000Z | # Required for Python to search this directory for module files
from webkitpy.tool.commands.commitannouncer import CommitAnnouncerCommand
from webkitpy.tool.commands.flakytests import FlakyTests
from webkitpy.tool.commands.prettydiff import PrettyDiff
from webkitpy.tool.commands.queries import *
from webkitpy.tool.commands.rebaseline import Rebaseline
from webkitpy.tool.commands.rebaselineserver import RebaselineServer
from webkitpy.tool.commands.layouttestsserver import LayoutTestsServer
| 49.5 | 73 | 0.872727 | 58 | 495 | 7.448276 | 0.37931 | 0.194444 | 0.259259 | 0.388889 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.080808 | 495 | 9 | 74 | 55 | 0.949451 | 0.123232 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
6568ad7868055414e5194a3a4bab360f669f2544 | 198 | py | Python | mmcls/models/carafe/__init__.py | zhaoyang97/mmclassification | 22362e4a351eea028a7fba081b274f435dbc872a | [
"Apache-2.0"
] | null | null | null | mmcls/models/carafe/__init__.py | zhaoyang97/mmclassification | 22362e4a351eea028a7fba081b274f435dbc872a | [
"Apache-2.0"
] | null | null | null | mmcls/models/carafe/__init__.py | zhaoyang97/mmclassification | 22362e4a351eea028a7fba081b274f435dbc872a | [
"Apache-2.0"
] | null | null | null | from .carafe_downsample_3_kernelexp import CARAFE_Downsample_3_kernelexp
from .carafe_downsample import CARAFE_Downsample
__all__ = [
'CARAFE_Downsample',
'CARAFE_Downsample_3_kernelexp',
] | 28.285714 | 72 | 0.833333 | 23 | 198 | 6.478261 | 0.304348 | 0.644295 | 0.342282 | 0.52349 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.017045 | 0.111111 | 198 | 7 | 73 | 28.285714 | 0.829545 | 0 | 0 | 0 | 0 | 0 | 0.231156 | 0.145729 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.333333 | 0 | 0.333333 | 0 | 1 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 6 |
657df53999f067ee2864e898c815d640a0f7e97d | 39 | py | Python | skclean/utils/__init__.py | Shihab-Shahriar/scikit-clean | 1f5d4266c91ca7074f1833de78526c39d97a5648 | [
"MIT"
] | 4 | 2020-11-02T14:01:50.000Z | 2021-12-06T09:18:33.000Z | skclean/utils/__init__.py | Shihab-Shahriar/scikit-clean | 1f5d4266c91ca7074f1833de78526c39d97a5648 | [
"MIT"
] | null | null | null | skclean/utils/__init__.py | Shihab-Shahriar/scikit-clean | 1f5d4266c91ca7074f1833de78526c39d97a5648 | [
"MIT"
] | null | null | null | from ._utils import load_data, compare
| 19.5 | 38 | 0.820513 | 6 | 39 | 5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.128205 | 39 | 1 | 39 | 39 | 0.882353 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
6597e9c496914377361af6a5dbe1bbd8d49b3a8f | 213 | py | Python | src/abaqus/StepMiscellaneous/RayleighDampingByFrequencyComponentArray.py | Haiiliin/PyAbaqus | f20db6ebea19b73059fe875a53be370253381078 | [
"MIT"
] | 7 | 2022-01-21T09:15:45.000Z | 2022-02-15T09:31:58.000Z | src/abaqus/StepMiscellaneous/RayleighDampingByFrequencyComponentArray.py | Haiiliin/PyAbaqus | f20db6ebea19b73059fe875a53be370253381078 | [
"MIT"
] | null | null | null | src/abaqus/StepMiscellaneous/RayleighDampingByFrequencyComponentArray.py | Haiiliin/PyAbaqus | f20db6ebea19b73059fe875a53be370253381078 | [
"MIT"
] | null | null | null | from .RayleighDampingByFrequencyComponent import RayleighDampingByFrequencyComponent
class RayleighDampingByFrequencyComponentArray(list[RayleighDampingByFrequencyComponent]):
def findAt(self):
pass
| 30.428571 | 90 | 0.849765 | 12 | 213 | 15.083333 | 0.833333 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.107981 | 213 | 6 | 91 | 35.5 | 0.952632 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.25 | false | 0.25 | 0.25 | 0 | 0.75 | 0 | 1 | 0 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 6 |
65e24839e176cd1b6284b6a5a119c7378363ae24 | 205 | py | Python | terra/msg/oracle/__init__.py | jooddang/terra-py | c048ffd53dad13cdfb0c516ccef3d06b1b968cb2 | [
"MIT"
] | null | null | null | terra/msg/oracle/__init__.py | jooddang/terra-py | c048ffd53dad13cdfb0c516ccef3d06b1b968cb2 | [
"MIT"
] | null | null | null | terra/msg/oracle/__init__.py | jooddang/terra-py | c048ffd53dad13cdfb0c516ccef3d06b1b968cb2 | [
"MIT"
] | null | null | null | from terra.msg.oracle.msgexchangerateprevote import MsgExchangeRatePrevote
from terra.msg.oracle.msgexchangeratevote import MsgExchangeRateVote
__all__ = ["MsgExchangeRatePrevote", "MsgExchangeRateVote"]
| 41 | 74 | 0.868293 | 17 | 205 | 10.235294 | 0.470588 | 0.103448 | 0.137931 | 0.206897 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.063415 | 205 | 4 | 75 | 51.25 | 0.90625 | 0 | 0 | 0 | 0 | 0 | 0.2 | 0.107317 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.666667 | 0 | 0.666667 | 0 | 1 | 0 | 1 | null | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
02b8ba85dafa3c8a81def1ef56b9dca1b42a848f | 34 | py | Python | tests/test_command.py | cleverinsightai/cognito | 7d643387d59ee31bff96dd0ae3e611a8aef4d876 | [
"BSD-3-Clause"
] | null | null | null | tests/test_command.py | cleverinsightai/cognito | 7d643387d59ee31bff96dd0ae3e611a8aef4d876 | [
"BSD-3-Clause"
] | null | null | null | tests/test_command.py | cleverinsightai/cognito | 7d643387d59ee31bff96dd0ae3e611a8aef4d876 | [
"BSD-3-Clause"
] | null | null | null | from .cognito.modules import Check | 34 | 34 | 0.852941 | 5 | 34 | 5.8 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.088235 | 34 | 1 | 34 | 34 | 0.935484 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
02d28253f1151f480d95d78d55c4c2f0210eff1f | 2,334 | py | Python | tests/test_git.py | hbasria/setuptools-scm-semver | 706665a48a785827b3b25b451b937342812ad5ff | [
"MIT"
] | null | null | null | tests/test_git.py | hbasria/setuptools-scm-semver | 706665a48a785827b3b25b451b937342812ad5ff | [
"MIT"
] | null | null | null | tests/test_git.py | hbasria/setuptools-scm-semver | 706665a48a785827b3b25b451b937342812ad5ff | [
"MIT"
] | 1 | 2019-10-24T13:54:16.000Z | 2019-10-24T13:54:16.000Z | import os
import pytest
@pytest.fixture
def wd(wd):
wd("git init")
wd("git config user.email test@example.com")
wd('git config user.name "a test"')
wd.add_command = "git add ."
wd.commit_command = "git commit -m test-{reason}"
return wd
def test_git_develop_branch_increments_patch(wd):
os.environ["BRANCH_NAME"] = "develop"
wd("git checkout -b develop")
assert wd.version == "0.0.1-beta.0"
wd.commit_testfile()
assert wd.version == "0.0.1-beta.1"
wd("git tag 0.1.0")
wd.commit_testfile()
assert wd.version == "0.1.1-beta.1"
def test_git_release_branch_increments_patch(wd):
os.environ["BRANCH_NAME"] = "release"
wd("git checkout -b release")
assert wd.version == "0.0.1-rc.0"
wd.commit_testfile()
assert wd.version == "0.0.1-rc.1"
wd("git tag 0.1.0")
wd.commit_testfile()
assert wd.version == "0.1.1-rc.1"
def test_git_master_branch_increments_patch(wd):
os.environ["BRANCH_NAME"] = "master"
wd("git checkout -b master")
assert wd.version == "0.0.1"
wd.commit_testfile()
wd("git tag 0.0.1")
wd.commit_testfile()
wd.commit_testfile()
assert wd.version == "0.0.2"
def test_git_pr_to_develop_branch_increments_patch(wd):
os.environ["BRANCH_NAME"] = "PR-123"
os.environ["CHANGE_TARGET"] = "develop"
wd("git checkout -b PR-123")
assert wd.version == "0.0.1-alpha.0"
wd.commit_testfile()
assert wd.version == "0.0.1-alpha.1"
wd("git tag 0.1.0")
wd.commit_testfile()
wd.commit_testfile()
assert wd.version == "0.1.1-alpha.2"
def test_git_pr_to_release_branch_increments_patch(wd):
os.environ["BRANCH_NAME"] = "PR-123"
os.environ["CHANGE_TARGET"] = "release"
wd("git checkout -b PR-123")
assert wd.version == "0.0.1-rc.0"
wd.commit_testfile()
assert wd.version == "0.0.1-rc.1"
wd("git tag 0.1.0")
wd.commit_testfile()
assert wd.version == "0.1.1-rc.1"
def test_git_pr_to_master_branch_increments_patch(wd):
os.environ["BRANCH_NAME"] = "PR-123"
os.environ["CHANGE_TARGET"] = "master"
wd("git checkout -b PR-123")
assert wd.version == "0.0.1"
wd.commit_testfile()
assert wd.version == "0.0.1"
wd("git tag 0.1.1")
wd.commit_testfile()
assert wd.version == "0.1.2"
| 19.948718 | 55 | 0.632819 | 381 | 2,334 | 3.716535 | 0.120735 | 0.031073 | 0.180085 | 0.19209 | 0.841102 | 0.780367 | 0.753531 | 0.723164 | 0.698446 | 0.53178 | 0 | 0.053226 | 0.203085 | 2,334 | 116 | 56 | 20.12069 | 0.708065 | 0 | 0 | 0.485294 | 0 | 0 | 0.276778 | 0 | 0 | 0 | 0 | 0 | 0.25 | 1 | 0.102941 | false | 0 | 0.029412 | 0 | 0.147059 | 0 | 0 | 0 | 0 | null | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
02d97fc056221a05f46ca4eba3b947b9df72ec82 | 13,357 | py | Python | tests/test_outputs/test_setters.py | wbuchwalter/polyaxon | a01396ea86a74082c457bfbc2c91d283b6ff6fba | [
"MIT"
] | null | null | null | tests/test_outputs/test_setters.py | wbuchwalter/polyaxon | a01396ea86a74082c457bfbc2c91d283b6ff6fba | [
"MIT"
] | null | null | null | tests/test_outputs/test_setters.py | wbuchwalter/polyaxon | a01396ea86a74082c457bfbc2c91d283b6ff6fba | [
"MIT"
] | null | null | null | from collections import namedtuple
import pytest
from rest_framework.exceptions import ValidationError
from db.models.experiments import Experiment
from db.models.jobs import Job
from factories.factory_experiments import ExperimentFactory
from factories.factory_jobs import JobFactory
from factories.factory_projects import ProjectFactory
from factories.factory_users import UserFactory
from polyaxon_schemas.environments import OutputsConfig
from signals.outputs import get_valid_outputs, get_valid_ref, set_outputs, set_outputs_refs
from tests.utils import BaseTest
class InstanceSpec(namedtuple("InstanceSpec", "user project")):
pass
@pytest.mark.outputs_mark
class TestOutputsSetters(BaseTest):
DISABLE_RUNNER = True
def setUp(self):
super().setUp()
self.user = UserFactory()
self.project = ProjectFactory(user=self.user)
self.project_job = JobFactory(project=self.project, name='unique')
self.job = JobFactory(name='unique2')
self.project_experiment = ExperimentFactory(project=self.project, name='unique')
self.experiment = ExperimentFactory(name='unique2')
self.instance_mock = InstanceSpec(self.user.id, self.project.id)
self.wrong_instance_mock = InstanceSpec(-1, -1)
def test_get_valid_ref_by_id(self):
assert get_valid_ref(
model=Experiment, entity_id=self.experiment.id)[0] == self.experiment.id
assert get_valid_ref(
model=Experiment, entity_id=self.project_experiment.id)[0] == self.project_experiment.id
assert get_valid_ref(
model=Job, entity_id=self.job.id)[0] == self.job.id
assert get_valid_ref(
model=Job, entity_id=self.project_job.id)[0] == self.project_job.id
assert get_valid_ref(model=Experiment, entity_id=-1).count() == 0
assert get_valid_ref(model=Job, entity_id=-1).count() == 0
def test_get_valid_ref_by_instance_name(self):
# Valid values
assert get_valid_ref(
model=Experiment,
instance=self.instance_mock,
entity_args=[self.project_experiment.name])[0] == self.project_experiment.id
assert get_valid_ref(
model=Job,
instance=self.instance_mock,
entity_args=[self.project_job.name])[0] == self.project_job.id
# Non valid project and user
assert get_valid_ref(
model=Experiment,
instance=self.wrong_instance_mock,
entity_args=[self.project_experiment.name]).count() == 0
assert get_valid_ref(
model=Job,
instance=self.wrong_instance_mock,
entity_args=[self.project_job.name]).count() == 0
# Non valid values
assert get_valid_ref(
model=Experiment,
instance=self.instance_mock,
entity_args=[self.experiment.name]).count() == 0
assert get_valid_ref(
model=Job,
instance=self.instance_mock,
entity_args=[self.job.name]).count() == 0
def test_get_valid_ref_by_instance_and_project_name(self):
# Valid values
assert get_valid_ref(
model=Experiment,
instance=self.instance_mock,
entity_args=[self.project.name,
self.project_experiment.name, ])[0] == self.project_experiment.id
assert get_valid_ref(
model=Job,
instance=self.instance_mock,
entity_args=[self.project.name,
self.project_job.name])[0] == self.project_job.id
# Non valid project and user
assert get_valid_ref(
model=Experiment,
instance=self.wrong_instance_mock,
entity_args=[self.project.name,
self.project_experiment.name]).count() == 0
assert get_valid_ref(
model=Job,
instance=self.wrong_instance_mock,
entity_args=[self.project.name,
self.project_job.name]).count() == 0
# Non valid values
assert get_valid_ref(
model=Experiment,
instance=self.instance_mock,
entity_args=[self.project.name,
self.experiment.name]).count() == 0
assert get_valid_ref(
model=Job,
instance=self.instance_mock,
entity_args=[self.project.name,
self.job.name]).count() == 0
def test_get_valid_ref_by_instance_and_project_name_user(self):
# Valid values
assert get_valid_ref(
model=Experiment,
instance=self.instance_mock,
entity_args=[self.user.username,
self.project.name,
self.project_experiment.name])[0] == self.project_experiment.id
assert get_valid_ref(
model=Job,
instance=self.instance_mock,
entity_args=[self.user.username,
self.project.name,
self.project_job.name])[0] == self.project_job.id
# Non valid project and user and all information should pass this time
assert get_valid_ref(
model=Experiment,
instance=self.wrong_instance_mock,
entity_args=[self.user.username,
self.project.name,
self.project_experiment.name]).count() == 1
assert get_valid_ref(
model=Job,
instance=self.wrong_instance_mock,
entity_args=[self.user.username,
self.project.name,
self.project_job.name]).count() == 1
# Non valid values
assert get_valid_ref(
model=Experiment,
instance=self.instance_mock,
entity_args=[self.user.username,
self.project.name,
self.experiment.name]).count() == 0
assert get_valid_ref(
model=Job,
instance=self.instance_mock,
entity_args=[self.user.username,
self.project.name,
self.job.name]).count() == 0
def test_get_valid_outputs(self):
# Valid outputs experiments
outputs = [
'{}'.format(self.experiment.id),
'{}'.format(self.project_experiment.id),
self.project_experiment.name,
'{}.{}'.format(self.project.name, self.project_experiment.name),
'{}/{}'.format(self.project.name, self.project_experiment.name),
'{}.{}.{}'.format(self.user.username, self.project.name, self.project_experiment.name),
'{}/{}/{}'.format(self.user.username, self.project.name, self.project_experiment.name),
]
assert len(get_valid_outputs(instance=self.instance_mock,
outputs=outputs,
model=Experiment,
entity='Experiment')) == len(outputs)
# Valid outputs jobs
outputs = [
'{}'.format(self.job.id),
'{}'.format(self.project_job.id),
self.project_job.name,
'{}.{}'.format(self.project.name, self.project_job.name),
'{}/{}'.format(self.project.name, self.project_job.name),
'{}.{}.{}'.format(self.user.username, self.project.name, self.project_job.name),
'{}/{}/{}'.format(self.user.username, self.project.name, self.project_job.name),
]
assert len(get_valid_outputs(instance=self.instance_mock,
outputs=outputs,
model=Job,
entity='Job')) == len(outputs)
# Non valid outputs
outputs = [
self.experiment.name,
'{}.{}'.format(self.project.name, self.experiment.name),
'{}/{}'.format(self.project.name, self.experiment.name),
'{}.{}.{}'.format(self.user.username, self.project.name, self.experiment.name),
'{}/{}/{}'.format(self.user.username, self.project.name, self.experiment.name),
]
with self.assertRaises(ValidationError):
get_valid_outputs(instance=self.instance_mock,
outputs=outputs,
model=Experiment,
entity='Experiment')
outputs = [
self.job.name,
'{}.{}'.format(self.project.name, self.job.name),
'{}/{}'.format(self.project.name, self.job.name),
'{}.{}.{}'.format(self.user.username, self.project.name, self.job.name),
'{}/{}/{}'.format(self.user.username, self.project.name, self.job.name),
]
with self.assertRaises(ValidationError):
get_valid_outputs(instance=self.instance_mock,
outputs=outputs,
model=Job,
entity='Job')
def test_set_outputs(self):
experiment_outputs = [
'{}'.format(self.experiment.id),
'{}'.format(self.project_experiment.id),
self.project_experiment.name,
'{}.{}'.format(self.project.name, self.project_experiment.name),
'{}/{}'.format(self.project.name, self.project_experiment.name),
'{}.{}.{}'.format(self.user.username, self.project.name, self.project_experiment.name),
'{}/{}/{}'.format(self.user.username, self.project.name, self.project_experiment.name),
]
job_outputs = [
'{}'.format(self.job.id),
'{}'.format(self.project_job.id),
self.project_job.name,
'{}.{}'.format(self.project.name, self.project_job.name),
'{}/{}'.format(self.project.name, self.project_job.name),
'{}.{}.{}'.format(self.user.username, self.project.name, self.project_job.name),
'{}/{}/{}'.format(self.user.username, self.project.name, self.project_job.name),
]
outputs_config = OutputsConfig(jobs=job_outputs, experiments=experiment_outputs).to_dict()
experiment = ExperimentFactory(user=self.user, project=self.project)
assert experiment.outputs is None
experiment.outputs = outputs_config
assert experiment.outputs is not None
assert len(experiment.outputs_experiments) == len(experiment_outputs)
assert len(experiment.outputs_jobs) == len(job_outputs)
set_outputs(instance=experiment)
del experiment.outputs_config
del experiment.outputs_experiments
del experiment.outputs_jobs
assert len(experiment.outputs_experiments) == 2
assert len(experiment.outputs_jobs) == 2
job = JobFactory(user=self.user, project=self.project)
assert job.outputs is None
job.outputs = outputs_config
assert job.outputs is not None
assert job.outputs is not None
assert len(job.outputs_experiments) == len(experiment_outputs)
assert len(job.outputs_jobs) == len(job_outputs)
set_outputs(instance=job)
del job.outputs_config
del job.outputs_experiments
del job.outputs_jobs
assert len(job.outputs_experiments) == 2
assert len(job.outputs_jobs) == 2
def test_set_outputs_refs(self):
experiment_outputs = [
'{}'.format(self.experiment.id),
'{}'.format(self.project_experiment.id),
self.project_experiment.name,
'{}.{}'.format(self.project.name, self.project_experiment.name),
'{}/{}'.format(self.project.name, self.project_experiment.name),
'{}.{}.{}'.format(self.user.username, self.project.name, self.project_experiment.name),
'{}/{}/{}'.format(self.user.username, self.project.name, self.project_experiment.name),
]
job_outputs = [
'{}'.format(self.job.id),
'{}'.format(self.project_job.id),
self.project_job.name,
'{}.{}'.format(self.project.name, self.project_job.name),
'{}/{}'.format(self.project.name, self.project_job.name),
'{}.{}.{}'.format(self.user.username, self.project.name, self.project_job.name),
'{}/{}/{}'.format(self.user.username, self.project.name, self.project_job.name),
]
outputs_config = OutputsConfig(jobs=job_outputs, experiments=experiment_outputs).to_dict()
experiment = ExperimentFactory(user=self.user, project=self.project)
assert experiment.outputs_refs is None
experiment.outputs = outputs_config
set_outputs(instance=experiment)
set_outputs_refs(instance=experiment)
assert experiment.outputs_refs is not None
assert len(experiment.outputs_refs_jobs) == 2
assert len(experiment.outputs_refs_experiments) == 2
job = JobFactory(user=self.user, project=self.project)
assert job.outputs_refs is None
job.outputs = outputs_config
set_outputs(instance=job)
set_outputs_refs(instance=job)
assert experiment.outputs_refs is not None
assert len(experiment.outputs_refs_jobs) == 2
assert len(experiment.outputs_refs_experiments) == 2
| 42.135647 | 100 | 0.598637 | 1,453 | 13,357 | 5.313145 | 0.060564 | 0.159585 | 0.089378 | 0.10829 | 0.820078 | 0.79987 | 0.766192 | 0.741062 | 0.723834 | 0.702332 | 0 | 0.00397 | 0.283297 | 13,357 | 316 | 101 | 42.268987 | 0.802465 | 0.020588 | 0 | 0.638783 | 0 | 0 | 0.023567 | 0 | 0 | 0 | 0 | 0 | 0.186312 | 1 | 0.030418 | false | 0.003802 | 0.045627 | 0 | 0.087452 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
02e5334b73da0c8133744b8bcf984ed805d0e830 | 57 | py | Python | tests/test_import.py | matthewfeickert/heputils | a810492b1bd8c67663810ba0e213dfc4e7c7d819 | [
"BSD-3-Clause"
] | 2 | 2020-11-12T13:07:16.000Z | 2020-12-01T16:53:12.000Z | tests/test_import.py | matthewfeickert/heputils | a810492b1bd8c67663810ba0e213dfc4e7c7d819 | [
"BSD-3-Clause"
] | 78 | 2020-08-27T08:15:55.000Z | 2022-03-07T19:49:58.000Z | tests/test_import.py | matthewfeickert/heputils | a810492b1bd8c67663810ba0e213dfc4e7c7d819 | [
"BSD-3-Clause"
] | null | null | null | import heputils
def test_import():
assert heputils
| 9.5 | 19 | 0.736842 | 7 | 57 | 5.857143 | 0.714286 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.210526 | 57 | 5 | 20 | 11.4 | 0.911111 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.333333 | 1 | 0.333333 | true | 0 | 0.666667 | 0 | 1 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
02fdcf109e305ad4f96054352f0ef81873004f02 | 23 | py | Python | __init__.py | aFrankLion/abaverify | 2b49ef2ecb618094757032b778ee9d8832be5219 | [
"NASA-1.3"
] | 23 | 2016-10-18T12:44:28.000Z | 2022-01-31T16:03:13.000Z | __init__.py | nasa/Abaverify | 2b49ef2ecb618094757032b778ee9d8832be5219 | [
"NASA-1.3"
] | 3 | 2018-07-02T16:41:38.000Z | 2019-04-24T20:11:14.000Z | __init__.py | nasa/Abaverify | 2b49ef2ecb618094757032b778ee9d8832be5219 | [
"NASA-1.3"
] | 22 | 2017-01-19T19:35:16.000Z | 2022-01-10T10:58:11.000Z | from abaverify import * | 23 | 23 | 0.826087 | 3 | 23 | 6.333333 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.130435 | 23 | 1 | 23 | 23 | 0.95 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
b88541b8570fc4f0d12dc08fd2bb3517aa0fcaca | 14,041 | py | Python | c_elegans_wiring/sub_modules/graph/graph_builder/class_graph_builder.py | adrameshiu/c-elegans-wiring | 5eff187bbec6991864f73f3f4652b98225eab8e8 | [
"MIT"
] | 1 | 2021-06-10T21:46:35.000Z | 2021-06-10T21:46:35.000Z | c_elegans_wiring/sub_modules/graph/graph_builder/class_graph_builder.py | adrameshiu/Celegans-search | 5eff187bbec6991864f73f3f4652b98225eab8e8 | [
"MIT"
] | null | null | null | c_elegans_wiring/sub_modules/graph/graph_builder/class_graph_builder.py | adrameshiu/Celegans-search | 5eff187bbec6991864f73f3f4652b98225eab8e8 | [
"MIT"
] | null | null | null | from c_elegans_wiring.sub_modules.api import graph_api
from c_elegans_wiring.sub_modules.graph import ConnectomeGraph
import c_elegans_wiring.sub_modules.c_elegans as c_elegans
def build_class_graph_from_cell_graph(cell_graph_obj, from_nodes_class, to_nodes_class,
max_cutoff, class_grouping_intensity=2,output_folder=None):
figures_drawn_till_now = 1 #todo: return class object
if class_grouping_intensity == 1:
figures_drawn_till_now = build_strong_class_graph(cell_graph_obj=cell_graph_obj,
figures_drawn_till_now=figures_drawn_till_now,
from_nodes_class=from_nodes_class,
to_nodes_class=to_nodes_class,
max_cutoff=max_cutoff,
output_folder=output_folder)
elif class_grouping_intensity == 2:
figures_drawn_till_now = build_moderate_class_graph(cell_graph_obj=cell_graph_obj,
figures_drawn_till_now=figures_drawn_till_now,
from_nodes_class=from_nodes_class,
to_nodes_class=to_nodes_class,
max_cutoff=max_cutoff,
output_folder=output_folder)
elif class_grouping_intensity == 3:
figures_drawn_till_now = build_lenient_class_graph(cell_graph_obj=cell_graph_obj,
figures_drawn_till_now=figures_drawn_till_now,
from_nodes_class=from_nodes_class,
to_nodes_class=to_nodes_class,
max_cutoff=max_cutoff,
output_folder=output_folder)
else:
figures_drawn_till_now = group_classes_three_ways(cell_graph_obj=cell_graph_obj,
figures_drawn_till_now=figures_drawn_till_now,
from_nodes_class=from_nodes_class,
to_nodes_class=to_nodes_class,
max_cutoff=max_cutoff,
output_folder=output_folder)
return figures_drawn_till_now
def group_classes_three_ways(cell_graph_obj, figures_drawn_till_now, from_nodes_class, to_nodes_class, max_cutoff,output_folder):
figures_drawn_till_now = build_strong_class_graph(cell_graph_obj=cell_graph_obj,
figures_drawn_till_now=figures_drawn_till_now,
from_nodes_class=from_nodes_class,
to_nodes_class=to_nodes_class,
max_cutoff=max_cutoff,
output_folder=output_folder)
figures_drawn_till_now = build_moderate_class_graph(cell_graph_obj=cell_graph_obj,
figures_drawn_till_now=figures_drawn_till_now,
from_nodes_class=from_nodes_class,
to_nodes_class=to_nodes_class,
max_cutoff=max_cutoff,
output_folder=output_folder)
figures_drawn_till_now = build_lenient_class_graph(cell_graph_obj=cell_graph_obj,
figures_drawn_till_now=figures_drawn_till_now,
from_nodes_class=from_nodes_class,
to_nodes_class=to_nodes_class,
max_cutoff=max_cutoff,
output_folder=output_folder)
return figures_drawn_till_now
def build_strong_class_graph(cell_graph_obj, figures_drawn_till_now, from_nodes_class, to_nodes_class, max_cutoff,output_folder):
class_graph_obj_strong = ConnectomeGraph()
graph_api.build_class_main_graph(class_graph_obj=class_graph_obj_strong,
cell_graph_obj=cell_graph_obj,
from_nodes_class=from_nodes_class,
to_nodes_class=to_nodes_class,
class_grouping_intensity=1,
dot_path=output_folder + '/main_class_graph_strong.dot',
csv_path=output_folder + '/inter_neuron_class_filtered_strong.csv')
graph_api.filter_class_graph(class_graph_obj=class_graph_obj_strong,
max_cutoff=max_cutoff,
is_incremental=False)
if class_graph_obj_strong.relevant_paths:
cell_pathways_count_for_class = len(c_elegans.get_cell_pathways_for_class_paths(
cell_paths=cell_graph_obj.relevant_paths,
class_paths=class_graph_obj_strong.relevant_paths,
all_neurons=cell_graph_obj.all_neuron_details))
c_elegans.build_edges_csv(cell_paths=cell_graph_obj.relevant_paths,
class_paths=class_graph_obj_strong.relevant_paths,
all_neuron_details=cell_graph_obj.all_neuron_details,
edges_csv_path=output_folder + '/complete_strong_class_paths.csv')
class_graph_obj_strong.set_cell_pathways_count(cell_pathways_count=cell_pathways_count_for_class)
if class_graph_obj_strong.main_graph:
figures_drawn_till_now = figures_drawn_till_now + 1
class_graph_obj_strong.draw_main_graph(figure_number=figures_drawn_till_now,
plot_title="Complete Strong Grouped Class Graph")
# if class_graph_obj_strong.sub_graph:
# figures_drawn_till_now = figures_drawn_till_now + 1
# class_graph_obj_strong.draw_sub_graph(figure_number=figures_drawn_till_now,
# plot_title="Maximal Strong Grouped Class Graph")
# figures_drawn_till_now = graph_api.filter_class_graph(class_graph_obj=class_graph_obj_strong,
# figure_number=figures_drawn_till_now,
# plot_title="Minimal Strong Grouped Class Graph",
# max_cutoff=max_cutoff,
# is_maximal=False,
# dot_path='out_files/dot_files/minimal_class_graph_strong.dot',
# csv_path='out_files/neuron_info/minimal_class_filtered_strong.csv',
# edges_csv_path='out_files/paths/minimal_class_filtered_strong_paths.csv')
return figures_drawn_till_now
def build_moderate_class_graph(cell_graph_obj, figures_drawn_till_now, from_nodes_class, to_nodes_class, max_cutoff, output_folder):
class_graph_obj_moderate = ConnectomeGraph()
graph_api.build_class_main_graph(class_graph_obj=class_graph_obj_moderate,
cell_graph_obj=cell_graph_obj,
from_nodes_class=from_nodes_class,
to_nodes_class=to_nodes_class,
class_grouping_intensity=2,
dot_path=output_folder + '/main_class_graph_moderate.dot',
csv_path= output_folder + '/inter_neuron_class_filtered_moderate.csv')
graph_api.filter_class_graph(class_graph_obj=class_graph_obj_moderate,
max_cutoff=max_cutoff,
is_incremental=False)
if class_graph_obj_moderate.relevant_paths:
cell_pathways_count_for_class = len(c_elegans.get_cell_pathways_for_class_paths(
cell_paths=cell_graph_obj.relevant_paths,
class_paths=class_graph_obj_moderate.relevant_paths,
all_neurons=cell_graph_obj.all_neuron_details))
c_elegans.build_edges_csv(cell_paths=cell_graph_obj.relevant_paths,
class_paths=class_graph_obj_moderate.relevant_paths,
all_neuron_details=cell_graph_obj.all_neuron_details,
edges_csv_path= output_folder + '/complete_class_filtered_moderate_paths.csv')
class_graph_obj_moderate.set_cell_pathways_count(cell_pathways_count=cell_pathways_count_for_class)
if class_graph_obj_moderate:
figures_drawn_till_now = figures_drawn_till_now + 1
class_graph_obj_moderate.draw_main_graph(figure_number=figures_drawn_till_now,
plot_title="Complete Moderate Grouped Class Graph")
# if class_graph_obj_moderate.sub_graph:
# figures_drawn_till_now = figures_drawn_till_now + 1
# class_graph_obj_moderate.draw_sub_graph(figure_number=figures_drawn_till_now,
# plot_title="Maximal Moderate Grouped Class Graph")
# figures_drawn_till_now = graph_api.filter_class_graph(class_graph_obj=class_graph_obj_moderate,
# figure_number=figures_drawn_till_now,
# plot_title="Minimal Moderate Grouped Class Graph",
# max_cutoff=max_cutoff,
# is_maximal=False,
# dot_path='out_files/dot_files/minimal_class_graph__moderate.dot',
# csv_path='out_files/neuron_info/minimal_class_filtered_moderate.csv',
# edges_csv_path='out_files/paths/minimal_class_filtered_moderate_paths.csv')
return figures_drawn_till_now
def build_lenient_class_graph(cell_graph_obj, figures_drawn_till_now, from_nodes_class, to_nodes_class, max_cutoff, output_folder):
class_graph_obj_lenient = ConnectomeGraph()
graph_api.build_class_main_graph(class_graph_obj=class_graph_obj_lenient,
cell_graph_obj=cell_graph_obj,
from_nodes_class=from_nodes_class,
to_nodes_class=to_nodes_class,
class_grouping_intensity=3,
dot_path=output_folder + '/main_class_graph_lenient.dot',
csv_path=output_folder + '/inter_neuron_class_filtered_lenient.csv')
graph_api.filter_class_graph(class_graph_obj=class_graph_obj_lenient,
max_cutoff=max_cutoff,
is_incremental=False)
if class_graph_obj_lenient.relevant_paths:
cell_pathways_count_for_class = len(c_elegans.get_cell_pathways_for_class_paths(
cell_paths=cell_graph_obj.relevant_paths,
class_paths=class_graph_obj_lenient.relevant_paths,
all_neurons=cell_graph_obj.all_neuron_details))
c_elegans.build_edges_csv(cell_paths=cell_graph_obj.relevant_paths,
class_paths=class_graph_obj_lenient.relevant_paths,
all_neuron_details=cell_graph_obj.all_neuron_details,
edges_csv_path=output_folder + '/complete_class_filtered_lenient_paths.csv')
class_graph_obj_lenient.set_cell_pathways_count(cell_pathways_count=cell_pathways_count_for_class)
if class_graph_obj_lenient.main_graph:
figures_drawn_till_now = figures_drawn_till_now + 1
class_graph_obj_lenient.draw_main_graph(figure_number=figures_drawn_till_now,
plot_title="Complete Lenient Grouped Class Graph")
# if class_graph_obj_lenient.sub_graph:
# figures_drawn_till_now = figures_drawn_till_now + 1
# class_graph_obj_lenient.draw_sub_graph(figure_number=figures_drawn_till_now,
# plot_title="Maximal Lenient Grouped Class Graph")
# figures_drawn_till_now = graph_api.filter_class_graph(class_graph_obj=class_graph_obj_lenient,
# figure_number=figures_drawn_till_now,
# plot_title="Minimal Lenient Grouped Class Graph",
# max_cutoff=max_cutoff,
# is_maximal=False,
# dot_path='out_files/dot_files/minimal_class_graph_lenient.dot',
# csv_path='out_files/neuron_info/minimal_class_filtered_lenient.csv',
# edges_csv_path='out_files/paths/minimal_class_filtered_lenient_paths.csv')
return figures_drawn_till_now
| 70.205 | 135 | 0.560929 | 1,444 | 14,041 | 4.848338 | 0.055402 | 0.093701 | 0.125696 | 0.149264 | 0.946436 | 0.920868 | 0.902157 | 0.859877 | 0.851878 | 0.800314 | 0 | 0.001652 | 0.396411 | 14,041 | 199 | 136 | 70.557789 | 0.824425 | 0.232177 | 0 | 0.625 | 0 | 0 | 0.040208 | 0.030156 | 0 | 0 | 0 | 0.005025 | 0 | 1 | 0.036765 | false | 0 | 0.022059 | 0 | 0.095588 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
b242b516b513ba3b06720cbd4d54f15c69639eaa | 95 | py | Python | denoiseg/models/__init__.py | jianzhangcs/DenoiSeg | 5dacfcc885c1f34379a39bceb8ed25202ad6e01f | [
"BSD-3-Clause"
] | 45 | 2020-05-13T01:45:20.000Z | 2022-03-27T17:43:20.000Z | denoiseg/models/__init__.py | jianzhangcs/DenoiSeg | 5dacfcc885c1f34379a39bceb8ed25202ad6e01f | [
"BSD-3-Clause"
] | 8 | 2020-06-20T18:22:32.000Z | 2021-11-22T20:11:08.000Z | denoiseg/models/__init__.py | juglab/DenoiSeg | 9803cbcf31c0510d28a9ff43be92b29cd47120a0 | [
"BSD-3-Clause"
] | 11 | 2020-05-17T11:50:11.000Z | 2022-01-20T09:25:32.000Z | # imports
from .denoiseg_config import DenoiSegConfig
from .denoiseg_standard import DenoiSeg
| 19 | 43 | 0.852632 | 11 | 95 | 7.181818 | 0.636364 | 0.303797 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.115789 | 95 | 4 | 44 | 23.75 | 0.940476 | 0.073684 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
b28d861d9b3a2b3a279764afd0d18128540f0b29 | 261,006 | py | Python | instances/passenger_demand/pas-20210422-1717-int16e/66.py | LHcau/scheduling-shared-passenger-and-freight-transport-on-a-fixed-infrastructure | bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11 | [
"BSD-3-Clause"
] | null | null | null | instances/passenger_demand/pas-20210422-1717-int16e/66.py | LHcau/scheduling-shared-passenger-and-freight-transport-on-a-fixed-infrastructure | bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11 | [
"BSD-3-Clause"
] | null | null | null | instances/passenger_demand/pas-20210422-1717-int16e/66.py | LHcau/scheduling-shared-passenger-and-freight-transport-on-a-fixed-infrastructure | bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11 | [
"BSD-3-Clause"
] | null | null | null |
"""
PASSENGERS
"""
numPassengers = 30419
passenger_arriving = (
(6, 7, 7, 4, 3, 1, 4, 4, 2, 1, 0, 0, 0, 10, 9, 5, 4, 7, 2, 2, 2, 8, 3, 3, 0, 0), # 0
(7, 4, 9, 15, 9, 3, 3, 3, 3, 1, 1, 0, 0, 7, 8, 2, 4, 4, 3, 5, 2, 4, 0, 1, 0, 0), # 1
(11, 7, 8, 7, 2, 5, 7, 3, 5, 2, 0, 1, 0, 9, 12, 7, 8, 9, 5, 3, 2, 2, 1, 2, 0, 0), # 2
(6, 6, 15, 7, 5, 4, 3, 4, 3, 4, 4, 1, 0, 12, 6, 5, 2, 5, 10, 3, 8, 5, 1, 1, 0, 0), # 3
(8, 10, 9, 5, 10, 2, 7, 4, 2, 2, 1, 0, 0, 8, 8, 8, 6, 5, 5, 5, 4, 2, 7, 1, 0, 0), # 4
(11, 6, 8, 10, 14, 5, 3, 7, 3, 0, 0, 1, 0, 17, 9, 11, 10, 7, 2, 4, 2, 7, 5, 3, 0, 0), # 5
(9, 13, 9, 19, 3, 5, 3, 7, 6, 4, 1, 0, 0, 13, 8, 8, 7, 11, 5, 6, 1, 5, 7, 4, 0, 0), # 6
(15, 10, 14, 9, 10, 3, 8, 6, 4, 0, 1, 1, 0, 8, 8, 10, 4, 11, 8, 8, 3, 2, 3, 4, 1, 0), # 7
(15, 14, 12, 14, 7, 4, 6, 4, 10, 2, 0, 0, 0, 14, 5, 10, 3, 6, 6, 4, 2, 4, 4, 3, 1, 0), # 8
(12, 10, 11, 18, 11, 4, 2, 7, 5, 3, 4, 2, 0, 12, 7, 8, 6, 9, 4, 3, 5, 7, 4, 0, 0, 0), # 9
(15, 18, 17, 11, 6, 3, 3, 2, 6, 3, 0, 1, 0, 18, 13, 4, 10, 12, 10, 2, 4, 12, 3, 5, 1, 0), # 10
(10, 12, 14, 11, 8, 5, 4, 2, 4, 2, 3, 2, 0, 9, 6, 5, 9, 14, 11, 3, 3, 3, 3, 0, 1, 0), # 11
(12, 17, 6, 18, 8, 3, 8, 6, 9, 3, 3, 0, 0, 15, 9, 9, 9, 17, 6, 6, 3, 6, 3, 2, 0, 0), # 12
(12, 18, 11, 14, 12, 6, 7, 5, 8, 1, 2, 1, 0, 17, 15, 14, 15, 8, 7, 4, 4, 9, 6, 1, 2, 0), # 13
(14, 16, 14, 16, 6, 2, 4, 3, 6, 2, 0, 1, 0, 16, 13, 9, 10, 10, 10, 3, 2, 4, 3, 2, 0, 0), # 14
(15, 15, 6, 17, 17, 4, 11, 1, 6, 2, 0, 0, 0, 14, 11, 8, 10, 8, 11, 6, 6, 4, 2, 0, 0, 0), # 15
(16, 11, 17, 12, 13, 10, 9, 5, 4, 6, 1, 0, 0, 16, 9, 11, 7, 16, 11, 6, 4, 3, 6, 1, 0, 0), # 16
(14, 15, 12, 20, 10, 4, 9, 7, 10, 4, 2, 2, 0, 11, 10, 15, 13, 14, 7, 10, 5, 2, 5, 2, 2, 0), # 17
(22, 14, 12, 12, 7, 6, 13, 2, 12, 2, 4, 3, 0, 15, 17, 16, 14, 11, 14, 7, 6, 8, 3, 0, 3, 0), # 18
(16, 11, 11, 18, 6, 7, 7, 4, 5, 4, 3, 0, 0, 16, 18, 9, 12, 14, 8, 8, 4, 5, 7, 1, 3, 0), # 19
(17, 11, 9, 13, 11, 4, 5, 8, 2, 6, 0, 1, 0, 12, 12, 10, 6, 12, 7, 3, 3, 9, 5, 1, 3, 0), # 20
(22, 16, 9, 12, 10, 10, 8, 6, 5, 4, 0, 0, 0, 21, 8, 12, 11, 16, 9, 8, 7, 4, 3, 3, 1, 0), # 21
(15, 12, 12, 21, 9, 4, 5, 9, 6, 4, 2, 1, 0, 29, 18, 11, 7, 9, 4, 8, 4, 8, 7, 2, 1, 0), # 22
(20, 14, 13, 15, 10, 4, 5, 4, 10, 3, 2, 3, 0, 16, 13, 14, 1, 18, 9, 3, 4, 4, 7, 1, 3, 0), # 23
(11, 15, 6, 19, 15, 11, 5, 2, 6, 7, 1, 2, 0, 18, 13, 11, 7, 13, 9, 4, 5, 11, 13, 1, 1, 0), # 24
(16, 13, 11, 17, 8, 3, 7, 5, 7, 2, 4, 1, 0, 19, 8, 12, 6, 11, 6, 8, 4, 5, 4, 2, 3, 0), # 25
(20, 20, 15, 13, 7, 3, 8, 3, 6, 3, 1, 0, 0, 11, 15, 7, 7, 13, 12, 4, 5, 5, 9, 4, 0, 0), # 26
(17, 16, 21, 8, 15, 7, 11, 4, 5, 3, 1, 4, 0, 16, 15, 10, 16, 17, 7, 10, 4, 4, 6, 6, 3, 0), # 27
(25, 11, 16, 11, 12, 7, 7, 8, 2, 3, 4, 1, 0, 14, 16, 11, 7, 15, 8, 5, 5, 5, 5, 4, 1, 0), # 28
(26, 19, 14, 13, 20, 9, 9, 2, 7, 5, 2, 0, 0, 13, 14, 13, 14, 11, 12, 8, 1, 7, 3, 4, 1, 0), # 29
(26, 20, 14, 12, 14, 8, 6, 7, 4, 2, 3, 0, 0, 19, 18, 7, 8, 8, 8, 6, 1, 7, 6, 4, 3, 0), # 30
(17, 20, 14, 23, 9, 8, 7, 9, 7, 4, 0, 0, 0, 14, 14, 10, 17, 18, 11, 7, 5, 5, 8, 1, 1, 0), # 31
(15, 13, 21, 13, 10, 3, 7, 6, 10, 4, 1, 0, 0, 12, 14, 15, 12, 22, 11, 9, 6, 3, 5, 1, 1, 0), # 32
(11, 15, 9, 20, 13, 6, 6, 5, 5, 3, 6, 1, 0, 13, 16, 14, 8, 11, 5, 7, 5, 6, 4, 3, 1, 0), # 33
(21, 21, 13, 15, 12, 5, 9, 14, 2, 4, 5, 2, 0, 21, 16, 11, 8, 9, 10, 9, 6, 7, 9, 1, 1, 0), # 34
(25, 22, 16, 10, 8, 6, 14, 9, 10, 3, 1, 2, 0, 15, 18, 15, 7, 8, 5, 1, 5, 6, 6, 2, 1, 0), # 35
(11, 6, 18, 24, 24, 5, 5, 8, 7, 5, 0, 1, 0, 10, 17, 13, 6, 16, 4, 4, 5, 7, 2, 2, 4, 0), # 36
(16, 19, 12, 17, 10, 8, 10, 3, 6, 4, 1, 0, 0, 15, 17, 11, 7, 11, 6, 3, 7, 6, 3, 3, 1, 0), # 37
(16, 12, 15, 14, 11, 1, 9, 5, 7, 3, 2, 2, 0, 14, 10, 11, 9, 13, 11, 7, 6, 7, 8, 3, 2, 0), # 38
(13, 19, 15, 14, 13, 7, 7, 5, 8, 2, 1, 5, 0, 18, 11, 9, 8, 12, 17, 5, 3, 5, 8, 2, 3, 0), # 39
(16, 11, 14, 14, 10, 3, 13, 6, 6, 1, 1, 1, 0, 16, 7, 17, 9, 13, 6, 4, 1, 5, 4, 1, 0, 0), # 40
(20, 20, 11, 14, 8, 9, 8, 4, 9, 1, 4, 2, 0, 15, 13, 8, 6, 11, 8, 4, 8, 6, 4, 0, 2, 0), # 41
(18, 14, 15, 13, 12, 9, 4, 7, 4, 1, 1, 3, 0, 20, 22, 12, 11, 11, 5, 4, 2, 10, 5, 1, 1, 0), # 42
(18, 19, 12, 19, 17, 4, 3, 10, 9, 3, 0, 1, 0, 17, 16, 15, 8, 11, 9, 4, 8, 6, 5, 6, 0, 0), # 43
(29, 17, 16, 13, 7, 6, 5, 5, 5, 2, 3, 0, 0, 17, 14, 9, 6, 14, 9, 7, 2, 3, 2, 2, 1, 0), # 44
(11, 19, 12, 20, 11, 6, 2, 2, 6, 5, 3, 0, 0, 10, 12, 11, 8, 12, 11, 10, 5, 10, 6, 4, 2, 0), # 45
(16, 13, 13, 17, 12, 6, 8, 8, 8, 2, 4, 1, 0, 15, 17, 11, 8, 9, 12, 5, 3, 5, 3, 4, 1, 0), # 46
(18, 17, 12, 13, 11, 1, 5, 9, 6, 5, 2, 1, 0, 22, 12, 10, 12, 11, 10, 8, 4, 4, 9, 2, 1, 0), # 47
(14, 15, 12, 14, 11, 5, 3, 7, 8, 2, 0, 2, 0, 17, 10, 11, 11, 9, 7, 8, 7, 8, 3, 3, 1, 0), # 48
(20, 19, 12, 18, 11, 9, 7, 5, 5, 2, 3, 2, 0, 12, 17, 11, 10, 11, 7, 3, 0, 3, 3, 5, 0, 0), # 49
(15, 15, 13, 16, 17, 8, 4, 3, 8, 2, 2, 3, 0, 15, 8, 8, 3, 17, 10, 4, 4, 5, 6, 6, 3, 0), # 50
(20, 18, 12, 22, 13, 6, 6, 4, 8, 3, 3, 4, 0, 15, 13, 12, 14, 18, 10, 7, 5, 5, 5, 1, 2, 0), # 51
(15, 11, 15, 20, 12, 3, 7, 6, 7, 4, 2, 0, 0, 15, 19, 8, 7, 12, 3, 9, 3, 10, 4, 2, 0, 0), # 52
(11, 15, 16, 13, 6, 7, 5, 8, 4, 1, 1, 1, 0, 23, 12, 8, 11, 11, 5, 4, 4, 3, 5, 3, 3, 0), # 53
(16, 16, 8, 13, 19, 2, 5, 3, 2, 5, 3, 3, 0, 12, 23, 8, 11, 14, 6, 7, 3, 6, 5, 0, 2, 0), # 54
(13, 9, 23, 13, 13, 6, 8, 5, 10, 5, 6, 1, 0, 6, 14, 6, 6, 11, 7, 6, 9, 9, 10, 3, 4, 0), # 55
(13, 12, 19, 8, 12, 5, 3, 7, 8, 1, 5, 1, 0, 22, 15, 15, 8, 11, 5, 6, 3, 11, 3, 4, 3, 0), # 56
(22, 13, 11, 19, 14, 2, 9, 4, 3, 1, 2, 1, 0, 12, 12, 7, 5, 17, 6, 4, 8, 6, 7, 4, 3, 0), # 57
(24, 12, 13, 14, 15, 5, 5, 6, 9, 1, 1, 3, 0, 16, 13, 9, 11, 10, 6, 5, 6, 9, 10, 4, 1, 0), # 58
(16, 19, 16, 14, 11, 5, 6, 11, 9, 2, 1, 1, 0, 15, 16, 6, 8, 11, 8, 4, 5, 3, 4, 1, 1, 0), # 59
(19, 15, 16, 11, 10, 2, 4, 6, 6, 4, 2, 2, 0, 19, 16, 12, 11, 13, 10, 10, 7, 9, 4, 1, 3, 0), # 60
(12, 13, 10, 25, 17, 6, 5, 5, 12, 0, 2, 3, 0, 25, 12, 5, 6, 10, 8, 4, 3, 7, 5, 1, 2, 0), # 61
(19, 20, 17, 12, 13, 3, 7, 4, 7, 3, 1, 3, 0, 23, 8, 11, 10, 13, 4, 5, 5, 8, 5, 0, 4, 0), # 62
(16, 19, 18, 9, 18, 4, 5, 6, 5, 6, 2, 1, 0, 10, 18, 9, 11, 15, 4, 5, 1, 12, 4, 5, 1, 0), # 63
(13, 16, 15, 10, 6, 7, 5, 1, 10, 3, 1, 1, 0, 24, 15, 7, 7, 13, 5, 7, 2, 5, 4, 1, 1, 0), # 64
(10, 14, 17, 18, 6, 5, 9, 6, 1, 3, 2, 3, 0, 9, 12, 13, 11, 7, 11, 12, 7, 10, 7, 3, 0, 0), # 65
(17, 14, 17, 10, 12, 6, 4, 5, 6, 3, 2, 0, 0, 22, 13, 7, 3, 14, 5, 4, 3, 6, 8, 4, 0, 0), # 66
(15, 14, 15, 13, 15, 12, 4, 8, 5, 4, 1, 2, 0, 22, 11, 9, 7, 8, 6, 3, 5, 4, 3, 0, 0, 0), # 67
(14, 15, 12, 17, 19, 9, 5, 9, 6, 2, 4, 1, 0, 21, 21, 8, 7, 15, 8, 6, 5, 10, 7, 4, 1, 0), # 68
(17, 4, 17, 22, 10, 8, 8, 3, 1, 2, 3, 2, 0, 19, 6, 11, 10, 8, 10, 6, 4, 7, 4, 1, 0, 0), # 69
(17, 17, 14, 16, 11, 9, 4, 2, 10, 4, 2, 2, 0, 12, 11, 10, 7, 16, 4, 3, 4, 8, 6, 2, 0, 0), # 70
(18, 15, 7, 11, 11, 4, 2, 6, 5, 2, 3, 2, 0, 16, 7, 9, 11, 14, 11, 7, 5, 5, 3, 5, 1, 0), # 71
(13, 21, 14, 19, 13, 9, 7, 5, 10, 3, 1, 2, 0, 17, 14, 13, 8, 7, 10, 4, 3, 5, 8, 1, 1, 0), # 72
(13, 15, 10, 22, 14, 7, 8, 5, 4, 1, 2, 0, 0, 17, 17, 10, 12, 13, 6, 2, 5, 5, 4, 6, 0, 0), # 73
(20, 17, 14, 16, 13, 6, 6, 3, 7, 3, 3, 0, 0, 11, 15, 11, 6, 7, 8, 4, 6, 5, 4, 4, 1, 0), # 74
(17, 11, 13, 11, 15, 3, 3, 3, 7, 2, 3, 2, 0, 11, 12, 6, 6, 10, 5, 7, 5, 10, 1, 2, 1, 0), # 75
(14, 15, 17, 16, 10, 8, 7, 5, 2, 3, 6, 1, 0, 20, 15, 14, 6, 11, 6, 3, 2, 7, 8, 3, 1, 0), # 76
(24, 9, 11, 16, 9, 8, 11, 4, 4, 4, 4, 2, 0, 13, 14, 11, 5, 14, 6, 5, 3, 7, 11, 4, 0, 0), # 77
(17, 19, 16, 22, 5, 11, 9, 6, 10, 2, 5, 2, 0, 10, 12, 8, 10, 12, 4, 4, 4, 8, 8, 3, 1, 0), # 78
(14, 11, 5, 11, 10, 4, 3, 8, 3, 5, 4, 1, 0, 22, 17, 9, 14, 10, 10, 6, 3, 4, 3, 3, 1, 0), # 79
(18, 12, 20, 16, 7, 5, 11, 7, 7, 2, 2, 0, 0, 15, 7, 10, 7, 14, 5, 7, 1, 4, 6, 4, 0, 0), # 80
(17, 9, 11, 14, 15, 5, 6, 6, 5, 2, 0, 2, 0, 20, 11, 7, 10, 14, 6, 5, 4, 4, 7, 4, 1, 0), # 81
(17, 16, 16, 13, 16, 7, 4, 4, 5, 2, 0, 1, 0, 19, 18, 12, 6, 5, 1, 7, 4, 6, 8, 1, 0, 0), # 82
(17, 10, 16, 10, 17, 9, 6, 3, 4, 3, 5, 0, 0, 16, 15, 13, 10, 14, 7, 6, 7, 6, 3, 2, 8, 0), # 83
(15, 8, 14, 14, 12, 6, 2, 6, 7, 6, 1, 1, 0, 17, 13, 6, 10, 21, 5, 8, 6, 1, 7, 5, 2, 0), # 84
(13, 14, 5, 16, 11, 3, 7, 5, 5, 1, 4, 4, 0, 16, 11, 8, 5, 8, 2, 5, 4, 6, 6, 1, 0, 0), # 85
(15, 12, 8, 12, 12, 7, 5, 4, 7, 1, 2, 1, 0, 18, 11, 13, 11, 17, 4, 4, 4, 4, 3, 0, 2, 0), # 86
(15, 13, 15, 7, 9, 3, 6, 4, 13, 2, 0, 2, 0, 18, 14, 5, 4, 10, 8, 9, 4, 4, 2, 2, 1, 0), # 87
(23, 10, 15, 7, 9, 8, 10, 6, 3, 2, 1, 0, 0, 9, 15, 8, 8, 19, 2, 5, 9, 9, 7, 2, 1, 0), # 88
(24, 11, 15, 13, 4, 4, 11, 6, 4, 2, 4, 0, 0, 19, 9, 13, 5, 10, 9, 5, 2, 7, 7, 1, 1, 0), # 89
(14, 4, 11, 16, 9, 9, 4, 9, 3, 1, 2, 1, 0, 16, 11, 17, 12, 12, 6, 6, 4, 3, 5, 2, 2, 0), # 90
(16, 6, 3, 13, 13, 6, 8, 4, 6, 2, 1, 1, 0, 13, 11, 15, 9, 13, 8, 4, 8, 12, 4, 4, 1, 0), # 91
(7, 11, 11, 19, 10, 6, 7, 3, 3, 3, 0, 1, 0, 18, 14, 10, 3, 13, 2, 3, 4, 2, 7, 4, 1, 0), # 92
(19, 12, 17, 14, 19, 4, 9, 8, 8, 3, 2, 1, 0, 11, 16, 9, 7, 9, 3, 7, 5, 1, 6, 4, 0, 0), # 93
(16, 12, 9, 9, 15, 2, 5, 1, 10, 3, 0, 3, 0, 15, 8, 3, 11, 11, 4, 10, 6, 5, 4, 3, 1, 0), # 94
(20, 11, 7, 10, 4, 5, 5, 4, 5, 0, 3, 2, 0, 16, 13, 8, 8, 9, 11, 9, 2, 8, 8, 1, 2, 0), # 95
(14, 8, 23, 12, 11, 7, 4, 4, 3, 2, 3, 0, 0, 16, 13, 13, 6, 15, 7, 7, 6, 7, 2, 4, 2, 0), # 96
(9, 15, 16, 9, 7, 6, 3, 6, 12, 3, 1, 0, 0, 14, 11, 8, 4, 16, 12, 3, 3, 6, 4, 4, 1, 0), # 97
(18, 14, 14, 17, 5, 8, 8, 2, 3, 0, 2, 0, 0, 11, 19, 13, 9, 14, 8, 2, 1, 1, 3, 2, 1, 0), # 98
(13, 13, 5, 11, 6, 10, 3, 4, 11, 1, 0, 0, 0, 15, 15, 7, 5, 9, 0, 3, 2, 4, 5, 3, 1, 0), # 99
(22, 9, 7, 19, 9, 4, 4, 4, 7, 3, 3, 1, 0, 17, 15, 12, 6, 9, 12, 6, 2, 5, 4, 1, 0, 0), # 100
(21, 16, 13, 13, 17, 6, 3, 2, 7, 3, 1, 1, 0, 10, 13, 10, 6, 8, 4, 6, 3, 1, 5, 2, 2, 0), # 101
(16, 10, 9, 17, 8, 4, 6, 3, 5, 4, 0, 1, 0, 14, 11, 12, 14, 12, 6, 4, 2, 6, 5, 2, 0, 0), # 102
(12, 17, 9, 10, 12, 6, 8, 4, 2, 1, 3, 0, 0, 14, 9, 10, 8, 14, 7, 5, 2, 10, 5, 3, 1, 0), # 103
(16, 10, 13, 13, 13, 6, 5, 9, 5, 3, 1, 1, 0, 16, 12, 8, 8, 9, 4, 4, 5, 7, 3, 1, 2, 0), # 104
(17, 16, 13, 9, 9, 4, 2, 7, 4, 1, 2, 1, 0, 9, 10, 4, 8, 14, 8, 3, 3, 6, 7, 1, 1, 0), # 105
(17, 16, 13, 12, 13, 4, 7, 5, 8, 3, 2, 1, 0, 17, 15, 8, 9, 10, 12, 4, 6, 5, 1, 4, 0, 0), # 106
(10, 13, 17, 10, 11, 4, 8, 8, 3, 2, 5, 1, 0, 11, 19, 5, 5, 10, 3, 7, 4, 7, 5, 3, 0, 0), # 107
(14, 11, 17, 10, 15, 6, 6, 5, 5, 9, 3, 0, 0, 20, 11, 11, 10, 13, 6, 6, 4, 3, 5, 2, 2, 0), # 108
(23, 16, 11, 10, 11, 6, 7, 5, 6, 2, 4, 2, 0, 17, 13, 10, 8, 9, 8, 4, 6, 4, 7, 2, 0, 0), # 109
(15, 18, 13, 13, 14, 4, 3, 3, 5, 2, 0, 0, 0, 19, 14, 13, 5, 13, 4, 10, 3, 9, 4, 3, 1, 0), # 110
(13, 12, 10, 10, 16, 6, 6, 3, 7, 2, 1, 1, 0, 16, 19, 11, 4, 14, 9, 2, 7, 9, 3, 3, 1, 0), # 111
(10, 12, 13, 15, 9, 7, 5, 4, 5, 4, 1, 6, 0, 15, 10, 14, 3, 14, 6, 5, 3, 5, 4, 2, 1, 0), # 112
(11, 10, 19, 13, 13, 4, 7, 3, 7, 1, 2, 1, 0, 21, 8, 10, 5, 7, 5, 6, 6, 7, 6, 4, 1, 0), # 113
(5, 14, 16, 13, 10, 2, 3, 3, 3, 1, 3, 1, 0, 19, 16, 5, 9, 18, 3, 3, 3, 9, 3, 1, 0, 0), # 114
(17, 7, 16, 9, 14, 6, 5, 5, 6, 1, 1, 0, 0, 16, 11, 9, 10, 13, 6, 2, 4, 12, 4, 1, 2, 0), # 115
(21, 6, 12, 7, 8, 7, 4, 8, 5, 2, 3, 3, 0, 21, 11, 10, 6, 16, 6, 5, 10, 4, 3, 3, 2, 0), # 116
(10, 9, 11, 10, 5, 8, 3, 2, 5, 3, 3, 1, 0, 6, 15, 6, 9, 11, 6, 3, 3, 4, 3, 1, 2, 0), # 117
(18, 12, 10, 20, 12, 8, 3, 3, 8, 3, 1, 0, 0, 14, 11, 10, 7, 12, 1, 3, 3, 3, 6, 1, 1, 0), # 118
(14, 11, 10, 17, 9, 8, 3, 9, 7, 7, 4, 1, 0, 15, 15, 5, 10, 13, 10, 5, 3, 6, 1, 2, 0, 0), # 119
(19, 15, 12, 10, 4, 2, 4, 4, 2, 4, 0, 1, 0, 16, 15, 7, 3, 10, 5, 9, 6, 2, 6, 3, 0, 0), # 120
(7, 11, 8, 16, 9, 5, 3, 4, 5, 0, 4, 0, 0, 13, 9, 9, 9, 11, 3, 4, 5, 3, 5, 4, 3, 0), # 121
(17, 11, 11, 19, 9, 7, 3, 4, 7, 3, 4, 1, 0, 13, 10, 10, 4, 12, 5, 2, 3, 1, 6, 4, 1, 0), # 122
(11, 4, 13, 8, 5, 3, 3, 4, 2, 0, 0, 0, 0, 11, 11, 10, 6, 7, 8, 6, 6, 4, 4, 2, 0, 0), # 123
(19, 8, 14, 14, 11, 10, 8, 5, 2, 1, 1, 2, 0, 20, 12, 10, 7, 5, 3, 3, 6, 4, 7, 4, 1, 0), # 124
(15, 13, 9, 5, 6, 3, 4, 5, 6, 1, 3, 3, 0, 9, 7, 9, 6, 15, 5, 3, 2, 3, 6, 2, 1, 0), # 125
(11, 8, 7, 10, 11, 5, 5, 4, 6, 2, 1, 1, 0, 13, 14, 6, 3, 12, 5, 6, 1, 17, 3, 3, 4, 0), # 126
(9, 12, 11, 14, 14, 2, 4, 3, 7, 1, 0, 1, 0, 19, 10, 7, 9, 9, 4, 4, 2, 3, 5, 2, 1, 0), # 127
(10, 14, 10, 17, 5, 5, 3, 7, 7, 1, 1, 0, 0, 12, 13, 10, 7, 9, 2, 8, 1, 2, 4, 3, 1, 0), # 128
(12, 10, 8, 7, 13, 4, 3, 7, 2, 2, 1, 0, 0, 13, 15, 10, 7, 10, 5, 6, 5, 6, 3, 3, 2, 0), # 129
(15, 14, 15, 9, 8, 4, 5, 4, 3, 0, 1, 2, 0, 10, 11, 7, 5, 8, 5, 7, 7, 6, 6, 1, 0, 0), # 130
(16, 8, 14, 6, 13, 3, 5, 2, 4, 2, 1, 1, 0, 12, 8, 6, 8, 15, 1, 0, 2, 6, 8, 5, 0, 0), # 131
(7, 6, 11, 10, 13, 6, 10, 6, 1, 2, 2, 0, 0, 13, 13, 18, 6, 10, 6, 2, 4, 5, 2, 0, 1, 0), # 132
(15, 14, 16, 13, 9, 9, 5, 6, 8, 0, 1, 1, 0, 15, 4, 6, 4, 12, 3, 2, 4, 3, 5, 3, 0, 0), # 133
(13, 8, 13, 13, 11, 3, 3, 4, 6, 1, 0, 0, 0, 17, 7, 12, 8, 8, 8, 6, 4, 6, 7, 5, 1, 0), # 134
(15, 4, 12, 12, 8, 7, 5, 6, 3, 2, 3, 1, 0, 12, 11, 11, 6, 13, 4, 3, 4, 3, 2, 2, 2, 0), # 135
(8, 5, 11, 17, 4, 8, 8, 1, 5, 6, 3, 2, 0, 12, 12, 3, 9, 10, 8, 5, 6, 2, 3, 1, 5, 0), # 136
(11, 5, 15, 15, 12, 5, 7, 5, 9, 2, 3, 1, 0, 12, 14, 4, 2, 13, 8, 2, 3, 3, 6, 2, 2, 0), # 137
(12, 11, 13, 19, 7, 5, 1, 4, 2, 1, 2, 0, 0, 10, 18, 8, 6, 19, 7, 7, 3, 7, 3, 1, 0, 0), # 138
(11, 10, 17, 14, 8, 3, 3, 5, 7, 1, 1, 0, 0, 13, 8, 8, 5, 10, 4, 3, 4, 3, 3, 4, 1, 0), # 139
(15, 13, 9, 10, 9, 6, 8, 3, 2, 2, 4, 0, 0, 13, 17, 13, 7, 11, 2, 5, 3, 6, 1, 1, 2, 0), # 140
(11, 14, 5, 12, 9, 2, 4, 4, 8, 3, 6, 0, 0, 18, 9, 11, 6, 9, 2, 7, 2, 1, 2, 3, 3, 0), # 141
(16, 12, 7, 12, 15, 3, 3, 4, 6, 3, 1, 0, 0, 13, 12, 9, 3, 12, 3, 7, 4, 3, 2, 1, 1, 0), # 142
(10, 11, 14, 15, 11, 4, 3, 5, 5, 3, 2, 0, 0, 8, 10, 9, 6, 6, 4, 5, 1, 3, 3, 2, 2, 0), # 143
(10, 15, 15, 7, 6, 5, 9, 2, 9, 1, 2, 3, 0, 9, 6, 8, 10, 12, 8, 2, 6, 3, 4, 2, 0, 0), # 144
(12, 10, 10, 6, 13, 4, 5, 3, 7, 1, 1, 0, 0, 17, 7, 14, 5, 15, 6, 4, 5, 2, 5, 3, 0, 0), # 145
(10, 19, 11, 16, 18, 8, 4, 3, 4, 3, 1, 2, 0, 7, 12, 7, 5, 15, 4, 2, 4, 5, 6, 2, 0, 0), # 146
(10, 9, 8, 8, 8, 3, 1, 3, 6, 0, 0, 3, 0, 13, 9, 7, 10, 9, 3, 4, 3, 9, 5, 4, 1, 0), # 147
(14, 8, 13, 5, 9, 3, 2, 3, 4, 1, 2, 1, 0, 12, 17, 5, 7, 10, 5, 4, 3, 7, 4, 2, 0, 0), # 148
(13, 6, 12, 10, 14, 3, 4, 6, 8, 4, 0, 0, 0, 14, 5, 10, 7, 10, 7, 5, 2, 8, 7, 2, 1, 0), # 149
(10, 12, 10, 9, 15, 1, 4, 5, 4, 3, 1, 0, 0, 10, 11, 6, 3, 9, 4, 3, 4, 6, 3, 2, 3, 0), # 150
(11, 9, 15, 12, 9, 4, 4, 3, 3, 2, 2, 0, 0, 13, 11, 11, 2, 6, 4, 3, 8, 7, 1, 5, 0, 0), # 151
(14, 12, 14, 8, 13, 5, 4, 2, 5, 1, 1, 0, 0, 13, 6, 4, 7, 10, 7, 4, 6, 5, 2, 1, 0, 0), # 152
(9, 7, 9, 13, 9, 3, 4, 3, 5, 4, 1, 0, 0, 16, 8, 7, 7, 7, 5, 3, 3, 2, 4, 1, 1, 0), # 153
(13, 5, 17, 16, 10, 5, 4, 2, 5, 5, 1, 0, 0, 10, 11, 3, 3, 8, 8, 1, 1, 4, 3, 2, 1, 0), # 154
(12, 8, 10, 5, 4, 5, 5, 1, 6, 2, 1, 0, 0, 16, 6, 3, 4, 9, 11, 6, 2, 9, 3, 4, 1, 0), # 155
(8, 6, 12, 13, 3, 4, 2, 2, 5, 2, 0, 2, 0, 10, 8, 8, 6, 14, 8, 1, 3, 7, 4, 3, 1, 0), # 156
(14, 7, 12, 12, 15, 4, 4, 6, 3, 1, 2, 1, 0, 22, 11, 9, 0, 18, 9, 5, 4, 4, 2, 2, 0, 0), # 157
(9, 9, 10, 16, 9, 2, 3, 0, 8, 3, 0, 0, 0, 19, 8, 10, 7, 8, 3, 4, 3, 6, 5, 2, 1, 0), # 158
(9, 9, 11, 13, 15, 4, 4, 6, 8, 3, 1, 1, 0, 12, 7, 7, 4, 11, 4, 4, 6, 5, 2, 2, 1, 0), # 159
(6, 8, 9, 7, 12, 6, 1, 4, 9, 1, 1, 0, 0, 16, 13, 9, 5, 16, 7, 2, 0, 6, 3, 1, 0, 0), # 160
(15, 3, 12, 17, 12, 5, 3, 4, 2, 0, 1, 0, 0, 15, 12, 13, 10, 4, 6, 4, 2, 7, 6, 3, 0, 0), # 161
(6, 17, 6, 12, 9, 8, 5, 3, 3, 1, 0, 3, 0, 12, 11, 5, 6, 12, 5, 3, 4, 5, 5, 2, 0, 0), # 162
(10, 8, 10, 11, 9, 4, 3, 7, 2, 1, 2, 1, 0, 9, 12, 13, 6, 8, 4, 3, 2, 4, 8, 1, 1, 0), # 163
(8, 5, 11, 11, 12, 3, 1, 4, 5, 1, 1, 0, 0, 9, 7, 4, 5, 8, 5, 5, 2, 6, 4, 1, 0, 0), # 164
(7, 7, 8, 10, 7, 6, 5, 3, 5, 4, 4, 0, 0, 12, 1, 8, 7, 11, 4, 5, 4, 5, 7, 3, 0, 0), # 165
(7, 7, 9, 6, 4, 3, 3, 4, 8, 1, 1, 0, 0, 12, 11, 4, 7, 15, 3, 4, 5, 5, 6, 2, 0, 0), # 166
(12, 9, 5, 11, 7, 3, 2, 4, 3, 2, 1, 2, 0, 7, 14, 1, 7, 5, 3, 3, 5, 4, 3, 2, 0, 0), # 167
(17, 3, 13, 10, 13, 5, 4, 7, 4, 0, 1, 1, 0, 11, 9, 7, 4, 12, 0, 5, 4, 4, 1, 4, 2, 0), # 168
(13, 4, 7, 8, 7, 3, 2, 2, 6, 2, 2, 5, 0, 16, 7, 11, 3, 12, 6, 2, 1, 4, 1, 1, 0, 0), # 169
(9, 5, 9, 10, 7, 5, 3, 4, 0, 2, 0, 2, 0, 10, 3, 4, 3, 8, 4, 3, 0, 0, 2, 1, 0, 0), # 170
(9, 4, 10, 5, 4, 6, 2, 3, 2, 3, 0, 0, 0, 11, 4, 5, 6, 11, 6, 1, 2, 2, 2, 2, 0, 0), # 171
(5, 5, 6, 6, 11, 1, 1, 4, 1, 3, 1, 1, 0, 12, 6, 4, 0, 9, 4, 2, 2, 2, 2, 3, 0, 0), # 172
(8, 5, 9, 8, 5, 5, 1, 0, 4, 2, 1, 4, 0, 12, 11, 5, 4, 8, 4, 3, 2, 3, 1, 1, 0, 0), # 173
(10, 8, 8, 7, 6, 1, 3, 8, 4, 2, 0, 0, 0, 17, 1, 5, 5, 7, 3, 1, 2, 6, 1, 2, 0, 0), # 174
(10, 5, 6, 6, 6, 3, 1, 1, 5, 1, 2, 1, 0, 6, 5, 9, 5, 7, 4, 3, 3, 4, 2, 1, 1, 0), # 175
(7, 3, 5, 8, 5, 5, 2, 2, 5, 2, 0, 0, 0, 10, 6, 5, 3, 2, 3, 4, 2, 2, 1, 1, 0, 0), # 176
(3, 3, 7, 5, 10, 2, 0, 3, 5, 0, 0, 0, 0, 7, 4, 4, 4, 5, 2, 1, 1, 1, 1, 0, 1, 0), # 177
(4, 2, 4, 3, 5, 5, 4, 1, 5, 1, 0, 0, 0, 8, 6, 5, 3, 2, 1, 3, 1, 5, 0, 2, 0, 0), # 178
(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), # 179
)
station_arriving_intensity = (
(8.033384925394829, 8.840461695509067, 8.33805316738001, 9.943468438181492, 8.887496972175379, 5.021847891259743, 6.6336569845982645, 7.445081876767077, 9.744158499468812, 6.332824024835792, 6.728424262216965, 7.836664125289878, 8.134208340125381), # 0
(8.566923443231959, 9.424097110631614, 8.888554546128244, 10.600230805242587, 9.475984539958779, 5.353573734468089, 7.07115030602191, 7.9352219566491335, 10.387592522132655, 6.75036910764344, 7.172953817529811, 8.353946657302968, 8.671666635903767), # 1
(9.09875681436757, 10.005416273425567, 9.436867656875862, 11.254380327463672, 10.062340757999591, 5.683976183219912, 7.506909612737127, 8.423400396647072, 11.028458891004078, 7.166262040032874, 7.615717038042101, 8.869172243284888, 9.206983725135505), # 2
(9.6268124690345, 10.582112803098315, 9.980817390911767, 11.903322252051318, 10.644258681603043, 6.011744996136181, 7.939205826636729, 8.907681851991212, 11.664216257473749, 7.578852317481889, 8.054957458923813, 9.380297095888738, 9.738036490006762), # 3
(10.149017837465571, 11.15188031885724, 10.518228639524859, 12.544461826212112, 11.219431366074389, 6.335569931837869, 8.366309869613534, 9.386130977911865, 12.292323272932332, 7.986489435468286, 8.48891861534492, 9.885277427767623, 10.262701812703709), # 4
(10.663300349893618, 11.712412439909741, 11.04692629400403, 13.17520429715263, 11.785551866718848, 6.654140748945943, 8.786492663560358, 9.856812429639348, 12.910238588770495, 8.387522889469862, 8.915844042475412, 10.382069451574637, 10.778856575412524), # 5
(11.167587436551466, 12.261402785463202, 11.564735245638186, 13.792954912079445, 12.34031323884167, 6.9661472060813825, 9.19802513037002, 10.317790862403982, 13.515420856378904, 8.780302174964413, 9.333977275485251, 10.868629379962893, 11.284377660319372), # 6
(11.65980652767195, 12.79654497472501, 12.069480385716217, 14.39511891819914, 12.881408537748086, 7.270279061865153, 9.599178191935335, 10.767130931436084, 14.105328727148231, 9.16317678742974, 9.74156184954443, 11.342913425585486, 11.777141949610431), # 7
(12.137885053487896, 13.31553262690256, 12.558986605527034, 14.979101562718284, 13.406530818743338, 7.565226074918224, 9.988222770149116, 11.20289729196596, 14.67742085246913, 9.53449622234364, 10.136841299822914, 11.802877801095525, 12.255026325471867), # 8
(12.599750444232136, 13.816059361203237, 13.031078796359527, 15.54230809284347, 13.913373137132655, 7.849678003861574, 10.363429786904192, 11.623154599223941, 15.229155883732279, 9.892609975183907, 10.518059161490685, 12.246478719146102, 12.71590767008986), # 9
(13.043330130137491, 14.295818796834425, 13.483581849502599, 16.08214375578126, 14.399628548221282, 8.122324607316171, 10.723070164093368, 12.025967508440338, 15.757992472328343, 10.235867541428343, 10.883458969717719, 12.671672392390324, 13.157662865650577), # 10
(13.466551541436809, 14.752504553003531, 13.914320656245145, 16.596013798738237, 14.862990107314454, 8.38185564390299, 11.065414823609466, 12.409400674845465, 16.26138926964799, 10.56261841655475, 11.231284259673998, 13.076415033481297, 13.57816879434018), # 11
(13.8673421083629, 15.183810248917917, 14.321120107876064, 17.08132346892098, 15.301150869717404, 8.626960872242991, 11.388734687345298, 12.771518753669634, 16.736804927081888, 10.871212096040916, 11.559778566529495, 13.45866285507211, 13.975302338344855), # 12
(14.243629261148602, 15.587429503784993, 14.701805095684259, 17.53547801353607, 15.711803890735363, 8.856330050957158, 11.69130067719369, 13.11038640014317, 17.181698096020693, 11.159998075364648, 11.86718542545419, 13.816372069815873, 14.346940379850777), # 13
(14.593340430026746, 15.961055936812143, 15.054200510958635, 17.95588267979007, 16.092642225673583, 9.068652938666455, 11.971383715047459, 13.424068269496395, 17.593527427855076, 11.427325850003735, 12.151748371618055, 14.147498890365696, 14.690959801044102), # 14
(14.914403045230168, 16.30238316720675, 15.376131244988068, 18.339942714889578, 16.441358929837293, 9.26261929399186, 12.227254722799401, 13.71062901695961, 17.96975157397571, 11.671544915435986, 12.411710940191071, 14.449999529374674, 15.00523748411101), # 15
(15.204744536991681, 16.609104814176213, 15.66542218906148, 18.685063366041145, 16.755647058531732, 9.436918875554335, 12.457184622342362, 13.968133297763139, 18.307829185773258, 11.891004767139194, 12.64531666634322, 14.721830199495905, 15.287650311237673), # 16
(15.46229233554412, 16.878914496927916, 15.919898234467764, 18.98864988045138, 17.033199667062142, 9.590241441974857, 12.659444335569138, 14.19464576713731, 18.605218914638375, 12.084054900591148, 12.850809085244478, 14.960947113382488, 15.536075164610265), # 17
(15.684973871120327, 17.10950583466924, 16.137384272495808, 19.248107505326846, 17.271709810733743, 9.721276751874406, 12.832304784372562, 14.388231080312417, 18.859379411961754, 12.249044811269659, 13.026431732064815, 15.165306483687544, 15.748388926414954), # 18
(15.870716573953118, 17.29857244660759, 16.315705194434525, 19.460841487874106, 17.468870544851786, 9.828714563873934, 12.974036890645431, 14.546953892518793, 19.067769329134048, 12.384323994652526, 13.170428141974206, 15.332864523064154, 15.922468478837914), # 19
(16.01744787427533, 17.44380795195034, 16.452685891572806, 19.624257075299766, 17.62237492472151, 9.91124463659443, 13.08291157628058, 14.668878858986748, 19.22784731754592, 12.488241946217535, 13.28104185014264, 15.461577444165426, 16.05619070406532), # 20
(16.123095202319785, 17.542905969904893, 16.54615125519955, 19.73575951481038, 17.729916005648143, 9.967556728656858, 13.157199763170816, 14.752070634946598, 19.337072028588036, 12.559148161442488, 13.356516391740096, 15.54940145964447, 16.147432484283325), # 21
(16.18558598831933, 17.59356011967863, 16.593926176603656, 19.79275405361254, 17.78918684293692, 9.996340598682188, 13.19517237320896, 14.794593875628664, 19.392902113651065, 12.595392135805188, 13.395095301936545, 15.594292782154383, 16.194070701678125), # 22
(16.208629381348224, 17.599557750342935, 16.599877091906723, 19.799889300411525, 17.804371289652156, 10.0, 13.199686403614942, 14.79919012345679, 19.399881975308645, 12.599667636031093, 13.399932859458785, 15.599836122542294, 16.2), # 23
(16.225619860854646, 17.59605925925926, 16.598903703703705, 19.799011111111113, 17.812972181783763, 10.0, 13.197206100217867, 14.7928, 19.398946666666667, 12.59704098765432, 13.39939932659933, 15.598538271604937, 16.2), # 24
(16.242251568338528, 17.589163237311386, 16.59698216735254, 19.797273662551444, 17.821383912951205, 10.0, 13.192318244170096, 14.78024691358025, 19.3970987654321, 12.591870141746686, 13.39834143908218, 15.595976223136716, 16.2), # 25
(16.258523230476854, 17.578975034293556, 16.594138820301787, 19.79469670781893, 17.82960618947377, 10.0, 13.185098749293955, 14.76176790123457, 19.39436197530864, 12.58424113397348, 13.396768774161368, 15.592185093735715, 16.2), # 26
(16.27443357394662, 17.5656, 16.5904, 19.7913, 17.837638717670742, 10.0, 13.175623529411766, 14.7376, 19.39076, 12.57424, 13.39469090909091, 15.587200000000003, 16.2), # 27
(16.2899813254248, 17.549143484224967, 16.585792043895747, 19.787103292181072, 17.845481203861443, 10.0, 13.163968498345842, 14.707980246913582, 19.386316543209876, 12.561952775491541, 13.39211742112483, 15.581056058527665, 16.2), # 28
(16.3051652115884, 17.52971083676269, 16.580341289437587, 19.78212633744856, 17.853133354365152, 10.0, 13.150209569918506, 14.673145679012345, 19.381055308641976, 12.547465496113398, 13.389057887517147, 15.57378838591678, 16.2), # 29
(16.319983959114396, 17.50740740740741, 16.574074074074073, 19.77638888888889, 17.860594875501178, 10.0, 13.13442265795207, 14.633333333333333, 19.375, 12.530864197530866, 13.385521885521886, 15.56543209876543, 16.2), # 30
(16.334436294679772, 17.482338545953365, 16.567016735253773, 19.76991069958848, 17.867865473588814, 10.0, 13.116683676268863, 14.588780246913581, 19.368174320987656, 12.512234915409238, 13.381518992393067, 15.556022313671699, 16.2), # 31
(16.34852094496153, 17.45460960219479, 16.55919561042524, 19.762711522633747, 17.874944854947355, 10.0, 13.097068538691198, 14.539723456790126, 19.360601975308644, 12.49166368541381, 13.377058785384712, 15.545594147233656, 16.2), # 32
(16.362236636636634, 17.424325925925924, 16.55063703703704, 19.75481111111111, 17.8818327258961, 10.0, 13.075653159041394, 14.486400000000001, 19.352306666666667, 12.469236543209878, 13.372150841750841, 15.534182716049381, 16.2), # 33
(16.375582096382097, 17.391592866941014, 16.541367352537723, 19.746229218106997, 17.888528792754347, 10.0, 13.052513451141776, 14.429046913580246, 19.343312098765438, 12.445039524462736, 13.36680473874548, 15.521823136716964, 16.2), # 34
(16.388556050874893, 17.356515775034293, 16.53141289437586, 19.736985596707818, 17.895032761841392, 10.0, 13.027725328814654, 14.367901234567903, 19.333641975308645, 12.419158664837678, 13.361030053622645, 15.508550525834478, 16.2), # 35
(16.40115722679201, 17.3192, 16.5208, 19.7271, 17.901344339476537, 10.0, 13.001364705882352, 14.303200000000002, 19.32332, 12.391680000000001, 13.354836363636364, 15.494400000000002, 16.2), # 36
(16.41338435081044, 17.27975089163237, 16.50955500685871, 19.71659218106996, 17.907463231979076, 10.0, 12.97350749616719, 14.23518024691358, 19.31236987654321, 12.362689565615, 13.348233246040657, 15.479406675811616, 16.2), # 37
(16.425236149607162, 17.238273799725654, 16.49770425240055, 19.70548189300412, 17.913389145668305, 10.0, 12.944229613491487, 14.164079012345681, 19.300815308641976, 12.332273397347967, 13.341230278089538, 15.4636056698674, 16.2), # 38
(16.436711349859177, 17.194874074074075, 16.485274074074077, 19.69378888888889, 17.919121786863524, 10.0, 12.913606971677561, 14.090133333333334, 19.288680000000003, 12.300517530864198, 13.333837037037037, 15.447032098765431, 16.2), # 39
(16.44780867824346, 17.149657064471878, 16.472290809327845, 19.6815329218107, 17.924660861884032, 10.0, 12.88171548454773, 14.013580246913584, 19.27598765432099, 12.267508001828991, 13.326063100137175, 15.429721079103798, 16.2), # 40
(16.458526861437004, 17.102728120713305, 16.458780795610426, 19.66873374485597, 17.930006077049125, 10.0, 12.848631065924312, 13.934656790123459, 19.262761975308642, 12.233330845907636, 13.317918044643973, 15.411707727480568, 16.2), # 41
(16.4688646261168, 17.054192592592596, 16.444770370370374, 19.655411111111114, 17.935157138678093, 10.0, 12.814429629629629, 13.8536, 19.24902666666667, 12.198072098765433, 13.30941144781145, 15.393027160493828, 16.2), # 42
(16.47882069895983, 17.00415582990398, 16.430285871056242, 19.641584773662554, 17.940113753090245, 10.0, 12.779187089486001, 13.770646913580249, 19.234805432098767, 12.161817796067673, 13.300552886893627, 15.373714494741657, 16.2), # 43
(16.488393806643085, 16.9527231824417, 16.4153536351166, 19.62727448559671, 17.944875626604873, 10.0, 12.742979359315743, 13.686034567901238, 19.220121975308643, 12.124653973479653, 13.291351939144532, 15.353804846822133, 16.2), # 44
(16.497582675843546, 16.900000000000002, 16.400000000000002, 19.6125, 17.949442465541274, 10.0, 12.705882352941178, 13.600000000000001, 19.205, 12.086666666666668, 13.281818181818181, 15.333333333333332, 16.2), # 45
(16.50638603323821, 16.846091632373113, 16.384251303155008, 19.59728106995885, 17.953813976218747, 10.0, 12.667971984184621, 13.512780246913582, 19.189463209876543, 12.04794191129401, 13.271961192168598, 15.312335070873344, 16.2), # 46
(16.514802605504055, 16.79110342935528, 16.36813388203018, 19.581637448559672, 17.957989864956588, 10.0, 12.629324166868395, 13.424612345679012, 19.173535308641977, 12.008565743026978, 13.261790547449806, 15.29084517604024, 16.2), # 47
(16.522831119318074, 16.735140740740743, 16.351674074074076, 19.565588888888893, 17.961969838074097, 10.0, 12.590014814814815, 13.335733333333335, 19.15724, 11.968624197530865, 13.251315824915824, 15.268898765432098, 16.2), # 48
(16.53047030135726, 16.67830891632373, 16.334898216735255, 19.549155144032923, 17.965753601890572, 10.0, 12.550119841846204, 13.246380246913581, 19.14060098765432, 11.928203310470966, 13.240546601820677, 15.246530955647007, 16.2), # 49
(16.537718878298588, 16.620713305898494, 16.31783264746228, 19.53235596707819, 17.969340862725304, 10.0, 12.50971516178488, 13.15679012345679, 19.12364197530864, 11.887389117512575, 13.22949245541838, 15.223776863283039, 16.2), # 50
(16.544575576819057, 16.56245925925926, 16.300503703703704, 19.515211111111114, 17.9727313268976, 10.0, 12.46887668845316, 13.0672, 19.10638666666667, 11.846267654320988, 13.218162962962964, 15.200671604938274, 16.2), # 51
(16.551039123595647, 16.503652126200276, 16.282937722908095, 19.497740329218107, 17.975924700726743, 10.0, 12.427680335673365, 12.977846913580246, 19.0888587654321, 11.8049249565615, 13.206567701708444, 15.177250297210794, 16.2), # 52
(16.55710824530535, 16.444397256515778, 16.26516104252401, 19.479963374485596, 17.978920690532046, 10.0, 12.386202017267813, 12.888967901234569, 19.071081975308644, 11.763447059899406, 13.194716248908842, 15.153548056698675, 16.2), # 53
(16.562781668625146, 16.384800000000002, 16.2472, 19.4619, 17.981719002632804, 10.0, 12.344517647058824, 12.800799999999999, 19.05308, 11.72192, 13.18261818181818, 15.1296, 16.2), # 54
(16.568058120232035, 16.324965706447188, 16.229080932784637, 19.443569958847736, 17.984319343348304, 10.0, 12.302703138868717, 12.71358024691358, 19.034876543209876, 11.68042981252858, 13.170283077690485, 15.10544124371285, 16.2), # 55
(16.572936326802996, 16.264999725651577, 16.210830178326475, 19.424993004115226, 17.986721418997856, 10.0, 12.26083440651981, 12.627545679012346, 19.016495308641975, 11.639062533150437, 13.157720513779774, 15.0811069044353, 16.2), # 56
(16.577415015015013, 16.205007407407408, 16.192474074074077, 19.40618888888889, 17.988924935900748, 10.0, 12.218987363834422, 12.542933333333336, 18.997960000000003, 11.597904197530866, 13.144940067340068, 15.056632098765432, 16.2), # 57
(16.581492911545087, 16.145094101508917, 16.174038957475997, 19.387177366255145, 17.99092960037628, 10.0, 12.177237924634875, 12.459980246913581, 18.979294320987655, 11.557040841335164, 13.131951315625393, 15.032051943301326, 16.2), # 58
(16.585168743070195, 16.085365157750342, 16.155551165980796, 19.367978189300413, 17.992735118743752, 10.0, 12.135662002743485, 12.378923456790124, 18.960521975308644, 11.516558500228626, 13.11876383588976, 15.007401554641062, 16.2), # 59
(16.588441236267325, 16.02592592592593, 16.137037037037036, 19.34861111111111, 17.99434119732246, 10.0, 12.094335511982571, 12.3, 18.94166666666667, 11.476543209876544, 13.105387205387206, 14.982716049382717, 16.2), # 60
(16.591309117813463, 15.966881755829906, 16.11852290809328, 19.329095884773665, 17.995747542431697, 10.0, 12.053334366174454, 12.223446913580247, 18.922752098765432, 11.437081005944217, 13.091831001371743, 14.958030544124373, 16.2), # 61
(16.593771114385607, 15.908337997256517, 16.100035116598082, 19.30945226337449, 17.996953860390775, 10.0, 12.01273447914145, 12.149501234567902, 18.903801975308642, 11.398257924096939, 13.078104801097394, 14.933380155464107, 16.2), # 62
(16.595825952660736, 15.8504, 16.0816, 19.289700000000003, 17.99795985751897, 10.0, 11.972611764705881, 12.078400000000002, 18.88484, 11.36016, 13.064218181818184, 14.9088, 16.2), # 63
(16.597472359315837, 15.793173113854596, 16.0632438957476, 19.26985884773663, 17.998765240135597, 10.0, 11.933042136690068, 12.010380246913583, 18.86588987654321, 11.322873269318702, 13.050180720788127, 14.884325194330135, 16.2), # 64
(16.5987090610279, 15.73676268861454, 16.04499314128944, 19.249948559670784, 17.999369714559947, 10.0, 11.894101508916325, 11.945679012345678, 18.846975308641976, 11.286483767718336, 13.036001995261257, 14.859990855052581, 16.2), # 65
(16.599534784473914, 15.681274074074077, 16.026874074074076, 19.22998888888889, 17.999772987111317, 10.0, 11.855865795206972, 11.884533333333335, 18.828120000000002, 11.251077530864197, 13.021691582491583, 14.835832098765435, 16.2), # 66
(16.59994825633087, 15.626812620027435, 16.00891303155007, 19.209999588477366, 17.99997476410901, 10.0, 11.81841090938433, 11.827180246913583, 18.809347654320987, 11.216740594421584, 13.007259059733137, 14.811884042066758, 16.2), # 67
(16.59966658316932, 15.573197822912517, 15.991049519890261, 19.189826784755773, 17.999804728475752, 9.99981441853376, 11.781624311727434, 11.77335016003658, 18.790540557841794, 11.183392706635466, 12.992457581664603, 14.788048035039589, 16.19980024005487), # 68
(16.597026731078905, 15.51879283154122, 15.97278148148148, 19.168453623188405, 17.99825708061002, 9.998347325102882, 11.744429090154583, 11.720158024691358, 18.770876543209877, 11.150090225127087, 12.975780542264753, 14.76355035737492, 16.198217592592595), # 69
(16.59181726009423, 15.463347935749368, 15.954029492455417, 19.14573939881911, 17.995198902606308, 9.995458009449779, 11.706656215298192, 11.667123914037496, 18.750244627343395, 11.116671239140375, 12.957038218441728, 14.738276418068494, 16.195091735253776), # 70
(16.584111457028687, 15.406896269746449, 15.93480013717421, 19.12171760601181, 17.990668926006617, 9.991193293705228, 11.668322655262381, 11.61426538637403, 18.728675537265662, 11.083136574948224, 12.936299793254179, 14.712244699540344, 16.190463820301783), # 71
(16.573982608695655, 15.349470967741935, 15.915099999999999, 19.096421739130435, 17.98470588235294, 9.985600000000002, 11.62944537815126, 11.5616, 18.706200000000003, 11.04948705882353, 12.913634449760767, 14.685473684210528, 16.184375), # 72
(16.561504001908514, 15.291105163945307, 15.894935665294923, 19.069885292538917, 17.977348503187283, 9.978724950464867, 11.590041352068948, 11.50914531321445, 18.682848742569732, 11.01572351703919, 12.889111371020142, 14.65798185449907, 16.1768664266118), # 73
(16.546748923480646, 15.231831992566043, 15.874313717421124, 19.04214176060118, 17.96863552005164, 9.970614967230606, 11.550127545119556, 11.456918884316416, 18.658652491998172, 10.9818467758681, 12.86279974009097, 14.629787692826028, 16.167979252400553), # 74
(16.52979066022544, 15.171684587813619, 15.85324074074074, 19.01322463768116, 17.95860566448802, 9.961316872427986, 11.509720925407201, 11.404938271604939, 18.63364197530864, 10.947857661583152, 12.834768740031897, 14.600909681611435, 16.157754629629633), # 75
(16.510702498956285, 15.11069608389752, 15.831723319615913, 18.98316741814278, 17.94729766803841, 9.950877488187778, 11.468838461035993, 11.353221033379059, 18.607847919524463, 10.913757000457247, 12.805087553901586, 14.571366303275333, 16.146233710562413), # 76
(16.48955772648655, 15.048899615027217, 15.809768038408777, 18.95200359634997, 17.934750262244815, 9.939343636640757, 11.427497120110047, 11.301784727937816, 18.581301051668955, 10.87954561876328, 12.7738253647587, 14.54117604023777, 16.13345764746228), # 77
(16.46642962962963, 14.98632831541219, 15.787381481481482, 18.919766666666668, 17.92100217864924, 9.926762139917695, 11.38571387073348, 11.250646913580248, 18.55403209876543, 10.845224342774147, 12.741051355661883, 14.510357374918781, 16.119467592592596), # 78
(16.441391495198904, 14.923015319261916, 15.76457023319616, 18.88649012345679, 17.906092148793675, 9.913179820149367, 11.343505681010402, 11.199825148605397, 18.52607178783722, 10.810793998762742, 12.706834709669796, 14.478928789738408, 16.104304698216733), # 79
(16.414516610007755, 14.858993760785877, 15.74134087791495, 18.852207461084273, 17.890058904220126, 9.898643499466544, 11.30088951904493, 11.149336991312301, 18.497450845907636, 10.776255413001962, 12.671244609841102, 14.446908767116696, 16.08801011659808), # 80
(16.385878260869568, 14.79429677419355, 15.7177, 18.816952173913048, 17.872941176470587, 9.8832, 11.257882352941177, 11.099200000000002, 18.4682, 10.741609411764706, 12.63435023923445, 14.414315789473685, 16.070625), # 81
(16.355549734597723, 14.728957493694413, 15.693654183813445, 18.780757756307032, 17.854777697087066, 9.866896143880508, 11.214501150803258, 11.049431732967536, 18.43834997713763, 10.706856821323866, 12.596220780908501, 14.381168339229419, 16.052190500685874), # 82
(16.323604318005607, 14.663009053497943, 15.669210013717422, 18.743657702630166, 17.835607197611555, 9.849778753238837, 11.170762880735285, 11.000049748513947, 18.40793150434385, 10.671998467952339, 12.55692541792191, 14.34748489880394, 16.03274777091907), # 83
(16.290115297906603, 14.59648458781362, 15.644374074074074, 18.70568550724638, 17.815468409586057, 9.831894650205761, 11.126684510841374, 10.95107160493827, 18.376975308641974, 10.637035177923023, 12.516533333333333, 14.313283950617285, 16.012337962962963), # 84
(16.255155961114095, 14.529417230850923, 15.61915294924554, 18.666874664519593, 17.794400064552573, 9.813290656912057, 11.08228300922564, 10.902514860539554, 18.345512117055325, 10.60196777750881, 12.47511371020143, 14.2785839770895, 15.991002229080934), # 85
(16.21879959444146, 14.46184011681933, 15.593553223593966, 18.627258668813745, 17.772440894053094, 9.794013595488494, 11.037575343992193, 10.854397073616827, 18.313572656607228, 10.566797092982599, 12.432735731584856, 14.24340346064063, 15.968781721536352), # 86
(16.18111948470209, 14.393786379928315, 15.567581481481481, 18.586871014492754, 17.749629629629634, 9.774110288065843, 10.99257848324515, 10.806735802469136, 18.28118765432099, 10.531523950617284, 12.389468580542264, 14.207760883690709, 15.945717592592594), # 87
(16.142188918709373, 14.325289154387361, 15.541244307270233, 18.54574519592056, 17.726005002824177, 9.753627556774882, 10.947309395088626, 10.75954860539552, 18.248387837219937, 10.496149176685762, 12.345381440132318, 14.171674728659784, 15.921850994513035), # 88
(16.102081183276677, 14.256381574405948, 15.51454828532236, 18.503914707461085, 17.701605745178732, 9.732612223746381, 10.901785047626733, 10.712853040695016, 18.21520393232739, 10.460673597460932, 12.30054349341367, 14.135163477967897, 15.897223079561043), # 89
(16.06086956521739, 14.187096774193549, 15.4875, 18.461413043478263, 17.676470588235297, 9.711111111111112, 10.856022408963586, 10.666666666666666, 18.18166666666667, 10.425098039215687, 12.255023923444977, 14.098245614035088, 15.871875000000001), # 90
(16.0186273513449, 14.117467887959643, 15.460106035665294, 18.41827369833602, 17.650638263535864, 9.689171040999847, 10.810038447203299, 10.621007041609511, 18.14780676726109, 10.389423328222922, 12.208891913284896, 14.060939619281399, 15.845847908093276), # 91
(15.975427828472597, 14.047528049913716, 15.432372976680384, 18.374530166398284, 17.624147502622446, 9.666838835543363, 10.763850130449988, 10.57589172382259, 18.113654961133975, 10.353650290755535, 12.162216645992086, 14.023263976126877, 15.819182956104251), # 92
(15.931344283413848, 13.977310394265235, 15.404307407407408, 18.33021594202899, 17.597037037037037, 9.644161316872427, 10.717474426807762, 10.53133827160494, 18.079241975308644, 10.31777975308642, 12.1150673046252, 13.985237166991553, 15.791921296296294), # 93
(15.886450002982048, 13.906848055223684, 15.375915912208507, 18.285364519592058, 17.569345598321632, 9.621185307117818, 10.670928304380737, 10.487364243255604, 18.044598536808415, 10.281812541488476, 12.067513072242896, 13.946877674295479, 15.764104080932785), # 94
(15.840818273990577, 13.836174166998541, 15.347205075445817, 18.240009393451423, 17.541111918018238, 9.597957628410304, 10.62422873127303, 10.443987197073618, 18.00975537265661, 10.245749482234594, 12.019623131903835, 13.908203980458689, 15.735772462277092), # 95
(15.79452238325282, 13.765321863799286, 15.318181481481483, 18.194184057971015, 17.512374727668846, 9.574525102880658, 10.577392675588754, 10.401224691358026, 17.974743209876543, 10.209591401597677, 11.971466666666668, 13.869234567901238, 15.706967592592594), # 96
(15.747635617582157, 13.694324279835394, 15.28885171467764, 18.14792200751476, 17.483172758815464, 9.550934552659655, 10.530437105432021, 10.359094284407867, 17.939592775491544, 10.173339125850616, 11.923112859590052, 13.829987919043152, 15.677730624142663), # 97
(15.700231263791975, 13.623214549316343, 15.259222359396432, 18.101256736446594, 17.453544743000084, 9.52723279987807, 10.48337898890695, 10.317613534522177, 17.904334796524918, 10.136993481266307, 11.87463089373265, 13.790482516304477, 15.648102709190674), # 98
(15.652382608695653, 13.552025806451613, 15.229300000000002, 18.054221739130437, 17.423529411764708, 9.503466666666666, 10.43623529411765, 10.276800000000001, 17.869, 10.100555294117648, 11.826089952153112, 13.750736842105264, 15.618125000000001), # 99
(15.60416293910658, 13.480791185450682, 15.19909122085048, 18.00685050993022, 17.393165496651335, 9.479682975156226, 10.389022989168232, 10.236671239140376, 17.833619112940102, 10.064025390677534, 11.777559217910095, 13.710769378865548, 15.58783864883402), # 100
(15.555645541838135, 13.409543820523034, 15.168602606310015, 17.959176543209878, 17.36249172920197, 9.455928547477518, 10.34175904216282, 10.19724481024234, 17.798222862368544, 10.027404597218862, 11.72910787406226, 13.670598609005365, 15.557284807956103), # 101
(15.506903703703706, 13.338316845878138, 15.13784074074074, 17.911233333333335, 17.331546840958605, 9.432250205761319, 10.294460421205521, 10.15853827160494, 17.762841975308643, 9.990693740014526, 11.680805103668263, 13.63024301494477, 15.526504629629631), # 102
(15.458010711516671, 13.267143395725476, 15.1068122085048, 17.86305437466452, 17.300369563463246, 9.408694772138395, 10.247144094400449, 10.120569181527207, 17.72750717878372, 9.953893645337423, 11.632720089786758, 13.589721079103796, 15.495539266117968), # 103
(15.409039852090416, 13.196056604274526, 15.075523593964334, 17.814673161567367, 17.268998628257886, 9.385309068739522, 10.199827029851722, 10.083355098308186, 17.692249199817102, 9.91700513946045, 11.584922015476401, 13.549051283902486, 15.464429869684501), # 104
(15.360064412238325, 13.125089605734766, 15.043981481481481, 17.766123188405796, 17.237472766884533, 9.362139917695474, 10.152526195663453, 10.046913580246915, 17.6570987654321, 9.880029048656501, 11.537480063795854, 13.508252111760886, 15.433217592592593), # 105
(15.311157678773782, 13.054275534315678, 15.012192455418381, 17.717437949543747, 17.205830710885177, 9.339234141137021, 10.105258559939752, 10.011262185642433, 17.622086602652033, 9.842966199198472, 11.490463417803769, 13.46734204509903, 15.401943587105624), # 106
(15.26239293851017, 12.983647524226738, 14.980163100137176, 17.66865093934514, 17.174111191801824, 9.31663856119494, 10.058041090784739, 9.976418472793783, 17.58724343850023, 9.805817417359263, 11.443941260558804, 13.426339566336967, 15.370649005486968), # 107
(15.21384347826087, 12.913238709677422, 14.947900000000002, 17.619795652173917, 17.14235294117647, 9.294400000000001, 10.010890756302521, 9.942400000000001, 17.5526, 9.768583529411766, 11.397982775119617, 13.38526315789474, 15.339375000000002), # 108
(15.16558258483927, 12.843082224877207, 14.915409739369, 17.570905582393987, 17.11059469055112, 9.272565279682976, 9.96382452459722, 9.90922432556013, 17.518187014174668, 9.731265361628877, 11.352657144544864, 13.34413130219238, 15.308162722908094), # 109
(15.117683545058746, 12.77321120403558, 14.882698902606315, 17.522014224369297, 17.078875171467768, 9.251181222374639, 9.916859363772943, 9.876909007773206, 17.484035208047555, 9.693863740283494, 11.308033551893201, 13.302962481649942, 15.277053326474624), # 110
(15.07021964573269, 12.703658781362009, 14.849774074074077, 17.47315507246377, 17.047233115468412, 9.230294650205762, 9.87001224193381, 9.845471604938272, 17.450175308641978, 9.656379491648512, 11.264181180223286, 13.261775178687461, 15.246087962962964), # 111
(15.02326417367448, 12.634458091065975, 14.816641838134434, 17.42436162104133, 17.015707254095055, 9.209952385307119, 9.823300127183934, 9.814929675354367, 17.41663804298125, 9.618813441996826, 11.221169212593775, 13.220587875724977, 15.215307784636488), # 112
(14.976806757924871, 12.565757790057525, 14.78338852520331, 17.375734211987265, 16.98428108827793, 9.190191630743222, 9.776841541850832, 9.78536411004897, 17.383540498013794, 9.581287578580367, 11.179078249844586, 13.179508698407085, 15.184710241349155), # 113
(14.930369436640104, 12.498235493640857, 14.75047308003459, 17.327663074043738, 16.952629367306123, 9.170967373647843, 9.731229133456928, 9.757138015208191, 17.351390457140898, 9.544504268660452, 11.137990939381115, 13.13905947538076, 15.154040662656056), # 114
(14.883815844806392, 12.431915517892875, 14.717915092331708, 17.280135208290847, 16.920652284621763, 9.152229619998023, 9.6864954403065, 9.730244246845935, 17.320199965870064, 9.508520524780923, 11.09784721828335, 13.099260132094162, 15.123210610656603), # 115
(14.837087797180216, 12.366701250066724, 14.685651503974197, 17.233065840426246, 16.888301642214046, 9.133934203659356, 9.64256770804463, 9.70460850063839, 17.28989014276453, 9.473269373519276, 11.05856949003437, 13.060037115979753, 15.092171615609425), # 116
(14.790127108518035, 12.302496077415555, 14.653619256841578, 17.18637019614759, 16.855529242072176, 9.116036958497425, 9.599373182316404, 9.680156472261736, 17.260382106387524, 9.438683841453006, 11.020080158117253, 13.021316874470001, 15.06087520777316), # 117
(14.742875593576338, 12.239203387192518, 14.621755292813388, 17.139963501152533, 16.82228688618535, 9.098493718377823, 9.556839108766905, 9.656813857392155, 17.231596975302296, 9.404696955159615, 10.98230162601508, 12.98302585499736, 15.02927291740644), # 118
(14.695275067111588, 12.176726566650768, 14.589996553769158, 17.09376098113873, 16.788526376542755, 9.081260317166132, 9.51489273304121, 9.634506351705832, 17.20345586807207, 9.371241741216595, 10.945156297210925, 12.945090504994296, 14.997316274767892), # 119
(14.647267343880259, 12.114969003043454, 14.55827998158842, 17.04767786180383, 16.754199515133596, 9.064292588727945, 9.473461300784406, 9.613159650878949, 17.175879903260093, 9.338251226201448, 10.908566575187866, 12.907437271893276, 14.964956810116156), # 120
(14.59879423863883, 12.053834083623727, 14.5265425181507, 17.001629368845496, 16.71925810394707, 9.047546366928849, 9.432472057641569, 9.592699450587691, 17.148790199429598, 9.305658436691674, 10.872454863428986, 12.869992603126756, 14.932146053709857), # 121
(14.549797566143766, 11.993225195644738, 14.494721105335538, 16.95553072796137, 16.683653944972374, 9.03097748563443, 9.391852249257788, 9.573051446508238, 17.122107875143822, 9.273396399264763, 10.836743565417363, 12.832682946127202, 14.898835535807633), # 122
(14.50021914115155, 11.933045726359639, 14.462752685022458, 16.90929716484911, 16.647338840198707, 9.01454177871028, 9.351529121278142, 9.554141334316773, 17.095754048966008, 9.24139814049822, 10.801355084636072, 12.795434748327075, 14.864976786668116), # 123
(14.450000778418648, 11.87319906302158, 14.430574199090993, 16.86284390520638, 16.61026459161526, 8.998195080021983, 9.311429919347711, 9.535894809689482, 17.069649839459384, 9.209596686969538, 10.766211824568192, 12.758174457158841, 14.830521336549939), # 124
(14.399084292701534, 11.813588592883713, 14.398122589420678, 16.816086174730817, 16.572383001211236, 8.98189322343513, 9.271481889111582, 9.518237568302546, 17.04371636518719, 9.177925065256215, 10.731236188696803, 12.720828520054958, 14.795420715711726), # 125
(14.347411498756685, 11.754117703199192, 14.365334797891038, 16.768939199120087, 16.53364587097583, 8.965592042815308, 9.231612276214832, 9.501095305832148, 17.017874744712667, 9.146316301935748, 10.696350580504982, 12.683323384447895, 14.759626454412127), # 126
(14.294924211340579, 11.69468978122116, 14.332147766381608, 16.72131820407184, 16.494005002898238, 8.949247372028104, 9.19174832630255, 9.484393717954474, 16.99204609659905, 9.114703423585638, 10.661477403475807, 12.645585497770107, 14.723090082909758), # 127
(14.241564245209673, 11.635208214202777, 14.29849843677192, 16.67313841528373, 16.453412198967666, 8.93281504493911, 9.151817285019812, 9.4680585003457, 16.966151539409577, 9.083019456783381, 10.626539061092359, 12.607541307454062, 14.68576313146326), # 128
(14.187273415120451, 11.575576389397186, 14.264323750941504, 16.624315058453412, 16.4118192611733, 8.916250895413912, 9.111746398011702, 9.452015348682016, 16.94011219170748, 9.051197428106473, 10.591457956837715, 12.569117260932218, 14.647597130331262), # 129
(14.131993535829388, 11.515697694057547, 14.229560650769887, 16.57476335927854, 16.36917799150434, 8.899510757318094, 9.0714629109233, 9.4361899586396, 16.913849172056, 9.019170364132412, 10.556156494194951, 12.530239805637045, 14.608543609772397), # 130
(14.07566642209295, 11.455475515437003, 14.19414607813661, 16.524398543456762, 16.32544019194999, 8.88255046451725, 9.030894069399695, 9.42050802589464, 16.887283599018378, 8.986871291438696, 10.52055707664715, 12.490835389000999, 14.568554100045299), # 131
(14.018233888667616, 11.39481324078871, 14.158016974921194, 16.47313583668574, 16.280557664499447, 8.865325850876964, 8.98996711908596, 9.404895246123317, 16.860336591157846, 8.954233236602823, 10.484582107677383, 12.450830458456547, 14.527580131408602), # 132
(13.959637750309861, 11.333614257365817, 14.121110283003175, 16.420890464663124, 16.2344822111419, 8.847792750262826, 8.948609305627183, 9.389277315001811, 16.832929267037642, 8.921189226202292, 10.448153990768738, 12.410151461436149, 14.485573234120938), # 133
(13.899819821776152, 11.271781952421478, 14.083362944262086, 16.367577653086567, 16.18716563386655, 8.829906996540425, 8.906747874668445, 9.37357992820631, 16.804982745221007, 8.887672286814597, 10.411195129404286, 12.368724845372267, 14.442484938440934), # 134
(13.838721917822966, 11.209219713208839, 14.044711900577454, 16.313112627653727, 16.138559734662593, 8.811624423575347, 8.86431007185483, 9.357728781412993, 16.77641814427117, 8.853615445017242, 10.373627927067108, 12.326477057697364, 14.398266774627231), # 135
(13.776285853206776, 11.145830926981056, 14.005094093828815, 16.25741061406225, 16.08861631551923, 8.792900865233184, 8.821223142831416, 9.341649570298044, 16.74715658275137, 8.818951727387716, 10.335374787240283, 12.283334545843907, 14.352870272938459), # 136
(13.712453442684055, 11.081518980991277, 13.964446465895698, 16.200386838009802, 16.037287178425654, 8.773692155379518, 8.77741433324329, 9.325267990537647, 16.717119179224852, 8.783614160503523, 10.296358113406889, 12.239223757244352, 14.306246963633242), # 137
(13.647166501011277, 11.016187262492654, 13.922705958657628, 16.141956525194022, 15.98452412537107, 8.753954127879942, 8.732810888735527, 9.308509737807984, 16.68622705225485, 8.747535770942156, 10.256500309050004, 12.194071139331164, 14.258348376970226), # 138
(13.58036684294491, 10.949739158738339, 13.879809513994145, 16.082034901312575, 15.930278958344665, 8.733642616600042, 8.687340054953216, 9.29130050778524, 16.654401320404595, 8.710649585281116, 10.215723777652705, 12.14780313953681, 14.20912604320803), # 139
(13.511996283241437, 10.88207805698148, 13.83569407378478, 16.020537192063113, 15.874503479335647, 8.712713455405407, 8.640929077541434, 9.273565996145594, 16.62156310223733, 8.672888630097898, 10.17395092269807, 12.100346205293746, 14.158531492605304), # 140
(13.44199663665733, 10.813107344475235, 13.790296579909057, 15.957378623143285, 15.817149490333206, 8.691122478161624, 8.593505202145272, 9.255231898565233, 16.587633516316288, 8.634185931970002, 10.131104147669182, 12.05162678403444, 14.106516255420662), # 141
(13.37030971794905, 10.742730408472745, 13.743553974246513, 15.892474420250753, 15.75816879332654, 8.668825518734284, 8.544995674409803, 9.236223910720339, 16.552533681204707, 8.594474517474925, 10.087105856049115, 12.001571323191351, 14.053031861912746), # 142
(13.29687734187308, 10.67085063622717, 13.695403198676681, 15.82573980908316, 15.697513190304846, 8.64577841098897, 8.49532773998011, 9.21646772828709, 16.516184715465837, 8.553687413190165, 10.04187845132095, 11.950106270196944, 13.998029842340188), # 143
(13.221641323185896, 10.597371414991658, 13.645781195079085, 15.757090015338171, 15.635134483257326, 8.621936988791274, 8.444428644501278, 9.195889046941678, 16.478507737662895, 8.511757645693216, 9.995344336967761, 11.897158072483679, 13.941461726961624), # 144
(13.144543476643964, 10.52219613201936, 13.594624905333262, 15.686440264713433, 15.570984474173173, 8.597257086006785, 8.39222563361839, 9.174413562360282, 16.439423866359128, 8.46861824156158, 9.947425916472632, 11.842653177484022, 13.88327904603568), # 145
(13.065525617003761, 10.445228174563427, 13.541871271318747, 15.613705782906601, 15.505014965041589, 8.57169453650109, 8.338645952976528, 9.151966970219084, 16.39885422011777, 8.424202227372753, 9.898045593318638, 11.786518032630433, 13.82343332982099), # 146
(12.98452955902176, 10.366370929877009, 13.487457234915055, 15.538801795615328, 15.437177757851764, 8.545205174139772, 8.28361684822077, 9.128474966194265, 16.356719917502065, 8.378442629704233, 9.847125770988859, 11.728679085355378, 13.761876108576189), # 147
(12.901497117454435, 10.285527785213262, 13.431319738001733, 15.461643528537275, 15.367424654592899, 8.517744832788429, 8.227065564996202, 9.103863245962012, 16.312942077075245, 8.331272475133515, 9.794588852966372, 11.669062783091313, 13.698558912559907), # 148
(12.81637010705826, 10.20260212782533, 13.37339572245831, 15.382146207370084, 15.295707457254194, 8.48926934631264, 8.168919348947906, 9.078057505198506, 16.26744181740054, 8.282624790238101, 9.740357242734255, 11.607595573270707, 13.63343327203078), # 149
(12.729090342589704, 10.117497344966367, 13.313622130164312, 15.30022505781142, 15.221977967824841, 8.459734548577998, 8.109105445720962, 9.05098343957993, 16.220140257041205, 8.232432601595482, 9.684353343775589, 11.544203903326022, 13.566450717247434), # 150
(12.63959963880524, 10.030116823889527, 13.251935902999268, 15.215795305558927, 15.146187988294043, 8.429096273450089, 8.047551100960453, 9.02256674478247, 16.170958514560464, 8.180628935783165, 9.626499559573448, 11.478814220689715, 13.49756277846851), # 151
(12.54783981046135, 9.940363951847957, 13.188273982842723, 15.128772176310271, 15.06828932065099, 8.397310354794502, 7.984183560311464, 8.992733116482306, 16.119817708521552, 8.12714681937864, 9.566718293610915, 11.411352972794255, 13.426720985952636), # 152
(12.453752672314497, 9.848142116094811, 13.12257331157419, 15.039070895763093, 14.988233766884889, 8.364332626476825, 7.918930069419071, 8.96140825035562, 16.06663895748772, 8.071919278959406, 9.504931949371066, 11.341746607072103, 13.353876869958444), # 153
(12.357280039121166, 9.75335470388324, 13.054770831073213, 14.946606689615056, 14.905973128984929, 8.330118922362647, 7.851717873928365, 8.928517842078596, 16.011343380022186, 8.014879341102965, 9.44106293033698, 11.26992157095572, 13.278981960744572), # 154
(12.258363725637818, 9.655905102466392, 12.984803483219322, 14.851294783563805, 14.821459208940315, 8.294625076317555, 7.782474219484418, 8.893987587327418, 15.953852094688205, 7.955960032386807, 9.375033639991733, 11.195804311877572, 13.201987788569642), # 155
(12.15694554662093, 9.555696699097421, 12.912608209892042, 14.753050403307, 14.734643808740238, 8.257806922207138, 7.71112635173232, 8.85774318177827, 15.894086220049003, 7.8950943793884365, 9.306766481818407, 11.119321277270117, 13.122845883692296), # 156
(12.05296731682698, 9.452632881029478, 12.838121952970909, 14.6517887745423, 14.645478730373895, 8.219620293896982, 7.637601516317151, 8.819710321107332, 15.831966874667822, 7.832215408685347, 9.236183859300079, 11.04039891456582, 13.041507776371162), # 157
(11.943489514248384, 9.344724993235614, 12.75774712624377, 14.54363133064199, 14.549889769393596, 8.177639162107376, 7.560170753484572, 8.777275123758995, 15.762659346558557, 7.76538546606583, 9.160953204062308, 10.956159302710944, 12.954377375064553), # 158
(11.811658827165445, 9.220904511359164, 12.65078050944478, 14.406363454061527, 14.424306095650605, 8.117903436811366, 7.469140421417146, 8.715541652423012, 15.658283617955432, 7.683649590557993, 9.06786709699039, 10.850180037892974, 12.840684235072311), # 159
(11.655795351846896, 9.080154765665142, 12.515073532729422, 14.237724016654177, 14.266272210154874, 8.038946073676295, 7.363589997414055, 8.632958703243755, 15.515880363565842, 7.58592904298063, 8.955615213775264, 10.720803118220555, 12.69827297422973), # 160
(11.477155287337537, 8.92339338892875, 12.352075155056495, 14.039316006010765, 14.077428998851381, 7.941723586512502, 7.244290313611002, 8.530560852975649, 15.337327627198428, 7.473053109073501, 8.825186647359532, 10.569227950252113, 12.528598471710556), # 161
(11.27699483268217, 8.751538013925183, 12.163234335384793, 13.812742409722123, 13.859417347685127, 7.827192489130329, 7.112012202143695, 8.409382678373124, 15.12450345266182, 7.3458510745763705, 8.677570490685794, 10.39665394054607, 12.333115606688533), # 162
(11.056570186925597, 8.565506273429639, 11.950000032673124, 13.559606215379095, 13.613878142601102, 7.696309295340116, 6.967526495147841, 8.2704587561906, 14.87928588376465, 7.205152225229, 8.513755836696653, 10.204280495660853, 12.113279258337407), # 163
(10.817137549112616, 8.366215800217313, 11.713821205880283, 13.281510410572508, 13.342452269544303, 7.550030518952207, 6.811604024759146, 8.114823663182511, 14.603552964315558, 7.05178584677115, 8.334731778334714, 9.993307022154886, 11.870544305830926), # 164
(10.559953118288028, 8.154584227063411, 11.45614681396507, 12.980057982893204, 13.046780614459719, 7.389312673776939, 6.6450156231133155, 7.943511976103274, 14.299182738123168, 6.8865812249425815, 8.141487408542579, 9.764932926586592, 11.606365628342832), # 165
(10.286273093496636, 7.931529186743127, 11.178425815886285, 12.656851919932002, 12.728504063292343, 7.215112273624654, 6.468532122346058, 7.757558271707324, 13.968053248996117, 6.71036764548306, 7.935011820262847, 9.520357615514403, 11.322198105046873), # 166
(9.997353673783238, 7.6979683120316595, 10.882107170602728, 12.31349520927975, 12.389263501987168, 7.028385832305694, 6.28292435459308, 7.557997126749083, 13.61204254074304, 6.523974394132343, 7.716294106438124, 9.260780495496734, 11.019496615116793), # 167
(9.694451058192634, 7.454819235704206, 10.568639837073198, 11.951590838527274, 12.030699816489188, 6.830089863630398, 6.088963151990087, 7.345863117982976, 13.233028657172568, 6.328230756630195, 7.48632336001101, 8.987400973092019, 10.69971603772634), # 168
(9.378821445769624, 7.202999590535967, 10.239472774256495, 11.572741795265413, 11.654453892743392, 6.621180881409112, 5.887419346672787, 7.122190822163432, 12.832889642093342, 6.123966018716379, 7.24608867392411, 8.701418454858675, 10.364311252049257), # 169
(9.051721035559014, 6.94342700930214, 9.896054941111416, 11.178551067084992, 11.262166616694774, 6.402615399452171, 5.679063770776885, 6.888014816044876, 12.413503539313982, 5.912009466130653, 6.996579141120026, 8.404032347355134, 10.014737137259289), # 170
(8.7144060266056, 6.677019124777921, 9.539835296596765, 10.770621641576858, 10.85547887428833, 6.175349931569918, 5.464667256438089, 6.644369676381733, 11.976748392643131, 5.693190384612782, 6.738783854541357, 8.096442057139818, 9.652448572530185), # 171
(8.368132617954185, 6.4046935697385114, 9.172262799671339, 10.350556506331834, 10.436031551469046, 5.940340991572694, 5.245000635792105, 6.392289979928433, 11.524502245889417, 5.468338059902528, 6.473691907130711, 7.779846990771154, 9.278900437035686), # 172
(8.014157008649567, 6.127367976959108, 8.79478640929394, 9.919958648940762, 10.005465534181923, 5.69854509327084, 5.02083474097464, 6.132810303439398, 11.058643142861477, 5.238281777739651, 6.202292391830685, 7.45544655480756, 8.89554760994954), # 173
(7.6537353977365505, 5.845959979214909, 8.408855084423363, 9.480431056994465, 9.565421708371947, 5.450918750474696, 4.792940404121401, 5.866965223669057, 10.581049127367942, 5.003850823863915, 5.9255744015838845, 7.124440155807469, 8.503844970445494), # 174
(7.288123984259929, 5.561387209281111, 8.015917784018413, 9.033576718083788, 9.11754095998411, 5.198418476994606, 4.562088457368093, 5.595789317371834, 10.09359824321745, 4.765874484015079, 5.644527029332911, 6.788027200329303, 8.105247397697292), # 175
(6.91857896726451, 5.274567299932917, 7.617423467037885, 8.58099861979956, 8.663464174963408, 4.942000786640907, 4.329049732850424, 5.3203171613021585, 9.598168534218628, 4.525182043932907, 5.360139368020368, 6.447407094931487, 7.701209770878679), # 176
(6.546356545795092, 4.986417883945522, 7.214821092440582, 8.124299749732613, 8.204832239254838, 4.682622193223941, 4.094595062704101, 5.0415833322144525, 9.096638044180112, 4.282602789357159, 5.073400510588858, 6.103779246172446, 7.2931869691634), # 177
(6.172712918896475, 4.697856594094126, 6.809559619185302, 7.665083095473786, 7.743286038803382, 4.421239210554052, 3.859495279064828, 4.760622406863145, 8.590884816910537, 4.0389660060276, 4.78529954998098, 5.758343060610604, 6.882633871725203), # 178
(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0), # 179
)
passenger_arriving_acc = (
(6, 7, 7, 4, 3, 1, 4, 4, 2, 1, 0, 0, 0, 10, 9, 5, 4, 7, 2, 2, 2, 8, 3, 3, 0, 0), # 0
(13, 11, 16, 19, 12, 4, 7, 7, 5, 2, 1, 0, 0, 17, 17, 7, 8, 11, 5, 7, 4, 12, 3, 4, 0, 0), # 1
(24, 18, 24, 26, 14, 9, 14, 10, 10, 4, 1, 1, 0, 26, 29, 14, 16, 20, 10, 10, 6, 14, 4, 6, 0, 0), # 2
(30, 24, 39, 33, 19, 13, 17, 14, 13, 8, 5, 2, 0, 38, 35, 19, 18, 25, 20, 13, 14, 19, 5, 7, 0, 0), # 3
(38, 34, 48, 38, 29, 15, 24, 18, 15, 10, 6, 2, 0, 46, 43, 27, 24, 30, 25, 18, 18, 21, 12, 8, 0, 0), # 4
(49, 40, 56, 48, 43, 20, 27, 25, 18, 10, 6, 3, 0, 63, 52, 38, 34, 37, 27, 22, 20, 28, 17, 11, 0, 0), # 5
(58, 53, 65, 67, 46, 25, 30, 32, 24, 14, 7, 3, 0, 76, 60, 46, 41, 48, 32, 28, 21, 33, 24, 15, 0, 0), # 6
(73, 63, 79, 76, 56, 28, 38, 38, 28, 14, 8, 4, 0, 84, 68, 56, 45, 59, 40, 36, 24, 35, 27, 19, 1, 0), # 7
(88, 77, 91, 90, 63, 32, 44, 42, 38, 16, 8, 4, 0, 98, 73, 66, 48, 65, 46, 40, 26, 39, 31, 22, 2, 0), # 8
(100, 87, 102, 108, 74, 36, 46, 49, 43, 19, 12, 6, 0, 110, 80, 74, 54, 74, 50, 43, 31, 46, 35, 22, 2, 0), # 9
(115, 105, 119, 119, 80, 39, 49, 51, 49, 22, 12, 7, 0, 128, 93, 78, 64, 86, 60, 45, 35, 58, 38, 27, 3, 0), # 10
(125, 117, 133, 130, 88, 44, 53, 53, 53, 24, 15, 9, 0, 137, 99, 83, 73, 100, 71, 48, 38, 61, 41, 27, 4, 0), # 11
(137, 134, 139, 148, 96, 47, 61, 59, 62, 27, 18, 9, 0, 152, 108, 92, 82, 117, 77, 54, 41, 67, 44, 29, 4, 0), # 12
(149, 152, 150, 162, 108, 53, 68, 64, 70, 28, 20, 10, 0, 169, 123, 106, 97, 125, 84, 58, 45, 76, 50, 30, 6, 0), # 13
(163, 168, 164, 178, 114, 55, 72, 67, 76, 30, 20, 11, 0, 185, 136, 115, 107, 135, 94, 61, 47, 80, 53, 32, 6, 0), # 14
(178, 183, 170, 195, 131, 59, 83, 68, 82, 32, 20, 11, 0, 199, 147, 123, 117, 143, 105, 67, 53, 84, 55, 32, 6, 0), # 15
(194, 194, 187, 207, 144, 69, 92, 73, 86, 38, 21, 11, 0, 215, 156, 134, 124, 159, 116, 73, 57, 87, 61, 33, 6, 0), # 16
(208, 209, 199, 227, 154, 73, 101, 80, 96, 42, 23, 13, 0, 226, 166, 149, 137, 173, 123, 83, 62, 89, 66, 35, 8, 0), # 17
(230, 223, 211, 239, 161, 79, 114, 82, 108, 44, 27, 16, 0, 241, 183, 165, 151, 184, 137, 90, 68, 97, 69, 35, 11, 0), # 18
(246, 234, 222, 257, 167, 86, 121, 86, 113, 48, 30, 16, 0, 257, 201, 174, 163, 198, 145, 98, 72, 102, 76, 36, 14, 0), # 19
(263, 245, 231, 270, 178, 90, 126, 94, 115, 54, 30, 17, 0, 269, 213, 184, 169, 210, 152, 101, 75, 111, 81, 37, 17, 0), # 20
(285, 261, 240, 282, 188, 100, 134, 100, 120, 58, 30, 17, 0, 290, 221, 196, 180, 226, 161, 109, 82, 115, 84, 40, 18, 0), # 21
(300, 273, 252, 303, 197, 104, 139, 109, 126, 62, 32, 18, 0, 319, 239, 207, 187, 235, 165, 117, 86, 123, 91, 42, 19, 0), # 22
(320, 287, 265, 318, 207, 108, 144, 113, 136, 65, 34, 21, 0, 335, 252, 221, 188, 253, 174, 120, 90, 127, 98, 43, 22, 0), # 23
(331, 302, 271, 337, 222, 119, 149, 115, 142, 72, 35, 23, 0, 353, 265, 232, 195, 266, 183, 124, 95, 138, 111, 44, 23, 0), # 24
(347, 315, 282, 354, 230, 122, 156, 120, 149, 74, 39, 24, 0, 372, 273, 244, 201, 277, 189, 132, 99, 143, 115, 46, 26, 0), # 25
(367, 335, 297, 367, 237, 125, 164, 123, 155, 77, 40, 24, 0, 383, 288, 251, 208, 290, 201, 136, 104, 148, 124, 50, 26, 0), # 26
(384, 351, 318, 375, 252, 132, 175, 127, 160, 80, 41, 28, 0, 399, 303, 261, 224, 307, 208, 146, 108, 152, 130, 56, 29, 0), # 27
(409, 362, 334, 386, 264, 139, 182, 135, 162, 83, 45, 29, 0, 413, 319, 272, 231, 322, 216, 151, 113, 157, 135, 60, 30, 0), # 28
(435, 381, 348, 399, 284, 148, 191, 137, 169, 88, 47, 29, 0, 426, 333, 285, 245, 333, 228, 159, 114, 164, 138, 64, 31, 0), # 29
(461, 401, 362, 411, 298, 156, 197, 144, 173, 90, 50, 29, 0, 445, 351, 292, 253, 341, 236, 165, 115, 171, 144, 68, 34, 0), # 30
(478, 421, 376, 434, 307, 164, 204, 153, 180, 94, 50, 29, 0, 459, 365, 302, 270, 359, 247, 172, 120, 176, 152, 69, 35, 0), # 31
(493, 434, 397, 447, 317, 167, 211, 159, 190, 98, 51, 29, 0, 471, 379, 317, 282, 381, 258, 181, 126, 179, 157, 70, 36, 0), # 32
(504, 449, 406, 467, 330, 173, 217, 164, 195, 101, 57, 30, 0, 484, 395, 331, 290, 392, 263, 188, 131, 185, 161, 73, 37, 0), # 33
(525, 470, 419, 482, 342, 178, 226, 178, 197, 105, 62, 32, 0, 505, 411, 342, 298, 401, 273, 197, 137, 192, 170, 74, 38, 0), # 34
(550, 492, 435, 492, 350, 184, 240, 187, 207, 108, 63, 34, 0, 520, 429, 357, 305, 409, 278, 198, 142, 198, 176, 76, 39, 0), # 35
(561, 498, 453, 516, 374, 189, 245, 195, 214, 113, 63, 35, 0, 530, 446, 370, 311, 425, 282, 202, 147, 205, 178, 78, 43, 0), # 36
(577, 517, 465, 533, 384, 197, 255, 198, 220, 117, 64, 35, 0, 545, 463, 381, 318, 436, 288, 205, 154, 211, 181, 81, 44, 0), # 37
(593, 529, 480, 547, 395, 198, 264, 203, 227, 120, 66, 37, 0, 559, 473, 392, 327, 449, 299, 212, 160, 218, 189, 84, 46, 0), # 38
(606, 548, 495, 561, 408, 205, 271, 208, 235, 122, 67, 42, 0, 577, 484, 401, 335, 461, 316, 217, 163, 223, 197, 86, 49, 0), # 39
(622, 559, 509, 575, 418, 208, 284, 214, 241, 123, 68, 43, 0, 593, 491, 418, 344, 474, 322, 221, 164, 228, 201, 87, 49, 0), # 40
(642, 579, 520, 589, 426, 217, 292, 218, 250, 124, 72, 45, 0, 608, 504, 426, 350, 485, 330, 225, 172, 234, 205, 87, 51, 0), # 41
(660, 593, 535, 602, 438, 226, 296, 225, 254, 125, 73, 48, 0, 628, 526, 438, 361, 496, 335, 229, 174, 244, 210, 88, 52, 0), # 42
(678, 612, 547, 621, 455, 230, 299, 235, 263, 128, 73, 49, 0, 645, 542, 453, 369, 507, 344, 233, 182, 250, 215, 94, 52, 0), # 43
(707, 629, 563, 634, 462, 236, 304, 240, 268, 130, 76, 49, 0, 662, 556, 462, 375, 521, 353, 240, 184, 253, 217, 96, 53, 0), # 44
(718, 648, 575, 654, 473, 242, 306, 242, 274, 135, 79, 49, 0, 672, 568, 473, 383, 533, 364, 250, 189, 263, 223, 100, 55, 0), # 45
(734, 661, 588, 671, 485, 248, 314, 250, 282, 137, 83, 50, 0, 687, 585, 484, 391, 542, 376, 255, 192, 268, 226, 104, 56, 0), # 46
(752, 678, 600, 684, 496, 249, 319, 259, 288, 142, 85, 51, 0, 709, 597, 494, 403, 553, 386, 263, 196, 272, 235, 106, 57, 0), # 47
(766, 693, 612, 698, 507, 254, 322, 266, 296, 144, 85, 53, 0, 726, 607, 505, 414, 562, 393, 271, 203, 280, 238, 109, 58, 0), # 48
(786, 712, 624, 716, 518, 263, 329, 271, 301, 146, 88, 55, 0, 738, 624, 516, 424, 573, 400, 274, 203, 283, 241, 114, 58, 0), # 49
(801, 727, 637, 732, 535, 271, 333, 274, 309, 148, 90, 58, 0, 753, 632, 524, 427, 590, 410, 278, 207, 288, 247, 120, 61, 0), # 50
(821, 745, 649, 754, 548, 277, 339, 278, 317, 151, 93, 62, 0, 768, 645, 536, 441, 608, 420, 285, 212, 293, 252, 121, 63, 0), # 51
(836, 756, 664, 774, 560, 280, 346, 284, 324, 155, 95, 62, 0, 783, 664, 544, 448, 620, 423, 294, 215, 303, 256, 123, 63, 0), # 52
(847, 771, 680, 787, 566, 287, 351, 292, 328, 156, 96, 63, 0, 806, 676, 552, 459, 631, 428, 298, 219, 306, 261, 126, 66, 0), # 53
(863, 787, 688, 800, 585, 289, 356, 295, 330, 161, 99, 66, 0, 818, 699, 560, 470, 645, 434, 305, 222, 312, 266, 126, 68, 0), # 54
(876, 796, 711, 813, 598, 295, 364, 300, 340, 166, 105, 67, 0, 824, 713, 566, 476, 656, 441, 311, 231, 321, 276, 129, 72, 0), # 55
(889, 808, 730, 821, 610, 300, 367, 307, 348, 167, 110, 68, 0, 846, 728, 581, 484, 667, 446, 317, 234, 332, 279, 133, 75, 0), # 56
(911, 821, 741, 840, 624, 302, 376, 311, 351, 168, 112, 69, 0, 858, 740, 588, 489, 684, 452, 321, 242, 338, 286, 137, 78, 0), # 57
(935, 833, 754, 854, 639, 307, 381, 317, 360, 169, 113, 72, 0, 874, 753, 597, 500, 694, 458, 326, 248, 347, 296, 141, 79, 0), # 58
(951, 852, 770, 868, 650, 312, 387, 328, 369, 171, 114, 73, 0, 889, 769, 603, 508, 705, 466, 330, 253, 350, 300, 142, 80, 0), # 59
(970, 867, 786, 879, 660, 314, 391, 334, 375, 175, 116, 75, 0, 908, 785, 615, 519, 718, 476, 340, 260, 359, 304, 143, 83, 0), # 60
(982, 880, 796, 904, 677, 320, 396, 339, 387, 175, 118, 78, 0, 933, 797, 620, 525, 728, 484, 344, 263, 366, 309, 144, 85, 0), # 61
(1001, 900, 813, 916, 690, 323, 403, 343, 394, 178, 119, 81, 0, 956, 805, 631, 535, 741, 488, 349, 268, 374, 314, 144, 89, 0), # 62
(1017, 919, 831, 925, 708, 327, 408, 349, 399, 184, 121, 82, 0, 966, 823, 640, 546, 756, 492, 354, 269, 386, 318, 149, 90, 0), # 63
(1030, 935, 846, 935, 714, 334, 413, 350, 409, 187, 122, 83, 0, 990, 838, 647, 553, 769, 497, 361, 271, 391, 322, 150, 91, 0), # 64
(1040, 949, 863, 953, 720, 339, 422, 356, 410, 190, 124, 86, 0, 999, 850, 660, 564, 776, 508, 373, 278, 401, 329, 153, 91, 0), # 65
(1057, 963, 880, 963, 732, 345, 426, 361, 416, 193, 126, 86, 0, 1021, 863, 667, 567, 790, 513, 377, 281, 407, 337, 157, 91, 0), # 66
(1072, 977, 895, 976, 747, 357, 430, 369, 421, 197, 127, 88, 0, 1043, 874, 676, 574, 798, 519, 380, 286, 411, 340, 157, 91, 0), # 67
(1086, 992, 907, 993, 766, 366, 435, 378, 427, 199, 131, 89, 0, 1064, 895, 684, 581, 813, 527, 386, 291, 421, 347, 161, 92, 0), # 68
(1103, 996, 924, 1015, 776, 374, 443, 381, 428, 201, 134, 91, 0, 1083, 901, 695, 591, 821, 537, 392, 295, 428, 351, 162, 92, 0), # 69
(1120, 1013, 938, 1031, 787, 383, 447, 383, 438, 205, 136, 93, 0, 1095, 912, 705, 598, 837, 541, 395, 299, 436, 357, 164, 92, 0), # 70
(1138, 1028, 945, 1042, 798, 387, 449, 389, 443, 207, 139, 95, 0, 1111, 919, 714, 609, 851, 552, 402, 304, 441, 360, 169, 93, 0), # 71
(1151, 1049, 959, 1061, 811, 396, 456, 394, 453, 210, 140, 97, 0, 1128, 933, 727, 617, 858, 562, 406, 307, 446, 368, 170, 94, 0), # 72
(1164, 1064, 969, 1083, 825, 403, 464, 399, 457, 211, 142, 97, 0, 1145, 950, 737, 629, 871, 568, 408, 312, 451, 372, 176, 94, 0), # 73
(1184, 1081, 983, 1099, 838, 409, 470, 402, 464, 214, 145, 97, 0, 1156, 965, 748, 635, 878, 576, 412, 318, 456, 376, 180, 95, 0), # 74
(1201, 1092, 996, 1110, 853, 412, 473, 405, 471, 216, 148, 99, 0, 1167, 977, 754, 641, 888, 581, 419, 323, 466, 377, 182, 96, 0), # 75
(1215, 1107, 1013, 1126, 863, 420, 480, 410, 473, 219, 154, 100, 0, 1187, 992, 768, 647, 899, 587, 422, 325, 473, 385, 185, 97, 0), # 76
(1239, 1116, 1024, 1142, 872, 428, 491, 414, 477, 223, 158, 102, 0, 1200, 1006, 779, 652, 913, 593, 427, 328, 480, 396, 189, 97, 0), # 77
(1256, 1135, 1040, 1164, 877, 439, 500, 420, 487, 225, 163, 104, 0, 1210, 1018, 787, 662, 925, 597, 431, 332, 488, 404, 192, 98, 0), # 78
(1270, 1146, 1045, 1175, 887, 443, 503, 428, 490, 230, 167, 105, 0, 1232, 1035, 796, 676, 935, 607, 437, 335, 492, 407, 195, 99, 0), # 79
(1288, 1158, 1065, 1191, 894, 448, 514, 435, 497, 232, 169, 105, 0, 1247, 1042, 806, 683, 949, 612, 444, 336, 496, 413, 199, 99, 0), # 80
(1305, 1167, 1076, 1205, 909, 453, 520, 441, 502, 234, 169, 107, 0, 1267, 1053, 813, 693, 963, 618, 449, 340, 500, 420, 203, 100, 0), # 81
(1322, 1183, 1092, 1218, 925, 460, 524, 445, 507, 236, 169, 108, 0, 1286, 1071, 825, 699, 968, 619, 456, 344, 506, 428, 204, 100, 0), # 82
(1339, 1193, 1108, 1228, 942, 469, 530, 448, 511, 239, 174, 108, 0, 1302, 1086, 838, 709, 982, 626, 462, 351, 512, 431, 206, 108, 0), # 83
(1354, 1201, 1122, 1242, 954, 475, 532, 454, 518, 245, 175, 109, 0, 1319, 1099, 844, 719, 1003, 631, 470, 357, 513, 438, 211, 110, 0), # 84
(1367, 1215, 1127, 1258, 965, 478, 539, 459, 523, 246, 179, 113, 0, 1335, 1110, 852, 724, 1011, 633, 475, 361, 519, 444, 212, 110, 0), # 85
(1382, 1227, 1135, 1270, 977, 485, 544, 463, 530, 247, 181, 114, 0, 1353, 1121, 865, 735, 1028, 637, 479, 365, 523, 447, 212, 112, 0), # 86
(1397, 1240, 1150, 1277, 986, 488, 550, 467, 543, 249, 181, 116, 0, 1371, 1135, 870, 739, 1038, 645, 488, 369, 527, 449, 214, 113, 0), # 87
(1420, 1250, 1165, 1284, 995, 496, 560, 473, 546, 251, 182, 116, 0, 1380, 1150, 878, 747, 1057, 647, 493, 378, 536, 456, 216, 114, 0), # 88
(1444, 1261, 1180, 1297, 999, 500, 571, 479, 550, 253, 186, 116, 0, 1399, 1159, 891, 752, 1067, 656, 498, 380, 543, 463, 217, 115, 0), # 89
(1458, 1265, 1191, 1313, 1008, 509, 575, 488, 553, 254, 188, 117, 0, 1415, 1170, 908, 764, 1079, 662, 504, 384, 546, 468, 219, 117, 0), # 90
(1474, 1271, 1194, 1326, 1021, 515, 583, 492, 559, 256, 189, 118, 0, 1428, 1181, 923, 773, 1092, 670, 508, 392, 558, 472, 223, 118, 0), # 91
(1481, 1282, 1205, 1345, 1031, 521, 590, 495, 562, 259, 189, 119, 0, 1446, 1195, 933, 776, 1105, 672, 511, 396, 560, 479, 227, 119, 0), # 92
(1500, 1294, 1222, 1359, 1050, 525, 599, 503, 570, 262, 191, 120, 0, 1457, 1211, 942, 783, 1114, 675, 518, 401, 561, 485, 231, 119, 0), # 93
(1516, 1306, 1231, 1368, 1065, 527, 604, 504, 580, 265, 191, 123, 0, 1472, 1219, 945, 794, 1125, 679, 528, 407, 566, 489, 234, 120, 0), # 94
(1536, 1317, 1238, 1378, 1069, 532, 609, 508, 585, 265, 194, 125, 0, 1488, 1232, 953, 802, 1134, 690, 537, 409, 574, 497, 235, 122, 0), # 95
(1550, 1325, 1261, 1390, 1080, 539, 613, 512, 588, 267, 197, 125, 0, 1504, 1245, 966, 808, 1149, 697, 544, 415, 581, 499, 239, 124, 0), # 96
(1559, 1340, 1277, 1399, 1087, 545, 616, 518, 600, 270, 198, 125, 0, 1518, 1256, 974, 812, 1165, 709, 547, 418, 587, 503, 243, 125, 0), # 97
(1577, 1354, 1291, 1416, 1092, 553, 624, 520, 603, 270, 200, 125, 0, 1529, 1275, 987, 821, 1179, 717, 549, 419, 588, 506, 245, 126, 0), # 98
(1590, 1367, 1296, 1427, 1098, 563, 627, 524, 614, 271, 200, 125, 0, 1544, 1290, 994, 826, 1188, 717, 552, 421, 592, 511, 248, 127, 0), # 99
(1612, 1376, 1303, 1446, 1107, 567, 631, 528, 621, 274, 203, 126, 0, 1561, 1305, 1006, 832, 1197, 729, 558, 423, 597, 515, 249, 127, 0), # 100
(1633, 1392, 1316, 1459, 1124, 573, 634, 530, 628, 277, 204, 127, 0, 1571, 1318, 1016, 838, 1205, 733, 564, 426, 598, 520, 251, 129, 0), # 101
(1649, 1402, 1325, 1476, 1132, 577, 640, 533, 633, 281, 204, 128, 0, 1585, 1329, 1028, 852, 1217, 739, 568, 428, 604, 525, 253, 129, 0), # 102
(1661, 1419, 1334, 1486, 1144, 583, 648, 537, 635, 282, 207, 128, 0, 1599, 1338, 1038, 860, 1231, 746, 573, 430, 614, 530, 256, 130, 0), # 103
(1677, 1429, 1347, 1499, 1157, 589, 653, 546, 640, 285, 208, 129, 0, 1615, 1350, 1046, 868, 1240, 750, 577, 435, 621, 533, 257, 132, 0), # 104
(1694, 1445, 1360, 1508, 1166, 593, 655, 553, 644, 286, 210, 130, 0, 1624, 1360, 1050, 876, 1254, 758, 580, 438, 627, 540, 258, 133, 0), # 105
(1711, 1461, 1373, 1520, 1179, 597, 662, 558, 652, 289, 212, 131, 0, 1641, 1375, 1058, 885, 1264, 770, 584, 444, 632, 541, 262, 133, 0), # 106
(1721, 1474, 1390, 1530, 1190, 601, 670, 566, 655, 291, 217, 132, 0, 1652, 1394, 1063, 890, 1274, 773, 591, 448, 639, 546, 265, 133, 0), # 107
(1735, 1485, 1407, 1540, 1205, 607, 676, 571, 660, 300, 220, 132, 0, 1672, 1405, 1074, 900, 1287, 779, 597, 452, 642, 551, 267, 135, 0), # 108
(1758, 1501, 1418, 1550, 1216, 613, 683, 576, 666, 302, 224, 134, 0, 1689, 1418, 1084, 908, 1296, 787, 601, 458, 646, 558, 269, 135, 0), # 109
(1773, 1519, 1431, 1563, 1230, 617, 686, 579, 671, 304, 224, 134, 0, 1708, 1432, 1097, 913, 1309, 791, 611, 461, 655, 562, 272, 136, 0), # 110
(1786, 1531, 1441, 1573, 1246, 623, 692, 582, 678, 306, 225, 135, 0, 1724, 1451, 1108, 917, 1323, 800, 613, 468, 664, 565, 275, 137, 0), # 111
(1796, 1543, 1454, 1588, 1255, 630, 697, 586, 683, 310, 226, 141, 0, 1739, 1461, 1122, 920, 1337, 806, 618, 471, 669, 569, 277, 138, 0), # 112
(1807, 1553, 1473, 1601, 1268, 634, 704, 589, 690, 311, 228, 142, 0, 1760, 1469, 1132, 925, 1344, 811, 624, 477, 676, 575, 281, 139, 0), # 113
(1812, 1567, 1489, 1614, 1278, 636, 707, 592, 693, 312, 231, 143, 0, 1779, 1485, 1137, 934, 1362, 814, 627, 480, 685, 578, 282, 139, 0), # 114
(1829, 1574, 1505, 1623, 1292, 642, 712, 597, 699, 313, 232, 143, 0, 1795, 1496, 1146, 944, 1375, 820, 629, 484, 697, 582, 283, 141, 0), # 115
(1850, 1580, 1517, 1630, 1300, 649, 716, 605, 704, 315, 235, 146, 0, 1816, 1507, 1156, 950, 1391, 826, 634, 494, 701, 585, 286, 143, 0), # 116
(1860, 1589, 1528, 1640, 1305, 657, 719, 607, 709, 318, 238, 147, 0, 1822, 1522, 1162, 959, 1402, 832, 637, 497, 705, 588, 287, 145, 0), # 117
(1878, 1601, 1538, 1660, 1317, 665, 722, 610, 717, 321, 239, 147, 0, 1836, 1533, 1172, 966, 1414, 833, 640, 500, 708, 594, 288, 146, 0), # 118
(1892, 1612, 1548, 1677, 1326, 673, 725, 619, 724, 328, 243, 148, 0, 1851, 1548, 1177, 976, 1427, 843, 645, 503, 714, 595, 290, 146, 0), # 119
(1911, 1627, 1560, 1687, 1330, 675, 729, 623, 726, 332, 243, 149, 0, 1867, 1563, 1184, 979, 1437, 848, 654, 509, 716, 601, 293, 146, 0), # 120
(1918, 1638, 1568, 1703, 1339, 680, 732, 627, 731, 332, 247, 149, 0, 1880, 1572, 1193, 988, 1448, 851, 658, 514, 719, 606, 297, 149, 0), # 121
(1935, 1649, 1579, 1722, 1348, 687, 735, 631, 738, 335, 251, 150, 0, 1893, 1582, 1203, 992, 1460, 856, 660, 517, 720, 612, 301, 150, 0), # 122
(1946, 1653, 1592, 1730, 1353, 690, 738, 635, 740, 335, 251, 150, 0, 1904, 1593, 1213, 998, 1467, 864, 666, 523, 724, 616, 303, 150, 0), # 123
(1965, 1661, 1606, 1744, 1364, 700, 746, 640, 742, 336, 252, 152, 0, 1924, 1605, 1223, 1005, 1472, 867, 669, 529, 728, 623, 307, 151, 0), # 124
(1980, 1674, 1615, 1749, 1370, 703, 750, 645, 748, 337, 255, 155, 0, 1933, 1612, 1232, 1011, 1487, 872, 672, 531, 731, 629, 309, 152, 0), # 125
(1991, 1682, 1622, 1759, 1381, 708, 755, 649, 754, 339, 256, 156, 0, 1946, 1626, 1238, 1014, 1499, 877, 678, 532, 748, 632, 312, 156, 0), # 126
(2000, 1694, 1633, 1773, 1395, 710, 759, 652, 761, 340, 256, 157, 0, 1965, 1636, 1245, 1023, 1508, 881, 682, 534, 751, 637, 314, 157, 0), # 127
(2010, 1708, 1643, 1790, 1400, 715, 762, 659, 768, 341, 257, 157, 0, 1977, 1649, 1255, 1030, 1517, 883, 690, 535, 753, 641, 317, 158, 0), # 128
(2022, 1718, 1651, 1797, 1413, 719, 765, 666, 770, 343, 258, 157, 0, 1990, 1664, 1265, 1037, 1527, 888, 696, 540, 759, 644, 320, 160, 0), # 129
(2037, 1732, 1666, 1806, 1421, 723, 770, 670, 773, 343, 259, 159, 0, 2000, 1675, 1272, 1042, 1535, 893, 703, 547, 765, 650, 321, 160, 0), # 130
(2053, 1740, 1680, 1812, 1434, 726, 775, 672, 777, 345, 260, 160, 0, 2012, 1683, 1278, 1050, 1550, 894, 703, 549, 771, 658, 326, 160, 0), # 131
(2060, 1746, 1691, 1822, 1447, 732, 785, 678, 778, 347, 262, 160, 0, 2025, 1696, 1296, 1056, 1560, 900, 705, 553, 776, 660, 326, 161, 0), # 132
(2075, 1760, 1707, 1835, 1456, 741, 790, 684, 786, 347, 263, 161, 0, 2040, 1700, 1302, 1060, 1572, 903, 707, 557, 779, 665, 329, 161, 0), # 133
(2088, 1768, 1720, 1848, 1467, 744, 793, 688, 792, 348, 263, 161, 0, 2057, 1707, 1314, 1068, 1580, 911, 713, 561, 785, 672, 334, 162, 0), # 134
(2103, 1772, 1732, 1860, 1475, 751, 798, 694, 795, 350, 266, 162, 0, 2069, 1718, 1325, 1074, 1593, 915, 716, 565, 788, 674, 336, 164, 0), # 135
(2111, 1777, 1743, 1877, 1479, 759, 806, 695, 800, 356, 269, 164, 0, 2081, 1730, 1328, 1083, 1603, 923, 721, 571, 790, 677, 337, 169, 0), # 136
(2122, 1782, 1758, 1892, 1491, 764, 813, 700, 809, 358, 272, 165, 0, 2093, 1744, 1332, 1085, 1616, 931, 723, 574, 793, 683, 339, 171, 0), # 137
(2134, 1793, 1771, 1911, 1498, 769, 814, 704, 811, 359, 274, 165, 0, 2103, 1762, 1340, 1091, 1635, 938, 730, 577, 800, 686, 340, 171, 0), # 138
(2145, 1803, 1788, 1925, 1506, 772, 817, 709, 818, 360, 275, 165, 0, 2116, 1770, 1348, 1096, 1645, 942, 733, 581, 803, 689, 344, 172, 0), # 139
(2160, 1816, 1797, 1935, 1515, 778, 825, 712, 820, 362, 279, 165, 0, 2129, 1787, 1361, 1103, 1656, 944, 738, 584, 809, 690, 345, 174, 0), # 140
(2171, 1830, 1802, 1947, 1524, 780, 829, 716, 828, 365, 285, 165, 0, 2147, 1796, 1372, 1109, 1665, 946, 745, 586, 810, 692, 348, 177, 0), # 141
(2187, 1842, 1809, 1959, 1539, 783, 832, 720, 834, 368, 286, 165, 0, 2160, 1808, 1381, 1112, 1677, 949, 752, 590, 813, 694, 349, 178, 0), # 142
(2197, 1853, 1823, 1974, 1550, 787, 835, 725, 839, 371, 288, 165, 0, 2168, 1818, 1390, 1118, 1683, 953, 757, 591, 816, 697, 351, 180, 0), # 143
(2207, 1868, 1838, 1981, 1556, 792, 844, 727, 848, 372, 290, 168, 0, 2177, 1824, 1398, 1128, 1695, 961, 759, 597, 819, 701, 353, 180, 0), # 144
(2219, 1878, 1848, 1987, 1569, 796, 849, 730, 855, 373, 291, 168, 0, 2194, 1831, 1412, 1133, 1710, 967, 763, 602, 821, 706, 356, 180, 0), # 145
(2229, 1897, 1859, 2003, 1587, 804, 853, 733, 859, 376, 292, 170, 0, 2201, 1843, 1419, 1138, 1725, 971, 765, 606, 826, 712, 358, 180, 0), # 146
(2239, 1906, 1867, 2011, 1595, 807, 854, 736, 865, 376, 292, 173, 0, 2214, 1852, 1426, 1148, 1734, 974, 769, 609, 835, 717, 362, 181, 0), # 147
(2253, 1914, 1880, 2016, 1604, 810, 856, 739, 869, 377, 294, 174, 0, 2226, 1869, 1431, 1155, 1744, 979, 773, 612, 842, 721, 364, 181, 0), # 148
(2266, 1920, 1892, 2026, 1618, 813, 860, 745, 877, 381, 294, 174, 0, 2240, 1874, 1441, 1162, 1754, 986, 778, 614, 850, 728, 366, 182, 0), # 149
(2276, 1932, 1902, 2035, 1633, 814, 864, 750, 881, 384, 295, 174, 0, 2250, 1885, 1447, 1165, 1763, 990, 781, 618, 856, 731, 368, 185, 0), # 150
(2287, 1941, 1917, 2047, 1642, 818, 868, 753, 884, 386, 297, 174, 0, 2263, 1896, 1458, 1167, 1769, 994, 784, 626, 863, 732, 373, 185, 0), # 151
(2301, 1953, 1931, 2055, 1655, 823, 872, 755, 889, 387, 298, 174, 0, 2276, 1902, 1462, 1174, 1779, 1001, 788, 632, 868, 734, 374, 185, 0), # 152
(2310, 1960, 1940, 2068, 1664, 826, 876, 758, 894, 391, 299, 174, 0, 2292, 1910, 1469, 1181, 1786, 1006, 791, 635, 870, 738, 375, 186, 0), # 153
(2323, 1965, 1957, 2084, 1674, 831, 880, 760, 899, 396, 300, 174, 0, 2302, 1921, 1472, 1184, 1794, 1014, 792, 636, 874, 741, 377, 187, 0), # 154
(2335, 1973, 1967, 2089, 1678, 836, 885, 761, 905, 398, 301, 174, 0, 2318, 1927, 1475, 1188, 1803, 1025, 798, 638, 883, 744, 381, 188, 0), # 155
(2343, 1979, 1979, 2102, 1681, 840, 887, 763, 910, 400, 301, 176, 0, 2328, 1935, 1483, 1194, 1817, 1033, 799, 641, 890, 748, 384, 189, 0), # 156
(2357, 1986, 1991, 2114, 1696, 844, 891, 769, 913, 401, 303, 177, 0, 2350, 1946, 1492, 1194, 1835, 1042, 804, 645, 894, 750, 386, 189, 0), # 157
(2366, 1995, 2001, 2130, 1705, 846, 894, 769, 921, 404, 303, 177, 0, 2369, 1954, 1502, 1201, 1843, 1045, 808, 648, 900, 755, 388, 190, 0), # 158
(2375, 2004, 2012, 2143, 1720, 850, 898, 775, 929, 407, 304, 178, 0, 2381, 1961, 1509, 1205, 1854, 1049, 812, 654, 905, 757, 390, 191, 0), # 159
(2381, 2012, 2021, 2150, 1732, 856, 899, 779, 938, 408, 305, 178, 0, 2397, 1974, 1518, 1210, 1870, 1056, 814, 654, 911, 760, 391, 191, 0), # 160
(2396, 2015, 2033, 2167, 1744, 861, 902, 783, 940, 408, 306, 178, 0, 2412, 1986, 1531, 1220, 1874, 1062, 818, 656, 918, 766, 394, 191, 0), # 161
(2402, 2032, 2039, 2179, 1753, 869, 907, 786, 943, 409, 306, 181, 0, 2424, 1997, 1536, 1226, 1886, 1067, 821, 660, 923, 771, 396, 191, 0), # 162
(2412, 2040, 2049, 2190, 1762, 873, 910, 793, 945, 410, 308, 182, 0, 2433, 2009, 1549, 1232, 1894, 1071, 824, 662, 927, 779, 397, 192, 0), # 163
(2420, 2045, 2060, 2201, 1774, 876, 911, 797, 950, 411, 309, 182, 0, 2442, 2016, 1553, 1237, 1902, 1076, 829, 664, 933, 783, 398, 192, 0), # 164
(2427, 2052, 2068, 2211, 1781, 882, 916, 800, 955, 415, 313, 182, 0, 2454, 2017, 1561, 1244, 1913, 1080, 834, 668, 938, 790, 401, 192, 0), # 165
(2434, 2059, 2077, 2217, 1785, 885, 919, 804, 963, 416, 314, 182, 0, 2466, 2028, 1565, 1251, 1928, 1083, 838, 673, 943, 796, 403, 192, 0), # 166
(2446, 2068, 2082, 2228, 1792, 888, 921, 808, 966, 418, 315, 184, 0, 2473, 2042, 1566, 1258, 1933, 1086, 841, 678, 947, 799, 405, 192, 0), # 167
(2463, 2071, 2095, 2238, 1805, 893, 925, 815, 970, 418, 316, 185, 0, 2484, 2051, 1573, 1262, 1945, 1086, 846, 682, 951, 800, 409, 194, 0), # 168
(2476, 2075, 2102, 2246, 1812, 896, 927, 817, 976, 420, 318, 190, 0, 2500, 2058, 1584, 1265, 1957, 1092, 848, 683, 955, 801, 410, 194, 0), # 169
(2485, 2080, 2111, 2256, 1819, 901, 930, 821, 976, 422, 318, 192, 0, 2510, 2061, 1588, 1268, 1965, 1096, 851, 683, 955, 803, 411, 194, 0), # 170
(2494, 2084, 2121, 2261, 1823, 907, 932, 824, 978, 425, 318, 192, 0, 2521, 2065, 1593, 1274, 1976, 1102, 852, 685, 957, 805, 413, 194, 0), # 171
(2499, 2089, 2127, 2267, 1834, 908, 933, 828, 979, 428, 319, 193, 0, 2533, 2071, 1597, 1274, 1985, 1106, 854, 687, 959, 807, 416, 194, 0), # 172
(2507, 2094, 2136, 2275, 1839, 913, 934, 828, 983, 430, 320, 197, 0, 2545, 2082, 1602, 1278, 1993, 1110, 857, 689, 962, 808, 417, 194, 0), # 173
(2517, 2102, 2144, 2282, 1845, 914, 937, 836, 987, 432, 320, 197, 0, 2562, 2083, 1607, 1283, 2000, 1113, 858, 691, 968, 809, 419, 194, 0), # 174
(2527, 2107, 2150, 2288, 1851, 917, 938, 837, 992, 433, 322, 198, 0, 2568, 2088, 1616, 1288, 2007, 1117, 861, 694, 972, 811, 420, 195, 0), # 175
(2534, 2110, 2155, 2296, 1856, 922, 940, 839, 997, 435, 322, 198, 0, 2578, 2094, 1621, 1291, 2009, 1120, 865, 696, 974, 812, 421, 195, 0), # 176
(2537, 2113, 2162, 2301, 1866, 924, 940, 842, 1002, 435, 322, 198, 0, 2585, 2098, 1625, 1295, 2014, 1122, 866, 697, 975, 813, 421, 196, 0), # 177
(2541, 2115, 2166, 2304, 1871, 929, 944, 843, 1007, 436, 322, 198, 0, 2593, 2104, 1630, 1298, 2016, 1123, 869, 698, 980, 813, 423, 196, 0), # 178
(2541, 2115, 2166, 2304, 1871, 929, 944, 843, 1007, 436, 322, 198, 0, 2593, 2104, 1630, 1298, 2016, 1123, 869, 698, 980, 813, 423, 196, 0), # 179
)
passenger_arriving_rate = (
(8.033384925394829, 8.103756554216645, 6.9483776394833425, 7.45760132863612, 5.924997981450252, 2.9294112699015167, 3.3168284922991322, 3.102117448652949, 3.2480528331562706, 1.5832060062089484, 1.1214040437028276, 0.6530553437741565, 0.0, 8.134208340125381, 7.183608781515721, 5.607020218514138, 4.749618018626844, 6.496105666312541, 4.342964428114128, 3.3168284922991322, 2.0924366213582264, 2.962498990725126, 2.4858671095453735, 1.3896755278966686, 0.7367051412924223, 0.0), # 0
(8.566923443231959, 8.638755684745645, 7.407128788440204, 7.95017310393194, 6.317323026639185, 3.122918011773052, 3.535575153010955, 3.306342481937139, 3.462530840710885, 1.6875922769108604, 1.1954923029216353, 0.6961622214419141, 0.0, 8.671666635903767, 7.657784435861053, 5.9774615146081755, 5.06277683073258, 6.92506168142177, 4.628879474711995, 3.535575153010955, 2.230655722695037, 3.1586615133195926, 2.650057701310647, 1.4814257576880407, 0.7853414258859679, 0.0), # 1
(9.09875681436757, 9.171631583973436, 7.864056380729885, 8.440785245597754, 6.708227171999727, 3.3156527735449486, 3.7534548063685635, 3.5097501652696135, 3.676152963668026, 1.7915655100082188, 1.269286173007017, 0.7390976869404075, 0.0, 9.206983725135505, 8.13007455634448, 6.346430865035084, 5.374696530024655, 7.352305927336052, 4.913650231377459, 3.7534548063685635, 2.3683234096749635, 3.3541135859998636, 2.8135950818659183, 1.5728112761459772, 0.8337846894521307, 0.0), # 2
(9.6268124690345, 9.70027006950679, 8.317347825759807, 8.927491689038488, 7.096172454402028, 3.5068512477461056, 3.9696029133183646, 3.7115341049963386, 3.8880720858245827, 1.8947130793704727, 1.3424929098206355, 0.7816914246573948, 0.0, 9.738036490006762, 8.598605671231342, 6.712464549103178, 5.684139238111417, 7.7761441716491655, 5.196147746994874, 3.9696029133183646, 2.5048937483900753, 3.548086227201014, 2.97583056301283, 1.6634695651519613, 0.8818427335915264, 0.0), # 3
(10.149017837465571, 10.222556958952469, 8.765190532937382, 9.408346369659084, 7.479620910716259, 3.6957491269054237, 4.183154934806767, 3.910887907463277, 4.097441090977444, 1.996622358867072, 1.4148197692241535, 0.8237731189806353, 0.0, 10.262701812703709, 9.061504308786986, 7.074098846120767, 5.9898670766012145, 8.194882181954888, 5.475243070448588, 4.183154934806767, 2.6398208049324454, 3.7398104553581293, 3.136115456553029, 1.7530381065874767, 0.9293233599047701, 0.0), # 4
(10.663300349893618, 10.736378069917262, 9.205771911670025, 9.881403222864472, 7.8570345778125645, 3.8815821035518008, 4.393246331780179, 4.1070051790163955, 4.303412862923498, 2.096880722367466, 1.4859740070792353, 0.8651724542978865, 0.0, 10.778856575412524, 9.51689699727675, 7.429870035396177, 6.290642167102396, 8.606825725846996, 5.749807250622953, 4.393246331780179, 2.772558645394143, 3.9285172889062823, 3.2938010742881585, 1.841154382334005, 0.9760343699924785, 0.0), # 5
(11.167587436551466, 11.239619220007935, 9.637279371365155, 10.344716184059584, 8.226875492561113, 4.06358587021414, 4.59901256518501, 4.299079526001659, 4.5051402854596345, 2.195075543741104, 1.555662879247542, 0.9057191149969079, 0.0, 11.284377660319372, 9.962910264965986, 7.77831439623771, 6.5852266312233105, 9.010280570919269, 6.018711336402323, 4.59901256518501, 2.902561335867243, 4.113437746280557, 3.448238728019862, 1.9274558742730312, 1.021783565455267, 0.0), # 6
(11.65980652767195, 11.73016622683126, 10.05790032143018, 10.796339188649355, 8.587605691832056, 4.2409961194213395, 4.799589095967668, 4.486304554765035, 4.701776242382744, 2.2907941968574352, 1.6235936415907386, 0.9452427854654573, 0.0, 11.777141949610431, 10.397670640120028, 8.117968207953693, 6.872382590572304, 9.403552484765488, 6.280826376671049, 4.799589095967668, 3.029282942443814, 4.293802845916028, 3.598779729549786, 2.0115800642860364, 1.066378747893751, 0.0), # 7
(12.137885053487896, 12.205904907994013, 10.465822171272528, 11.234326172038713, 8.937687212495558, 4.413048543702297, 4.994111385074558, 4.667873871652484, 4.89247361748971, 2.3836240555859103, 1.6894735499704858, 0.9835731500912939, 0.0, 12.255026325471867, 10.81930465100423, 8.447367749852429, 7.150872166757729, 9.78494723497942, 6.535023420313477, 4.994111385074558, 3.152177531215927, 4.468843606247779, 3.744775390679572, 2.093164434254506, 1.1096277189085468, 0.0), # 8
(12.599750444232136, 12.664721081102966, 10.859232330299607, 11.656731069632603, 9.27558209142177, 4.578978835585919, 5.181714893452096, 4.842981083009976, 5.076385294577426, 2.4731524937959772, 1.7530098602484476, 1.0205398932621754, 0.0, 12.71590767008986, 11.225938825883926, 8.765049301242238, 7.41945748138793, 10.152770589154851, 6.780173516213966, 5.181714893452096, 3.270699168275656, 4.637791045710885, 3.8855770232108684, 2.1718464660599213, 1.1513382801002698, 0.0), # 9
(13.043330130137491, 13.104500563764889, 11.236318207918833, 12.061607816835945, 9.599752365480853, 4.7380226876011005, 5.361535082046684, 5.010819795183474, 5.252664157442781, 2.558966885357086, 1.8139098282862867, 1.0559726993658605, 0.0, 13.157662865650577, 11.615699693024464, 9.069549141431432, 7.676900656071258, 10.505328314885562, 7.015147713256865, 5.361535082046684, 3.3843019197150714, 4.799876182740427, 4.020535938945316, 2.247263641583767, 1.1913182330695355, 0.0), # 10
(13.466551541436809, 13.52312917358657, 11.595267213537621, 12.447010349053677, 9.908660071542968, 4.889415792276744, 5.532707411804733, 5.170583614518944, 5.420463089882663, 2.640654604138688, 1.8718807099456667, 1.0897012527901082, 0.0, 13.57816879434018, 11.986713780691188, 9.359403549728333, 7.921963812416063, 10.840926179765326, 7.238817060326522, 5.532707411804733, 3.4924398516262456, 4.954330035771484, 4.14900344968456, 2.3190534427075247, 1.229375379416961, 0.0), # 11
(13.8673421083629, 13.918492728174757, 11.934266756563387, 12.810992601690735, 10.200767246478268, 5.032393842141746, 5.694367343672649, 5.321466147362347, 5.578934975693962, 2.7178030240102293, 1.9266297610882495, 1.1215552379226759, 0.0, 13.975302338344855, 12.337107617149433, 9.633148805441246, 8.153409072030687, 11.157869951387925, 7.4500526063072865, 5.694367343672649, 3.5945670301012465, 5.100383623239134, 4.270330867230246, 2.3868533513126775, 1.26531752074316, 0.0), # 12
(14.243629261148602, 14.288477045136244, 12.251504246403549, 13.151608510152053, 10.474535927156907, 5.166192529725009, 5.845650338596845, 5.462661000059654, 5.727232698673564, 2.7899995188411624, 1.9778642375756985, 1.1513643391513229, 0.0, 14.346940379850777, 12.66500773066455, 9.889321187878492, 8.369998556523486, 11.454465397347128, 7.647725400083517, 5.845650338596845, 3.6901375212321494, 5.237267963578454, 4.383869503384019, 2.45030084928071, 1.2989524586487495, 0.0), # 13
(14.593340430026746, 14.630967942077797, 12.54516709246553, 13.466912009842552, 10.728428150449055, 5.2900475475554325, 5.9856918575237295, 5.593361778956831, 5.864509142618358, 2.856831462500934, 2.0252913952696763, 1.1789582408638082, 0.0, 14.690959801044102, 12.968540649501888, 10.12645697634838, 8.570494387502801, 11.729018285236716, 7.830706490539565, 5.9856918575237295, 3.778605391111023, 5.3642140752245275, 4.488970669947518, 2.509033418493106, 1.3300879947343454, 0.0), # 14
(14.914403045230168, 14.943851236606186, 12.813442704156724, 13.754957036167184, 10.960905953224861, 5.403194588161918, 6.1136273613997005, 5.7127620903998375, 5.989917191325237, 2.917886228858997, 2.0686184900318456, 1.2041666274478897, 0.0, 15.00523748411101, 13.245832901926784, 10.343092450159226, 8.753658686576989, 11.979834382650473, 7.997866926559773, 6.1136273613997005, 3.8594247058299413, 5.480452976612431, 4.584985678722395, 2.562688540831345, 1.3585319306005625, 0.0), # 15
(15.204744536991681, 15.225012746328195, 13.054518490884568, 14.013797524530858, 11.170431372354487, 5.504869344073363, 6.228592311171181, 5.820055540734641, 6.102609728591085, 2.972751191784799, 2.1075527777238703, 1.2268191832913256, 0.0, 15.287650311237673, 13.495011016204579, 10.53776388861935, 8.918253575354395, 12.20521945718217, 8.148077757028497, 6.228592311171181, 3.932049531480973, 5.585215686177244, 4.671265841510287, 2.6109036981769136, 1.384092067848018, 0.0), # 16
(15.46229233554412, 15.472338288850588, 13.266581862056471, 14.241487410338536, 11.355466444708094, 5.594307507818667, 6.329722167784569, 5.914435736307213, 6.201739638212791, 3.021013725147788, 2.141801514207413, 1.2467455927818742, 0.0, 15.536075164610265, 13.714201520600614, 10.709007571037066, 9.063041175443361, 12.403479276425582, 8.280210030830098, 6.329722167784569, 3.9959339341561906, 5.677733222354047, 4.747162470112846, 2.6533163724112945, 1.4065762080773265, 0.0), # 17
(15.684973871120327, 15.683713681780135, 13.447820227079841, 14.436080628995136, 11.514473207155827, 5.670744771926737, 6.416152392186281, 5.995096283463507, 6.286459803987251, 3.0622612028174157, 2.171071955344136, 1.2637755403072954, 0.0, 15.748388926414954, 13.901530943380248, 10.855359776720679, 9.186783608452245, 12.572919607974502, 8.39313479684891, 6.416152392186281, 4.050531979947669, 5.757236603577914, 4.812026876331712, 2.689564045415968, 1.4257921528891033, 0.0), # 18
(15.870716573953118, 15.857024742723624, 13.596420995362104, 14.59563111590558, 11.645913696567856, 5.733416828926462, 6.4870184453227155, 6.061230788549498, 6.355923109711349, 3.0960809986631324, 2.1950713569957014, 1.2777387102553464, 0.0, 15.922468478837914, 14.055125812808807, 10.975356784978505, 9.288242995989394, 12.711846219422698, 8.485723103969297, 6.4870184453227155, 4.095297734947473, 5.822956848283928, 4.865210371968527, 2.7192841990724212, 1.441547703883966, 0.0), # 19
(16.01744787427533, 15.990157289287811, 13.710571576310672, 14.718192806474825, 11.748249949814339, 5.781559371346751, 6.54145578814029, 6.112032857911145, 6.409282439181973, 3.1220604865543846, 2.213506975023774, 1.2884647870137858, 0.0, 16.05619070406532, 14.17311265715164, 11.067534875118868, 9.366181459663151, 12.818564878363945, 8.556846001075604, 6.54145578814029, 4.129685265247679, 5.874124974907169, 4.9060642688249425, 2.7421143152621346, 1.4536506626625285, 0.0), # 20
(16.123095202319785, 16.080997139079486, 13.78845937933296, 14.801819636107783, 11.819944003765428, 5.8144080917165, 6.578599881585408, 6.1466960978944165, 6.445690676196012, 3.139787040360623, 2.226086065290016, 1.2957834549703726, 0.0, 16.147432484283325, 14.253618004674097, 11.13043032645008, 9.419361121081867, 12.891381352392024, 8.605374537052183, 6.578599881585408, 4.153148636940357, 5.909972001882714, 4.933939878702596, 2.757691875866592, 1.461908830825408, 0.0), # 21
(16.18558598831933, 16.12743010970541, 13.82827181383638, 14.844565540209405, 11.85945789529128, 5.83119868256461, 6.59758618660448, 6.164414114845277, 6.464300704550355, 3.148848033951298, 2.232515883656091, 1.2995243985128655, 0.0, 16.194070701678125, 14.294768383641518, 11.162579418280455, 9.446544101853892, 12.92860140910071, 8.630179760783388, 6.59758618660448, 4.1651419161175784, 5.92972894764564, 4.948188513403136, 2.7656543627672763, 1.4661300099732195, 0.0), # 22
(16.208629381348224, 16.132927937814358, 13.83323090992227, 14.849916975308645, 11.869580859768103, 5.833333333333334, 6.599843201807471, 6.166329218106997, 6.466627325102881, 3.149916909007774, 2.233322143243131, 1.2999863435451913, 0.0, 16.2, 14.299849778997103, 11.166610716215654, 9.44975072702332, 12.933254650205763, 8.632860905349796, 6.599843201807471, 4.166666666666667, 5.9347904298840515, 4.949972325102882, 2.7666461819844543, 1.4666298125285782, 0.0), # 23
(16.225619860854646, 16.12972098765432, 13.832419753086421, 14.849258333333335, 11.875314787855842, 5.833333333333334, 6.598603050108934, 6.163666666666667, 6.466315555555555, 3.149260246913581, 2.2332332210998884, 1.2998781893004117, 0.0, 16.2, 14.298660082304526, 11.166166105499443, 9.44778074074074, 12.93263111111111, 8.629133333333334, 6.598603050108934, 4.166666666666667, 5.937657393927921, 4.949752777777779, 2.7664839506172845, 1.4663382716049385, 0.0), # 24
(16.242251568338528, 16.1233996342021, 13.830818472793784, 14.847955246913582, 11.880922608634137, 5.833333333333334, 6.596159122085048, 6.158436213991771, 6.465699588477367, 3.1479675354366723, 2.233056906513697, 1.2996646852613931, 0.0, 16.2, 14.296311537875322, 11.165284532568485, 9.443902606310015, 12.931399176954734, 8.62181069958848, 6.596159122085048, 4.166666666666667, 5.940461304317068, 4.949318415637862, 2.766163694558757, 1.4657636031092822, 0.0), # 25
(16.258523230476854, 16.114060448102425, 13.828449016918157, 14.846022530864197, 11.886404126315846, 5.833333333333334, 6.592549374646977, 6.150736625514405, 6.46478732510288, 3.146060283493371, 2.2327947956935614, 1.2993487578113097, 0.0, 16.2, 14.292836335924404, 11.163973978467807, 9.43818085048011, 12.92957465020576, 8.611031275720167, 6.592549374646977, 4.166666666666667, 5.943202063157923, 4.948674176954733, 2.7656898033836312, 1.46491458619113, 0.0), # 26
(16.27443357394662, 16.1018, 13.825333333333333, 14.843475, 11.891759145113827, 5.833333333333334, 6.587811764705883, 6.140666666666667, 6.463586666666666, 3.143560000000001, 2.232448484848485, 1.2989333333333337, 0.0, 16.2, 14.288266666666669, 11.162242424242425, 9.430679999999999, 12.927173333333332, 8.596933333333334, 6.587811764705883, 4.166666666666667, 5.945879572556914, 4.947825000000001, 2.765066666666667, 1.4638000000000002, 0.0), # 27
(16.2899813254248, 16.08671486053955, 13.821493369913123, 14.840327469135804, 11.896987469240962, 5.833333333333334, 6.581984249172921, 6.12832510288066, 6.462105514403292, 3.140488193872886, 2.232019570187472, 1.2984213382106389, 0.0, 16.2, 14.282634720317025, 11.160097850937358, 9.421464581618656, 12.924211028806583, 8.579655144032923, 6.581984249172921, 4.166666666666667, 5.948493734620481, 4.946775823045269, 2.764298673982625, 1.462428623685414, 0.0), # 28
(16.3051652115884, 16.0689016003658, 13.816951074531323, 14.83659475308642, 11.902088902910101, 5.833333333333334, 6.575104784959253, 6.113810699588477, 6.460351769547325, 3.1368663740283504, 2.2315096479195247, 1.2978156988263985, 0.0, 16.2, 14.27597268709038, 11.157548239597624, 9.41059912208505, 12.92070353909465, 8.559334979423868, 6.575104784959253, 4.166666666666667, 5.951044451455051, 4.945531584362141, 2.763390214906265, 1.460809236396891, 0.0), # 29
(16.319983959114396, 16.04845679012346, 13.811728395061728, 14.832291666666666, 11.907063250334119, 5.833333333333334, 6.567211328976035, 6.097222222222222, 6.458333333333333, 3.1327160493827173, 2.230920314253648, 1.297119341563786, 0.0, 16.2, 14.268312757201645, 11.15460157126824, 9.398148148148149, 12.916666666666666, 8.536111111111111, 6.567211328976035, 4.166666666666667, 5.953531625167059, 4.944097222222223, 2.7623456790123457, 1.458950617283951, 0.0), # 30
(16.334436294679772, 16.02547700045725, 13.805847279378145, 14.82743302469136, 11.911910315725876, 5.833333333333334, 6.558341838134432, 6.078658436213992, 6.456058106995885, 3.1280587288523103, 2.2302531653988447, 1.296335192805975, 0.0, 16.2, 14.259687120865724, 11.151265826994223, 9.384176186556928, 12.91211621399177, 8.510121810699589, 6.558341838134432, 4.166666666666667, 5.955955157862938, 4.942477674897121, 2.761169455875629, 1.4568615454961138, 0.0), # 31
(16.34852094496153, 16.00005880201189, 13.799329675354366, 14.82203364197531, 11.916629903298237, 5.833333333333334, 6.548534269345599, 6.058218106995886, 6.453533991769548, 3.1229159213534534, 2.229509797564119, 1.2954661789361381, 0.0, 16.2, 14.250127968297518, 11.147548987820594, 9.368747764060357, 12.907067983539095, 8.48150534979424, 6.548534269345599, 4.166666666666667, 5.958314951649118, 4.940677880658438, 2.759865935070873, 1.4545508001828993, 0.0), # 32
(16.362236636636634, 15.972298765432097, 13.792197530864199, 14.816108333333332, 11.921221817264065, 5.833333333333334, 6.537826579520697, 6.0360000000000005, 6.450768888888889, 3.1173091358024703, 2.228691806958474, 1.2945152263374486, 0.0, 16.2, 14.239667489711932, 11.143459034792368, 9.351927407407409, 12.901537777777778, 8.450400000000002, 6.537826579520697, 4.166666666666667, 5.960610908632033, 4.938702777777778, 2.75843950617284, 1.452027160493827, 0.0), # 33
(16.375582096382097, 15.942293461362596, 13.784472793781436, 14.809671913580248, 11.92568586183623, 5.833333333333334, 6.526256725570888, 6.012102880658436, 6.447770699588479, 3.111259881115685, 2.2278007897909133, 1.2934852613930805, 0.0, 16.2, 14.228337875323884, 11.139003948954567, 9.333779643347052, 12.895541399176958, 8.41694403292181, 6.526256725570888, 4.166666666666667, 5.962842930918115, 4.93655730452675, 2.7568945587562874, 1.449299405578418, 0.0), # 34
(16.388556050874893, 15.9101394604481, 13.776177411979882, 14.802739197530864, 11.930021841227594, 5.833333333333334, 6.513862664407327, 5.986625514403293, 6.4445473251028815, 3.1047896662094203, 2.226838342270441, 1.2923792104862066, 0.0, 16.2, 14.216171315348271, 11.134191711352205, 9.314368998628257, 12.889094650205763, 8.381275720164611, 6.513862664407327, 4.166666666666667, 5.965010920613797, 4.934246399176955, 2.755235482395977, 1.4463763145861912, 0.0), # 35
(16.40115722679201, 15.87593333333333, 13.767333333333335, 14.795325, 11.934229559651024, 5.833333333333334, 6.500682352941176, 5.959666666666668, 6.441106666666666, 3.097920000000001, 2.225806060606061, 1.2912000000000003, 0.0, 16.2, 14.203200000000002, 11.129030303030303, 9.29376, 12.882213333333333, 8.343533333333335, 6.500682352941176, 4.166666666666667, 5.967114779825512, 4.931775000000001, 2.753466666666667, 1.4432666666666667, 0.0), # 36
(16.41338435081044, 15.839771650663007, 13.757962505715593, 14.78744413580247, 11.938308821319383, 5.833333333333334, 6.486753748083595, 5.931325102880659, 6.437456625514404, 3.090672391403751, 2.2247055410067764, 1.2899505563176348, 0.0, 16.2, 14.18945611949398, 11.123527705033881, 9.27201717421125, 12.874913251028808, 8.303855144032923, 6.486753748083595, 4.166666666666667, 5.969154410659692, 4.929148045267491, 2.751592501143119, 1.4399792409693644, 0.0), # 37
(16.425236149607162, 15.801750983081849, 13.748086877000459, 14.77911141975309, 11.942259430445535, 5.833333333333334, 6.4721148067457435, 5.901699588477367, 6.433605102880659, 3.0830683493369926, 2.22353837968159, 1.2886338058222835, 0.0, 16.2, 14.174971864045116, 11.11769189840795, 9.249205048010975, 12.867210205761317, 8.262379423868314, 6.4721148067457435, 4.166666666666667, 5.971129715222768, 4.926370473251031, 2.7496173754000917, 1.4365228166438047, 0.0), # 38
(16.436711349859177, 15.761967901234568, 13.737728395061731, 14.770341666666667, 11.94608119124235, 5.833333333333334, 6.456803485838781, 5.8708888888888895, 6.42956, 3.0751293827160504, 2.2223061728395064, 1.2872526748971194, 0.0, 16.2, 14.159779423868311, 11.111530864197531, 9.225388148148149, 12.85912, 8.219244444444445, 6.456803485838781, 4.166666666666667, 5.973040595621175, 4.923447222222223, 2.7475456790123465, 1.4329061728395065, 0.0), # 39
(16.44780867824346, 15.720518975765888, 13.726909007773205, 14.761149691358025, 11.949773907922687, 5.833333333333334, 6.440857742273865, 5.838991769547327, 6.425329218106996, 3.0668770004572488, 2.2210105166895295, 1.2858100899253166, 0.0, 16.2, 14.143910989178481, 11.105052583447646, 9.200631001371743, 12.850658436213992, 8.174588477366258, 6.440857742273865, 4.166666666666667, 5.974886953961343, 4.920383230452676, 2.745381801554641, 1.42913808870599, 0.0), # 40
(16.458526861437004, 15.677500777320528, 13.71565066300869, 14.751550308641978, 11.953337384699417, 5.833333333333334, 6.424315532962156, 5.806106995884774, 6.420920658436214, 3.05833271147691, 2.2196530074406624, 1.2843089772900476, 0.0, 16.2, 14.12739875019052, 11.09826503720331, 9.174998134430727, 12.841841316872427, 8.128549794238685, 6.424315532962156, 4.166666666666667, 5.976668692349708, 4.9171834362139935, 2.743130132601738, 1.4252273433927756, 0.0), # 41
(16.4688646261168, 15.633009876543213, 13.70397530864198, 14.741558333333336, 11.956771425785394, 5.833333333333334, 6.4072148148148145, 5.772333333333334, 6.416342222222223, 3.049518024691359, 2.2182352413019086, 1.282752263374486, 0.0, 16.2, 14.110274897119341, 11.091176206509541, 9.148554074074074, 12.832684444444446, 8.081266666666668, 6.4072148148148145, 4.166666666666667, 5.978385712892697, 4.913852777777779, 2.740795061728396, 1.421182716049383, 0.0), # 42
(16.47882069895983, 15.587142844078647, 13.69190489254687, 14.731188580246915, 11.960075835393496, 5.833333333333334, 6.389593544743001, 5.737769547325104, 6.4116018106995885, 3.040454449016919, 2.2167588144822714, 1.281142874561805, 0.0, 16.2, 14.092571620179852, 11.083794072411356, 9.121363347050755, 12.823203621399177, 8.032877366255146, 6.389593544743001, 4.166666666666667, 5.980037917696748, 4.9103961934156395, 2.738380978509374, 1.4170129858253318, 0.0), # 43
(16.488393806643085, 15.539996250571559, 13.679461362597166, 14.720455864197532, 11.963250417736582, 5.833333333333334, 6.371489679657872, 5.702514403292183, 6.4067073251028805, 3.031163493369914, 2.2152253231907557, 1.279483737235178, 0.0, 16.2, 14.074321109586954, 11.076126615953777, 9.09349048010974, 12.813414650205761, 7.983520164609057, 6.371489679657872, 4.166666666666667, 5.981625208868291, 4.906818621399179, 2.7358922725194335, 1.4127269318701419, 0.0), # 44
(16.497582675843546, 15.491666666666667, 13.66666666666667, 14.709375000000001, 11.966294977027516, 5.833333333333334, 6.352941176470589, 5.666666666666668, 6.4016666666666655, 3.021666666666668, 2.213636363636364, 1.277777777777778, 0.0, 16.2, 14.055555555555554, 11.068181818181818, 9.065000000000001, 12.803333333333331, 7.9333333333333345, 6.352941176470589, 4.166666666666667, 5.983147488513758, 4.903125000000001, 2.733333333333334, 1.4083333333333337, 0.0), # 45
(16.50638603323821, 15.442250663008686, 13.653542752629173, 14.697960802469137, 11.969209317479164, 5.833333333333334, 6.333985992092311, 5.63032510288066, 6.396487736625514, 3.0119854778235036, 2.2119935320281, 1.2760279225727789, 0.0, 16.2, 14.036307148300564, 11.059967660140499, 9.035956433470508, 12.792975473251028, 7.882455144032924, 6.333985992092311, 4.166666666666667, 5.984604658739582, 4.899320267489713, 2.730708550525835, 1.4038409693644263, 0.0), # 46
(16.514802605504055, 15.391844810242342, 13.640111568358483, 14.686228086419753, 11.971993243304391, 5.833333333333334, 6.3146620834341975, 5.593588477366255, 6.391178436213992, 3.0021414357567453, 2.210298424574968, 1.2742370980033535, 0.0, 16.2, 14.016608078036885, 11.051492122874839, 9.006424307270233, 12.782356872427984, 7.831023868312758, 6.3146620834341975, 4.166666666666667, 5.985996621652196, 4.895409362139919, 2.728022313671697, 1.3992586191129404, 0.0), # 47
(16.522831119318074, 15.340545679012347, 13.626395061728397, 14.674191666666669, 11.974646558716064, 5.833333333333334, 6.295007407407407, 5.556555555555557, 6.385746666666667, 2.9921560493827166, 2.208552637485971, 1.272408230452675, 0.0, 16.2, 13.996490534979422, 11.042763187429854, 8.976468148148149, 12.771493333333334, 7.77917777777778, 6.295007407407407, 4.166666666666667, 5.987323279358032, 4.891397222222224, 2.7252790123456796, 1.3945950617283953, 0.0), # 48
(16.53047030135726, 15.288449839963418, 13.612415180612713, 14.661866358024692, 11.977169067927047, 5.833333333333334, 6.275059920923102, 5.519325102880659, 6.380200329218106, 2.982050827617742, 2.2067577669701133, 1.2705442463039174, 0.0, 16.2, 13.97598670934309, 11.033788834850565, 8.946152482853226, 12.760400658436213, 7.727055144032923, 6.275059920923102, 4.166666666666667, 5.9885845339635235, 4.887288786008232, 2.7224830361225427, 1.389859076360311, 0.0), # 49
(16.537718878298588, 15.235653863740286, 13.598193872885233, 14.649266975308642, 11.979560575150202, 5.833333333333334, 6.25485758089244, 5.481995884773663, 6.3745473251028795, 2.971847279378144, 2.204915409236397, 1.2686480719402533, 0.0, 16.2, 13.955128791342785, 11.024577046181985, 8.91554183813443, 12.749094650205759, 7.674794238683129, 6.25485758089244, 4.166666666666667, 5.989780287575101, 4.883088991769548, 2.7196387745770467, 1.385059442158208, 0.0), # 50
(16.544575576819057, 15.182254320987655, 13.583753086419755, 14.636408333333335, 11.981820884598399, 5.833333333333334, 6.23443834422658, 5.4446666666666665, 6.368795555555556, 2.9615669135802474, 2.2030271604938276, 1.2667226337448563, 0.0, 16.2, 13.933948971193416, 11.015135802469137, 8.88470074074074, 12.737591111111112, 7.622533333333334, 6.23443834422658, 4.166666666666667, 5.9909104422991994, 4.878802777777779, 2.716750617283951, 1.380204938271605, 0.0), # 51
(16.551039123595647, 15.128347782350252, 13.56911476909008, 14.623305246913581, 11.983949800484496, 5.833333333333334, 6.213840167836683, 5.407436213991769, 6.3629529218107, 2.9512312391403754, 2.2010946169514076, 1.2647708581008996, 0.0, 16.2, 13.912479439109894, 11.005473084757037, 8.853693717421125, 12.7259058436214, 7.570410699588477, 6.213840167836683, 4.166666666666667, 5.991974900242248, 4.874435082304528, 2.713822953818016, 1.3753043438500232, 0.0), # 52
(16.55710824530535, 15.074030818472796, 13.554300868770008, 14.609972530864198, 11.985947127021364, 5.833333333333334, 6.1931010086339064, 5.370403292181071, 6.357027325102881, 2.940861764974852, 2.1991193748181406, 1.2627956713915565, 0.0, 16.2, 13.890752385307119, 10.995596874090701, 8.822585294924554, 12.714054650205762, 7.518564609053499, 6.1931010086339064, 4.166666666666667, 5.992973563510682, 4.8699908436214, 2.710860173754002, 1.3703664380429816, 0.0), # 53
(16.562781668625146, 15.019400000000001, 13.539333333333333, 14.596425, 11.987812668421869, 5.833333333333334, 6.172258823529412, 5.333666666666667, 6.351026666666667, 2.9304800000000006, 2.19710303030303, 1.2608000000000001, 0.0, 16.2, 13.8688, 10.98551515151515, 8.791440000000001, 12.702053333333334, 7.467133333333333, 6.172258823529412, 4.166666666666667, 5.993906334210934, 4.865475000000001, 2.707866666666667, 1.3654000000000004, 0.0), # 54
(16.568058120232035, 14.964551897576587, 13.524234110653865, 14.582677469135803, 11.989546228898869, 5.833333333333334, 6.151351569434358, 5.2973251028806585, 6.344958847736625, 2.9201074531321454, 2.1950471796150812, 1.2587867703094042, 0.0, 16.2, 13.846654473403445, 10.975235898075404, 8.760322359396435, 12.68991769547325, 7.416255144032922, 6.151351569434358, 4.166666666666667, 5.994773114449434, 4.860892489711935, 2.704846822130773, 1.360413808870599, 0.0), # 55
(16.572936326802996, 14.909583081847279, 13.509025148605396, 14.56874475308642, 11.991147612665237, 5.833333333333334, 6.130417203259905, 5.261477366255145, 6.338831769547324, 2.9097656332876096, 2.1929534189632958, 1.2567589087029418, 0.0, 16.2, 13.824347995732358, 10.964767094816478, 8.729296899862828, 12.677663539094649, 7.366068312757203, 6.130417203259905, 4.166666666666667, 5.995573806332619, 4.856248251028807, 2.7018050297210796, 1.3554166438042983, 0.0), # 56
(16.577415015015013, 14.85459012345679, 13.493728395061732, 14.554641666666669, 11.99261662393383, 5.833333333333334, 6.109493681917211, 5.226222222222224, 6.332653333333334, 2.899476049382717, 2.1908233445566783, 1.254719341563786, 0.0, 16.2, 13.801912757201645, 10.95411672278339, 8.69842814814815, 12.665306666666668, 7.316711111111113, 6.109493681917211, 4.166666666666667, 5.996308311966915, 4.851547222222224, 2.6987456790123465, 1.3504172839506174, 0.0), # 57
(16.581492911545087, 14.79966959304984, 13.478365797896664, 14.540383024691359, 11.99395306691752, 5.833333333333334, 6.088618962317438, 5.191658436213992, 6.326431440329218, 2.8892602103337914, 2.1886585526042324, 1.2526709952751107, 0.0, 16.2, 13.779380948026215, 10.943292763021162, 8.667780631001373, 12.652862880658436, 7.2683218106995895, 6.088618962317438, 4.166666666666667, 5.99697653345876, 4.846794341563787, 2.695673159579333, 1.3454245084590766, 0.0), # 58
(16.585168743070195, 14.744918061271147, 13.462959304983997, 14.525983641975309, 11.995156745829167, 5.833333333333334, 6.067831001371743, 5.157884773662552, 6.320173991769548, 2.879139625057157, 2.1864606393149604, 1.2506167962200887, 0.0, 16.2, 13.756784758420972, 10.9323031965748, 8.63741887517147, 12.640347983539096, 7.221038683127573, 6.067831001371743, 4.166666666666667, 5.9975783729145835, 4.841994547325104, 2.6925918609968, 1.3404470964791952, 0.0), # 59
(16.588441236267325, 14.690432098765434, 13.44753086419753, 14.511458333333334, 11.996227464881638, 5.833333333333334, 6.0471677559912855, 5.125000000000001, 6.31388888888889, 2.8691358024691365, 2.184231200897868, 1.2485596707818931, 0.0, 16.2, 13.734156378600822, 10.921156004489339, 8.607407407407408, 12.62777777777778, 7.175000000000001, 6.0471677559912855, 4.166666666666667, 5.998113732440819, 4.837152777777779, 2.6895061728395064, 1.3354938271604941, 0.0), # 60
(16.591309117813463, 14.636308276177413, 13.432102423411067, 14.496821913580249, 11.997165028287798, 5.833333333333334, 6.026667183087227, 5.093102880658437, 6.3075840329218105, 2.8592702514860546, 2.1819718335619576, 1.246502545343698, 0.0, 16.2, 13.711527998780674, 10.909859167809786, 8.577810754458163, 12.615168065843621, 7.130344032921811, 6.026667183087227, 4.166666666666667, 5.998582514143899, 4.832273971193417, 2.6864204846822135, 1.3305734796524924, 0.0), # 61
(16.593771114385607, 14.582643164151806, 13.416695930498403, 14.482089197530867, 11.997969240260517, 5.833333333333334, 6.006367239570725, 5.062292181069959, 6.301267325102881, 2.849564481024235, 2.1796841335162327, 1.2444483462886757, 0.0, 16.2, 13.68893180917543, 10.898420667581162, 8.548693443072704, 12.602534650205762, 7.0872090534979435, 6.006367239570725, 4.166666666666667, 5.998984620130258, 4.827363065843623, 2.6833391860996807, 1.3256948331047098, 0.0), # 62
(16.595825952660736, 14.529533333333333, 13.401333333333335, 14.467275000000003, 11.998639905012647, 5.833333333333334, 5.986305882352941, 5.0326666666666675, 6.294946666666666, 2.8400400000000006, 2.1773696969696976, 1.2424000000000002, 0.0, 16.2, 13.6664, 10.886848484848487, 8.52012, 12.589893333333332, 7.045733333333335, 5.986305882352941, 4.166666666666667, 5.999319952506323, 4.822425000000002, 2.6802666666666672, 1.3208666666666669, 0.0), # 63
(16.597472359315837, 14.477075354366713, 13.386036579789668, 14.452394135802471, 11.999176826757065, 5.833333333333334, 5.966521068345034, 5.004325102880659, 6.288629958847737, 2.830718317329676, 2.1750301201313547, 1.2403604328608446, 0.0, 16.2, 13.64396476146929, 10.875150600656774, 8.492154951989026, 12.577259917695473, 7.006055144032923, 5.966521068345034, 4.166666666666667, 5.999588413378532, 4.817464711934158, 2.6772073159579337, 1.316097759487883, 0.0), # 64
(16.5987090610279, 14.425365797896662, 13.370827617741199, 14.437461419753088, 11.999579809706631, 5.833333333333334, 5.947050754458163, 4.977366255144033, 6.282325102880659, 2.8216209419295843, 2.1726669992102097, 1.238332571254382, 0.0, 16.2, 13.6216582837982, 10.863334996051048, 8.464862825788751, 12.564650205761318, 6.968312757201646, 5.947050754458163, 4.166666666666667, 5.999789904853316, 4.812487139917697, 2.67416552354824, 1.3113968907178786, 0.0), # 65
(16.599534784473914, 14.374501234567903, 13.35572839506173, 14.422491666666668, 11.99984865807421, 5.833333333333334, 5.927932897603486, 4.95188888888889, 6.27604, 2.81276938271605, 2.170281930415264, 1.2363193415637863, 0.0, 16.2, 13.599512757201648, 10.851409652076319, 8.438308148148149, 12.55208, 6.932644444444446, 5.927932897603486, 4.166666666666667, 5.999924329037105, 4.807497222222223, 2.6711456790123465, 1.3067728395061733, 0.0), # 66
(16.59994825633087, 14.324578235025148, 13.340760859625059, 14.407499691358025, 11.999983176072671, 5.833333333333334, 5.909205454692165, 4.927991769547327, 6.269782551440329, 2.8041851486053964, 2.1678765099555233, 1.23432367017223, 0.0, 16.2, 13.577560371894528, 10.839382549777614, 8.412555445816189, 12.539565102880658, 6.899188477366257, 5.909205454692165, 4.166666666666667, 5.999991588036336, 4.802499897119342, 2.6681521719250116, 1.3022343850022864, 0.0), # 67
(16.59966658316932, 14.275431337669806, 13.325874599908552, 14.39237008856683, 11.999869818983834, 5.833225077478026, 5.890812155863717, 4.905562566681908, 6.263513519280598, 2.795848176658867, 2.1654095969441007, 1.2323373362532992, 0.0, 16.19980024005487, 13.555710698786289, 10.827047984720503, 8.3875445299766, 12.527027038561195, 6.867787593354672, 5.890812155863717, 4.166589341055733, 5.999934909491917, 4.797456696188944, 2.6651749199817103, 1.29776648524271, 0.0), # 68
(16.597026731078905, 14.22556009557945, 13.310651234567901, 14.376340217391304, 11.998838053740013, 5.832369272976682, 5.872214545077291, 4.8833991769547325, 6.256958847736625, 2.7875225562817723, 2.162630090377459, 1.2302958631145768, 0.0, 16.198217592592595, 13.533254494260342, 10.813150451887294, 8.362567668845315, 12.51391769547325, 6.8367588477366255, 5.872214545077291, 4.165978052126201, 5.999419026870006, 4.792113405797102, 2.66213024691358, 1.2932327359617684, 0.0), # 69
(16.59181726009423, 14.174735607770254, 13.295024577046181, 14.359304549114333, 11.996799268404205, 5.8306838388457045, 5.853328107649096, 4.861301630848957, 6.2500815424477985, 2.7791678097850943, 2.159506369740288, 1.228189701505708, 0.0, 16.195091735253776, 13.510086716562785, 10.797531848701441, 8.337503429355282, 12.500163084895597, 6.80582228318854, 5.853328107649096, 4.164774170604074, 5.998399634202102, 4.786434849704778, 2.6590049154092363, 1.2886123279791142, 0.0), # 70
(16.584111457028687, 14.122988247267578, 13.279000114311843, 14.341288204508857, 11.993779284004411, 5.828196087994717, 5.8341613276311906, 4.8392772443225125, 6.242891845755221, 2.7707841437370564, 2.1560499655423633, 1.226020391628362, 0.0, 16.190463820301783, 13.486224307911982, 10.780249827711817, 8.312352431211167, 12.485783691510441, 6.774988142051518, 5.8341613276311906, 4.162997205710512, 5.9968896420022055, 4.780429401502953, 2.6558000228623686, 1.2839080224788708, 0.0), # 71
(16.573982608695655, 14.070348387096773, 13.262583333333334, 14.322316304347826, 11.989803921568626, 5.824933333333335, 5.81472268907563, 4.817333333333334, 6.2354, 2.762371764705883, 2.1522724082934617, 1.2237894736842108, 0.0, 16.184375, 13.461684210526316, 10.761362041467306, 8.287115294117648, 12.4708, 6.744266666666667, 5.81472268907563, 4.160666666666668, 5.994901960784313, 4.7741054347826095, 2.6525166666666666, 1.2791225806451614, 0.0), # 72
(16.561504001908514, 14.016846400283198, 13.245779721079103, 14.302413969404189, 11.984899002124855, 5.820922887771173, 5.795020676034474, 4.795477213839354, 6.227616247523244, 2.753930879259798, 2.1481852285033574, 1.2214984878749227, 0.0, 16.1768664266118, 13.436483366624147, 10.740926142516786, 8.261792637779392, 12.455232495046488, 6.713668099375096, 5.795020676034474, 4.157802062693695, 5.992449501062428, 4.76747132313473, 2.649155944215821, 1.274258763662109, 0.0), # 73
(16.546748923480646, 13.962512659852205, 13.228594764517604, 14.281606320450884, 11.979090346701094, 5.816192064217854, 5.775063772559778, 4.773716201798507, 6.219550830666057, 2.7454616939670253, 2.143799956681829, 1.219148974402169, 0.0, 16.167979252400553, 13.410638718423858, 10.718999783409142, 8.236385081901075, 12.439101661332113, 6.683202682517909, 5.775063772559778, 4.154422903012753, 5.989545173350547, 4.760535440150296, 2.645718952903521, 1.269319332713837, 0.0), # 74
(16.52979066022544, 13.90737753882915, 13.211033950617283, 14.259918478260868, 11.972403776325345, 5.810768175582992, 5.754860462703601, 4.752057613168724, 6.211213991769547, 2.7369644153957884, 2.13912812333865, 1.2167424734676198, 0.0, 16.157754629629633, 13.384167208143815, 10.695640616693249, 8.210893246187364, 12.422427983539094, 6.652880658436215, 5.754860462703601, 4.150548696844995, 5.986201888162673, 4.7533061594202906, 2.6422067901234567, 1.2643070489844683, 0.0), # 75
(16.510702498956285, 13.851471410239393, 13.193102766346595, 14.237375563607085, 11.964865112025606, 5.804678534776205, 5.734419230517997, 4.730508763907942, 6.2026159731748205, 2.728439250114312, 2.134181258983598, 1.2142805252729445, 0.0, 16.146233710562413, 13.357085778002387, 10.67090629491799, 8.185317750342936, 12.405231946349641, 6.622712269471118, 5.734419230517997, 4.146198953411575, 5.982432556012803, 4.745791854535696, 2.638620553269319, 1.259224673658127, 0.0), # 76
(16.48955772648655, 13.794824647108282, 13.174806698673981, 14.21400269726248, 11.956500174829877, 5.797950454707109, 5.7137485600550235, 4.70907696997409, 6.193767017222985, 2.7198864046908207, 2.1289708941264505, 1.2117646700198144, 0.0, 16.13345764746228, 13.329411370217956, 10.64485447063225, 8.15965921407246, 12.38753403444597, 6.592707757963726, 5.7137485600550235, 4.141393181933649, 5.9782500874149385, 4.738000899087494, 2.6349613397347964, 1.254074967918935, 0.0), # 77
(16.46642962962963, 13.737467622461173, 13.156151234567902, 14.189825, 11.94733478576616, 5.790611248285322, 5.69285693536674, 4.687769547325104, 6.184677366255142, 2.711306085693537, 2.123508559276981, 1.2091964479098987, 0.0, 16.119467592592596, 13.301160927008882, 10.617542796384903, 8.13391825708061, 12.369354732510285, 6.562877366255145, 5.69285693536674, 4.136150891632373, 5.97366739288308, 4.729941666666668, 2.6312302469135807, 1.248860692951016, 0.0), # 78
(16.441391495198904, 13.679430709323423, 13.1371418609968, 14.164867592592593, 11.93739476586245, 5.782688228420464, 5.671752840505201, 4.666593811918916, 6.1753572626124065, 2.702698499690686, 2.117805784944966, 1.2065773991448674, 0.0, 16.104304698216733, 13.27235139059354, 10.58902892472483, 8.108095499072057, 12.350714525224813, 6.533231336686482, 5.671752840505201, 4.130491591728903, 5.968697382931225, 4.721622530864199, 2.6274283721993603, 1.243584609938493, 0.0), # 79
(16.414516610007755, 13.620744280720386, 13.117784064929126, 14.139155595813204, 11.92670593614675, 5.774208708022151, 5.650444759522465, 4.645557079713459, 6.165816948635879, 2.694063853250491, 2.111874101640184, 1.2039090639263914, 0.0, 16.08801011659808, 13.242999703190304, 10.559370508200919, 8.082191559751472, 12.331633897271757, 6.503779911598843, 5.650444759522465, 4.1244347914443935, 5.963352968073375, 4.713051865271069, 2.6235568129858255, 1.23824948006549, 0.0), # 80
(16.385878260869568, 13.56143870967742, 13.098083333333335, 14.112714130434785, 11.915294117647058, 5.765200000000001, 5.628941176470589, 4.624666666666667, 6.156066666666666, 2.685402352941177, 2.1057250398724086, 1.2011929824561405, 0.0, 16.070625, 13.213122807017545, 10.528625199362043, 8.05620705882353, 12.312133333333332, 6.474533333333334, 5.628941176470589, 4.118, 5.957647058823529, 4.704238043478263, 2.619616666666667, 1.2328580645161293, 0.0), # 81
(16.355549734597723, 13.501544369219879, 13.078045153177872, 14.085568317230274, 11.903185131391377, 5.75568941726363, 5.607250575401629, 4.603929888736474, 6.146116659045877, 2.676714205330967, 2.099370130151417, 1.198430694935785, 0.0, 16.052190500685874, 13.182737644293633, 10.496850650757084, 8.030142615992899, 12.292233318091753, 6.445501844231063, 5.607250575401629, 4.111206726616879, 5.951592565695688, 4.695189439076759, 2.6156090306355746, 1.2274131244745345, 0.0), # 82
(16.323604318005607, 13.441091632373114, 13.057675011431185, 14.057743276972625, 11.890404798407703, 5.745704272722655, 5.585381440367643, 4.5833540618808115, 6.135977168114616, 2.667999616988085, 2.0928209029869853, 1.195623741566995, 0.0, 16.03274777091907, 13.151861157236944, 10.464104514934926, 8.003998850964255, 12.271954336229232, 6.416695686633136, 5.585381440367643, 4.104074480516182, 5.945202399203851, 4.6859144256575425, 2.6115350022862374, 1.2219174211248287, 0.0), # 83
(16.290115297906603, 13.380110872162485, 13.036978395061729, 14.029264130434784, 11.876978939724037, 5.735271879286694, 5.563342255420687, 4.562946502057613, 6.125658436213991, 2.659258794480756, 2.0860888888888893, 1.1927736625514405, 0.0, 16.012337962962963, 13.120510288065844, 10.430444444444445, 7.977776383442267, 12.251316872427982, 6.388125102880658, 5.563342255420687, 4.096622770919067, 5.938489469862018, 4.676421376811596, 2.607395679012346, 1.2163737156511352, 0.0), # 84
(16.255155961114095, 13.318632461613346, 13.015960791037951, 14.000155998389694, 11.862933376368382, 5.724419549865368, 5.54114150461282, 4.542714525224815, 6.115170705685108, 2.650491944377203, 2.0791856183669055, 1.1898819980907918, 0.0, 15.991002229080934, 13.088701978998708, 10.395928091834525, 7.951475833131607, 12.230341411370215, 6.35980033531474, 5.54114150461282, 4.088871107046691, 5.931466688184191, 4.666718666129899, 2.6031921582075905, 1.210784769237577, 0.0), # 85
(16.21879959444146, 13.256686773751051, 12.994627686328306, 13.970444001610309, 11.84829392936873, 5.713174597368289, 5.518787671996097, 4.522665447340345, 6.104524218869075, 2.64169927324565, 2.0721226219308098, 1.1869502883867193, 0.0, 15.968781721536352, 13.05645317225391, 10.360613109654047, 7.9250978197369495, 12.20904843773815, 6.331731626276483, 5.518787671996097, 4.080838998120206, 5.924146964684365, 4.656814667203437, 2.5989255372656612, 1.2051533430682777, 0.0), # 86
(16.18111948470209, 13.194304181600955, 12.972984567901234, 13.940153260869565, 11.833086419753089, 5.7015643347050755, 5.496289241622575, 4.5028065843621405, 6.093729218106997, 2.6328809876543215, 2.0649114300903775, 1.1839800736408925, 0.0, 15.945717592592594, 13.023780810049816, 10.324557150451888, 7.898642962962963, 12.187458436213994, 6.303929218106997, 5.496289241622575, 4.072545953360768, 5.9165432098765445, 4.646717753623189, 2.594596913580247, 1.1994821983273598, 0.0), # 87
(16.142188918709373, 13.131515058188414, 12.951036922725194, 13.90930889694042, 11.817336668549451, 5.689616074785349, 5.473654697544313, 4.483145252248133, 6.082795945739979, 2.624037294171441, 2.0575635733553868, 1.1809728940549822, 0.0, 15.921850994513035, 12.990701834604803, 10.287817866776932, 7.8721118825143215, 12.165591891479957, 6.276403353147386, 5.473654697544313, 4.064011481989534, 5.908668334274726, 4.636436298980141, 2.5902073845450393, 1.193774096198947, 0.0), # 88
(16.102081183276677, 13.068349776538785, 12.928790237768634, 13.877936030595814, 11.80107049678582, 5.677357130518723, 5.4508925238133665, 4.463688766956257, 6.07173464410913, 2.6151683993652335, 2.050090582235612, 1.1779302898306583, 0.0, 15.897223079561043, 12.957233188137238, 10.250452911178058, 7.845505198095699, 12.14346928821826, 6.24916427373876, 5.4508925238133665, 4.055255093227659, 5.90053524839291, 4.625978676865272, 2.585758047553727, 1.1880317978671624, 0.0), # 89
(16.06086956521739, 13.004838709677419, 12.906250000000002, 13.846059782608698, 11.784313725490197, 5.664814814814815, 5.428011204481793, 4.444444444444445, 6.060555555555556, 2.606274509803922, 2.04250398724083, 1.1748538011695908, 0.0, 15.871875000000001, 12.923391812865496, 10.212519936204147, 7.818823529411765, 12.121111111111112, 6.222222222222222, 5.428011204481793, 4.046296296296297, 5.892156862745098, 4.615353260869567, 2.5812500000000003, 1.1822580645161291, 0.0), # 90
(16.0186273513449, 12.941012230629672, 12.883421696387746, 13.813705273752014, 11.767092175690575, 5.652016440583244, 5.405019223601649, 4.4254196006706294, 6.049268922420364, 2.597355832055731, 2.0348153188808165, 1.17174496827345, 0.0, 15.845847908093276, 12.889194651007948, 10.174076594404081, 7.792067496167191, 12.098537844840727, 6.195587440938882, 5.405019223601649, 4.037154600416603, 5.883546087845287, 4.604568424584006, 2.5766843392775494, 1.1764556573299705, 0.0), # 91
(15.975427828472597, 12.876900712420905, 12.86031081390032, 13.780897624798712, 11.749431668414964, 5.638989320733629, 5.381925065224994, 4.406621551592746, 6.037884987044658, 2.5884125726888843, 2.027036107665348, 1.1686053313439067, 0.0, 15.819182956104251, 12.85465864478297, 10.135180538326738, 7.765237718066651, 12.075769974089315, 6.169270172229845, 5.381925065224994, 4.027849514809735, 5.874715834207482, 4.593632541599572, 2.5720621627800644, 1.1706273374928098, 0.0), # 92
(15.931344283413848, 12.812534528076466, 12.836922839506174, 13.747661956521743, 11.731358024691357, 5.625760768175583, 5.358737213403881, 4.388057613168725, 6.026413991769548, 2.5794449382716054, 2.0191778841042, 1.1654364305826295, 0.0, 15.791921296296294, 12.819800736408922, 10.095889420521, 7.738334814814815, 12.052827983539096, 6.143280658436215, 5.358737213403881, 4.018400548696845, 5.865679012345678, 4.582553985507248, 2.567384567901235, 1.1647758661887697, 0.0), # 93
(15.886450002982048, 12.74794405062171, 12.813263260173755, 13.714023389694043, 11.712897065547754, 5.612358095818728, 5.335464152190369, 4.369735101356501, 6.014866178936138, 2.5704531353721194, 2.01125217870715, 1.16223980619129, 0.0, 15.764104080932785, 12.784637868104188, 10.056260893535747, 7.711359406116356, 12.029732357872277, 6.117629141899102, 5.335464152190369, 4.008827211299091, 5.856448532773877, 4.571341129898015, 2.5626526520347515, 1.1589040046019738, 0.0), # 94
(15.840818273990577, 12.683159653081995, 12.789337562871514, 13.680007045088567, 11.694074612012159, 5.598808616572678, 5.312114365636515, 4.351661332114007, 6.003251790885536, 2.561437370558649, 2.0032705219839726, 1.1590169983715575, 0.0, 15.735772462277092, 12.749186982087132, 10.016352609919863, 7.684312111675945, 12.006503581771073, 6.09232586495961, 5.312114365636515, 3.999149011837627, 5.847037306006079, 4.560002348362857, 2.5578675125743033, 1.1530145139165453, 0.0), # 95
(15.79452238325282, 12.61821170848268, 12.765151234567902, 13.645638043478261, 11.674916485112563, 5.585139643347051, 5.288696337794377, 4.333843621399177, 5.991581069958848, 2.55239785039942, 1.9952444444444448, 1.1557695473251033, 0.0, 15.706967592592594, 12.713465020576134, 9.976222222222225, 7.657193551198258, 11.983162139917695, 6.067381069958849, 5.288696337794377, 3.9893854595336076, 5.8374582425562815, 4.5485460144927545, 2.553030246913581, 1.1471101553166074, 0.0), # 96
(15.747635617582157, 12.553130589849111, 12.740709762231369, 13.61094150563607, 11.655448505876976, 5.571378489051465, 5.265218552716011, 4.316289285169945, 5.979864258497181, 2.5433347814626543, 1.9871854765983423, 1.152498993253596, 0.0, 15.677730624142663, 12.677488925789556, 9.93592738299171, 7.630004344387961, 11.959728516994362, 6.042804999237923, 5.265218552716011, 3.9795560636081895, 5.827724252938488, 4.536980501878691, 2.5481419524462736, 1.141193689986283, 0.0), # 97
(15.700231263791975, 12.487946670206647, 12.71601863283036, 13.575942552334945, 11.635696495333388, 5.557552466595541, 5.241689494453475, 4.299005639384241, 5.968111598841639, 2.5342483703165772, 1.9791051489554419, 1.1492068763587067, 0.0, 15.648102709190674, 12.64127563994577, 9.89552574477721, 7.60274511094973, 11.936223197683278, 6.018607895137937, 5.241689494453475, 3.969680333282529, 5.817848247666694, 4.525314184111649, 2.5432037265660723, 1.1352678791096953, 0.0), # 98
(15.652382608695653, 12.422690322580646, 12.691083333333335, 13.540666304347827, 11.615686274509805, 5.543688888888889, 5.218117647058825, 4.282000000000001, 5.956333333333333, 2.5251388235294123, 1.9710149920255189, 1.1458947368421055, 0.0, 15.618125000000001, 12.604842105263158, 9.855074960127594, 7.575416470588236, 11.912666666666667, 5.9948000000000015, 5.218117647058825, 3.9597777777777776, 5.807843137254903, 4.51355543478261, 2.5382166666666675, 1.129335483870968, 0.0), # 99
(15.60416293910658, 12.357391919996457, 12.665909350708734, 13.505137882447666, 11.595443664434223, 5.529815068841132, 5.194511494584116, 4.265279682975157, 5.944539704313367, 2.516006347669384, 1.9629265363183495, 1.1425641149054624, 0.0, 15.58783864883402, 12.568205263960085, 9.814632681591746, 7.54801904300815, 11.889079408626735, 5.97139155616522, 5.194511494584116, 3.9498679063150943, 5.797721832217111, 4.501712627482556, 2.533181870141747, 1.1233992654542237, 0.0), # 100
(15.555645541838135, 12.292081835479447, 12.640502171925013, 13.469382407407409, 11.574994486134646, 5.515958319361886, 5.17087952108141, 4.248852004267642, 5.932740954122847, 2.506851149304716, 1.9548513123437101, 1.1392165507504473, 0.0, 15.557284807956103, 12.531382058254918, 9.77425656171855, 7.520553447914146, 11.865481908245695, 5.948392805974699, 5.17087952108141, 3.9399702281156324, 5.787497243067323, 4.48979413580247, 2.528100434385003, 1.1174619850435863, 0.0), # 101
(15.506903703703706, 12.22679044205496, 12.614867283950618, 13.433425000000002, 11.554364560639069, 5.5021459533607695, 5.1472302106027605, 4.2327242798353915, 5.920947325102881, 2.497673435003632, 1.9468008506113774, 1.135853584578731, 0.0, 15.526504629629631, 12.49438943036604, 9.734004253056886, 7.493020305010894, 11.841894650205761, 5.925813991769548, 5.1472302106027605, 3.93010425240055, 5.7771822803195345, 4.477808333333335, 2.522973456790124, 1.1115264038231782, 0.0), # 102
(15.458010711516671, 12.161548112748353, 12.589010173754001, 13.397290780998391, 11.533579708975497, 5.488405283747397, 5.123572047200224, 4.2169038256363365, 5.909169059594573, 2.4884734113343563, 1.9387866816311266, 1.132476756591983, 0.0, 15.495539266117968, 12.457244322511812, 9.693933408155633, 7.4654202340030675, 11.818338119189146, 5.903665355890872, 5.123572047200224, 3.920289488390998, 5.766789854487748, 4.465763593666131, 2.5178020347508006, 1.1055952829771232, 0.0), # 103
(15.409039852090416, 12.096385220584981, 12.562936328303612, 13.361004871175524, 11.512665752171923, 5.474763623431389, 5.099913514925861, 4.201397957628411, 5.897416399939034, 2.479251284865113, 1.9308203359127338, 1.129087606991874, 0.0, 15.464429869684501, 12.419963676910612, 9.654101679563668, 7.437753854595337, 11.794832799878067, 5.881957140679775, 5.099913514925861, 3.9105454453081343, 5.756332876085962, 4.4536682903918425, 2.5125872656607227, 1.099671383689544, 0.0), # 104
(15.360064412238325, 12.031332138590201, 12.536651234567902, 13.324592391304346, 11.491648511256354, 5.461248285322361, 5.076263097831727, 4.186213991769549, 5.885699588477366, 2.470007262164126, 1.922913343965976, 1.125687675980074, 0.0, 15.433217592592593, 12.382564435780811, 9.61456671982988, 7.410021786492376, 11.771399176954732, 5.860699588477368, 5.076263097831727, 3.9008916323731144, 5.745824255628177, 4.44153079710145, 2.5073302469135803, 1.093757467144564, 0.0), # 105
(15.311157678773782, 11.96641923978937, 12.510160379515318, 13.28807846215781, 11.470553807256785, 5.44788658232993, 5.052629279969876, 4.1713592440176805, 5.8740288675506775, 2.4607415497996183, 1.9150772363006283, 1.1222785037582528, 0.0, 15.401943587105624, 12.345063541340778, 9.575386181503141, 7.382224649398854, 11.748057735101355, 5.839902941624753, 5.052629279969876, 3.8913475588070923, 5.735276903628392, 4.429359487385938, 2.5020320759030636, 1.0878562945263066, 0.0), # 106
(15.26239293851017, 11.901676897207842, 12.483469250114315, 13.251488204508856, 11.449407461201215, 5.434705827363715, 5.0290205453923695, 4.156841030330743, 5.862414479500076, 2.451454354339816, 1.9073235434264675, 1.1188616305280807, 0.0, 15.370649005486968, 12.307477935808887, 9.536617717132337, 7.354363063019447, 11.724828959000153, 5.819577442463041, 5.0290205453923695, 3.8819327338312255, 5.724703730600607, 4.417162734836286, 2.496693850022863, 1.081970627018895, 0.0), # 107
(15.21384347826087, 11.83713548387097, 12.456583333333336, 13.214846739130437, 11.428235294117645, 5.421733333333335, 5.0054453781512604, 4.142666666666667, 5.850866666666667, 2.442145882352942, 1.8996637958532698, 1.1154385964912283, 0.0, 15.339375000000002, 12.26982456140351, 9.498318979266347, 7.326437647058825, 11.701733333333333, 5.799733333333334, 5.0054453781512604, 3.8726666666666674, 5.714117647058822, 4.40494891304348, 2.4913166666666675, 1.076103225806452, 0.0), # 108
(15.16558258483927, 11.772825372804107, 12.429508116140834, 13.17817918679549, 11.40706312703408, 5.408996413148403, 4.98191226229861, 4.128843468983388, 5.839395671391555, 2.4328163404072196, 1.8921095240908108, 1.112010941849365, 0.0, 15.308162722908094, 12.232120360343014, 9.460547620454054, 7.298449021221657, 11.67879134278311, 5.780380856576743, 4.98191226229861, 3.8635688665345733, 5.70353156351704, 4.392726395598498, 2.485901623228167, 1.0702568520731008, 0.0), # 109
(15.117683545058746, 11.708776937032614, 12.402249085505263, 13.141510668276972, 11.385916780978512, 5.396522379718539, 4.9584296818864715, 4.1153787532388355, 5.828011736015851, 2.423465935070874, 1.8846722586488671, 1.108580206804162, 0.0, 15.277053326474624, 12.194382274845779, 9.423361293244335, 7.27039780521262, 11.656023472031702, 5.76153025453437, 4.9584296818864715, 3.8546588426560997, 5.692958390489256, 4.380503556092325, 2.4804498171010527, 1.0644342670029652, 0.0), # 110
(15.07021964573269, 11.64502054958184, 12.374811728395064, 13.104866304347826, 11.36482207697894, 5.384338545953361, 4.935006120966905, 4.102279835390947, 5.816725102880659, 2.4140948729121283, 1.8773635300372145, 1.1051479315572885, 0.0, 15.246087962962964, 12.156627247130173, 9.386817650186073, 7.242284618736384, 11.633450205761317, 5.743191769547326, 4.935006120966905, 3.845956104252401, 5.68241103848947, 4.368288768115943, 2.474962345679013, 1.0586382317801675, 0.0), # 111
(15.02326417367448, 11.581586583477144, 12.347201531778696, 13.068271215781, 11.34380483606337, 5.372472224762486, 4.911650063591967, 4.089554031397653, 5.805546014327083, 2.404703360499207, 1.8701948687656293, 1.101715656310415, 0.0, 15.215307784636488, 12.118872219414563, 9.350974343828147, 7.214110081497619, 11.611092028654166, 5.725375643956714, 4.911650063591967, 3.837480160544633, 5.671902418031685, 4.356090405260334, 2.469440306355739, 1.0528715075888313, 0.0), # 112
(14.976806757924871, 11.51861130755273, 12.319490437669426, 13.031800658990448, 11.322854058851952, 5.3609451179335466, 4.888420770925416, 4.077235045853738, 5.794513499337931, 2.3953218946450923, 1.8631797083074313, 1.098292391533924, 0.0, 15.184710241349155, 12.081216306873161, 9.315898541537155, 7.185965683935276, 11.589026998675863, 5.708129064195233, 4.888420770925416, 3.829246512809676, 5.661427029425976, 4.343933552996817, 2.4638980875338854, 1.0471464825047938, 0.0), # 113
(14.930369436640104, 11.456715869170786, 12.292060900028826, 12.995747305532802, 11.301752911537415, 5.349730967961242, 4.865614566728464, 4.065474173003413, 5.783796819046966, 2.3861260671651134, 1.8563318232301862, 1.094921622948397, 0.0, 15.154040662656056, 12.044137852432362, 9.28165911615093, 7.1583782014953385, 11.567593638093932, 5.691663842204779, 4.865614566728464, 3.821236405686601, 5.6508764557687075, 4.331915768510935, 2.4584121800057654, 1.0415196244700715, 0.0), # 114
(14.883815844806392, 11.395922558068468, 12.264929243609757, 12.960101406218136, 11.280434856414509, 5.338800611665514, 4.84324772015325, 4.054268436185806, 5.773399988623354, 2.3771301311952313, 1.8496412030472253, 1.091605011007847, 0.0, 15.123210610656603, 12.007655121086316, 9.248206015236125, 7.131390393585693, 11.546799977246708, 5.675975810660129, 4.84324772015325, 3.8134290083325095, 5.640217428207254, 4.320033802072713, 2.452985848721952, 1.0359929598244064, 0.0), # 115
(14.837087797180216, 11.336142812561162, 12.238042919978499, 12.924799380319683, 11.25886776147603, 5.328128285467958, 4.821283854022315, 4.043586875265996, 5.763296714254843, 2.3683173433798195, 1.8430949150057288, 1.0883364263316462, 0.0, 15.092171615609425, 11.971700689648106, 9.215474575028642, 7.104952030139457, 11.526593428509686, 5.661021625372395, 4.821283854022315, 3.8058059181913984, 5.629433880738015, 4.308266460106562, 2.4476085839957, 1.0305584375055605, 0.0), # 116
(14.790127108518035, 11.277288070964257, 12.211349380701316, 12.88977764711069, 11.237019494714783, 5.317688225790165, 4.799686591158202, 4.033398530109057, 5.753460702129175, 2.359670960363252, 1.8366800263528757, 1.085109739539167, 0.0, 15.06087520777316, 11.936207134930834, 9.183400131764378, 7.079012881089755, 11.50692140425835, 5.6467579421526795, 4.799686591158202, 3.7983487327072605, 5.6185097473573915, 4.296592549036898, 2.4422698761402635, 1.0252080064512963, 0.0), # 117
(14.742875593576338, 11.21926977159314, 12.18479607734449, 12.854972625864399, 11.214857924123566, 5.3074546690537305, 4.7784195543834524, 4.023672440580065, 5.743865658434098, 2.351174238789904, 1.8303836043358468, 1.0819188212497801, 0.0, 15.02927291740644, 11.901107033747579, 9.151918021679233, 7.053522716369711, 11.487731316868196, 5.633141416812091, 4.7784195543834524, 3.791039049324093, 5.607428962061783, 4.284990875288134, 2.436959215468898, 1.0199336155993766, 0.0), # 118
(14.695275067111588, 11.161999352763203, 12.158330461474298, 12.820320735854047, 11.192350917695169, 5.297401851680244, 4.757446366520605, 4.014377646544097, 5.734485289357356, 2.3428104353041492, 1.824192716201821, 1.0787575420828581, 0.0, 14.997316274767892, 11.866332962911438, 9.120963581009105, 7.028431305912447, 11.468970578714712, 5.620128705161736, 4.757446366520605, 3.7838584654858884, 5.5961754588475845, 4.273440245284683, 2.43166609229486, 1.014727213887564, 0.0), # 119
(14.647267343880259, 11.105388252789831, 12.131899984657018, 12.785758396352872, 11.169466343422396, 5.287504010091301, 4.736730650392203, 4.005483187866229, 5.7252933010866975, 2.3345628065503625, 1.818094429197978, 1.0756197726577732, 0.0, 14.964956810116156, 11.831817499235502, 9.090472145989889, 7.003688419651086, 11.450586602173395, 5.60767646301272, 4.736730650392203, 3.7767885786366437, 5.584733171711198, 4.2619194654509585, 2.4263799969314035, 1.0095807502536214, 0.0), # 120
(14.59879423863883, 11.049347909988416, 12.105452098458917, 12.751222026634121, 11.146172069298046, 5.277735380708496, 4.716236028820784, 3.9969581044115383, 5.716263399809866, 2.326414609172919, 1.812075810571498, 1.0724993835938965, 0.0, 14.932146053709857, 11.797493219532859, 9.060379052857488, 6.979243827518756, 11.432526799619732, 5.595741346176154, 4.716236028820784, 3.769810986220354, 5.573086034649023, 4.250407342211375, 2.4210904196917835, 1.0044861736353108, 0.0), # 121
(14.549797566143766, 10.993789762674343, 12.078934254446281, 12.716648045971027, 11.122435963314915, 5.268070199953418, 4.695926124628894, 3.9887714360450994, 5.707369291714607, 2.3183490998161913, 1.8061239275695606, 1.0693902455106004, 0.0, 14.898835535807633, 11.763292700616601, 9.030619637847803, 6.955047299448573, 11.414738583429214, 5.584280010463139, 4.695926124628894, 3.762907285681013, 5.561217981657458, 4.238882681990344, 2.4157868508892566, 0.9994354329703949, 0.0), # 122
(14.50021914115155, 10.938625249163001, 12.052293904185383, 12.681972873636834, 11.098225893465804, 5.258482704247664, 4.675764560639071, 3.9808922226319887, 5.698584682988669, 2.3103495351245553, 1.8002258474393456, 1.0662862290272563, 0.0, 14.864976786668116, 11.729148519299818, 9.001129237196727, 6.931048605373665, 11.397169365977337, 5.573249111684785, 4.675764560639071, 3.7560590744626166, 5.549112946732902, 4.227324291212279, 2.4104587808370765, 0.9944204771966367, 0.0), # 123
(14.450000778418648, 10.883765807769782, 12.025478499242494, 12.647132928904785, 11.073509727743506, 5.248947130012824, 4.655714959673856, 3.9732895040372846, 5.689883279819794, 2.302399171742385, 1.794368637428032, 1.063181204763237, 0.0, 14.830521336549939, 11.694993252395603, 8.971843187140161, 6.907197515227153, 11.379766559639588, 5.562605305652198, 4.655714959673856, 3.74924795000916, 5.536754863871753, 4.215710976301596, 2.405095699848499, 0.9894332552517985, 0.0), # 124
(14.399084292701534, 10.82912287681007, 11.9984354911839, 12.612064631048113, 11.048255334140823, 5.239437713670492, 4.635740944555791, 3.965932320126061, 5.68123878839573, 2.294481266314054, 1.7885393647828007, 1.0600690433379134, 0.0, 14.795420715711726, 11.660759476717045, 8.942696823914003, 6.883443798942161, 11.36247757679146, 5.552305248176485, 4.635740944555791, 3.7424555097646373, 5.524127667070411, 4.204021543682705, 2.39968709823678, 0.9844657160736429, 0.0), # 125
(14.347411498756685, 10.774607894599258, 11.971112331575865, 12.576704399340066, 11.022430580650552, 5.229928691642264, 4.615806138107416, 3.958789710763395, 5.6726249149042225, 2.2865790754839375, 1.7827250967508306, 1.0569436153706582, 0.0, 14.759626454412127, 11.626379769077237, 8.913625483754151, 6.859737226451811, 11.345249829808445, 5.542305595068753, 4.615806138107416, 3.735663351173045, 5.511215290325276, 4.192234799780023, 2.394222466315173, 0.9795098085999328, 0.0), # 126
(14.294924211340579, 10.720132299452729, 11.943456471984673, 12.54098865305388, 10.996003335265492, 5.220394300349728, 4.595874163151275, 3.951830715814364, 5.664015365533016, 2.27867585589641, 1.7769129005793014, 1.0537987914808424, 0.0, 14.723090082909758, 11.591786706289264, 8.884564502896506, 6.836027567689229, 11.328030731066033, 5.53256300214011, 4.595874163151275, 3.728853071678377, 5.498001667632746, 4.1803295510179606, 2.388691294396935, 0.97455748176843, 0.0), # 127
(14.241564245209673, 10.665607529685879, 11.915415363976601, 12.504853811462798, 10.968941465978443, 5.210808776214481, 4.575908642509906, 3.9450243751440417, 5.655383846469858, 2.2707548641958457, 1.7710898435153934, 1.0506284422878387, 0.0, 14.68576313146326, 11.556912865166222, 8.855449217576966, 6.812264592587535, 11.310767692939717, 5.523034125201659, 4.575908642509906, 3.722006268724629, 5.484470732989221, 4.168284603820934, 2.3830830727953205, 0.9696006845168982, 0.0), # 128
(14.187273415120451, 10.610945023614088, 11.886936459117921, 12.468236293840059, 10.9412128407822, 5.201146355658116, 4.555873199005851, 3.938339728617507, 5.646704063902494, 2.2627993570266187, 1.765242992806286, 1.0474264384110183, 0.0, 14.647597130331262, 11.5216908225212, 8.82621496403143, 6.788398071079855, 11.293408127804987, 5.51367562006451, 4.555873199005851, 3.7151045397557967, 5.4706064203911, 4.156078764613354, 2.377387291823584, 0.9646313657830989, 0.0), # 129
(14.131993535829388, 10.556056219552751, 11.857967208974907, 12.431072519458905, 10.91278532766956, 5.191381275102222, 4.53573145546165, 3.9317458160998338, 5.637949724018666, 2.2547925910331035, 1.7593594156991588, 1.044186650469754, 0.0, 14.608543609772397, 11.48605315516729, 8.796797078495793, 6.764377773099309, 11.275899448037332, 5.504444142539767, 4.53573145546165, 3.7081294822158726, 5.45639266383478, 4.1436908398196355, 2.3715934417949813, 0.9596414745047956, 0.0), # 130
(14.07566642209295, 10.500852555817252, 11.828455065113841, 12.393298907592571, 10.883626794633326, 5.181487770968396, 4.515447034699847, 3.9252116774560997, 5.629094533006126, 2.2467178228596745, 1.7534261794411918, 1.0409029490834167, 0.0, 14.568554100045299, 11.449932439917582, 8.767130897205957, 6.740153468579022, 11.258189066012251, 5.49529634843854, 4.515447034699847, 3.701062693548854, 5.441813397316663, 4.131099635864191, 2.3656910130227686, 0.9546229596197504, 0.0), # 131
(14.018233888667616, 10.445245470722984, 11.798347479100995, 12.354851877514303, 10.853705109666297, 5.171440079678229, 4.49498355954298, 3.918706352551382, 5.620112197052615, 2.238558309150706, 1.7474303512795641, 1.0375692048713792, 0.0, 14.527580131408602, 11.413261253585167, 8.73715175639782, 6.715674927452117, 11.24022439410523, 5.486188893571935, 4.49498355954298, 3.693885771198735, 5.4268525548331485, 4.1182839591714355, 2.3596694958201994, 0.949567770065726, 0.0), # 132
(13.959637750309861, 10.38914640258533, 11.767591902502646, 12.315667848497343, 10.822988140761264, 5.161212437653315, 4.474304652813592, 3.9121988812507547, 5.61097642234588, 2.2302973065505736, 1.7413589984614566, 1.0341792884530125, 0.0, 14.485573234120938, 11.375972172983136, 8.706794992307282, 6.690891919651719, 11.22195284469176, 5.477078433751057, 4.474304652813592, 3.686580312609511, 5.411494070380632, 4.105222616165782, 2.3535183805005295, 0.9444678547804848, 0.0), # 133
(13.899819821776152, 10.332466789719687, 11.736135786885072, 12.275683239814924, 10.791443755911033, 5.150779081315248, 4.453373937334223, 3.9056583034192958, 5.601660915073669, 2.2219180717036497, 1.7351991882340478, 1.030727070447689, 0.0, 14.442484938440934, 11.337997774924577, 8.675995941170239, 6.6657542151109475, 11.203321830147338, 5.467921624787015, 4.453373937334223, 3.6791279152251772, 5.395721877955516, 4.091894413271643, 2.3472271573770147, 0.9393151627017899, 0.0), # 134
(13.838721917822966, 10.275118070441435, 11.703926583814546, 12.234834470740296, 10.759039823108395, 5.14011424708562, 4.432155035927415, 3.8990536589220803, 5.592139381423722, 2.213403861254311, 1.7289379878445184, 1.0272064214747805, 0.0, 14.398266774627231, 11.299270636222584, 8.64468993922259, 6.640211583762932, 11.184278762847445, 5.458675122490913, 4.432155035927415, 3.671510176489728, 5.379519911554198, 4.0782781569134325, 2.340785316762909, 0.9341016427674034, 0.0), # 135
(13.776285853206776, 10.217011683065968, 11.670911744857346, 12.193057960546685, 10.725744210346152, 5.129192171386024, 4.410611571415708, 3.892353987624185, 5.5823855275837895, 2.2047379318469296, 1.7225624645400475, 1.0236112121536591, 0.0, 14.352870272938459, 11.259723333690248, 8.612812322700236, 6.614213795540787, 11.164771055167579, 5.44929558267386, 4.410611571415708, 3.6637086938471604, 5.362872105173076, 4.064352653515563, 2.3341823489714693, 0.9288192439150881, 0.0), # 136
(13.712453442684055, 10.15805906590867, 11.63703872157975, 12.15029012850735, 10.691524785617101, 5.117987090638052, 4.388707166621645, 3.885528329390686, 5.572373059741617, 2.1959035401258813, 1.716059685567815, 1.0199353131036961, 0.0, 14.306246963633242, 11.219288444140656, 8.580298427839075, 6.587710620377642, 11.144746119483234, 5.439739661146961, 4.388707166621645, 3.6557050647414657, 5.345762392808551, 4.050096709502451, 2.3274077443159498, 0.9234599150826065, 0.0), # 137
(13.647166501011277, 10.098171657284933, 11.602254965548024, 12.106467393895517, 10.656349416914047, 5.106473241263299, 4.366405444367763, 3.8785457240866603, 5.56207568408495, 2.1868839427355393, 1.7094167181750008, 1.016172594944264, 0.0, 14.258348376970226, 11.1778985443869, 8.547083590875005, 6.560651828206616, 11.1241513681699, 5.4299640137213245, 4.366405444367763, 3.6474808866166426, 5.3281747084570235, 4.035489131298506, 2.320450993109605, 0.9180156052077213, 0.0), # 138
(13.58036684294491, 10.037260895510144, 11.566507928328454, 12.061526175984431, 10.620185972229777, 5.094624859683358, 4.343670027476608, 3.8713752115771833, 5.551467106801532, 2.1776623963202795, 1.7026206296087845, 1.0123169282947344, 0.0, 14.20912604320803, 11.135486211242075, 8.513103148043921, 6.532987188960837, 11.102934213603064, 5.419925296208056, 4.343670027476608, 3.6390177569166844, 5.3100929861148884, 4.020508725328145, 2.313301585665691, 0.912478263228195, 0.0), # 139
(13.511996283241437, 9.97523821889969, 11.529745061487317, 12.015402894047334, 10.583002319557098, 5.082416182319821, 4.320464538770717, 3.863985831727331, 5.54052103407911, 2.168222157524475, 1.6956584871163454, 1.008362183774479, 0.0, 14.158531492605304, 11.091984021519266, 8.478292435581725, 6.504666472573423, 11.08104206815822, 5.409580164418264, 4.320464538770717, 3.6302972730855863, 5.291501159778549, 4.005134298015779, 2.3059490122974635, 0.9068398380817901, 0.0), # 140
(13.44199663665733, 9.912015065768964, 11.491913816590882, 11.968033967357464, 10.544766326888803, 5.069821445594281, 4.296752601072636, 3.8563466244021805, 5.529211172105429, 2.158546482992501, 1.688517357944864, 1.00430223200287, 0.0, 14.106516255420662, 11.047324552031569, 8.442586789724318, 6.4756394489775015, 11.058422344210857, 5.398885274163053, 4.296752601072636, 3.6213010325673434, 5.272383163444402, 3.989344655785822, 2.2983827633181764, 0.9010922787062696, 0.0), # 141
(13.37030971794905, 9.84750287443335, 11.452961645205429, 11.919355815188066, 10.505445862217693, 5.056814885928333, 4.272497837204901, 3.848426629466808, 5.517511227068235, 2.1486186293687317, 1.6811843093415195, 1.0001309435992793, 0.0, 14.053031861912746, 11.001440379592072, 8.405921546707596, 6.445855888106194, 11.03502245413647, 5.3877972812535315, 4.272497837204901, 3.612010632805952, 5.252722931108846, 3.973118605062689, 2.2905923290410857, 0.8952275340393956, 0.0), # 142
(13.29687734187308, 9.781613083208239, 11.412835998897235, 11.86930485681237, 10.465008793536564, 5.043370739743566, 4.247663869990055, 3.840194886786288, 5.505394905155279, 2.1384218532975416, 1.6736464085534917, 0.9958421891830788, 0.0, 13.998029842340188, 10.954264081013864, 8.368232042767458, 6.415265559892624, 11.010789810310557, 5.376272841500803, 4.247663869990055, 3.6024076712454045, 5.232504396768282, 3.956434952270791, 2.282567199779447, 0.8892375530189309, 0.0), # 143
(13.221641323185896, 9.714257130409019, 11.37148432923257, 11.817817511503629, 10.423422988838217, 5.029463243461577, 4.222214322250639, 3.8316204362256996, 5.492835912554298, 2.1279394114233043, 1.6658907228279605, 0.99142983937364, 0.0, 13.941461726961624, 10.905728233110038, 8.329453614139801, 6.383818234269912, 10.985671825108597, 5.364268610715979, 4.222214322250639, 3.592473745329698, 5.2117114944191085, 3.9392725038345437, 2.2742968658465146, 0.8831142845826383, 0.0), # 144
(13.144543476643964, 9.64534645435108, 11.328854087777719, 11.764830198535075, 10.380656316115449, 5.015066633503958, 4.196112816809195, 3.8226723176501176, 5.479807955453042, 2.1171545603903956, 1.6579043194121055, 0.9868877647903354, 0.0, 13.88327904603568, 10.855765412693687, 8.289521597060528, 6.351463681171186, 10.959615910906084, 5.351741244710165, 4.196112816809195, 3.582190452502827, 5.190328158057724, 3.921610066178359, 2.265770817555544, 0.8768496776682801, 0.0), # 145
(13.065525617003761, 9.574792493349808, 11.284892726098956, 11.710279337179951, 10.33667664336106, 5.000155146292303, 4.169322976488264, 3.813319570924618, 5.4662847400392565, 2.1060505568431886, 1.6496742655531065, 0.9822098360525362, 0.0, 13.82343332982099, 10.804308196577896, 8.248371327765533, 6.318151670529565, 10.932569480078513, 5.338647399294466, 4.169322976488264, 3.5715393902087875, 5.16833832168053, 3.903426445726651, 2.2569785452197917, 0.870435681213619, 0.0), # 146
(12.98452955902176, 9.502506685720592, 11.239547695762546, 11.654101346711496, 10.291451838567841, 4.984703018248201, 4.141808424110385, 3.803531235914277, 5.4522399725006885, 2.094610657426059, 1.6411876284981433, 0.9773899237796149, 0.0, 13.761876108576189, 10.751289161575762, 8.205938142490716, 6.2838319722781755, 10.904479945001377, 5.324943730279988, 4.141808424110385, 3.5605021558915717, 5.145725919283921, 3.884700448903833, 2.2479095391525097, 0.8638642441564175, 0.0), # 147
(12.901497117454435, 9.428400469778822, 11.192766448334778, 11.596232646402957, 10.2449497697286, 4.968684485793251, 4.113532782498101, 3.7932763524841717, 5.437647359025082, 2.082818118783379, 1.6324314754943956, 0.9724218985909429, 0.0, 13.698558912559907, 10.69664088450037, 8.162157377471978, 6.248454356350136, 10.875294718050164, 5.310586893477841, 4.113532782498101, 3.5490603469951787, 5.1224748848643, 3.8654108821343196, 2.2385532896669558, 0.8571273154344385, 0.0), # 148
(12.81637010705826, 9.352385283839885, 11.144496435381926, 11.536609655527563, 10.197138304836129, 4.9520737853490395, 4.084459674473953, 3.7825239604993777, 5.42248060580018, 2.0706561975595257, 1.6233928737890426, 0.9672996311058923, 0.0, 13.63343327203078, 10.640295942164814, 8.116964368945213, 6.211968592678575, 10.84496121160036, 5.295533544699129, 4.084459674473953, 3.5371955609635997, 5.098569152418064, 3.845536551842522, 2.2288992870763855, 0.8502168439854443, 0.0), # 149
(12.729090342589704, 9.274372566219169, 11.09468510847026, 11.475168793358566, 10.147985311883227, 4.934845153337166, 4.054552722860481, 3.771243099824971, 5.406713419013735, 2.058108150398871, 1.614058890629265, 0.9620169919438353, 0.0, 13.566450717247434, 10.582186911382186, 8.070294453146325, 6.174324451196611, 10.81342683802747, 5.27974033975496, 4.054552722860481, 3.524889395240833, 5.0739926559416135, 3.825056264452856, 2.2189370216940523, 0.8431247787471974, 0.0), # 150
(12.63959963880524, 9.194273755232066, 11.043279919166057, 11.411846479169196, 10.097458658862696, 4.916972826179219, 4.023775550480226, 3.759402810326029, 5.390319504853488, 2.0451572339457917, 1.6044165932622414, 0.956567851724143, 0.0, 13.49756277846851, 10.522246368965572, 8.022082966311206, 6.135471701837374, 10.780639009706976, 5.263163934456441, 4.023775550480226, 3.5121234472708704, 5.048729329431348, 3.8039488263897328, 2.2086559838332116, 0.8358430686574607, 0.0), # 151
(12.54783981046135, 9.11200028919396, 10.990228319035603, 11.346579132232703, 10.045526213767326, 4.898431040296793, 3.992091780155732, 3.7469721318676275, 5.373272569507184, 2.0317867048446603, 1.5944530489351527, 0.950946081066188, 0.0, 13.426720985952636, 10.460406891728066, 7.9722652446757625, 6.09536011453398, 10.746545139014367, 5.245760984614678, 3.992091780155732, 3.4988793144977093, 5.022763106883663, 3.7821930440775686, 2.198045663807121, 0.8283636626539964, 0.0), # 152
(12.453752672314497, 9.027463606420243, 10.935477759645158, 11.27930317182232, 9.992155844589925, 4.8791940321114815, 3.9594650347095355, 3.7339201043148416, 5.355546319162572, 2.017979819739852, 1.5841553248951779, 0.945145550589342, 0.0, 13.353876869958444, 10.39660105648276, 7.920776624475889, 6.053939459219555, 10.711092638325145, 5.227488146040779, 3.9594650347095355, 3.485138594365344, 4.996077922294963, 3.759767723940774, 2.187095551929032, 0.8206785096745677, 0.0), # 153
(12.357280039121166, 8.940575145226303, 10.878975692561012, 11.209955017211293, 9.937315419323285, 4.859236038044878, 3.9258589369641825, 3.7202157675327485, 5.337114460007395, 2.0037198352757417, 1.5735104883894968, 0.9391601309129768, 0.0, 13.278981960744572, 10.330761440042743, 7.867552441947483, 6.011159505827224, 10.67422892001479, 5.208302074545848, 3.9258589369641825, 3.4708828843177697, 4.968657709661643, 3.736651672403765, 2.1757951385122025, 0.8127795586569367, 0.0), # 154
(12.258363725637818, 8.851246343927524, 10.820669569349436, 11.138471087672855, 9.880972805960209, 4.838531294518574, 3.891237109742209, 3.705828161386424, 5.317950698229401, 1.9889900080967022, 1.562505606665289, 0.9329836926564644, 0.0, 13.201987788569642, 10.262820619221108, 7.812528033326444, 5.966970024290106, 10.635901396458802, 5.188159425940994, 3.891237109742209, 3.456093781798981, 4.940486402980104, 3.712823695890952, 2.1641339138698874, 0.804658758538866, 0.0), # 155
(12.15694554662093, 8.759388640839303, 10.760506841576703, 11.06478780248025, 9.823095872493491, 4.817054037954164, 3.85556317586616, 3.690726325740946, 5.298028740016334, 1.9737735948471096, 1.5511277469697347, 0.9266101064391765, 0.0, 13.122845883692296, 10.19271117083094, 7.755638734848673, 5.921320784541328, 10.596057480032668, 5.167016856037325, 3.85556317586616, 3.440752884252974, 4.911547936246746, 3.688262600826751, 2.1521013683153405, 0.7963080582581185, 0.0), # 156
(12.05296731682698, 8.664913474277022, 10.698434960809092, 10.988841580906726, 9.76365248691593, 4.79477850477324, 3.8188007581585754, 3.6748793004613884, 5.27732229155594, 1.958053852171337, 1.5393639765500133, 0.9200332428804852, 0.0, 13.041507776371162, 10.120365671685335, 7.696819882750066, 5.87416155651401, 10.55464458311188, 5.1448310206459436, 3.8188007581585754, 3.4248417891237426, 4.881826243457965, 3.662947193635576, 2.1396869921618182, 0.7877194067524566, 0.0), # 157
(11.943489514248384, 8.56599791046598, 10.631455938536474, 10.907723497981493, 9.699926512929064, 4.7702895112293024, 3.780085376742286, 3.6571979682329148, 5.254219782186185, 1.9413463665164579, 1.5268255340103847, 0.9130132752259121, 0.0, 12.954377375064553, 10.043146027485031, 7.634127670051924, 5.824039099549372, 10.50843956437237, 5.120077155526081, 3.780085376742286, 3.407349650878073, 4.849963256464532, 3.6359078326604983, 2.126291187707295, 0.7787270827696345, 0.0), # 158
(11.811658827165445, 8.452495802079234, 10.542317091203984, 10.804772590546145, 9.61620406376707, 4.7354436714732975, 3.734570210708573, 3.6314756885095885, 5.21942787265181, 1.9209123976394986, 1.5113111828317318, 0.9041816698244146, 0.0, 12.840684235072311, 9.94599836806856, 7.556555914158659, 5.762737192918495, 10.43885574530362, 5.084065963913424, 3.734570210708573, 3.3824597653380692, 4.808102031883535, 3.6015908635153826, 2.108463418240797, 0.7684087092799304, 0.0), # 159
(11.655795351846896, 8.323475201859713, 10.429227943941186, 10.678293012490633, 9.51084814010325, 4.689385209644506, 3.6817949987070273, 3.5970661263515646, 5.171960121188613, 1.896482260745158, 1.4926025356292107, 0.893400259851713, 0.0, 12.69827297422973, 9.827402858368842, 7.463012678146054, 5.689446782235472, 10.343920242377227, 5.0358925768921905, 3.6817949987070273, 3.3495608640317895, 4.755424070051625, 3.559431004163545, 2.0858455887882372, 0.7566795638054286, 0.0), # 160
(11.477155287337537, 8.179777273184687, 10.293395962547079, 10.529487004508074, 9.38495266590092, 4.632672092132293, 3.622145156805501, 3.5544003554065204, 5.112442542399476, 1.8682632772683756, 1.4708644412265888, 0.8807689958543429, 0.0, 12.528598471710556, 9.68845895439777, 7.354322206132943, 5.6047898318051255, 10.224885084798952, 4.976160497569129, 3.622145156805501, 3.3090514943802094, 4.69247633295046, 3.509829001502692, 2.058679192509416, 0.7436161157440625, 0.0), # 161
(11.27699483268217, 8.022243179431417, 10.136028612820661, 10.359556807291593, 9.239611565123418, 4.565862285326026, 3.5560061010718473, 3.503909449322135, 5.041501150887273, 1.836462768644093, 1.4462617484476323, 0.8663878283788393, 0.0, 12.333115606688533, 9.530266112167231, 7.231308742238162, 5.509388305932278, 10.083002301774545, 4.9054732290509895, 3.5560061010718473, 3.261330203804304, 4.619805782561709, 3.4531856024305316, 2.0272057225641325, 0.7292948344937653, 0.0), # 162
(11.056570186925597, 7.851714083977169, 9.958333360560937, 10.169704661534322, 9.075918761734068, 4.489513755615068, 3.4837632475739206, 3.4460244817460834, 4.959761961254883, 1.8012880563072504, 1.418959306116109, 0.8503567079717379, 0.0, 12.113279258337407, 9.353923787689116, 7.0947965305805445, 5.40386416892175, 9.919523922509766, 4.824434274444517, 3.4837632475739206, 3.2067955397250487, 4.537959380867034, 3.3899015538447745, 1.9916666721121876, 0.71379218945247, 0.0), # 163
(10.817137549112616, 7.669031150199204, 9.761517671566903, 9.961132807929381, 8.894968179696201, 4.404184469388787, 3.405802012379573, 3.3811765263260463, 4.867850988105186, 1.762946461692788, 1.3891219630557858, 0.8327755851795738, 0.0, 11.870544305830926, 9.160531436975312, 6.945609815278928, 5.288839385078362, 9.735701976210372, 4.733647136856465, 3.405802012379573, 3.1458460495634197, 4.447484089848101, 3.320377602643128, 1.9523035343133808, 0.6971846500181095, 0.0), # 164
(10.559953118288028, 7.475035541474793, 9.546789011637559, 9.735043487169904, 8.697853742973145, 4.310432393036548, 3.3225078115566578, 3.3097966567096977, 4.766394246041056, 1.7216453062356458, 1.35691456809043, 0.8137444105488828, 0.0, 11.606365628342832, 8.951188516037709, 6.7845728404521495, 5.164935918706936, 9.532788492082112, 4.633715319393577, 3.3225078115566578, 3.078880280740391, 4.348926871486572, 3.245014495723302, 1.909357802327512, 0.6795486855886177, 0.0), # 165
(10.286273093496636, 7.270568421181199, 9.315354846571905, 9.492638939949002, 8.485669375528229, 4.208815492947715, 3.234266061173029, 3.2323159465447184, 4.656017749665372, 1.6775919113707654, 1.322501970043808, 0.7933631346262003, 0.0, 11.322198105046873, 8.726994480888202, 6.612509850219039, 5.0327757341122945, 9.312035499330744, 4.525242325162606, 3.234266061173029, 3.0062967806769394, 4.242834687764114, 3.1642129799830014, 1.8630709693143812, 0.6609607655619273, 0.0), # 166
(9.997353673783238, 7.056470952695688, 9.06842264216894, 9.235121406959813, 8.259509001324778, 4.099891735511655, 3.14146217729654, 3.1491654694787847, 4.537347513581013, 1.6309935985330861, 1.2860490177396875, 0.7717317079580612, 0.0, 11.019496615116793, 8.489048787538673, 6.430245088698436, 4.892980795599257, 9.074695027162026, 4.408831657270299, 3.14146217729654, 2.928494096794039, 4.129754500662389, 3.0783738023199385, 1.8136845284337881, 0.6414973593359717, 0.0), # 167
(9.694451058192634, 6.833584299395522, 8.807199864227664, 8.963693128895455, 8.020466544326124, 3.9842190871177325, 3.0444815759950434, 3.0607762991595733, 4.411009552390856, 1.5820576891575493, 1.247720560001835, 0.7489500810910016, 0.0, 10.69971603772634, 8.238450892001017, 6.2386028000091756, 4.746173067472647, 8.822019104781711, 4.285086818823403, 3.0444815759950434, 2.8458707765126663, 4.010233272163062, 2.987897709631819, 1.7614399728455332, 0.6212349363086839, 0.0), # 168
(9.378821445769624, 6.602749624657969, 8.53289397854708, 8.67955634644906, 7.769635928495594, 3.8623555141553156, 2.9437096733363934, 2.9675795092347634, 4.277629880697781, 1.5309915046790952, 1.2076814456540184, 0.7251182045715564, 0.0, 10.364311252049257, 7.976300250287119, 6.038407228270092, 4.592974514037284, 8.555259761395561, 4.154611312928669, 2.9437096733363934, 2.7588253672537966, 3.884817964247797, 2.8931854488163538, 1.706578795709416, 0.6002499658779973, 0.0), # 169
(9.051721035559014, 6.3648080918602945, 8.24671245092618, 8.383913300313743, 7.508111077796515, 3.7348589830137664, 2.8395318853884426, 2.870006173352032, 4.137834513104661, 1.4780023665326634, 1.1660965235200045, 0.7003360289462612, 0.0, 10.014737137259289, 7.7036963184088725, 5.830482617600023, 4.43400709959799, 8.275669026209322, 4.018008642692845, 2.8395318853884426, 2.6677564164384044, 3.7540555388982577, 2.7946377667712485, 1.649342490185236, 0.5786189174418451, 0.0), # 170
(8.7144060266056, 6.12060086437976, 7.949862747163971, 8.077966231182643, 7.23698591619222, 3.602287460082452, 2.7323336282190445, 2.7684873651590554, 3.992249464214377, 1.4232975961531957, 1.1231306424235596, 0.6747035047616515, 0.0, 9.652448572530185, 7.421738552378166, 5.615653212117798, 4.269892788459586, 7.984498928428754, 3.8758823112226777, 2.7323336282190445, 2.5730624714874657, 3.61849295809611, 2.692655410394215, 1.5899725494327943, 0.5564182603981601, 0.0), # 171
(8.368132617954185, 5.870969105593635, 7.643552333059449, 7.762917379748876, 6.9573543676460305, 3.4651989117507385, 2.6225003178960526, 2.663454158303514, 3.8415007486298056, 1.3670845149756323, 1.0789486511884518, 0.648320582564263, 0.0, 9.278900437035686, 7.1315264082068905, 5.3947432559422595, 4.101253544926896, 7.683001497259611, 3.7288358216249198, 2.6225003178960526, 2.475142079821956, 3.4786771838230153, 2.587639126582959, 1.52871046661189, 0.5337244641448761, 0.0), # 172
(8.014157008649567, 5.616753978879182, 7.328988674411616, 7.439968986705571, 6.6703103561212815, 3.3241513044079904, 2.51041737048732, 2.5553376264330825, 3.6862143809538255, 1.309570444434913, 1.0337153986384477, 0.62128721290063, 0.0, 8.89554760994954, 6.83415934190693, 5.168576993192238, 3.9287113333047383, 7.372428761907651, 3.5774726770063157, 2.51041737048732, 2.37439378886285, 3.3351551780606408, 2.479989662235191, 1.4657977348823235, 0.5106139980799257, 0.0), # 173
(7.6537353977365505, 5.358796647613667, 7.00737923701947, 7.110323292745849, 6.376947805581297, 3.179702604443573, 2.3964702020607005, 2.4445688431954404, 3.527016375789314, 1.250962705965979, 0.9875957335973142, 0.5937033463172892, 0.0, 8.503844970445494, 6.53073680949018, 4.93797866798657, 3.7528881178979363, 7.054032751578628, 3.4223963804736166, 2.3964702020607005, 2.2712161460311235, 3.1884739027906486, 2.370107764248617, 1.401475847403894, 0.4871633316012425, 0.0), # 174
(7.288123984259929, 5.097938275174352, 6.679931486682011, 6.7751825385628415, 6.078360639989406, 3.0324107782468537, 2.2810442286840464, 2.331578882238264, 3.36453274773915, 1.19146862100377, 0.9407545048888186, 0.5656689333607753, 0.0, 8.105247397697292, 6.222358266968527, 4.703772524444093, 3.574405863011309, 6.7290654954783, 3.26421043513357, 2.2810442286840464, 2.1660076987477526, 3.039180319994703, 2.2583941795209475, 1.3359862973364023, 0.46344893410675936, 0.0), # 175
(6.91857896726451, 4.835020024938507, 6.347852889198238, 6.435748964849671, 5.775642783308939, 2.882833792207196, 2.164524866425212, 2.216798817209233, 3.199389511406209, 1.131295510983227, 0.8933565613367281, 0.537283924577624, 0.0, 7.701209770878679, 5.910123170353863, 4.46678280668364, 3.39388653294968, 6.398779022812418, 3.103518344092926, 2.164524866425212, 2.0591669944337117, 2.8878213916544695, 2.1452496549498905, 1.2695705778396478, 0.4395472749944098, 0.0), # 176
(6.546356545795092, 4.570883060283395, 6.012350910367152, 6.093224812299459, 5.469888159503225, 2.731529612713966, 2.0472975313520503, 2.100659721756022, 3.0322126813933705, 1.07065069733929, 0.8455667517648098, 0.5086482705143706, 0.0, 7.2931869691634, 5.595130975658075, 4.227833758824048, 3.211952092017869, 6.064425362786741, 2.9409236104584306, 2.0472975313520503, 1.9510925805099755, 2.7349440797516125, 2.0310749374331536, 1.2024701820734305, 0.4155348236621269, 0.0), # 177
(6.172712918896475, 4.306368544586282, 5.6746330159877525, 5.74881232160534, 5.162190692535588, 2.5790562061565305, 1.929747639532414, 1.9835926695263104, 2.863628272303512, 1.0097415015069002, 0.7975499249968301, 0.4798619217175504, 0.0, 6.882633871725203, 5.278481138893053, 3.98774962498415, 3.0292245045207, 5.727256544607024, 2.7770297373368344, 1.929747639532414, 1.8421830043975218, 2.581095346267794, 1.916270773868447, 1.1349266031975505, 0.3914880495078438, 0.0), # 178
(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0), # 179
)
passenger_allighting_rate = (
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 0
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 1
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 2
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 3
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 4
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 5
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 6
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 7
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 8
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 9
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 10
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 11
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 12
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 13
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 14
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 15
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 16
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 17
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 18
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 19
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 20
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 21
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 22
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 23
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 24
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 25
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 26
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 27
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 28
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 29
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 30
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 31
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 32
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 33
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 34
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 35
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 36
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 37
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 38
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 39
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 40
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 41
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 42
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 43
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 44
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 45
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 46
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 47
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 48
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 49
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 50
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 51
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 52
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 53
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 54
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 55
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 56
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 57
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 58
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 59
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 60
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 61
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 62
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 63
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 64
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 65
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 66
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 67
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 68
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 69
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 70
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 71
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 72
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 73
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 74
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 75
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 76
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 77
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 78
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 79
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 80
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 81
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 82
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 83
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 84
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 85
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 86
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 87
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 88
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 89
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 90
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 91
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 92
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 93
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 94
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 95
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 96
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 97
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 98
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 99
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 100
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 101
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 102
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 103
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 104
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 105
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 106
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 107
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 108
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 109
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 110
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 111
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 112
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 113
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 114
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 115
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 116
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 117
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 118
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 119
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 120
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 121
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 122
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 123
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 124
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 125
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 126
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 127
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 128
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 129
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 130
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(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 161
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 162
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 163
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 164
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 165
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 166
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 167
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 168
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 169
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 170
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 171
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 172
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 173
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 174
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 175
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 176
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 177
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 178
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 179
)
"""
parameters for reproducibiliy. More information: https://numpy.org/doc/stable/reference/random/parallel.html
"""
#initial entropy
entropy = 8991598675325360468762009371570610170
#index for seed sequence child
child_seed_index = (
1, # 0
65, # 1
)
| 279.150802 | 491 | 0.771944 | 32,987 | 261,006 | 6.107588 | 0.23103 | 0.353798 | 0.339503 | 0.643269 | 0.365283 | 0.359913 | 0.359387 | 0.359307 | 0.359228 | 0.359228 | 0 | 0.851527 | 0.094764 | 261,006 | 934 | 492 | 279.449679 | 0.001181 | 0.015367 | 0 | 0.200873 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0.005459 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
a2420b8b636ce9b943a3feb0176ac8f77c7a31c1 | 92 | py | Python | test_tapas/params/tapa.py | tapas-scaffold-tool/tapas | 00109ec8aec2d590207a3e6ecca72061f2ad03f5 | [
"MIT"
] | 1 | 2020-10-29T17:24:15.000Z | 2020-10-29T17:24:15.000Z | test_tapas/params/tapa.py | tapas-scaffold-tool/tapas | 00109ec8aec2d590207a3e6ecca72061f2ad03f5 | [
"MIT"
] | 9 | 2019-09-16T11:32:35.000Z | 2020-11-05T20:59:57.000Z | test_tapas/params/tapa.py | tapas-scaffold-tool/tapas | 00109ec8aec2d590207a3e6ecca72061f2ad03f5 | [
"MIT"
] | null | null | null | from tapas.tools import prompt_str
def ask():
prompt_str("a.b")
prompt_str("a.c")
| 13.142857 | 34 | 0.652174 | 16 | 92 | 3.5625 | 0.6875 | 0.473684 | 0.350877 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.195652 | 92 | 6 | 35 | 15.333333 | 0.77027 | 0 | 0 | 0 | 0 | 0 | 0.065217 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.25 | true | 0 | 0.25 | 0 | 0.5 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
a25b992d077b44859b7a758b0de9ca0bab7e23c1 | 151 | py | Python | Scripts/my-ip-hostname.py | yogeshwaran01/Mini-Projects | c1a8790079d904405d49c71d6903ca4daaa77b38 | [
"MIT"
] | 4 | 2020-09-30T17:18:13.000Z | 2021-06-11T21:02:10.000Z | Scripts/my-ip-hostname.py | yogeshwaran01/Mini-Projects | c1a8790079d904405d49c71d6903ca4daaa77b38 | [
"MIT"
] | null | null | null | Scripts/my-ip-hostname.py | yogeshwaran01/Mini-Projects | c1a8790079d904405d49c71d6903ca4daaa77b38 | [
"MIT"
] | 1 | 2021-04-02T14:51:00.000Z | 2021-04-02T14:51:00.000Z | import socket
print("Computer Host Name is : ", socket.gethostname())
print("Computer IP Address is : ", socket.gethostbyname(socket.gethostname()))
| 25.166667 | 78 | 0.741722 | 18 | 151 | 6.222222 | 0.611111 | 0.232143 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.119205 | 151 | 5 | 79 | 30.2 | 0.842105 | 0 | 0 | 0 | 0 | 0 | 0.324503 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.333333 | 0 | 0.333333 | 0.666667 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 6 |
a26972b0f9f12bd38806c01a445d4d3d2bc367bc | 105 | py | Python | profile/views.py | melvinchia3636/notesdb | fb4ec1742713501be13cac0965242da1421228bd | [
"MIT"
] | null | null | null | profile/views.py | melvinchia3636/notesdb | fb4ec1742713501be13cac0965242da1421228bd | [
"MIT"
] | null | null | null | profile/views.py | melvinchia3636/notesdb | fb4ec1742713501be13cac0965242da1421228bd | [
"MIT"
] | null | null | null | from django.shortcuts import render
def HomeView(request):
return render(request, 'profile/index.html') | 26.25 | 45 | 0.8 | 14 | 105 | 6 | 0.857143 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.095238 | 105 | 4 | 45 | 26.25 | 0.884211 | 0 | 0 | 0 | 0 | 0 | 0.169811 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.333333 | false | 0 | 0.333333 | 0.333333 | 1 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 6 |
a272cabc00a7f9a244b89a1d7bd1440fa99c0dc3 | 72 | py | Python | loss/__init__.py | marshuang80/StyleTransfer | 08dcdf447c6e6e6495847f295036f2ccfe682078 | [
"Apache-2.0"
] | 2 | 2019-06-07T23:37:09.000Z | 2019-12-06T04:25:06.000Z | loss/__init__.py | marshuang80/StyleTransfer | 08dcdf447c6e6e6495847f295036f2ccfe682078 | [
"Apache-2.0"
] | null | null | null | loss/__init__.py | marshuang80/StyleTransfer | 08dcdf447c6e6e6495847f295036f2ccfe682078 | [
"Apache-2.0"
] | null | null | null | from .content_loss import ContentLoss
from .style_loss import StyleLoss
| 24 | 37 | 0.861111 | 10 | 72 | 6 | 0.7 | 0.333333 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.111111 | 72 | 2 | 38 | 36 | 0.9375 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
a273e27c013b1d9fedb54c02a6e0f1ea4bcbb636 | 149 | py | Python | test.py | YuhengZhi/demix | e7bd400b3901f51bfeb4012753a84c3c69aa78a6 | [
"MIT"
] | null | null | null | test.py | YuhengZhi/demix | e7bd400b3901f51bfeb4012753a84c3c69aa78a6 | [
"MIT"
] | null | null | null | test.py | YuhengZhi/demix | e7bd400b3901f51bfeb4012753a84c3c69aa78a6 | [
"MIT"
] | null | null | null | import os
os.environ['LD_LIBRARY_PATH'] = '/usr/local/cuda-9.0/lib64'
print(os.environ['LD_LIBRARY_PATH'])
import tensorflow as tf
# import torch | 29.8 | 60 | 0.744966 | 25 | 149 | 4.28 | 0.68 | 0.168224 | 0.205607 | 0.336449 | 0.411215 | 0 | 0 | 0 | 0 | 0 | 0 | 0.030075 | 0.107383 | 149 | 5 | 61 | 29.8 | 0.774436 | 0.080537 | 0 | 0 | 0 | 0 | 0.416667 | 0.189394 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.5 | 0 | 0.5 | 0.25 | 1 | 0 | 0 | null | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 6 |
a294a61dcb2ede5203450588b5f3de486f43e066 | 18,648 | py | Python | core/posts/tests/test_posts_view.py | cmackenzie1/cmput404-project | 4067a05c0d3c20b18765f4bc6359ca7c5f59cfee | [
"Apache-2.0"
] | null | null | null | core/posts/tests/test_posts_view.py | cmackenzie1/cmput404-project | 4067a05c0d3c20b18765f4bc6359ca7c5f59cfee | [
"Apache-2.0"
] | 74 | 2019-01-12T21:16:03.000Z | 2022-03-02T03:04:54.000Z | core/posts/tests/test_posts_view.py | cmackenzie1/cmput404-project | 4067a05c0d3c20b18765f4bc6359ca7c5f59cfee | [
"Apache-2.0"
] | 2 | 2019-02-06T16:18:10.000Z | 2019-02-09T22:06:34.000Z | import json
from django.contrib.auth import get_user_model
from django.core.files.uploadedfile import SimpleUploadedFile
from django.test import TestCase
from django.urls import reverse
from rest_framework import status
from rest_framework.test import APIRequestFactory, force_authenticate
from core.authors.tests.util import setupUser
from core.posts.models import Posts
from core.posts.views import PostsViewSet
class PostViewsTest(TestCase):
def setUp(self):
self.author1 = setupUser("cry", "password")
self.author2 = setupUser("user2", "password")
self.factory = APIRequestFactory()
self.client.login(username="cry", password="password")
def test_create_post_wrong_user_authentication(self):
self.client.login(username="user2", password="password")
author_id = str(self.author1.id)
data = {
"author": author_id,
"title": "wild",
"unlisted": True,
"visibility": "PRIVATE",
"categories": ["cool", "fun", "sad"]
}
post_data = json.dumps(data)
response = self.client.post('/posts/', {'query': 'createpost', 'postData': post_data})
self.assertEqual(response.status_code, 403)
self.assertEqual(Posts.objects.all().count(), 0)
def test_create_post_unauthenticated(self):
self.client.logout()
author_id = str(self.author1.id)
data = {
"author": author_id,
"title": "wild",
"unlisted": True,
"visibility": "PRIVATE",
"categories": ["cool", "fun", "sad"]
}
post_data = json.dumps(data)
response = self.client.post('/posts/', {'query': 'createpost', 'postData': post_data})
self.assertEqual(response.status_code, 403)
self.assertEqual(Posts.objects.all().count(), 0)
def test_unfound_users(self):
author_id = str(self.author1.id)
data = {
"author": author_id,
"title": "wild",
"unlisted": True,
"visibility": "PRIVATE",
"categories": ["cool", "fun", "sad"]
}
post_data = json.dumps(data)
response = self.client.post('/posts/', {'query': 'createpost', 'postData': post_data, 'visibleTo': json.dumps(["a", "b"])})
self.assertEqual(response.status_code, 400)
self.assertEqual(sorted(response.data["invalidUsers"]), ["a", "b"])
self.assertEqual(Posts.objects.all().count(), 0)
def test_one_unfound_user(self):
author_id = str(self.author1.id)
data = {
"author": author_id,
"title": "wild",
"unlisted": True,
"visibility": "PRIVATE",
"categories": ["cool", "fun", "sad"]
}
post_data = json.dumps(data)
response = self.client.post('/posts/', {'query': 'createpost', 'postData': post_data, 'visibleTo': json.dumps(["b"])})
self.assertEqual(response.status_code, 400)
self.assertEqual(sorted(response.data["invalidUsers"]), ["b"])
self.assertEqual(Posts.objects.all().count(), 0)
def test_all_valid_users(self):
author_id = str(self.author1.id)
data = {
"author": author_id,
"title": "wild",
"unlisted": True,
"visibility": "PRIVATE",
"categories": ["cool", "fun", "sad"]
}
post_data = json.dumps(data)
response = self.client.post('/posts/', {'query': 'createpost', 'postData': post_data, 'visibleTo': json.dumps(["cry", "user2"])})
self.assertEqual(response.status_code, 200)
self.assertIsNone(response.data.get("invalidUsers"))
posts = Posts.objects.all()
self.assertEqual(len(posts), 1)
self.assertEqual(posts.first().visibility, "PRIVATE")
self.assertEqual(posts.first().title, "wild")
self.assertEqual(posts.first().categories, ["cool", "fun", "sad"])
self.assertTrue(posts.first().unlisted)
self.assertEqual(sorted(posts.first().visibleTo), sorted([self.author1.get_url(), self.author2.get_url()]))
def test_all_valid_users_external(self):
author_id = str(self.author1.id)
data = {
"author": author_id,
"title": "wild",
"unlisted": True,
"visibility": "PRIVATE",
"categories": ["cool", "fun", "sad"]
}
post_data = json.dumps(data)
response = self.client.post('/posts/', {'query': 'createpost', 'postData': post_data, 'visibleTo': json.dumps(["cry", "user2", "https://somevaliduser.awekawieawe/author/25"])})
self.assertEqual(response.status_code, 200)
self.assertIsNone(response.data.get("invalidUsers"))
posts = Posts.objects.all()
self.assertEqual(len(posts), 1)
self.assertEqual(posts.first().visibility, "PRIVATE")
self.assertEqual(posts.first().title, "wild")
self.assertEqual(posts.first().categories, ["cool", "fun", "sad"])
self.assertTrue(posts.first().unlisted)
self.assertEqual(sorted(posts.first().visibleTo), sorted([self.author1.get_url(), self.author2.get_url(), "https://somevaliduser.awekawieawe/author/25"]))
def test_create_private_add_author(self):
author_id = str(self.author1.id)
data = {
"author": author_id,
"title": "wild",
"unlisted": True,
"visibility": "PRIVATE",
"categories": ["cool", "fun", "sad"]
}
post_data = json.dumps(data)
response = self.client.post('/posts/', {'query': 'createpost', 'postData': post_data, 'visibleTo': json.dumps(["user2"])})
self.assertEqual(response.status_code, 200)
self.assertIsNone(response.data.get("invalidUsers"))
posts = Posts.objects.all()
self.assertEqual(len(posts), 1)
self.assertEqual(posts.first().visibility, "PRIVATE")
self.assertEqual(posts.first().title, "wild")
self.assertEqual(posts.first().categories, ["cool", "fun", "sad"])
self.assertTrue(posts.first().unlisted)
self.assertEqual(sorted(posts.first().visibleTo), sorted([self.author1.get_url(), self.author2.get_url()]))
def test_create_post_public_no_visible_to(self):
author_id = str(self.author1.id)
data = {
"author": author_id,
"title": "wild",
"unlisted": True,
"visibility": "PUBLIC",
"categories": ["cool", "fun", "sad"]
}
post_data = json.dumps(data)
response = self.client.post('/posts/', {'query': 'createpost', 'postData': post_data})
self.assertEqual(response.status_code, 200)
self.assertIsNone(response.data.get("invalidUsers"))
posts = Posts.objects.all()
self.assertEqual(len(posts), 1)
self.assertEqual(posts.first().visibility, "PUBLIC")
self.assertEqual(posts.first().title, "wild")
self.assertEqual(posts.first().categories, ["cool", "fun", "sad"])
self.assertTrue(posts.first().unlisted)
self.assertEqual(len(posts.first().visibleTo), 0)
def test_create_post(self):
author_id = str(self.author1.id)
data = {
"author": author_id,
"title": "wild",
"unlisted": True,
"visibility": "PRIVATE",
"categories": ["cool", "fun", "sad"],
"description": "A description about the post",
"content": "!! POST CONTENT !! [imgTag](https://somefakeimage11111111111.core)"
}
post_data = json.dumps(data)
response = self.client.post('/posts/', {'query': 'createpost', 'postData': post_data})
self.assertEqual(response.status_code, 200)
self.assertEqual(response.data["query"], "createpost")
self.assertEqual(response.data["message"], "Post created")
posts = Posts.objects.all()
self.assertEqual(len(posts), 1)
self.assertEqual(len(posts.first().visibleTo), 0)
post = posts[0]
self.assertEqual(post.author, self.author1)
self.assertEqual(post.title, data["title"])
self.assertEqual(post.unlisted, data["unlisted"])
self.assertEqual(post.visibility, data["visibility"])
self.assertEqual(post.categories, data["categories"])
self.assertEqual(post.description, data["description"])
self.assertEqual(post.content, data["content"])
self.assertEqual(post.contentType, "text/markdown")
self.assertEqual(post.comments.count(), 0)
# From https://stackoverflow.com/a/27345260, credits to Danilo Cabello
def test_valid_file_type(self):
author_id = str(self.author1.id)
data = {
"author": author_id,
"title": "wild"
}
post_data = json.dumps(data)
fp = SimpleUploadedFile("file.jpg", b"file_content", content_type="image/jpeg")
response = self.client.post('/posts/', {'imageFiles': fp, 'query': 'createpost', 'postData': post_data})
self.assertEqual(response.status_code, 200)
self.assertTrue(Posts.objects.all().count(), 2)
text_posts = Posts.objects.filter(contentType="text/markdown")
text_post = text_posts.first()
self.assertEqual(len(text_posts), 1)
self.assertEqual(text_post.author, self.author1)
self.assertEqual(text_post.title, "wild")
image_posts = Posts.objects.filter(post_id=text_post.post_id, contentType="image/jpeg;base64")
self.assertEqual(len(image_posts), 1)
self.assertEqual(image_posts.first().content, "image/jpeg;base64,ZmlsZV9jb250ZW50")
def test_application_base64_file(self):
author_id = str(self.author1.id)
data = {
"author": author_id,
"title": "wild"
}
post_data = json.dumps(data)
fp = SimpleUploadedFile("app.dat", b"file_content", content_type="application/base64")
response = self.client.post('/posts/', {'imageFiles': fp, 'query': 'createpost', 'postData': post_data})
self.assertEqual(response.status_code, 200)
self.assertTrue(Posts.objects.all().count(), 2)
text_posts = Posts.objects.filter(contentType="text/markdown")
text_post = text_posts.first()
self.assertEqual(len(text_posts), 1)
self.assertEqual(text_post.author, self.author1)
self.assertEqual(text_post.title, "wild")
data_posts = Posts.objects.filter(post_id=text_post.post_id, contentType="application/base64")
self.assertEqual(len(data_posts), 1)
self.assertEqual(data_posts.first().content, "application/base64,ZmlsZV9jb250ZW50")
def test_invalid_file_type(self):
author_id = str(self.author1.id)
data = {
"author": author_id,
"title": "wild"
}
post_data = json.dumps(data)
fp = SimpleUploadedFile("file.mp4", b"file_content", content_type="video/mp4")
response = self.client.post('/posts/', {'imageFiles': fp, 'query': 'createpost', 'postData': post_data})
self.assertEqual(response.status_code, 400)
self.assertEqual(response.data["message"], "Invalid file type")
self.assertTrue(Posts.objects.all().count(), 0)
def test_multiple_files(self):
author_id = str(self.author1.id)
data = {
"author": author_id,
"title": "wild",
"contentType": "text/plain"
}
post_data = json.dumps(data)
fp = SimpleUploadedFile("file.jpg", b"file_content", content_type="image/jpeg")
fp2 = SimpleUploadedFile("file2.jpg", b"file_content", content_type="image/png")
response = self.client.post('/posts/', {'imageFiles': {fp, fp2}, 'query': 'createpost', 'postData': post_data})
self.assertEqual(response.status_code, 200)
self.assertEqual(len(Posts.objects.filter(author=author_id)), 3)
text_post = Posts.objects.filter(contentType="text/plain")
self.assertEqual(len(text_post), 1)
# Checks the post_id attribute to make sure it matches the actual text post
image_posts = Posts.objects.filter(post_id=text_post[0].post_id, contentType__icontains="image/")
self.assertEqual(len(image_posts), 2)
# Checks the content of each image (base64 encoded)
self.assertEqual(Posts.objects.get(contentType="image/jpeg;base64").content, "image/jpeg;base64,ZmlsZV9jb250ZW50")
self.assertEqual(Posts.objects.get(contentType="image/png;base64").content, "image/png;base64,ZmlsZV9jb250ZW50")
def test_delete_post(self):
author_id = str(self.author1.id)
data = {
"author": author_id,
"title": "wild"
}
post_data = json.dumps(data)
response = self.client.post('/posts/', {'query': 'createpost', 'postData': post_data})
post = Posts.objects.get(author=author_id)
response = self.client.delete('/posts/{0}/'.format(post.id))
self.assertEqual(response.status_code, 200)
self.assertEqual(response.data["message"], "Post deleted successfully")
self.assertEqual(len(Posts.objects.filter(id=post.id)), 0)
def test_list_posts_unauthenticated(self):
author_id = str(self.author1.id)
data = {
"author": author_id,
"title": "wild"
}
post_data = json.dumps(data)
fp = SimpleUploadedFile("file.jpg", b"file_content", content_type="image/jpeg")
response = self.client.post('/posts/', {'imageFiles': fp, 'query': 'createpost', 'postData': post_data})
self.assertEqual(response.status_code, 200)
view = PostsViewSet.as_view({'get': 'list'})
request = self.factory.get(reverse('posts-list'))
response = view(request)
self.assertEqual(response.status_code, status.HTTP_200_OK)
for post in response.data['posts']:
self.assertNotIn("image", post['contentType'])
def test_list_posts_authenticated(self):
author_id = str(self.author1.id)
data = {
"author": author_id,
"title": "wild"
}
post_data = json.dumps(data)
fp = SimpleUploadedFile("file.jpg", b"file_content", content_type="image/jpeg")
response = self.client.post('/posts/', {'imageFiles': fp, 'query': 'createpost', 'postData': post_data})
self.assertEqual(response.status_code, 200)
view = PostsViewSet.as_view({'get': 'list'})
request = self.factory.get(reverse('posts-list'))
force_authenticate(request, user=get_user_model().objects.get(username="cry"))
response = view(request)
self.assertEqual(response.status_code, status.HTTP_200_OK)
for post in response.data['posts']:
if 'text' in post['contentType']:
continue
self.assertIn("image", post['contentType'])
def test_list_posts_unlisted(self):
author_id = str(self.author1.id)
data = {
"author": author_id,
"title": "wild",
"unlisted": True
}
post_data = json.dumps(data)
response = self.client.post('/posts/', {'query': 'createpost', 'postData': post_data})
self.assertEqual(response.status_code, 200)
view = PostsViewSet.as_view({'get': 'list'})
request = self.factory.get(reverse('posts-list'))
force_authenticate(request, user=get_user_model().objects.get(username="cry"))
response = view(request)
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertEqual(len(response.data["posts"]), 0)
self.assertEqual(len(Posts.objects.all()), 1)
def test_deletion_unauthenticated(self):
self.client.logout()
post = Posts.objects.create(author=self.author1)
res = self.client.delete("/posts/{0}/".format(post.id))
self.assertEqual(res.status_code, 403)
self.assertEqual(len(Posts.objects.all()), 1)
def test_deletion_wrong_user_authentication(self):
self.client.login(username="user2", password="password")
post = Posts.objects.create(author=self.author1)
res = self.client.delete("/posts/{0}/".format(post.id))
self.assertEqual(res.status_code, 403)
self.assertEqual(len(Posts.objects.all()), 1)
def test_put_unauthenticated(self):
self.client.logout()
post = Posts.objects.create(author=self.author1, title="one")
response = self.client.put("/posts/{0}/".format(post.id), json.dumps({
"title": "new title",
"unlisted": False,
"contentType": "text/markdown",
"description": "Hello",
"author": str(self.author1.id)
}), content_type='application/json')
self.assertEqual(response.status_code, 403)
self.assertEqual(Posts.objects.all().first().title, "one")
def test_put_wrong_user(self):
self.client.login(username="user2", password="password")
post = Posts.objects.create(author=self.author1, title="one")
response = self.client.put("/posts/{0}/".format(post.id), json.dumps({
"title": "new title",
"unlisted": False,
"contentType": "text/markdown",
"description": "Hello",
"author": str(self.author1.id)
}), content_type='application/json')
self.assertEqual(response.status_code, 403)
self.assertEqual(Posts.objects.all().first().title, "one")
def test_put_valid(self):
post = Posts.objects.create(author=self.author1, title="one")
response = self.client.put("/posts/{0}/".format(post.id), json.dumps({
"title": "new title",
"unlisted": False,
"contentType": "text/markdown",
"description": "Hello",
"content": "Test post content",
"author": str(self.author1.id)
}), content_type='application/json')
self.assertEqual(response.status_code, 200)
posts = Posts.objects.all()
new_post = posts.first()
self.assertEqual(posts.count(), 1)
self.assertEqual(new_post.title, "new title")
self.assertEqual(new_post.unlisted, False)
self.assertEqual(new_post.contentType, "text/markdown")
self.assertEqual(new_post.description, "Hello")
self.assertEqual(new_post.content, "Test post content")
self.assertEqual(new_post.author, self.author1)
| 43.981132 | 184 | 0.612398 | 2,061 | 18,648 | 5.42164 | 0.086851 | 0.127528 | 0.055575 | 0.059692 | 0.803204 | 0.768928 | 0.747181 | 0.719617 | 0.718006 | 0.7138 | 0 | 0.016067 | 0.235682 | 18,648 | 423 | 185 | 44.085106 | 0.767909 | 0.010296 | 0 | 0.679245 | 0 | 0 | 0.157219 | 0.00737 | 0 | 0 | 0 | 0 | 0.291105 | 1 | 0.061995 | false | 0.016173 | 0.026954 | 0 | 0.091644 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
a2a9987bb63997e1f5da97f9f0e7173126b3a106 | 14,479 | py | Python | tests/open_api/open_api_spec/test_open_api_spec.py | ericmharris/gc3-query | 0bf5226130aafbb1974aeb96d93ee1996833e87d | [
"MIT"
] | null | null | null | tests/open_api/open_api_spec/test_open_api_spec.py | ericmharris/gc3-query | 0bf5226130aafbb1974aeb96d93ee1996833e87d | [
"MIT"
] | null | null | null | tests/open_api/open_api_spec/test_open_api_spec.py | ericmharris/gc3-query | 0bf5226130aafbb1974aeb96d93ee1996833e87d | [
"MIT"
] | null | null | null | from pathlib import Path
import pytest
from gc3_query.lib import *
from gc3_query.lib.gc3_config import GC3Config
from gc3_query.lib.iaas_classic.instances import Instances
from gc3_query.lib.iaas_classic.sec_rules import SecRules
from gc3_query.lib.open_api.open_api_spec import OpenApiSpec
from gc3_query.lib.base_collections import NestedOrderedDictAttrListBase
BASE_DIR = gc3_cfg.BASE_DIR
TEST_BASE_DIR: Path = Path(__file__).parent
# config_dir = TEST_BASE_DIR.joinpath("config")
config_dir = gc3_cfg.BASE_DIR.joinpath("etc/config")
output_dir = TEST_BASE_DIR.joinpath('output')
spec_files_dir = TEST_BASE_DIR.joinpath('spec_files')
def test_setup():
assert TEST_BASE_DIR.exists()
assert gc3_cfg.OPEN_API_CATALOG_DIR.exists()
assert gc3_cfg.OPEN_API_SPEC_BASE.exists()
if not config_dir.exists():
config_dir.mkdir()
if not output_dir.exists():
output_dir.mkdir()
if not spec_files_dir.exists():
spec_files_dir.mkdir()
def test_init():
service = 'Instances'
gc3_config = GC3Config(atoml_config_dir=config_dir)
service_cfg = gc3_config.iaas_classic.services.compute[service]
open_api_specs_cfg = gc3_config.iaas_classic.open_api_specs
idm_cfg = gc3_config.idm.domains.gc30003
# oapi_spec = OpenApiSpec(service_cfg=service_cfg, open_api_specs_cfg=api_catalog_config)
oapi_spec = OpenApiSpec(service_cfg=service_cfg, open_api_specs_cfg=open_api_specs_cfg, idm_cfg=idm_cfg)
assert oapi_spec.name == service
def test_build():
service = 'Instances'
gc3_config = GC3Config(atoml_config_dir=config_dir)
service_cfg = gc3_config.iaas_classic.services.compute[service]
open_api_specs_cfg = gc3_config.iaas_classic.open_api_specs
idm_cfg = gc3_config.idm.domains.gc30003
# oapi_spec = OpenApiSpec(service_cfg=service_cfg, open_api_specs_cfg=api_catalog_config)
oapi_spec = OpenApiSpec(service_cfg=service_cfg, open_api_specs_cfg=open_api_specs_cfg, idm_cfg=idm_cfg)
assert oapi_spec.name == service
is_built = oapi_spec.build()
assert is_built
def test_validate():
service = 'Instances'
gc3_config = GC3Config(atoml_config_dir=config_dir)
service_cfg = gc3_config.iaas_classic.services.compute[service]
open_api_specs_cfg = gc3_config.iaas_classic.open_api_specs
idm_cfg = gc3_config.idm.domains.gc30003
# oapi_spec = OpenApiSpec(service_cfg=service_cfg, open_api_specs_cfg=api_catalog_config)
oapi_spec = OpenApiSpec(service_cfg=service_cfg, open_api_specs_cfg=open_api_specs_cfg, idm_cfg=idm_cfg)
assert oapi_spec.name == service
is_valid = oapi_spec.validate()
assert is_valid
def test_str_repr():
service = 'Instances'
gc3_config = GC3Config(atoml_config_dir=config_dir)
service_cfg = gc3_config.iaas_classic.services.compute[service]
open_api_specs_cfg = gc3_config.iaas_classic.open_api_specs
idm_cfg = gc3_config.idm.domains.gc30003
oapi_spec = OpenApiSpec(service_cfg=service_cfg, open_api_specs_cfg=open_api_specs_cfg, idm_cfg=idm_cfg)
s = str(oapi_spec)
r = repr(oapi_spec)
assert s
assert r
assert s==r
assert service in s
assert 'OpenApiSpec' in r
def test_init_properties():
idm_domain = 'gc30003'
service = 'Instances'
gc3_config = GC3Config(atoml_config_dir=config_dir)
idm_cfg = gc3_config.idm.domains[idm_domain]
service_cfg = gc3_config.iaas_classic.services.compute[service]
open_api_specs_cfg = gc3_config.iaas_classic.open_api_specs
api_catalog_config = gc3_config.iaas_classic.open_api_spec_catalog
oapi_spec = OpenApiSpec(service_cfg=service_cfg, open_api_specs_cfg=open_api_specs_cfg, idm_cfg=idm_cfg)
assert oapi_spec.name == service
assert oapi_spec._spec_data['schemes'] == ['https']
assert oapi_spec.spec_file.exists()
assert oapi_spec.spec_file.suffix=='.yaml'
assert oapi_spec.spec_export_dir.exists()
assert isinstance(oapi_spec._spec_dict, dict)
assert isinstance(oapi_spec._spec_data, NestedOrderedDictAttrListBase)
def test_get_spec():
idm_domain = 'gc30003'
service = 'Instances'
gc3_config = GC3Config(atoml_config_dir=config_dir)
idm_cfg = gc3_config.idm.domains[idm_domain]
service_cfg = gc3_config.iaas_classic.services.compute[service]
open_api_specs_cfg = gc3_config.iaas_classic.open_api_specs
api_catalog_config = gc3_config.iaas_classic.open_api_spec_catalog
oapi_spec = OpenApiSpec(service_cfg=service_cfg, open_api_specs_cfg=open_api_specs_cfg, idm_cfg=idm_cfg)
assert oapi_spec.name == service
assert oapi_spec._spec_data['schemes'] == ['https']
bravado_core_spec = oapi_spec.get_swagger_spec(rest_endpoint=idm_cfg.rest_endpoint)
assert bravado_core_spec.origin_url == idm_cfg.rest_endpoint
# assert bravado_core_spec.spec_dict['info']['title'] == service
assert bravado_core_spec.spec_dict['info']['title'] == service_cfg.title
def test_get_spec_from_kwargs():
idm_domain = 'gc30003'
service = 'Instances'
gc3_config = GC3Config(atoml_config_dir=config_dir)
idm_cfg = gc3_config.idm.domains[idm_domain]
service_cfg = gc3_config.iaas_classic.services.compute[service]
open_api_specs_cfg = gc3_config.iaas_classic.open_api_specs
api_catalog_config = gc3_config.iaas_classic.open_api_spec_catalog
# oapi_spec = OpenApiSpec(service_cfg=service_cfg, open_api_specs_cfg=api_catalog_config, rest_endpoint=idm_cfg.rest_endpoint)
oapi_spec = OpenApiSpec(service_cfg=service_cfg, open_api_specs_cfg=open_api_specs_cfg, idm_cfg=idm_cfg, rest_endpoint=idm_cfg.rest_endpoint)
assert oapi_spec.name == service
assert oapi_spec._spec_data['schemes'] == ['https']
bravado_core_spec = oapi_spec.get_swagger_spec()
assert bravado_core_spec.origin_url == idm_cfg.rest_endpoint
assert oapi_spec.rest_endpoint == idm_cfg.rest_endpoint
#
# def test_from_url():
# idm_domain = 'gc30003'
# service = 'Instances'
# gc3_config = GC3Config(atoml_config_dir=config_dir)
# idm_cfg = gc3_config.idm.domains[idm_domain]
# service_cfg = gc3_config.iaas_classic.services.compute[service]
# open_api_specs_cfg = gc3_config.iaas_classic.open_api_specs
# api_catalog_config = gc3_config.iaas_classic.open_api_spec_catalog
# oapi_spec = OpenApiSpec(service_cfg=service_cfg, open_api_specs_cfg=open_api_specs_cfg, idm_cfg=idm_cfg)
# assert oapi_spec.name == service
# assert oapi_spec._spec_data['schemes'] == ['https']
# assert oapi_spec.from_url == True
#
# bravado_core_spec = oapi_spec.get_swagger_spec(rest_endpoint=idm_cfg.rest_endpoint)
# assert bravado_core_spec.origin_url == idm_cfg.rest_endpoint
# assert bravado_core_spec.spec_dict['info']['title'] == service
def test_check_spec_properties():
idm_domain = 'gc30003'
service: str = 'Instances'
gc3_config = GC3Config(atoml_config_dir=config_dir)
idm_cfg = gc3_config.idm.domains[idm_domain]
service_cfg = gc3_config.iaas_classic.services.compute[service]
open_api_specs_cfg = gc3_config.iaas_classic.open_api_specs
oapi_spec = OpenApiSpec(service_cfg=service_cfg, open_api_specs_cfg=open_api_specs_cfg, idm_cfg=idm_cfg)
assert oapi_spec.name == service
assert oapi_spec._spec_data['schemes'] == ['https']
assert oapi_spec.title == service_cfg.title
version_tupple = oapi_spec.version.split('.')
assert version_tupple[0].isnumeric()
assert service.lower() in oapi_spec.description
service_paths = oapi_spec.paths
in_paths = ['instance' in p for p in service_paths]
assert all(in_paths)
operation_ids = oapi_spec.operation_ids
assert len(operation_ids) > 0
assert 'discoverInstance' in operation_ids
assert 'deleteInstance' in operation_ids
operation_id_descrs = oapi_spec.operation_id_descrs
assert operation_id_descrs
assert 'deleteInstance' in operation_id_descrs
def test_get_paas_spec():
idm_domain = 'gc30003'
service = 'ServiceInstances'
paas_type = 'database'
service_cfg = gc3_cfg.paas_classic.services.get(paas_type)[service]
idm_cfg = gc3_cfg.idm.domains[idm_domain]
open_api_specs_cfg = gc3_cfg.paas_classic.open_api_specs
oapi_spec = OpenApiSpec(service_cfg=service_cfg, open_api_specs_cfg=open_api_specs_cfg, idm_cfg=idm_cfg)
assert oapi_spec.name == service
assert oapi_spec._spec_data['schemes'] == ['https']
bravado_core_spec = oapi_spec.get_swagger_spec(rest_endpoint=idm_cfg.rest_endpoint)
assert bravado_core_spec.origin_url == idm_cfg.rest_endpoint
assert bravado_core_spec.spec_dict['info']['title'] == service_cfg.title
def test_paas_rest_endpoint_dbcs():
idm_domain = 'gc30003'
service = 'ServiceInstances'
paas_type = 'database'
service_cfg = gc3_cfg.paas_classic.services.get(paas_type)[service]
idm_cfg = gc3_cfg.idm.domains[idm_domain]
open_api_specs_cfg = gc3_cfg.paas_classic.open_api_specs
oapi_spec = OpenApiSpec(service_cfg=service_cfg, open_api_specs_cfg=open_api_specs_cfg, idm_cfg=idm_cfg)
assert oapi_spec.name == service
assert oapi_spec._spec_data['schemes'] == ['https']
bravado_core_spec = oapi_spec.get_swagger_spec(rest_endpoint=idm_cfg.rest_endpoint)
assert oapi_spec.rest_endpoint=='https://dbaas.oraclecloud.com/'
assert oapi_spec._spec_data['schemes'] == ['https']
assert oapi_spec.spec_dict['schemes'] == ['https']
assert oapi_spec.spec_dict['host'] == 'dbaas.oraclecloud.com'
def test_paas_rest_endpoint_jaas():
idm_domain = 'gc30003'
service = 'ServiceInstances'
paas_type = 'java'
service_cfg = gc3_cfg.paas_classic.services.get(paas_type)[service]
idm_cfg = gc3_cfg.idm.domains[idm_domain]
open_api_specs_cfg = gc3_cfg.paas_classic.open_api_specs
oapi_spec = OpenApiSpec(service_cfg=service_cfg, open_api_specs_cfg=open_api_specs_cfg, idm_cfg=idm_cfg)
assert oapi_spec.name == service
assert oapi_spec._spec_data['schemes'] == ['https']
bravado_core_spec = oapi_spec.get_swagger_spec(rest_endpoint=idm_cfg.rest_endpoint)
assert oapi_spec.rest_endpoint=='https://jaas.oraclecloud.com/'
assert oapi_spec._spec_data['schemes'] == ['https']
assert oapi_spec.spec_dict['host'] == 'jaas.oraclecloud.com'
def test_get_rest_endpoint_iaas():
idm_domain = 'gc30003'
service: str = 'Instances'
gc3_config = GC3Config(atoml_config_dir=config_dir)
idm_cfg = gc3_config.idm.domains[idm_domain]
service_cfg = gc3_config.iaas_classic.services.compute[service]
open_api_specs_cfg = gc3_config.iaas_classic.open_api_specs
oapi_spec = OpenApiSpec(service_cfg=service_cfg, open_api_specs_cfg=open_api_specs_cfg, idm_cfg=idm_cfg)
assert oapi_spec.name == service
assert oapi_spec._spec_data['schemes'] == ['https']
assert oapi_spec.rest_endpoint=='https://compute.uscom-central-1.oraclecloud.com'
assert oapi_spec.title == service_cfg.title
assert oapi_spec.spec_dict['host'] == 'compute.uscom-central-1.oraclecloud.com'
# @pytest.fixture()
# def test_equality_setup():
# idm_domain = 'gc30003'
# gc3_config = GC3Config(atoml_config_dir=config_dir)
# idm_cfg = gc3_config.idm.domains[idm_domain]
# api_catalog_config = gc3_config.iaas_classic.open_api_spec_catalog
#
# instances_service: str = 'Instances'
# instances_service_cfg = gc3_config.iaas_classic.services[instances_service]
# instances_oapi_spec = OpenApiSpec(service_cfg=instances_service_cfg, open_api_specs_cfg=api_catalog_config, idm_cfg=idm_cfg)
# open_api_specs_cfg = gc3_config.iaas_classic.open_api_specs
# assert instances_oapi_spec.name == instances_service
#
# secrules_service: str = 'SecRules'
# secrules_service_cfg = gc3_config.iaas_classic.services[secrules_service]
#
# sec_rules = SecRules(service_cfg=secrules_service_cfg, idm_cfg=idm_cfg, from_url=True)
# instances = Instances(service_cfg=instances_service_cfg, idm_cfg=idm_cfg, from_url=True)
# yield sec_rules, instances, idm_cfg, idm_domain
#
#
# def test_equality(test_equality_setup):
# sec_rules, instances, idm_cfg, idm_domain = test_equality_setup
# idm_domain = 'gc30003'
#
# instances_service: str = 'Instances'
# gc3_config = GC3Config(atoml_config_dir=config_dir)
# idm_cfg = gc3_config.idm.domains[idm_domain]
# instances_service_cfg = gc3_config.iaas_classic.services[instances_service]
# api_catalog_config = gc3_config.iaas_classic.open_api_spec_catalog
# instances_oapi_spec = OpenApiSpec(service_cfg=instances_service_cfg, open_api_specs_cfg=api_catalog_config, idm_cfg=idm_cfg)
# instances_oapi_spec_2 = OpenApiSpec(service_cfg=instances_service_cfg, open_api_specs_cfg=api_catalog_config, idm_cfg=idm_cfg)
# assert instances_oapi_spec.name == instances_service
# assert instances_oapi_spec._spec_data['schemes'] == ['https']
# assert instances_oapi_spec.title == instances_service
#
# secrules_service: str = 'SecRules'
# gc3_config = GC3Config(atoml_config_dir=config_dir)
# idm_cfg = gc3_config.idm.domains[idm_domain]
# sec_rules_service_cfg = gc3_config.iaas_classic.services[secrules_service]
# api_catalog_config = gc3_config.iaas_classic.open_api_spec_catalog
# sec_rules_oapi_spec = OpenApiSpec(service_cfg=sec_rules_service_cfg, open_api_specs_cfg=api_catalog_config, idm_cfg=idm_cfg)
# assert sec_rules_oapi_spec.name == secrules_service
# assert sec_rules_oapi_spec._spec_data['schemes'] == ['https']
# assert sec_rules_oapi_spec.title == secrules_service
#
# assert instances_oapi_spec == instances_oapi_spec_2
# assert sec_rules_oapi_spec != instances_oapi_spec
# assert sec_rules_oapi_spec != instances_oapi_spec_2
#
#
# def test_export():
# idm_domain = 'gc30003'
# service = 'Instances'
# gc3_config = GC3Config(atoml_config_dir=config_dir)
# idm_cfg = gc3_config.idm.domains[idm_domain]
# service_cfg = gc3_config.iaas_classic.services.compute[service]
# api_catalog_config = gc3_config.iaas_classic.open_api_spec_catalog
# oapi_spec = OpenApiSpec(service_cfg=service_cfg, open_api_specs_cfg=open_api_specs_cfg, idm_cfg=idm_cfg)
# assert oapi_spec.name == service
# assert oapi_spec._spec_data['schemes'] == ['https']
# bravado_core_spec = oapi_spec.get_swagger_spec(rest_endpoint=idm_cfg.rest_endpoint)
# exported_file_paths = oapi_spec.export()
# for exported_file_path in exported_file_paths:
# assert exported_file_path.exists()
#
| 45.81962 | 145 | 0.772843 | 2,104 | 14,479 | 4.863593 | 0.057985 | 0.072706 | 0.075051 | 0.073292 | 0.831232 | 0.794293 | 0.759699 | 0.72188 | 0.710153 | 0.689143 | 0 | 0.014685 | 0.134609 | 14,479 | 315 | 146 | 45.965079 | 0.801995 | 0.324401 | 0 | 0.601064 | 0 | 0 | 0.066866 | 0.006191 | 0 | 0 | 0 | 0 | 0.31383 | 1 | 0.069149 | false | 0 | 0.042553 | 0 | 0.111702 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
a2b0d892d49137b8202d2a7ede6871a2ecdbbf48 | 143 | py | Python | bill_helper/__init__.py | Genpeng/bill-helper | e9c04bbcb5cad3c4bbeaee6f82b1d9ebbcb9bdca | [
"MIT"
] | null | null | null | bill_helper/__init__.py | Genpeng/bill-helper | e9c04bbcb5cad3c4bbeaee6f82b1d9ebbcb9bdca | [
"MIT"
] | null | null | null | bill_helper/__init__.py | Genpeng/bill-helper | e9c04bbcb5cad3c4bbeaee6f82b1d9ebbcb9bdca | [
"MIT"
] | null | null | null | from bill_helper.bill_classify import classify_bill
from bill_helper.bill_similarity_search import find_k_nearest_bills
__version__ = '0.0.1'
| 28.6 | 67 | 0.867133 | 23 | 143 | 4.826087 | 0.608696 | 0.144144 | 0.252252 | 0.324324 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.022901 | 0.083916 | 143 | 4 | 68 | 35.75 | 0.824427 | 0 | 0 | 0 | 0 | 0 | 0.034965 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.666667 | 0 | 0.666667 | 0 | 1 | 0 | 0 | null | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
a2e204d23375853d368bd2a404d87a06f709c01e | 22,296 | py | Python | tests/scheduling/test_system_newsched.py | JeshuaT/PsyNeuLink | 912f691028e848659055430f37b6c15273c762f1 | [
"Apache-2.0"
] | 67 | 2018-01-05T22:18:44.000Z | 2022-03-27T11:27:31.000Z | tests/scheduling/test_system_newsched.py | JeshuaT/PsyNeuLink | 912f691028e848659055430f37b6c15273c762f1 | [
"Apache-2.0"
] | 1,064 | 2017-12-01T18:58:27.000Z | 2022-03-31T22:22:24.000Z | tests/scheduling/test_system_newsched.py | JeshuaT/PsyNeuLink | 912f691028e848659055430f37b6c15273c762f1 | [
"Apache-2.0"
] | 25 | 2017-12-01T20:27:07.000Z | 2022-03-08T21:49:39.000Z | import numpy
from psyneulink.core.compositions.composition import Composition
from psyneulink.core.components.functions.stateful.integratorfunctions import SimpleIntegrator
from psyneulink.core.components.functions.nonstateful.distributionfunctions import DriftDiffusionAnalytical
from psyneulink.core.components.functions.nonstateful.transferfunctions import Linear, Logistic
from psyneulink.core.components.mechanisms.processing.integratormechanism import IntegratorMechanism
from psyneulink.core.components.mechanisms.processing.transfermechanism import TransferMechanism
from psyneulink.core.components.projections.modulatory.controlprojection import ControlProjection
from psyneulink.core.scheduling.condition import AfterNCalls, All, Any, AtNCalls, AtPass, EveryNCalls, JustRan
from psyneulink.core.scheduling.scheduler import Scheduler
from psyneulink.core.scheduling.time import TimeScale
from psyneulink.library.components.mechanisms.processing.integrator.ddm import DDM
class TestInit:
def test_create_scheduler_from_system_StroopDemo(self):
Color_Input = TransferMechanism(name='Color Input', function=Linear(slope=0.2995))
Word_Input = TransferMechanism(name='Word Input', function=Linear(slope=0.2995))
# Processing Mechanisms (Control)
Color_Hidden = TransferMechanism(
name='Colors Hidden',
function=Logistic(gain=(1.0, ControlProjection)),
)
Word_Hidden = TransferMechanism(
name='Words Hidden',
function=Logistic(gain=(1.0, ControlProjection)),
)
Output = TransferMechanism(
name='Output',
function=Logistic(gain=(1.0, ControlProjection)),
)
# Decision Mechanisms
Decision = DDM(
function=DriftDiffusionAnalytical(
drift_rate=(1.0),
threshold=(0.1654),
noise=(0.5),
starting_point=(0),
t0=0.25,
),
name='Decision',
)
# Outcome Mechanism:
Reward = TransferMechanism(name='Reward')
myComposition = Composition(pathways=[[Color_Input, Color_Hidden, Output, Decision],
[Word_Input, Word_Hidden, Output, Decision],
[Reward]])
sched = Scheduler(composition=myComposition)
expected_consideration_queue = [
{Color_Input, Word_Input, Reward},
{Color_Hidden, Word_Hidden},
{Output},
{Decision}
]
assert sched.consideration_queue == expected_consideration_queue
class TestLinear:
def test_one_run_twice(self):
A = IntegratorMechanism(
name='A',
default_variable=[0],
function=SimpleIntegrator(
rate=.5,
)
)
c = Composition(pathways=[A])
term_conds = {TimeScale.TRIAL: AfterNCalls(A, 2)}
stim_list = {A: [[1]]}
c.run(
inputs=stim_list,
termination_processing=term_conds
)
terminal_mech = A
expected_output = [
numpy.array([1.]),
]
for i in range(len(expected_output)):
numpy.testing.assert_allclose(expected_output[i], terminal_mech.get_output_values(c)[i])
def test_two_AAB(self):
A = IntegratorMechanism(
name='A',
default_variable=[0],
function=SimpleIntegrator(
rate=.5
)
)
B = TransferMechanism(
name='B',
default_variable=[0],
function=Linear(slope=2.0),
)
c = Composition(pathways=[A, B])
term_conds = {TimeScale.TRIAL: AfterNCalls(B, 1)}
stim_list = {A: [[1]]}
sched = Scheduler(composition=c)
sched.add_condition(B, EveryNCalls(A, 2))
c.scheduler = sched
c.run(
inputs=stim_list,
termination_processing=term_conds
)
terminal_mech = B
expected_output = [
numpy.array([2.]),
]
for i in range(len(expected_output)):
numpy.testing.assert_allclose(expected_output[i], terminal_mech.get_output_values(c)[i])
def test_two_ABB(self):
A = TransferMechanism(
name='A',
default_variable=[0],
function=Linear(slope=2.0),
)
B = IntegratorMechanism(
name='B',
default_variable=[0],
function=SimpleIntegrator(
rate=.5
)
)
c = Composition(pathways=[A, B])
term_conds = {TimeScale.TRIAL: AfterNCalls(B, 2)}
stim_list = {A: [[1]]}
sched = Scheduler(composition=c)
sched.add_condition(A, Any(AtPass(0), AfterNCalls(B, 2)))
sched.add_condition(B, Any(JustRan(A), JustRan(B)))
c.scheduler = sched
c.run(
inputs=stim_list,
termination_processing=term_conds
)
terminal_mech = B
expected_output = [
numpy.array([2.]),
]
for i in range(len(expected_output)):
numpy.testing.assert_allclose(expected_output[i], terminal_mech.get_output_values(c)[i])
class TestBranching:
def test_three_ABAC(self):
A = IntegratorMechanism(
name='A',
default_variable=[0],
function=SimpleIntegrator(
rate=.5
)
)
B = TransferMechanism(
name='B',
default_variable=[0],
function=Linear(slope=2.0),
)
C = TransferMechanism(
name='C',
default_variable=[0],
function=Linear(slope=2.0),
)
c = Composition(pathways=[[A,B],[A,C]])
term_conds = {TimeScale.TRIAL: AfterNCalls(C, 1)}
stim_list = {A: [[1]]}
sched = Scheduler(composition=c)
sched.add_condition(B, Any(AtNCalls(A, 1), EveryNCalls(A, 2)))
sched.add_condition(C, EveryNCalls(A, 2))
c.scheduler = sched
c.run(
inputs=stim_list,
termination_processing=term_conds
)
terminal_mechs = [B, C]
expected_output = [
[
numpy.array([1.]),
],
[
numpy.array([2.]),
],
]
for m in range(len(terminal_mechs)):
for i in range(len(expected_output[m])):
numpy.testing.assert_allclose(expected_output[m][i], terminal_mechs[m].get_output_values(c)[i])
def test_three_ABAC_convenience(self):
A = IntegratorMechanism(
name='A',
default_variable=[0],
function=SimpleIntegrator(
rate=.5
)
)
B = TransferMechanism(
name='B',
default_variable=[0],
function=Linear(slope=2.0),
)
C = TransferMechanism(
name='C',
default_variable=[0],
function=Linear(slope=2.0),
)
c = Composition(pathways=[[A,B],[A,C]])
term_conds = {TimeScale.TRIAL: AfterNCalls(C, 1)}
stim_list = {A: [[1]]}
c.scheduler.add_condition(B, Any(AtNCalls(A, 1), EveryNCalls(A, 2)))
c.scheduler.add_condition(C, EveryNCalls(A, 2))
c.run(
inputs=stim_list,
termination_processing=term_conds
)
terminal_mechs = [B, C]
expected_output = [
[
numpy.array([1.]),
],
[
numpy.array([2.]),
],
]
for m in range(len(terminal_mechs)):
for i in range(len(expected_output[m])):
numpy.testing.assert_allclose(expected_output[m][i], terminal_mechs[m].get_output_values(c)[i])
def test_three_ABACx2(self):
A = IntegratorMechanism(
name='A',
default_variable=[0],
function=SimpleIntegrator(
rate=.5
)
)
B = TransferMechanism(
name='B',
default_variable=[0],
function=Linear(slope=2.0),
)
C = TransferMechanism(
name='C',
default_variable=[0],
function=Linear(slope=2.0),
)
c = Composition(pathways=[[A,B],[A,C]])
term_conds = {TimeScale.TRIAL: AfterNCalls(C, 2)}
stim_list = {A: [[1]]}
sched = Scheduler(composition=c)
sched.add_condition(B, Any(AtNCalls(A, 1), EveryNCalls(A, 2)))
sched.add_condition(C, EveryNCalls(A, 2))
c.scheduler = sched
c.run(
inputs=stim_list,
termination_processing=term_conds
)
terminal_mechs = [B, C]
expected_output = [
[
numpy.array([3.]),
],
[
numpy.array([4.]),
],
]
for m in range(len(terminal_mechs)):
for i in range(len(expected_output[m])):
numpy.testing.assert_allclose(expected_output[m][i], terminal_mechs[m].get_output_values(c)[i])
def test_three_2_ABC(self):
A = IntegratorMechanism(
name='A',
default_variable=[0],
function=SimpleIntegrator(
rate=.5
)
)
B = IntegratorMechanism(
name='B',
default_variable=[0],
function=SimpleIntegrator(
rate=1
)
)
C = TransferMechanism(
name='C',
default_variable=[0],
function=Linear(slope=2.0),
)
c = Composition(pathways=[[A,C],[B,C]])
term_conds = {TimeScale.TRIAL: AfterNCalls(C, 1)}
stim_list = {A: [[1]], B: [[2]]}
sched = Scheduler(composition=c)
sched.add_condition(C, All(EveryNCalls(A, 1), EveryNCalls(B, 1)))
c.scheduler = sched
c.run(
inputs=stim_list,
termination_processing=term_conds
)
terminal_mechs = [C]
expected_output = [
[
numpy.array([5.]),
],
]
for m in range(len(terminal_mechs)):
for i in range(len(expected_output[m])):
numpy.testing.assert_allclose(expected_output[m][i], terminal_mechs[m].get_output_values(c)[i])
def test_three_2_ABCx2(self):
A = IntegratorMechanism(
name='A',
default_variable=[0],
function=SimpleIntegrator(
rate=.5
)
)
B = IntegratorMechanism(
name='B',
default_variable=[0],
function=SimpleIntegrator(
rate=1
)
)
C = TransferMechanism(
name='C',
default_variable=[0],
function=Linear(slope=2.0),
)
c = Composition(pathways=[[A,C],[B,C]])
term_conds = {TimeScale.TRIAL: AfterNCalls(C, 2)}
stim_list = {A: [[1]], B: [[2]]}
sched = Scheduler(composition=c)
sched.add_condition(C, All(EveryNCalls(A, 1), EveryNCalls(B, 1)))
c.scheduler = sched
c.run(
inputs=stim_list,
termination_processing=term_conds
)
terminal_mechs = [C]
expected_output = [
[
numpy.array([10.]),
],
]
for m in range(len(terminal_mechs)):
for i in range(len(expected_output[m])):
numpy.testing.assert_allclose(expected_output[m][i], terminal_mechs[m].get_output_values(c)[i])
def test_three_integrators(self):
A = IntegratorMechanism(
name='A',
default_variable=[0],
function=SimpleIntegrator(
rate=1
)
)
B = IntegratorMechanism(
name='B',
default_variable=[0],
function=SimpleIntegrator(
rate=1
)
)
C = IntegratorMechanism(
name='C',
default_variable=[0],
function=SimpleIntegrator(
rate=1
)
)
c = Composition(pathways=[[A,C],[B,C]])
term_conds = {TimeScale.TRIAL: AfterNCalls(C, 2)}
stim_list = {A: [[1]], B: [[1]]}
sched = Scheduler(composition=c)
sched.add_condition(B, EveryNCalls(A, 2))
sched.add_condition(C, Any(EveryNCalls(A, 1), EveryNCalls(B, 1)))
c.scheduler = sched
c.run(
inputs=stim_list,
termination_processing=term_conds
)
mechs = [A, B, C]
expected_output = [
[
numpy.array([2.]),
],
[
numpy.array([1.]),
],
[
numpy.array([4.]),
],
]
for m in range(len(mechs)):
for i in range(len(expected_output[m])):
numpy.testing.assert_allclose(expected_output[m][i], mechs[m].get_output_values(c)[i])
def test_four_ABBCD(self):
A = TransferMechanism(
name='A',
default_variable=[0],
function=Linear(slope=2.0),
)
B = IntegratorMechanism(
name='B',
default_variable=[0],
function=SimpleIntegrator(
rate=.5
)
)
C = IntegratorMechanism(
name='C',
default_variable=[0],
function=SimpleIntegrator(
rate=.5
)
)
D = TransferMechanism(
name='D',
default_variable=[0],
function=Linear(slope=1.0),
)
c = Composition(pathways=[[A,B,D],[A,C,D]])
term_conds = {TimeScale.TRIAL: AfterNCalls(D, 1)}
stim_list = {A: [[1]]}
sched = Scheduler(composition=c)
sched.add_condition(B, EveryNCalls(A, 1))
sched.add_condition(C, EveryNCalls(A, 2))
sched.add_condition(D, Any(EveryNCalls(B, 3), EveryNCalls(C, 3)))
c.scheduler = sched
c.run(
inputs=stim_list,
termination_processing=term_conds
)
terminal_mechs = [D]
expected_output = [
[
numpy.array([4.]),
],
]
for m in range(len(terminal_mechs)):
for i in range(len(expected_output[m])):
numpy.testing.assert_allclose(expected_output[m][i], terminal_mechs[m].get_output_values(c)[i])
def test_four_integrators_mixed(self):
A = IntegratorMechanism(
name='A',
default_variable=[0],
function=SimpleIntegrator(
rate=1
)
)
B = IntegratorMechanism(
name='B',
default_variable=[0],
function=SimpleIntegrator(
rate=1
)
)
C = IntegratorMechanism(
name='C',
default_variable=[0],
function=SimpleIntegrator(
rate=1
)
)
D = IntegratorMechanism(
name='D',
default_variable=[0],
function=SimpleIntegrator(
rate=1
)
)
c = Composition(pathways=[[A,C],[A,D],[B,C],[B,D]])
term_conds = {TimeScale.TRIAL: All(AfterNCalls(C, 1), AfterNCalls(D, 1))}
stim_list = {A: [[1]], B: [[1]]}
sched = Scheduler(composition=c)
sched.add_condition(B, EveryNCalls(A, 2))
sched.add_condition(C, EveryNCalls(A, 1))
sched.add_condition(D, EveryNCalls(B, 1))
c.scheduler = sched
c.run(
inputs=stim_list,
termination_processing=term_conds
)
mechs = [A, B, C, D]
expected_output = [
[
numpy.array([2.]),
],
[
numpy.array([1.]),
],
[
numpy.array([4.]),
],
[
numpy.array([3.]),
],
]
for m in range(len(mechs)):
for i in range(len(expected_output[m])):
numpy.testing.assert_allclose(expected_output[m][i], mechs[m].get_output_values(c)[i])
def test_five_ABABCDE(self):
A = TransferMechanism(
name='A',
default_variable=[0],
function=Linear(slope=2.0),
)
B = TransferMechanism(
name='B',
default_variable=[0],
function=Linear(slope=2.0),
)
C = IntegratorMechanism(
name='C',
default_variable=[0],
function=SimpleIntegrator(
rate=.5
)
)
D = TransferMechanism(
name='D',
default_variable=[0],
function=Linear(slope=1.0),
)
E = TransferMechanism(
name='E',
default_variable=[0],
function=Linear(slope=2.0),
)
c = Composition(pathways=[[A,C,D],[B,C,E]])
term_conds = {TimeScale.TRIAL: AfterNCalls(E, 1)}
stim_list = {A: [[1]], B: [[2]]}
sched = Scheduler(composition=c)
sched.add_condition(C, Any(EveryNCalls(A, 1), EveryNCalls(B, 1)))
sched.add_condition(D, EveryNCalls(C, 1))
sched.add_condition(E, EveryNCalls(C, 1))
c.scheduler = sched
c.run(
inputs=stim_list,
termination_processing=term_conds
)
terminal_mechs = [D, E]
expected_output = [
[
numpy.array([3.]),
],
[
numpy.array([6.]),
],
]
for m in range(len(terminal_mechs)):
for i in range(len(expected_output[m])):
numpy.testing.assert_allclose(expected_output[m][i], terminal_mechs[m].get_output_values(c)[i])
#
# A B
# |\/|
# C D
# |\/|
# E F
#
def test_six_integrators_threelayer_mixed(self):
A = IntegratorMechanism(
name='A',
default_variable=[0],
function=SimpleIntegrator(
rate=1
)
)
B = IntegratorMechanism(
name='B',
default_variable=[0],
function=SimpleIntegrator(
rate=1
)
)
C = IntegratorMechanism(
name='C',
default_variable=[0],
function=SimpleIntegrator(
rate=1
)
)
D = IntegratorMechanism(
name='D',
default_variable=[0],
function=SimpleIntegrator(
rate=1
)
)
E = IntegratorMechanism(
name='E',
default_variable=[0],
function=SimpleIntegrator(
rate=1
)
)
F = IntegratorMechanism(
name='F',
default_variable=[0],
function=SimpleIntegrator(
rate=1
)
)
c = Composition(pathways=[[A,C,E],[A,C,F],[A,D,E],[A,D,F],[B,C,E],[B,C,F],[B,D,E],[B,D,F]])
term_conds = {TimeScale.TRIAL: All(AfterNCalls(E, 1), AfterNCalls(F, 1))}
stim_list = {A: [[1]], B: [[1]]}
sched = Scheduler(composition=c)
sched.add_condition(B, EveryNCalls(A, 2))
sched.add_condition(C, EveryNCalls(A, 1))
sched.add_condition(D, EveryNCalls(B, 1))
sched.add_condition(E, EveryNCalls(C, 1))
sched.add_condition(F, EveryNCalls(D, 2))
c.scheduler = sched
c.run(
inputs=stim_list,
termination_processing=term_conds
)
# Intermediate time steps
#
# 0 1 2 3
#
# A 1 2 3 4
# B 1 2
# C 1 4 8 14
# D 3 9
# E 1 8 19 42
# F 23
#
expected_output = {
A: [
numpy.array([4.]),
],
B: [
numpy.array([2.]),
],
C: [
numpy.array([14.]),
],
D: [
numpy.array([9.]),
],
E: [
numpy.array([42.]),
],
F: [
numpy.array([23.]),
],
}
for m in expected_output:
for i in range(len(expected_output[m])):
numpy.testing.assert_allclose(expected_output[m][i], m.get_output_values(c)[i])
class TestTermination:
def test_termination_conditions_reset(self):
A = IntegratorMechanism(
name='A',
default_variable=[0],
function=SimpleIntegrator(
rate=.5
)
)
B = TransferMechanism(
name='B',
default_variable=[0],
function=Linear(slope=2.0),
)
c = Composition(pathways=[[A,B]])
term_conds = {TimeScale.TRIAL: AfterNCalls(B, 2)}
stim_list = {A: [[1]]}
sched = Scheduler(composition=c)
sched.add_condition(B, EveryNCalls(A, 2))
c.scheduler = sched
c.run(
inputs=stim_list,
termination_processing=term_conds
)
# A should run four times
terminal_mech = B
expected_output = [
numpy.array([4.]),
]
for i in range(len(expected_output)):
numpy.testing.assert_allclose(expected_output[i], terminal_mech.get_output_values(c)[i])
c.run(
inputs=stim_list,
)
# A should run an additional two times
terminal_mech = B
expected_output = [
numpy.array([6.]),
]
for i in range(len(expected_output)):
numpy.testing.assert_allclose(expected_output[i], terminal_mech.get_output_values(c)[i])
| 27.124088 | 111 | 0.50287 | 2,194 | 22,296 | 4.96217 | 0.071103 | 0.059153 | 0.064664 | 0.096996 | 0.815468 | 0.795536 | 0.749334 | 0.726187 | 0.712777 | 0.712777 | 0 | 0.019452 | 0.379754 | 22,296 | 821 | 112 | 27.157125 | 0.767807 | 0.014308 | 0 | 0.651908 | 0 | 0 | 0.005011 | 0 | 0 | 0 | 0 | 0 | 0.024427 | 1 | 0.022901 | false | 0.003053 | 0.018321 | 0 | 0.047328 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
0c0f4814460a7f18b89565172e5bf3b0e1305d5c | 133 | py | Python | admin.py | Ole-ks/Docker-Django-Nginx-Postgres | 37e6f4bfe8f215284919c377669f8c009f884b3b | [
"MIT"
] | 24 | 2016-08-25T09:35:25.000Z | 2021-08-31T13:59:57.000Z | admin.py | Ole-ks/Docker-Django-Nginx-Postgres | 37e6f4bfe8f215284919c377669f8c009f884b3b | [
"MIT"
] | 2 | 2018-11-19T18:51:06.000Z | 2020-10-04T08:58:07.000Z | admin.py | Ole-ks/Docker-Django-Nginx-Postgres | 37e6f4bfe8f215284919c377669f8c009f884b3b | [
"MIT"
] | 26 | 2016-10-14T03:07:27.000Z | 2021-11-13T13:02:19.000Z | from django.contrib import admin
from .models import Model_example
# Register your models here.
admin.site.register(Model_example)
| 19 | 34 | 0.819549 | 19 | 133 | 5.631579 | 0.631579 | 0.224299 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.120301 | 133 | 6 | 35 | 22.166667 | 0.91453 | 0.195489 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.666667 | 0 | 0.666667 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
0c1601f3f58ab5925c9be73c6980e0fc9b2f52ab | 266 | py | Python | src/genie/libs/parser/iosxe/tests/ShowSdwanOmpTlocPath/cli/equal/golden_output_expected.py | jmedina0911/genieparser | 2fcd6d3e44891551af4b9d05e2c053218ee25c32 | [
"Apache-2.0"
] | null | null | null | src/genie/libs/parser/iosxe/tests/ShowSdwanOmpTlocPath/cli/equal/golden_output_expected.py | jmedina0911/genieparser | 2fcd6d3e44891551af4b9d05e2c053218ee25c32 | [
"Apache-2.0"
] | null | null | null | src/genie/libs/parser/iosxe/tests/ShowSdwanOmpTlocPath/cli/equal/golden_output_expected.py | jmedina0911/genieparser | 2fcd6d3e44891551af4b9d05e2c053218ee25c32 | [
"Apache-2.0"
] | null | null | null | expected_output = {
"tloc_path": {
"100.100.100.10": {"tloc": {"default": {"transport": "ipsec"}}},
"100.100.100.20": {"tloc": {"default": {"transport": "ipsec"}}},
"100.100.100.30": {"tloc": {"default": {"transport": "ipsec"}}},
}
}
| 33.25 | 72 | 0.496241 | 28 | 266 | 4.642857 | 0.392857 | 0.276923 | 0.207692 | 0.576923 | 0.523077 | 0.523077 | 0.523077 | 0 | 0 | 0 | 0 | 0.15566 | 0.203008 | 266 | 7 | 73 | 38 | 0.457547 | 0 | 0 | 0 | 0 | 0 | 0.473684 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
a769df940c8447e72467be403f7852c6a75cf740 | 688 | py | Python | webtest/__init__.py | azmeuk/webtest | ca58f4d1712d87397e84ed30fd87475c6a814d32 | [
"MIT"
] | 2,151 | 2020-04-18T07:31:17.000Z | 2022-03-31T08:39:18.000Z | webtest/__init__.py | azmeuk/webtest | ca58f4d1712d87397e84ed30fd87475c6a814d32 | [
"MIT"
] | 4,640 | 2015-07-08T16:19:08.000Z | 2019-12-02T15:01:27.000Z | webtest/__init__.py | azmeuk/webtest | ca58f4d1712d87397e84ed30fd87475c6a814d32 | [
"MIT"
] | 698 | 2015-06-02T19:18:35.000Z | 2022-03-29T16:57:15.000Z | # (c) 2005 Ian Bicking and contributors; written for Paste
# (http://pythonpaste.org)
# Licensed under the MIT license:
# http://www.opensource.org/licenses/mit-license.php
"""
Routines for testing WSGI applications.
"""
from webtest.app import TestApp
from webtest.app import TestRequest
from webtest.app import TestResponse
from webtest.app import AppError
from webtest.forms import Form
from webtest.forms import Field
from webtest.forms import Select
from webtest.forms import Radio
from webtest.forms import Checkbox
from webtest.forms import Text
from webtest.forms import Textarea
from webtest.forms import Hidden
from webtest.forms import Submit
from webtest.forms import Upload
| 29.913043 | 58 | 0.813953 | 100 | 688 | 5.6 | 0.44 | 0.275 | 0.285714 | 0.392857 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.006623 | 0.122093 | 688 | 22 | 59 | 31.272727 | 0.92053 | 0.297965 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
a7b0263d2d0d92a195af487b15f50cb154c06054 | 36 | py | Python | pickled/__init__.py | TurkuNLP/finngen-tools | 45d95b228ea7044a2d56bd414b8f44e418c42a57 | [
"MIT"
] | null | null | null | pickled/__init__.py | TurkuNLP/finngen-tools | 45d95b228ea7044a2d56bd414b8f44e418c42a57 | [
"MIT"
] | null | null | null | pickled/__init__.py | TurkuNLP/finngen-tools | 45d95b228ea7044a2d56bd414b8f44e418c42a57 | [
"MIT"
] | null | null | null | from .pickled import PickledDataset
| 18 | 35 | 0.861111 | 4 | 36 | 7.75 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.111111 | 36 | 1 | 36 | 36 | 0.96875 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
38fc1476aa35e5836c0d62912f3c444da6fac2aa | 164 | py | Python | introduction-to-python/python-basics/script_01.py | nhutnamhcmus/datacamp-playground | 25457e813b1145e1d335562286715eeddd1c1a7b | [
"MIT"
] | 1 | 2021-05-08T11:09:27.000Z | 2021-05-08T11:09:27.000Z | introduction-to-python/python-basics/script_01.py | nhutnamhcmus/datacamp-playground | 25457e813b1145e1d335562286715eeddd1c1a7b | [
"MIT"
] | 1 | 2022-03-12T15:42:14.000Z | 2022-03-12T15:42:14.000Z | introduction-to-python/python-basics/script_01.py | nhutnamhcmus/datacamp-playground | 25457e813b1145e1d335562286715eeddd1c1a7b | [
"MIT"
] | 1 | 2021-04-30T18:24:19.000Z | 2021-04-30T18:24:19.000Z | def sum(firstNumber, secondNumber):
return firstNumber + secondNumber
# Example, do not modify!
print(5 / 8)
# Print the sum of 7 and 10
print(sum(7,10)) | 23.428571 | 38 | 0.689024 | 25 | 164 | 4.52 | 0.68 | 0.40708 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.061538 | 0.207317 | 164 | 7 | 39 | 23.428571 | 0.807692 | 0.29878 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.25 | false | 0 | 0 | 0.25 | 0.5 | 0.5 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 6 |
ac1a554860dc63c784286394830d269a9065c6c3 | 131 | py | Python | util/test/tests/Vulkan/VK_Draw_Zoo.py | PLohrmannAMD/renderdoc | ea16d31aa340581f5e505e0c734a8468e5d3d47f | [
"MIT"
] | 6,181 | 2015-01-07T11:49:11.000Z | 2022-03-31T21:46:55.000Z | util/test/tests/Vulkan/VK_Draw_Zoo.py | PLohrmannAMD/renderdoc | ea16d31aa340581f5e505e0c734a8468e5d3d47f | [
"MIT"
] | 2,015 | 2015-01-16T01:45:25.000Z | 2022-03-25T12:01:06.000Z | util/test/tests/Vulkan/VK_Draw_Zoo.py | PLohrmannAMD/renderdoc | ea16d31aa340581f5e505e0c734a8468e5d3d47f | [
"MIT"
] | 1,088 | 2015-01-06T08:36:25.000Z | 2022-03-30T03:31:21.000Z | import rdtest
import renderdoc as rd
class VK_Draw_Zoo(rdtest.Draw_Zoo):
demos_test_name = 'VK_Draw_Zoo'
internal = False | 18.714286 | 35 | 0.763359 | 21 | 131 | 4.428571 | 0.666667 | 0.225806 | 0.193548 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.175573 | 131 | 7 | 36 | 18.714286 | 0.861111 | 0 | 0 | 0 | 0 | 0 | 0.083333 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.4 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
ac42b55f25f353caf0f13944db0ccb3d3082a1b3 | 9,401 | py | Python | tests/algos/common.py | Utschie/tf2rl | cd8b25a8fdccaa581afebc659cee5d2f509cf1f5 | [
"MIT"
] | 1 | 2020-10-12T23:44:04.000Z | 2020-10-12T23:44:04.000Z | tests/algos/common.py | Utschie/tf2rl | cd8b25a8fdccaa581afebc659cee5d2f509cf1f5 | [
"MIT"
] | null | null | null | tests/algos/common.py | Utschie/tf2rl | cd8b25a8fdccaa581afebc659cee5d2f509cf1f5 | [
"MIT"
] | null | null | null | import unittest
import numpy as np
import gym
class CommonAlgos(unittest.TestCase):
@classmethod
def setUpClass(cls):
# TODO: Remove dependencies to gym
cls.discrete_env = gym.make("CartPole-v0")
cls.continuous_env = gym.make("Pendulum-v0")
cls.batch_size = 32
cls.agent = None
class CommonOffPolAlgos(CommonAlgos):
@classmethod
def setUpClass(cls):
super().setUpClass()
cls.env = None
cls.action_dim = None
cls.is_discrete = True
def test_get_action(self):
if self.agent is None:
return
# Single input
state = self.env.reset().astype(np.float32)
action_train = self.agent.get_action(state, test=False)
action_test = self.agent.get_action(state, test=True)
if self.is_discrete:
self.assertTrue(isinstance(action_train, (np.int32, np.int64, int)))
self.assertTrue(isinstance(action_test, (np.int32, np.int64, int)))
else:
self.assertEqual(action_train.shape[0], self.action_dim)
self.assertEqual(action_test.shape[0], self.action_dim)
# Multiple inputs
states = np.zeros(
shape=(self.batch_size, state.shape[0]), dtype=np.float32)
actions_train = self.agent.get_action(states, test=False)
actions_test = self.agent.get_action(states, test=True)
if self.is_discrete:
self.assertEqual(
actions_train.shape, (self.batch_size,))
self.assertEqual(
actions_test.shape, (self.batch_size,))
else:
self.assertEqual(
actions_train.shape, (self.batch_size, self.action_dim))
self.assertEqual(
actions_test.shape, (self.batch_size, self.action_dim))
def test_get_action_greedy(self):
if self.agent is None:
return
# Multiple inputs
states = np.zeros(
shape=(self.batch_size, self.env.reset().astype(np.float32).shape[0]), dtype=np.float32)
actions_train = self.agent.get_action(states, test=False)
actions_test = self.agent.get_action(states, test=True)
# All actions should be same if `test=True`, and not same if `test=False`
if self.is_discrete:
self.assertEqual(np.prod(np.unique(actions_test).shape), 1)
self.assertGreater(np.prod(np.unique(actions_train).shape), 1)
else:
self.assertEqual(np.prod(np.all(actions_test == actions_test[0, :], axis=0)), 1)
self.assertEqual(np.prod(np.all(actions_train == actions_train[0, :], axis=0)), 0)
def test_train(self):
if self.agent is None:
return
rewards = np.zeros(shape=(self.batch_size, 1), dtype=np.float32)
dones = np.zeros(shape=(self.batch_size, 1), dtype=np.float32)
obses = np.zeros(
shape=(self.batch_size,)+self.env.observation_space.shape,
dtype=np.float32)
acts = np.zeros(
shape=(self.batch_size, self.action_dim,),
dtype=np.float32)
self.agent.train(
obses, acts, obses, rewards, dones)
def test_compute_td_error(self):
if self.agent is None:
return
rewards = np.zeros(shape=(self.batch_size, 1), dtype=np.float32)
dones = np.zeros(shape=(self.batch_size, 1), dtype=np.float32)
obses = np.zeros(
shape=(self.batch_size,)+self.env.observation_space.shape,
dtype=np.float32)
acts = np.zeros(
shape=(self.batch_size, self.continuous_env.action_space.low.size,),
dtype=np.float32)
self.agent.compute_td_error(
states=obses, actions=acts, next_states=obses,
rewards=rewards, dones=dones)
class CommonOffPolContinuousAlgos(CommonOffPolAlgos):
@classmethod
def setUpClass(cls):
super().setUpClass()
cls.env = cls.continuous_env
cls.action_dim = cls.continuous_env.action_space.low.size
cls.is_discrete = False
class CommonOffPolDiscreteAlgos(CommonOffPolAlgos):
@classmethod
def setUpClass(cls):
super().setUpClass()
cls.env = cls.discrete_env
cls.action_dim = 1
cls.is_discrete = True
class CommonOnPolActorCritic(CommonAlgos):
def test_get_action(self):
if self.agent is None:
return
# Single input
state = self.env.reset().astype(np.float32)
action_train, logp_train = self.agent.get_action(state, test=False)
action_test, logp_test = self.agent.get_action(state, test=True)
if self.is_discrete:
self.assertTrue(isinstance(action_train, (np.int32, np.int64, int)))
self.assertTrue(isinstance(action_test, (np.int32, np.int64, int)))
else:
self.assertEqual(action_train.shape[0], self.action_dim)
self.assertEqual(action_test.shape[0], self.action_dim)
self.assertEqual(logp_train.shape[0], 1)
self.assertEqual(logp_test.shape[0], 1)
# Multiple inputs
states = np.zeros(
shape=(self.batch_size, state.shape[0]), dtype=np.float32)
actions_train, logps_train = self.agent.get_action(states, test=False)
actions_test, logps_test = self.agent.get_action(states, test=True)
if self.is_discrete:
self.assertEqual(
actions_train.shape, (self.batch_size,))
self.assertEqual(
actions_test.shape, (self.batch_size,))
else:
self.assertEqual(
actions_train.shape, (self.batch_size, self.action_dim))
self.assertEqual(
actions_test.shape, (self.batch_size, self.action_dim))
self.assertEqual(logps_train.shape, (self.batch_size,))
self.assertEqual(logps_test.shape, (self.batch_size,))
def test_train(self):
if self.agent is None:
return
state = self.env.reset().astype(np.float32)
obses = np.zeros(
shape=(self.batch_size,)+state.shape,
dtype=np.float32)
acts = np.zeros(
shape=(self.batch_size, self.action_dim),
dtype=np.int32 if self.is_discrete else np.float32)
advs = np.ones(
shape=(self.batch_size, 1),
dtype=np.float32)
logps = np.ones_like(advs)
returns = np.zeros(
shape=(self.batch_size, 1),
dtype=np.float32)
self.agent.train(obses, acts, advs, logps, returns)
class CommonOnPolActorCriticContinuousAlgos(CommonOnPolActorCritic):
@classmethod
def setUpClass(cls):
super().setUpClass()
cls.env = cls.continuous_env
cls.action_dim = cls.continuous_env.action_space.low.size
cls.is_discrete = False
class CommonOnPolActorCriticDiscreteAlgos(CommonOnPolActorCritic):
@classmethod
def setUpClass(cls):
super().setUpClass()
cls.env = cls.discrete_env
cls.action_dim = 1
cls.is_discrete = True
class CommonIRLAlgos(CommonAlgos):
@classmethod
def setUpClass(cls):
super().setUpClass()
cls.irl_discrete = None
cls.irl_continuous = None
def test_inference_discrete(self):
if self.irl_discrete is None:
return
state = np.zeros(
shape=(self.discrete_env.observation_space.low.size,),
dtype=np.float32)
action = np.zeros(
shape=(self.discrete_env.action_space.n,),
dtype=np.float32)
action[self.discrete_env.action_space.sample()] = 1.
self.irl_discrete.inference(state, action, state)
def test_inference_continuous(self):
if self.irl_continuous is None:
return
state = np.zeros(
shape=(self.continuous_env.observation_space.low.size,),
dtype=np.float32)
action = np.zeros(
shape=(self.continuous_env.action_space.low.size,),
dtype=np.float32)
self.irl_continuous.inference(state, action, state)
def test_train_discrete(self):
if self.irl_discrete is None:
return
states = np.zeros(
shape=(self.batch_size, self.discrete_env.observation_space.low.size),
dtype=np.float32)
actions = np.zeros(
shape=(self.batch_size, self.discrete_env.action_space.n),
dtype=np.float32)
self.irl_discrete.train(
agent_states=states,
agent_acts=actions,
agent_next_states=states,
expert_states=states,
expert_acts=actions,
expert_next_states=states)
def test_train_continuous(self):
if self.irl_continuous is None:
return
states = np.zeros(
shape=(self.batch_size, self.continuous_env.observation_space.low.size),
dtype=np.float32)
actions = np.zeros(
shape=(self.batch_size, self.continuous_env.action_space.low.size),
dtype=np.float32)
self.irl_continuous.train(
agent_states=states,
agent_acts=actions,
agent_next_states=states,
expert_states=states,
expert_acts=actions,
expert_next_states=states)
if __name__ == '__main__':
unittest.main()
| 36.019157 | 100 | 0.617913 | 1,130 | 9,401 | 4.967257 | 0.09292 | 0.052913 | 0.072332 | 0.092998 | 0.817388 | 0.799216 | 0.778728 | 0.748619 | 0.704614 | 0.647426 | 0 | 0.015258 | 0.274971 | 9,401 | 260 | 101 | 36.157692 | 0.808245 | 0.018934 | 0 | 0.687783 | 0 | 0 | 0.003256 | 0 | 0 | 0 | 0 | 0.003846 | 0.108597 | 1 | 0.076923 | false | 0 | 0.013575 | 0 | 0.171946 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
ac65496ec20c93eaeb39af113936731db595d5e6 | 24,998 | py | Python | parser/team21/Analisis_Ascendente/AST.py | susanliss/tytus | a613a2352cf4a1d0e90ce27bb346ab60ed8039cc | [
"MIT"
] | null | null | null | parser/team21/Analisis_Ascendente/AST.py | susanliss/tytus | a613a2352cf4a1d0e90ce27bb346ab60ed8039cc | [
"MIT"
] | null | null | null | parser/team21/Analisis_Ascendente/AST.py | susanliss/tytus | a613a2352cf4a1d0e90ce27bb346ab60ed8039cc | [
"MIT"
] | null | null | null | from subprocess import check_call
from expresion import *
from instruccion import *
class AST:
def __init__(self, sentencias):
self.contador = 0
self.c = ""
self.sentencias = sentencias
self.pe = 0
def ReportarAST(self):
print('en clase ast')
#print(self.sentencias)
f = open('AST.dot', 'w')
self.c = 'digraph G{\n'
self.c += 'edge [color=blue]; rankdir = TB;\n'
self.c += 'Nodo'+ str(self.contador)+ '[label="S"]\n'
self.contador = self.contador + 1
self.c += 'Nodo'+ str(self.contador)+ '[label="Instrucciones"]\n'
self.c += 'Nodo' + '0' +' -> ' + 'Nodo'+ str(self.contador) + ';\n'
self.TiposInstruccion(self.sentencias, str(self.contador))
self.c += "}\n"
#print(arbol)
f.write(self.c)
f.close()
check_call(['dot', '-Tpng', 'AST.dot', '-o', 'AST.png'])
def TiposInstruccion(self, inst, padre):
if inst != None:
for i in inst:
if isinstance(i, CreateTable):
self.CreateTable(i, padre)
if isinstance(i, InsertInto):
self.InsertInto(i, padre)
if isinstance(i, CreateReplace):
self.CreateReplace(i, padre)
if isinstance(i, Show):
self.Show(padre)
if isinstance(i, AlterDatabase):
self.AlterDatabase(i, padre)
if isinstance(i, AlterTable):
self.AlterTable(i, padre)
if isinstance(i, Update):
self.Update(i, padre)
def CreateTable(self, inst, padre):
self.contador = self.contador + 1
self.c += 'Nodo'+ str(self.contador)+ '[label="Instruccion: CREATE TABLE"]\n'
self.c += 'Nodo' + padre +' -> ' + 'Nodo'+ str(self.contador) + ';\n'
np = str(self.contador)
self.contador = self.contador + 1
self.c += 'Nodo'+ str(self.contador)+ '[label="Id: ' + inst.id + '"]\n'
self.c += 'Nodo' + np +' -> ' + 'Nodo'+ str(self.contador) + ';\n'
#recorrer campos
for campo in inst.campos:
if isinstance(campo, Campo):
self.contador = self.contador + 1
self.c += 'Nodo'+ str(self.contador)+ '[label="Campo"]\n'
self.c += 'Nodo' + np +' -> ' + 'Nodo'+ str(self.contador) + ';\n'
ncp = str(self.contador)
if campo.caso == 1:
self.contador = self.contador + 1
self.c += 'Nodo'+ str(self.contador)+ '[label="Id: ' + campo.id + '"]\n'
self.c += 'Nodo' + ncp +' -> ' + 'Nodo'+ str(self.contador) + ';\n'
self.contador = self.contador + 1
if campo.tipo.longitud != None:
self.c += 'Nodo'+ str(self.contador)+ '[label="Tipo: ' + campo.tipo.tipo + '(' + str(campo.tipo.longitud.valor) + ')' + '"]\n'
else:
self.c += 'Nodo'+ str(self.contador)+ '[label="Tipo: ' + campo.tipo.tipo + '"]\n'
self.c += 'Nodo' + ncp +' -> ' + 'Nodo'+ str(self.contador) + ';\n'
if campo.acompaniamiento != None:
for acom in campo.acompaniamiento:
self.contador = self.contador + 1
a = str(self.contador)
if acom.tipo == 'DEFAULT':
self.c += 'Nodo'+ str(self.contador)+ '[label="' + acom.tipo + ': '+ str(acom.valorDefault.valor) + '"]\n'
elif acom.tipo == 'UNIQUE':
if acom.valorDefault != None:
if isinstance(acom.valorDefault, Id):
self.c += 'Nodo'+ str(self.contador)+ '[label="' + acom.tipo + ': ' + acom.valorDefault.id +'"]\n'
else:
self.c += 'Nodo'+ str(self.contador)+ '[label="' + acom.tipo + '"]\n'
#listaId padre self.contador
self.listaID(acom.valorDefault, str(self.contador))
else:
self.c += 'Nodo'+ str(self.contador)+ '[label="' + acom.tipo + '"]\n'
else:
self.c += 'Nodo'+ str(self.contador)+ '[label="' + acom.tipo + '"]\n'
self.c += 'Nodo' + ncp +' -> ' + 'Nodo'+ a + ';\n'
if campo.caso == 2 or campo.caso == 3:
if campo.caso == 2:
self.contador = self.contador + 1
self.c += 'Nodo'+ str(self.contador)+ '[label="CONSTRAINT: ' + campo.id + '"]\n'
self.c += 'Nodo' + ncp +' -> ' + 'Nodo'+ str(self.contador) + ';\n'
self.contador = self.contador + 1
a = str(self.contador)
if len(campo.idFk) == 1:
self.c += 'Nodo'+ str(self.contador)+ '[label="FOREIGN KEY: ' + campo.idFk[0].id + '"]\n'
else:
self.c += 'Nodo'+ str(self.contador)+ '[label="FOREIGN KEY"]\n'
self.listaID(campo.idFk, str(self.contador))
self.c += 'Nodo' + ncp +' -> ' + 'Nodo'+ a + ';\n'
self.contador = self.contador + 1
self.c += 'Nodo'+ str(self.contador)+ '[label="REFERENCES: ' + campo.tablaR + '"]\n'
self.c += 'Nodo' + ncp +' -> ' + 'Nodo'+ str(self.contador) + ';\n'
self.contador = self.contador + 1
a = str(self.contador)
if len(campo.idR) == 1:
self.c += 'Nodo'+ str(self.contador)+ '[label="Columna: ' + campo.idR[0].id + '"]\n'
else:
self.c += 'Nodo'+ str(self.contador)+ '[label="Columnas"]\n'
self.listaID(campo.idR, str(self.contador))
self.c += 'Nodo' + ncp +' -> ' + 'Nodo'+ a + ';\n'
if campo.caso == 4:
self.contador = self.contador + 1
a = str(self.contador)
#if len(campo.id) == 1:
# self.c += 'Nodo'+ str(self.contador)+ '[label="PRIMARY KEY: '+ campo.id +'"]\n'
#else:
self.c += 'Nodo'+ str(self.contador)+ '[label="PRIMARY KEY"]\n' #
self.listaID(campo.id, str(self.contador))#
self.c += 'Nodo' + ncp +' -> ' + 'Nodo'+ a + ';\n'
if inst.idInherits != None:
self.contador = self.contador + 1
self.c += 'Nodo'+ str(self.contador)+ '[label="INHERITS: '+ inst.idInherits +'"]\n'
self.c += 'Nodo' + np +' -> ' + 'Nodo'+ str(self.contador) + ';\n'
def InsertInto(self, inst, padre):
self.contador = self.contador + 1
self.c += 'Nodo'+ str(self.contador)+ '[label="Instruccion: INSERT INTO"]\n'
self.c += 'Nodo' + padre +' -> ' + 'Nodo'+ str(self.contador) + ';\n'
np = str(self.contador)
self.contador = self.contador + 1
self.c += 'Nodo'+ str(self.contador)+ '[label="Id: ' + inst.id + '"]\n'
self.c += 'Nodo' + np +' -> ' + 'Nodo'+ str(self.contador) + ';\n'
if inst.listaId != None:
self.contador = self.contador + 1
self.c += 'Nodo'+ str(self.contador)+ '[label="Columnas"]\n'
self.c += 'Nodo' + np +' -> ' + 'Nodo'+ str(self.contador) + ';\n'
self.listaID(inst.listaId, str(self.contador))
self.contador = self.contador + 1
self.c += 'Nodo'+ str(self.contador)+ '[label="Values"]\n'
self.c += 'Nodo' + np +' -> ' + 'Nodo'+ str(self.contador) + ';\n'
a = str(self.contador)
if len(inst.values) == 1:
self.listaID(inst.values[0], str(self.contador))
else:
for val in inst.values:
self.contador = self.contador + 1
self.c += 'Nodo'+ str(self.contador)+ '[label="Value"]\n'
self.c += 'Nodo' + a +' -> ' + 'Nodo'+ str(self.contador) + ';\n'
self.listaID(val, str(self.contador))
def CreateReplace(self, inst, padre):
label = ""
if inst.caso == 1:
label = 'CREATE'
elif inst.caso == 2:
label = 'CREATE OR REPLACE'
self.contador = self.contador + 1
self.c += 'Nodo'+ str(self.contador)+ '[label="Instruccion: '+label+'"]\n'
self.c += 'Nodo' + padre +' -> ' + 'Nodo'+ str(self.contador) + ';\n'
np = str(self.contador)
if inst.exists:
self.contador = self.contador + 1
self.c += 'Nodo'+ str(self.contador)+ '[label="IF NOT EXISTS"]\n'
self.c += 'Nodo' + np +' -> ' + 'Nodo'+ str(self.contador) + ';\n'
self.contador = self.contador + 1
self.c += 'Nodo'+ str(self.contador)+ '[label="' + inst.id + '"]\n'
self.c += 'Nodo' + np +' -> ' + 'Nodo'+ str(self.contador) + ';\n'
if inst.complemento != None:
if isinstance(inst.complemento, ComplementoCR):
self.contador = self.contador + 1
self.c += 'Nodo'+ str(self.contador)+ '[label="OWNER: ' + inst.complemento.idOwner + '"]\n'
self.c += 'Nodo' + np +' -> ' + 'Nodo'+ str(self.contador) + ';\n'
if inst.complemento.mode != None:
self.contador = self.contador + 1
self.c += 'Nodo'+ str(self.contador)+ '[label="MODE: ' + str(inst.complemento.mode) + '"]\n'
self.c += 'Nodo' + np +' -> ' + 'Nodo'+ str(self.contador) + ';\n'
def Show(self, padre):
self.contador = self.contador + 1
self.c += 'Nodo'+ str(self.contador)+ '[label="Instruccion: SHOW DATABASES"]\n'
self.c += 'Nodo' + padre +' -> ' + 'Nodo'+ str(self.contador) + ';\n'
def AlterDatabase(self, inst, padre):
self.contador = self.contador + 1
self.c += 'Nodo'+ str(self.contador)+ '[label="Instruccion: ALTER DATABASE"]\n'
self.c += 'Nodo' + padre +' -> ' + 'Nodo'+ str(self.contador) + ';\n'
np = str(self.contador)
self.contador = self.contador + 1
self.c += 'Nodo'+ str(self.contador)+ '[label="' + inst.name + '"]\n'
self.c += 'Nodo' + np +' -> ' + 'Nodo'+ str(self.contador) + ';\n'
self.contador = self.contador + 1
label = ""
if inst.caso == 1:
label = 'RENAME'
elif inst.caso == 2:
label = 'OWNER'
self.c += 'Nodo'+ str(self.contador)+ '[label="' + label + '"]\n'
self.c += 'Nodo' + np +' -> ' + 'Nodo'+ str(self.contador) + ';\n'
self.contador = self.contador + 1
self.c += 'Nodo'+ str(self.contador)+ '[label="' + inst.newName + '"]\n'
self.c += 'Nodo' + np +' -> ' + 'Nodo'+ str(self.contador) + ';\n'
def AlterTable(self, inst, padre):
self.contador = self.contador + 1
self.c += 'Nodo'+ str(self.contador)+ '[label="Instruccion: ALTER TABLE"]\n'
self.c += 'Nodo' + padre +' -> ' + 'Nodo'+ str(self.contador) + ';\n'
np = str(self.contador)
self.contador = self.contador + 1
self.c += 'Nodo'+ str(self.contador)+ '[label="Tabla: ' + inst.id + '"]\n'
self.c += 'Nodo' + np +' -> ' + 'Nodo'+ str(self.contador) + ';\n'
#ADD
if inst.caso == 1:
self.contador = self.contador + 1
self.c += 'Nodo'+ str(self.contador)+ '[label="ADD"]\n'
self.c += 'Nodo' + np +' -> ' + 'Nodo'+ str(self.contador) + ';\n'
#ADD COLUMN
if inst.idAdd != None:
self.contador = self.contador + 1
self.c += 'Nodo'+ str(self.contador)+ '[label="COLUMN"]\n'
self.c += 'Nodo' + np +' -> ' + 'Nodo'+ str(self.contador) + ';\n'
self.contador = self.contador + 1
self.c += 'Nodo'+ str(self.contador)+ '[label="' + inst.idAdd + '"]\n'
self.c += 'Nodo' + np +' -> ' + 'Nodo'+ str(self.contador) + ';\n'
self.contador = self.contador + 1
if inst.tipoAdd != None:
self.c += 'Nodo'+ str(self.contador)+ '[label="Tipo: ' + inst.tipoAdd.tipo + '(' + str(inst.tipoAdd.longitud.valor) + ')' + '"]\n'
else:
self.c += 'Nodo'+ str(self.contador)+ '[label="Tipo: ' + inst.tipoAdd.tipo + '"]\n'
self.c += 'Nodo' + np +' -> ' + 'Nodo'+ str(self.contador) + ';\n'
#ADD CONSTRAINT
if inst.constraintId != None:
self.contador = self.contador + 1
self.c += 'Nodo'+ str(self.contador)+ '[label="CONSTRAINT"]\n'
self.c += 'Nodo' + np +' -> ' + 'Nodo'+ str(self.contador) + ';\n'
self.contador = self.contador + 1
self.c += 'Nodo'+ str(self.contador)+ '[label="' + inst.constraintId + '"]\n'
self.c += 'Nodo' + np +' -> ' + 'Nodo'+ str(self.contador) + ';\n'
self.contador = self.contador + 1
self.c += 'Nodo'+ str(self.contador)+ '[label="UNIQUE"]\n'
self.c += 'Nodo' + np +' -> ' + 'Nodo'+ str(self.contador) + ';\n'
if inst.columnId != None:
self.contador = self.contador + 1
self.c += 'Nodo'+ str(self.contador)+ '[label="' + inst.columnId + '"]\n'
self.c += 'Nodo' + np +' -> ' + 'Nodo'+ str(self.contador) + ';\n'
#ADD FOREIGN
if inst.listaFK != None:
self.contador = self.contador + 1
a = str(self.contador)
if len(inst.listaFK) == 1:
self.c += 'Nodo'+ str(self.contador)+ '[label="FOREIGN KEY: ' + inst.listaFK[0].id + '"]\n'
else:
self.c += 'Nodo'+ str(self.contador)+ '[label="FOREIGN KEY"]\n'
self.listaID(inst.listaFK, str(self.contador))
self.c += 'Nodo' + np +' -> ' + 'Nodo'+ a + ';\n'
if inst.listaReferences != None:
self.contador = self.contador + 1
a = str(self.contador)
if len(inst.listaReferences) == 1:
self.c += 'Nodo'+ str(self.contador)+ '[label="REFERENCES: ' + inst.listaReferences[0].id + '"]\n'
else:
self.c += 'Nodo'+ str(self.contador)+ '[label="REFERENCES"]\n'
self.listaID(inst.listaReferences, str(self.contador))
self.c += 'Nodo' + np +' -> ' + 'Nodo'+ a + ';\n'
#ADD CHECK
#DROP
if inst.caso == 2:
self.contador = self.contador + 1
self.c += 'Nodo'+ str(self.contador)+ '[label="DROP"]\n'
self.c += 'Nodo' + np +' -> ' + 'Nodo'+ str(self.contador) + ';\n'
if inst.columnConstraint != None:
self.contador = self.contador + 1
self.c += 'Nodo'+ str(self.contador)+ '[label="' + inst.columnConstraint + '"]\n'
self.c += 'Nodo' + np +' -> ' + 'Nodo'+ str(self.contador) + ';\n'
if inst.idDrop != None:
self.contador = self.contador + 1
self.c += 'Nodo'+ str(self.contador)+ '[label="' + inst.idDrop + '"]\n'
self.c += 'Nodo' + np +' -> ' + 'Nodo'+ str(self.contador) + ';\n'
#ALTER
if inst.caso == 3:
self.contador = self.contador + 1
self.c += 'Nodo'+ str(self.contador)+ '[label="ALTER COLUMN"]\n'
self.c += 'Nodo' + np +' -> ' + 'Nodo'+ str(self.contador) + ';\n'
if inst.columnAlter != None:
self.contador = self.contador + 1
self.c += 'Nodo'+ str(self.contador)+ '[label="' + inst.columnAlter + '"]\n'
self.c += 'Nodo' + np +' -> ' + 'Nodo'+ str(self.contador) + ';\n'
self.contador = self.contador + 1
self.c += 'Nodo'+ str(self.contador)+ '[label="SET NOT NULL"]\n'
self.c += 'Nodo' + np +' -> ' + 'Nodo'+ str(self.contador) + ';\n'
def Update(self, inst, padre):
self.contador = self.contador + 1
self.c += 'Nodo'+ str(self.contador)+ '[label="Instruccion: UPDATE"]\n'
self.c += 'Nodo' + padre +' -> ' + 'Nodo'+ str(self.contador) + ';\n'
np = str(self.contador)
self.contador = self.contador + 1
self.c += 'Nodo'+ str(self.contador)+ '[label="Id: ' + inst.id + '"]\n'
self.c += 'Nodo' + np +' -> ' + 'Nodo'+ str(self.contador) + ';\n'
for asign in inst.asignaciones:
print('asignar')
if isinstance(asign, Asignacion):
self.Asignacion(asign, np)
#---------------------LISTAS----------------------------------------
#es una lista de entero, decimal, cadena, id
def listaID(self, inst, padre):
for var in inst:
if isinstance(var, Id):
self.contador += self.contador
self.c += 'Nodo'+ str(self.contador)+ '[label="' + var.id + '"]\n'
self.c += 'Nodo' + padre +' -> ' + 'Nodo'+ str(self.contador) + ';\n'
if isinstance(var, Primitivo):
self.contador += self.contador
self.c += 'Nodo'+ str(self.contador)+ '[label="' + str(var.valor) + '"]\n'
self.c += 'Nodo' + padre +' -> ' + 'Nodo'+ str(self.contador) + ';\n'
#EXPRESION
def E(self, inst, padre):
self.contador += self.contador
self.c += 'Nodo'+ str(self.contador)+ '[label="E"]\n'
self.c += 'Nodo' + padre +' -> ' + 'Nodo'+ str(self.contador) + ';\n'
np = str(self.contador)
eiz = False
edr = False
prim = False
prim2 = False
if isinstance(inst, Unario):
print('unario')
print(inst)
print(inst.op)
print(inst.operador)
self.contador = self.contador + 1
self.c += 'Nodo'+ str(self.contador) + '[label="Operador\nUnario: '+str(inst.operador)+' "]\n'
self.c += 'Nodo' + np +' -> ' + 'Nodo'+ str(self.contador) + ';\n'
npu = str(self.contador)
if isinstance(inst.op, Primitivo):
self.Primitivo(inst.op, npu)
if isinstance(inst.op, Id):
self.Id(inst.op.id, npu)
if isinstance(inst.op, IdId):
pass
if isinstance(inst.op, Expresion):
self.E(inst.op, npu)
else :
if isinstance(inst.iz, Unario):
self.contador = self.contador + 1
self.c += 'Nodo'+ str(self.contador) + '[label="Operador\nUnario: '+inst.iz.operador+' "]\n'
self.c += 'Nodo' + np +' -> ' + 'Nodo'+ str(self.contador) + ';\n'
npu = str(self.contador)
if isinstance(inst.iz.op, Primitivo):
self.Primitivo(inst.iz.op, npu)
if isinstance(inst.iz.op, Id):
self.Id(inst.iz.op, npu)
if isinstance(inst.iz.op, IdId):
pass
if isinstance(inst.iz.op, Expresion):
self.E(inst.iz.op, npu)
self.contador += self.contador
self.c += 'Nodo'+ str(self.contador) + '[label="Operador: ' + inst.iz.operador+'"]\n'
self.c += 'Nodo' + np +' -> ' + 'Nodo'+ str(self.contador) + ';\n'
if isinstance(inst.dr, Unario):
self.contador = self.contador + 1
self.c += 'Nodo'+ str(self.contador) + '[label="Operador\nUnario: '+inst.dr.operador+' "]\n'
self.c += 'Nodo' + np +' -> ' + 'Nodo'+ str(self.contador) + ';\n'
npu = str(self.contador)
if isinstance(inst.dr.op, Primitivo):
self.Primitivo(inst.dr.op, npu)
if isinstance(inst.dr.op, Id):
self.Id(inst.dr.op, npu)
if isinstance(inst.dr.op, IdId):
pass
if isinstance(inst.dr.op, Expresion):
self.E(inst.dr.op, npu)
if isinstance(inst.iz, Primitivo):
self.contador += self.contador
self.c += 'Nodo'+ str(self.contador) + '[label="Primitivo: ' + str(inst.iz.valor) + '"]\n'
self.c += 'Nodo' + np +' -> ' + 'Nodo'+ str(self.contador) + ';\n'
self.contador = self.contador + 1
self.c += 'Nodo'+ str(self.contador) + '[label="Operador: ' + inst.operador+'"]\n'
self.c += 'Nodo' + np +' -> ' + 'Nodo'+ str(self.contador) + ';\n'
prim = True
if isinstance(inst.dr, Primitivo):
self.contador += self.contador
self.c += 'Nodo'+ str(self.contador) + '[label="Primitivo: ' + str(inst.dr.valor) + '"]\n'
self.c += 'Nodo' + np +' -> ' + 'Nodo'+ str(self.contador) + ';\n'
prim2 = True
if isinstance(inst.iz, Id):
self.Id(inst.iz.id, np)
self.contador = self.contador + 1
self.c += 'Nodo'+ str(self.contador) + '[label=" Operador: ' + inst.operador+'"]\n'
self.c += 'Nodo' + np +' -> ' + 'Nodo'+ str(self.contador) + ';\n'
prim = True
if isinstance(inst.dr, Id):
self.Id(inst.dr.id, np)
prim2 = True
if isinstance(inst.iz, IdId):
pass
if isinstance(inst.dr, IdId):
pass
if isinstance(inst.iz, Expresion):
print('expresion iz')
self.E(inst.iz, np)
self.contador += self.contador
self.c += 'Nodo'+ str(self.contador) + '[label="Operador: ' + inst.operador+'"]\n'
self.c += 'Nodo' + np +' -> ' + 'Nodo'+ str(self.contador) + ';\n'
eiz = True
if isinstance(inst.dr, Expresion):
self.E(inst.dr, np)
diz = True
def Id(self, id, padre):
self.contador += self.contador
self.c += 'Nodo'+ str(self.contador)+ '[label="Id: ' + id + '"]\n'
self.c += 'Nodo' + padre +' -> ' + 'Nodo'+ str(self.contador) + ';\n'
def Primitivo(self, prim, padre):
self.contador += self.contador
self.c += 'Nodo'+ str(self.contador)+ '[label="' + str(prim.valor) + '"]\n'
self.c += 'Nodo' + padre +' -> ' + 'Nodo'+ str(self.contador) + ';\n'
def Unario(self, unario, padre):
self.contador += self.contador
self.c += 'Nodo'+ str(self.contador)+ '[label="' + unario.operador + '"]\n'
self.c += 'Nodo' + padre +' -> ' + 'Nodo'+ str(self.contador) + ';\n'
a = str(self.contador)
self.contador += self.contador
print('UNARIO')
#if isinstance(unario.op, Id):
# print(unario.op)
# self.c += 'Nodo'+ str(self.contador)+ '[label="' + unario.op.id + '"]\n'
#elif isinstance(unario.op, Primitivo):
# print(unario.op)
# self.c += 'Nodo'+ str(self.contador)+ '[label="' + str(unario.op.val) + '"]\n'
#elif isinstance(unario.op, IdId):
# pass
# self.c += 'Nodo' + a +' -> ' + 'Nodo'+ str(self.contador) + ';\n'
#es una expresion
self.E(unario.op, a)
#ASIGNACION
def Asignacion(self, inst, padre):
self.contador += self.contador
self.c += 'Nodo'+ str(self.contador)+ '[label="Asignacion"]\n'
self.c += 'Nodo' + padre +' -> ' + 'Nodo'+ str(self.contador) + ';\n'
padre = str(self.contador)
self.contador += self.contador
if isinstance(inst.id, Id):
self.c += 'Nodo'+ str(self.contador)+ '[label="' + inst.id.id + '"]\n'
if isinstance(inst.id, IdId):
self.c += 'Nodo'+ str(self.contador)+ '[label="' + str(inst.id.id1.id) + '.' + str(inst.id.id2.id) + '"]\n'
self.c += 'Nodo' + padre +' -> ' + 'Nodo'+ str(self.contador) + ';\n'
self.contador += self.contador
self.c += 'Nodo'+ str(self.contador)+ '[label="="]\n'
self.c += 'Nodo' + padre +' -> ' + 'Nodo'+ str(self.contador) + ';\n'
#self.contador += self.contador
#self.c += 'Nodo'+ str(self.contador)+ '[label="E"]\n'
#self.c += 'Nodo' + padre +' -> ' + 'Nodo'+ str(self.contador) + ';\n'
self.E(inst.expresion, padre)
| 53.07431 | 151 | 0.459037 | 2,722 | 24,998 | 4.213446 | 0.060985 | 0.326445 | 0.22757 | 0.238556 | 0.778446 | 0.731537 | 0.709391 | 0.692214 | 0.684018 | 0.646351 | 0 | 0.0052 | 0.353828 | 24,998 | 471 | 152 | 53.07431 | 0.704823 | 0.036323 | 0 | 0.455422 | 0 | 0 | 0.125114 | 0.0069 | 0.163855 | 0 | 0 | 0 | 0 | 1 | 0.038554 | false | 0.012048 | 0.007229 | 0 | 0.048193 | 0.019277 | 0 | 0 | 0 | null | 1 | 1 | 1 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
ac870f60703500418fa6ee8506e0a8e5f24f90d6 | 340 | py | Python | euler_distributed/_slurm_env.py | kareido/my_packages | 509da783fd259d30c365ed38db29ffb88521336c | [
"Unlicense"
] | null | null | null | euler_distributed/_slurm_env.py | kareido/my_packages | 509da783fd259d30c365ed38db29ffb88521336c | [
"Unlicense"
] | null | null | null | euler_distributed/_slurm_env.py | kareido/my_packages | 509da783fd259d30c365ed38db29ffb88521336c | [
"Unlicense"
] | null | null | null | import os
def get_world_size():
return int(os.environ['SLURM_NTASKS'])
def get_rank():
return int(os.environ['SLURM_PROCID'])
def get_jobid():
return int(os.environ['SLURM_JOB_ID'])
def get_backend():
return os.environ.get('DISTRIBUTED_BACKEND', None)
def get_nodelist():
return os.environ['SLURM_NODELIST']
| 14.166667 | 54 | 0.697059 | 49 | 340 | 4.591837 | 0.408163 | 0.133333 | 0.248889 | 0.24 | 0.306667 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.161765 | 340 | 23 | 55 | 14.782609 | 0.789474 | 0 | 0 | 0 | 0 | 0 | 0.204142 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.454545 | true | 0 | 0.090909 | 0.454545 | 1 | 0 | 0 | 0 | 0 | null | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 6 |
3bd1bee36febf40d8606cda18d67176dad78bf36 | 2,460 | py | Python | authors/apps/authentication/tests/test_social_auth.py | andela/Ah-backend-valkyrie | f0eb64c27e1fe37d5c81e4b9a8762dcf3c336a79 | [
"BSD-3-Clause"
] | null | null | null | authors/apps/authentication/tests/test_social_auth.py | andela/Ah-backend-valkyrie | f0eb64c27e1fe37d5c81e4b9a8762dcf3c336a79 | [
"BSD-3-Clause"
] | 46 | 2019-01-08T13:16:41.000Z | 2021-04-30T20:47:08.000Z | authors/apps/authentication/tests/test_social_auth.py | andela/Ah-backend-valkyrie | f0eb64c27e1fe37d5c81e4b9a8762dcf3c336a79 | [
"BSD-3-Clause"
] | 3 | 2019-01-07T08:21:59.000Z | 2019-09-20T06:43:18.000Z | from .base import BaseTestMethods
from rest_framework import status
class TestSocialAuth(BaseTestMethods):
def test_login_with_invalid_facebook_token(self):
# user login with invalid facebook token
response = self.client.post(
"/api/v1/auth/facebook/", {"user": {
"auth_token": self.invalid_facebook_token}}, format='json')
print(self.valid_facebook_token)
self.assertEqual(
response.status_code, status.HTTP_400_BAD_REQUEST)
self.assertIn(
'Invalid or expired token. Please login again.', str(
response.data))
def test_login_with_valid_facebook_token(self):
# user login with valid facebook token
response = self.client.post(
"/api/v1/auth/facebook/", {"user": {
"auth_token": self.valid_facebook_token}}, format='json')
print(self.valid_facebook_token)
self.assertEqual(
response.status_code, status.HTTP_200_OK)
def test_login_with_invalid_google_token(self):
# user login with invalid google token
response = self.client.post(
"/api/v1/auth/google/", {"user": {
"auth_token": self.invalid_google_token}}, format='json')
print(self.valid_facebook_token)
self.assertEqual(
response.status_code, status.HTTP_400_BAD_REQUEST)
self.assertIn(
'Invalid or expired token. Please login again.', str(
response.data))
def test_login_with_invalid_twitter_token(self):
# user login with invalid google token
response = self.client.post(
"/api/v1/auth/twitter/", {"user": {
"auth_token": self.invalid_twitter_token}}, format='json')
self.assertEqual(
response.status_code, status.HTTP_400_BAD_REQUEST)
self.assertIn(
'Invalid or expired token. Please login again', str(
response.data)
)
def test_login_with_valid_twitter_token(self):
# user login with invalid google token
response = self.client.post(
"/api/v1/auth/twitter/", {"user": {
"auth_token": self.valid_twitter_token}}, format='json')
self.assertEqual(
response.status_code, status.HTTP_200_OK)
| 39.047619 | 80 | 0.593902 | 266 | 2,460 | 5.24812 | 0.169173 | 0.083811 | 0.080229 | 0.057307 | 0.925501 | 0.845272 | 0.797278 | 0.797278 | 0.797278 | 0.790115 | 0 | 0.011792 | 0.310569 | 2,460 | 63 | 81 | 39.047619 | 0.811321 | 0.07561 | 0 | 0.630435 | 0 | 0 | 0.145439 | 0.037902 | 0 | 0 | 0 | 0 | 0.173913 | 1 | 0.108696 | false | 0 | 0.043478 | 0 | 0.173913 | 0.065217 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
3becf867e4ea16735938b5eb1a302234db0d714b | 42 | py | Python | dev/Tools/Python/2.7.13/mac/Python.framework/Versions/2.7/lib/python2.7/site-packages/pyxb/bundles/saml20/dce.py | jeikabu/lumberyard | 07228c605ce16cbf5aaa209a94a3cb9d6c1a4115 | [
"AML"
] | 123 | 2015-01-12T06:43:22.000Z | 2022-03-20T18:06:46.000Z | dev/Tools/Python/2.7.13/mac/Python.framework/Versions/2.7/lib/python2.7/site-packages/pyxb/bundles/saml20/dce.py | jeikabu/lumberyard | 07228c605ce16cbf5aaa209a94a3cb9d6c1a4115 | [
"AML"
] | 103 | 2015-01-08T18:35:57.000Z | 2022-01-18T01:44:14.000Z | dev/Tools/Python/2.7.13/mac/Python.framework/Versions/2.7/lib/python2.7/site-packages/pyxb/bundles/saml20/dce.py | jeikabu/lumberyard | 07228c605ce16cbf5aaa209a94a3cb9d6c1a4115 | [
"AML"
] | 54 | 2015-02-15T17:12:00.000Z | 2022-03-07T23:02:32.000Z | from pyxb.bundles.saml20.raw.dce import *
| 21 | 41 | 0.785714 | 7 | 42 | 4.714286 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.052632 | 0.095238 | 42 | 1 | 42 | 42 | 0.815789 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
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